TRANSFERABILITY OF POST-CONSTRUCTION STORMWATER QUALITY BMP EFFECTIVENESS STUDIES

Requested by:

American Association of State Highway and Transportation Officials (AASHTO)

Standing Committee on the Environment

Prepared by:

Geosyntec Consultants, Inc. Portland, Oregon

Wright Water Engineers, Inc. Denver, Colorado

Venner Consulting Lakewood, Colorado

July 2015

The information contained in this report was prepared as part of NCHRP Project 25-25, Task 92, National Cooperative Highway Research Program, Transportation Research Board.

SPECIAL NOTE: This report IS NOT an official publication of the National Cooperative Highway Research Program, Transportation Research Board, National Research Council, or The National

Academies.

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Acknowledgements

This study was requested by the American Association of State Highway and Transportation Officials (AASHTO), and conducted as part of the National Cooperative Highway Research Program (NCHRP) Project 25-25. The NCHRP is supported by annual voluntary contributions from the state Departments of Transportation. Project 25-25 is intended to fund quick response studies on behalf of the AASHTO Standing Committee on the Environment. The report was prepared by Marc Leisenring, Daniel Pankani, and Eric Strecker of Geosyntec Consultants, Inc. in Portland, Oregon; Jane Clary, Andrew Earles, and Jonathan Jones of Wright Water Engineers, Inc. in Denver, Colorado; and Marie Venner of Venner Consulting in Lakewood, Colorado. The work was guided by a task group chaired by G. Scott McGowen, California DOT, and included the following members: William Fletcher, Oregon DOT; Rich McLaughlin, North Caroline State University; Fred Noble, Florida DOT; Karuna Pujara, Maryland SHA; Michelle Reynolds, Wisconsin DOT; and Kenneth M. Stone, Washington State DOT. The FHWA liaison was Marcel Tchaou. The project was managed by Crawford Jencks, NCHRP staff.

Disclaimer

The opinions and conclusions expressed or implied are those of the research agency that performed the research and are not necessarily those of the Transportation Research Board or its sponsors. The information contained in this document was taken directly from the submission of the author(s). This document is not a report of the Transportation Research Board or of the National Research Council.

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CONTENTS

EXECUTIVE SUMMARY ....................................................................................................................... V

1 INTRODUCTION AND BACKGROUND ....................................................................................... 1

2 SURVEY OF STATE PRACTICES AND LITERATURE/DATA REVIEW ............................... 2 2.1 State Regulatory Frameworks and BMP Assessment Protocols ........................................... 2 2.2 Evaluation of Existing BMP Study Clearinghouses ............................................................. 14 2.3 Summary of BMP Effectiveness Studies and Data Applicable to Highways ..................... 29

3 EFFECT OF GEOGRAPHIC AND OTHER VARIABLES ON BMP EFFECTIVENESS ...... 40 3.1 Background―BMP Performance Factors ............................................................................ 40 3.2 Effects of Climate And Hydrology ......................................................................................... 43 3.3 Effects of Soils and Topography ............................................................................................. 51 3.4 Effects of Traffic Volumes and Adjacent Land Uses ........................................................... 54 3.5 DOT BMP vs. Non-DOT BMP Performance ........................................................................ 69 3.6 Summary of BMP Performance Data Transferability Considerations .............................. 72

4 SUMMARY OF BMP EFFECTIVENESS STANDARDIZATION CONSIDERATIONS ....... 75 4.1 Recommended Standardized Monitoring and Reporting Protocols ................................... 75 4.2 Recommended Preliminary Study Screening Criteria ......................................................... 78 4.3 Other Considerations if BMPDB Adopted by DOTs ........................................................... 79

5 RESOURCES NEEDED TO DEVELOP BMP DATABASE PORTAL FOR STATE DOTS .. 81 5.1 Potential Scope of Work.......................................................................................................... 82 5.2 Preliminary Draft Budget ....................................................................................................... 84 5.3 Preliminary Draft Schedule .................................................................................................... 86 5.4 Conclusion ................................................................................................................................ 86

6 SUMMARY CONCLUSIONS ......................................................................................................... 87 6.1 Existing BMP Assessment Protocols and Study Clearinghouses ........................................ 87 6.2 Variables Affecting Transferability of Performance Findings ............................................ 88 6.3 Development of a DOT-Focused BMP Study Repository .................................................... 90

7 REFERENCES .................................................................................................................................. 91

BMP GLOSSARY ..................................................................................................................................... 95

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LIST OF FIGURES

Figure 2-1. Conceptual overview of BMPDB relational structure. ..............................................17 Figure 2-2. User interface of the HRDB. ....................................................................................20 Figure 2-3. EPA rainfall zones. ..................................................................................................34 Figure 3-1. Geographic distribution of average annual precipitation (1981-2010). .....................44 Figure 3-2. Geographic distribution of normal mean annual temperature (1981-2010). .............45 Figure 3-3. BMP influent vs effluent concentration (TSS). .........................................................61 Figure 3-4. BMP influent vs effluent concentration (NOx). .........................................................62 Figure 3-5. BMP influent vs effluent concentration (TKN). .........................................................63 Figure 3-6. BMP influent vs effluent concentration (TP). ...........................................................64 Figure 3-7. BMP influent vs effluent concentration (TCu). .........................................................65 Figure 3-8. BMP influent vs effluent concentration (TPb). .........................................................66 Figure 3-9. BMP influent vs effluent concentration (TZn). ..........................................................67 Figure 3-10. BMP influent vs effluent concentration (FC). .........................................................68 Figure 3-11. Key for Tables 3-16 to 3-17. ..................................................................................71 Figure 5-1. Preliminary draft schedule for developing BMP database portal for state DOTs. .....86

LIST OF TABLES Table 2-1. Stormwater treatment BMPs approved under TAPE (as of August 2014). ................. 4 Table 2-2. Technology use level application requirements for TAPE. ......................................... 6 Table 2-3. TAPE reciprocity outside of Washington State (Washington Stormwater Center). ..... 7 Table 2-4. Stormwater treatment BMPs approved under TARP (as of July 2014). ..................... 8 Table 2-5. Stormwater management BMPs approved under ETV. ............................................10 Table 2-6. Summary of DOT BMP certification and evaluation practices. ..................................12 Table 2-7. Number of transportation-related BMP studies in the BMPDB as of July 2014. ........15 Table 2-8. BMPDB relational database tables. ..........................................................................16 Table 2-9. Data elements in the BMPDB watersheds table. ......................................................18 Table 2-10. Data elements in the BMPDB general BMP information table. ...............................19 Table 2-11. Highway data sets included in HRDB (Version 1.0.0a, May 2010)..........................20 Table 2-12. HRDB data elements in Highway Site table. ...........................................................21 Table 2-13. Reporting parameters for CWP Database. .............................................................23 Table 2-14. Examples of BMP performance data clearinghouses/repositories. .........................25 Table 2-15. Delaware DOT BMP inspection parameters. ..........................................................28 Table 2-16. Research categories and studies reviewed. ...........................................................31 Table 2-17. Commonly used BMPs according to NCHRP25-25/83 survey of DOTs. .................31 Table 2-18. Rarely used BMPs according to NCHRP25-25/83 survey of DOTs. .......................32 Table 2-19. Proposed list of BMPs to be evaluated versus literature study availability. .............32 Table 2-20. Relevant pollutant categories and pollutant types. ..................................................33 Table 2-21. Geographical distribution of BMP performance-related studies. .............................34 Table 2-22. Overview of all studies and data in BMPDB for BMP types analyzed. ....................35 Table 2-23. Number of transportation-related studies in BMPDB analyzed. ..............................36 Table 2-24. Number of influent/effluent data sets in BMPDB for solids. .....................................36 Table 2-25. Number of influent/effluent data sets in BMPDB for metals. ...................................37 Table 2-26. Number of influent/effluent data sets in BMPDB for nutrients. ................................37 Table 2-27. Number of influent/effluent data sets in BMPDB for oxygen demanding substances. .................................................................................................................................................38

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Table 2-28. Number of influent/effluent data sets in BMPDB for hydrocarbons. ........................38 Table 3-1. Stormwater BMP UOP categories. ...........................................................................42 Table 3-2. Unit processes effective for removal of pollutants commonly found in stormwater. ...43 Table 3-3. Number of highway runoff EMCs per EPA rainfall zones from NSQD and HRDB. ....46 Table 3-4. Median highway runoff EMCs per EPA rainfall zones from NSQD and HRDB. .........46 Table 3-5. Median highway runoff EMCs by EPA rainfall zone that are statistically similar. .......47 Table 3-6. Number of EMCs by constituents by average annual daily traffic. ............................55 Table 3-7. Medians and confidence intervals for combined NSQD and HRDB data. .................56 Table 3-8. Number of storm EMCs by constituent per land use. ................................................57 Table 3-9. Percent difference in land use median EMCs compared to highway land use. .........57 Table 3-10. Comparison of land use pollutant median EMCs to rural AADT median EMCs (0 – 30K) and ultra-urban AADT median EMCs (90K +). ..................................................................58 Table 3-11. Percent difference in land use median EMCs to rural AADT median EMCs (0 – 30K) and ultra-urban AADT median EMCs (90K +). ..........................................................................58 Table 3-12. BMP influent/effluent correlation results from BMPDB. ...........................................60 Table 3-13. Grouping of BMPDB land uses. ..............................................................................69 Table 3-14. Comparison of median land use EMCs from the BMPDB to rural AADT median EMCs (0-30K) and ultra-urban AADT median EMCs (90K +). ...................................................70 Table 3-15. Percent difference of median land use EMCs from the BMPDB compared to rural AADT median EMCs (0-30K) and ultra-urban AADT median EMCs (90K +). ............................70 Table 3-16. Median BMP influent concentrations for transportation land use studies compared to all other developed land use studies. ........................................................................................71 Table 3-17. Median BMP effluent concentrations for transportation land use studies compared to all other developed land use studies. ....................................................................................72 Table 5-1: Preliminary draft budget of costs for proposed NCHRP/DOT enhancements ...........85

LIST OF APPENDICES

Appendix A – Highway Runoff Boxplots by EPA Rain Zone Appendix B – Highway Runoff Boxplots and Scatter Plots by Average Annual Daily Traffic Appendix C – BMP Studies in Highway Settings Targeted for Future Entry into BMP Database Appendix D – Letter of Support from WERF for DOT Portal on BMPDB Website

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LIST OF ACRONYMS µg/L Micrograms per Liter AADT Average Annual Daily Traffic APWA American Public Works Association ASCE American Society of Civil Engineers BCa Bias Corrected and Accelerated BMP Best Management Practice BMPDB International Stormwater BMP Database BOD Biochemical Oxygen Demand COD Chemical Oxygen Demand CULD Conditional Use Level Designation DL Detection Limit ETV Environmental Technology Verification DOT Department of Transportation DP Dissolved Phosphorus E. coli Escherichia coli EMC Event Mean Concentration EPA Environmental Protection Agency ET Evapotranspiration EWRI Environmental and Water Resources Institute FC Fecal Coliform FHWA Federal Highway Administration GULD General Use Level Designation HRDB Highway Runoff Database mg/L Milligrams per Liter NCHRP National Cooperative Highway Research Program NO2 Nitrite NO3 Nitrate NOx Nitrate plus Nitrite NPDES National Pollutant Discharge Elimination System NSQD National Stormwater Quality Database PAH Polycyclic Aromatic Hydrocarbons PULD Pilot Use Level Designation ROS Regression-on-Order Statistics TAPE Technology Assessment Protocol―Ecology TARP Technology Acceptance and Reciprocity Partnership TCu Total Copper TDS Total Dissolved Solids TKN Total Kjeldahl Nitrogen TN Total Nitrogen TP Total Phosphorus TPb Total Lead TPH Total Petroleum Hydrocarbons TSS Total Suspended Solids TZn Total Zinc UOP Unit Operations and Processes USEPA United States Environmental Protection Agency USGS United States Geological Survey WERF Water Environment Research Foundation

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Executive Summary Understanding the factors that affect best management practice (BMP) performance allows for the

improved selection and design of BMPs that are targeted to specific constituents and hydrologic conditions of concern. In addition, BMP data availability can help identify BMP design improvements that reduce cost of implementation and maintenance. BMP performance studies, particularly studies that report both BMP design attributes and performance data, tend to be concentrated in certain regions of the country. The ability to use BMP performance data collected in geographically different locations is therefore desirable to departments of transportation (DOTs).

This study evaluated state practices for assessing BMPs, reviewed BMP effectiveness data applicable to highway runoff, evaluated variables potentially affecting data transferability, and assessed the feasibility of establishing a highway BMP database. These were all accomplished to answer the following questions:

• What BMP performance monitoring studies applicable for highways are available in data clearinghouses and as published literature?

• What are the observed effects of geographic variables on highway BMP performance?

• How can highway BMP performance monitoring evaluations be standardized?

• What recommendations can be provided for assessing the feasibility of establishing a BMP performance monitoring study central repository for transportation agencies?

The review of transportation-related BMP performance studies and data clearinghouses revealed several data gaps, particularly with regard to the national distribution of studies. In some areas of the country, BMP monitoring studies are completely lacking, while other areas have limited studies for particular BMP types or monitored parameters. The International Stormwater BMP Database (BMPDB) was identified as the most comprehensive national-scale database that already contains many highway BMP studies. The literature review identified 21 additional studies for potential entry into the BMPDB.

To assess the combined effect of geographic factors, hypothesis testing was conducted on a dataset of highway runoff event mean concentrations (EMCs) for different Environmental Protection Agency (EPA) rain zones. Statistically significant differences in median concentrations were identified for total suspended solids (TSS), total Kjeldahl nitrogen (TKN), nitrate+nitrite (NOx), total phosphorus (TP), total copper (TCu), total lead (TPb), and total zinc (TZn). The effects of traffic volumes and land uses were also evaluated. With the exception of chemical oxygen demand (COD) and fecal coliform (both sparse datasets), all constituents analyzed (TSS, TKN, NOx, TP, TCu, TPb, and TZn) showed statistically significant increases in median concentrations with increases in average annual daily traffic (AADT), but AADT correlations with TSS and TP were relatively weak compared to the other constituents. Comparisons of DOT-specific to non-DOT BMP performance data indicate that some non-DOT studies may be appropriate for assessing BMP performance for the highway environment and that influent concentration magnitudes are a potentially significant variable to consider when assessing performance.

With regard to a central repository for highway BMP studies, it is recommended, with the Project Panel concurrence, that DOTs utilize and build upon the existing BMPDB, as opposed to creating a separate transportation BMP database. The BMPDB has a proven track record and has an organization (Water Environment Research Foundation [WERF]) committed to its long-term growth and maintenance. A targeted number of enhancements have been proposed for the BMPDB to better support DOT objectives, including the addition of a DOT-specific portal and additional database fields for storing DOT-specific information. The resources necessary to complete these enhancements have been estimated and described.

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1 Introduction and Background Since the Nationwide Urban Runoff Program (NURP) (EPA 1983) and the Federal Highway

Administration's (FHWA’s) Highway Runoff evaluations (Driscoll et al. 1990), there has been a significant amount of research on the performance of post-construction stormwater BMPs for urban and transportation land uses. Through this research, BMP designs have continued to be improved and refined to address specific pollutants and hydrologic conditions of concern, as well as to increase the cost-effectiveness of their continued operation and maintenance.

However, available performance studies that report both results and BMP design attributes tend to be concentrated in only some parts of the nation due to a number of factors such as regulatory pressures, environmentally progressive public agencies, access to funding, and affiliations with university researchers. State DOTs that either have not been able to conduct BMP performance monitoring research or only limited efforts, must, at least in part, rely on research by other DOTs, as well as non-transportation BMP researchers, when deciding which BMPs they should implement within their rights of way. To ensure that BMP performance study data can be transferable and comparable between locations and types of BMP systems, there is a need to consider differences in geographic location and other factors that influence performance, practicability of implementation given site constraints, and overall study quality. Monitoring study design and data reporting that are not standardized contribute significantly to potential issues with the transferability of BMP studies. Along with varying scientific approaches to BMP studies that DOTs have taken, there are also various state regulatory frameworks that have an effect on study design and protocols.

Local evaluations of BMP performance for planning and design purposes are preferred where available. However, they are often not available, and schedule and budget constraints may prevent timely testing. The ability to effectively use data in studies from other areas and other land uses would save DOTs time and money. In fact, conducting a wide range of studies on various BMP types and sizing configurations on a local basis is likely not feasible in most cases. Finally, locating BMP effectiveness data useful to DOTs is difficult because much of such data are not indexed or collected in a single repository, but rather are kept with the various research agencies, with most data only available in the gray literature.

The objectives of this study were to: (1) identify transportation-related BMP performance monitoring studies, (2) review existing BMP monitoring and assessment protocols, (3) evaluate conditions and factors influencing the transferability of BMP performance monitoring results, and (4) investigate the feasibility of establishing a central repository (BMP Database) for DOT post-construction stormwater quality research studies to facilitate the exchange of BMP effectiveness information.

Following this introductory section, the report is organized into five primary additional sections: • Section 2: Survey of State Practices and Literature/Data Review

• Section 3: Effect of Geographic and Other Variables on BMP Effectiveness

• Section 4: Summary of BMP Effectiveness Standardization Considerations

• Section 5: Resources Needed to Develop a BMP Database Portal for State DOTs

• Section 6: Summary and Conclusions

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2 Survey of State Practices and Literature/Data Review The effectiveness of stormwater BMPs has been evaluated by researchers for several decades using a

variety of approaches. The earlier evaluations focused primarily on water quality performance (concentration reductions, load reductions, effluent quality, etc.), while more recently the focus has been on the hydrologic/hydraulic performance (volume captured, volume lost, hydraulic residence time, etc.), as well as water quality. More advanced evaluations have attempted to associate performance metrics with site-specific conditions, BMP design variables, and maintenance activities to help improve BMP designs and operations. While many approaches are possible, the approach ultimately selected is largely dependent on the goals of the research, type of BMP(s) being evaluated, conveyance system configuration and design, regulatory/permit requirements, and the physical characteristics of the site.

This section summarizes available data and studies that are potentially useful for evaluating the variables influencing the transferability of BMP effectiveness information (see Section 3) and the standardization of BMP study information (see Section 4). Specifically, this section includes:

• An evaluation of state regulatory frameworks and BMP assessment protocols to determine the criteria currently used in the evaluation and acceptance of BMPs (Section 2.1).

• Identification and evaluation of existing BMP data clearinghouses to investigate data storage structures, types of BMP data currently being collected, and existing data gaps (Section 2.2).

• A summary of literature and BMPDB data related to BMP effectiveness studies applicable to highways to complement the protocols and data clearinghouse evaluations (Section 2.3).

2.1 State Regulatory Frameworks and BMP Assessment Protocols

Most states do not have established BMP assessment protocols that standardize monitoring procedures and reporting of BMP study information. However, most states do have a mechanism for accepting new treatment technologies that are not specifically listed in state-approved BMP design manuals. The Washington State Department of Ecology has the Technology Assessment Protocol – Ecology (TAPE) that establishes a rigorous process for evaluating and reporting the field-monitored performance of stormwater BMPs prior to being approved for use in Washington State. The Technology Acceptance and Reciprocity Partnership (TARP) Protocol for Stormwater BMP Demonstrations is a similar program endorsed by California, Massachusetts, Maryland, New Jersey, Pennsylvania, and Virginia. Both programs are intended to promote consistency in data collection and reporting of BMP performance to allow state regulators to verify performance claims. The Water Environment Federation (WEF) and the American Public Works Association (APWA) have developed a workgroup dedicated to the investigation of a national stormwater testing and evaluation program and an interest group was formed on Stormwater Testing and Evaluation for Products and Practices (STEPP) (WEF 2014). In 1995, the EPA Office of Research and Development established the agency’s Environmental Technology Verification (ETV) Program. The ETV program stopped taking applications for technology verifications in 2013 (USEPA 2013a). This section briefly summarizes the TAPE, TARP, and ETV programs, followed by discussions of other relevant efforts.

2.1.1 Technology Assessment Protocol―Ecology (TAPE)

The Washington State Technology Assessment Protocol―Ecology (TAPE) was developed in 2002 and updated in 2011 by the Washington State Department of Ecology for evaluating emerging stormwater

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technologies in various treatment categories including Pretreatment, Basic, Enhanced, Phosphorus, and Oil Treatment, as defined below.

• Pretreatment BMPs are intended to achieve at least 50% removal of TSS for influent concentrations greater than 100 mg/L. For influent concentrations between 50 and 100 mg/L, the facilities are intended to achieve effluent goals of less than 50 mg/L. For most constituents, the effluent quality value was added to address percent removal issues when influent to a BMP already has relatively low pollutant concentrations (i.e., percent removal is low because the influent is already relatively clean).

• Basic treatment BMPs are intended to achieve a goal at least 80% removal of TSS for an influent concentration greater than 100 mg/L. For influent concentrations less than 100 mg/L, the effluent goal is less than or equal to 20 mg/L TSS.

• Enhanced treatment BMPs (“Dissolved Metals Treatment”) are intended to achieve a higher level of treatment than basic treatment and provide removal of dissolved metals. Greater than 30% removal of dissolved copper is required for influent concentrations ranging from 0.005 to 0.02 mg/L. Greater than 60% removal of dissolved zinc is required for influent concentrations ranging from 0.02 to 0.3 mg/L.

• Phosphorus treatment BMPs are intended to achieve a goal of at least 50% total phosphorus removal for an influent concentration range of 0.1 to 0.5 mg/L, as well as achieve basic treatment.

• Oil treatment BMPs are intended to achieve the goals of no ongoing or recurring visible sheen and a daily average total petroleum hydrocarbon concentration no greater than 10 mg/L, with a maximum of 15 mg/L for discrete (grab) samples.

• For all of the above, the BMP must meet the hydrologic treatment requirements of 91% capture and treatment of the total runoff volume using a Washington State Department of Ecology-approved continuous simulation model.

The new 2011 TAPE Guidance Manual (Publication #11-10-061) must be followed for studies submitted as of January 2013. The protocol produces designated use levels as defined below.

• General Use Level Designation (GULD) may be used in Washington subject to use level designation conditions.

• Conditional Use Level Designation (CULD) allows continued use of the technology for a specified time period during which field testing must be completed by the vendor and/or developer.

• Pilot Use Level Designation (PULD) allows limited use of the technology to allow field testing to be conducted. PULD technologies may be installed, provided that the vendor and/or developer agree to conduct field testing based on the TAPE at all installations.

The objectives of TAPE are to characterize, with a reasonable level of statistical confidence, an emerging technology’s effectiveness in removing pollutants from stormwater runoff and to compare test results with the proponent’s claims. The test protocol also assesses technologies with respect to other factors such as maintenance, reliability, and longevity. The technology performance evaluation process consists of:

1. Preliminary testing of the product by the proponent. 2. Use level application submission to Ecology and the Technology Review Committee (TRC) for

review. 3. Denial or approval of a use level designation by Ecology.

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4. Quality Assurance Project Plan submittal by proponent and approval by Ecology and the TRC. 5. Testing at a site indicative of the Pacific Northwest. 6. Submission of a Technical Evaluation Report (TER) to Ecology and the TRC. 7. Denial or approval of a general use level designation by Ecology.

At this time, TAPE does not support separate certifications for eastern and western Washington. All

field testing must be reflective of western conditions, and the western Washington hydraulic sizing approach must be followed. However, in the final TER, methods for the hydraulic sizing of the BMP using both the western and eastern approaches are requested.

2.1.1.1 Technologies Approved The list of post-construction BMP technologies currently approved by TAPE is shown in Table 2-1 for

pretreatment, basic treatment, enhanced treatment, and phosphorus treatment. Expiration dates are shown in parentheses for technologies with CULD and PULD use designations. Almost all of the approved BMPs, with the exception of those submitted by the Washington State DOT (WSDOT), are proprietary BMPs manufactured and sold by private companies. Washington State has additional “public domain” BMPs that are also approved, but these have not been through the TAPE approval process. In general, a problem with BMP approval programs is that they are typically self-funded (i.e., the submitting vendor or government organization wishing to use the technology, such as WSDOT in the example above, submits the data and pays for both the study and the application).

Table 2-1. Stormwater treatment BMPs approved under TAPE (as of August 2014).

Device Manufacturer or Proponent

Pre-treatment

Basic Treatment

Enhanced Treatment

Phosphorus Treatment

Filterra® Boxless™ Americast GULD GULD GULD Filterra® System Americast GULD GULD GULD

Aqua-Filter System AquaShield™, Inc. PULD (2/1/2015)

PULD (2/1/2015)

PULD (2/1/2015)

Aqua-Swirl System AquaShield™, Inc. GULD CULD (2/1/2016)

BayFilter® BaySaver Technologies, Inc. GULD

CULD (12/1/2014)

CULD (12/1/2014)

MWS-Linear Modular Wetland

Bio Clean Environmental Services, Inc. GULD GULD GULD

CDS™ Stormwater Treatment System

CONTECH Engineered Solutions, LLC. GULD

Media Filtration System

CONTECH Engineered Solutions, LLC. GULD

Media Filtration System with Perlite Media at 2 gpm

CONTECH Engineered Solutions, LLC.

CULD (6/30/2017)

CULD (6/30/2017)

StormFilter using Perlite Media at 2 gpm

CONTECH Engineered

CULD (9/30/2015)

PULD (9/30/2015)

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Device Manufacturer or Proponent

Pre-treatment

Basic Treatment

Enhanced Treatment

Phosphorus Treatment

Solutions, LLC. StormFilter using PhosphoSorb Media at 1.67 gpm/sq. ft.

CONTECH Engineered Solutions, LLC.

CULD (12/31/2014)

CULD (12/31/2014)

StormFilter using ZPG Media

CONTECH Engineered Solutions, LLC. GULD

Stormwater Management StormFilter® using MetalRx™ Media

CONTECH Engineered Solutions, LLC.

CULD (6/30/2016)

CULD (6/30/2016)

UrbanGreen™ BioFilter

CONTECH Engineered Solutions, LLC.

CULD (6/30/2017)

CULD (6/30/2017)

Vortechs System

CONTECH Engineered Solutions, LLC. GULD

Downstream Defender Hydro International GULD

Up-Flo™ Filter Hydro International CULD (3/1/2016)

Jellyfish™ Filter Imbrium Systems CULD (6/30/2015)

PULD (6/30/2015)

Stormceptor Imbrium Systems GULD

FloGard Perk Filter® Kristar/Oldcastle Precast, Inc. GULD GULD

Enpurion®Metals Treatment

Lean Environment, Inc.

CULD (2/29/2016)

CULD (2/29/2016)

ecoStorm plus

Royal Environmental Systems GULD

Aquip StormwateRx, LLC CULD (1/1/2017)

CULD (1/1/2017)

CULD (1/1/2017)

Maxwell Plus Drainage System Torrent Resources

PULD (4/1/2017)

Compost-Amended Biofiltration Swale WSDOT GULD GULD Media Filter Drain WSDOT GULD GULD GULD

Source: http://www.ecy.wa.gov/programs/wq/stormwater/newtech/technologies.html

2.1.1.2 Data Requirements The TAPE program may grant a PULD to BMP submitters based solely upon laboratory-based data

results or results from monitoring studies outside the Pacific Northwest. This designation allows submitters to complete up to five installations within Washington to obtain field-based performance data with the ultimate goal of achieving GULD status. The submitter’s representative must monitor influent

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and effluent at all locations. Selecting sites with consistent drainage areas allows for aggregation of results that may help facilitate the development of a final report for submission.

For the CULD use level, field data supporting performance claims must be available and up to 10 field sites in Washington are permitted to collect the additional data needed to achieve GULD. At a minimum, one field site must conduct monitoring for the GULD use level. Generally, the conditional-use approach would allow products and practices to reach the field faster, thereby reducing the time to either reach markets or initiate data gathering for field-testing purposes. Table 2-2 summarizes some of the minimum application requirements for the PULD, CULD, and GULD use level designations.

Table 2-2. Technology use level application requirements for TAPE.

Treatment Technology Use Level Application Requirement PULD CULD GULD Cover letter and detailed description of technology X X X Description of system hydraulic capacity and performance X X X Field or laboratory data in support of the performance claims, collected by a protocol that is reasonably consistent but does not necessarily fully meet the TAPE protocol X

Field data in support of the performance claims. These data may be collected by a protocol that is reasonably consistent but does not necessarily fully meet the TAPE protocol. Field data may be supplemented with laboratory data to reflect TAPE requirements.

X

Field data collected in accordance with the TAPE protocol

X Data analysis with conclusions about system performance and any relevant statistical information X X X Prescribed statistical analysis

X

Technical Evaluation Report (TER) with prescribed analysis methods

X Third party review

X X

Once an application has been received and a PULD or CULD has been granted, the technology

proponents are allowed a maximum of 30 months to prepare a Quality Assurance Project Plan (QAPP), receive QAPP approval from Ecology, conduct stormwater monitoring, and prepare a Technical Evaluation Report (TER). The QAPP must describe the measures taken to ensure that collected samples represent a wide range of water quality conditions during storm flow. Influent and effluent concentrations must then be monitored for discrete storm events greater than 0.15 inches and where influent concentrations fall within specified ranges depending on the treatment category being targeted.

For GULD applications, Ecology has established an expert panel to advise them on the acceptability of the QAPP and then acceptance of the TER. This is an important component of the process that helps ensure technical rigor, but also allows for consideration of specific site issues.

TAPE approvals have been accepted by several states or agencies outside of Washington State. A list is provided in Table 2-3 along with the conditions of acceptance, if applicable. If a city is not specified, the condition is applicable statewide.

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Table 2-3. TAPE reciprocity outside of Washington State (Washington Stormwater Center).

State City Conditions

California Sacramento Will consider TAPE GULD certifications for basic treatment Santa Monica Accepted

Colorado Denver Accepted Maine - Accepted Missouri St. Louis The Metropolitan Sewer District (MSD) allows certification New York - Accepted by Department of Environmental Conservation

Oregon - Accepted by ODOT

Portland The Bureau of Environmental Services (BES) requires GULD certification as a submission requirement

Rhode Island -

Accepted for bacteria, nitrogen, and phosphorus treatment

2.1.2 Technology Acceptance and Reciprocity Partnership (TARP)

The Technology Acceptance Reciprocity Partnership (TARP) Program was developed in 2001 and is currently endorsed by California, Illinois, Massachusetts, Maryland, New Jersey, New York, Pennsylvania, and Virginia (see Section 2.1.4 for discussion of state DOT BMP evaluations and acceptance protocols). Endorsement means these states have agreed to:

1. Address technology review and approval barriers in policy and regulations that do not advance knowledge of a technology’s performance or recognize innovative approaches to meet environmental protection goals.

2. Accept the performance tests and data, and acknowledge the approval results of a partner’s review of a technology demonstration, as appropriate, in order to reduce subsequent review and approval time.

3. Increase expertise in the applications and advantages of technologies that may have superior environmental and economic benefits for controlling stormwater pollution.

4. Use the Protocol, as appropriate, for state-led initiatives, grants, and verification or certification programs where the objective is to document performance efficiency and cost of BMPs.

5. Share technology information with potential users in the public and private sectors using existing state supported programs.

6. Monitor and evaluate the results of using this Protocol, and periodically review and revise the Protocol to maintain its viability.

2.1.2.1 Technologies Approved The list of technologies approved of this writing is shown in Table 2-4. A current list of approved

technologies is available on line.

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Table 2-4. Stormwater treatment BMPs approved under TARP (as of July 2014).

Manufacturer Device Name MTD1 Laboratory

Test Certifications

Field Test Certifications

Certified TSS

Removal Rate

AquaShield, Inc. AquaFilter Filtration Chamber

Certification Certification 80%

Aqua-Shield. Inc. Aqua-Swirl Concentrator

Certification 50%

BaySaver Technologies, Inc.

Bayfilter

Certification 80%

BaySaver Technologies, Inc.

BaySeparator

Certification 50%

Hydro International, Inc. Downstream Defender

Certification 50%

KriStar Enterprises, Inc. FloGard Dual-Vortex Hydrodynamic Separator

Certification 50%

CONTECH Stormwater Solutions, Inc.

High Efficiency Continuous Deflective Separator (CDS) Unit

Certification 50%

Hydroworks, LLC Hydroguard Certification 50% Imbrium Systems Corporation

Jellyfish Filter

Certification 80%

CONTECH Stormwater Solutions, Inc.

Media Filtration Systems

Certification Certification 80%

Suntree Technologies, Inc.

Nutrient Separating Baffle Box

Certification 50%

Imbrium Systems Corporation

Stormceptor OSR Certification 50%

Imbrium Systems Corporation

Stormceptor STC

Certification 50%

Jensen Precast, Inc. StormVault Certification 80% CONTECH Stormwater Solutions, Inc.

Stormwater Management StormFilter

Certification Certification 80%

Terre Hill Concrete Products

TerreKleen Stormwater Device

Certification 50%

Hydro International Up-Flo Filter Certification 80% Environment 21, LLC V2B1 Certification 50% CONTECH Stormwater Solutions, Inc.

Vortechs Stormwater Treatment System

Certification Certification 50%

CONTECH Stormwater Solutions, Inc.

VortSentry System Certification 50%

1MTD―Manufactured Treatment Device

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2.1.2.2 Data Requirements In TARP, the requirements for a BMP performance demonstration are limited to a common set of

uniform criteria, acceptable to all participating states. Specific state requirements must be considered when a technology proponent is pursuing certification or verification of a stormwater BMP in that state. In general, reviewers look for these characteristics:

• A minimum of 15 storm events per monitoring location.

• At least 50% of the annual average rainfall sampled for a minimum of 15 inches of precipitation.

• Average particle size: mean < 100 microns; approximate TSS distribution: 55% sand, 40% silt, and 5% clay.

• TSS influent concentration: 100 – 300 mg/L.

• Flows with a range up to 125% of design capacity.

• Scour tests.

The following data are required to be submitted within the product specifications: 1. A summary of the underlying scientific and engineering principles for the technology. 2. Technology specifications, alternative technology configurations, and any associated disadvantages,

such as physical constraints and limitations, weight and buoyancy, transportability, durability, energy requirements, and consumable materials.

3. Minimum siting and design specifications to achieve stated performance, including but not limited to: pollutants that should and could be addressed; minimum and maximum influent concentrations; pollutants that will not be addressed or that may be increased; and siting, location, land use, and land activity limitations or restrictions.

4. A discussion of the advantages of the technology when compared to conventional stormwater systems providing comparable stormwater control.

5. Standard drawings, including a schematic of the technology and a process flow diagram. 6. A discussion of technology hydraulics and system sizing to meet performance standards and goals

(e.g., to handle the water quality volume, rate of runoff, type of storm, or recharge requirements). 7. Full range of operating conditions for the technology, including minimum, maximum, and optimal

conditions to achieve the performance goals and standards and for reliability of the technology. 8. Minimum maintenance requirements to sustain performance. 9. Significant modifications and technical advancements in the technology design. 10. Technology limitations, such as performance limits for control of certain water quality parameters,

and predicted impacts from construction, operation, and maintenance of the technology. 11. Identified secondary impacts. 12. Discussion of the generation, handling, removal, and disposal of discharges, emissions, and waste

byproducts in terms of mass balance, maintenance requirements, and cost. 13. Discussion of pretreatment and preconditioning of stormwater, if appropriate to achieve stated

performance of the BMP. 14. Identification of any special licensing or hauling requirements, safety issues, and access requirements

associated with operation or maintenance of the technology.

Objective, quantifiable, replicable, and defensible performance claims are expected as well, typically including evaluations for contaminant removal efficiency and/or pollution prevention claims. The procedures for a stormwater BMP field test must be described in the Quality Assurance Plan scope, and

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reviewed and validated to ensure that the procedures for collecting, handling, and analyzing samples and data will be accurate, precise, representative, complete, and comparable.

2.1.3 EPA Environmental Technology Verification (ETV) Program

In 1995, the EPA Office of Research and Development established the agency’s Environmental Technology Verification (ETV) Program. The latest protocol (Draft 4.1) was developed in 2002. The goal of the program was to “provide credible performance data for commercial-ready environmental technologies to speed their implementation for the benefit of purchasers, permitters, vendors and the public;” however, the program stopped taking applications for technology verifications in 2013 (USEPA, 2013).

2.1.3.1 Technologies Approved Results from approximately a dozen different technologies have been submitted to the ETV program,

and Verification Reports and Verification Statements are posted on EPA’s ETV website (USEPA, 2013). Stormwater Source-Area Treatment Devices covered by the EPA program are listed online (http://www.epa.gov/nrmrl/std/etv/vt-wqp.html#SWSATD) and shown in Table 2-5.

Table 2-5. Stormwater management BMPs approved under ETV. Manufacturer Device Name Verification Report

and Statement

BaySaver Technologies, Inc. BaySaver Separation System, Model 10K

Report (PDF) (49 pp) 2005

Hydro International Downstream Defender®, 6-ft Diameter

Report (PDF) (74 pp)(EPA/600/R-07/121) September 2007 Statement (PDF) (5 pp)

Practical Best Management of Georgia, Inc.

CrystalStream Water Quality Vault Model 1056

Report (PDF) (46 pp) 2005 Statement (PDF) (5 pp)

Stormwater Management, Inc. (now Contech)

CatchBasin StormFilter Report (PDF) (68 pp (EPA/600/R-05/138) August 2005 Statement (PDF) (6 pp)

Stormwater Management, Inc. (now Contech)

StormFilter Using Perlite Filter Media

Report (PDF) (56 pp) 2005 Statement (PDF) (5 pp)

Stormwater Management, Inc. (now Contech)

StormFilter Using ZPG Filter Media

Report (PDF) (65 pp) 2004 Statement (PDF) (5 pp)

Stormwater Management, Inc. (now Contech)

StormScreen Treatment System

Report (PDF) (67 pp, 4.26 MB)April 2005 Statement (PDF) (5 pp, 200 KB)

Terre Hill Concrete Products Terre Kleen™ 09 Report (PDF) (61 pp, 1.94 MB)July 2008 Statement (PDF) (5 pp, 80 KB)

Vortechnics, Inc. Vortechs System, Model 1000

Report (PDF) (66 pp) 2005 Statement (PDF) (5 pp)

Zeta Technology, Inc. Arkal Pressurized Storm Water Filtration System

Report (PDF) (75 pp) 2004 Statement (PDF) (5 pp)

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2.1.4 DOT BMP Certification and Evaluation Practices

DOT BMP certification and evaluation practices are summarized in Table 2-6. Of the six state programs detailed in the table, two of the state DOTs, California and Oregon, operate their own stormwater BMP programs, including evaluation programs, without the assistance of their state’s environmental agency. Oregon DOT’s (ODOT’s) stormwater BMP program includes a BMP certification process where proprietary BMPs must have the TAPE certification in order to be included on the ODOT Qualified Products List and non-proprietary BMPs are evaluated based on unit processes and available testing data, with the BMPDB being the primary source. Three of the six states, New Jersey, Virginia, and Washington, rely heavily on their state’s environmental agencies for support and approval of stormwater BMPs for highway applications.

In Massachusetts, the Massachusetts Highway Department (MassHighway) worked with the Massachusetts Department of Environmental Protection to develop the initial statewide list of approved BMPs. However, MassHighway is the responsible agency for assessing and approving additional or proprietary BMPs for use on highway projects.

2.1.4.1 BMP Certification Criteria In three of the six state programs summarized in Table 2-6, BMPs are certified and assigned specific

performance ratings for percent removal of TSS, total phosphorus and/or nitrogen (Massachusetts, New Jersey, and Virginia). Percent removal values are provided to support BMP selection and design, and are based on established testing protocols. In the other three states, BMP certification is conducted with internal procedures based on review of literature information and testing data.

2.1.4.2 BMP Evaluation and Certification Protocol There are a variety of testing protocols considered by state DOTs when evaluating BMP performance

and certifying BMPs. These include: • Massachusetts Strategic Envirotechnology Partnership (STEP);

• The Technology and Reciprocity Partnership (TARP);

• The Environmental Technology Verification Program (ETV);

• Washington State Department of Ecology’s Technology Assessment Protocol (TAPE); and

• BMP Monitoring Protocols from the International Stormwater BMP Database (although the BMP Database does not “certify” BMP performance).

Many states that accept data from one of the above protocols require state-specific criteria (e.g., different inter-event periods) to be considered in addition to those required by the individual protocols before they will approve a BMP. There is an ongoing effort within the American Society of Civil Engineers (ASCE) Environmental and Water Resources Institute (EWRI) to unify evaluation and certification protocols. The ASCE/EWRI has formed a Task Committee on Guidelines for Certification of Manufactured Stormwater BMPs (see http://watertech.rutgers.edu/) to review existing certification programs for various manufactured stormwater BMPs and seek input on certification methods and contents from a variety of stakeholders. This review and input will be used to develop new guidelines, although the guidelines have not yet been finalized, as of the date of this report.

2.1.4.3 Use of Non-Approved BMPs A key consideration for stormwater management is flexibility in BMP selection and design, and

allowances for the use of non-approved BMPs (or reduced sizing, etc., as compared to BMPs for new

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highways). This is especially important for retrofitting in space-limited, ultra-urban environments because site constraints may preclude the use of many of the approved BMPs, which are based on full treatment compliance, in some cases by parameter, for new and re-development projects. All of the states whose policies were examined allow for use of BMPs that are not included on their approved lists; however, it is not always clear what process must be followed to use a different BMP or reduced sizing of an approved BMP. Many of the processes are focused on proprietary/manufactured BMPs that will be most applicable in space-constrained, ultra-urban environments, and most states appear to be receptive to introduction of new products. All of the evaluation and certification procedures are rigorous and require monitoring, but these can be completed through a pilot study.

Table 2-6. Summary of DOT BMP certification and evaluation practices.

State Evaluation (Protocol) California Caltrans has a well-defined approval process in their SWMP and Project Planning and Design Guide. BMPs evaluated in a fiscal year are reported in the Caltrans Stormwater Management Program Annual Reports, and Treatment BMP Technology Report. The Caltrans Treatment BMP Technology Report is updated annually and includes listing of BMPs that are being considered for a pilot-study, BMPs that are approved, or BMPs that have been rejected.

Guidance Manual: Caltrans BMP Pilot Study Guidance Manual (2009), Caltrans Stormwater Monitoring Guidance Manual (2013). Caltrans has four Stormwater Advisory Teams (SWAT)

• Maintenance SWAT O Reviews and/or evaluates proposed/existing

BMPs used by the Division of Maintenance • Project Design SWAT

o Reviews proposed/existing BMPs used in the planning and design of projects

• Construction SWAT o Reviews proposed/existing construction BMPs

and measures used for the stabilization of soils • Water Quality SWAT

o Reviews proposed/existing treatment BMPs and prioritizes research/studies of treatment BMPs

The SWATs evaluate new technologies submitted via the Department’s New Product Submittal process. SWATs may also review pilot-studies and BMPs deployed by other DOT agencies. SWAT Division Chiefs have final approval authority for proposed new BMPs.

Massachusetts MassHighway and Department of Environmental Protection (DEP) worked together on creating the DEP’s Stormwater Management Volume 2: Stormwater Policy Handbook that identifies BMPs that are acceptable for use in Massachusetts. MassHighway has also prepared the MassHighway Storm Water Handbook for Highways and Bridges. For critical source areas, MassHighway also requires additional BMPs, above and beyond those required in the DEP Stormwater Policy Handbook.

The designer of a particular project that uses innovative BMPs should consider available evaluation protocols and resources. Acceptable protocols for determining if a BMP will adequately achieve the water quality goals of the project include those from the Massachusetts Strategic Envirotechnology Partnership (STEP), the Technology and Reciprocity Partnership (TARP), and the Environmental Technology Verification Program (ETV).

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State Evaluation (Protocol) New Jersey Department of Environmental Protection (DEP) lists certified manufactured treatment devices as well as those being evaluated on www.njstormwater.org. Acceptable BMPs are listed in the NJDEP (Department of Environmental Protection) Stormwater Best Management Practices Manual that NJDOT helped write. NJDEP is still reviewing (3/31) Section 10 for the 2008 revision. The New Jersey Roadway Design Manual references the NJDEP Stormwater Best Management Practices for acceptable BMPs.

NJCAT screens emerging technologies and allows only the best candidates into the acceptance program. The NJDEP Division of Science, Research & Technology (DSRT) is responsible for certifying final pollutant removal rates for all manufactured treatment devices. This final certification process must be based on verification of the device’s pollutant removal rates by one of the following: 1. The N.J. Corporation for Advanced Technology (NJCAT) in

accordance with the protocol “Stormwater Best Management Practices Demonstration Tier II Protocol for Interstate Reciprocity” as developed under the Environmental Council of States (ECOS) and Technology Acceptance and Reciprocity Partnership (TARP).

2. Another TARP state, or another state or government agency that is recognized by NJ through a formal reciprocity agreement.

3. Other third party testing organizations (i.e., NSF). Oregon Department of Transportation has finished a revision to its Hydraulics Manual to include a chapter on water quality and a listing of “Preferred BMPs.”

Preferred BMPs were identified as part of literature review for treatment effectiveness. There is no defined formal process other than assigning each BMP primary unit processes and judging performance based on unit processes. Proprietary BMPs receiving a TAPE GULD Enhanced rating designation are given preferred status. Hydraulic Design Deviation Requests are submitted to the Regional Hydraulics Engineer who reviews the request and submits recommendation to Technical Services Geo-Environmental and/or Bridge Section Senior Hydraulics Engineer for final review. Hydraulic Engineering Staff in Technical Services will conduct the review and provide approval, suggested revisions, or deny the request.

Virginia Virginia’s Department of Conservation and Recreation (DCR) Stormwater Management Handbook includes approved BMPs. This handbook appears to apply to the Virginia Department of Transportation. DCR also maintains a website entitled, “Virginia Stormwater BMP Clearinghouse.”

According to the Virginia Stormwater BMP Clearinghouse, BMP evaluation is based on the TARP protocols and Virginia-specific requirements to the TARP protocol.

Washington BMPs that are acceptable for use in highway projects are listed in the WSDOT Highway Runoff Manual (HRM). Acceptable highway BMPs are approved by the Washington Department of Ecology and are a subset of the BMPs included in Ecology’s Stormwater Management Manuals for Eastern and Western WA that WSDOT has determined are appropriate for highways.

Ecology has developed Technology Assessment Protocol (TAPE) for evaluating emerging technologies, which is intended for ultra-urban treatment technologies such as short detention, flow-based BMPs. The TAPE protocols specify sampling criteria, site and technology information, quality assurance, and quality control measures, target pollutants, and evaluation report content. The TAPE protocols also suggest that technologies are evaluated on factors other than treatment performance, including costs, operations and maintenance, reliability, and longevity.

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2.2 Evaluation of Existing BMP Study Clearinghouses

A number of states and organizations provide access to BMP performance studies. In most cases, access to these studies is provided either as list of individual downloadable papers on performance studies, a report summarizing findings of multiple studies or as tabulated pollutant removal tables (often as percent reduction) by BMP category based on literature reviews. For example, Section 2.1 discussed a variety of technology verification programs, which typically provide access to individual performance studies, but not a master compiled database of underlying data. Similarly, a variety of organizations provide “clearinghouses” of BMP performance information (e.g., Virginia Stormwater Clearinghouse—http://vwrrc.vt.edu/swc/), but this is typically not in the form of a standardized, searchable on-line database with underlying metadata about the study conditions and event-based performance information. In other cases, a state agency may maintain an internal water quality database that includes BMP performance data, but it is often not publically searchable or open to studies outside the organization’s jurisdiction. In such cases, the organization maintains its database internally, but provides access to findings in the form of interpretive reports that are accessible on-line. For an example, see the Caltrans website (e.g., http://dot.ca.gov/hq/env/stormwater/special/newsetup/index.htm). In other cases, a static BMP performance database may have been compiled, but not funded or maintained over the long-run (e.g., EPA Performance Tool).

For purposes of supporting state DOTs across the U.S., the most versatile and useful BMP performance database approach is one that is conducted at a national scale, provides underlying monitoring data and metadata about the study site and the BMP design, and is publically accessible in a searchable database format. As a result of this review, the only known database currently meeting these criteria is the International Stormwater BMP Database (BMPDB, see www.bmpdatabase.org), which is cosponsored by WERF, FHWA, ASCE-EWRI, EPA, and APWA. Given that it already contains more than 140 highway-related data sets and is actively maintained as of 2015, it is used as the primary benchmark for comparison to other potential databases or clearinghouses that may be useful to the National Cooperative Highway Research Program (NCHRP). Additional discussion is provided for these BMP performance data sources:

• FHWA National Highway Runoff Database (HRDB)—focuses on highway runoff, rather than BMP performance, but is the repository most similar to the BMPDB.

• EPA website providing guidance on BMP performance.

• Center for Watershed Protection National Pollutant Removal Performance Database.

• State or regional databases.

• Summary of “clearinghouses” that may be a source of BMP performance data, although not in a searchable database format.

• Databases of other types of BMP-related information maintained by DOTs such as maintenance/inspection records or standardized attributes for BMP locations and features.

2.2.1 International Stormwater BMP Database

The BMPDB (www.bmpdatabase.org) currently contains nearly 600 BMP performance studies with over 300,000 water quality records for over 430 constituents during over 14,000 storm events. The BMPDB includes over 140 transportation-related BMP performance monitoring studies from 10 states around the country, as summarized in Table 2-7. Precipitation and flow data are also provided for storm events. The monitoring data are supported by metadata about the study site and the BMP design. The

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BMPDB is the largest known database of stormwater BMP performance data that is regularly updated and maintained and that contains data from many parts of the U.S., as well as several other countries. The project is a long-term effort; the initially populated database (originally released in 1999) has been updated approximately annually for 15 years. It is also the largest known repository of performance data for transportation-related BMPs. FHWA provides annual funding to the BMPDB, with its representatives serving on both the Project Steering Committee and the Project Subcommittee in accordance with WERF’s project management process. The BMPDB project has also developed detailed BMP performance reporting protocols and monitoring guidance (Geosyntec and WWE 2009) in an effort to make data from various BMP studies in various locations more comparable and informative regarding the potential causes of successful or poor BMP performance.

Table 2-7. Number of transportation-related BMP studies in the BMPDB as of July 2014.

BMP Category Transportation Related Studies by State CA DE FL MD MN NC TX VA WA WI Total

Biofilter (Strip) 34 0 0 0 0 3 2 1 0 0 40 Biofilter (Swale) 6 0 6 0 0 2 0 10 0 0 24 Bioretention 0 1 0 0 0 0 0 0 1 0 2 Composite (Train) 0 0 1 0 0 4 0 1 0 0 6 Control 0 0 0 0 0 0 2 0 0 0 2 Detention Basin 5 0 0 0 1 0 2 4 0 0 12 Manufactured Device 9 7 0 1 0 0 0 3 0 1 21 Sand Filter 8 1 0 0 0 0 0 1 0 0 10 Other Media Bed Filter 3 0 0 0 0 0 2 0 0 0 5 Porous Pavement 0 0 0 0 0 4 3 0 0 0 7 Permeable Friction Coarse Overlay 0 0 0 0 0 3 3 0 0 0 6 Retention Pond 1 0 2 0 0 0 0 0 0 0 3 Wetland Basin 0 0 0 0 0 0 0 5 0 0 5 Wetland Swale/Channel 0 0 1 0 0 2 0 0 0 0 3 Total 66 9 10 1 1 18 14 25 1 1 140

For a complete description of the BMPDB structure, the User’s Guide (WWE and Geosyntec 2010)

should be referenced; however, a brief overview of the BMP Database structure is provided in Table 2-8 and Figure 2-1. The BMPDB is populated through entry of data into Excel spreadsheets that are batch-uploaded from all data providers to a Microsoft Access Database approximately once per year. Table 2-8 provides a list of the Excel spreadsheets (within an overall workbook) and corresponding Microsoft Access table names.

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Table 2-8. BMPDB relational database tables.

Excel Worksheet Name Microsoft Access Database Table

Name PART 1. GENERAL TEST SITE 1. Test Site (General Information) TESTSITE 2. Study Info (Study Documentation) LAYOUTS PHOTOS 3. Agencies (Monitoring and/or Sponsoring Study) AGENCIES 4. Location Info (Site Location) TESTSITE PART 2. ESTABLISH MONITORED EVENTS 5. Monitoring Events EVENT 6. Monitoring Costs (for Overall Site) MONITORINGCOSTS PART 3. WATERSHED (TRIBUTARY AREA) 7. Watershed (General Information) WATERSHED NS01 8. Roads and Parking Lots (in Watershed) WATERSHED NS01 9. Land Use LANDUSE PART 4. GENERAL BMP (required for all sites) 10 BMP (General) BMP INFO S02 11. BMP Costs BMP COSTS PART 5. MONITORING STATIONS 12. Monitoring Station Relation (to BMPs) MONITORING STATION 13. Instrumentation (at Monitoring Stations) INSTRUMENTS PART 6. MONITORING RESULTS 14. Precipitation PRECIPITATION 15. Flow FLOW 16. Water Quality WATER QUALITY 17. Settling Velocity TSSVELOC PART 7. INDIVIDUAL BMP DESIGN SPREADSHEETS 18. Detention Basin DETENTION BASIN S02d 19. Retention Pond RETENPOND S02r 20. Grass Filters (Buffer Strips and Swales) GRASSFILTER S02g 21. Media Filters MEDIAFILT S02f 22. Permeable Pavement POROUSPAV S02p 23. Infiltration Basin INFBASINS S02i 24. Perc Trench (Percolation Trench and Dry Wells) INFTRENCH S02t 25. Wetland Channel WETLAND CHANNEL S02c 26. Wetland Basin WETLANDBASIN S02w 27. Manufactured Device (Multiple Types) MANUFACTURED DEVICE S02 28. Bioretention BIORETENTION S02b 29. Green Roof GREENROOF S02g 30. Rain Harvest (Rainwater Harvesting) RAINWATERHARVEST S02r 31. LID (Low Impact Development) LIDSITE S02L 32. Non-structural (BMPs) NONSTRUCT INFO N02 33. Other BMP OTHERBMP S02o 34. Composite BMP COMPOSITE BMP S02c

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Figure 2-1. Conceptual overview of BMPDB relational structure.

Each database table contains standardized reporting parameters (or data elements) related to BMP performance studies. To enable meaningful analysis of BMP data, a fairly large amount of information is requested in the spreadsheet-based data submittal package. These data requests are prioritized as “required,” “important, but not required” or “nice to have.” These data request categories were developed to recognize that not all data providers will be able to provide all of the requested information. The priority level for each data element is color coded in the spreadsheet cells according to these three priority levels:

• Required: “Required” data are necessary for proper evaluation and comparison of BMP performance. If these data are not provided, then the BMP study may either be rejected from inclusion in the BMPDB or excluded from certain types of analysis.

• Important: “Important” data are also necessary for proper evaluation and comparison of BMP data. If these data are currently unavailable, they should be collected in future monitoring efforts. Some of the watershed (tributary drainage area) data elements fall into this category.

• Nice to have: “Nice to have” fields provide data that are useful in BMP evaluation but not essential for BMP evaluation. For example, “comments” and cost data are considered nice to have. Nice-to-have fields are color-coded in yellow in the spreadsheets.

Most of the information requested in the BMPDB is relevant to both highways and other urban land

uses; however, two tables that may be particularly relevant to highway-related agencies are the Watersheds and General BMP Information tables. The Watersheds table provides information on the characteristics of the drainage area tributary to the BMP and is also linked to a separate Land Use table, which allows multiple land uses to be associated with a watershed. Transportation-specific land uses can be characterized as Park & Ride, Maintenance Station, or Highway. The Watersheds table allows entry of both test and control (reference) watershed characteristics associated with the study. The General BMP Information table is common to all BMP types in the database, with more detailed BMP design information provided in separate tables based on specific BMP type, as shown in Table 2-10. The data elements for the Watersheds and General BMP Information tables are summarized below because they represent metadata that could be modified to support highway-related research objectives, if needed.

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Table 2-9. Data elements in the BMPDB watersheds table.

General Watershed Characteristic

Road and Parking Lot Characteristics

Highway-specific Characteristics

Watershed Type Code (reference or test site)

Watershed Name Characterize Highway Conditions (see notes below)*

Watershed Description Total Paved Roadway Area Average Annual Daily Traffic (cars/day)

Total Watershed Area Total Length of Curb and Gutter on Paved Roads

Number of Lanes

Total Length of Watershed Total Unpaved Roadway Area Deicing Method** Average Overland Flow Length

Total Length of Curb/Gutter on Unpaved Roads

*Narrative description that can be used to address characteristics such as cruising, acceleration, deceleration, intersections, degree of truck traffic, parking/high turnover (e.g., toll plazas, rest stops, etc.), parking/low turnover (e.g., park and rides). **Options include: (1) Sand; (2) Sand/Salt; (3) Magnesium Chloride; (4) Other Chemical; (5) None; (6) Sand, Salt, and Magnesium Chloride.

Maximum Overland Flow Length

% Paved Roads Draining to Grass Swales/Ditches

Narrative Description of Flow Paths (for LID sites)

% Unpaved Roads Draining to Grass Swales/Ditches

Total Length of Grass-Lined Channels

Type of Pavement on Roadways

Total Disturbed Area Total Paved Parking Lot Area % Irrigated Lawn and/or Agriculture

Total Length of Curb/Gutter on Paved Parking Lots

% Total Impervious Area in Watershed

Total Unpaved Parking Lot Area

% of Total Impervious Area that is Hydraulically Connected

Total Length of Curb/Gutter on Unpaved Parking Lots

% of Watershed Served by Storm Sewers

% Paved Parking Lot Draining to Grass Swales/Ditches

% of Impervious Area with Canopy (estimated)

% Unpaved Parking Lot Draining to Grass Swales/Ditches

Storm Sewer Design Return Period (yrs.)

Type of Pavement in Parking Lots

Average Watershed Slope % Porous Concrete Average Runoff Coefficient % Porous Asphalt Hydrologic Soil Group % Porous Modular Soil Type Distribution of Hydrologic Soil Groups on LID Site

Narrative Description of Soil Conditions

Type of Vegetation

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Table 2-10. Data elements in the BMPDB general BMP information table.

General Description Maintenance Description Type of BMP Being Tested (Enter Code) Maintenance Type and Frequency Basis of Design (e.g., 2-yr, 24 hr. storm or design treatment flow rate)

Last Rehabilitation Date

Purpose of BMP (treatment objectives) Type of Rehabilitation Source of Design Guidance for BMP Qualitative Evaluation of BMP Condition

(vegetation, soils, odors, etc.) Date Facility Placed in Service For BMPs without permanent pool, does surface

ponding exist beyond design drain time? (Y/N) Number of Inflow Points If clogging present, estimate % of total surface

area of structure affected BMP Designed to Bypass or Overflow Description, Types, and Designs of Outlets Upstream Treatment Provided? Describe Upstream Treatment (if any) Name of Upstream BMP(s) (upstream to downstream)

General Configuration of BMP in Tributary Watershed (i.e., end of pipe, source control, off-line, on-line)

Was qualified engineering oversight provided at construction? (Y/N; unknown)

Was structure installed as designed? (Y/N; unknown)

General Description of Site Activities/Conditions Influencing Pollutant Loading to BMP

Describe BMP/Comments

2.2.2 Federal Highway Runoff Database

The U.S. Geological Survey (USGS) developed the Highway Runoff Database (HRDB) in cooperation with FHWA to serve as a data warehouse for current and future highway-runoff data sets (Granato and Cazenas 2009). The database is intended to be used to document information about a highway runoff monitoring study including the characteristics of the monitoring site(s), highway runoff data (including precipitation, runoff, and event mean concentrations [EMCs] of water-quality constituents), quality assurance/quality control (QA/QC) data, and sediment quality data. The HRDB provides information and data that may be used to assess potential impacts of highway runoff on receiving waters and the need for management measures to mitigate those impacts. The HRDB application (Figure 2-2) also was developed to serve as a data preprocessor for the Stochastic Empirical Loading and Dilution Model (SELDM). The USGS, in cooperation with FHWA, developed SELDM to replace the FHWA runoff quality model developed in the 1980s and published in 1990. SELDM uses information and data about a highway site, a receiving-water basin, precipitation events, storm flow, water-quality, and the performance of mitigation measures to produce a stochastic population of runoff quality variables. SELDM is a planning-level model that estimates event mean concentrations, volumes, and loads from a site of interest and from an

20

upstream basin to predict the effects of stormwater runoff on receiving waters while considering dilution and the uncertainties in hydrology and land use concentrations. The HRDB application, which is the graphical user interface and associated computer code, can be used to facilitate estimation of SELDM parameters including runoff coefficients, runoff-quality statistics, and relations between water-quality variables in highway runoff from the available data (Granato and Cazenas 2009).

As of July 2014, the HRBD included 37 tables with data for over 55,760 measurements (including over 100 water-quality constituents) from over 4,210 storm events, monitored at 119 highway-runoff monitoring sites in the conterminous United States, as documented in 10 selected highway runoff data sets shown in Table 2 11 (Granato and Cazenas 2009). The vast majority of the data sets are runoff characterization data, rather than BMP performance monitoring; however, a few BMP data sets are included. The BMP monitoring studies in the HRDB overlap with the BMPDB (e.g., Caltrans, Texas) for most of the studies, with the exception of two USGS studies, which were uploaded to the BMP Database in 2014.

Table 2-11. Highway data sets included in HRDB (Version 1.0.0a, May 2010).

HRDB Highway Data Set Period of Record FHWA 1990 Runoff Model Working Data 1975-1984 MA 2002 Highway BMP Data 1999-2000 CALTRANS 2003 Highway Runoff Data 1999-2003 WI 2000 DOT Urban Highway Sweeping Data 1999-2000 WA 2005 DOT Highway Runoff BMP Data 2001-2005 TX 1997 Highway Runoff Data 1994-1997 MI 1998 Highway Runoff Data 1995-1997 OH DOT 1995-1996 1995-1996 MA 2009 Highway Runoff Data 2005-2007 MA 2010 Highway Runoff Data 2008-2009

The HRDB and BMPDB have some similarities in terms of basic database structure and content. For

example, both databases are in Microsoft Access, following standard protocols for relational databases. Both allow entry of event-based monitoring data and request information documenting the tributary drainage area to monitoring locations. The major differences in the databases are that the HRDB is designed to characterize runoff, whereas the BMPDB is designed to characterize BMP performance, which affects both the content and structure of the databases. The BMPDB contains 18 tables related to

Figure 2-2. User interface of the HRDB.

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design characteristics of various BMP types and requests maintenance and cost data for the BMPs. Additionally, the BMPDB is structured to enable multiple monitoring stations to be associated with one test site. In the HRDB, each monitoring location is essentially a separate “site” with a different ID so pairing of the influent and effluent data requires some additional steps. Despite the differences in the purposes and content of the database, there may be data elements associated with the HRDB that could be integrated in the BMPDB to enhance its usefulness to highway-related entities. In terms of site-related characterization, the Highway Site table in the HRDB is of particular interest. Data elements included in the Highway Site table are summarized in Table 2-12. Most of these data elements are also represented in the BMPDB, with exceptions highlighted in grey.

Table 2-12. HRDB data elements in Highway Site table.

Site_ID ADT Avg Annual Precip

Receiving Water Name

Site Name Drainage Area Avg Wind Speed Hydrologic Unit Code Highway Data Set_ID

Impervious Fraction Number of Events

USEPA River Reach

State_ID Highway Traffic Lanes Number of Snow Events

Highway Site Narrative

County/City Monitored Traffic Lanes BMP (upstream BMP, if any)

Source Site ID

Location Description

Lane Width Begin Month Data Qualifier

Highway Mile Post

Length of Road (draining to monitoring site)

Begin Year

Latitude Pavement Type_ID End Month Longitude Curb (curb or raised berm at

the edge of the road) End Year

Lat/Long Accuracy

Section Type_ID (Unknown, Grade, Cut, Fill, Cut and Fill, Bridge, Other)

Altitude

Lat/Long Datum Drainage System Type_ID (Unknown, Swale, Pipe, Combined Sewer, Other)

Altitude Accuracy

Land Use Type (urban, non-urban, unknown)

Altitude Datum

Land Use Class (narrative)

In terms of data retrieval, the HRDB currently relies on a user downloading the database itself and

utilizing pre-formulated queries to extract and analyze data (or writing queries of their own). The BMPDB has a less developed graphical user interface (GUI) for the downloaded Microsoft Access database because it generally directs inexperienced database users to on-line search tools on the www.bmpdatabase.org website, including a Google Earth map interface that can be used to identify studies and extract specific studies, entire data sets, or summary statistics.

Other differences include increased focus on sediment characterization in the HRDB. Although the BMPDB allows entry of sediment data (Total Suspended Solids, Suspended Sediment Concentration, and Particle Settling Velocity Distribution), sediment is addressed more explicitly in the HRDB. Another difference is that the HRDB storm event table combines information about the event, precipitation, and

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flow into one table, rather than three separate tables. This partitioning is needed in the BMPDB due to the multiple monitoring stations typically associated with BMP studies. The BMPDB also includes a Monitoring Station table that enables multiple monitoring stations at a site to be related to each other as inflow, outflow, etc. The HRDB was not designed to relate multiple monitoring stations to a single test site due to its primary focus on runoff characterization, so each test site in the HRDB is essentially a separate monitoring station. Despite these differences, it would be relatively straightforward to integrate data elements and/or tables from the HRDB into the BMPDB to enhance consistency between these databases and to improve the overall information contained in each of them.

2.2.3 EPA’s BMP Performance Website

EPA does not currently provide its own BMP performance database on its website. Instead, EPA provides a compilation of BMP performance resources on its Green Infrastructure webpage (http://water.epa.gov/infrastructure/greeninfrastructure/gi_performance.cfm) that directs users to the following databases and summary reports:

• International Stormwater Best Management Practices Database (BMPDB): (www.bmpdatabase.org, as discussed above in Section 2.2.1).

• National Pollutant Removal Performance Database: Center for Watershed Protection Database (2000 CWP Database) (discussed below in Section 2.2.4).

• Runoff Reduction Method Technical Memo―Appendix F: BMP Research Summary Tables―Center for Watershed Protection summary tables for BMP performance (this is basically an update to the 2000 CWP Database).

• Illinois Green Infrastructure: This report prepared for the Illinois EPA summarizes the pollutant removal and volume reduction results reported in more than 50 peer-reviewed journal articles.

• University of New Hampshire Stormwater Center (UNHSC): 2009 Biannual Report―UNHSC operates a field research facility including several types of stormwater treatment systems. (Most of these studies are included in the BMPDB.)

In addition to directing users to these compilations, EPA provides links to individual studies for a variety of green infrastructure practices. Few of these studies are currently in highway settings, and the site does not specify reporting protocols for highway-related BMPs. For BMP performance monitoring protocols, EPA (http://water.epa.gov/scitech/wastetech/guide/stormwater/monitor.cfm) directs interested parties to the BMPDB performance monitoring manual (Geosyntec and WWE 2009), which provides the BMPDB reporting protocols.

In 2008, EPA had also developed an on-line searchable database called the Urban BMP Performance Tool, which incorporated findings from several existing databases, drawing primarily on the BMPDB and the CWP Database. As of July 2014, this tool appeared to be no longer publically accessible on EPA’s website.

2.2.4 National Pollutant Removal Performance Database: Center for Watershed Protection Database

The National Pollutant Removal Performance Database, Version 3, includes 166 performance studies published through 2006 contained in one Microsoft Access database table. The CWP Database focuses primarily on characterization of percent removal associated with various urban stormwater BMP types.

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Although the underlying database can be requested from CWP, it is not currently accessible in an on-line search engine format. Findings are primarily disseminated through interpretive reports prepared by CWP. CWP reviewed studies for potential inclusion into the database based on three target criteria: 1. Five or more storm samples were collected. 2. Automated equipment that enabled flow or time-based composite samples was used. 3. The method used to compute removal efficiency was documented.

In addition to percent removal estimates, a limited amount of study metadata is provided to document certain factors that may affect BMP performance, as summarized in Table 2-13.

Table 2-13. Reporting parameters for CWP Database.

ID

Land Use Study Number

BMP Size

BMP Category

BMP Age* BMP Type

Land Use Percentages (imperviousness, commercial, residential, industrial, forest, meadow)*

CWP Library (for internal use by CWP)

Watershed inches*

Author

Impervious inches* Reference

Drainage Class*

Facility Name

Performance Notes State

BMP Notes

Country

Load Efficiency (allows entry of notes on method used to calculate % removal)

Number of Storms

Concentration Efficiency (allows entry of notes on method used to calculate % removal)

Treatment Volume

% removal reported for various constituents load and concentration data for overall study (each water quality constituent is a separate data element)

Drainage Area

Load or concentration data (in and out) for overall study used to calculate the % removal (reported for some studies)

Soil Type Characteristics Slope

*Not included in Version 1.0 of database.

2.2.5 State or Regional Databases

Several states have developed BMP performance databases. A challenge associated with any database effort is an on-going funding mechanism that enables the database to continue to grow and be maintained over the long-term. For databases that are maintained over the long-term by state agencies, the focus is typically limited to their geographic jurisdiction and regulatory requirements, thereby limiting national applicability. Nonetheless, there may be features associated with state databases that would be useful at a national scale. A few examples of state databases include:

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• Missouri DOT Database: The Missouri DOT developed a Microsoft Access water quality database, with ArcGIS tools for the purpose of storing both general water quality data and BMP data relevant to impaired water bodies on the 303(d) list. The primary objective of the project is to create a “Monitor and Response Decision Tool” for MoDOT which would address the federally mandated stormwater management plan (SWMP) as it pertains to discharges to Water Quality Impaired Waters. Both the BMPDB and the HRDB were significantly referenced in developing the database structure; however, the primary BMP focus is erosion and sediment control practices (based on a pick-list of BMP types). A key objective of the database tools is the ability to use ArcGIS to overlay impaired waters with BMP locations and performance data. In terms of BMP design information, a basic table is provided named “BMP Measurements.” Data elements requested include: project/activity name, results of a baseline sample of the water body (e.g., water quality concentration before BMP implementation), the type of BMP being used, the spacing of the BMPs (e.g., distance in feet), the post BMP measurement of the water body, and the water body identification number (WBID). A Field Measurements table enables entry of water quality sampling data. See http://library.modot.mo.gov/RDT/reports/Ri08031/or10017.pdf for more information.

• Harris County Flood Control District (HCFCD), TX: HCFCD developed a regional BMP performance database for the greater Houston, TX, area modeled after the BMPDB. The database application provides HCFCD, regional partners, and other interested parties with a tool to access and evaluate the effectiveness of structural BMPs constructed within the southeast Texas region. Additional analysis and development tools were created to meet the needs of HCFCD, including access to BMP effectiveness data through a mapping interface that enables creation of maps, reports and statistical plots of the BMP effectiveness data. Data sets from the regional database are periodically exported to the BMPDB. No new highway-related parameters were added the HCFCD database. This database can be accessed at: http://www.hcfcd.org/bmp.html and will continue to be populated by the HCFCD.

• State of Florida BMP Database: Similar to the HCFCD database, the State of Florida Department of Environmental Protection (DEP) adapted the BMPDB structure for its use. The purpose of the Florida Stormwater BMP Database was to develop a centralized storage system to organize, store, and support assessments related to the performance of BMPs in Florida. Once the database was populated with 78 BMPs, the project transitioned to a static phase (i.e., a funding mechanism was not in place for on-going population of the database). The State of Florida DEP shared the populated database with the BMPDB, and the Florida data sets were uploaded to the BMPDB. An on-line web link to the database is not currently available.

• Oregon BMP Database: A Microsoft Access database was prepared based on a literature review for the Oregon Association of Clean Water Agencies in March 2005 (CH2MHill 2005). Any BMP study in the literature review that provided percent removal or effluent concentration data was included and was categorized according to BMP type. Each BMP study summary provided general site and location information, bibliographic information, and percent removal and effluent concentration data for selected water quality constituents. Percent removal and effluent concentration data were provided as presented in the study, according to these categories: mean, median, high, and low. Additional information recorded in narrative form included: effectiveness at reducing pollutants, effectiveness at achieving desired effluent quality, effectiveness at reducing discharge volumes, importance of and description of relevant physical variables; and costs to implement, operate, and maintain (CH2MHill 2005). The database findings were used to support a GIS-based model (PLOAD) to calculate pollutant loadings. See http://ci.klamath-

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falls.or.us/sites/ci.klamath-falls.or.us/files/Recycling/sw-bestmgmtpractices_0505.pdf for additional information. Similar to the Florida BMP Database, this database has not continued to be updated.

• Agricultural BMP Databases: Several states and organizations have developed agricultural BMP Databases (e.g., Arkansas, Florida, Minnesota, and Chesapeake Bay Area [via Virginia Tech]). These are not reviewed in this report since their primary focus is not highway-related; however, some of these databases contain some urban BMP performance information (perhaps a few studies per database). In most cases, these databases focus on summaries of water quality data as percent removals or as load reductions. Event-based data and standardized BMP design parameters are typically not provided. Limited tributary watershed information is sometimes provided (e.g., drainage area, soil type). For more information, see literature review prepared for the National Corn Growers Association and WERF (Wright Water Engineers and Geosyntec 2012).

2.2.6 Other BMP Performance Clearinghouses

In addition to formal databases, a variety of organizations provide “clearinghouses” of BMP performance data. These tend to be links to individual performance studies or overall interpretive reports. Table 2-14 summarizes some of the sources that may be useful for identifying studies that relevant to DOTs, but these sources were not reviewed in detail for this report.

Table 2-14. Examples of BMP performance data clearinghouses/repositories.

Study and Weblink State Description Washington State DOT http://www.wsdot.wa.gov/Environment/WaterQuality/Research/

WA Washington DOT stormwater research reports can be accessed on-line. A few references have a link to the full report in PDF format; others provide an abstract of the report. Additionally, WSDOT has formal database documentation available for their Stormwater Features Inventory Database, which may be useful for supplementing BMP performance reporting parameters in highway settings (as discussed in Section 2.2.7).

Caltrans Monitoring & Research and Applied Studies http://www.dot.ca.gov/hq/env/stormwater/special/newsetup/#monitoring

CA The California Department of Transportation (Caltrans) operates an extensive stormwater program with the objectives of integrating appropriate stormwater control activities into ongoing activities. A substantial number of BMP performance studies at highways and highway-related facilities, including those in retrofit contexts, has been completed. These studies can be downloaded in PDF format from the Caltrans website. Caltrans maintains an internal water quality database, but it is not accessible on-line. Much of the Caltrans BMP monitoring data set through 2008 has been uploaded to the BMPDB.

Lake Tahoe BMP Monitoring Evaluation Process - Synthesis of Existing Research (2nd Nature 2006) http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsm9_045816.pdf

CA In 2005, a BMP database structure was developed for BMPs in the Lake Tahoe area, along with a literature review evaluating and synthesizing BMP performance in the watershed. A publically accessible database was not ultimately completed; however, research completed in support of this effort may be useful for DOTs from this website.

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Study and Weblink State Description Washington Stormwater Center http://www.wastormwatercenter.org/

WA Washington Stormwater Center serves NPDES permittees and stormwater managers by providing tools to control stormwater and protect water quality. The website provides a gateway to the TAPE program and provides information on monitoring technologies. A database of LID locations is provided.

VA Stormwater BMP Clearinghouse http://vwrrc.vt.edu/swc/index.html

VA The BMP Clearinghouse is jointly administered by the Virginia Department of Environmental Quality (DEQ) and the Virginia Water Resources Research Center (VWRRC). Currently, the BMP Clearinghouse website is fairly static, pointing the user to documents in Adobe PDF format. In the future, the website may be driven by a database of textual elements, which will allow for more flexible access to the information contained in the site. The website directs users to BMPDB, TAPE, NJCAT and MASTEP for data, along with several university research programs.

Villanova Urban Stormwater Partnership (VUSP) http://www1.villanova.edu/villanova/engineering/research/centers/vcase/vusp1.html

PA The VUSP’s Stormwater Control Measure (SCM) Research and Demonstration Park includes several types of SCMs/BMPs. Performance data for these studies can be accessed from the VUSP website. (Note: Several of VUSP’s studies have been uploaded to the BMPDB.)

North Carolina State University http://www.bae.ncsu.edu/stormwater/pubs.htm

NC The North Carolina State Biological and Agricultural Sciences Group (NCSU-BAE) group conducts extensive stormwater BMP performance research and provides training on BMP inspection and maintenance. Publications can be downloaded from their website, with many having been conducted in highway settings. (Note: Many of NCSU-BAE’s studies have been uploaded to the BMPDB.)

University of Maryland/ Mid-Atlantic Water Program http://archive.chesapeakebay.net/pubs/bmp/BMP_ASSESSMENT_FINAL_REPORT.pdf and http://www.ence.umd.edu/~apdavis/LID-Publications.htm

MD The University of Maryland (UMD) is significantly involved in assessment of stormwater BMPs to support nutrient reduction measures associated with the Chesapeake Bay. Although an on-line searchable database is not available, the synthesis of BMP performance literature by Simpson and Weammert (2009) provides a BMP assessment providing a summary of studies relied upon for BMP performance estimates for nutrient reduction. More recent studies, including some highway applications, can be accesses on the UMD website.

University of Minnesota http://stormwater.safl.umn.edu/

MN The University of Minnesota Stormwater Research Program is currently developing new treatment technologies, providing guidance for assessment and maintenance of treatment practices, investigating groundwater impacts from stormwater infiltration, and improving models for runoff and treatment practices. Their website enables access to research being conducting in conjunction with their program. Strong guidance for inspection and assessment of BMPs in a maintenance context is also provided.

University of New Hampshire (UNH) Stormwater Center http://www.unh.edu/unhsc/

NH Provides a publications page, which includes findings of research conducted at the UNH Stormwater Center, as well as other locations where UNH is involved with research. Parking lots and roadways are included in these research reports. (Note: Many of UNH’s studies have been uploaded to the BMPDB.)

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Study and Weblink State Description Delaware DOT https://www.deldot.gov/stormwater/publications.shtml

DE Provides a publications page with a series of reports on highway-related BMP performance studies. (Some of these have been uploaded to the BMPDB.)

National LID Clearinghouse http://www.lid-stormwater.net/clearinghouse/

Natl. This website provides a web-based clearinghouse for LID-related permit strategies, technical guidance, and demonstration of effectiveness, outreach and education. Although a placeholder page (“Projects Database”) was created for information on research and case studies that have documented the effectiveness of LID techniques, this page was not completed under the grant that funded the website creation. Website last updated in 2007.

Low Impact Development Center http://www.lid-stormwater.net/

Natl. The Low Impact Development Center website focuses primarily on design guidance and examples, rather than on reporting BMP performance.

Green Highways Program http://www.lowimpactdevelopment.org/green_highways.htm

Natl. The Green Highways Program portion of the Low Impact Development Center website provides an overview of several projects; however, BMP monitoring data are not available through this site.

Transportation Research Information Database http://trid.trb.org/

Natl. The Transportation Research Information Database (TRID) is an integrated database that combines the records from Transportation Research Board’s Transportation Research Information Services (TRIS) Database and the OECD’s Joint Transport Research Centre’s International Transport Research Documentation (ITRD) Database. TRID provides access to more than one million records of transportation research worldwide. The information provided is in bibliographic format, with links to downloadable PDFs. This database includes NCHRP research, which is also accessible at http://www.trb.org/NCHRP/NCHRP.aspx.

USGS Transportation-Related Research http://water.usgs.gov/osw/TRB/index.html

Natl. Provides a webpage with links to PDFs summarizing the USGS’s annual research activities for the Transportation Research Board. This research is specific to highways.

University of Central Florida Stormwater Management Academy http://www.stormwater.ucf.edu/

FL Provides a research publications page which includes reports on pervious pavements and Biosorption Activated Media (BAM). Supported by the University of Central Florida, Florida DOT, and Florida Department of Environmental Protection.

2.2.7 Other Types of DOT Databases

Although most state DOTs do not maintain BMP performance databases that are linked with site and BMP metadata, many DOTs maintain some type of database/GIS inventory of BMP locations, characteristics, inspection records and/or maintenance records. The content and degree of sophistication of such databases varies and has not been inventoried in detail for purposes of this report. Nonetheless, DOTs with such databases may be useful resources for identification of additional data elements that

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should be considered when evaluating BMP performance in highway settings. Two examples are provided below. As one example, the Delaware DOT has a BMP inspection protocol that considers the condition of a variety of site and BMP characteristics, which are briefly summarized in Table 2-15. A scoring schema accompanies the reporting fields to indicate a range of conditions indicating that the BMP is functioning as designed, has operational problems or is non-functional. These types of parameters can be important in assessing BMP performance and could be considered as data elements in a BMP performance database. (Currently, the BMPDB includes some of these data elements or allows for entry in narrative form; however, these or similar elements could be refined for transportation agency-related purposes.)

Table 2-15. Delaware DOT BMP inspection parameters.

Information Type Parameters Considered Site Conditions Access, Fencing, Invasive Vegetation, Public Hazards Water Quality Influences (related to BMP conditions)

Inflow condition (outfall, swale, sheetflow), conveyance condition within BMP (surface & subsurface), downstream condition, pretreatment, ponding (unwanted), water quality contamination (organic debris, garbage, excessive sediment)

Embankments (for ponds) Upstream/downstream embankment cover, embankment seepage Outlet Structure (for ponds with embankments)

Riser (opening, low flow, structural integrity), primary spillway/outfall, emergency spillway

Source: KCI Technologies (2007) As a second example, Washington State Department of Transportation maintains a Stormwater

Features Inventory Database (WSDOT 2012), with formal documentation of stormwater features and attributes. This database includes documentation required under stormwater permit requirements for mapping and inventory, but also includes information related to BMP design characteristics. The “data dictionary” accompanying the WSDOT database provides useful definitions of site and BMP characteristics that could be considered as a resource to enhance the BMPDB or other national database.

2.2.8 Conclusions Regarding BMP Performance Databases

Based on a review of various BMP performance databases and “clearinghouses”, particularly with regard to highway-related applications, the International Stormwater BMP Database (BMPDB) is the only national-scale database that is actively populated, analyzed, and maintained, with both the database and associated interpretive reports accessible to the public. Additionally, the BMPDB includes the largest single known compilation of highway-related BMP performance studies with supporting study characteristics, BMP design information, and event-based monitoring data for precipitation, flow, and water quality. Although the BMPDB already includes a variety of highway-related reporting parameters, it would be relatively straightforward to create a highway-related table of data elements to further enhance the value of information reported with DOT studies. Data elements in the HRDB as well as inspection/maintenance-related parameters documented in various state DOT databases are areas where additional data elements could be considered for the BMPDB (see discussion in Section 5).

Although a separate transportation BMP database could be developed, the level of effort required would greatly exceed the effort required to enhance and amend the existing BMPDB to include more transportation agency fields. Additionally, long-term support for maintaining such a database would need

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to be addressed by DOTs, whereas the BMPDB benefits from a coalition of project sponsors that share the cost of maintenance and management of the BMPDB and related tools.

2.3 Summary of BMP Effectiveness Studies and Data Applicable to Highways

This section summarizes existing BMP studies that have been identified as being potentially applicable to state DOTs. An effort was made to identify studies to cover a diversity of geographic and climatic regions including both cold and warm and both wet and dry conditions. A searchable bibliographic database has been compiled that includes author, date, title, abstract, keywords, research categories, and BMPs and pollutant categories. The bibliography builds upon existing literature synopses and databases assembled by the project team, such as those developed for International Stormwater BMP Database project, NCHRP 25-20(02): Identification of Research Needs Related to Highway Runoff Management (NCHRP Report 521) and NCHRP 25-40: Long-Term Performance and Life-Cycle Costs of Stormwater Best Management Practices (NCHRP Report 792).

2.3.1 Study Evaluation Criteria

To focus the scope of the literature search, the Project Team’s research approach was to develop a set of criteria covering the major aspects of BMPs and their environment that are considered relevant to performance. The study evaluation criteria consist of a shortlist of relevant research topics, BMP types, pollutants, and geographic and physical site considerations that are deemed potentially influential and relevant to BMP performance. Once the research criteria were developed, studies and data sources were catalogued, reviewed, and evaluated accordingly.

2.3.1.1 Research Categories The literature search focused on the research areas considered relevant to BMP performance. Each of

the studies reviewed was entered into a bibliographic database developed to support this project, and annotated to indicate which of the research topics were covered by the study. The tagging of the individual studies with research categories facilitates quick search and retrieval of studies for further evaluation (see Section 3). The research categories and topics include:

• Runoff quality characterization: Studies categorized under the runoff quality characterization designation in the bibliographic database typically contain information related to the characterization of runoff and include information about expected quantity and quality of runoff from various land uses and their impact on BMPs. This research area is relevant to the current effort because the influent quality to a BMP impacts its performance; understanding influent characteristics allows the development of influent/effluent relationships that might be useful in predicting effluent concentrations.

• Capture efficiency: Studies in this research category typically include information about design variables that influence capture efficiency (the amount of runoff that is managed for water quality), full-brim drawdown times, and average detention times. Understanding the tradeoff between capture efficiency and water quality performance for both flow-limited and volume-limited BMPs in the context of design variables is useful in predicting overall BMP performance.

• Water quality performance: Studies in this research category typically include information about the water quality performance (percent removal of pollutants) of structural BMPs under various conditions, including site characteristics, climate, and pollutant loadings. Understanding

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BMP performance under various conditions is the cornerstone to answering the key questions of this project.

• Volume reductions and flow-duration control: Volume reduction and flow-duration control are increasingly becoming established as expected performance metrics for stormwater BMPs. Flow-duration control refers to matching pre-development flow rates and duration of those flow rates to minimize hydro-modification impacts to streams. Volume reduction and flow-duration control have been included here to complement water quality performance and to achieve a more holistic recognition of the factors that influence BMP performance.

• Design variables and unit treatment processes: Design criteria and unit treatment processes are the objective variables that will allow the construction of deterministic relationships for understanding BMP performance and extrapolating known performance to other locations with less information. Studies that relate design criteria variation and unit treatment processes to other characteristics of BMPs are therefore considered highly relevant.

• Long-term pollutant retention: Pollutant retention in BMPs is an important consideration in long term performance, and therefore studies in this category enhance understanding of BMP performance over time.

• Maintenance: Maintenance is related to long-term pollutant retention and overall performance. Maintenance practices and costs differ geographically, which can affect BMP performance. Studies that relate maintenance practices to performance could allow performance prediction relationships to be adjusted based on how BMPs are maintained. However, research to date, including research recently completed for NCHRP Report 792, has not shown a relationship between maintenance and performance.

• Climatic and metrological factors: The transferability of BMP performance is highly dependent on geography and spatial location, primarily due to variations in climatic and meteorological factors. Studies that allow us to understand which climatic and meteorological factors are important for BMP performance are considered highly relevant.

• BMP treatment trains: To ensure that any conclusions derived for individual BMPs are applicable to combinations of BMPs, research related to the performance of BMP treatment trains is considered relevant since BMPs are increasingly deployed in combination with other BMPs.

Table 2-16 shows the number of studies identified for each research category. The literature review was not exhaustive; however, the number of studies in each research area serves as an indication of the relative ease of obtaining studies related to any one topic as compared to another. Table 2-16 confirms our general understanding of known data gaps and compares favorably to other past literature review efforts.

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Table 2-16. Research categories and studies reviewed.

Research Category Number of Studies Reviewed

Capture Efficiency 5 Climatic and Meteorological Factors 9 Design Variables and Unit Treatment Processes 8 Hydro-modification Performance 3 Long-Term Pollutant Retention 1 Maintenance 9 Runoff Quality Characterization 27 Treatment Trains 1 Volume Reduction Performance 9 Water Quality Performance 44 TOTAL 116

2.3.1.2 BMP Types NCHRP 25-25/83: Current Practice of Post-Construction Structural Stormwater Control

Implementation for Highways conducted a survey in which all 50 state DOTs participated to determine the post-construction structural stormwater BMPs types commonly being used by DOTs (Venner et al. 2013). DOTs were asked to indicate whether they used various BMPs “frequently,” “sometimes,” “rarely,” or “never.” Rankings were compiled and summed for each BMP type by assigning a value of 2 to “frequently,” 1 to “sometimes,” 0 to “rarely,” and -1 to “never” for all the responses in the survey. The NCHRP 25-25/83 BMP list is the basis of the set of BMPs evaluated in this effort. The list of BMPs “frequently” and “sometimes” used by DOTs is presented in Table 2-17 and the “rarely” and “never” used BMPs are shown in 1 Ranks were determined by summing DOT responses after assigning a value of 2 to “frequently,” 1 to “sometimes,” 0 to “rarely,” and -1 to “never” for all the responses in the survey.

Table 2-18. Ranks were determined by summing DOT responses after assigning a value of 2 to “frequently,” 1 to “sometimes,” 0 to “rarely,” and -1 to “never” for all the responses in the survey.

Table 2-17. Commonly used BMPs according to NCHRP25-25/83 survey of DOTs.

Rank1 “Frequently” and “Sometimes” Used BMPs 86 Vegetated Swale 52 Rock Swale 47 Filter Strip 44 Dry Detention Basin 31 Wet Pond/Retention Basin 16 Infiltration Basin 11 Infiltration Trench 10 Compost-amended Slope 10 Wetland Swale/Channel

8 Oil/Water/Grit Separator Vault 5 Bioretention/Rain Garden (Bioretention without Underdrain) 3 Wetland Basin

1 Ranks were determined by summing DOT responses after assigning a value of 2 to “frequently,” 1 to “sometimes,” 0 to “rarely,” and -1 to “never” for all the responses in the survey.

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Table 2-18. Rarely used BMPs according to NCHRP25-25/83 survey of DOTs.

Rank “Rarely” and “Never” used BMPs -4 Hydrodynamic Device -4 Other BMPs -7 Permeable Shoulders or Parking -9 Catch Basin Insert

-10 Sand Filter -13 Permeable (Open-Graded) Friction Course Overlay for Water Quality Purposes -13 Underground Detention Vault -19 Bioslope/Ecology Embankment//Filter Strip with Soil Amendment/Media Filter Drain -26 Underground Infiltration Vault -26 Dry Well (Class V Injection Well) -31 MCTT―Multi-Chambered Treatment Train (e.g., with Tube Settlers) -33 Batch Detention (Real-Time Automated Outlet) -34 Cartridge Filter

1 Ranks were determined by summing DOT responses after assigning a value of 2 to “frequently,” 1 to “sometimes,” 0 to “rarely,” and -1 to “never” for all the responses in the survey.

Using the NCHRP 25-25/83 list of “frequently” and “sometimes” used BMPs, and comparing those

BMPs to the BMP categories in the BMPDB, the project team created the shortlist of BMPs shown in Table 2-19. The list covers all the NCHRP 25-25/83 “frequently” and “sometimes” used BMPs with minor changes in BMP names and aligns well with the BMP categories in the BMPDB. This list of BMPs is the set of BMPs evaluated in Section 3.

As is evident from the DOT popularity rankings in Table 2-17, some of the selected BMPs are more pervasive and one would therefore expect more studies to be available for the BMPs that are more actively used and have been in use for a longer time. Table 2-19 shows the relative distribution of studies by BMP type. The number of studies for the various BMP types roughly correlates with the DOT popularity rankings from NCHRP 25-25/83, as would be expected.

Table 2-19. Proposed list of BMPs to be evaluated versus literature study availability.

BMP Type Available Studies from Literature Search

Bioretention 9 Constructed & Pocket Wetlands 5 Dry Detention Basins 12 Infiltration Trenches/Basins 7 Manufactured Device 17 Permeable Pavement 7 Wet Retention Ponds 10 Sand/Media Filter 14 Vegetated Swales & Filter Strips 19 Wetland Basin/Channel 2

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2.3.1.3 Pollutant Types DOT stormwater management practitioners encounter many of the same pollutants that urban

stormwater management practitioners encounter, albeit in different quantities and concentrations. Therefore the ideal set of pollutants considered relevant for this study would be typical stormwater pollutants as tracked and stored in the existing Stormwater BMP data clearinghouses discussed in Section 2.2. Using the stormwater pollutants in the BMPDB as a starting point for creating a shortlist of pollutants to consider, the BMPDB pollutants have been grouped into categories including nutrients, solids, metals, polycyclic aromatic hydrocarbons (PAHs), bacteria, and other. At least one pollutant was selected from each category to formulate a set of pollutants to be evaluated in Section 3. The shortlist of pollutants used for the remainder of the report is shown in Table 2-20. For nutrients, total nitrogen (TN) and total phosphorus (TP) were initially selected, but the number of data points for TN in the BMPDB is limited, so nitrate (NO3) was selected. For solids, TSS and turbidity were initially selected, but turbidity data are lacking. For metals, copper (Cu) and zinc (Zn) were selected due to these being frequently monitored and detected. Data sets for other metals are more limited and are often confounded by a high number of non-detects. For hydrocarbons, all of the constituent data sets are limited but total petroleum hydrocarbons (TPH) is the largest data set. For bacteria, fecal coliform and E. coli were selected because these are the most frequently monitored and detected constituents in this category. For the general “Other” category, data are sparse, but chemical oxygen demand (COD) had the largest dataset to conduct analyses.

Table 2-20. Relevant pollutant categories and pollutant types.

Constituent Category Constituent Subcategory Representative Constituents Evaluated

Nutrients Nitrogen (TN, NH3, NO2, NO3, DN, Organic-N, TKN) Phosphorus (P, TP, SRP, DP, Ortho-P) Organic Carbon (DOC, TOC)

TP, NO3

Solids Trash and Debris Dissolved Solids (TDS) Suspended Solids (TSS) Suspended Sediment Concentration (SSC) Settleable Solids Turbidity Volatile Solids Particle Size Distribution (PSD)

TSS

Metals Al, As, Cu, Pb, Zn, Ni, Cd, Fe, Cr (total and dissolved)

Cu, Zn (total and dissolved)

Hydrocarbons Oil and grease, Total Petroleum Hydrocarbons (TPH), Total PAHs, Anthracene, chrysene, etc.

TPH

Bacteria Enterococcus, E. coli, Fecal Coliform, Fecal Streptococcus, Total Coliform

Fecal Coliform, E. coli

Other Conductivity, Biochemical Oxygen Demand (BOD), COD, Dissolved Oxygen, Hardness, pH, Temperature, ORP

COD

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2.3.1.4 Geographic Locations A key consideration in BMP performance variability is geographic location, which determines the

climatic and meteorological conditions under which BMPs operate as well as local geology and topography. The literature search focused on BMP studies from all over the country. To facilitate spatial categorization/aggregation of studies in the bibliographic database, studies are tagged geographically using EPA’s rainfall zones as shown in Figure 2-3. Each zone corresponds to a geographical region with similar climatic conditions (USEPA 1986). However, it is acknowledged that there is significant variability within each region. Table 2-21 shows a count of the studies found for each of the regions. Due to the limited number of studies reviewed, the table is not intended to conclusively demonstrate the abundance of studies in one region versus a lack of studies in another; rather it provides a quick inventory of what will be available for further evaluation of geographic location related to performance (Section 3).

Source: NPDES Phase I regulations, 40 CFR Part 122, Appendix E (USEPA 1990) Figure 2-3. EPA rainfall zones.

Table 2-21. Geographical distribution of BMP performance-related studies.

EPA Rainfall Zone Number Number of Studies Found Zone 1 - Great Lakes 12 Zone 2 - Northeast 8 Zone 6 - Southwest 7 Zone 3 - Southeast 6 Zone 7 - Northwest 5 Zone 5 - Texas 3 Zone 9 - Rocky Mountain 2

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2.3.2 Study Evaluation Approach

The Project Team’s approach for evaluating the transferability of post construction BMP performance studies is based on a combination of using data from existing BMP performance data clearinghouses in combination with published literature studies. As previously stated, the BMPDB is the largest, most established BMP data repository and therefore the primary source of data for studies related to BMP performance. Thus, in addition to the broader literature search, the Project Team reviewed and summarized relevant studies in the BMPDB to attain a more complete picture of data availability and to fill data gaps.

As of May 2015, there are approximately 434 studies in the BMPDB that are considered relevant to this project based on the BMP types selected for analysis as shown in Table 2-19. Table 2-22 provides an overview of the relevant studies in the BMPDB. Table 2-23

Table 2-23 shows the number of transportation-related studies by state. California leads the nation as the state with the most transportation-related studies in the BMPDB, followed by Delaware and Florida. In terms of BMP types, filter strips are the most represented category of BMPs, followed by vegetated swales and manufactured devices. Tables 2-23 through 2-27 summarize the number of influent and effluent data sets by storm event for selected constituents potentially relevant to this study. These tables summarize all available data (transportation-related and others) for the selected set of BMP types.

Table 2-22. Overview of all studies and data in BMPDB for BMP types analyzed.

Total number of studies 434 Total number of transportation-related studies 126 Total number of states represented 23 Total number of states represented in transportation-related studies 10 Highest number of studies for any one state 78 (FL) Highest number of transportation-related studies for any one state 66 (CA) Total number of states with no studies 27 Total number of BMP types in all studies 12

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Table 2-23. Number of transportation-related studies in BMPDB analyzed.

State TOTAL BMP Type CA DE FL MD MN NC TX VA WA WI

Bioretention 1 1 2 Dry Detention Basins 5 1 2 4 12 Filter Strips 34 3 2 1 40 Manufactured Device 9 7 1 3 1 21 Permeable Pavement 1 1 Retention Ponds 1 2 3 Sand/Media Filter 11 1 2 1 15 Vegetated Swales 6 6 2 10 24 Wetland Basin 5 5 Wetland Channel 1 2 3 Total 66 9 9 1 1 8 6 24 1 1 126

Table 2-24. Number of influent/effluent data sets in BMPDB for solids.

Total dissolved solids Total suspended solids

Bioretention 116/77 319/265 Dry Detention Basins 100/86 464/443 Filter Strips 617/434 863/659 Infiltration Trenches 32/0 35/3 Manufactured Device 292/321 1118/1042 Permeable Pavement 19/4 28/253 Retention Ponds 172/161 1170/1077 Sand/Media Filter 218/205 563/535 Vegetated Swales 95/82 283/454 Wetland Basin 21/12 491/324 Wetland Channel 92/79 306/234 Grand Total 1774/1461 5640/5289

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Table 2-25. Number of influent/effluent data sets in BMPDB for metals.

Aluminum, Total

Cadmium, Total

Copper, Dissolved

Copper, Total

Lead, Total

Zinc, Dissolved

Zinc, Total

Bioretention -/- 52/48 92/66 75/67 59/54 50/46 118/110 Dry Detention Basins -/- 230/224 210/210 295/275 301/280 209/211 343/344

Filter Strips -/- 617/436 628/446 679/484 710/511 628/446 730/538 Infiltration Trenches -/- 32/0 -/- 33/0 35/3 -/- 35/3

Manufactured Device -/- 329/316 305/389 479/526 337/386 303/388 636/683

Permeable Pavement -/- 0/127 19/196 0/233 0/213 0/179 11/215

Retention Ponds 50/25 528/471 347/383 888/828 887/790 321/321 955/851 Sand/Media Filter -/- 317/299 237/218 471/445 456/420 237/211 546/517 Vegetated Swales -/- 142/127 114/96 144/373 168/392 114/96 199/419 Wetland Basin 8/8 179/92 28/25 243/153 214/119 28/25 313/198 Wetland Channel -/- 80/64 52/47 139/122 219/156 64/56 151/132 Grand Total 58/33 2506/2204 2032/2076 3446/3506 3386/3324 1954/1979 4037/4010

Table 2-26. Number of influent/effluent data sets in BMPDB for nutrients.

Kjeldahl Nitrogen

(TKN)

Nitrogen, Ammonium (NH4) as N

Nitrogen, Nitrite (NO2)

+ Nitrate (NO3) as N

Phosphorus as P,

Dissolved Phosphorus as P, Total

Bioretention 251/219 36/38 320/294 22/21 380/328 Dry Detention Basins 287/279 -/- 198/185 142/124 418/402 Filter Strips 841/625 43/28 148/116 21/17 862/639 Infiltration Trenches 34/3 -/- 2/0 -/- 35/0 Manufactured Device 508/443 -/- 529/407 277/267 767/702 Permeable Pavement 0/240 0/34 12/230 0/102 9/233 Retention Ponds 692/643 -/- 672/609 460/428 1136/1039 Sand/Media Filter 501/480 -/- 265/236 131/123 536/515 Vegetated Swales 178/393 43/41 132/371 76/59 245/464 Wetland Basin 217/201 -/- 312/228 175/115 439/295 Wetland Channel 221/193 43/38 67/62 148/98 283/217 Grand Total 3730/3719 165/179 2657/2738 1452/1354 5110/4834

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Table 2-27. Number of influent/effluent data sets in BMPDB for oxygen demanding substances.

Biological Oxygen

Demand (5-day) Chemical Oxygen

Demand Bioretention 37/32 59/53 Dry Detention Basins 123/114 172/159 Filter Strips 11/0 99/88 Infiltration Trenches 1/0 2/3 Manufactured Device 167/129 354/304 Permeable Pavement 0/4 0/129 Retention Ponds 284/308 485/430 Sand/Media Filter 147/139 237/212 Vegetated Swales 49/48 105/101 Wetland Basin 27/26 74/50 Wetland Channel 40/43 93/66 Grand Total 886/843 1680/1595

Table 2-28. Number of influent/effluent data sets in BMPDB for hydrocarbons.

Tota

l Pet

role

um

Hyd

ro-c

arbo

ns,

(TPH

)

Oil

and

Gre

ase

Ben

zo[a

]pyr

ene

Chr

ysen

e

Fluo

rant

hene

Phen

anth

rene

Pyre

ne

Tota

l PA

Hs

Bioretention -/- 43/34 -/- -/- -/- -/- -/- -/- Dry Detention Basins 8/0 74/77 -/- -/- -/- -/- -/- 8/0 Filter Strips 9/0 -/- -/- -/- -/- -/- -/- -/- Infiltration Trenches -/- -/- -/- -/- -/- -/- -/- -/- Manufactured Device 73/54 191/185 50/41 50/44 101/92 50/42 101/91 26/15 Permeable Pavement -/- 0/4 -/- -/- -/- -/- -/- -/- Retention Ponds 13/11 56/52 15/13 15/13 27/25 27/25 27/25 -/- Sand/Media Filter 20/1 44/26 -/- -/- -/- -/- -/- -/- Vegetated Swales -/- 51/46 -/- -/- -/- -/- -/- -/- Wetland Basin 1/0 26/21 13/10 13/10 13/10 13/10 13/10 -/- Wetland Channel -/- 28/29 -/- -/- -/- -/- -/- -/- Grand Total 124/66 513/474 78/64 78/67 141/127 90/77 141/126 34/15

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2.3.3 List of Potentially Relevant Studies

The list of potentially relevant studies consists of 50 studies stored in the bibliographic database that accompanies this document. The bibliographic database contains forms that allow quick entry and categorization of additional studies; a feature that can be utilized by other researchers in the future.

2.3.4 Discussion of Potential Data Gaps

Recall that the study approach consisted of reviewing studies from literature and BMP data clearinghouses. The literature reviewed was categorized according to the 10 topic areas shown in Table 2-16, which show the number of studies found for each topic. The topic with the most studies found is runoff quality characterization, while the topics with the lowest number of studies found are long-term pollutant retention and treatment trains. In terms of BMP types, vegetated swales and filters were the BMP type with the most studies found followed by manufactured devices. Natural wetland basins and channels had the fewest studies (see Table 2-19).

The geographic distribution of the reviewed studies by EPA rainfall region is shown in Table 2-21. As indicated, Zone 1 (Great Lakes) leads with 12 studies found while Zone 9 (Rocky Mountain) is at the bottom of the list with 2 studies.

In reviewing the available data from the BMP data clearinghouses, the BMPDB was determined to be the most complete source of BMP data and Tables 2-22 through 2-28 summarize the available data according to various criteria and areas of interest. Table 2-22 presents an overview of the studies in the BMPDB and shows that 23 states are currently represented in the BMPDB, which reveals a data gap in terms of the geographic representation.

Table 2-23 further underscores this point by showing the number of transportation-related studies from only nine states with four states having only one study each. In terms of BMP types, filters strips lead with the most transportation-related studies (40; mostly from Caltrans) followed by vegetated swales (24). In terms of pollutants, Tables 2-24 to 2-28 summarize the available influent/effluent data by BMP types for five pollutant categories including solids, metals, nutrients, oxygen demanding substances, and hydrocarbons. Total suspended solids lead with the most data followed by total phosphorus and total zinc. Hydrocarbons make up the smallest available data set (particularly PAHs) and these data are primarily associated with manufactured devices.

40

3 Effect of Geographic and Other Variables on BMP Effectiveness One of the goals of this project is to develop guidance to inform DOTs on the comparability and

transferability of post-construction BMP effectiveness studies and provide recommendations for standardizing BMP data collection and management efforts. This section evaluates the variables that potentially influence the transferability of BMP effectiveness information and includes:

• Assessment of influent characteristics and BMP unit operations and processes (UOPs) impacted by geographic and land use variables (Section 3.1).

• Evaluation of effects of specific variables on BMP effectiveness including: climate and hydrology (Section 3.2), soils and topography (Section 3.3), and land use, including traffic volumes and adjacent land uses (Section 3.4).

• Comparison of DOT and non-DOT BMP studies and water quality data for transferability (Section 3.5).

BMP performance is influenced by two main factors, namely, influent characteristics and BMP UOPs. Both of these factors are affected by geographic variables such as climate, site soils, topography, and site land uses. The next section (Section 3.1) provides some background on these two BMP performance factors and sets the stage for evaluating the effects of geographic variables on performance. Sections 3.2, 3.3, and 3.4 discuss specific geographic and land use variables and their impacts on the two BMP performance factors.

3.1 Background―BMP Performance Factors

Watershed characteristics and weather patterns directly influence the quality and flow regime of runoff introduced into a BMP, which can thereby influence the quality of the effluent that leaves the BMP after treatment or bypass/overflow. Similarly, the treatment mechanisms, or UOPs, within the BMP provided by various design features and components directly affect the quantity of managed runoff and the quality of the effluent that leaves the BMP. While other factors may influence water quality downstream of a BMP and within receiving water systems, those factors will not be evaluated as part of this effort.

While influent characteristics determine the nature of the stormwater flows that are delivered to a BMP, the UOPs that occur within a BMP control how the influent is managed and transformed prior to leaving the BMP. The primary UOPs provided by stormwater BMPs include flow attenuation, surface runoff volume reduction, filtration, settling/sedimentation, sorption, microbially-mediated transformation and plant uptake. Flow attenuation refers to the difference in peak discharge between inflows and outflows with the outflows being lower. Volume reduction refers to the loss of stormwater runoff within a BMP via infiltration, re-use or evapotranspiration. UOPs can be loosely grouped into four main categories:

• hydrologic/hydraulic operations, • physical processes, • biological processes, and • chemical processes.

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3.1.1 Hydrologic/Hydraulic Operations

Hydrologic/hydraulic operations (flow alteration) are limited to operations that control the quantity and flow characteristics of stormwater with little or no direct effect on concentrations. Flow alteration operations include modifications to components of the hydrologic cycle (runoff, infiltration, and evaporation [ET]) and detention/storage with the goal of controlling volumes and runoff rates. Hydrologic modification is ubiquitous in the built environment and may be intentional or inadvertent, as well as beneficial or detrimental. Examples of intentional hydrologic modifications that have potential water quality and quantity benefits include infiltration and detention/flow equalization. Detrimental hydrologic modifications include impervious area creation, compaction of urban soils and loss of vegetation.

3.1.2 Physical Operations

UOPs classified under physical operations (in contrast to chemical or biological processes), are forms of stormwater treatment that are brought about by physical mechanisms such as settling/sedimentation, filtration, and aeration/volatilization. Physical unit operations are the dominant forms of treatment in many stormwater BMPs.

3.1.3 Biological Processes

Biological unit processes for stormwater treatment utilize living organisms (e.g., plants, algae, and microbes) to transform or sequester organic and inorganic constituents from water and soil. They primarily include microbially-mediated transformations and uptake and storage processes.

3.1.4 Chemical Processes

Chemical characteristics, such as pH, alkalinity, hardness, redox conditions, organic carbon, and ionic concentrations, dictate dissolved solids partitioning and speciation of stormwater pollutants, which in turn controls the type of UOPs necessary to treat those pollutants. Three common chemical UOPs are sorption, coagulation/flocculation, and disinfection (e.g., ozonation, irradiation, etc.). For some pollutants and BMP types, chemical UOPs can be the dominant removal mechanism (for example, dissolved copper in a media filter).

3.1.5 Summary of UOPs by BMP Type and Pollutants Removed

The UOPs in each of the broad categories and the representative BMPs that provide these processes are summarized in Table 3-1.

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Table 3-1. Stormwater BMP UOP categories.

UOP Category Unit treatment process/operation

Typical BMPs with Significant UOP Category Functions

Hydrologic operations Flow attenuation Surface runoff volume reduction

Detention basins Bioretention Biofilters (swales, filter strips) Porous pavement Infiltration basins/trenches/wells

Physical unit treatment process

Filtration (size separation/ exclusion) Gravity(density) separation/ flotation/skimming Aeration & volatilization

Gross solids removal devices Detention basins Infiltration basins Wet ponds and wetlands Porous pavement Biofilters (swales, filter strips) Bioretention

Biological unit treatment process

Microbially mediated transformations Uptake and storage

Biofilters (swales, filter strips) Wet ponds/wetlands Bioretention

Chemical unit treatment process

Sorption Coagulation/precipitation/ flocculation Disinfection

Media filters Bioretention Active treatment BMPs (e.g., chitosan-enhanced sand filter, electrocoagulation, alum or polyacrylamide injection, etc.) Wetlands Disinfection

The factors that influence the effectiveness of these UOPs, such as particle size distribution, residence

time, settling rates, temperature, pH, vegetation density, etc., will be discussed within the context of the highway environment and the various climate regimes of the United States in subsequent sections of this document. Table 3-2 shows which UOPs are effective for removal of various pollutants. For detailed information on UOPs, refer to Critical Assessment of Stormwater Treatment and Control Selection Issues, published by WERF (Strecker et al. 2005).

43

Table 3-2. Unit processes effective for removal of pollutants commonly found in stormwater.

Pollutants

Unit Process Hydrologic Physical Biological Chemical

Infil

tratio

n

Gra

vity

Set

tling

/ Fl

otat

ion/

Skim

min

g

Filtr

atio

n/S

orpt

ion

Aera

tion/

Vola

tiliz

atio

n

Mic

robi

ally

Med

iate

d Tr

ansf

orm

atio

n

Plan

t Upt

ake

Coa

gula

tion/

Flo

ccul

atio

n

Prec

ipita

tion/

Fl

occu

latio

n

Dis

infe

ctio

n

Particulates (sediments, solids, heavy metals, organics, nutrients)

Solubles (heavy metals, organics/BOD, nutrients)

Trash and Debris

Flotables (oil and grease)

Bacteria

3.2 Effects of Climate And Hydrology

Climate and hydrology are critical, interrelated driving forces for stormwater management in the U.S. and around the world. Climatic factors including wind speeds, temperatures, surface/atmospheric moisture and pressure, precipitation, and others affect stormwater runoff flow characteristics and pollutant loadings as well as the performance of stormwater BMPs. Figure 3-1 and Figure 3-2 show average annual precipitation and normal mean temperatures, respectively, from 1981-2010 for the continental U.S. Note that both figures show significant geographic variability across the nation. Other climatic variables including minimum and maximum temperatures, daily temperature range, evapotranspiration (ET), and others show similar geographic variability. Differences due to climate and hydrology affect BMP performance by impacting runoff before it enters the BMP (influent impacts), and while the flow is within the BMP (UOP impacts). These climatic/hydrologic variations include:

• Differing rainfall intensity-duration-frequency relationships across the country • Differences in ET, soil moisture, temperature and other related factors that affect types and

success of vegetation in stormwater systems. • Temperature can affect viscosity of water which can affect particle settling rates. • Seasonal differences that affect the growing season, nutrient cycling in vegetative communities,

seasonal groundwater levels, and decay/decomposition. • Presence or absence of snow and frozen surfaces due to changes in temperature and differences in

snow melt characteristics due to variations in sunshine, precipitation, temperature, and wind.

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Of the various climatic and hydrologic factors mentioned, precipitation and temperature are arguably the two most influential factors as far as BMP performance is concerned. Precipitation and temperature are connected and inter-related drivers of the hydrologic cycle and it is difficult to separate and quantify the effect of one without the other.

Temperature is one of the primary drivers of the hydrologic cycle (others include locations of large water bodies/oceans, prevailing winds, elevation/topography, etc.), and despite local and regional variations, which will fall on either side of the predicted changes in global temperatures, the balance between the amount of water on the surface of the earth and the amount in the atmosphere will indisputably shift toward more atmospheric moisture and less surface water on a global scale as temperatures increase. Recent results from the White House’s National Science and Technology Council and the U.S. Global Change Research Program’s recently published 3rd National Climate Assessment (NCA) state U.S. mean temperature increases of 1.3 to 1.9°F since 1895 and project continuing increases (Walsh et al. 2014). While local effects are uncertain, the global effect is a matter of thermodynamics and will influence stormwater management strategies in many locations in different ways depending on geography, geology, hydrology, and other factors over the long term. Recent research has also shown that climatic extremes of temperature, rainfall, drought, and other climatic and hydrologic parameters are becoming more variable as temperatures increase, which is important for design of treatment measures. Accounting for average or design conditions will provide a basis for design, but planning for how these facilities function during very wet or dry periods is an increasingly important consideration.

Source: PRISM, Oregon State University, 2013 Figure 3-1. Geographic distribution of average annual precipitation (1981-2010).

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Source: PRISM, Oregon State University, 2013 Figure 3-2. Geographic distribution of normal mean annual temperature (1981-2010).

Precipitation, including both rain and snow, is a key component of the hydrologic cycle. Precipitation produces runoff, which mobilizes pollutants from land surfaces creating the influent that is routed to stormwater treatment BMPs. The intensity, frequency, and duration of precipitation events affect the flow characteristics of the influent stream and its ability to mobilize and transport stormwater quality constituents, therefore directly impacting BMP performance.

3.2.1 Climate and Hydrology Effects on BMP Influent Characteristics

The effect of geographic variations in climate and hydrology (especially temperature and precipitation) on BMP influent characteristics (quantity, flow regime, and quality) can be evaluated according to their influence on stormwater flow characteristics and their effects on stormwater quality constituents. While impacts to stormwater flow characteristics can be easily quantified by analysis of variations in precipitation patterns across the nation, climatic effects on stormwater constituents are harder to quantify due to additional impacts from anthropogenic factors related to land uses. Local design requirements or standards typically account for the local precipitation patterns, therefore lessening the impacts related to varying precipitation depths and intensities on the performance of BMPs. For this reason, this section focuses on discussing impacts to stormwater constituents. For example, precipitation patterns can affect particle size distributions in stormwater runoff and these may vary within and between rainfall events due to differences in antecedent dry period, rainfall intensity, rainfall duration, and other factors (Leisenring et al., 2013). Precipitation patterns can also have a significant effect on pollutant build up and wash off since the frequency and intensity of rainfall can be directly related to these physical processes.

Highway runoff quality data were obtained from the Highway-Runoff Database (HRDB) (Granato and Cazenas 2009; Smith and Granato 2010) and the National Stormwater Quality Database (NSQD) (Pitt 2008) to evaluate geographic differences in climate that affect concentrations of stormwater constituents. Event mean concentration (EMC) data were grouped by EPA Rain Zone (Figure 2-3). The available data

46

were evaluated for selected parameters including TSS, total Kjeldahl nitrogen (TKN), total nitrate plus nitrite (NOx), total and dissolved phosphorus (TP & DP), total copper (TCu), total lead (TPb), total zinc (TZn), chemical oxygen demand (COD), and fecal coliform (FC). To improve the representativeness of the statistics generated, only data collected after 1986 were included in the analysis since data prior to this time are influenced by leaded gasoline and less stringent emission control requirements on vehicles.

Table 3-3 summarizes the number of EMCs per rain zone, while Table 3-4 summarizes the median EMC per rain zone. Mann-Whitney hypothesis testing was conducted to statistically compare median EMCs for each rain zone. These results are summarized in Table 3-5. For each rain zone/EMC combination, the other rain zones with statistically similar EMCs are listed. Appendix A includes side-by-side boxplots of highway runoff concentrations by EPA rain zone.

Table 3-3. Number of highway runoff EMCs per EPA rainfall zones from NSQD and HRDB.

EPA Rainfall Zones Constituent 1 2 3 4 5 6 7 8 9 TSS 106 231 93 0 181 834 262 0 0 TKN 19 93 0 0 249 853 187 0 0 NOx 3 85 0 0 147 738 216 0 0 TP 203 179 94 0 407 874 258 0 0 DP 19 12 14 0 0 159 7 0 0 TCu 278 101 94 0 245 841 243 0 0 TPb 205 98 32 0 263 852 227 0 0 TZn 278 202 94 0 419 839 262 0 0 COD 148 31 46 20 181 117 320 79 16 FC 0 18 0 0 0 26 22 0 0

Table 3-4. Median highway runoff EMCs per EPA rainfall zones from NSQD and HRDB.

EPA Rainfall Zones Constituent 1 2 3 4 5 6 7 8 9 TSS (mg/L) 134.1 38.9 30.7

83.9 81.1 51.3

TKN (mg/L) 2.02 1.81

1.58 1.75 1.03 NOx (mg/L) 0.83 1.43

0.90 0.62 0.26

TP (mg/L) 0.12 0.42 0.09

0.19 0.23 0.12 DP (mg/L) 0.05 0.10 0.04 0.13 0.08

TCu (µg/L) 46.1 18.7 15.9

6.00 28.7 11.0 TPb (µg/L) 12.2 72.3 3.24

58.9 15.5 4.74

TZn (µg/L) 193 108 85

58.8 154 70.1 COD (mg/L) 90.2 133 39.3 109 57.3 179 66.3 101 294

FC (MPN/100 mL)

2095

1883 2287

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Table 3-5. Median highway runoff EMCs by EPA rainfall zone that are statistically similar.

EPA Rainfall Zones Constituent 1 2 3 4 5 6 7 8 9 TSS None 7 None 6 5 2 TKN 2,5,6 1,5,6 1,2,6 1,2,5 None NOx 2,5,6,7 1 1 1 None TP 7 None None None None 1 DP 3,7 6,7 1 2,7 1,2,6 TCu None None 7 None None 3 TPb 6 5 7 2 1 3 TZn None None 5,7 3 None 3 COD 4,8 4,8 5 1,2,8 3,7 None 5 1,2,4 None FC 6,7 2,7 2,6

Key observations based on this analysis include the following: • Total Suspended Solids (TSS): TSS is one of the larger data sets available with more than 90

EMCs for Rain Zones 1, 2, 3, 5, 6, and 7. Mann-Whitney hypothesis testing shows statistically significant differences in median TSS concentrations between most of these Zones. Rain Zones 5 and 6 are statistically similar in TSS concentrations as well as Zones 2 and 7. Highest median TSS concentrations occur in the northeastern states (Rain Zone 1), which is likely largely due to the application of traction sands in winter months. Rain Zone 7 (Pacific Northwest), which is also a northern climate area, has a lower median TSS concentration of 81 mg/L; however, precipitation in the form of snow and ice is less common in this area due to its temperate maritime climate, in addition, intensities of precipitation are also lower reducing the mobilization of sediment during rainfall. For southern Rain Zones, areas with arid and semi-arid climates (e.g., Zones 5 & 6) typically have higher median TSS concentrations than those with wetter climates (e.g., Zones 2 & 3), which may be a function of how climate and availability of water influence growth of vegetation and erosion (both wind and water caused) as well as the potential for more build-up of particulates on surfaces and in conveyance systems during extended dry periods.

• Nitrogen: Highway runoff data are available for total Kjeldahl nitrogen (TKN) and nitrate plus nitrite (NOx). TKN and NOx can be summed to calculate total nitrogen, where paired samples for both constituents are collected. The largest data set available is for TKN with more than 100 data points available for Rain Zones 5, 6, and 7. For these Rain Zones, median TKN concentrations (as well NOx) are statistically different. Rain Zone 2 has the highest median NOx concentrations, while Zone 7 has the lowest. Western states typically have lower median concentrations for TKN and NOx, which may reflect differences in productivity of vegetation in wetter versus drier climates, as well as effects of prevailing winds on industrial and automotive emissions. However, given the complexity of variables involved, it is not easy to draw definitive conclusions on which climatic, hydrologic and geographic differences cause differences in median concentrations for TKN and other nitrogen species.

• Total Phosphorus (TP): TP data are primarily available for Rain Zones 1, 2, and 3 (East Coast) and 5, 6, and 7 (Southwest and West). Data from the Northern Plains and Northern Mountain states are not available for comparison. Mann-Whitney hypothesis testing shows statistically

48

significant differences in median TP concentrations between all Rain Zones except for Rain Zones 1 and 7. Rain Zone 2 (Mid-Atlantic) has the highest median TP concentration by far (0.42 mg/L), followed by Rain Zone 6 (0.23 mg/L) and Rain Zone 5 (0.19 mg/L). From available data, TP is generally lower in highway runoff in northern climates. TP is influenced by factors that vary substantially with geography and climate including soil types, erosion potential, vegetative growth, and nutrient cycling. Given the multitude of climatic and hydrologic variables as well as geographic differences in soils, geology, and other factors, it is not easy to draw cause-and-effect relationships between climatic variables and median TP concentrations. However, the data clearly show that there is significant variability based on geography and climate. Dissolved phosphorus (DP) data are more limited (fewer than 20 points for Rain Zones 1, 2, 3, and 7 and no data for Rain Zones 4, 5, 8, and 9). Therefore, reliable identification of geographic and/or climatic trends for DP is not possible as of this writing given the size of the available data set.

• Total Metals: There are substantial data sets for TCu, TPb, and TZn, with more than 100 data points for several rain zones. In general, statistically significant differences in median concentrations for all rain zones are observed except for Rain Zones 3 and 7 for all three metals and for Rain Zones 1 and 6 for TPb. Rain Zones 3 and 7 are the wettest parts of the country and have the lowest median EMCs for TCu, TPb, and TZn. This finding is consistent with Pitt et al. (2004) who found that wetter climates often have lower concentrations, particularly for pollutants that tend to have a first flush effect. Total zinc and copper concentrations are highest in Rain Zones 1 and 6, which may be due to dry weather build-up in Zone 6 and build-up in snow pack in Zone 1. However, traffic volumes at the monitored sites also play a role in metals concentrations as described in Section 5. All of the metals exhibit similar trends to TSS, which is likely a function of the particulate fraction of these metals, which would be highly correlated with TSS.

• Fecal Coliform (FC): FC data are very limited. Rain Zones 2, 6, and 7 have approximately 20 data points each. Data are not available for the other Rain Zones. Median FC concentrations are similar for the Rain Zones where data are available, on the order of 2,000 MPN/100 mL; although the range of data spans four orders of magnitude from less than 100 MPN/100 mL to greater than 100,000 MPN/100 mL and the upper quantitation limit may vary substantially among studies. As a result, variability is too great for meaningful statistical comparisons given the size of the data sets. It is clear, however, that stormwater runoff has FC concentrations an order of magnitude or more higher than primary contact recreation standards. A biological parameter such as FC would be expected to vary with climate, given the influence that temperature and other climatic variables have on bacterial growth and decay. Although data were not available for Hawaii and Alaska for FC, these extremes of climate would result in very different conditions for FC and other biological parameters. Additionally, fecal indicator bacteria have been found in a number of environmental reservoirs including soils and sands in tropical, subtropical, and temperate climates (Fujioka et al. 1999; Byappanahalli et al. 2006; Yamahara et al. 2007).

• Chemical Oxygen Demand (COD): COD data are available for all rain zones, but are limited in Rain Zones 2, 3, 4, 8, and 9. COD is quite variable, but significantly lower median concentrations are noted for the wetter climates (Rain Zones 3 and 7). The highest COD concentrations are noted for Rain Zones 2, 6, and 9. However, there are too few data points to determine whether these medians are representative of the rain zones. As with the metals, Pitt et al. (2004) found that COD had a strong first flush effect. Therefore, runoff concentrations are likely due to the rate of build-up and the frequency and intensity of wash-off.

In summary, climate and hydrology are important factors influencing the concentration of runoff for most highway pollutants. Hydrologic factors including rainfall intensity-duration-frequency, inter-event

49

time, seasonal precipitation trends, and others affect pollutant build-up and wash-off relationships and the median runoff concentrations observed. However, as discussed in Sections 3.4 and 3.5, other factors such as topography, soils, adjacent land use, and traffic volume also play important roles.

3.2.2 Effect on BMP UOP Performance

Geographic variations in climatic and hydrologic factors primarily related to temperature and precipitation affect BMP UOPs in all four UOP categories including hydrologic operations and physical, biological, and chemical unit treatment processes. Many of the effects are difficult (if not impossible) to quantify given existing data and monitoring and reporting methods; however, increasing interest in unit treatment processes continues to advance the state of knowledge in this area.

3.2.2.1 Effect on Hydrologic Operations As described in Section 3.1, hydrologic operations influence the quantity and flow characteristics of

stormwater. Climate and hydrology directly affect hydrologic operations as follows: • Variations in precipitation patterns affect the inflow hydrograph and therefore directly influence

the amount of flow that is available within the BMP to be infiltrated, evapotranspired or stored to attenuate flows as well as how much runoff is managed versus bypasses or overflows.

• Solar radiation varies greatly throughout the year. High summer radiation leads to warm conditions and high levels of ET in treatment systems. Low winter radiation, and the resulting cold temperatures and reduced vapor pressures, can reduce ET to fractions of their summer values. Relative humidity and wind speeds also influence rates of evaporation from open water surfaces within the BMP and hence affect the rate at which storage is recovered for subsequent flow attenuation.

• Variations in temperature affect the ability of the BMP to infiltrate flows into surfaces that may be frozen and covered with ice. Climatic factors that affect snow melt also influence freeze/thaw cycles within the BMP and the rate at which storage is recovered under frozen conditions for subsequent flow attenuation. Temperature controls thermal stratification, which directly affects mixing and the propensity for short-circuiting in detention systems.

3.2.2.2 Effect on Physical Unit Treatment Processes As discussed in Section 2, physical unit treatment processes include gravity separation (sedimentation),

size exclusion (filtration and screening), and aeration/volatilization. Climatic and hydrologic factors affect physical unit treatment processes in the following ways:

• As mentioned in Section 3.2.1, precipitation patterns can affect particle size, which can thereby affect sedimentation and filtration processes and associated BMP maintenance frequencies. Increased sedimentation can result in more washout and reduced infiltration rates. Higher loads of fine particulates can clog filtration-based BMPs such as sand filters and bioretention systems with underdrains.

• Temperature directly affects sedimentation. According to Minton (2013) the effect of temperature on settling velocities is significant for stormwater. Settling velocities decrease as temperature decreases due to the increase in the viscosity of water. The viscosity of the water basically doubles as the temperature declines from 80°F to near freezing. In Stokes formulation, this has the effect of reducing the settling velocity by half, making sedimentation a much less effective process in cold water situations. Saline stormwater has a similar effect on settling velocities. In areas with cold temperatures and high use of road salts, the impact to settling is even greater.

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Temperature also affects hydraulic conductivity and therefore soil infiltration rates with higher and lower infiltration rates occurring during warmer and colder months of the year (Bragga et al. 2007).

• Filtration of particulates and particulate bound pollutants are little affected by changes in temperature (Blecken et al. 2007). However, vegetation density, which can affect infiltration rates in vegetated BMPs such as swales, filter strips, and bioretention systems, is affected by climate.

• Direct precipitation over the surface of well-designed BMPs of adequate depth is expected to have little impact on sedimentation and re-suspension of previously settled constituents.

• Temperature and wind speed have a direct effect on the rate of oxygen transfer from the air into the ponded water of a BMP system. The rate of volatilization of organic compounds also increases with increased temperature and wind speeds. Temperature and wind speed also impact evapotranspiration rates.

3.2.2.3 Effect on Biological Unit Treatment Processes Microbes and plants are the main agents of biological unit treatment processes. Geographic variations

in climatic and hydrologic factors that impact the health and availability of these agents thereby indirectly affect these processes. The following are some of the ways in which climate and hydrology affect biological unit treatment processes in BMPs:

• Precipitation affects the health of plants in vegetated BMPs and hence supports plant uptake of pollutants. Plant uptake is, however, a minor process and may not significantly influence pollutant removal performance. Too much (flooding) or too little (drought) precipitation negatively impacts plants.

• Temperature similarly affects the health of plants, and seasonal variations may either support vibrant plant and microbial communities or cause die offs that negatively impact water quality.

• Several other inter-related climatic and hydrologic factors affect plant and microbe health including variations in sunlight, wind speeds, and relative humidity.

• Several microbial processes are controlled by temperature, and temperature (and direct sunlight) has been found to be an important factor in natural inactivation of bacteria. Studies (as summarized by Olivieri et al. 2007) consistently find that warmer water temperatures result in faster inactivation of bacteria.

3.2.2.4 Effect on Chemical Unit Treatment Processes Chemical unit treatment processes (see Section 3.1.4) are primarily affected by temperature. The effect

of other climatic and hydrologic factors such as precipitation, wind speeds, solar radiation, etc. are the subject of fewer studies in literature. The following are ways in which climatic and hydrologic factors (primarily temperature) affect chemical unit treatment processes:

• Temperature affects sorption processes and warm temperatures can contribute to leaching of previously captured stormwater constituents such as nitrogen. Blecken et al. (2007) found dissolved nitrogen exported at higher temperatures due to increased nitrification with increasing temperature in their study of biofilter treatment performance. Temperature has also been shown to affect the release rate of metals in stormwater sediments.

• Temperature affects speciation of metals and other stormwater constituents and therefore directly impacts BMP performance by influencing the division between particulate-bound and dissolved forms of stormwater constituents.

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3.3 Effects of Soils and Topography Soils have many different properties that can affect the stormwater BMP influent characteristics and

BMP performance in the highway environment. Soils vary in their degree of compaction, hydraulic conductivity, chemistry, pH, erodability, particle density/size distribution, cohesion, and ability to retain water―all factors which affect stormwater runoff composition and UOPs within BMPs. Topography refers to the steepness of the slopes in the highway environment and tributary areas and in highway BMPs. Topography can also greatly affect stormwater BMP influent characteristics and BMP performance. Some of the ways in which soils and topography affect stormwater BMP influent characteristics and BMP UOPs are discussed in this section.

3.3.1 Effect on BMP Influent Characteristics

Soils within road shoulders and natural conveyance systems can have a significant effect on BMP influent concentrations. Soils with a lesser degree of compaction and/or higher hydraulic conductivity, absent high groundwater levels, allow more rainfall and runoff to infiltrate into the ground, thus causing decreased surface runoff volume and less frequent runoff events (Strecker et al. 2005). Lower runoff volume can also lead to less road shoulder and conveyance system erosion and more infiltration of pollutants into the soil, effectively decreasing pollutant loads to BMPs. Soils with a higher fraction of smaller-sized particles and high erodability can increase TSS and particulate-bound pollutant loads in runoff. Particles with natural deposits of phosphorus or metals can release these compounds into runoff under certain conditions, thereby increasing pollutant loads. Soils that are more easily erodible by wind can be blown onto impervious surfaces resulting in increased TSS levels in runoff.

Stormwater BMP influent that is high in fine particulates (which can be a function of the soil types), clog infiltration and media filtration BMPs more quickly, leading to decreased performance. Finally, soils can affect the pH and hardness of stormwater runoff depending upon the amount of soluble calcium, magnesium, iron, and other divalent cations they contain.

Steeper slopes increase the velocity of runoff allowing less time for infiltration, and resulting in less surface runoff volume reduction and more frequent runoff events with higher flow rates. This directly affects the influent quality because increased frequency and velocity of runoff typically mobilizes more sediment and associated pollutants.

3.3.2 Effect on BMP UOP Performance

In addition to impacting stormwater BMP influent, soils, and topography can influence BMP UOPs that occur within the BMP. All four categories of UOPs including hydrologic operations, physical, biological, and chemical treatment processes are affected by soils and topography.

3.3.2.1 Effect on Hydrologic Operations As stated in Section 3.1, hydrologic operations refer to flow attenuation and volume reduction

operations in BMPs. Soils and topography influence hydrologic operations within a BMP as follows: • Soils with less compaction and/or higher hydraulic conductivity allow more water to infiltrate,

increasing flow attenuation and surface runoff volume reduction, thereby decreasing surface and near surface retention times within BMPs.

• Soils that swell when they absorb water can decrease retention volume and infiltration capacity.

• Steeper slopes in BMPs such as swales and filter strips typically decrease contact time and the ability of the BMP to attenuate flows, reduce surface runoff volumes, and provide treatment.

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• Steep slopes can also cause erosion of soils and planting media and washout of previously captured sediment. However, Navickis-Brasch (2011) found that low vegetation coverage and high percentage of sand were greater contributors to erosion severity than the slope of a highway embankment and suggests that vegetated filter strips and dispersion BMPs can be effective at slopes up to 33% when vegetation cover can be established.

3.3.2.2 Effect on Physical Unit Treatment Processes Physical unit treatment processes are the processes that physically remove pollutants through processes

such as filtration, sedimentation, or volatilization. Soils and topography influence physical UOPs within a BMP in the following ways:

• Particle density has a substantial impact on particle settling velocity. A review of thirteen papers and reports found that densities of particles in stormwater ranged from 1.1 to 2.86 g/cm3, with the most common values in the 1.4 to 1.8 g/cm3 range (Karamalegos et al. 2005), which is significantly lower than the commonly assumed density of sediment (~2.65 g/cm3). While the relationship between soil characteristics and stormwater sediment is an area of needed research, it is presumed that stormwater sediment is at least partially derived from local soils―the finer particles from local soils that may be transported by wind, water, or vehicles are assumed to be present in stormwater sediment.

• Soils with a higher fraction of smaller size particles can more effectively filter out pollutants; however, they typically slow flow rates and can clog more easily.

• If soils underlying a BMP are compacted or have low hydraulic conductivity, this can diminish infiltration capacity and load reduction. Compaction may occur on the watershed, site, or BMP scale. Many researchers have shown that the degree of compaction, whether caused by construction activities or the method of BMP construction, can dramatically reduce infiltration capacity of soils and BMPs (Pitt et al. 2008; Gregory et al. 2006; Brown and Hunt 2010). Winston et al. (2011) found increased loads of TP and TSS from roadside filter strips with highly compacted soils. Additionally, soil conditions leading to reduced infiltration may impact the health and eventual coverage of vegetation within vegetated BMPs.

• Smaller-sized particles or charged soil particles, such as clays, in influent are far less likely to be removed by sedimentation BMPs, making sedimentation BMPs far less effective at removing TSS in areas with fine-grained soils.

• Steeper longitudinal slopes in linear BMPs can decrease retention times, decreasing the sedimentation and/or filtration performance.

3.3.2.3 Effect on Biological Unit Treatment Processes Biological unit treatment processes refer to the microbially mediated transformations of pollutants and

uptake of pollutants into biomass. Variations in soils and topography influence biological processes indirectly through their effects on the plants and microbes as follows:

• Soil composition greatly affects the types of vegetation and microbes that live in the soil, as well as the ability for these organisms to proliferate. In most cases, amendments can be added to provide both the physical structure and essential nutrients to promote healthy plants and microbes beneficial to the biological treatment functions of vegetated BMPs.

• Vegetation and microbes are sensitive to the pH of the soil, the moisture retention capacity of the soil, the aeration of the soil, and the chemistry of the soil. Most bacteria are very sensitive to

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acidic conditions, while fungi may thrive under both acidic and basic conditions (Strecker et al. 2005).

• Emergent plants in wetlands and wet ponds uptake nutrients and other compounds from the soil pore water. The presence of silicate clays and organic matter affects nutrient speciation and the formation of metal chelates and, thus, the ability for plant to uptake these pollutants (Miller and Gardiner 1998).

• Compacted soils or soils with a lower hydraulic conductivity will slow the infiltration rate in BMPs, which can allow for longer retention times and longer biological treatment, but they also typically provide less pore space for roots and microbes, which will decrease the rate of biological transformation and plant uptake. On the other hand, soils with a very high hydraulic conductivity may not allow sufficient retention time for much biological treatment to take place, and may have pore spaces too large for microbes to avoid being flushed out. In some applications, such as wetlands, it may be desirable to maintain saturated soils to maintain anaerobic conditions, while in others, such as bioretention, consistently saturated soils may inhibit growth of the planted vegetation.

• Slopes affect biological treatment by affecting the rate of flow through treatment BMPs, which affects the time available for biological treatment processes to occur. Steep slopes that result in erosive velocities may also affect the ability for plants become established.

3.3.2.4 Effect on Chemical Unit Treatment Processes Chemical unit treatment processes refer to processes that chemically remove or destroy pollutants such

as sorption, coagulation, and disinfection. Variations in soils and topography affect chemical unit treatment processed in BMPs in the following ways:

• Sorption of pollutants to suspended sediment can take place within the water column, as well as within soil and filtration media. Sorption is a function of the number and strength of adsorption sites, the pH, temperature, and oxidation-reduction potential of the media.

• While sorption isotherms can be developed for different types of media or media mixtures to estimate pollutant sorption potential, the episodic and highly variable characteristics of stormwater limit the applicability of isotherms developed for equilibrium conditions (Strecker et al., 2005).

• Soils with smaller particles also have more surface area available for sorption of pollutants than coarser soils, and typically have slower flow rates allowing more contact time for sorption, and less leaching of previously sorbed pollutants. Sorption of metals and other charged pollutants is a function of pH, therefore, the pH of soils can affect how well some pollutants are removed.

• Cohesive soils may not allow much water to get into the pores once wetted, which limits their ability to remove pollutants by sorption.

• Clays and other small, charged soil particles are resistant to coagulation without flocculants or coagulation agents, which limits their ability to be removed. These particles can bind some types of pollutants, preventing them from coagulating or sorbing to media enabling their release into effluent.

• Site slopes have limited direct effect on chemical unit treatment processes; however the increases (or decreases) in flow rates can have a significant effect on contact times.

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3.4 Effects of Traffic Volumes and Adjacent Land Uses

Traffic volumes and land use activities in areas that are tributary to a stormwater BMP can directly influence the quantity and quality of stormwater runoff influent to the BMP, and to a lesser extent, the efficiency of the UOPs within the BMP.

3.4.1 Effect on BMP Influent Characteristics

The imperviousness of an area tributary to a BMP is typically related to land use, and therefore greatly influences the volume and flow characteristics of the runoff it generates. Urban land uses typically have higher imperviousness along with compacted soils that limit infiltration. Urban areas therefore typically produce relatively high peak flows with few opportunities for volume reduction via infiltration and ET as compared to the pre-developed condition, unless there are specific measures to infiltrate under such circumstances. Land use therefore has a direct effect on BMP influent characteristics related to flow and volume.

Nearly all anthropogenic activities (e.g., construction, agriculture, manufacturing, transport, etc.) produce stormwater pollutants. The kinds of pollutant generating activities that occur in an area and the relative magnitude of pollutant loadings can be correlated to land use. As discussed below, both average annual daily traffic (AADT) and adjacent land uses and general location in urban or agricultural areas can have a direct impact on highway runoff quality.

3.4.1.1 Constituents Impacted by Traffic Volume To assess the impact of AADT on constituent concentrations, the post-1986 highway runoff quality

data from HRDB and NSQD were analyzed. Three AADT categories were created: 0-30,000; 30,000-90,000; and 90,000+. As shown in Table 3-6, these categories provide a reasonable division of the data, with a fairly balanced distribution of the data between AADT categories. They also generally represent rural, urban, and ultra-urban highway environments. Data for fecal coliform are sparse in all categories and there was not a large enough dataset to analyze E. coli and TPH results.

Highway runoff median concentrations are presented in Table 3-7 and side-by-side boxplots and scatter plots by AADT are provided in Appendix B. To handle non-detects in the analyzed data sets, a robust regression-on-order statistics (ROS) method as described by Helsel and Cohn (1988) was used to provide probabilistic estimates of non-detects before computing descriptive statistics. As compared to simple substitution methods (e.g., ½ detection limit [DL], DL, or zero), the ROS method reduces the potential bias caused by the presence of non-detects. Confidence intervals were generated using the bias corrected and accelerated (BCa) bootstrap method described by Efron and Tibishirani (1993). This method for computing confidence intervals is resistant to outliers and does not require any restrictive distributional assumptions common with parametric confidence intervals.

As indicated in Table 3-7, there appears to be a relationship between AADT categories and pollutant concentrations. In general, pollutant concentrations increase as the AADT increases and median concentrations of TSS, TKN, NOx, TP, TCu, TPb, and TZn are statistically different among the three AADT bins. TSS and TP tend to have a weaker correlation to AADT then TKN, NOx, TCu, TPb, and TZn. The median concentration of COD for the 0-30K AADT bin is statistically less than the larger two bins, but the 30-90K and 90K + median concentrations are not statistically different from each other. Fecal coliform appears to negatively correlate with AADT, but the correlation is not significant. While the data are too sparse to make any definitive conclusions, fecal coliform in highway runoff may be more influenced by adjacent land uses and associated sources of bacteria in more rural highway settings (e.g., more wildlife crossings, agricultural/livestock operations adjacent to roadways, vegetated embankments and natural drainage systems that attract wildlife, more trucks hauling livestock, etc.). However,

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overpasses where a high density of birds nest or roost may be a localized source of bacteria in both urban and rural highway settings. For example, Sejkora et al. (2011) found that nesting colonies of cliff swallows on bridges near Austin, Texas are a significant source of E. coli and fecal coliform.

The results of this analysis are consistent with the findings by Kayhanian et al. (2003) who found that most highway runoff constituent concentrations can be associated with AADT, but the relationship is non-linear and dependent on other watershed factors, such as total drainage area, rainfall characteristics, and adjacent land uses. Smith and Granato (2010) reported some positive correlation between AADT and concentrations of total-recoverable metals, polycyclic aromatic hydrocarbons (PAHs), and phthalates. Driscoll et al. (1990) noted that as the AADT bins (in their case less than and greater than 30,000 AADT) also aligned with the surrounding land uses (rural and urban respectively) that it could be the urban vs. rural location vs. the actual traffic volumes that may be influencing runoff concentrations. This concept is further discussed in Section 3.4.2.

Table 3-6. Number of EMCs by constituents by average annual daily traffic.

Constituent AADT Category 0 - 30K 30 - 90K 90K +

Total Suspended Solids (TSS) 412 435 603 Total Kjeldahl Nitrogen (TKN) 360 294 416

Nitrite+Nitrate (NOx) 386 332 361 Total Phosphorus (TP) 488 496 548

Total Copper (TCu) 486 468 574 Total Lead (TPb) 462 440 496 Total Zinc (TZn) 484 477 608

Chemical Oxygen Demand (COD) 359 295 304 Fecal Coliform 3 0 23

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Table 3-7. Medians and confidence intervals for combined NSQD and HRDB data.

Constituent Medians (90%Confidence Intervals) by AADT Category

0 - 30K 30 - 90K 90K + TSS 44.0 63.5 100.0

(mg/L) (36.3 - 52.0) (53.9 - 67.0) (91.4 - 107.0) TKN 1.03 1.66 2.15

(mg/L) (0.85 - 1.18) (1.50 - 1.75) (1.96 - 2.39) NOx 0.24 0.66 1.10

(mg/L) (0.20 - 0.29) (0.60 - 0.71) (0.85 - 1.18) TP 0.12 0.18 0.24

(mg/L) (0.10 - 0.13) (0.15 - 0.19) (0.22 - 0.26) TCu 9.81 21.20 48.55

(µg/L) (8.20 - 11.00) (17.03 - 22.00) (43.00 - 52.00) TPb 4.85 9.13 30.48

(µg/L) (3.51 - 5.56) (7.33 - 10.88) (25.95 - 34.50) TZn 54.98 113.29 217.41

(µg/L) (48.48 - 62.50) (100.00 - 125.00) (200.0 - 235.90) COD 49.3 108.2 95.8

(mg/L) (43.0 - 54.0) (84.5 - 118.5) (86.0 - 107.3) FC 5,418 No Data 1,735

(MPN/100mL) (300 - 13,000) (1,200 - 2,300)

3.4.1.2 Constituents Impacted by Adjacent Land Use Stormwater runoff constituent concentrations can be affected through the mobilization of pollutants

from tributary surfaces, atmospheric deposition, or through the sharing of a common drainage network with an adjacent land use such as open ditches (common along rural highways). Smith and Granato reported that concentrations of total-recoverable metals and PAHs increased with the amount of urbanization or surrounding impervious area (2010). A similar conclusion was reached by Driscoll et al. (1990) who found that highways in non-urban settings have lower runoff concentrations than urban highways. Atmospheric deposition can occur locally or on a regional/world scale and have a significant impact on pollutant loads, particularly for solids, metals, and nutrients (Barrett et al. 1995; Herrara 2007).

In the Rocky Mountain National Park in Colorado an interagency team found a baseline atmospheric nitrogen deposition rate of 3.1 kg N/ha/yr and that Colorado sources only accounted for approximately 65% and 55% of the deposition within the park during the spring and summer, respectively (Rocky Mountain National Park Initiative 2010). In a highly impervious urban catchment in Los Angeles, researchers found atmospheric deposition could potentially account for 57 – 100% of trace metal loading in stormwater, assuming the entire quantity deposited was available for removal in stormwater runoff (Sabin et al. 2005).

To assess potential impacts from surrounding land uses in the highway environment, the NSQD database was used to compare average highway median EMCs to the average median EMCs of other land uses. Only post-1986 events were used in the comparisons and seasonality was not taken into account. Sample counts of less than 5 were similarly excluded and are presented in Table 3-8. The results are presented in Table 3-9 with absolute differences in median EMC concentration of magnitude 10% or greater highlighted.

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Table 3-8. Number of storm EMCs by constituent per land use.

Land Use TSS TKN NOx TP TCu TPb TZn COD Fecal Coliform

E. Coli TPH

Commercial 918 794 832 995 799 652 886 708 312 40 21

Freeway 369 439 210 594 349 364 596 448 67 13 26

Industrial 701 667 615 712 627 648 694 548 417 24 26

Open Space 117 94 120 122 53 54 60 52 33 5 1

Residential 2356 2200 1876 2471 1784 1425 2058 1615 596 44 45

Table 3-9. Percent difference in land use median EMCs compared to highway land use.

Land Use TSS TKN NOx TP TCu TPb TZn COD Fecal Coliform

E. Coli TPH

Commercial 11% -22% -43% -13% 0% -71% 24% -1% 82% -31% -57% Industrial 44% -30% -32% -8% 20% -62% 59% -14% 16% -84% -47%

Residential 13% -25% -32% 13% -20% -79% -24% -21% 286% -58% -60% Open Space 17% -56% -51% -21% -37% -76% -28% -54% 126% -42% -80% Note: Red indicates where other land use concentrations are more than 10% higher than freeway land use. Green indicates where other land use concentrations are more than 10% lower than the freeway land use. White indicates where differences are less than 10%.

As Table 3-9 illustrates, none of the investigated land uses have median EMCs that are always greater

than or less than the highway pollutant medians. However, TSS and fecal coliform concentrations in runoff from other land uses are typically greater than those produced from highways. TKN, NOx, and TPb concentrations from other land uses are typically less than highway runoff. The number of data points for E. coli and TPH are too limited to draw strong conclusions, but the data indicate highways produce higher median concentrations. TCu, TZn, and COD are more variable, but the data generally indicate that TZn may be higher in commercial and industrial runoff than highway runoff and TCu concentrations are higher in highway runoff than residential and open space runoff.

The type of highway environment was also considered in the assessment. A comparison between the average land use median EMCs and those for a more rural highway environment (AADT of 0 – 30K) and an ultra-urban highway environment (AADT of 90K+) is given in Table 3-10 and the resultant percent differences are presented in Table 3-11. Due to the sparse results for E. coli and TPH in the AADT analysis, these constituents were excluded from the tables. As shown, rural highways have lower loadings of TSS, nutrients, and metals than adjacent land uses, but higher fecal coliform loadings. This trend reverses for ultra-urban highways, where all highway loadings are generally larger than those of other land uses except for fecal coliform. This finding supports the conclusion that fecal coliform may be more influenced by associated sources of bacteria in a rural setting than highly urban settings.

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Table 3-10. Comparison of land use pollutant median EMCs to rural AADT median EMCs (0 – 30K) and ultra-urban AADT median EMCs (90K +).

Constituent

Median EMCs by AADT Category Median EMCs by Land Use

0 - 30K 90K + Commercial Industrial Residential Open Space

TSS (mg/L) 44.0 100.0 60.0 78.0 61.0 63.0 TKN (mg/L) 1.03 2.15 1.34 1.20 1.29 0.76 NOx (mg/L) 0.24 1.10 0.54 0.65 0.65 0.47 TP (mg/L) 0.12 0.24 0.21 0.22 0.27 0.19 TCu (µg/L) 9.81 48.55 15.00 18.00 12.00 9.50 TPb (µg/L) 4.85 30.48 12.24 16.09 8.67 10.00 TZn (µg/L) 54.98 217.41 120.00 154.00 74.00 70.00 COD (mg/L) 49.3 95.8 62.4 54.0 50.0 29.2 FC (MPN/100mL) 5,418 1,735 3,300 2,100 7,000 4,100

Table 3-11. Percent difference in land use median EMCs to rural AADT median EMCs (0 – 30K) and ultra-urban AADT median EMCs (90K +).

Land Use TSS TKN NOx TP TCu TPb TZn COD Fecal Coliform

Perc

ent C

hang

e fr

om 0

- 30

K

Med

ian

EMC

s Commercial 36% 30% 125% 80% 53% 152% 118% 26% -39%

Industrial 77% 16% 170% 89% 84% 232% 180% 9% -61%

Residential 39% 25% 171% 132% 22% 79% 35% 1% 29%

Open Space 43% -27% 94% 63% -3% 106% 27% -41% -24%

Perc

ent C

hang

e fr

om 9

0K +

M

edia

n EM

Cs Commercial -40% -38% -51% -11% -69% -60% -45% -35% 90%

Industrial -22% -44% -41% -7% -63% -47% -29% -44% 21%

Residential -39% -40% -41% 14% -75% -72% -66% -48% 303%

Open Space -37% -65% -58% -20% -80% -67% -68% -70% 136%

Note: Red indicates where other land use concentrations are more than 10% higher than freeway land use. Green indicates where other land use concentrations are more than 10% lower than the freeway land use. White indicates where differences are less than 10%.

Note that Table 3-9 and Table 3-11 compare the relative magnitudes of pollutant concentrations from

highways as compared to pollutant concentrations from potential adjacent land uses and not directly the likelihood of those land uses to be a direct source of atmospheric deposition to the highway surface or runoff from those land uses to intermingle with highway stormwater runoff.

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3.4.2 Effect on BMP UOP Performance

As previously stated, traffic volumes, adjacent land uses and general climate region have the potential to impact stormwater runoff before it is conveyed to a BMP. Impacts from traffic levels and nearby and surrounding land uses to the treatment processes within the BMP itself are relatively minimal. Physical unit treatment process impacts can be affected if adjacent land uses increase the amount of trash and debris that enters a BMP. Land uses that produce trace metals in the dissolved form and atmospheric deposition of toxic pollutants from industrial and commercial land uses can impact plants and microbes and degrade biological treatment processes. In terms of chemical treatment process impacts, the speciation of phosphorus and other pollutants varies depending on the land use. This has an impact on BMP performance because the effectiveness of sorption and coagulation/flocculation is dependent on the speciation.

Beyond UOP impacts, changes in influent quality can affect the performance of various BMP types to varying extents. BMPs that are sensitive to influent quality changes can therefore be indirectly affected by all the factors that significantly impact influent quality. Table 3-12 shows the results of a correlation analysis comparing influent versus effluent concentrations for several BMP types contained in the International BMP Database. The Spearman’s rho values and associated p-values shown in the table indicate that the effluent concentrations of most BMPs are statistically correlated with influent concentrations (bold values). However, only weak correlation was found for some pollutants and BMPs (green values). For example, detention basins show a relatively strong influent/effluent correlation for most pollutants (rho>0.5), while bioretention cells typically show weak influent/effluent correlation for most pollutants (rho≤0.5). This assessment indicates that detention basins are more sensitive to influent quality than bioretention cells. This difference is likely due to the differences in the dominant UOPs in bioretention cells (filtration) versus detention basins (gravity separation). Looking at wet retention ponds, one might conclude that the longer holding times in wet retention ponds makes them less sensitive to influent quality as compared to detention basins.

Another way to evaluate the potential dependence of effluent concentrations on influent concentrations for various BMPs is to compare the influent/effluent regression equations developed for NCHRP Report 792 (Taylor et al., 2014) from data from the BMPDB. Figure 3-3 through Figure 3-10 include plots of these regression equations, which were developed using the Kendall-Theil robust line procedure described by Granato (2006) along with an assumption of no removal below laboratory reporting limits and no net export from BMPs (see Taylor et al. [2014] for details).

Figure 3-3 shows the fitted curves for effluent vs. influent TSS concentrations for various BMP types. The curves indicates that bioretention and sand filters are the most effective for TSS removal and the effluent concentrations are not that sensitive to influent concentrations. Retention ponds are the next most effective followed by grass strips and detention basins. Swales are the least effective for reducing TSS concentrations.

Figure 3-4 shows the fitted curves for effluent vs. influent NOx concentrations. All BMPs show a fairly linear relationship between effluent and influent concentrations with limited reductions and only retention ponds, grass strips, and detention basins show any reductions. The curves for sand filters, bioretention, and swales are on top of each other and indicate no removal of NOx based on the available studies in the BMP database. The extended holding times and potential denitrifying conditions in retention ponds is likely one of the factors affecting NOx removal performance.

Figure 3-5 indicates that TKN can be effectively removed by retention ponds, sand filters, and bioretention. Detention basins indicate some removal, but are not reliable for significant reductions and appear to be highly dependent on influent concentrations. The data for swales and filter strips in the BMPDB indicate that these BMPs cannot consistently remove TKN.

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Table 3-12. BMP influent/effluent correlation results from BMPDB.

Pollutant

Spearman’s rho Values (and associated p-values) by BMP Type

Bioretention Grass Swale

Filter Strip

Retention Pond

Detention Basin Sand Filter

TSS 0.30 (<0.001)

0.46 (<0.001)

0.46 (<0.001)

0.46 (<0.001)

0.55 (<0.001)

0.41 (<0.001)

NO3- NA 0.89

(<0.001) 0.65

(<0.001) 0.53

(<0.001) 0.79

(<0.001) 0.75

(<0.001)

TKN 0.57 (<0.001)

0.73 (<0.001)

0.57 (<0.001)

0.59 (<0.001)

0.70 (<0.001)

0.71 (<0.001)

DP -0.06 (0.786)

0.68 (<0.001) NA 0.52

(<0.001) 0.67

(<0.001) 0.69

(<0.001)

TP 0.38 (<0.001)

0.63 (<0.001)

0.46 (<0.001)

0.63 (<0.001)

0.66 (<0.001)

0.71 (<0.001)

TCu 0.41 (<0.001)

0.81 (<0.001)

0.70 (<0.001)

0.58 (<0.001)

0.87 (<0.001)

0.61 (<0.001)

TPb NA NA 0.78 (<0.001)

0.55 (<0.001)

0.90 (<0.001)

0.71 (<0.001)

TZn 0.49 (<0.001)

0.82 (<0.001)

0.63 (<0.001)

0.50 (<0.001)

0.72 (<0.001)

0.43 (<0.001)

FC 0.70 (<0.001)

0.83 (<0.001)

0.31 (0.177)

0.78 (<0.001)

0.65 (<0.001)

0.70 (<0.001)

E. coli 0.34 (0.012)

0.83 (<0.001) NA 0.78

(<0.001) 0.58

(<0.001) NA

Note: Bold text indicates statistically significant correlation. Green text indicates statistically significant, but weak correlation (rho ≤ 0.5) meaning effluent quality is only loosely correlated with influent. Source: Adapted from Taylor et al. (2014).

For total phosphorus, Figure 3-6 shows removals by all BMPs except bioretention. The apparent failure of bioretention to mitigate total phosphorus is largely due to the use of phosphorus-rich compost in the media mixes for the studies in the BMPDB. Retention ponds and sand filters appear to be the most effective at TP removal even at low influent concentrations. Sand filters appear to be relatively insensitive to influent TP.

For TCu, TPb, and TZn (Figure 3-7, Figure 3-8, and Figure 3-9, respectively), most BMPs appear to have some dependence on influent concentrations. Figure 3-7 indicates that retention ponds are the most effective at TCu removal followed by grass strips. Figure 3-8 indicates that retention ponds, sand filters, and bioretention are the most effective for TPb removal and the effluent concentrations of these BMPs are not very dependent on the influent concentrations, which likely reflects the highly particulate fraction of TPb. Figure 3-9 indicates that effluent TZn concentrations of all BMPs are somewhat dependent on influent concentrations, but as with the other metals, sand filters, bioretention, and retention ponds are the most effective followed by filter strips. Detention basins and swales are generally the least effective for metals concentration reductions. Due to laboratory detection limits encountered in the available BMP performance studies, removal of any of the metals below approximately 5-10 µg/L is highly uncertain.

Figure 3-10 includes influent/effluent curves for fecal coliform. As indicated, bioretention appears to be the most effective followed by sand filters. Retention ponds and detention basins are partially effective and grass swales and strips do not show any significant removals (i.e., curves are on top of each other on the 1:1 line).

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Figure 3-3. BMP influent vs effluent concentration (TSS).

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Figure 3-4. BMP influent vs effluent concentration (NOx).

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Figure 3-5. BMP influent vs effluent concentration (TKN).

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Figure 3-6. BMP influent vs effluent concentration (TP).

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Figure 3-7. BMP influent vs effluent concentration (TCu).

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Figure 3-8. BMP influent vs effluent concentration (TPb).

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Figure 3-9. BMP influent vs effluent concentration (TZn).

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Figure 3-10. BMP influent vs effluent concentration (FC).

Overall, the influent/effluent correlation analyses and the regression curves indicate that that comparison between BMP performance results may be feasible if influent concentrations are considered for those BMPs and pollutants where strong correlations exist. As discussed in other sections, other factors such as hydrology, hydraulics, and climate should also be taken into account.

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3.5 DOT BMP vs. Non-DOT BMP Performance

3.5.1 Comparison of DOT and Non-DOT Studies

Influent and effluent data from transportation and DOT BMP studies were compared to influent and effluent data from non-transportation studies in the International Stormwater BMP Database (BMPDB). Ten different types of BMPs were evaluated:

• Filter strips (FS) • Vegetated swales (VS) • Wetland channels (WC) • Wetland basins (WB) • Bioretention (BR) • Dry detention basin (DB) • Sand/media filter (SF) • Manufactured devices (MD) • Retention Ponds (RP)

Insufficient data were available for evaluation of infiltration trenches and permeable pavement. TSS, TKN, NOx, TP, TCu, TPb, TZn, COD, and FC BMP influent concentrations were compared for each BMP type above to determine if water quality is significantly different between transportation-related and non-transportation related BMP studies in the BMPDB. Very limited data were available for TPH, so this constituent was not analyzed. Non-detect values were estimated using the ROS method (Helsel and Cohn 1988) prior to calculating summary statistics.

To determine if a subset of the BMP performance data contained in the BMPDB from other land use sites may be used to supplement DOT data, studies from the BMPDB were broken down into 5 land use groups for all studies where a single land use made up at least 50 percent of the drainage area to the BMP. The grouped land uses are shown in Table 3-13.

Table 3-13. Grouping of BMPDB land uses.

Grouped Land Use BMP Database Land Uses

Transportation/DOT studies Park & Ride, Maintenance Station, Roads/Highway

Industrial Light Industrial, Heavy Industrial

Residential Medium Density Residential, High Density Residential, Low Density Residential, Multi-Family Residential

Commercial Office Commercial, Retail, Restaurants, Institutional, College Campus, Automotive Services

Open Space Forest, Open Space, Orchard, Vegetable Farming, Rangeland, Open Space (Manicured), Open Space (Undisturbed)

Influent median EMCs from each land use group were compared to median highway EMCs (see

Table 3-14 and Table 3-15). Significant differences in influent pollutant median EMCs were observed for

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nearly all pollutants across most land uses for both rural AADT (0-30K) median EMCs and ultra-urban AADT (90K+) median EMCs (see Table 3-15).

Table 3-14. Comparison of median land use EMCs from the BMPDB to rural AADT median EMCs (0-30K) and ultra-urban AADT median EMCs (90K +).

Constituent

Median Highway EMCs by AADT Category Median Influent EMCs by Land Use

0 - 30K 90K + Commercial Industrial Residential Open Space

TSS (mg/L) 44.0 100.0 56.7 37.7 153.6 43.6 TKN (mg/L) 1.03 2.15 0.96 0.97 1.91 1.20 NOx (mg/L) 0.24 1.10 0.29 0.36 0.77 0.35 TP (mg/L) 0.12 0.24 0.15 0.10 0.40 0.19 TCu (µg/L) 9.81 48.55 12.6 8.5 38.1 8.0 TPb (µg/L) 4.85 30.48 8.37 8.39 22.11 5.92 TZn (µg/L) 54.98 217.41 69.2 125.0 129.0 39.3

COD (mg/L) 49.3 95.8 61.0 29.2 48.1 52.1 FC

(MPN/100mL) 5,418 1,735 8,961 3,709 6,654

Table 3-15. Percent difference of median land use EMCs from the BMPDB compared to rural AADT median EMCs (0-30K) and ultra-urban AADT median EMCs (90K +).

Land Use TSS TKN NOx TP TCu TPb TZn COD FC

Perc

ent

Cha

nge

fr

om 0

- 30

K

Med

ian

EMC

s Commercial 29% -7% 20% 25% 28% 73% 26% 24% 65%

Industrial -14% -6% 50% -11% -13% 73% 127% -41% -32%

Residential 249% 85% 222% 240% 289% 356% 135% -2% -100%

Open Space -1% 16% 47% 66% -18% 22% -28% 5% 23%

Perc

ent

Cha

nge

from

90K

+

Med

ian

EMC

s Commercial -43% -55% -74% -38% -74% -73% -68% -36% 416%

Industrial -62% -55% -67% -56% -82% -72% -43% -70% 114%

Residential 54% -11% -30% 68% -22% -27% -41% -50% -100%

Open Space -56% -44% -68% -18% -84% -81% -82% -46% 283% Note: Yellow indicates where other land use concentrations are more than 10% higher than highway land use. Blue indicates where other land use concentrations are more than 10% lower than the highway land use. White indicates where differences are less than 10%.

All other land use-related studies in the BMPDB were compared to transportation-related studies.

Statistically significant differences were found between DOT and non-DOT studies for several constituents and several BMPs using Mann-Whitney tests at 99-percent confidence level, which means that medians would need to be significantly different to reject the null hypothesis. Table 3-16 and Table

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3-17 summarize the median influent and effluent concentrations, respectively, for transportation-related studies compared to all other developed land use studies. Figure 3-11 provides a key for these tables. The other developed land use studies consist of a combination of BMP studies from the industrial, commercial, and residential land use categories shown in Table 3-13.

While limited data are available in the BMPDB for several BMP/pollutant/land use combinations, the two tables indicate that both the influent and effluent concentrations for most pollutants are statistically similar to transportation-related studies for TSS. Bioretention, retention ponds, and sand filters appear to have the largest number of pollutants with similar influent and effluent characteristics among the different land uses.

Table 3-16. Median BMP influent concentrations for transportation land use studies compared to all other developed land use studies.

Bio

rete

ntio

n

Dry

D

eten

tion

Bas

ins

Filte

r Str

ips

Ret

entio

n Po

nds

Sand

/Med

ia

Filte

r

Vege

tate

d Sw

ales

Wet

land

B

asin

Wet

land

C

hann

el

TSS (mg/L) 35.8 ≈ 84.1 76.2 ≈ 93.8 47.5 ≈ 49.5

84.1 ≈ 133.5 68.4 ≈ 52.6

40.5 ≈ 35.2

48.8 ≠ 389.2

37.6 ≠ 11.1

TKN (mg/L) 0.9 ≈ 1.5 1 ≠ 1.9 1 ≠ 1.4 1.1 ≠ 2.4 0.8 ≠ 1.3 0.8 ≠ 1.6 1.9 ≠ 1 NOx (mg/L) 0.3 ≈ 0.4 0.5 ≠ 0.9 0.2 ≠ 0.6 0.5 ≈ 0.7 0.3 ≠ 0.4 0.1 ≠ 0.7 0.3 ≠ 0.5 TP (mg/L) 0.1 ≠ 0.2 0.3 ≠ 0.3 0.2 ≈ 0.2 0.2 ≈ 0.3 0.2 ≈ 0.2 0.1 ≈ 0.1 0.3 ≠ 0.1 TCu (µg/L) 11.6 ≠ 38.5 9 ≠ 39.7 5.9 ≠ 23.6 9.2 ≠ 94 9.8 ≠ 13 3.9 ≠ 36.8 TPb (µg/L) 5.3 ≈ 8.6 4.5 ≠ 56.3 2.5 ≠ 7.3 9.8 ≠ 150.4 12.3 ≠ 8.4 3.5 ≠ 47.7

TZn (µg/L) 61.5 ≠ 192 46 ≠ 275.5 37.1 ≠ 108.3

46.9 ≠ 231.3

61.3 ≠ 118.6

25.6 ≠ 274.6

64.1 ≠ 394.4

COD (mg/L) 50.5 ≈ 57.1 56.1 ≠ 147.1 38.9 ≠ 64.1 36.7 ≈ 81.1

FC (MPN/100mL)

10069 ≠ 703 6394 ≠ 347

3131 ≈ 5000

Note: See Figure 3-11 for the key to the values, symbols, and color coding of this table. Blank cells indicate the number of EMCs for the transportation or other land use is less than 10 and therefore deemed insufficient.

Figure 3-11. Key for Tables 3-16 to 3-17.

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Table 3-17. Median BMP effluent concentrations for transportation land use studies compared to all other developed land use studies.

Bio

rete

ntio

n Dry

D

eten

tion

Bas

ins

Filte

r St

rips

Ret

entio

n Po

nds

Sand

/Med

ia

Filte

r

Vege

tate

d Sw

ales

Wet

land

B

asin

Wet

land

C

hann

el

TSS (mg/L) 9 ≈ 7.5 28.9 ≈ 34.3 23.2 ≈ 23.4 7.9 ≈ 26 11 ≠ 7.1

28.5 ≈ 30.3 12.7 ≠ 312

10.9 ≈ 17.7

TKN (mg/L) 0.5 ≈ 1.3 0.8 ≠ 1.5 0.8 ≠ 1.3 0.8 ≠ 2 0.4 ≠ 0.7 0.7 ≠ 1.3 1.5 ≠ 0.9 NOx (mg/L) 0.2 ≈ 0.4 0.3 ≠ 0.8 0.2 ≠ 0.5 0.2 ≈ 0.2 0.4 ≠ 0.7 0.2 ≠ 0.5 0.1 ≈ 0.1 TP (mg/L) 0.1 ≠ 0.1 0.2 ≈ 0.2 0.1 ≠ 0.3 0.1 ≠ 0.3 0.1 ≈ 0.1 0.2 ≠ 0.2 0.2 ≠ 0.1 TCu (µg/L) 6.1 ≠ 13.1 6.2 ≠ 20.3 4 ≠ 8.6 6.3 ≈ 6.7 3 ≠ 17.2 TPb (µg/L) 1.9 ≠ 10.2 2.8 ≠ 22.1 2.3 ≠ 8.2 2 ≠ 1 2.4 ≠ 19.8

TZn (µg/L) 7.2 ≠ 36.5 28.3 ≠ 100.5 19.1 ≠ 41 18.3 ≈ 22.3

20.9 ≠ 69.1

11.4 ≠ 211.9

COD (mg/L) 36 ≈ 63.1 18.2 ≈ 22.7 28.9 ≈ 66.5

FC (MPN/100mL)

65.3 ≠ 8360 4524 ≠ 960 2772 ≠ 137

3745 ≈ 2219

Note: See Figure 3-11 for the key to the values, symbols, and color coding of this table. Blank cells indicate the number of EMCs for the transportation or other land use is less than 10 and therefore deemed insufficient.

As indicated in Section 3.4.2, the effluent concentrations of some BMPs for some pollutants are not strongly correlated with their influent concentrations. For example, the effluent concentrations of bioretention cells are not strongly correlated with influent concentration except for TKN, DP, and fecal coliform. Also, except for detention basins, most BMPs are not sensitive to influent TSS concentrations. Finally, as indicated in Section 3.4.1, the runoff quality from a highway may be comparable to other urban land uses depending on the traffic volume. Therefore, depending on the BMP and constituent of interest, some non-transportation studies may still be comparable (transferable) to DOTs. However, as the regression curves presented in Section 3.4.2 indicate, the effluent concentration dependence on influent concentrations, as well as the other factors influencing BMP performance (hydrology, hydraulics, and climate) must be taken into consideration.

3.6 Summary of BMP Performance Data Transferability Considerations

As described throughout this section, there are several factors that should be considered when assessing whether the results of a BMP performance study conducted at one location are applicable for use in evaluating potential performance in another location. The primary factors are the environmental and site conditions that may affect the influent characteristics and BMP unit operations and processes.

Land use, climate and hydrology are the three particularly important factors that can affect influent quality and quantity, as well as the effectiveness of treatment processes. Local design requirements or standards typically account for differences in rainfall depths and intensities as well as runoff rates based upon imperviousness; therefore, local precipitation patterns may not significantly affect the transferability of performance data except that precipitation may affect influent concentrations. For example, locations with long dry periods and high rainfall intensities may have more significant pollutant mobilization affects than locations with short dry periods and mild rainfall intensities. Precipitation intensity can

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influence the type and character (e.g., speciation, particle size distribution) of pollutants mobilized. In addition, temperature can affect pollutant generation, mobility, and treatment. For example, urban snowpack may accumulate pollutants from atmospheric deposition and winter road maintenance activities (e.g., sanding, salting, and plowing). During large snowmelt events, particularly rain-on-snow, episodic discharges of pollutants may occur. Also, cold temperatures can affect processes such as infiltration, filtration, sedimentation, and biological activity. Because of these various effects, climate is recommended as an important consideration for BMP performance transferability. The EPA Rain Zones (Figure 2-3) can be used as a guideline for dividing climate characteristics, but other factors such as elevation and rain shadow effects may also need to be considered.

Exposed soils that are erodible by wind or rain and topography can also have a significant effect on the rates and quality of runoff, which in turn, can affect BMP performance, both in terms of solids and hydraulic loadings to the BMP and volume losses within the BMP. In the highway environment, soil characteristics may be particularly important in areas where run-on from pervious to impervious areas occur or natural roadside drainage systems are utilized. Without run-on or natural drainage systems, topography may be less important from a BMP performance study transferability perspective since the rate of runoff to the BMP and the rate of infiltration within the BMP should be accounted for during selection and design. Therefore, in general, soils and topography information should be evaluated when reviewing the results of a particular performance study―especially if evaluating volume losses. However, if other characteristics such as influent concentrations and design features at the site are expected to be similar to another site, then minor differences in soil and topography may not warrant screening out the study.

Based on the analysis of available highway stormwater data (Section 3.4), traffic volumes and/or adjacent land use appear to have a significant effect on runoff quality. Typically, as traffic volumes increase and areas become more urbanized, the runoff concentrations of sediment, metals, nutrients, and oxygen demanding substances (e.g., COD) increase. Available highway data for bacteria are too sparse to make any strong conclusions; however, given the limited sources of bacteria and conditions needed in the urban highway environment, bacteria concentrations are expected to be higher in rural areas except potentially in locations where a high density of birds congregate, such as highway overpasses. While the relative effect of adjacent land use versus traffic volumes on runoff quality is uncertain, it is surmised that adjacent land use mostly influences sediment and nutrient concentrations due to run-on, leaf-fall, and aerial deposition, while most metals and hydrocarbons are more directly related to vehicular and roadway infrastructure sources. Given the discussion above, it is recommended that AADT and adjacent land use be considered when assessing data transferability. However, magnitudes of influent concentrations may be used as reasonable surrogates to these site conditions and BMP studies for other urban land uses may be applicable to highways. For example, commercial and industrial runoff quality is relatively similar to an urban highway runoff quality. As noted in Table 3-11 and 3-15, rural highways (0-30K AADT) typically have lower concentrations and ultra-urban highways (>90K AADT) typically have higher concentrations than urban land uses. This indicates that highways with moderate traffic levels (30-90K AADT) produce runoff quality that is closer to other urban land uses.

While influent quality is an important consideration, the effluent concentrations of some BMP types and pollutants are not strongly correlated to influent concentrations. For example, filtration BMPs (e.g., sand filters and bioretention with underdrains) or BMPs with large wet pools (e.g., retention ponds and wetlands) are generally insensitive to influent concentrations. While BMPs without permanent pools or with short residence times (e.g., dry detention basins, swales, and filter strips) tend to be very sensitive to influent concentrations. Comparisons of effluent concentrations from transportation BMP studies to effluent concentrations from other urban BMP studies indicate there are differences in performance, with the possible exception of TSS. However, the BMP data sets are still limited when dividing them into these

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broad land use groups and the potential for the performance to be influenced by the numerous other factors discussed above is high. Therefore, limited conclusions can be drawn by simply comparing performance of a BMP from one land use type to another land use type.

In summary, the transferability of BMP performance study data depends on several factors. Environmental conditions, site characteristics, and BMP design features should be evaluated. However, the relative importance of each of these can vary significantly depending on the pollutant, BMP type, and potential data application. While the available BMP performance data are still too limited to draw statistically-based conclusions, the qualitative and quantitative analyses presented in this section provide some lines of evidence to support data transferability assessments. Climate may be one of the most important considerations due to the potential affect it can have on both influent quality and BMP treatment processes. Once climate is accounted for, the next recommended variable to consider is land use, but only for those BMPs and pollutants which are strongly tied to land use and where effluent concentrations are strongly correlated with influent concentrations. As the BMPDB grows, the ability to conduct additional statistical analyses of the effects of various factors on performance will improve, as will the ability to isolate studies that meet particular screening criteria to best match a particular site.

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4 Summary of BMP Effectiveness Standardization Considerations This section provides recommended monitoring and reporting protocols for BMPs monitored by DOTs,

along with recommendations for screening criteria to identify studies appropriate for inclusion in a central performance data repository. The International Stormwater BMP Database (BMPDB; www.bmpdatabase.org) has been identified as the best starting point to address monitoring and reporting protocols for DOT studies of BMP performance. With some modifications and additions to individual fields contained in data tables within the BMPDB, the project team concludes that the information needed to meet DOT objectives related to BMP performance evaluation is largely in place and recommends that DOTs adopt/adapt the BMPDB for this purpose (discussed further in Section 5).

This section focuses on information to be reported with DOT BMP monitoring performance studies and does not focus on the BMPDB data entry process, database queries/retrieval processes, or consolidated data analysis of DOT performance studies. Although the BMPDB structure (schema) and data elements (fields) are expected to require only limited modification or additions to meet all or most DOT needs, there are more significant opportunities for DOT-related enhancements to the BMPDB data entry and retrieval tools and user interfaces to streamline these processes for DOT purposes as discussed in Section 4.3.

4.1 Recommended Standardized Monitoring and Reporting Protocols

To facilitate evaluation of DOT-related BMP performance at individual sites and to enable comparison of performance among multiple sites, the following broad categories of information are needed:

• Test Site: This category includes general information about the geographic and climate-related characteristics of a study site, enabling evaluation of BMP performance among geographic subgroups and comparison of performance of BMPs in various climates/regions.

• Watershed (Tributary Land Use Characteristics): This category provides specific information regarding the physical characteristics of the land area tributary to the BMP being monitoring, including both anthropogenic (e.g., roads, buildings, conveyance, etc.) and natural (e.g., soils, vegetative cover, etc.) characteristics.

• BMP Design and Maintenance: BMP design characteristics and maintenance condition are critical factors that affect BMP performance. Although these conditions are sometimes weakly characterized when BMP performance monitoring data are reported, these characteristics are critical to understanding which BMPs work well (or fail) for various water quality and volume control objectives.

• Monitoring Program Information: Information about the monitoring program design (e.g., monitoring station configuration, etc.), instrumentation and QA/QC measures is important for assessing the quality of the study data and identifying limiting factors related to transferability and use of the data set.

• Monitoring Data (Characteristics of Monitored Events): Precipitation, flow, and BMP influent and effluent pollutant concentration data for each monitoring event are also needed for hydrologic, hydraulic and water quality performance evaluation.

Additionally, supplemental information about studies can also be useful for broader purposes, including information on BMP costs and monitoring program costs.

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These broad categories of BMP performance study reporting protocols are needed for both DOT and non-DOT studies. As discussed in Section 2, the BMPDB schema and data elements have been developed to systematically record this type of information. The BMPDB User’s Guide (WWE and Geosyntec 2010), provides a description of each data table and data element in the BMPDB. For convenience, a brief overview of the BMP Database structure is provided in Table 2-8 and Figure 2-1. The BMPDB is populated through entry of data into Excel spreadsheets that after QA/QC by the BMPDP team are uploaded to a Microsoft Access Database. Table 2-8 provides a list of the Excel spreadsheets (within an overall workbook) and corresponding Microsoft Access table name. These data tables (major data reporting categories) are expected to support the needs of DOTs, with the following additions, options, enhancements, and comments that are recommended for consideration to assist DOTs:

• Adding Particle Size Distribution Table: Although the Settling Velocity table (Item 17 in Table 1) included in the original peer-reviewed recommendation for the BMPDB, this table is rarely populated in BMP performance studies submitted to the BMPDB (it is a “nice to have” parameter currently). Particle size data are highly valuable for assessing and explaining the observed performance of BMPs. A more useful and more commonly reported data set, which would also be consistent with certification protocols (e.g., TAPE/TARP) and the Highway Runoff Database (HRDB) would be a Particle Size Distribution table. Historically, in the BMPDB, particle size data have been stored in the Water Quality table. However, this information could be more standardized, visible, and accessible in a separate table.

• Simplifying the Instrumentation Meta-Data Entry: The Instrumentation table (Item 13 in Table 1) currently requests standardized information about monitoring equipment in a manner that can be queried based on instrument type. An alternative data entry approach for instrumentation could be a narrative field associated with the Monitoring Station table. The benefit of such a change is more flexibility and simplicity during data entry and potentially a clearer narrative describing the monitoring station characteristics. (A limitation of narrative fields is that they are less conducive to standardized queries that screen data based on certain characteristics).

• Improve Characterization of Manufactured Devices: The BMPDB is in the process of refining the manner in which manufactured devices (Item 27 in Table 1) are characterized, with input from the Stormwater Equipment Manufacturers’ Association (SWEMA). A detailed discussion of these potential changes is beyond the scope of this report, but the general concept is to characterize these devices according to their fundamental unit treatment processes and components rather than the proprietary trade names and/or materials. Non-proprietary public works similar type designs such as baffle boxes, tree box filters, and Multi-Chamber Treatment Trains would be similarly categorized.

• Retain Existing or Add Additional BMP Types: Although some BMP types may not be used in highway settings (e.g., green roofs) and could be considered for exclusion for DOT reporting protocols, these BMPs may be present at highway-related facilities addressed under stormwater discharge permits such as park and rides, maintenance yards, rest stops and/or other DOT facilities. Thus, the project team recommends that the complete list of BMP types currently included in the BMPDB be retained, even if certain BMP types are not currently widely used by DOTs. Most BMP types used in highway settings are also used in non-highway settings. A potential exception may include permeable friction course (PFC) overlays - although there may be local street applications. Currently, PFC studies can be entered and retrieved from the permeable pavement table. However, this may not be ideal

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given the significant difference in design and treatment mechanisms of PFC vs. permeable pavements that infiltrate. Additional PFC design characteristics could be added, if needed. Innovative BMP designs that do not fit into the standard categories can still be entered into the BMPDB with the “Other BMP” category (Item 33 in Table 1), but additional DOT-specific BMP types could be added. For example, the Washington State DOT Media Filter Drain is one such BMP design that does not easily fit (would be considered a media filter, but its sloped configuration is different than most media filters) into the existing categories in the BMPDB that may warrant its own data table.

As described in Section 2, most of the information requested in the BMPDB is relevant to both

highways and other urban land uses; however, two tables that are particularly relevant to highway-related agencies are the Watershed and General BMP Information tables (see Table 2-9 and Table 2-10, respectively). The Watershed table provides information on the characteristics of the drainage area tributary to the BMP and is also linked to a separate Land Use table, which allows multiple land uses to be associated with a watershed (tributary drainage area). Transportation-specific land uses can be characterized as Park & Ride, Maintenance Station, or Highway in the Land Use table. The Watershed table allows entry of both test and control (reference) watershed characteristics associated with the study. In addition to the general land use, road and parking lot information can be entered including total paved and unpaved roadway area, length of curb and gutter, and percent of road area draining to swales and ditches. Highway-specific information is currently requested for highway condition, average daily traffic, number of lanes, and deicing method. The General BMP Information table is common to all BMP types in the database, with more detailed BMP design information provided in separate tables based on specific BMP type, as shown in Table 2-10. The data elements for the Watershed and General BMP Information tables are summarized below because they represent metadata that could be modified to support highway-related research objectives, if needed.

Based on review of other databases, most notably the Highway Runoff Database (HRDB) prepared by the USGS in cooperation with FHWA (Granato and Cazenas 2009), several additional data elements are recommended for the tables described above. These include the following data elements, listed by table: Test Site Table

• DOT study: Provide a yes/no field to flag the study as a DOT-related study to support customized queries for DOTs.

Watershed Table—Roads and Highways Spreadsheet (Subset of Watershed Table)

• Roadway type: rural (significant undeveloped adjacent areas), urban (limited right of way [ROW] available), ultra-urban (no ROW available)

• Average ROW width (beyond the edge of pavement)* • Description of adjacent land use and whether there is commingled flow or prevalent wind

direction) • Road shoulder condition: fully stabilized or vegetated, partially stabilized or vegetated, not

stabilized • Monitored traffic lanes • Lane widths*

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• Curb presence and type (concrete curb, raised berm at the edge of the road, other, none, unknown)

• Vegetation clear zone maintained adjacent to pavement: yes/no • Section type (unknown, grade, cut, fill, cut and fill, bridge, other) • Drainage system type (unknown, swale, pipe, combined sewer, other) • Highway mile post • Roadway maintenance practices/frequencies (e.g., sweeping, mowing, sediment removal, trash

removal) • Surface pavement type (asphalt or cement concrete) • Date of last resurfacing and surfacing material and sealants used. • Deicing events/dates • Use of studded tires (yes/no and if yes dates allowed).

*Note: units of measurement fields also needed, unless units are specified in the field type.

4.2 Recommended Preliminary Study Screening Criteria

Each BMPDB table contains standardized reporting parameters (or data elements) related to BMP performance studies. To enable meaningful analysis of BMP data, a fairly large amount of metadata is requested in the spreadsheet based data submittal package, prioritized according to these three priority levels:

• Required: “Required” data are necessary for proper evaluation and comparison of BMP performance. If these data are not provided, then the BMP study may either be rejected from inclusion in the BMPDB or excluded from certain types of analysis.

• Important: “Important” data are also necessary for proper evaluation and comparison of BMP data, but limited evaluations/comparisons could still be made. If these data are currently unavailable, they should be collected in future monitoring efforts. Some of the watershed (tributary drainage area) data elements fall into this category.

• Nice to have: “Nice to have” fields provide data that are useful in BMP evaluation but not essential for BMP evaluation. For example, “comments” and cost data are considered nice to have.

Ideally, screening criteria for study inclusion would be to include studies that provide all of the

“required” fields, at a minimum. In practice, this is not realistic, with experience with the BMPDB demonstrating that it is very difficult to obtain studies meeting the criteria completely. Thus, the project team recommends that studies that have at least some minimum amount of data (e.g., 3 storm events) and site description be included in a DOT BMPDB, but that studies with limited data should be ranked according to overall study usefulness. In other words, the criteria for study inclusion could be less restrictive, with the option to be more restrictive or cautionary on use of study data entered, depending on the intended use of the information. Such a ranking system should be kept simple, such as high (=1), medium (=2), and low (=3) categories. Studies with larger numbers of storms sampled, EMC data and quantitative site and BMP design data would be ranked as high usefulness, whereas studies with fewer number of storms and less complete descriptions would be ranked as medium or low usefulness (or other appropriate terminology).

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4.3 Other Considerations if BMPDB Adopted by DOTs

This section has thus far focused on the types of information that should be reported in a centralized repository of DOT studies and Section 5 discusses the feasibility of developing such a repository; however, there is not significant focus on some important related aspects of such a repository, particularly if an existing database, presumably the BMPDB, was adopted/enhanced for this purpose for DOTs. Additional considerations include:

• Data Retrieval Features: Currently, the BMPDB offers a variety of on-line data and performance analysis retrieval tools. It could be feasible to develop DOT-related data retrieval tools through development of a “DOT Portal” on the BMPDB website. Such as portal could provide additional query options for DOTs. As an example, the BMPDB currently provides a Chesapeake Bay portal, specific to performance studies in the Chesapeake Bay region. A similar tool (or set of customized tools) could be developed for DOTs.

• Analysis Protocols and Customized DOT Performance Reports: Currently, the BMPDB project includes annual to biannual data analysis summaries, typically grouping BMP performance by BMP category-pollutant groups. Specially focused analyses could be completed for DOTs focusing on DOT-related land uses and BMPs by regional subgroupings, or other subgroupings of interest to project sponsors.

• Training and Outreach: The long-term usefulness of any performance database is based on continued population of the database with additional performance studies and/or longer records for included studies and then active use of the database data and results summaries. Given the competing demands for DOT staff time, the success of a DOT-related performance study effort over the long-term would be enhanced by providing training and outreach to DOT staff responsible for BMP monitoring and reporting as well as those that would use the data for BMP selection and design. This could be accomplished by providing workshops and/or conference presentations on entering and managing data using the BMPDB as well as use and findings. These training sessions should not only provide information on the mechanics of entering data, but also on the benefits of participating in a centralized database effort that helps to maximize (leverage) the usefulness of multiple national monitoring efforts. Training on BMP performance data collection and analysis (i.e., Customized DOT performance reports above), as well as tips on data management, could also be part of an outreach effort.

• Database Ownership: A key question raised during the December 11, 2014, project webinar related to ownership of the BMPDB. Background information on the BMPDB includes the following:

o WERF is the legal owner of the BMPDB; however, the BMPDB is publically available and WERF funds the BMPDB through a coalition of partners who direct the priorities and annual activities related to the BMPDB. Led by WERF, the coalition of partners currently includes the FHWA, the EWRI of the ACSE, the EPA, and the APWA. Each of these organizations provides input on the project direction and technical review of work products and provides either annual financial support or support of single project targeted efforts (e.g., EPA provided funding for the 2002 Stormwater BMP Monitoring Manual and subsequent 2009 update).

o The FHWA is a primary funding partner for the BMPDB, with annual financial contribution typically equal to WERF’s. As a result, FHWA has significant input on the priorities and direction of the project, working with other funding partners.

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o WERF uses a targeted collaborative research approach to conduct research and welcomes partnering agencies to accomplish projects such as the BMPDB. Provided that DOT objectives are complimentary to the objectives of the overall BMPDB project and changes are approved by the WERF Project Steering Committee (which includes a representative of FHWA), then WERF would be open to enhancements/tools to support DOTs. A similar approach was used to support National Fish and Wildlife Foundation (NFWF) objectives for the Chesapeake Bay through a special “portal” on the project website. A similar model could be adopted for a “DOT Portal.”

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5 Resources Needed to Develop BMP Database Portal for State DOTs As a result of work completed in this project, the International Stormwater Best Management Practices

Database (BMPDB) was identified by the Project Team, with concurrence by the Project Panel, as the most appropriate repository for archiving and maintaining DOT-related BMP performance studies.

The literature review (Section 2) uncovered data gaps in highway BMP performance data availability and yielded a collection of potentially useful studies now housed in a bibliographic database. A review of various available data clearinghouses as part of this effort led to the conclusion that the BMPDB was the most comprehensive national-scale, actively maintained database of BMP performance study information currently available and it already includes a significant number of highway BMP datasets. The summary of BMP effectiveness standardization considerations (Section 4), as well as the long-term track record of maintenance and enhancement of the database led to the recommendation that the BMPDB be adopted as the data clearinghouse for DOT BMP studies and that a customized “DOT Portal” be developed to facilitate easier access to DOT-specific BMP information. In addition, Section 4 also identified additional transportation-specific data fields and analysis tools that would be useful for DOTs (and in some cases others as well). Based on these recommendations and the concurrence from the NCHRP Panel on those recommendations, this section focuses on the tasks and resources needed to develop additional transportation-specific data fields, enhanced tools and targeted analyses to best support DOT objectives and provide training and outreach to DOTs on using and submitting data to the BMPDB.

Relatively few modifications are needed to the BMPDB reporting protocols to meet the objectives of DOTs; however, there are several enhancements that can be made in terms of additional data fields, retrieval, and analyses that would benefit DOTs. Additionally, the BMPDB becomes more useful as new studies are added to the database; therefore, targeted BMPDB-related outreach to DOT researchers and stormwater permit managers to encourage identification and submission of data sets and demonstrate applications of the BMPDB would help to maximize the investment in transportation-related BMP research nationally.

This section provides an estimate of resources needed to complete recommended tasks based on the following assumptions:

1. Funding: The baseline annual funding of the BMPDB is assumed to remain in place. For the past several years, the baseline funding has been $110,000 per year, with $50,000 provided by WERF, $50,000 provided by the FHWA, and $10,000 provided by the EWRI of the ASCE. Actual funding may be greater or less than this baseline; however, maintaining the baseline funding at this level enables continued maintenance and growth of the database, continued selected updating and development of web-based tools, and completion of periodic targeted analyses. Funding has also been provided in the past by the EPA (most recently for special projects such as monitoring guidance) and the American Public Works Association (APWA). Organizations such as the Stormwater Manufacturers Association (SWEMA) are also considering funding of special projects and analyses for the BMPDB. These long-term partnerships and cost-sharing are a key benefit of working with an existing, actively managed and updated database, rather than creating a new transportation-only database.

2. Sponsoring Organization(s): The BMPDB project is managed by WERF, which handles sponsoring partner agreements and funding, contractual agreements with project contractors, and facilitates project planning by Project Sponsors and peer review by the Project Sponsors and Project Subcommittee (technical reviewers). For the purposes of this task report, the Project Team assumes that this arrangement will continue with WERF serving as the lead sponsoring organization and that FHWA will continue to be actively engaged as a Project Sponsor and

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active participant on the technical oversight committee. Given WERF’s strong history of cooperative research with other organizations, including FHWA, it is likely that NCHRP, AASHTO, and/or other Project Sponsors could be added to the existing coalition of sponsors as appropriate.

3. Project Team: The cost estimate provided in this report was developed as if the existing BMPDB Project Team would complete the services described in this report due to the significant historical and institutional knowledge and experience that the Project Team has developed from building and then maintaining and operating the BMPDB since its inception in 1996.

4. Scope of Work: The scope of work described in this report assumes that assumptions 1 through 3 are in place; therefore, the tasks described are essentially “add-ons” to the existing BMPDB scope of work, leveraging existing funding for tasks that DOTs or their organizations would otherwise incur if a completely new DOT Database were developed. The remainder of this report is focused on the “incremental” or additional DOT/NCHRP funding needed to accomplish the goals recommended in this NCHRP report.

5.1 Potential Scope of Work

The potential scope of work has been divided into two phases. Phase 1 will includes adding additional data fields to the BMPDB and developing a portal for state DOTs. Phase 2 includes adding additional DOT data to the BMPDB and preparing a DOT-focused data analysis report.

5.1.1 Advisory Panel Meeting/Kickoff (Task 1.1)

Early in the process of refining and implementing DOT-related enhancements to the BMPDB, the Project Team recommends an in-person Advisory Panel of experts meeting with the Project Team to review the recommendations of this NCHRP report and develop a more detailed path forward for implementation of new features and enhancements that support DOT objectives. The scope of work envisions approximately six Advisory Panel members and three Project Team members participating in a half-day meeting. Review of recommended additional transportation-related data fields would occur at this meeting along with discussion of planned analyses updates.

5.1.2 Communication and Training (Task 1.2)

A primary component of the baseline operations for the BMPDB project involves on-going communication with users of the BMPDB, data providers, governmental agencies and other stakeholders. Publicity for the BMPDB is a key component of the project’s success, particularly with regard to acquiring new data sets. Communication-related activities include articles, journal papers, conference presentations and proceedings, periodic webinars, and basic website updates.

In order for a centralized repository for DOT-related BMP performance information to continue growing, state DOTs would need to be committed to providing their monitoring study data to the BMPDB. The likelihood of this occurring will be enhanced by providing DOT training and outreach on data submittal and use of the BMPDB. The Project Team recommends additional communication activities specifically targeted at DOTs as follows [NOTE these items would be completed following completion of appropriate tasks below]:

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• Conduct one NCHRP/DOT webinar focusing on the enhanced DOT-related features of the BMPDB, along with basic information regarding how to submit performance studies to the BMPDB. Record webinar and make it available for web viewing.

• Develop and conduct training sessions on data submittal and use of BMPDB to support DOT objectives at two annual conferences or other national or regional DOT meetings. These would provide hands-on experience with using the BMPDB and provide an opportunity for discussion with the Project Team. In-person communication has historically been the most effective tool to encourage researchers and agencies to submit data to the BMPDB.

• Produce one or more short articles on the new DOT enhancements and publicize to DOT audiences through DOT forums, mailing lists, and/or other internal communications venues. In addition to publicizing enhanced features, these articles will solicit future data submissions from DOTs.

• Conduct a survey of DOTs to determine which have ongoing or historic BMP performance monitoring data suitable for entry into BMPDB. Questions included on the survey would be further refined and developed through the Advisory Panel meeting.

• Respond to inquiries from DOTs interested in providing data and/or utilizing the BMPDB in independent analyses.

5.1.3 Update Web-based Retrieval and Analysis Tools (Task 1.3)

User-friendly access to BMP performance data is essential to the long-term success of the project, both for DOTs and the broader user community. The BMPDB website (www.bmpdatabase.org) currently offers retrieval and analysis tools that have evolved over time and that are being updated as part of the BMPDB scope of work for 2015. Additional enhancements for DOT-specific studies through the addition of a “DOT Portal” to the website are recommended. Subject to refinement by the Advisory Panel, these enhancements are envisioned to include features such as:

• DOT-specific data retrieval forms with custom DOT-related fields and the ability to search and retrieve DOT data only.

• DOT-specific data summaries to present the results of queries and the display of DOT-targeted studies in formats that relevant to DOT audiences. The Project Team will evaluate the order and type of fields presented in summary listings as well as complete listings of query results to determine formats that are most useful to DOT practitioners.

• Either an enhanced version of the existing web map that allows users to quickly exclude non-DOT data from consideration and display, or a custom DOT-specific version of the web map displaying/operating on DOT data only. Filtering and display of study locations and study data on the web map will be evaluated and modified as needed to cater to DOT audiences.

• A Documents section that includes links to DOT-specific studies and data. • The Help section of the website can be updated with a quick start guide for DOTs highlighting

DOT-specific features and enhancements as they are rolled out and made available to the public.

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5.1.4 Enhancements/Maintenance to Database Structure, Data Entry Spreadsheets, and User's Guide (Task 1.4)

Over the past several years, BMPDB users, Project Sponsors and the Project Team have identified a number of recommended improvements to the BMPDB structure and associated tools such as the data entry spreadsheets and the Microsoft Access database itself. Section 4 completed the review of the BMPDB data fields and structure and identified recommended enhancements to meet the needs of DOTs. These improvements have been included in the 2015 Scope of Work for the BMPDB, given the relatively minor nature of these changes and the opportunity to complete these during the course of work already planned for the BMPDB. However, additional funding is recommended to support updates to the data entry spreadsheet and user’s guide that reflect the additional DOT data fields added to the BMPDB.

5.1.5 New BMP Data Entry and Upload (Task 2.1)

Ongoing BMP data entry is an annual baseline task for the continued management and growth of the BMPDB. Data entry involves pursuing data sets, corresponding with data providers prior to and during data entry, reviewing submitted data, corresponding with data providers to fill data gaps, and uploading the accepted studies to the master database. The Project Team recommends that funds be allocated for this task to enable backfilling of critical metadata for existing DOT studies that are already in the BMPDB in accordance with the Section 4 recommendations. Additionally, new DOT studies could also be entered under this task (see Appendix C for a list of candidate studies). The number of studies that can be added depends on the budget available, the format of the original data source and the relative allocation of funds between backfilling meta-data for existing DOT studies and the entry of new studies. The literature review summarized in Section 2.3 and the bibliographic database developed as part of this project is a useful starting place for identification of additional studies that may be appropriate for entry into the BMPDB.

5.1.6 Special Data Analysis Reports (Task 2.2)

On approximately a biennial basis, the Project Team completes special data analysis reports that may include updates of summaries that characterize categories of BMPs or that involve special advanced or targeted analyses. Updates of the BMP category-level statistical analysis reports focus on selected water quality analytes including TSS, selected metals, selected nutrients, and fecal indicator bacteria. Summaries on changes in runoff volumes have also been performed. This category-level analysis includes summary statistics for each BMP category-analyte combination, hypothesis testing comparing inflows versus outflows. Also included are boxplots, time series plots and probability plots.

Although DOTs generally benefit from periodic updates of these category-level and special analysis reports, there would be additional benefits of completing a DOT-focused analysis report, which includes performance findings to date, along with recommendations for additional research needs. Section 3 provides an example of the types of analysis that could be completed to support DOT objectives. It is recommended that the Advisory Panel input be used to further refine the content of DOT-targeted analysis reports completed under this task.

5.2 Preliminary Draft Budget

Table 5-1 shows an approximate budget for the tasks described in Section 5.1. The budget is presented for two phases of work with subtotals for each phase. The first phase is for work related to BMP

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enhancement and DOT outreach while the second phase is for DOT-focused database population and analyses.

Table 5-1: Preliminary draft budget of costs for proposed NCHRP/DOT enhancements

Task Brief Description Budget

Estimate Comment Phase 1. BMP Database Enhancements and DOT Outreach

1.1 Advisory Panel Meeting/Kickoff $15,000 Half-day Advisory Panel meeting to discuss new tools, enhancements, and outreach strategies. Three Project Team members plus travel expenses for approximately 6-8 DOT representatives.

1.2 Communication and Training Promoting Use of the Database, Basic Website Updates, Administration and Coordination/Communication with Data Providers (8 hrs/month for 12 months)

$15,000 to $20,000

One NCHRP/DOT webinar and training at two annual conferences/training sessions. Completion of short articles on DOT listserves and/or other internal communications venues soliciting data. Conduct survey of DOTs to determine which have ongoing or historic BMP performance monitoring suitable for entry into BMP Database.

1.3 Update Web-based Retrieval and Analysis Tools

$15,000 to $20,000

Provide DOT webpage "portal" with enhanced search features for DOTs. These enhancements would be based on customizing existing tools.

1.4 Enhancements/Maintenance to Database Structure, Data Entry Spreadsheets, and User's Guide

$5,000 to 10,000

Includes the recommended additions to the BMPDB presented in Section 4 and updates to the data entry spreadsheet and user’s guide.

Phase 1 Subtotal $50,000 to $65,000 Phase 2. DOT-focused Database Population and Analysis

2.1 New BMP Data Entry (typically, approx. 25-50 studies) & Upload: includes new studies and may include expanded data sets/backfilling for existing studies

$20,000 to $30,000

Assumes that FHWA funding will continue to support entry/upload of the DOT studies, where such opportunities exist. As part of the DOT enhancement project, it is recommended that funding is provided for additional targeted DOT-related data entry and/or backfilling of meta data into existing BMPDB DOT studies.

2.2 Special Data Analysis Reports (Pollutant Categories or Advanced Analysis Reports; other Special Reports)

$25,000 to $35,000

Prepare DOT-focused analysis report, including performance summaries and recommendations for additional analysis.

Phase 2 Subtotal $45,000 to $65,000 Total $95,000 to $130,000

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5.3 Preliminary Draft Schedule

The figure below shows an approximate schedule for a possible 12-month project schedule for the described tasks above. The schedule and budget allocations are expected to change once the project objectives have been refined and prioritized.

Figure 5-1. Preliminary draft schedule for developing BMP database portal for state DOTs.

5.4 Conclusion

This section summarized the recommended tasks and estimated resources needed to enhance the BMPDB to meet the specific objectives of DOTs described in Sections 2, 3, and 4. By choosing to utilize and enhance the existing BMPDB, NCHRP, and DOTs will benefit from over 17 years of investment in a national database, as well as benefit from a coalition of sponsoring organizations, including FHWA, that enables cost-sharing for on-going maintenance and growth of the database. This section has focused on the estimated resources necessary to provide enhancements specific to DOTs that go beyond the baseline operations of the BMPDB. To complete these enhancements, next steps for NCHRP would include coordination with the WERF-led coalition of project sponsors, particularly FHWA, to establish a cooperative agreement and process to complete these enhancements for DOTs. Given WERF’s strong history and commitment to collaborative research, FHWA’s role as a BMPDB Project Sponsor, and precedent established through a similar project model used for the Chesapeake Bay Portal on the BMP Database website, the proposed scope of work is expected to be readily achievable and highly beneficial to DOTs. Appendix D is a letter from WERF supporting this effort.

1.1 Advisory Panel Meeting/Kickoff

1.2 Communication, Training, and Outreach

1.3 Update Web-based Retrieval and Analysis Tools

1.4 Enhancements to Database Structure

2.1 New BMP Data Entry and Upload

2.2 Special DOT Data Analysis Reports

Weeks from Notice to Proceed

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6 Summary Conclusions As more BMP performance data become available, BMP designs have continued to be improved and

refined to address specific constituents and hydrologic conditions of concern. However, BMP performance monitoring studies that report performance results as well as BMP design attributes tend to be concentrated in certain parts of the nation due to varying regulatory requirements (including in some locations the requirement to monitor and report on BMP performance), access to funding and other factors. State DOTs that have not yet conducted their own research on BMP performance monitoring or only have completed a limited amount typically have to rely on data from other DOTs or in some cases, non-DOT studies, in order to select appropriate BMPs based on their needs and to document BMP performance in a regulatory context. There is therefore a need to understand the transferability of BMP performance monitoring data and the factors that affect the applicability of data collected in geographically different areas. The objectives of this research project were to:

• Evaluate existing study clearing houses, published literature and BMP performance protocols to determine the availability of BMP performance monitoring studies applicable for highways.

• Evaluate variabilities affecting the transferability of BMP performance monitoring data • Provide recommendations for assessing the feasibility of establishing a BMP performance

monitoring study central repository for transportation agencies. These objectives were accomplished as a series of four tasks (see Sections 2, 3, 4, and 5) and the

conclusions and recommendations from the work conducted are presented next.

6.1 Existing BMP Assessment Protocols and Study Clearinghouses

The following is a summary of the findings from our review of state BMP testing and acceptance protocols, our review of literature related to the transferability of post-construction BMP effectiveness studies, and our review of BMP database clearinghouses.

Our survey of state practices leads us to conclude that most states do not have established BMP monitoring protocols that standardize monitoring procedures and reporting of information. However, most states do have a mechanism for accepting new treatment technologies that are not specifically listed in state-approved BMP design manuals. TAPE and TARP are the two primary BMP assessment and acceptance protocols upon which most the other formal/informal protocols are based. Therefore developing nationwide protocols for the acceptance of BMP performance studies based on compatibility with TAPE and/or TARP is highly recommended.

Our review of the various BMP performance monitoring data clearinghouses leads us to conclude that the BMPDB is currently the most appropriate national-scale database that is actively maintained, populated and analyzed with public access to the underlying data as well as interpretive reports. This database is also the most thorough regarding study data reporting protocols as it was designed to allow scientific analyses on design vs. performance. The BMPDB also contains the largest collection of highway-related BMP performance studies and is therefore the best available source of data needed to explore relationships and trends to support conclusions related to the transferability of BMP performance studies. The success of the BMPDB also makes it an appropriate prototype and model for the proposed highway BMP database in terms of database structure, data submission protocols, and data analysis and reporting capabilities as well as a long-term commitment by funding agencies.

Our review of the available literature related to the transferability of BMP performance studies has yielded a collection of potentially useful DOT BMP monitoring studies now housed in the bibliographic

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database and has also revealed potential data gaps that might make it challenging to develop and support trends and relations that would be useful for use and application of BMP performance data on a national scale. Data gaps related to the geographic distribution of the available studies and data gaps arising from the limited number of studies that have attempted to correlate BMP design parameters to long term BMP performance are key examples of limitations of the currently available literatures.

6.2 Variables Affecting Transferability of Performance Findings

Our survey of state practices, BMP performance data clearinghouses, including the BMPDB, and literature summarized in Section 6.1, above, demonstrated the non-uniformity in the availability of BMP performance monitoring data across the nation. The lack of BMP effectiveness data in some parts of the country makes it desirable to investigate the transferability of data from geographically different locations. This document summarizes the results of analysis performed to assess the BMP performance factors that are influenced by geographic changes and to evaluate the transferability of BMP effectiveness data.

In evaluating BMP performance factors that are impacted by geographical factors, we found it necessary to group the effects into two categories: (1) impacts to BMP influent characteristics and (2) impacts to the UOPs within a BMP. Geographic factors that impact influent characteristics are relatively more well-known and studied compared to factors that impact UOPs within BMPs. The three primary categories of geographic factors evaluated were: (1) climate and hydrology, (2) soils/topography, and (3) traffic volumes and adjacent land use.

6.2.1 BMP Performance Factors Affected by Climate and Hydrology

Climatic and hydrologic factors are interrelated since hydrologic factors such as precipitation, runoff characteristics, and evapotranspiration are inseparable from climatic influences. With regard to BMP performance, hydrologic impacts due to variations in runoff peak flows and volumes are primarily limited to impacts on percent capture (the amount of runoff managed by the BMP meeting its design treatment rates/volumes), surface runoff volume loss, and contact/retention times, all of which can be mitigated by proper design that considers site hydrology. Beyond precipitation, the effects of other climatic factors such as temperature, solar radiation, relative humidity, and wind speeds and their effects on pollutant-generating human activities such as road sanding, are far more intricate and more difficult to quantify and relate to effects on BMP performance. To evaluate the combined impact of these climatic factors, Mann-Whitney hypothesis testing was conducted on a dataset of highway runoff EMCs for different EPA rain zones for various stormwater constituents. The results of the analysis indicate that climate and hydrology appear to be important factors influencing highway runoff concentrations, but the trends are not the same for all constituents. For example, TSS concentrations are highest in northeastern and Great Lakes states (Zone 1), which is likely due to the combination of frequent application of traction sands and high intensity rainfall events. The mid-Atlantic states (Rain Zone 2) have the highest median TP and NOx concentrations, which may due to higher densities of agricultural and industrial land uses.

Climatic and hydrologic effects on BMP UOP performance were also discussed qualitatively and are expected to have significant effects on BMP performance primarily related to hydraulics and temperature variations. For example, temperature and solar radiation can have a significant effect on evapotranspiration rates, thermal stratification, sedimentation, filtration, infiltration, freeze/thaw cycles, and biological activities. Many of these variables and associated effects are difficult (if not impossible) to quantify given their complex interactions and the lack of large and robust BMP datasets.

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6.2.2 BMP Performance Factors Affected by Soils and Topography

In addition to climatic and hydrologic variations, soils and topography vary geographically as well, and can impact BMP performance. While hydrologic impacts can be mitigated by proper design, soil and topographic impacts on the BMP itself can be mitigated by properly siting a BMP on suitable soils and mild slopes. The evaluation of the effects of soil and topography therefore primarily focuses on impacts to BMP influent characteristics, such as the influence of soils on pH, particle sizes, and densities and soil containing pollutants (for example phosphorus), and the effects of steep slopes on runoff flow rates and ability to mobilize and suspend pollutants.

Soils and topography can also affect BMP UOPs, particularly infiltration and evapotranspiration rates. Soils with high hydraulic conductivity allow more water to infiltrate, increasing flow attenuation and surface runoff volume reductions. Soils rich in organic material can absorb more water and retain more pollutants. Soil composition also greatly affects the types of vegetation and microbes that live in the soil and enhance the physical and biological treatment functions of vegetated BMPs. In most cases, soil amendments may be used to provide both the physical structure and essential nutrients to promote healthy plants and microbes; however, these amendments can contain significant levels of nutrients so must be used with caution. Topography has less of an influence on BMP UOPs, but it can constrain the types of BMPs that can be used and/or require additional design features to overcome steep or flat topography.

6.2.3 BMP Performance Factors Affected by Traffic Volumes and Adjacent Land Use

The effect of traffic volumes and land uses were also evaluated to determine their potential impacts on BMP performance. To assess the impact of AADT on constituent concentrations, highway runoff quality data from the HRDB were grouped into three categories based on AADT (0-30K, 30-90K and 90K +) and analyzed. With the exception of COD and FC (both of which were sparse datasets), all the constituents analyzed showed a statistically significant increase in median concentrations with increased AADT. Increased AADT is often associated with surrounding land uses (rural, urban, and ultra-urban; i.e. higher AADTs are often associated with more intense land uses). To assess the potential effect of surrounding land uses, data from the NSQD database were used to compare average highway median EMCs to the average median EMCs of other land use types. The results were mixed for most constituents; however, TSS and fecal coliform loadings from other land uses were consistently greater than the highway median EMCs. Typically, median runoff concentrations for AADT of 0-30K were lower than concentrations from other land use types, while median runoff concentrations for AADT of 90K + concentrations were higher than other land uses. While adjacent land use may influence highway runoff quality, there is no information on the adjacent land uses contained within the available databases to adequately evaluate this potential affect. Therefore, it remains possible that AADT itself may not be the only factor that is resulting in higher EMCs.

Finally, since there are significantly less BMP performance monitoring data associated with highways than there is for non-highway land uses (particularly in some parts of the country and for some BMPs), the project team also evaluated the transferability of non-highway land use BMP monitoring data for use in DOT applications. The analyses indicate that influent and effluent concentrations for non-highway BMP performance data are typically statistically different from influent and effluent concentrations of highway BMP performance data. However, this depends on the constituents of interest and, as discussed previously, traffic volumes or adjacent land use. TSS appears to be the most similar constituent across BMPs and land uses. Also, not all BMP types are sensitive to influent concentrations particularly those that depend on infiltration and filtration or have a large permanent pool. Therefore, depending on highway site conditions and the BMP and constituent of interest, some non-highway BMP monitoring studies data may still be applicable to the highway environment. With use of the influent and effluent

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relationships developed by Taylor et al. (2014) for various BMP types, studies from different locations having different influent characteristics may still be transferrable. However, the hydrologic and climatic factors influencing percent capture and volume loss should also be considered if the load reduction results from one study are to be applied to a different setting.

Impacts to BMP UOPs from traffic levels and nearby and surrounding land uses are expected to be relatively minimal and are primarily related to the influent quality. For example, trash and debris can cause blinding of filters, and toxic pollutants running off road surfaces or entering BMPs through aerial deposition may impact plants and microbes that support biological treatment processes. The speciation of some pollutants may also vary by land use, which may affect sorption and coagulation/flocculation processes. However, pollutant speciation and its effect on treatment processes and receiving waters is an area needing further research.

6.3 Development of a DOT-Focused BMP Study Repository

Based on a review of various BMP performance databases and “clearinghouses” as discussed in Section 2, particularly with regard to highway-related applications, the International Stormwater BMP Database (BMPDB) is the only national-scale database that we are aware of that is actively populated, analyzed, and maintained, with both the database and associated interpretive reports accessible to the public. Additionally, the BMPDB includes the largest single known compilation of highway-related BMP performance studies with supporting study characteristics, BMP design information and event-based monitoring data for precipitation, flow, and water quality. Reporting protocols for the BMPDB are comprehensive and in general support DOT BMP performance evaluation objectives. FHWA is already a key funding partner and leader in the BMPDB effort. As a result of these findings, the project team with Panel concurrence recommends that DOTs utilize and build upon the existing BMPDB effort.

A targeted number of data reporting enhancements for the BMPDB have been recommended to better support DOT objectives, as described in this report. Additional enhancements, including a DOT “portal” to the BMPDB, have also been recommended with regard to customized data retrieval and analysis options. Finally, training and outreach have been recommended to encourage data upload and use of the BMPDB by DOTs. A scope of work and budget was prepared to accomplish these tasks.

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7 References 2ndNature. (2006). Lake Tahoe BMP Monitoring Evaluation Process. Prepared for USFS Lake Tahoe Basin Management

Unit. Accessible at: http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsm9_045816.pdf. Barrett, M. E., Zuber, R. D., Collins, E. R., III., Malina, J. F., Jr., Charbeneau, R. J., and Ward, G. H. (1995). A Review

and Evaluation of Literature Pertaining to the Quantity and Control of Pollution from Highway Runoff and Construction (CRWR Online Report No. 95-5). Austin, TX: Center for Research in Water Resources, Bureau of Engineering Research, The University of Texas at Austin. April.

Blecken, G.T., Viklander, M., Muthanna, T., Zinger, Y., Deletic, A., Fletcher, T. (2007). Biofilter treatment of stormwater: temperature influence on the removal of nutrients. Proceedings of the 6th International Conference on Sustainable Techniques and Strategies in Urban Water Management. Novatech 2007. Lyon, France.

Bragga, A. Horst, M., and Traver, R (2007) Temperature Effects on the Infiltration Rate through an Infiltration Basin BMP. Journal of Irrigation and Drainage Engineering.

Brown, R.A. and Hunt, W.F. (2010). Impacts of Construction Activity on Bioretention Performance. Journal of Hydrologic Engineering. 15(6), 386-394.

Byappanahalli, M.N., Whitman, R.L., Shively, D.A., Sadowsky, M.J., and Ishii, S. (2006). Population structure, persistence, and seasonality of autochthonous Escherichia coli in temperate, coastal forest soil from a Great Lakes watershed. Environ. Microbiol., 8, 504-513.

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BMP GLOSSARY

Biofilter (Strip) See Filter Strip Biofilter (Swale) See Vegetated Swale

Bioretention Shallow vegetated depressions with planting layer and optional storage layer and/or underdrain

Bioslope See Media Filter Drain

Catch Basin Insert Manufactured filters, fabric, or other devices placed in catch basins and drainage inlets to remove or exclude coarse sediment, trash, and debris

Compost Amended Slope See Filter Strip Composite (Train) See Treatment Train Detention Basin See Dry Detention Basin Detention Vault Structure constructed to provide storage and detention of incoming flows

Dry Detention Basin Surface depressions, sometimes vegetated, with storage for temporarily detaining stormwater

Ecology Embankment See Media Filter Drain

Filter Strip Vegetated surfaces designed to provide treatment of shallow sheets flows from adjacent surfaces; typically planted with turf grass and may be amended with compost

Grass Filter Generically refers to filter strips and vegetated swales

Green Roof Also known as ecoroofs, roof gardens, or vegetated roof covers consist of vegetative cover, growing media, a drainage layer and a waterproof membrane.

Hydrodynamic Device Remove trash debris and coarse sediment from incoming flow using screening, gravity settling and centrifugal forces generated by forcing the influent into a circular motion

Infiltration Basin Surface depressions, sometimes vegetated with storage for detaining and infiltrating stormwater

Infiltration Vault Structure constructed to provide storage, detention and infiltration of influent stormwater

Infiltration Trenches Typically narrow, relatively shallow gravel and sand filled trenches designed to infiltrate stormwater

Low Impact Development (LID)

An approach to land development or re-development that works with nature to manage stormwater as close to its source as possible.

Media Bed Filter BMPs that utilize a bed of engineered media to filter influent stormwater Media Filter Refers to media bed filters and media cartridge filters

Media Filter Drain

Linear flow-through stormwater treatment device typically consisting of a gravel no-vegetation zone, a vegetated filter strip, ecology mix bed and a gravel-filled under-drain trench, typically sited along highway side-slopes and medians, or other linear depressions.

Manufactured Device See Proprietary BMPs

Oil/Water/Grit Separator Vault Gross solids removal devices for removing floatables, trash, debris and coarse sediment

Other Media Bed Filter See media bed filters

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Permeable Pavement An alternative to conventional impervious pavements that allows some water to pass through into a subsurface gravel or other storage layer and optionally infiltrate into surrounding soils

Permeable Shoulder Permeable pavement installed as road shoulders Percolation Trench See Infiltration Trench Porous Pavement See Permeable Pavement Permeable Friction Course Overlay

Typically consists of a layer of porous asphalt placed as an overlay on top of an existing conventional concrete or asphalt surface

Rain Garden See Bioretention

Rainwater Harvesting Category of stormwater BMPs that collect and store rainfall for later use, includes cisterns and rain barrels

Retention Pond See Wet Pond

Rock Swale Linear conveyance system designed, shaped and lined with rocks or gravel to convey surface runoff in a non-erosive manner

Sand Filter Typically two-chambered with a settling chamber that drains into a filter bed filled with sand or other filtering media

Wetland Basin Surface depressions with open water, wetland soils, a variety of submerged and emergent aquatic plants and varying water depths

Wetland Swale/Channel Linear conveyance surface depressions with wetland soils, a variety of submerged and emergent aquatic plants and varying water depths

Wet Pond Surface ponds similar to wetland basins with typically deeper depths and permanent pools for detaining stormwater

A P P E N D I X A

Highway Runoff Boxplots by EPA Rain Zone

A P P E N D I X B

Highway Runoff Boxplots and Scatter Plots by Average Annual Daily Traffic (AADT)

The boxplots provide a representation of the spread and central tendency of a pollutant’s EMC per AADT category and the scatterplots provide information on non-detects and the relationship of a pollutant’s EMCs between AADT categories. A key for the boxplots is given below.

A P P E N D I X C

BMP Studies in Highway Settings Targeted for Future Entry into BMP Database

Appendix C BMP Studies in Highway Settings Targeted for Future Entry into BMP Database

DID Year Published Authors Title Additional

Description State Pollutants BMPs Web Link

64065 2011Herrera Environmental Consultants

Highway Runoff Characterization Monitoring Study OR

BOD, Cd, Cu, DOC, E. Coli, Fecal Coliform, NH3, NO3, Oil & Grease, Ortho-P, Pb, TKN, Total PAHs, TP, TSS, Zn

ftp://ftp.odot.state.or.us/techserv/Geo-Environmental/Environmental/Other%20Enviromental%20Materials/Stormwater_Program/Monitoring_Report_2011.pdf

64068 2012 Matthew A. Lebens and Brett Troyer

Porous Asphalt Pavement Performance in Cold Regions MN

Cd, Conductivity, Cr, Cu, Fe, Ni, Pb, pH, Temperature, TKN, TN, TP, TSS, Tubidity, Volatitle Solids, Zn

Permeable Pavement

http://www.dot.state.mn.us/research/documents/201212.pdf

64069 2008Ming-Han Li, Aditya B. Raut Desai, and Michael E. Barrett

Undergound Stormwater Quality Detention BMP for Sediment Trapping in Ultra-Urban Environments: Final Results and Design Guidelines

TX TSS Dry Basins http://tti.tamu.edu/documents/0-4611-2.pdf

64055 2006Caltrans Division of Environmental Analysis

Highway 267 Filter Fabric Sand Trap Pilot Study

Caltrans Interim Report CA TSS, Turbidity Infiltration

Facilitieshttp://www.dot.ca.gov/hq/env/stormwater/pdf/CTSW-RT-05-157-01-2.pdf

64056 2013 S. Ali Abbasi and Antti Koskelo

Pollutant Load Reductions for Total Maximum Daily Loads for Highways

NCHRP Synthesis Report

COD, Cu, Fe, Fecal Coliform, NO3, Pb, TKN, TN, TOC, TP, TSS, Tubidity, Zn

Biofilters, Dry Basins,

Infiltration Facilities,

Media Filters, Permeable Pavement, Wet Basins

http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_444.pdf

64057 2010Alan Black, Robert Armstrong and Angela Deardorff

Adapting a Roadside Media Filtration BMP for Dissolved Metals Removal at the End of Pipe

Presentation at StormCon 2010 WA Cu, SRP, TP, TSS, Zn

Biofilters, Dry Basins, Media

Filters

http://news.hntb.com/white-papers/technical-papers/adapting-a-roadside-media-filtration-bmp-for-dissolved-ls-removal-at-the-end-of-pipe.download.

64060 2009

Tatsuji Ebihara, C. Bryan Young, Vibhuti Tiwari and Lisa M. Agee

Treatment of Contaminated Roadway Runoff Using Vegetated Filter Strips

Report on Research KS Cr, Cu, Pb, Total PAHs, Zn Biofilters

http://nacto.org/docs/usdg/treatment_of_contaminated_roadway_runoff_using_vegetated_filter_strips_ebihara.pdf

64062 2004 Kelly Renee FlintWater Quality Characterization of Highway Stormwater Runoff from an Ultra Urban Area

Graduate School of the University of

MarylandDC Cd, Cu, NO2, NO3, Pb, TKN, TP,

TSS, Zn Biofilters http://drum.lib.umd.edu/bitstream/1903/210/1/umi-umd-1284.pdf

64063 2005

Jeffrey Hauser, JeffryCurtis, John Johnston, Dipen Patel and Mark Keisler

Small-Scale Pilot Testing of Stormwater Treatment Systems to Meet Numerical Effluent Limits in the Lake Tahoe Basin

Proceedings of WEFTEC CA TP, Tubidity

Manufactures Media

Filtration, Media Filters

http://www.owp.csus.edu/research/papers/papers/PP063.pdf

64072 2013Gayle F. Mitchell, Shad Sargand, and Andrew Russ

Exfiltration Trench for Post Construction Storm Water Management for Linear Transportation Projects: Volumes 1-3

OH

Cd, COD, Cr, Cu, Fe, Ni, Oil & Grease, Particle Size Distribution, Pb, pH, TSS, Tubidity, Zn

Media Filters

http://www.dot.state.oh.us/Divisions/Planning/SPR/Research/reportsandplans/Reports/2013/Engineering/FINAL%20REPORT%20VOL%201-3.pdf

June 2015 Page 1 of 3 NCHRP 25-25

Appendix C BMP Studies in Highway Settings Targeted for Future Entry into BMP Database

DID Year Published Authors Title Additional

Description State Pollutants BMPs Web Link

64426 2005 Jeffrey S. Brown and Steven M. Bay

Assessment of Best Management Practice (BMP) Effectiveness

Southern California Coastal Water Research

Project - Technical Report

#461

CAAl, Ar, Cd, Conductivity, Cr, Cu, DOC, Hardness, NH3, Ni, pH, TDS, TSS, Zn

Constructed & Pocket

Wetlands, Hydrodynamic,

Sub-surface Flow Wetlands

http://www.swrcb.ca.gov/rwqcb4/html/programs/funding/SCCRWP/BMPEval_FinalReport_16Dec05.pdf

64514 2013

Robert M. Roseen, James J. Houle, Timothy A. Puls, Thomas P. Ballestero

Final Report on a Cold Climate Permeable Interlocking Concrete Pavement Test Facility at the University of New Hampshire Stormwater Center

NH

NH4, NO2, NO3, Ortho-P, Particle Size Distribution, SSC, TKN, TN, Total PAHs, TP, TSS, Zn

Permeable Pavement

http://www.unh.edu/unhsc/sites/unh.edu.unhsc/files/UNHSC_ICPI_Final%20Report_7-8-13.pdf

64518 2008

Christina E. Stanard, Michael E. Barrett, and Randall J. Charbeneau

Stormwater Quality Benefits of a Permeable Friction Course TX COD, Cu, DP, NO2, NO3, Pb,

TKN, TP, TSS, ZnPermeable Pavement

http://www.crwr.utexas.edu/reports/pdf/2008/rpt08-03.pdf

64512 2012J. Moores, J. Gadd, P. Pattinson, C. Hyde, and P. Miselis

Field Evaluation of Media Filtration Stormwater Treatment Devices

New Zealand Transportation

Agency Research Report

Cu, TSS, Zn Media Filters http://www.nzta.govt.nz/resources/research/reports/493/docs/493.pdf

64527 2012 Doug Hutchinson CatchBasin StormFilter Performance Evaluation Report WA

Cd, Cu, Hardness, Oil & Grease, Ortho-P, Particle Size Distribution, Pb, pH, Total PAHs, TP, TSS, Volatitle Solids, Zn

Manufactures Media Filtration

http://www.seattle.gov/util/groups/public/@spu/@drainsew/documents/webcontent/01_016486.pdf

64549 2010 URS Corporation - North Carolina

Stormwater Runoff from Bridges: Final Report to Joint Legislation Transportation Oversight Committee

NC

Cd, Conductivity, Cr, Cu, Fe, NH3, Ni, NO2-NO3, Oil & GreaseOrtho-P, Pb, pH, TDS, TKN, TN, Total PAHs, TP, TSS, Zn

Biofilters, Bioretention,

Constructed & Pocket

Wetlands, Dry Basins

https://connect.ncdot.gov/resources/hydro/stormwater%20resources/stormwater%20runoff%20from%20bridges%20-%20may%202012.pdf

64516 2002 Kirk P. Smith

Effectiveness of Three Best Management Practices for Highway-Runoff Quality along the Southeast Expressway, Boston, Massachusetts

USGS Water Resources

InvestigationsMA

Al, Ar, Cd, Cr, Cu, Fe, Ni, Particle Size Distribution, Pb, SSC, Total PAHs, Trash and Debris, Zn

Oil/Grit Sep. http://pubs.usgs.gov/wri/wri024059/pdfs/wri024059.pdf

64550 2013Washington State Department of Transportation

WSDOT NPDES Municipal StormwaterPermit Highway Runoff and BMP Effectiveness Stormwater Monitoring Report, Water Year 2012

WACd, Cu, Hardness, NO2-NO3, Particle Size Distribution, Pb, TKN, Total PAHs, TP, TSS, Zn

Biofiltershttp://www.wsdot.wa.gov/NR/rdonlyres/5D30C83E-2292-47FE-A1D7-646817E2F0BD/0/HighwaysBMPsRpt.pdf

64553 2010 GPI Southeast Baffle Box Effectiveness Monitoring Project FL

Cd, Cr, Cu, Fecal Coliform, NH4, Ni, NOx, Organic-N, Ortho-P, TKN, TN, Total Coliform, Total PAHs, TP, TSS, Zn

Baffle Boxeshttp://www.dep.state.fl.us/water/nonpoint/docs/nonpoint/fdeps0236-baffle-box-final-report.pdf

64555 2005DP Environmental and Community Wastershed Fund

Quantifying the Effect of a Vegetated Littoral Zone on Wet Detention Pond Pollutant Load Reduction

FL Alkalinity, COD, Cu, NH3, NO2-NO3, Pb, SRP, TKN, TP, TSS Wet Basins

http://www.dep.state.fl.us/water/nonpoint/docs/nonpoint/EffectivenessLittoraZoneVegetationFinalReport.pdf

June 2015 Page 2 of 3 NCHRP 25-25

Appendix C BMP Studies in Highway Settings Targeted for Future Entry into BMP Database

DID Year Published Authors Title Additional

Description State Pollutants BMPs Web Link

64556 2010

Cape Canaveral Scientific, Inc. and Stormwater Solutions, Inc.

Poppleton Creek Wet Detention Pond FLAlkalinity, Cd, Conductivity, Cr, Cu, DN, DP, NH3, NOx, P, pH, SRP, TN, TP, Turbidity, Zn

Wet Basinshttp://www.dep.state.fl.us/water/nonpoint/docs/nonpoint/s0278-poppleton-creek-fr.pdf

June 2015 Page 3 of 3 NCHRP 25-25

A P P E N D I X D

Letter of Support from WERF for DOT Portal on BMPDB Website

May 4, 2015

Crawford F. Jencks

National Cooperative Highway Research Program (NCHRP)

Transportation Research Board of the National Academies

500 5th Street, NW

Washington, DC 20001

Subject: International Urban Stormwater BMP Database

Dear Mr. Jenks:

The Water Environment Research Foundation has managed the International Urban

Stormwater BMP Database and led the coalition of project sponsors for over ten years.

The sponsors include the Federal Highway Administration and others. We have been

maintaining and populating the BMP Database over time and it has become the largest

clearinghouse of BMP design and related performance monitoring data in the world. It is

a critical resource for many organizations managing and regulating stormwater systems

including federal, state, regional, and local agencies responsible for stormwater

management.

WERF is aware that the National Cooperative Highway Research Program has been

conducting a project to evaluate potential BMP data repositories for departments of

transportation (DOTs) under its “25-25 Task 92” project. We understand that the project

has recommended that DOTs utilize the BMP Database for DOT studies, but would like

to add some additional DOT-specific parameters, as well as new tools and a portal for

DOTs. WERF is receptive to these recommendations and would view a DOT portal as an

excellent opportunity to enhance the utility of the BMP Database project.

WERF’s approach to the BMP Database is based on working collaboratively with

multiple agencies and organizations that have common needs for BMP performance

information. This synergistic approach provides a cost-effective, strategic way to manage

creating and maintaining these tools. Our efforts on this project are guided through two

different project committees, which are open to additional partners, including:

Steering Committee – comprised of funding partners to the BMP Database and

responsible for strategic direction (currently includes FHWA).

Project SubCommittee (PSC) – technical experts that volunteer their time to review

the work products of the BMP Database for accuracy and relevance to the industry.

In the past, we have also partnered to develop special portals, analyses, and reports that

are outside of our normal annual work program. For example, in 2012, WERF

collaborated with the National Fish and Wildlife Foundation to develop a research portal,

complete a targeted statistical analysis and hold a special webinar for information

pertaining to the Chesapeake Bay Watershed.

WERF would be pleased to further discuss how the BMP Database and its associated

tools and analyses could be enhanced to better meet the needs of DOTs. Please don’t

hesitate to contact me via phone at 571-384-2105 or via email tconnor@werf.org.

Best Regards,

Theresa Connor, P.E.

Stormwater Research Program Director

Chair Kevin L. Shafer Executive Director Metro Milwaukee Sewerage District

Vice-Chair Glen Daigger, Ph.D., P.E., BCEE, NAE President One Water Solutions, LLC

Secretary Eileen J. O’Neill, Ph.D. Executive Director Water Environment Federation

Treasurer Brian L. Wheeler Executive Director Toho Water Authority

Rajendra P. Bhattarai, P.E., BCEE Division Manager, Environmental & Regulatory Services Division Austin Water Utility

Paul L. Bishop, Ph.D., P.E., BCEE Associate Dean of Engineering for Research University of Rhode Island

Scott D. Dyer, Ph.D. Principal Scientist The Procter & Gamble Company

Catherine R. Gerali District Manager Metro Wastewater Reclamation District

Philippe Gislette Scientific, Technical & Innovation Director Degrémont, Suez-Environnement

Julia J. Hunt, P.E. Assistant Northern Region Manager, Operations Trinity River Authority of Texas

Douglas M. Owen, P.E., BCEE, ENV SP Executive Vice President & Chief Technical Officer ARCADIS U.S.

Jim Matheson President/CEO Oasys Water, Inc.

Ed McCormick, P.E. President Water Environment Federation

James Anthony (Tony) Parrott Executive Director Metropolitan Sewer District of Greater Cincinnati

Rick Warner, P.E. Senior Engineer Washoe County Community Services Department – Water Utility

Interim Executive Director Lawrence P. Jaworski, P.E., BCEE

635 Slaters Lane, Suite G-110 Alexandria, VA 22314-1177 Tel: 571-384-2100 Fax: 703-299-0742 Email: werf@werf.org www.werf.org