Measurement System Evaluation for Fugitive Dust Emissions Detection and Quantification

Prepared by

John G. Watson Judith C. Chow

Li Chen Xiaoliang Wang

Desert Research Institute 2215 Raggio Parkway

Reno, NV, USA

Prepared for

South Coast Air Quality Management District Diamond Bar, CA

June 15, 2010

i

EXECUTIVE SUMMARY The South Coast Air Quality Management District (SCAQMD) adopted and amended

Rule 403 to protect the public from excessive fugitive dust emissions caused by man-made activities, including those occurring on mining, aggregate handling, and construction sites. Rule 403 requires downwind PM10 (particles with aerodynamic diameters <10 µm) levels to be less than 50 µg/m3 over upwind concentrations. As monitoring to comply with Rule 403 has been implemented, questions have been raised about the definitions of upwind and downwind locations, zones of influence for emissions from different facilities, and the accuracy and precision of alternatives to the high-volume integrated filter sampler that is currently used to measure PM10.

SCAQMD and aggregate producing plants have a shared interest in protecting the public from fugitive dust emissions by ensuring that ambient air monitoring conducted in accordance with Rule 403 provides data that are accurate and representative of a facility’s contributions to regulated PM10 as close as possible to the facility’s fence line. Accordingly, SCAQMD and aggregate producing plants are conducting a Joint Technical Review (JTR) to assess alternate Rule 403 ambient air monitoring methodologies.

To inform this process, this study was conducted to evaluate measurement alternatives to the current practice. Study objectives included: 1) identifying, describing, and selecting PM10 and meteorological measurement systems; 2) developing sampling configurations and procedures; 3) completing a field study; and 4) evaluating measurement methodologies for implementing Rule 403. Rule 403 requires upwind and downwind monitoring as close to fugitive dust source fence lines as possible.

Literature and manufacturer descriptions were identified and surveyed for PM10 samplers and meteorological instruments. PM10 can be measured by filter sampling with laboratory weighing, the attenuation of electrons (beta rays) through PM10 deposits on a paper tape, changes in the frequency of a vibrating element as aerosol loadings increase, particle light scattering, and optical counting and sizing of individual particles. Wind monitors consist of mechanical wind vanes and anemometers and sonic anemometers. Published PM10 comparison studies show mixed results, with good comparability for non-volatile particles with a low PM10-2.5 fraction relative to PM2.5. Collocated measurements near fugitive dust sources with large spatial gradients and high PM10-2.5 fractions show moderate to low comparability.

Samplers were selected for inclusion in the study based on their availability within time and budget constraints, but these represented a broad range of manufacturers and measurement principles. Integrated filter samplers included: 1) the Sierra-Andersen high-volume (1130 L/min) sampler (hivol), a PM10 federal reference method (FRM); 2) the BGI low-volume (16.7 L/min) PQ200, a PM10 FRM; and 3) the BGI OMNI mini-volume (5 L/min) sampler, which is not a FRM. Light scattering was measured with the Met-One E-Sampler and TSI Model 8250 DustTrak nephelometers. Particles were counted with Grimm Model 1.108 and TSI Model 8533 DRX optical particle counters (OPCs). The Met-One E-BAM used the beta attenuation principle.

Both mechanical and sonic wind measurements were implemented, but the TacMAT sonic anemometer yielded physically unreasonable data soon after developing a dust coating. This type of sonic anemometer may be more suitable for less dusty environments.

Equipment manuals were only marginally useful for setting up, calibrating, and operating instruments. More complete and practical standard operating procedures (SOPs) were developed for each instrument and are included in Appendix A of this report.

ii

Sampling sites were configured within the fence lines at United Rock in Irwindale, CA, and at Vulcan Materials in Upland, CA. This was necessary to provide for security. As a result, the measurements taken do not represent concentrations that go beyond the property line. The instruments were closer to fugitive dust emitters than sites that would be selected as part of Rule 403.

Inlets were uniformly located at 2 m above ground level. Most of the samplers are adjustable to this height, but the hivol samplers required leg extensions. Samplers were placed at 2 m intervals, but concentration gradients across the array caused differences in the measurements, especially at the United Rock downwind Site 1. Mechanical anemometers were located on 5 m masts, the tallest that is feasible without a guyed tower. The E-BAM has a mechanical wind vane and anemometer integrated with its support stand, but 1 m above ground level measurements did not represent the general transport flows. Wind directions were more variable the United Rock downwind site than at other locations owing to the nearby presence of tall structures (e.g., crushers, dryers, conveyors, and storage piles) and a tree line on the north side of the property.

Collocated sampling was carried out at the United Rock downwind site from 9/8 through 9/10/08 and at the Vulcan downwind site from 10/23 – 30/08. Owing to lack of equipment availability, only the hivols, OMNIs, DustTraks, and E-Samplers had sufficient collocated measurement for comparison. Upwind/downwind sampling was conducted at United Rock from 9/11 through 9/27/2008 and at Vulcan Materials from 10/2 through 10/20/2008.

Continuous data were acquired at one-minute intervals for 24 hours each day. Filters were sampled between 1100 and 1600 PDT on each day of the study because consistent winds have previously been observed during this period. Data from each instrument was converted to common units, edited for instrument down time, and compiled into a data base in Microsoft Excel. On-minute data were averaged for the 1100 to 1600 PDT filter sampling periods for comparison purposes.

The hivol samplers were comparable to each other during collocated sampling, even under high dust loading conditions. The PQ200 lovol FRM measured ~18% lower PM10 levels than the collocated hivols, even though tests in ambient environments show they are equivalent. The particle size measurements showed large concentrations with sizes >10 µm, and previous comparisons have shown large differences among hivol samplers with different inlets under these conditions. Rule 403 requires “…high-volume particulate matter samplers or other U.S. EPA-approved equivalent method for PM10 monitoring…” so as an FRM, the PQ200 could be used with no modification to the rule. The PQ200 is much smaller and lighter, has its own support stand, and operates on a rechargeable 12 V battery, so it would be much easier to deploy for temporary monitoring.

None of the other instruments correlated well with the hivol PM10. The OMNI minivols did not acquire sufficient mass over the five hour sampling period to be comparable with each other or with the other samplers. The factory calibrations for the nephelometers did not represent the light scatting to mass relationships for a fugitive dust environment. Light scattering is sensitive to the PM2.5 fraction, but less sensitive to larger particles. While the DRX PM10 concentration did not equal the hivol, it was highly correlated (R = 0.9) with hivol PM10 at United Rock and moderately correlated (R = 0.7) at Vulcan Materials. Multiple size ranges from the DRX allow different sources to be identified. A 10/9/08 PM2.5 episode at Vulcan showed consistently high PM2.5 that was not associated with larger particle sizes. This probably

iii

represents secondary aerosol formation and/or wildfire contributions. On the other hand, high PM2.5 at United Rock during simulated fugitive dust episodes showed PM2.5 increasing over periods of a few minutes along with the larger particle sizes, indicating a local dust source. This experiment emphasized the constrained nature of nearby plumes, as the Grimm OPC that was within 20 m of the DRX did not register the event.

The concept of upwind and downwind in Rule 403 may be refined. The upwind site at United Rock consistently measured higher PM10 than the downwind site, even though they were along the prevailing wind vector. The Vulcan “upwind” site was isolated from nearby sources and provided a reasonable representation of neighbor-hood-scale concentrations, as indicated by similar PM10 levels at both sites on Sunday when the facility was not in operation.

iv

ACKNOWLEDGEMENTS Study investigators are indebted to many people and companies for facilitating the field

experiment. Ken Barker of United Rock and Charles St. John of Vulcan Materials assisted in site preparation and electrical installation and offered instruction and advice on potential dust sources and plant operations. Philip Fine and Rudy Eden of SCAQMD made available hivol, E-BAM, E-Sampler, and TacMAT monitoring equipment. SCAQMD field monitoring staff Sumner Wilson, Paul Chavez, and Richard Parent provided technical support and trouble-shooting throughout the experiment. Tom Merrifield of BGI, Incorporated, provided the OMNI minivol and PQ200 FRM lovol samplers and assisted in operator training and sampler installation. TSI, Incorporated, provided the new DustTrak DRX prototype which was used for the first time in an experiment of this type. Bill Roe of Grimm USA provided extensive instruction on use of the optical particle counters, as did Bob Hammer of Climatronics on the use of the TacMAT sonic anemometers. Steve Kohl and Brenda Cristiani of DRI coordinated the filter processing and shipping and compiled the integrated sample data base.

v

TABLE OF CONTENTS Page

Executive Summary ................................................................................................................... i 

Acknowledgements .................................................................................................................. iv 

Table of Contents .......................................................................................................................v 

List of Tables .......................................................................................................................... vii 

List of Figures ........................................................................................................................ viii 

List of Figures, Continued ....................................................... Error! Bookmark not defined. 

1.  Introduction ..................................................................................................................... 1-1 1.1  Background .............................................................................................................. 1-1 1.2  Project Goals and Objectives ................................................................................... 1-1 1.3  Report Overview ..................................................................................................... 1-2 

2.  Suspended Particulate Matter ......................................................................................... 2-1 2.1  Particle Size Distributions ....................................................................................... 2-1 2.2  Fugitive Dust Emission Mechanisms and Amounts ................................................ 2-2 2.3  Fugitive Dust Deposition and Residence Times ..................................................... 2-6 2.4  Source Profiles ......................................................................................................... 2-8 2.5  PM Health Effects ................................................................................................... 2-9 

3.  PM and Meteorological Measurement Systems ............................................................. 3-1 3.1  Monitoring Purposes ............................................................................................... 3-1 3.2  Filter Samplers ......................................................................................................... 3-1 

3.2.1  Size-Selective Inlets ....................................................................................... 3-1 3.2.2  Filter Media .................................................................................................... 3-3 3.2.3  Filter Sampling Systems ................................................................................ 3-4 

3.3  Continuous PM10 Monitors ...................................................................................... 3-6 3.3.1  Tapered Element Oscillating Microbalance (TEOM) .................................... 3-6 3.3.2  Beta Attenuation Monitor (BAM) ................................................................. 3-8 3.3.3  Light Scattering Nephelometers .................................................................... 3-8 3.3.4  Optical Particle Counters (OPC) .................................................................... 3-9 

3.4  Meteorological Measurements .............................................................................. 3-10 3.5  Monitors Selected for Testing ............................................................................... 3-11 

4.  Experimental Configuration and Procedures .................................................................. 4-1 4.1  Sampling Locations ................................................................................................. 4-1 4.2  Site Configurations .................................................................................................. 4-2 4.3  Network Operations ................................................................................................. 4-3 4.4  Flow Rate Performance Tests .................................................................................. 4-9 4.5  Database .................................................................................................................. 4-9 

vi

5.  Study Results .................................................................................................................. 5-1 5.1  Collocated Sampler Comparisons ........................................................................... 5-1 5.2  Inter-Sampler Comparisons ..................................................................................... 5-4 5.3  Particle Size distributions ........................................................................................ 5-9 5.4  Wind Direction and Wind Speed Variability ........................................................ 5-14 5.5  Downwind/Upwind PM10 Differences .................................................................. 5-19 

6.  Summary And Conclusions ............................................................................................ 6-1 6.1  PM10 Measurement Systems .................................................................................... 6-1 6.2  Sampling Configurations and Procedures ............................................................... 6-1 6.3  Field Study Completion ........................................................................................... 6-2 6.4  PM10 and Meteorological Monitoring Relevant to Rule 403 .................................. 6-2 

7.  References ....................................................................................................................... 7-1 

vii

LIST OF TABLES Page Table 2-1. Daily 2008 emission rate estimates in the South Coast Air Basin (CARB,

2009). ................................................................................................................... 2-7 Table 3-1. Commonly-used PM10 inlets. ..................................................................................... 3-3 Table 4-1. Sampling locations at United Rock and Vulcan Materials. All inlets were

placed two meters above ground level. ................................................................ 4-1 Table 4-2. Summary of the Standard Operating Procedures (SOPs) applied for the

Southern California Fugitive Dust Emissions Study. .......................................... 4-7 Table 4-3. Major activities during the 8/9/08 through 11/4/08 sampling period. ........................ 4-8 Table 4-4. Activities and observations at United Rock. ............................................................ 4-10 Table 4-5. Activities and observations at Vulcan Materials. ..................................................... 4-11 Table 4-6. Laboratory verification of flow rate transfer standards for filter samplers. The

primary standard is a Gillibrator bubble-meter. ................................................. 4-13 Table 5-1. PM10 correlation coefficients (R) for downwind and upwind sites. Downwind

data include 9 samples from United Rock Site 1 and 18 samples from Vulcan Materials Site 1, excluding dust generation events on 9/25 and 9/26. Upwind data include 13 samples from United Rock Site 2 and 11 samples from Vulcan Materials Site 2. Insufficient Grimm OPC data corresponded with that from the other samplers for inclusion. ........................... 5-9 

Table 5-2. Daily wind direction and wind speed frequencies (% of time) at United Rock Site 1 from 1100 to 1600 PDT. .......................................................................... 5-15 

Table 5-3. Daily wind direction and wind speed frequencies (% of time) at United Rock Site 2 from 1100 to 1600 PDT. .......................................................................... 5-16 

Table 5-4. Daily wind direction and wind speed frequencies (% of time) at Vulcan Materials Site 1 from 1100 to 1600 PDT. .......................................................... 5-17 

Table 5-5. Daily wind direction and wind speed frequencies (% of time) at Vulcan Materials Site 2 from 1100 to 1600 PDT. .......................................................... 5-18 

viii

LIST OF FIGURES Page Figure 2-1. Illustration of different modes in a typical atmospheric particle size

distribution. Nucleation and Aitken modes often overlap. Note that the tail (dotted line) of the accumulation mode penetrates into sizes <0.1 µm, as does the PM10-2.5 mode into the accumulation mode. Sources and processes that affect each mode are indicated. Total suspended particulate (TSP) refers to all particle sizes, but the peaked-roof inlet hivol sampler provides a 50% cut-point of ~30 to 50 µm. ......................................................... 2-1 

Figure 2-2. Size distributions of several California PM source emissions as a fraction of TSP (Ahuja et al., 1989; Houck et al., 1989; 1990). ............................................ 2-3 

Figure 2-3. Attenuation of mass concentrations for 2.5 and 10 µm aerodynamic diameter particles with time and vertical mixing height (1 to 100 m). This assumes a stirred tank model (Hinds, 1999) in which particles are homogeneously redistributed throughout the mixed layer at each time step and gravitational settling velocities. ........................................................................... 2-8 

Figure 2-4. Ultrafine and larger particle deposition in different portions of the human respiratory tract (Chow, 1995). .......................................................................... 2-10 

Figure 3-1. Example sampling effectiveness curve for the Airmetrics (2009) MRI10 PM10 inlet. ..................................................................................................................... 3-2 

Figure 3-2. Examples of integrated filter samplers: a) hivol TSP, b) hivol PM10, c) Thermo-Fisher lovol Partisol Plus PM10, d) BGI lovol PQ100 PM10, e) BGI lovol PQ200 PM10, f) BGI OMNI minivol PM10, g) EcoTech minivol PM10, h) Airmetrics minivol PM10, and i) Airmetrics updated minivol PM10. .................................................................................................................... 3-5 

Figure 3-3. Examples of continuous particle monitors: a) Thermo-Fisher TEOM with Filter Dynamics Measurement System (FDMS) attachment, b) MetOne E-BAM, c) MetOne E-SAMPLER nephelometer, d) TSI Model 8250 DustTrak nephelometer, e) TSI Model 8533 DustTrak DRX aerosol monitor (nephelometer and optical particle counter [OPC]), and f) Grimm OPC. ..................................................................................................................... 3-7 

Figure 3-4. Particle scattering efficiencies (Watson, 2002) as a function of size distribution for different particle compositions for λ = 550 nm light. Soot includes extinction due to both scattering and absorption (particles are assumed to be spherical). ..................................................................................... 3-9 

Figure 4-1. Locations of sampling sites at United Rock. Site 1 was nominally downwind and Site 2 was nominally upwind of the sand and gravel operations. North is in the vertical direction. Some roadway and storage pile configurations differed from those depicted in this satellite picture that was taken at an earlier date. ........................................................................................................... 4-2 

Figure 4-2. Locations of sampling sites at Vulcan Materials. Site 1 was nominally downwind and Site 2 was nominally upwind of the sand and gravel operations. North is in the vertical direction. Some roadway and storage pile configurations differed from those depicted in this satellite picture that was taken at an earlier date. ................................................................................. 4-3 

ix

Figure 4-3. Sampler configuration at the United Rock downwind Site 1. This is facing north toward the flood control area behind the trees. .......................................... 4-4 

Figure 4-4. Sampler configuration at the United Rock upwind Site 2. The northeast corner of Arrow Highway and Avenida Barbosa is in the background beyond the trees. .................................................................................................. 4-4 

Figure 4-5. Sampler configuration at the Vulcan downwind Site 1, facing northeast. ................ 4-5 Figure 4-6. Sampler configuration at the Vulcan upwind Site 2, facing south. ........................... 4-5 Figure 5-1. Collocated comparison of PM10 mass for collocated: a) Sierra Andersen hivol

and b) BGI OMNI minivol integrated filter samplers at United Rock and Vulcan Materials. Trendlines are derived from unweighted ordinary linear regression with zero intercept. ............................................................................. 5-2 

Figure 5-2. Collocated comparison of PM10 mass for collocated MetOne E-Samplers: a) with factory calibration and b) normalized to filter mass measurements at United Rock and Vulcan Materials. Trendlines are derived from unweighted ordinary linear regression with zero intercept. ................................. 5-3 

Figure 5-3. Collocated comparison of 5-hour PM10 mass for collocated DustTraks with factory calibration for: a) Units 2 and 3 at United Rock Site 1 from 9/8 – 27, excluding 9/25 – 26 during dust generation events and b) Units 3 and 4 at Vulcan Site 1 from 10/23 – 30. Trendlines are derived from unweighted ordinary linear regression with zero intercept. .................................................... 5-5 

Figure 5-4. Five-hour average PM10 concentrations at United Rock for: a) downwind Site 1 and b) upwind Site 2 for the period from 9/11 – 27. Fugitive dust episodes were simulated near United Rock Site 1 on 9/25 and 9/26. .................. 5-6 

Figure 5-5. Five-hour average PM10 concentrations at Vulcan Materials for: a) downwind Site 1 and b) upwind Site 2 for the period from 10/2 – 20. ................................. 5-7 

Figure 5-6. Comparison between five-hour average PM10 from collocated FRMs, PQ200 lovol and hivol samplers at United Rock and Vulcan Materials downwind sites. Samples during dust generation events at United Rock Site 1 on 9/25 and 9/26 are excluded. ......................................................................................... 5-8 

Figure 5-7. DRX mass size distributions at: a) United Rock downwind Site 1 and b) Vulcan Materials downwind Site 1. ................................................................... 5-10 

Figure 5-8. Grimm OPC mass size distributions at: a) United Rock downwind Site 1 and b) United Rock upwind Site 2. ........................................................................... 5-11 

Figure 5-9. Grimm OPC mass size distributions at: a) Vulcan Materials downwind Site 1 and b) Vulcan Materials upwind Site 2. ............................................................. 5-12 

Figure 5-10. One-minute DRX size variations for highest 5-hour PM1.0 concentrations for: a) United Rock Site 1 on 9/26/08 and b) Vulcan Site 1 on 10/9/08. ........... 5-13 

Figure 5-11. Wind direction frequencies from 1100 to 1600 PDT at: a) United Rock Sites 1 and 2 from 9/11 through 9/27 and b) Vulcan Materials Sites 1 and 2 from 10/2 through 10/20. Scale is percent of time from the indicated direction. ............................................................................................................ 5-20 

Figure 5-12. Downwind/upwind PM10 differences for hivol and OMNI minivol filter samplers at: a) United Rock and b) Vulcan Materials. ...................................... 5-21 

1-1

1. INTRODUCTION 1.1 Background

The South Coast Air Quality Management District (SCAQMD) adopted and amended Rule 403 (SCAQMD, 2005) to protect the public from excessive fugitive dust emissions caused by man-made activities, including those occurring on mining, aggregate handling, and construction sites. SCAQMD and aggregate producing plants have a shared interest in protecting the public from fugitive dust emissions by ensuring that ambient air monitoring conducted in accordance with Rule 403 provides data that are accurate and representative of a facility’s contributions to regulated PM10 (particles with aerodynamic diameters <10 µm) near facility fence lines. Accordingly, SCAQMD and aggregate producing plants are conducting a Joint Technical Review (JTR) to assess alternate Rule 403 ambient air monitoring methodologies. Rule 403 Provision (d)(3) states:

“No person shall cause or allow PM10 levels to exceed 50 micrograms per cubic meter when determined, by simultaneous sampling, as the difference between upwind and downwind samples collected on high-volume particulate matter samplers or other U.S. EPA-approved equivalent method for PM10 monitoring. If sampling is conducted, samplers shall be: (A) operated, maintained, and calibrated in accordance with 40 Code of Federal Regulations (CFR), Part 50, Appendix J, or appropriate U.S. EPA-published documents for U.S. EPA-approved equivalent method(s) for PM10; and (B) reasonably placed upwind and downwind of key activity areas and as close to the property line as feasible, such that other sources of fugitive dust between the sampler and the property line are minimized.”

Rule 403 was first adopted in 1976 and was amended in 1992, 1993, 1997, 1998, 2004, and 2005. The high volume (hivol) PM10 Federal Reference Method (FRM) monitor is a large device that is difficult to move and locate for short-term monitoring. It requires substantial power, usually supplied by a portable generator, to operate. The hivol needs to operate for several hours (at least five hours for Rule 403) during which wind directions may change, thereby allowing nearby source contributions to potentially interfere with those from the monitored fugitive dust sources.

The monitoring methods should: 1) be rapidly deployable (light, durable, and self powered to the extent practicable for service in Rule 403 monitoring locations with no line power), 2) provide short-term average PM10 data that can be analyzed with respect to short-term average wind direction data; and 3) capture PM10 mass loadings comparable (but not necessarily equivalent) to those from PM10 FRMs; and 4) have reasonable initial and operating costs.

1.2 Project Goals and Objectives

The goal of this project is to identify and characterize ambient air PM10 monitoring instruments and meteorological monitoring systems that can be effectively and economically used in Rule 403 monitoring to accurately estimate incremental PM10 concentrations contributed by specific commercial fugitive dust sources, such as sand and gravel operations and quarries. Although the scope of this study does not specifically address possible modifications to Rule 403, it is expected that study results will provide a technical basis for future modification. Specific study objectives are:

Identify and evaluate PM10 measurement systems that provide continuous PM10 data in the necessary time.

Develop sampling and analysis configurations and procedures that include the continuous PM10 and meteorological monitoring instruments.

1-2

Execute a field study to evaluate PM10 and meteorological monitoring instruments under representative conditions at fugitive dust sources with adjacent roads and at stationary sources that potentially affect the Rule 403 PM10 incremental impact analyses.

Identify and describe PM10 and meteorological monitoring procedures that can be used effectively and economically to confirm compliance with Rule 403 incremental impact requirements.

1.3 Report Overview

This section defines the scope and objectives of the study. Section 2 describes the properties of fugitive dust and ambient aerosols that affect their suspension and transport. Section 3 summarizes monitoring instruments and procedures and the rationale for selecting those used in this study. Section 4 documents the experimental locations, site configurations, and instrument performance. Section 5 analyzes ambient aerosol data to determine the precision of the different measurement methods and examines upwind and downwind PM10 concentrations, to determine the extent to which different methods might achieve the requirements of Rule 403. Section 6 summarizes the study results and provides recommendations for further measurements, while Section 7 provides references to more complete treatments of different topics. Appendix A, a separate volume, contains standard operating procedures for each of the instruments used in the study. The project data base is compiled in self-documenting Excel data files.

2-1

2. SUSPENDED PARTICULATE MATTER This section describes the properties of suspended particulate matter (PM) that are

relevant to suspension, transport, and adverse health effects. These properties are also relevant to the measurement systems used to quantify atmospheric PM10 concentrations.

2.1 Particle Size Distributions

PM10 contains several different size fractions that indicate emission sources and affect PM transport distances. Figure 2-1 represents portions of the PM mass size distribution. Fugitive dust emissions, typical of those from sand and gravel processing, reside primarily in the coarse particle (2.5 to 10 µm) fraction (i.e., PM10-2.5). PM10-2.5 also contains sea salt near coastal areas, as well as pollen, spores, and other plant parts. Modern particle removal devices, such as precipitators and baghouses, are more efficient for removing coarse and larger particles than they are for accumulation mode particles, so well-controlled industrial processes are minimal emitters.

Figure 2-1. Illustration of different modes in a typical atmospheric particle size distribution. Nucleation and Aitken modes often overlap. Note that the tail (dotted line) of the accumulation mode penetrates into sizes <0.1 µm, as does the PM10-2.5 mode into the accumulation mode. Sources and processes that affect each mode are indicated. Total suspended particulate (TSP) refers to all particle sizes, but the peaked-roof inlet hivol sampler provides a 50% cut-point of ~30 to 50 µm.

The accumulation mode is comprised of direct PM emissions from combustion sources, such as gasoline- and diesel-fueled engines, and the conversion of oxides of nitrogen (NOx), sulfur dioxide (SO2), ammonia (NH3), and some reactive organic gases (ROG) to PM through atmospheric chemical processes. The condensation portion of the accumulation mode forms

0

2

4

6

8

10

0.001 0.01 0.1 1 10 100Particle Aerodynamic Diameter (µm)

Re

lati

ve

Mas

s C

on

ce

ntr

ati

on

Accumulation Coarse

PM 10

PM 2.5

Geological Material, Pollen,

Sea Salt

Sulfate, Nitrate, Ammonium,

Organic Carbon, Elemental Carbon, Heavy Metals, Fine

Geological

Condensed Organic

Carbon or Sulfuric Acid

Vapors, Clean Environment

Aitken

Condensation Mode

Droplet Mode

Nucleation

Fresh High Temperature Emissions,

Organic Carbon,

Sulfuric Acid, Metal Vapors

Ultrafine (PM 0.1)

TSP

2-2

mostly under dry gas-to-particle conversion conditions. The droplet mode is consistent with aqueous-phase reactions in fogs and clouds; more material accumulates within the water droplet that leaves larger particles when the water evaporates (Hering and Friedlander, 1982; John et al., 1990; Meng and Seinfeld, 1994). Another interpretation of these modes for relative humidity (RH) > 80% is that the water-absorbing (hygroscopic) materials (e.g., sulfate [SO4

=] and nitrate [NO3

-]) have grown into the droplet mode while the water-repellent (hydrophobic) materials (e.g., soot and some organic carbon [OC]) have retained their original sizes (Aklilu et al., 2006; Lowenthal et al., 2003; Watson, 2002; Watson et al., 2008a).

Ultrafine particles (UP; particles with aerodynamic diameters < 100 nm or 0.1 µm) are both directly emitted by combustion sources and can form by nucleation and growth in the atmosphere. Fossil fuels often include trace amounts of sulfur that can oxidize to sulfuric acid and ROG that can oxidize to condensable compounds. As these gases cool upon dilution with ambient air, they may condense onto larger particles or nucleate into UP. Black carbon soot is produced during oxygen-starved combustion. Some of this soot is UP, but these particles increase in size with time owing to condensation and adsorption of vapors and by coagulation with other small particles. UP number concentrations decrease and particle sizes increase rapidly with distance from the source (Zhu et al., 2002).

Figure 2-2 compares the relative amounts of PM1.0, PM2.5, and PM10 (particles with aerodynamic diameters <1, 2.5, and 10 µm, respectively) in TSP for bulk material from several California emission sources. Fugitive dust was collected from exposed surfaces, dried, sieved, and resuspended in a chamber for sampling through size-selective inlets onto filters for mass quantification (Chow et al., 1994). Construction dusts, road dusts, and soil dusts formed from pulverization of larger soil particles are predominantly in the PM10-2.5 particle size range, with minor to moderate quantities in the PM2.5 fraction. Biomass burning and diesel engine emissions are mostly in the PM1.0 size fraction, consistent with Figure 2-1.

2.2 Fugitive Dust Emission Mechanisms and Amounts

Reviews of fugitive dust sources, suspension mechanisms, and control measures (Amato, 2000; Chow and Watson, 1992; Cowherd, 2001; Kinsey and Cowherd, 1992; Watson et al., 2000) emphasize the large uncertainty of generalizing results from a single experiment to other situations. These reviews identify gravel and mining pits, open fields and parking lots, paved and unpaved roads, agricultural fields, construction sites, dry lakes (playas), unenclosed storage piles, and material transfer systems are the major sources of fugitive dust. Visible dust plumes may be noticed over these sources when wind speeds are high or when vehicles are moving, but these plumes dilute and deposit rapidly with distance from the source.

PM2.5 and PM10 source apportionment studies show that, on average, fugitive dust contributes ~5% to ~20% of PM2.5 and ~40% to ~60% of PM10 measured in the atmosphere (Watson and Chow, 2000). Fugitive dust contributions to ambient measurements are often overestimated by dispersion models that simulate contributions to receptor concentrations. This occurs because fugitive dust emission factors are usually measured at or near the source, then they are allocated over emission grid squares of 1 km x 1 km or more. Few dispersion models account for deposition of large particles that occur within the grid square.

Fugitive dust emissions depend on particle sizes, surface loadings, surface conditions, atmospheric and surface moisture, wind speeds, and dust-suspending activities. Emission rates and control measures are also closely related to these properties. The silt fraction of surface dust is most often used as a surrogate for suspendable particles. The silt fraction consists of particles

2-3

with geometric diameters <75 µm as determined by sieving dried soil acquired from surface samples. The 75 µm geometric diameter corresponds to an aerodynamic diameter of ~120 µm because the aerodynamic diameter varies inversely with the square root of the density, which is ~2.65 g/cm3 for minerals. Similarly, a 2.5 µm aerodynamic diameter dust particle has a geometric diameter of ~1.5 µm and a 10 µm aerodynamic diameter dust particle has a geometric diameter of ~6 µm. Little is known about the PM2.5 and PM10 in surface dust deposits as these fractions are too small to be determined by simple sieving methods. In spite of its limitations, silt fractions or quantities appear as explicit variables in many fugitive dust emission factors (U.S.EPA, 2006a). The processes related to particle size indicate that actual emissions of PM2.5 and PM10 are influenced by more detailed size distributions above and below the 75 µm geometric diameter that specifies silt content. An approach similar to that used to obtain the distributions in Figure 2-2 provides a more accurate representation.

Figure 2-2. Size distributions of several California PM source emissions as a fraction of TSP (Ahuja et al., 1989; Houck et al., 1989; 1990).

When surfaces are continually disturbed by intense winds, vehicular movement, or other human activities, unlimited reservoirs are created that emit dust whenever winds exceed threshold suspension velocities. Suspendable dust loadings may vary substantially, even over periods of a few minutes, when there are no mechanisms to replenish the reservoir.

Surface conditions refer to the landform shape and cohesion of a potential dust reservoir. The effects of landform shape on dust suspension are embodied in the concept of “surface roughness.” Surface roughness is related to the heights of obstructions within and around exposed dust areas. Agriculturists often orient their furrows perpendicular to prevailing winds, or

2-4

plant rows of trees upwind of their fields, to minimize soil losses from wind erosion by increasing surface roughness. Larger surface roughness decreases the force exerted by the wind on suspendable surface particles, thereby decreasing emissions. However, larger surface roughness increases vertical turbulence that can mix suspended particles higher into the atmosphere for longer transport distances.

The aerodynamic roughness length is the apparent distance above the surface at which the average wind speed approaches zero. In reality, wind speed does not become zero at this level, but it deviates from the logarithmic increase of wind speed with height that is commonly found in the atmosphere. Aerodynamic surface roughness is one-eighth to one-thirtieth the height of obstructions in and around an exposed area (Greeley and Iversen, 1985), but it is site-specific and quantified with wind speed measurements taken at different elevations between ~1 m and ~10 m above ground level. The ratio of wind speeds at lower levels is plotted against the logarithm of the height of the measurement and extrapolated to a wind velocity of zero. The intersection with the elevation axis at wind speed equals zero is the surface roughness. In practice, estimates from many hours of wind measurements are averaged to determine typical surface roughness. The slope of this relationship is termed the “friction velocity” and indicates the wind shear forces near an erodible surface (Pasquill, 1961).

The effects of different variables are embodied in a threshold friction velocity (Belnap et al., 2007; Chepil, 1959; Gillette, 1978; Gillette et al., 1979; 1980; 1982; 1983; 1988; 1990; 1997; 2001; 2004; Gillette and Chen, 2001; Ibrahim et al., 2004; Ishizuka et al., 2005; Jie, 2004; King et al., 2005; Kjelgaard et al., 2004; Li and Pomeroy, 1997; Lyles and Krauss, 1971; Marticorena et al., 1997; Nickling and Ecclestone, 1981; Okin, 2005; Ono, 2006; Raupach et al., 1993; Zhao et al., 2006) that is experimentally determined by placing a wind tunnel over an example of the affected soil and measuring the surface speed at which soil movement first becomes visible. With the more common use of continuous particulate monitors (Chow, 1995; Chow et al., 2008), threshold friction velocities might be inferred from hourly PM2.5, PM10, and wind speed measurements at ambient sampling sites. Averaging times of one to five minutes would provide more precise estimates than the hourly averages.

Gillette et al. (1980) shows threshold friction velocities that vary from 0.19 to 1.82 m/s for soils with different degrees of disturbance. Most ambient wind speed measurements are made at elevations between 5 and 10 m above ground level, and these must be translated to surface friction velocities to determine suspension. This is done using estimates of surface roughness and friction velocities from the actual or similar sites. For this range of surface threshold values, emissions will be initiated at ambient wind speeds (measured at 7 m above the ground level, the height of most National Weather Service wind sensors) between 7 and 10 m/s (25 to 36 km/h). Even though emissions begin at these velocities, the wind force contains insufficient energy to suspend very much of the erodible soil mass. The amount of dust suspended increases at approximately the cube of the wind speed above the threshold velocity.

Water adhering to soil particles increases their mass and surface tension forces, thereby decreasing suspension and transport. Watering trucks provide effective means of increasing the surface tension. Cohesion of wetted particles may persist after the water has evaporated due to the formation of aggregates and surface crusts. Rosbury and Zimmer (1983) found that moisture content affects the ejection of particles by vehicles, as well as the strength of the road bed and hence its ability to deform under vehicle weight. The addition of water to create surface moisture contents exceeding 2% resulted in >80% reductions for PM10 emissions compared to a control surface with an average moisture content of 0.56% (Flocchini et al., 1994a). Road surface

2-5

moisture content enhances the strength characteristics of surface crusts and the stability of aggregates (Bradford and Grosman, 1982; Lehrsch and Jolley, 1992). Kinsey and Cowherd (1992) show how watering reduces emissions at a construction site. Significant dust control benefits are derived initially by doubling the area that is watered; however, benefits are reduced as more water is applied to the site. Ultimately, control efficiency is limited because grading operations are continually exposing dry earth and burying the moistened topsoil.

Excessive moisture causes dust to adhere to vehicle surfaces so that it can be carried out of unpaved roads, parking lots, and staging areas. Carryout also occurs when trucks exit heavily watered construction and mining sites (Englehart and Kinsey, 1983). This dust is deposited on paved (or unpaved) roadway surfaces as it dries, where it is available for suspension far from its point of origin. Fugitive dust emissions from paved roads are often higher after rainstorms in areas where unpaved accesses are abundant, even though the rain may have flushed existing dust from the paved streets. Wheel and truck washing requirements, such as those implemented at United Rock and Vulcan, are needed to reduce the carryout from sand and gravel operations.

Vehicles represent the other common source of fugitive dust emissions from paved roads (Etyemezian et al., 2003a; 2003b; 2006; Kuhns et al., 2001; 2005; Patra et al., 2008), unpaved roads and staging areas (Barnard et al., 1986; Claiborn et al., 1995; Dyck and Stukel, 1976; Etyemezian et al., 2003b; Flocchini et al., 1994b; Gillies et al., 1999; Kuhns et al., 2005; Midwest Research Institute, 1991; Muleski and Stevens, 1992; Stevens, 1991; Tsai and Chang, 2002; Williams et al., 2008), as does materials movement associated with mineral processing, quarrying, and construction (Armstrong and Russell, 1980; Axetell and Cowherd, 1984; Baxter, 1983; Chakrabarty et al., 2002; Chang et al., 1999; Chaulya, 2006; Dietrich et al., 1980; Hubbard, 1976a; 1976b; 1976c; Lee et al., 2001; U.S.EPA, 1976; 1978). This movement creates small particle reservoirs and provides the energy to inject them into the atmosphere.

Vehicle shape, speed, weight, number of wheels as well as dust acquisition for trackout interact with different surfaces to change the particle size, surface loading, wind effects, and surface moisture. Vehicular traffic in these areas adds to particle suspension because tire contact creates a shearing force with the road that lifts particles into the air (Nicholson et al., 1989; Nicholson and Branson, 1990). Moving vehicles also create turbulent wakes that act much like natural winds to raise particles (Moosmüller et al., 1998). Natural crusts are often disturbed by vehicular movement, increasing the reservoir available for wind erosion.

Minimizing the deposition of fresh dust onto these surfaces by wheel and truck washing, and collecting deposited dust with street sweepers, are viable methods for reducing their PM emissions, as demonstrated at the United Rock and Vulcan Materials facilities. Dust loadings on a paved road surface build up by being tracked out from unpaved areas such as construction sites, unpaved roads, parking lots, and shoulders; by spills from trucks carrying dirt and other particulate materials; by transport of dirt collected on vehicle undercarriages; by wear of vehicle components such as tires, brakes, clutches, and exhaust system components; by wear of the pavement surface; by deposition of suspended particles from many emissions sources; and by water and wind erosion from adjacent areas.

Storage piles, material conveyance, and loading activities (Badr and Harion, 2005; Badr and Harion, 2007; Balentine et al., 1985; Billman and Arya, 1985; Cai et al., 1983; Chang, 2006; Cowherd et al., 1979; Cowherd, 1981; 1982; 1983; 1988; Cowherd and Englehart, 1988; de Faveri et al., 1990; Nalpanis and Hunt, 1986; Park and Lee, 2002; Rappen, 1972; Stunder and Arya, 1988; Xuan and Robins, 1994) create dust reservoirs, mechanically inject dust into the

2-6

atmosphere, and present targets for wind erosion. Most of these operations are affected by the same variables described above, but the frequency, spatial extent, and magnitudes differ from those of urban and non-urban dust sources.

Table 2-1 summarizes daily emissions for PM size fractions and gaseous pollutants in the South Coast Air Basin (SoCAB). CO plays a small role in PM formation, but it is included for completeness. As explained above, the other gases are precursors to particle formation. Sand and gravel production is included in the “Mineral Industrial Processes” category. Emissions for this category constitute 2.8%, 2.6%, and 2.2% of total SoCAB TSP, PM10, and PM2.5 emissions, respectively. Construction and demolition and paved road dust are the largest PM10 emitters, accounting for 58% of total SoCAB PM10 emissions. Directly-emitted PM2.5 accounts for 39% of PM10, and its major components are fuel combustion, engine exhaust, cooking, biomass burning, and paved and unpaved road dust. NOx emissions are much higher than SO2 emissions, and this is reflected in higher amounts of nitrate than sulfate found in SoCAB ambient PM2.5 samples (SCAQMD, 2000; 2007). ROG and TOG are also major emission components, and a portion of them engenders the formation of secondary organic aerosol (Vutukuru et al., 2006).

2.3 Fugitive Dust Deposition and Residence Times

Pollutants eventually leave the atmosphere by deposition to the Earth’s surface. The amount deposited is proportional to the ambient concentration, with the proportionality constant termed the deposition velocity. While simple in concept, deposition velocities are highly variable, depending on the pollutant composition, its uniformity of mixing throughout the atmosphere, atmospheric turbulence, and the nature of the surface to which it deposits.

A small fraction of suspendable particles are regionally transportable particles. Ground level emissions of large mechanically generated particles are often removed near the source, due to deposition or impaction on nearby obstacles. The initial vertical energy is typically short-lived. In the absence of violent winds with large vertical components, there is little, if any, “residual” or “continuing” source of energy to sustain vertical motion. These vertical updrafts can loft fugitive dust emissions to great heights that increase residence time and transport distances. Coarse particles have a higher probability than accumulation mode particles of being removed from the atmosphere due to losses by impaction with obstacles, such as trees and buildings, and by deposition to the ground due to gravitational settling.

Based on gravitational setting velocities that apply to particles with aerodynamic diameters >~2 µm (Slinn, 1982), Figure 2-3 shows that half of the 10 µm particles mixed within the first meter are removed after ~3.5 minutes, and that half of the 2.5 µm particles in this layer are gone after an hour. Less than 10% of the 10 µm particles remain after 12 minutes, with 90% of the 2.5 µm particles depleted after 3.5 hours. A 1 m/s wind speed results in a transport distance of 3.6 km/hr. In an average 5 m/s wind, only 10% of the 10 µm particles uniformly mixed through a 10 m depth would travel more than 36 km from the source within two hours after suspension, while 10% of the 2.5 µm particles could achieve distances of nearly 600 km. PM2.5 emissions are more relevant to regional inventories than are PM10 emissions from fugitive dust sources, but empirical emission factors for this size fraction are lacking for fugitive dust source categories.

Figure 2-3 also shows that residence time increases with the mixing depth. Particles emitted at higher elevations above ground level, such as from a smoke stack, mix through layers >100 m which substantially increases their residence times and potential transport distances. Upwind obstructions lower wind speeds and turbulence near the ground. This lowers the

2-7

probability of achieving a suspension threshold velocity at the surface, reduces the upward lift of particles that are suspended mechanically or by wind, and limits the downwind transport distance of suspended particles because the wind speed is lower. The effectiveness of these obstructions depends on their height, length, and permeability (i.e., the fraction of their area which is open, typically the density of leaf and branch cover).

Table 2-1. Daily 2008 emission rate estimates in the South Coast Air Basin (CARB, 2009).

Emission Rate (tons/day)a Source Type TSP PM10 PM2.5 NOx SO2 CO ROG TOG

STATIONARY SOURCES Fuel Combustion 5.9 5.7 5.7 45.1 6.7 35.7 6.0 27.4 Waste Disposal 1.2 0.7 0.3 2.0 0.5 1.1 9.2 72.7 Cleaning and Surface Coatings 0.6 0.5 0.5 0.1 0.0 0.1 39.2 48.3 Petroleum Production and Marketing 4.0 2.6 2.2 4.3 6.2 8.9 33.1 38.1 Chemical Industrial Processes 0.7 0.6 0.5 0.2 1.1 0.1 8.4 9.3 Food and Agriculture Industrial Processes

0.8 0.4 0.2 0.0 - 0.0 2.6 2.8

Mineral Industrial Processes 14.5 7.8 2.5 3.5 1.3 0.6 0.6 0.7 Metal Industrial Processes 0.8 0.6 0.4 0.2 0.0 1.2 0.1 0.1 Wood and Paper Industrial Processes 5.7 4.0 2.4 - - - 0.2 0.2 Glass and Related Product Industrial Processes

0.2 0.2 0.1 0.0 0.0 0.0 0.0 0.0

Electronics Industrial Processes 0.0 0.0 0.0 0.0 0.0 - 0.1 0.1 Other Industrial Processes 1.3 0.9 0.6 0.6 0.3 0.5 7.6 8.4 AREA SOURCES Solvent Evaporation 0.0 0.0 0.0 - - - 127.1 145.6 Residential Fuel Combustion 8.9 8.4 8.2 24.2 0.4 57.5 4.1 9.4 Farming Operations 1.4 0.6 0.1 - - - 5.6 70.6 Construction and Demolition 101.8 49.8 5.0 - - - - - Paved Road Dust 267.8 122.4 18.4 - - - - - Unpaved Road Dust 17.6 10.3 1.0 - - - - - Fugitive Windblown Dust 4.5 2.3 0.3 - - - - - Fires 0.5 0.4 0.4 0.1 - 3.0 0.2 0.3 Managed Burning and Disposal 5.4 5.2 4.6 1.5 0.5 50.7 3.6 5.7 Cooking 16.6 15.3 14.0 - - - 1.9 2.8 MOBILE SOURCES On-road Motor Vehicles 25.2 24.9 17.9 450.4 2.1 2115.8 210.8 231.8 Other Mobile Sources 19.1 18.5 16.4 287.8 18.9 974.2 150.8 165.5 NATURAL SOURCES Biogenic Sources - - - - - - 75.6 82.5 Wildfires 17.3 16.6 14.1 5.0 1.5 164.2 10.9 18.1 Total 521.8 298.7 115.8 825.0 39.5 3413.6 697.7 940.4 aTSP = total suspended particulates (~30-50 µm); PM10 = particles < 10 µm; PM2.5 = particles < 2.5 µm; NOx = oxides of nitrogen (NO + NO2 expressed as NO2); SO2 = sulfur dioxide; CO = carbon monoxide; ROG = reactive organic gases (expressed as equivalent propane); and TOG = total organic gases (expressed as equivalent propane).

2-8

van Eimern et al. (1964) examined how wind speeds and turbulence change behind shelterbelts and wind fences that are often used to reduce crop erosion and snow blow, and to minimize deposition on roads during dust storms. Permeability is an important parameter, with a medium value (~50%) giving the longest fetch with a greater than 60% reduction in wind speed. Although a solid wall provides a precipitous reduction, it does not persist for long distances downwind of the obstruction. The implication is that a thin row of trees upwind of a dust source provides greater downwind reductions than a dense row. A stabilized berm around loose tailings would provide a similar effect.

Figure 2-3. Attenuation of mass concentrations for 2.5 and 10 µm aerodynamic diameter particles with time and vertical mixing height (1 to 100 m). This assumes a stirred tank model (Hinds, 1999) in which particles are homogeneously redistributed throughout the mixed layer at each time step and gravitational settling velocities.

2.4 Source Profiles

Fugitive dust contributions to PM2.5 and PM10 concentrations in ambient air are easily distinguished from other source contributions by their chemical compositions. Chow et al., (1994) found that geological profiles typically contain large abundances of aluminum, silicon, potassium, calcium, and iron. The abundance of total potassium in geological material is six to ten times the abundance of water-soluble potassium, which is 100% of total potassium in biomass burning emissions. The abundances of these elements are often similar among fugitive dust profiles. Lead is often enriched in paved road dust, even though leaded fuels are no longer used in the United States. It is believed that decomposition of tire weights may be the source of this enrichment (Root, 2000). Carbon constitutes 5 – 15% of road dust and some agricultural soils. Soluble ions such as nitrate (NO3

-), sulfate (SO4=), and ammonium (NH4

+) are generally

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.001 0.01 0.1 1 10 100 1000

Time Since Suspension (Hours)

Mas

s F

ract

ion

Su

spen

ded

2.5 µm, 1 m

2.5 µm, 10 m

2.5 µm, 100 m

10 µm, 1 m

10 µm, 10 m

10 µm, 100 m

2-9

low, in the range of 0.1 – 0.2%. Water-soluble sodium (Na+) and chloride (Cl-) are also low, except for situations when salt is used as a de-icing agent or in the presence of dust from a dry lake bed. A wide assortment of organic compounds is found in road dusts and agricultural soils (Labban et al., 2006; Rogge et al., 2006; 2007; Song et al., 1999), but these are specific to their origins. Mine tailings and ores are often enriched in the metals that are the object of their extraction (Freeman et al., 1990; Rabinowitz, 2005; Vincent et al., 2001).

Fugitive dust from sand and gravel operations is not easily separated from different emitting activities within the facility or from other fugitive dust sources because the chemical profiles are similar. Raw and finished materials are the basis for nearly all other geological material in the SoCAB. No contaminants are added during processing, and any organic material is probably lost in the grinding and washing processes. On the other hand, the PM10 composition for sand and gravel operations is expected to differ greatly from non-dust sources such as exhaust emissions from nearby highways, wildfire emissions, and secondary NO3

-, SO4=, and

organic aerosols.

2.5 PM Health Effects

PM must be inhaled and deposited in different parts of the human body to cause adverse health effects. Figure 2-4 shows approximately how much of different particle sizes deposit in different parts of the human respiratory system. The extra-thoracic region consists of the nasal, oral, pharyngeal and laryngeal airways. The main mechanisms for deposition in this region are impaction and diffusion. Most particles in the PM10-2.5 fraction typified by fugitive dust are removed by impaction in this upper part of the respiratory system, sometimes resulting in allergic reactions and sinus ailments. The intrathoracic region includes the trachea, bronchial airways, and alveoli in the lung. Particles reaching this region can cause lung damage and potentially transfer to the bloodstream for transport to other organs. Most of the UP pass through the upper respiratory tract and deposit in the lower respiratory tract. Deposition efficiencies increase at higher breathing rates, such as those experienced during exercise.

The primary evidence for PM health effects comes from epidemiological studies that relate mortality and morbidity to PM concentrations on short and long time scales. Pope and Dockery (2006) document substantial progress since Vedal (1997) in all areas of understanding, with notable advances in the areas of: 1) short-term exposure and mortality; 2) long-term exposure and mortality; 3) time-scales of exposure; 4) the shape of the concentration-response function; 5) cardiovascular disease; and 6) biological plausibility.

Pope and Dockery (2006) summarize a large number of epidemiological studies that consistently show statistically significant associations between cardiopulmonary mortality and daily PM2.5 or PM10 concentrations. Long-term exposures show larger effects than short-term exposures across all of the studies. PM health effects appear to depend on both the concentrations and the length of exposure, with repeated exposures to high levels over many years being more serious than less frequent short-term exposures to higher concentrations. Pope and Dockery (2006) note that setting a standard implies that there is a “threshold” below which no effects are observed, and that estimated concentration-response functions appear to be linear with no evidence of a lower limit at which no effects are observed.

2-10

Figure 2-4. Ultrafine and larger particle deposition in different portions of the human respiratory tract (Chow, 1995).

Associations between short-term exposure to PM10-2.5 and mortality were found in Mexico City, Mexico (Castillejos et al., 2000); in Phoenix, AZ (Smith et al., 2000), Detroit, MI (Ito, 2003), the Coachella Valley, CA (Ostro et al., 2000) of the USA; and in eight Canadian cities (Burnett and Goldberg, 2003). Smith et al. (2000) reported significant effects for PM10-2.5 but not PM2.5. Conversely, Schwartz (1996) found no association between episodes of PM10-2.5 associated with dust storms and mortality in Spokane, WA, USA. Associations between PM10-2.5 and hospital admissions for respiratory illness were reported for Reno, NV, USA, Anchorage, AK, USA (Choudhury et al., 1997), and Vancouver, Canada (Cheng et al., 2004; Cheng and Li, 2005). In contrast, Schwartz and Neas (2000) found that asthma in children was more closely related to PM2.5, especially the SO4

= fraction, than to PM10-2.5, in six eastern U.S. cities.

Coarse particle endotoxins are of concern, especially in agricultural areas. Endotoxin Units (EU) were measured in PM10 samples at 13 locations in southern California by Mueller-Anneling et al. (2004). The lowest concentrations (0.21 EU/m3) were found in the Los Angeles urban area. Higher concentrations were found at Lancaster (1.30 EU/m3) and Rubidoux (1.85 EU/m3) sites described as impacted by agricultural activities. Endotoxins are not expected to be part of emissions from sand and gravel operations.

Health effects of the combination of carbonaceous material in PM and volatile organic compounds (VOCs) are little studied but are becoming of concern. Epidemiological and toxicological studies indicate associations between organic fractions of ambient PM and adverse respiratory and cardiovascular health outcomes (Mauderly and Chow, 2008). The broad, diverse

0

20

40

60

80

100

120

0.01 0.1 1 10 100

Particle Aerodynamic Diameter (microns)

De

po

sit

ion

(P

erc

en

t)

Rest Normal Exercise

Nose

Lung

Trachea

Mouth (ISO)

2-11

class organic aerosols, such as those from engine exhaust and biomass burning, may be more important to public health than the modest attention given to them. Again, the organic constituents of sand and gravel emissions are believed to be small, although they will be prominent in emissions from diesel engines used in the different processes.

The UP fraction is becoming of greater concern as a health hazard in southern California (Becker et al., 2003; Delfino et al., 2005; 2008; Hu et al., 2008; Li et al., 2003), especially in the exhaust of diesel engines which appear to be among the highest SoCAB emitters. UP is negligible in all fugitive dust emissions, including those from sand and gravel operations.

3-1

3. PM AND METEOROLOGICAL MEASUREMENT SYSTEMS This Section describes different methods by which PM can be measured and identifies

those with the most promise for potential use in Rule 403 implementation.

3.1 Monitoring Purposes

PM is sampled to: 1) determine compliance with National Ambient Air Quality Standards (NAAQS; Bachmann, 2007); 2) enhance understanding of the chemical and physical properties of atmospheric pollution (Chow, 1995); 3) apportion PM chemical constituents, including toxic metals, to sources (Watson et al., 2008b); and 4) evaluate the extent and causes of adverse health effects (Mauderly and Chow, 2008; Pope and Dockery, 2006), ecosystem damage (McLaughlin, 1985), and visibility impairment (Watson, 2002). A sampling system that serves one objective does not necessarily meet the needs of other objectives. Filter samplers designed to determine compliance with mass-based NAAQS have limited applicability to particle sizing, hourly and daily sequential sampling, sampling for chemical characterization, and quantification of volatile aerosols (Chow and Watson, 2008). Rule 403’s stated objective is to evaluate potential effects on public health near sand and gravel operations and is more relevant to the fourth objective. Understanding the causes of excessive levels, objective 2, would help to focus remediation efforts. Since the sampling locations and monitoring periods are not consistent with NAAQS, Rule 403 data would not be used for NAAQS compliance purposes.

3.2 Filter Samplers

Most of the world’s data on PM mass concentrations is obtained by drawing a measured volume of air through a size-selective inlet, then through a collection filter over a 24-hour period. The filter is weighed in a temperature- and RH-controlled laboratory before and after sampling to determine the particle deposit by difference. The deposit weight is then divided by the air volume sampled to determine the PM mass concentration. U.S. EPA (2009) designates filter-based FRMs for lead (U.S. EPA, 2008) in TSP and for PM10 and PM2.5 mass (U.S.EPA, 2006b) to determine NAAQS compliance (Bachmann, 2007; Chow et al., 2007).

3.2.1 Size-Selective Inlets

Inertial classifiers are used as size-selective inlets to remove particles exceeding a specified aerodynamic diameter. Inlets are characterized by sampling effectiveness curves, as illustrated in Figure 3-1, showing the fraction of particles that pass through as a function of aerodynamic diameter. Sampling effectiveness is summarized by a 50% cut-point (d50), the diameter at which half of the particles pass through the inlet, and a slope (or geometric standard deviation) that is the square root of the ratio of the diameter of particles with 84% removal (d84) to the diameter with a 16% removal (d16). A slope of 1 indicates a step-function that is impossible to obtain in practice. Many inlets have slopes of up to 1.5; the smaller the slope, the “sharper” the cut-point. Table 3-1 summarizes the most commonly used PM10 inlets.

Sampling efficiency quantifies the total PM mass fraction passing through the inlet and depends on the particle size distribution as well as the inlet sampling effectiveness. Sampling efficiency is obtained by integrating the product of sampling effectiveness and aerosol mass-size distribution across all expected particle sizes (Keywood et al., 1999; Watson et al., 1983; Wedding and Carney, 1983). Since many ambient mass-size distributions peak near 10 µm but are at a minimum near 2.5 µm, as in Figure 2-1, changes in PM10 sampling effectiveness have a

3-2

Figure 3-1. Example sampling effectiveness curve for the Airmetrics (2009) MRI10 PM10 inlet.

larger effect on mass concentrations than do changes in PM2.5 sampling effectiveness (Burton and Lundgren, 1987; Lundgren et al., 1984). PM10 inlets with sharper cut-points often collect less mass than those with broader cut-points in dusty areas.

Inlet transmission characteristics should be independent of wind speed and wind direction. The rectangular peaked-roof inlet of the hivol sampler for TSP (Code of Federal Regulations, 2007), which is still the FRM for the lead NAAQS, has a variable sampling effectiveness in response to wind direction and speed (McFarland et al., 1980; Wedding et al., 1977). PM10 inlets are symmetrically constructed to minimize these changes. Wind directions and speeds do not appreciably affect PM2.5 cut-points.

All of the inlets in Table 3-1 are direct impaction systems (Marple and Willeke, 1976) consisting of one or more jets positioned above an impaction plate. The impactor dimensions are selected to allow particles with diameters exceeding the desired cut-point to strike and adhere to the plate. Impaction inlets require frequent cleaning and sometimes oiling or greasing to prevent impacted particles from disaggregating or becoming re-entrained in the airflow (John et al., 1991). U.S. EPA (1987) requires a PM10 cut-point of 10±0.5 µm for FRMs. The Wedding IP10 inlet, which is no longer manufactured but is still in use, had a 9.6 µm cut-point, while the original Andersen SA-321A inlet had a 10.2 µm cut-point (Watson and Chow, 2009). This difference made the Wedding inlet more attractive to some users, since sampling with a lower cut-point decreased the PM10 measured and lowered the probability of exceeding the NAAQS. The Andersen SA-321A was replaced by the SA-321B with a 9.7 µm cut-point to meet this competition; new impactor jets were also provided for the SA-321B inlet. Although both of these were replaced with the G1200 inlet (now with different designations depending on manufacturer), some of the original inlets are still in use and it is difficult to distinguish them from each other by their appearance.

d16

d50

d84

3-3

Table 3-1. Commonly-used PM10 inlets.

Name and Referencesa

Inlet ID: d50 (µm), Slopeb, Flow (L/min)

Description and Comments

Hivol PM10 (Hall et al., 1988; John et al., 1983; John and Wang, 1991; McFarland et al., 1984; McFarland and Ortiz, 1984a; 1984b; 1985; Wedding et al., 1985)

G1200: 9.7,1.4, 1133 Anodized spun aluminum with a single stage of opposing jets. The body is hinged to facility cleaning and re-greasing of the removable impaction plate that is sprayed with an aerosol adhesive after cleaning. The G1200 was preceded by the SA-320 single stage PM15 inlet and the SA321A and SA321B dual stage and SA321C single stage PM10 inlets that are no longer sold but may still be in use. It is not entirely clear which sampling effectiveness tests apply to each of these inlets.

Flat Top Dichot PM10 (Lai and Chen, 2000; McFarland et al., 1978; van Osdell and Chen, 1990; Wedding et al., 1980)

246B Flat Top: 10.2,1.41, 16.7

Machined aluminum with one impactor tubes and three vertical elutriator tubes. Rain drops are blown into the inlet beneath the flat top and accumulate on the impaction surface. Water exits through a small drain attached to a bottle on the outside of the inlet. The top unscrews for cleaning the impactor surface.

BGI FRM Louvered PM10 (Kenny et al., 2005; Tolocka et al., 2001)

Louvered PM10: BGI16: 10,1.4,16.7 BGI5: 10,1.4,5

Same materials and design as the SA246B but with a top that curves over the inlet bug screen to minimize the entry of windblown raindrops. Also available with a polytetrafluorethylene (PTFE) coating. The 5 L/min version is used on the BGI OMNI MiniVol sampler. The 16.7L/min inlet is supplied by many vendors.

Airmetrics Minivol Impactors (Airmetrics, 2009; Turner, 1998; Wiener and Vanderpool, 1992)

MRI10: 10,1.2,5 MV10: ~10,1.4,5

MRI10 is a stainless steel impactor with a louvered rain cover and a non-greased impaction surface. MV10 is machined polymeric propylene plastic or machined aluminum. Apiezon vacuum grease dissolved in hexane is pipetted onto impaction surfaces before each sample to minimize re-entrainment.

a References cited provide a more complete description of the inlet and tests of its collection and transmission properties.

b Inlet IDs have been assigned to facilitate later reference. These are contractions of the manufacturer’s part number, where possible. “d50” is the aerodynamic diameter at which half of the particles pass through the inlet and the other half deposit in the inlet, as determined by presentation and detection of known particle sizes. Slope =

1684 d/d , the square root of the particle diameter ratios for inlet penetration at 84 and 16%. Values given are

those provided by the vendor for the specified flow rate, and these may differ from those reported in some of the citations owing to different test and inlet conditions. “NA” in the slope position indicates that this value was not available.

3.2.2 Filter Media

After passing through the inlet, the air stream is drawn onto a filter which is weighed before and after sampling to determine the mass. NAAQS compliance sampling (U.S. EPA, 2006b) include 0.3 µm DOP (dioctyl phthalate) sampling efficiency in excess of 99.9%, weight losses or gains due to mechanical or chemical instability of less than a 5 µg/m3 equivalent, and alkalinity of less than 25 microequivalents/gm to minimize SO2 and NOx absorption. These are only the minimal requirements for samples that require chemical analyses. The most commonly used filter media for mass measurements are Teflon-membrane and reinforced quartz-fiber.

3-4

Ringed Teflon-membrane filters (Pall Life Sciences, 2009a; Whatman, 2009a) consist of a thin, porous polytetrafluorethylene (PTFE) Teflon sheet stretched across a polymethylpentane ring; the thin membrane collapses without the ring and the filter cannot be accurately sectioned into smaller pieces. PTFE Teflon is very stable, absorbing negligible amounts of water or gases. It has inherently low contamination levels, but chemicals have been found in some batches by acceptance testing. This filter is commonly used for mass and elemental analyses. The thin membrane is especially appropriate for X-ray fluorescence (XRF) or proton-induced X-ray emissions (PIXE) analyses that obtain elemental concentrations while leaving the filter intact (Kasahara, 1999; Watson et al., 1999). Teflon-membrane filters are used in samplers with the 16.7 and 5 liters per minute (L/min) inlets for this study. Owing to its carbon content, Teflon-membrane filters cannot be used for thermal carbon analyses.

Quartz-fiber filters (Pall Life Sciences, 2009b; Whatman, 2009b) consist of a tightly-woven mat of quartz filaments. These filters meet requirements in most categories. Quartz-fiber filters adsorb organic gases during sampling, as determined by placing quartz backup filters behind Teflon or quartz front filters or passively exposing field blank quartz filter to ambient air (Watson et al., 2009). The extent to which this is a positive bias due to gas adsorption, or a negative bias due to the collection of volatilized particles, is uncertain (Eatough et al., 2003). The greatest drawback of quartz-fiber filters is their fragility; they require careful handling for accurate mass measurements. The Whatman QMA quartz-fiber filter contains a 5% borosilicate glass binder that minimizes its friability. This filter is used in hivol (1130 L/min) PM10 samplers for mass measurements in this study.

3.2.3 Filter Sampling Systems

Figure 3-2 illustrates several PM10 filter sampling systems and inlets. The hivol FRM PM10 sampler is a variation on the hivol TSP sampler that replaces the peaked roof with the G1200 impactor inlet (e.g., Ecotech, 2009a; Thermo Scientific, 2009a; and Tisch, 2009a). A carbon-vane blower is used to pull air through a 20.32 cm × 25.4 cm QMA quartz-fiber filter. The flow rate is set at 1130 L/min to attain the desired PM10 cut-point. The flow is monitored continuously by a mass flow meter that increases the blower speed as the pressure drop across the filter loading increases. The hivol weighs ~40 kg and requires 110 V AC line power at ~6 Amps. A portable generator is needed for power at remote locations and a platform or leg extensions are needed to place the inlet at the 2 m height above ground level typically used to represent the human breathing zone. The inlet housing consists of a clamshell that can be opened to grease and clean the impaction plates. The sampler is switched on and off with a timer.

Hivol PM10 FRMs are still in use for NAAQS compliance monitoring, but many are being replaced with low-volume (lovol) FRMs (e.g., BGI, 2009a; 2009b; and Thermo Scientific, 2009b; 2009c) using an equivalent of the BGI 16.7 L/min louvered inlet in Table 3-1. These are smaller than the hivol and use Teflon-membrane filters that can be more precisely weighed in the laboratory. The 16.7 L/min flow rate for lovol samplers was selected because it equals 1 m3/hr, providing 24 m3 of air over a compliance-oriented 24-hr measurement period. These samplers are more compact, but still relatively heavy at ~30 kg, mostly owing the weight of the pumps. These are available with built-in platforms that locate the inlet at 2 m above the surface. The Thermo Scientific units require 110V AC line power, while the BGI units operate on built-in rechargeable 12 V batteries. Mass and volumetric flow sensors and computer feedback to the flow mover allow for adjustments to changes in filter loading and ambient temperature and pressure during sampling. Sample start and stop times and data acquisition are monitored with a built-in computer.

Figure 3Fisher loOMNI mupdated m

a)

d)

g)

3-2. Examplovol Partisolminivol PM10

minivol PM

les of integr Plus PM10,0, g) EcoTec10.

rated filter s d) BGI lovch minivol P

3-5

b)

e)

h)

samplers: a)vol PQ100 PPM10, h) Air

hivol TSP,PM10, e) BGrmetrics min

c)

f)

i)

, b) hivol PI lovol PQ2

nivol PM10, a

M10, c) The200 PM10, f)and i) Airme

ermo-) BGI etrics

3-6

Minivol samplers (5 L/min; Airmetrics, 2009; BGI, 2009c; Ecotech, 2009b) are not designated as PM10 FRMs and are not used for NAAQS compliance monitoring. Minivols are light (<10 kg) and small, so they can be mounted on power poles, fence posts, rooftops, and tripods in areas that are inaccessible to the hivol and lovol FRMs. They have low power requirements and internal batteries that can be recharged from AC line power, solar panels, or switched with batteries charged off-site. They can use Teflon-membrane filters mounted in filter holders identical to those used in the lovol FRMs, so laboratory weighing and filter processing procedures need only minor modification. The lower volume sampled, ~30% of that of the lovol, means that detection limits are about three times higher, but these are comparable to those of the hivol for a 24-hr sampling period. Minivols have been most widely used in PM10 neighborhood-scale studies (Adams et al., 1994; Blanchard et al., 1999; Chao and Wong, 2002; Chow et al., 1999; 2000; 2002; Chow and Watson, 2001; Gillies et al., 1999; Kemp, 1990; Ringler et al., 1993; Vinitketkumnuen et al., 2002) where they have revealed large spatial gradients near fugitive dust sources.

Several PM10 comparison studies have been reported among filter samplers (Chow et al., 2006; Chow and Watson, 1997; Heal et al., 2000; Mathai et al., 1988; 1990; Williams et al., 2000) with generally comparable results to within ±10% to 15% deviations at 24-hour concentrations exceeding 10 µg/m3. Deviations between collocated samplers, even within the same class of hivol, lovol, and minivol, are usually highest when the sampled aerosol has a high PM10-2.5 component. A partial explanation, as noted above, is that small shifts in the inlet cut-point occur where the size distribution is near its maximum (see Figure 2-1). The localized nature of dust plumes, that may affect one monitor more than another in a collocated sampling array, is also believed to degrade comparability when there are nearby PM10-2.5 emitters.

3.3 Continuous PM10 Monitors

Filter monitors require a time-integrated sample over several hours and the need to change the filters on a regular basis. Data cannot be acquired in real-time owing to the need for filter processing in the laboratory. This has engendered efforts to develop and deploy continuous PM10 monitors (Chow et al., 2008; McMurry, 2000; Solomon and Sioutas, 2008; Watson et al., 1998). FRMs are defined as integrated filter samplers, so no continuous monitors can be designated as an FRM. Federal Equivalent Monitors (FEMs) can be used for compliance under certain situations in which they can produce results comparable to those of collocated FRMs. U.S. EPA (2009) has specified the Tapered Element Oscillating Microbalance (TEOM) and the Beta Attenuation Monitor (BAM) as PM10 FEMs, subject to certain design and operating constraints. Several of these continuous particle monitors are illustrated in Figure 3-3.

3.3.1 Tapered Element Oscillating Microbalance (TEOM)

In the TEOM (Patashnick and Rupprecht, 1991; Thermo Scientific, 2009d), air is drawn through a size-selective inlet through a filter mounted on an oscillating hollow tube. The oscillation frequency changes with mass loading on the filter, which is used to calculate mass concentration by calibrating the measured frequency with standards. To minimize thermal expansion, the TEOM filter is kept at a constant temperature of 50 °C, and this evaporates volatile PM2.5 components such as ammonium nitrate, liquid water, and semi-volatile organic compounds (SVOCs; Lee et al., 2005; Pang et al., 2002). Although this does not affect the PM10-

2.5 fraction, it does affect the PM10 fraction. This difficulty has been largely solved by preceding

Figure 3DynamicSAMPLEDustTrakGrimm O

a)

c)

e)

3-3. Examplcs MeasuremER nephelomk DRX aeroOPC.

les of continment Systemmeter, d) Tosol monito

nuous particm (FDMS)TSI Model 8or (nephelom

3-7

b

d

f

cle monitorsattachment

8250 DustTmeter and o

b)

d)

f)

s: a) Thermt, b) MetOnTrak nephelooptical parti

mo-Fisher TEne E-BAM,ometer, e) Ticle counter

EOM with F, c) MetOnTSI Model

[OPC]), an

Filter ne E-8533 nd f)

3-8

the filter with a Filter Dynamics Measurement System (FDMS), that alternates the sample stream between a base flow and a reference flow every 6 min (Grover et al., 2008). Particles collected during the base flow are allowed to volatilize during the reference flow. If a negative mass is measured during the reference flow, due to loss of volatiles from the filter, this mass is added to the mass measurement made for the prior particle-laden samples to obtain total PM concentration. The TEOM requires line power, weighs ~40 kg, and must be placed on a vibration-free platform to minimize interferences with the vibrating element. The FDMS requires additional support facilities. The TEOM with an FDMS was recently designated as a FEM for PM2.5. Despite the demonstrated losses of volatile species and several comparison studies showing lower PM10 concentrations, the 50 °C TEOM without the FDMS is also designated as an FEM for PM10.

3.3.2 Beta Attenuation Monitor (BAM)

BAMs (Lillienfeld, 1970; Met One Instruments, 2009a; Met One Instruments, 2009b; Thermo Scientific, 2009e; Tisch, 2009b) draw air through a size-selective inlet, then through a quartz-fiber filter tape. A radioactive source, usually low-level carbon-14, generates a stream of electrons (beta rays) through the sample spot as the particle deposit accumulates. As the filter spot loads up, the penetrated electron count decreases, and the decrease is proportional to the sample loading. The filter tape can be set to advance every hour, or to advance when it reaches a selected mass loading. If the filter tape is regularly marked, the sample spots can be recovered and submitted to elemental analyses (Watson et al., 2007) to better understand the nature of the contributing sources. BAMs sometimes measure higher PM10 and PM2.5 concentrations than collocated FRMs because volatile species such as NO3

-, liquid water, and SVOCs may have partially evaporated from the FRM filter during sampling or during laboratory equilibration prior to weighing (Chow et al., 2006; Huang and Tai, 2008; Schwab et al., 2006; Takahashi et al., 2008). The MetOne E-BAM is a portable, lightweight (~12 kg), real-time monitor that can be operated with 12 V battery, solar, or AC line power.

3.3.3 Light Scattering Nephelometers

Small particles scatter light, and this scattering is used as a detection method in instruments called nephelometers (Varma et al., 2003); nephos is the Greek word for cloud. Nephelometers used for particle detection are often termed “photometers,” but photometer refers more to the detection device rather than the light scattering principle on which the instruments operate. Sampled air is drawn into a detection zone where it is illuminated by a light source, and the scattered light detected at some angle (e.g., 90°) relative to the light source is related to the PM concentration. The amount of scattering depends strongly on particle composition and size distribution, as illustrated in Figure 3-4 for different chemical compounds. Note that fugitive dust (soil) has lower peak scattering efficiencies than other compounds and that this efficiency decreases rapidly with increasing particle size. The consequence of this is that the scattering is dominated by PM2.5, and PM10-2.5 scattering is a small signal on a large background when PM2.5 levels are high as they are in the SoCAB.

The TSI Model 8520 DustTrak uses a long-wavelength laser (λ = 780 nm), is calibrated using Arizona road dust, and has been used to estimate fugitive dust emission factors

3-9

Figure 3-4. Particle scattering efficiencies (Watson, 2002) as a function of size distribution for different particle compositions for λ = 550 nm light. Soot includes extinction due to both scattering and absorption (particles are assumed to be spherical).

(Etyemezian et al., 2006; Gillies et al., 2007; Kuhns et al., 2001; Kuhns et al., 2005). The Model 8520 is no longer manufactured, but there are hundreds of these units still in use and available on the secondary market. The Model 8520 provides outputs in mg/m3 which are related to the fugitive dust calibration, and its readings are much higher than actual PM concentrations, up to a factor of 10, when PM2.5 concentrations are high. Model 8520 output can be normalized to collocated PM2.5 concentrations collected with a filter sampler. The successor of the Model 8520 is the DustTrak II which uses a λ = 655 nm laser as the light source.

Met One Instruments (2009c) recently released the E-Sampler, which consists of a nephelometer with a backup filter drawing air at 2 L/min. The backup filter can be weighed and used to normalize the light scattering signal over the filter sampling period. A low flow rate is used so that the filter can stay in place for up to 7 days. This flow rate is too low to obtain sufficient filter loading for 24 hour or shorter sample durations.

3.3.4 Optical Particle Counters (OPC)

OPCs (Hodkinson and Greenfield, 1965) use light scattering to detect the size and number of individual particles, rather than scattering from a group of particles as with the nephelometer. A narrow air stream containing the particles is directed through a small sensing zone where it is illuminated by an intense light beam usually generated by a laser. Light scattered by an individual particle is sensed by a fast and sensitive detector, resulting in an electrical pulse. Particle size is determined from the pulse amplitude, and particle number is determined from the

0

1

2

3

4

5

6

7

8

0.01 0.1 1 10

Mass Median Geometric Particle Diameter (µm)

Sca

tter

ing

or

Ab

sorp

tio

n E

ffic

ien

cy (

m2 /g

)

Amm Sulfate Scat Amm Nitrate Scat Organics ScatSoil Scat Soot Scat Water ScatSoil Abs Soot Abs Soot Ext

3-10

number of pulses. The size of particles that can be detected with OPCs ranges from about 0.3 to 20 µm. UP are not detectable by OPCs owing to their low scattering efficiencies, as shown in Figure 3-4.

Particle sizes and numbers are translated to mass concentration by assuming a spherical particle shape and a particle density. The sum over all particle size bins can be further related to mass loadings by comparison with a collocated filter sample. Particle size measurement is commonly calibrated with a National Institute of Standards and Technology (NIST)-traceable, monodisperse polystyrene latex spheres (PSL). While size measurements with OPCs can be very precise, their accuracy depends on particle composition and shape. OPCs have been designed mostly for indoor use in clean rooms and workplaces. Most of the units are bulky and require line power or are handheld, relatively fragile, and unsuited for field use.

Though originally developed for indoor industrial hygiene monitoring, the Grimm Model 1.108 OPC (Grimm, 2009; Grimm and Eatough, 2009; Heim et al., 2008; Hoffmann et al., 2008; Peters et al., 2006) has been used in outdoor applications. The individual particles passing through the sensing cell are illuminated by a 785 nm diode laser. The raw data consists of number counts in size bins ranging from 0.3 to >20 µm. Mass concentrations are estimated by assuming spherical particles and uniform particle densities for each size bin and summing up to the desired size range (e.g., PM2.5 and PM10). The Model 1.108 has a “mass” mode that outputs a mass concentration, but the specific algorithm used to convert from number to mass is considered proprietary and is hardwired into the Grimm OPC software.

TSI recently released the TSI Model 8533 DustTrak DRX aerosol monitor (TSI, 2009; Wang et al., 2009) which combines a light scattering nephelometer (λ = 655 nm) for PM2.5 with an OPC for sizing particles 1-15 µm, yielding mass readings in mg/m3 for PM1, PM2.5, PM4, PM10, and PM15. The DRX is still calibrated against Arizona road dust by factory default, but the user can create custom calibration factors. The DRX is more accurate than nephelometers for measuring PM10 because it sizes and counts individual coarse particles that have low scattering efficiencies (Figure 3-4) that are usually underestimated by nephelometers. The DRX is also able to measure higher concentrations than an OPC because it uses nephelometry for PM2.5, which reduces coincidence losses for large numbers of small particles.

3.4 Meteorological Measurements

Rule 403 requires PM10 measurements upwind and downwind of the facility being tested. Knowledge of wind directions in the vicinity of the selected sampling locations is needed both before monitors are located and during the test to verify that the monitoring locations reasonably attain the upwind/downwind criteria. As explained in Section 2, wind speed at the surface may also be a useful parameter for determining the erosion potential of surface dust and estimating potential transport distances of low-level sources.

Mechanical and sonic anemometers are used to determine wind speeds and directions and are available from several suppliers (Climatronics Corp., 2009a; Davis Instruments, 2009; Met One Instruments, 2009d). The mechanical wind vane consists of a horizontal rod with a vertical fin on one end and a counterweight on the other. The fin rotates on a potentiometer that provides a voltage to the data recorder proportional to its angle relative to a reference point. The fin aligns itself in the downwind direction at levels above a certain wind speed threshold, usually ~0.2 m/s, which is determined by the friction in the bearing attaching the vane to the potentiometer. The vane is aligned with a sighting compass to make appropriate corrections for magnetic declination. The mechanical anemometer consists of cups mounted on rods extending from a

3-11

vertical spindle that catch the wind and rotate the spindle. The number of complete rotations per second is counted and related to wind speed by calibration in a wind tunnel. Sonic anemometers use changes in the speed of sound with wind direction and speed. Ultrasonic sound is generated by a speaker and detected by a microphone. Two speaker/microphone pairs are oriented perpendicular to each other to obtain independent directions. The detected pitch changes with wind speed in the particular direction, and this is related to the wind speed (Doppler effect). Wind direction is determined by the vector average of wind speeds in the independent perpendicular directions.

Mechanical weather stations are the least expensive and easiest to work with. They are also the most durable, as sonic anemometers can become easily damaged and must be frequently cleaned in dusty environments. The TacMAT (Climatronics Corp., 2009b) weather station is a new type of sonic anemometer, developed for emergency response, that has no moving parts and orients itself to true north with an internal compass. The TacMAT is no longer available, but was recently improved and released during 2008 as the All-In-One (AIO) weather station (Climatronics Corp., 2009c). The AIO claims to require no maintenance and no extensive orientation. It mounts directly on a mast with no apparent need for orientation.

The most difficult aspect of wind speed and direction measurements is in locating the sensors. For urban-scale flows, the sensors should be at least 10 m above ground level to be above nearby obstructions. The distance to these obstructions should be greater than 10 times the height of the tower. Towers higher than ~5 m require dedicated concrete supports and guy wires. Such towers are not portable enough for short-term upwind/downwind monitoring. Property fence lines are often near roadways or structures that can interfere with the measurements. As noted in Section 2, passing vehicles create wakes that influence the microscale flows. For determining dust suspension, it is desirable to have the measurements closer to the surface of the potential dust reservoirs.

The Met One E-BAM and the E-Sampler include options for integrated meteorological measurement using a mechanical wind vane and anemometer. The data acquisition system acquires meteorological data along with the PM data. Davis Instruments (2009) has a telemetry system and rechargeable batteries on solar cells that allow for remote placement and wireless data acquisition for up to eight different locations.

3.5 Monitors Selected for Testing

Resources were insufficient to obtain copies of all potentially useful instruments. The choices made for this study were: 1) two Sierra-Andersen PM10 FRM high-volume filter samplers provided by the SCAQMD; 2) one PQ200 PM10 lovol filter sampler provided by BGI Incorporated; 3) two BGI OMNI minivol samplers provided by BGI Incorporated; 4) two Met One E-BAM beta attenuation monitors with built-in mechanical weather stations provided by the SCAQMD; 5) one Tisch/Kimoto PM10/PM2.5 BAM provided by DRI; 6) two Met One E-SAMPLER nephelometers provided by the SCAQMD; 7) four DustTrak Model 8520 nephelometers provided by DRI; 8) two Grimm Model 1.108 OPCs provided by DRI; 8) one TSI DustTrak DRX OPC/nephelometer provided by TSI, Incorporated; 9) two Davis Instruments mechanical weather stations provided by DRI; and 10) two TacMAT self-orienting sonic anemometers provided by the SCAQMD. This represented ~US$200,000 of hardware at list prices.

4-1

4. EXPERIMENTAL CONFIGURATION AND PROCEDURES 4.1 Sampling Locations

Table 4-1 summarizes the sampling locations and instrument operating periods. The experiment was conducted during the August through October, 2008, period to avoid the autumn rain storms. Figures 4-1 and 4-2 show the locations of the sampling sites at the United Rock and Vulcan Materials, respectively. Since the samplers were operated unattended, except for daily maintenance and sample changing, it was necessary to locate them within the property boundaries at both facilities to obtain adequate security. As a result, monitors were closer to dust-generating activities than would be the case for a Rule 403 test; it is expected that fugitive dust source contributions, and PM10 levels in general, are higher than concentrations outside of each facility’s property lines.

Table 4-1. Sampling locations at United Rock and Vulcan Materials. All inlets were placed two meters above ground level.

Site Name Site Site Address Latitude Longitude

Elevation (m above sea level)

Operating Period (2008)

United Rock Downwind

1 1245 East Arrow Highway, Irwindale, CA, 91706

N34°7’1” W117°58’43” 138 8/17 – 9/27

United Rock Upwind

2 N34°6’52” W117°58’57” 138 9/10 – 9/27

Vulcan Downwind

1 1975 North Benson Avenue, Upland, CA, 91784

N34°8’7” W117°41’0” 520 10/2 – 10/31

Vulcan Upwind

2 N34°7’50” W117°41’17” 510 10/2 – 10/22

United Rock Site 1 was located on a concrete pad near the northeast fence line. An unpaved turnout was just southwest of the pad which was blocked from traffic during the experiment. A truck wash station on the main paved road through the facility was within 10 m south of the site. The paved road carried substantial heavy-duty truck traffic during weekdays and was swept regularly with a vacuum sweeper. Conveyors and storage piles were 10 to 30 m southeast of the site. Interstate 605, a heavily-travelled highway, was within 100 m to the west, while a flood control area was located to the north.

United Rock Site 2 was located on a graded area just inside the fence line at the southwest corner of the facility, the northeast corner of Avenida Barbosa. and Arrow Highway. Arrow Highway was being repaired to the west of this corner. The main raw material conveyor was immediately to the north of the monitors. Intense diesel truck traffic was observed throughout the sampling period along Arrow Highway. A construction material dump site was in operation within a spent gravel pit on the south side of Arrow Highway.

Vulcan Site 1 was located on a concrete patio west of the employee lunchroom and weigh station. Dust was cleaned from the patio and nearby areas prior to locating the samplers. The truck scales were located within 5 m south of the monitors and experienced heavy diesel traffic. Employee cars were parked to the immediate north and west of the patio. The area to the north was flat and unpaved, but regularly watered. This area was frequently traversed by large (75 ton haul) trucks and maintenance vehicles within 5 to 25 m north of the samplers. Loading, dumping, and grading took place ~30 m northeast of the monitors.

4-2

Vulcan Site 2 was located in a large sandy plain to the southwest of Site 1. Monitors were within 100 m of Interstate 210 to the southeast and south. Heavy truck traffic was occasional. This increased near the end of the measurement period when a storage pile ~50 m of the site was being moved to another location.

Figure 4-1. Locations of sampling sites at United Rock. Site 1 was nominally downwind and Site 2 was nominally upwind of the sand and gravel operations. North is in the vertical direction. Some roadway and storage pile configurations differed from those depicted in this satellite picture that was taken at an earlier date.

4.2 Site Configurations

Figures 4-3 to 4-6 show the location of sampling instruments during upwind and downwind sampling. Particle sampler inlets were located 2 m above ground level. All but the hivol allowed for this adjustment. Leg extensions were put on the hivols to bring them to the desired heights. Samplers were located at least 2 m from each other to avoid interference in sampling from the same air. Even so, samplers were at different distances from nearby sources which might affect their particle collection.

All of the samplers were located in a collocated fashion at the upwind site after each experiment. Similar samplers were placed within 2 m of each other for these collocated measurements.

4-3

Figure 4-2. Locations of sampling sites at Vulcan Materials. Site 1 was nominally downwind and Site 2 was nominally upwind of the sand and gravel operations. North is in the vertical direction. Some roadway and storage pile configurations differed from those depicted in this satellite picture that was taken at an earlier date.

4.3 Network Operations

Detailed Standard Operating Procedures (SOPs) were created for each measurement system and are included in the companion document (Appendix A). Table 4-2 summarizes the 15 SOPs applied in this study. SOPs include: 1) the instrument operating principle; 2) equipment and accessory lists; 3) calibration and performance tests methods and frequencies; 4) daily checklists and data sheets; 5) data acquisition and downloading instructions; 6) quality assurance and quality control instructions; and 7) references to operating manuals and scientific publications.

Filters were weighed in the DRI laboratory and shipped in cooled containers by overnight mail to and from the field technician’s residence in Temple City, CA. Weighing room cleanliness, temperature, and RH requirements were consistent with those required for FRM filters, as specified in the SOP. A laboratory area was set up at the residence to load the filters into cassettes appropriate for each sampler.

Vulcan Site 2

Vulcan Site 1

----------150m-----------

Interstate 210

4-4

Figure 4-3. Sampler configuration at the United Rock downwind Site 1. This is facing north toward the flood control area behind the trees.

Figure 4-4. Sampler configuration at the United Rock upwind Site 2. The northeast corner of Arrow Highway and Avenida Barbosa is in the background beyond the trees.

Grimm OPC Kimoto BAM DustTrak DRX

DustTrak 8520

hivol E-Sampler

PQ200 lovol

E-BAM

Omni minivol

Davis Met

TacMAT Met

E-BAM Omni minivol

TacMAT Met

Davis Met

DustTrak 8520Grimm OPC

hivol E-Sampler

4-5

Figure 4-5. Sampler configuration at the Vulcan downwind Site 1, facing northeast.

Figure 4-6. Sampler configuration at the Vulcan upwind Site 2, facing south.

DustTrak 8520Grimm OPC

hivol

E-Sampler

PQ200 lovol

E-BAM

Omni minivol

Davis Met

DustTrak 8520 DustTrak DRX

Omni minivol DustTrak 8520 Grimm OPC

E-BAM

E-Sampler

hivol

Davis Met

4-6

Table 4-3 summarizes the major segments of the sampling period. Measurement systems were installed and began operating as they became available. This allowed time for site technician training, SOP creation and revision, identification and resolution of problems, and instrument shakedowns. It became apparent soon after their installation that the TacMAT sonic anemometer and the Kimoto BAM were not functioning properly, so these were eliminated from further testing rather than detracting from maintenance of the other instruments. The experiment was too short to troubleshoot malfunctioning instruments.

It was found that over-tightening the OMNI screw-type filter holders put a torque on the Teflon-membrane filters that would sometime result in a tear. Some practice was needed to tighten the filter holder sufficiently to prevent leaks, but not so tight that the filters are torn. This could be remediated with a pin in the two-part press-fit filter cassette that would keep the top part from rotating with respect to the bottom part. Initial examination of the filter weights and continuous data showed that there were differences in concentration across the sample array at United Rock Site 1, so samplers were clustered closer together after samplers were relocated at United Rock Site 2.

Power outages were common during shakedown sampling during the last week of August at United Rock Site 1 owing to faulty circuit grounding. A major overhaul of the wiring was implemented by the facility electrician to resolve this difficulty.

Five-hour duration hivol, PQ200 lovol, and OMNI minivol filter samples were acquired each day between 1100 and 1600 PDT for upwind/downwind and collocated sampling. The other monitors operated continuously, both during and between the filter sampling periods. Vulcan Site 2 did not have line power, so a 3500 watt generator was installed ~10 m downwind of the monitors. Only the hivol required 110 V AC line power, so the generator was filled with gasoline (~4 gallon capacity) and started each morning at ~0930 PDT to set the hivol timer and check the flows. The hivol timer is apparently tied to the 60 cycle line current, and 60 cycles was not precisely maintained by the generator. As a result, sample start and stop times at Vulcan Site 2 could deviate from the 1100 and 1600 PDT set points by up to 20 minutes. This deviation was minimized by replacing the timer’s 9 V backup battery every three days (about its lifetime). The generator was allowed to operate until it ran out of fuel, which occurred 12 to 15 hours after startup as indicated by an AC clock.

The generator at Vulcan Site 2 also powered a 12 V charger connected to two deep cycle 12 V batteries in parallel. The E-BAM, E-Sampler, OMNI, and Grimm were all connected to these batteries so they could operate continuously after the generator shut down. The internal 12 V battery in the Grimm had a ~8 hour lifetime, and a splice was needed to connect it to the external 12 V power source. The DustTraks operate on four C cells that supply 6 V, so they could not be powered from the deep cycle battery. The step-down transformer was used while the generator was operating, and the backup batteries took over after the generator ceased to operate. The DustTraks operated for ~18 hours on a single set of batteries without line current.

A daily routine was established in which the technician would arrive at the downwind site at ~0900 PDT and verify that the continuous monitors were operating. He would then go to the upwind Site 2 to verify instrument operation. The generator was refilled and started at Vulcan Site 2. Exposed filters were removed and replaced with fresh filters. Data were downloaded from the continuous instruments (i.e., E-BAM, E-Sampler, DustTraks, Grimms, and meteorological

4-7

Table 4-2. Summary of the Standard Operating Procedures (SOPs) applied for the Southern California Fugitive Dust Emissions Study.

DRI SOP Number

Title Observables Last Revision Date

1-201.1 General Metal Works HIVOL SSI Particle mass (µg/m3) 9/1/2000

1-214.2 TSI Incorporated Model 8520 DustTrak Aerosol Monitor

Particle Mass (mg/m3) 8/11/2009

1-238.1 TSI Incorporated Model DRX8533 DustTrak Aerosol Monitor

Size-segregated PM mass (mg/m3)

8/11/2009

1-239.1 Grimm Dust Monitor Model 1.108

Particle size distribution (counts/m3)

3/18/2009

1-240.1 BGI FRM Omni Ambient Air Sampler

Particle mass (µg/m3) 3/29/2009

1-241.1 BGI Federal Reference Method (FRM) Model PQ200

Particle mass (µg/m3) 3/29/2009

1-242.1 MetOne E-Sampler Particle light scattering (mg/m3)

3/30/2009

1-243.1 MetOne E-BAM Particle mass (mg/m3), wind speed, wind direction, temperature, relative humidity, pressure

3/30/2009

1-313.1 Davis Instruments Wind Wizard III

Wind speed, wind direction, temperature, relative humidity, pressure

3/27/2009

1-314.1 Davis Instruments Vantage Pro II Wind speed, wind direction, temperature, relative humidity, pressure

3/27/2009

2-102.6 Hi Volume (HIVOL) Gravimetric Analysis

Particle mass (µg/m3) 4/23/2008

2-110.4 Filter Pack Assembly, Disassembly, and Cleaning

N/A 11/24/1998

2-113.3 PM2.5 FRM Sample Shipping, Receiving and Chain-of-Custody

N/A 7/30/2007

2-114.6 PM2.5 FRM Gravimetric Analysis Particle mass (µg/m3) 12/2/2008

2-115.5 HIVOL Sample Shipping, Receiving, and Chain-of-Custody

N/A 6/13/2008

4-8

Table 4-3. Major activities during the 8/9/08 through 11/4/08 sampling period.

Date (2008) Location (CA) Event Description 8/9 – 8/10 Temple City Trained field technician and prepared for field

sampling. 8/11 Irwindale Conducted field survey.

8/13 Irwindale Completed security training at United Rock. 8/14 – 8/16 Irwindale Installed equipment at United Rock downwind

Site 1: two Davis Met, two DustTraks, two OMNIs, one TacMAT, and one E-BAM.

8/17 – 9/5 Irwindale Resolved power problems, conducted equipment testing, and developed operating procedures.

9/5 Irwindale Installed two hivols, a second E-BAM, and two E-Samplers at United Rock Site 1.

9/8 – 9/10 Irwindale Conducted collocated sampling at United Rock downwind Site 1. Installed sampler bases for United Rock upwind Site 2.

9/10 Irwindale Installed PQ200 lovol and Kimoto BAM at United Rock Site 1.

9/11 – 9/27 Irwindale Started sampling at the United Rock upwind/downwind Sites.

9/13 Irwindale Found Kimoto malfunction (removed from service). Found TacMAT wind speed and direction data inconsistent with observations (removed from service).

9/25, ~1400 PDT Irwindale Generated dust episode 1r. Removed DustTrak 2 for testing.

9/26, ~1000 PDT Irwindale Visited by SCAQMD field and laboratory staff.

9/26, ~1400 PDT Irwindale Generated dust episode 2r. Removed DustTrak2 and DRX for testing.

9/28 Irwindale Completed flow checks, dismantled and cleaned equipment at United Rock Sites 1 and 2.

9/29 Upland Moved equipment from United Rock to Vulcan Materials.

9/30 – 10/1 Upland Set up Vulcan downwind Site 1 and upwind Site 2. 10/2 – 10/20 Upland Started sampling at the Vulcan upwind/downwind

sites. 10/13 Upland Removed E-BAMs at 0900PDT for SCAQMD

wildfire assessment.

10/21 – 10/22 Upland Disassembled Vulcan Site 2 and relocated remaining instruments to Vulcan Site 1 for collocated sampling.

10/23 – 10/30 Upland Conducted collocated sampling at Vulcan downwind Site 1.

10/31 – 11/1 Upland Performed flow checks and inlet cleaning at Vulcan Site 1.

11/2 – 11/3 Upland Packed and shipped samplers. 11/4 ~1100 PDT Upland SCAQMD picked up hivols and E-Samplers.

4-9

monitors). Flow checks were made and timers were set for sample start. The technician then returned to the downwind Site 1 and went through the same procedures. He would wait until 1100 PDT to verify that the filter samplers had started, then would return to the upwind site to verify that the samplers there had started. The technician then returned to his residence where he removed the exposed filters from the cassettes and replaced them with fresh filters for the next day’s sampling. Continuous data were reformatted and appended to the growing database daily. Dates for performance tests and deviations from procedures were recorded in the logbook. Filters were sent to and received from DRI on a weekly basis.

The size-selective inlets were cleaned before and after monitoring at each facility. They accumulated moderate amounts of dust on the impaction plates after ~2 weeks of sampling. The meteorological instruments acquired substantial dust loadings due to deposition at all sites, and these were disassembled and cleaned before and after each of the sampling periods. The TacMAT was very dirty, and its performance deteriorated after the first day of measurement. It is probable that the dust coating interferes with the sound generation and sensing of this sonic anemometer.

Tables 4-4 and 4-5 summarize deviations from procedures during the experimental periods at United Rock and Vulcan Materials, respectively.

4.4 Flow Rate Performance Tests

Flow rates were calibrated and verified against transfer standards. BGI DeltaCal and BGI Challenger critical throat flow meters were used for the lovol and minivol samplers, and their verification against a primary standard bubble-meter is shown in Table 4-6. Deviations from the standard were less than ±2%. Hivol samplers were calibrated using a recently-verified variable plate orifice provided by the SCAQMD. A TSI-calibrated 0 to 5 L/min rotameter was also used to cross-check the 2-3 L/min flow rates for the DustTraks and Grimm OPCs. Flow checks and adjustments were made throughout the sampling period. These flow checks demonstrated that flows remained reasonably stable for all of the instruments and monitoring periods.

4.5 Database

Data are contained in self-documenting Excel spreadsheets as companions to this report. All of the continuous data were acquired as one-minute averages, which contains much information. One-minute PM concentrations were averaged over the 1100 to 1600 PDT intervals for comparison with the integrated filter measurements.

4-10

Table 4-4. Activities and observations at United Rock.

Date Time (PDT)

Activity Observation

8/16 0945 Manual Calibrate OMNI2 at Site 2, T=23.2 °C, P= 677 mm, adjust to 4.93 L/min.

8/16 1045 Manual Calibrate OMNI1 at Site 1, T=30.4 °C, P= 648 mm, adjust to 5.24 L/min.

8/17 0945 Circuits blown at Site 1, Reset ground faults at 1000 PDT.

8/17 1357 OMNI2 at Site 2 displayed SP too low. Pump stopped. Filter broken.

OMNI2 sample invalid.

9/9 0930 Calibrated hivol1 and hivol2 at Sites 1 and 2, respectively

9/10 1100 hivol2 at Site 2 did not turn on. Timer reset to 1125 PDT to 1600 PDT.

Timer misprogrammed.

9/11 0748 Calibrated PQ200 lovol sampler at Site 1.

9/13 0848 Reset DTRK4 time at Site 1 from 1048 PDT to proper time at 0848 PDT.

9/13 0825 E-Sampler1 power out at Site 1. Reset ground fault. Power is unstable. Repeated ground fault trips.

9/15 1800 Calibration check on OMNI2 at Site 2.

9/17 1000 Calibration check on PQ200 at Site 1.

9/20 0932 Power outage observed at Sites 1 and 2. Called electrician 9/21 0932 Power outage observed at Sites 1 and 2. 9/28 1040 Flow check on hivol2 at Site 2. Within spec.

9/28 1050 Flow check on E-BAM2 at Site 2. Within spec.

9/28 1110 Flow check on OMNI2 at Site 2. Readjusted from 5.42 L/min to 5 L/min.

9/28 1130 E-BAM2 at Site 2 Calibrator displayed "under 2.0" Adjusted to 2 L/min.

9/28 1300 Flow check on E-Sampler1 at Site 1. Readjusted from 2.25 L/min to 2.0 L/min.

9/28 1315 Flow check on OMNI2 at Site 2. Within spec.

9/28 1345 Flow check on E-BAM at Site 1. Within spec.

9/30 0750 Flow check on hivol1 at Site 1. Within spec.

9/30 0810 Flow check on OMNI1 at Site 1. Within spec.

9/30 0850 Flow check on E-Sampler1 at Site 1. Within spec.

9/30 0930 Flow check on PQ200 at Site 1. Within spec.

9/30 1040 Flow check on E-BAM1 at Site1. Readjusted from 16.89 L/min to 16.7 L/min.

10/1 0850 Flow check on E-BAM2 at Site 2. Within spec.

10/1 1005 Flow check on E-Sampler2 at Site 2. Within spec.

10/1 1035 Flow check on OMNI2 at Site 2. Within spec.

4-11

Table 4-5. Activities and observations at Vulcan Materials.

Date Time (PDT)

Activity Observation

10/4/2008 All day Drain holes drilled to minimize accumulation and seals placed around sample intake tube at Site 2.

Heavy rainstorm. Water entered Site 2 cabinet and accumulated in the bottom.

10/4/2008 0830 Grimm2 malfunctioned at Vulcan Site 2.

Rain leaked into shelter. Took back to residence and dried for 48 hours, then it worked.

10/5/2008 0830 Verify instruments. Heavy overnight rainstorm.

10/5/2008 0930 OMNI2 display blank at Site 2. Looks like it got wet.

Disconnected power and dried sampler for 30 minutes. Screen responded.

10/5/2008 1030 Changed generator oil. Operating correctly after oil change.

10/6/2008 0815 Reinstalled Grimm2 at Site 2. Operating correctly.

10/7/2008 0925 DustTrak 8520 at Site 1 operating, but battery connection loose. Displayed 11% battery

Battery was not recharging because of loose connections. Put shims behind battery to better connect it to leads. Partially successful.

10/7/2008 1049 Cleaned E-Sampler 1 inlet at Site 1. Regular check revealed excessive debris in inlet. Other inlets were not dirty.

10/8/2008 0901 Clock on hivol2 at Site 2 read 0917 PDT. Reset to 0901 PDT

Timer is synchronized to current cycle which is not exactly 60 cps from generator.

10/9/2008 1139 Busy truck traffic Every three to five minutes large trucks pass near sampling Site 1 on scale.

10/11/2008 0800 – 1100 Large dump trucks washed north of sampling Site 1 with power spray.

10/12/2008 0800 9V battery dead in Wind Wizard II2 at Site 2. Replaced it.

Battery life is 6 to 7 days. Instituted change every six days.

10/14/2008 0010 E-BAM2 at Site 2 registered ERROR LOG, with flow rate 16.6 L/min.

Flow rate within tolerance. Error was ignored.

10/18/2008 0830 Clock display blank on hivol2 at Site 2. Replaced 9V battery for the clock.

No effect on sample because timer is powered by generator when it is on. 9V battery backup lasts ~190 hours.

10/20/2008 1610 Flow check on hivol2 at Site 2. Within spec.

10/20/2008 1620 Change batteries for DustTrak4 and Wind Wizard II2 at Site 2.

10/20/2008 1630 Flow check on E-Sampler2 at Site 2. Within spec.

10/20/2008 1710 Flow check on hivol1 at Site 1. Within spec.

10/20/2008 1755 Flow check on E-Sampler. Within spec.

10/22/2008 0713 Flow check on hivol1 at Site 1. Within spec.

4-12

Table 4-5. (continued) Date Time

(PDT) Activity Observation

10/22/2008 0725 Flow check on hivol2 at Site 1. Within spec.

Instrument was originally at Site 2.

10/22/2008 0810 Flow check on E-Sampler1 at Site 1. Within spec.

10/22/2008 0820 Flow check on E-Sampler2 at Site 1. Within spec.

Instrument was originally at Site 2.

10/22/2008 0840 Flow check on OMNI1 at Site 1. Set to 5 L/min with DeltaCal calibrator.

Discrepancy found between BGI and DeltaCal calibrators. BGI read 4.6 L/min while DeltaCal read 5.0 L/min. Need to compare calibrations in standards lab.

10/22/2008 0845 Flow check on OMNI1 at Site 1. Set to 5 L/min with DeltaCal and BGI calibrators.

Discrepancy found between Challenge and DeltaCal calibrators. Challenge read 4.6 L/min while DeltaCal read 5.0 L/min. Need to compare calibrations in standards laboratory.

10/22/2008 0850 Flow check on OMNI2 at Site 1. Set to 5 L/min with DeltaCal and BGI calibrators.

Discrepancy found between Challenge and DeltaCal calibrators. Challenge read 4.6 L/min while DeltaCal read 5.0 L/min. Need to compare calibrations in standards laboratory.

10/22/2008 0910 Flow check on PQ200 at Site 1. Within spec.

10/23/2008 0835 Flow check on E-Sampler1 at Site 1. Within spec.

DeltaCal read 1.8 L/min while rotameter (21659) read 2.0 L/min.

10/23/2008 0840 Flow check on E-Sampler2 at Site 1. Within spec.

DeltaCal read 1.8 L/min while rotameter (21659) read 2.0 L/min.

10/23/2008 0845 Flow check on OMNI1 at Site 1 with EAF501 rotameter, Challenge, and DeltaCal calibrators.

EAF501=5.0 L/min. Challenge (with 0-6 orifice)=5.5 L/min.DeltaCal=5.55 L/min. Didn't adjust flow.

10/23/2008 0855 Flow check on OMNI2 at Site 1 with EAF501 rotameter, Challenge, and DeltaCal calibrators.

EAF501=5.0 L/min Challenge (with 0-6 orifice)=5.5 L/minDeltaCal=5.55 L/min Didn't adjust flow.

10/23/2008 0920 Flow check on Grimm1 at Site 1 with TSI216549 rotameter.

1.1 L/min. No adjustment.

10/23/2008 0925 Flow check on Grimm2 at Site 1 with TSI216549 rotameter.

1.3 L/min. No adjustment.

10/23/2008 0940 Flow check on PQ200. Within spec. DeltaCal=16.66 L/min. Challenger (6-30 orifice)=16.95 L/min.No adjustment.

10/23/2008 0945 GRIM2 was not functioning. Did not function for the rest of the project.

4-13

Table 4-6. Laboratory verification of flow rate transfer standards for filter samplers. The primary standard is a Gillibrator bubble-meter.

Primary Standard (L/min)

DeltaCal (L/min)

DeltaCal (% difference)

Challenger (L/min)

Challenger (% difference)

2.41 2.40 -0.41 2.38 -1.24 2.48 2.48 0.00 2.45 -1.21 4.44 4.37 -1.58 4.38 -1.35 5.86 5.82 -0.68 5.75 -1.88 8.60 8.49 -1.28 8.49 -1.28

12.16 11.97 -1.56 12.05 -0.90 16.74 16.51 -1.37 16.52 -1.31

5-1

5. STUDY RESULTS 5.1 Collocated Sampler Comparisons

This study was intended to obtain collocated samples before and after the upwind/downwind sampling at both United Rock and Vulcan Materials. Owing to limited equipment availability and reliability, a sufficient number of collocated samples was only available for the hivols, OMNI minivols, E-Samplers, and DustTraks. The second Grimm OPC and E-BAM were not available until upwind/downwind sampling had commenced at United Rock, and the E-BAMs had to be removed from Vulcan Materials on 10/13 to support a SCAQMD wildfire exposure assessment. The second Grimm OPC malfunctioned prior to the final collocated testing at Vulcan Materials. Only one PQ200 lovol was available for the study.

Previous PM10 comparison studies among these and other particle measurement devices show mixed results (Buser et al., 2008; Cheng, 2008; Chow et al., 2006; Chow and Watson, 1997; Gehrig et al., 2005; Gertler et al., 1993; Green et al., 2009; Grimm and Eatough, 2009; Heal et al., 2000; Kingham et al., 2006; Kolak and Visalli, 1981; Magliano et al., 1999; Mathai et al., 1988; 1990; McFarland and Ortiz, 1985; Motallebi et al., 2003; Ono et al., 2000; Park et al., 2009; Purdue et al., 1986; Rodes et al., 1985; Rodes and Evans, 1985; Salminen and Karlsson, 2003; Shimp, 1988; Sweitzer, 1985; Tsai, 1995; Tsai and Cheng, 1995; Watson et al., 2000; Wedding et al., 1985a; 1985b; 1985c; Williams et al., 2000), with high comparability in laboratory tests and in ambient environments with non-volatile aerosols and minimal PM10-2.5 levels. Moderate to low comparability was found in source-oriented environments with large spatial gradients, large PM10-2.5 fractions, and/or high levels of semi-volatile aerosol components. This study did not have the resources for a definitive collocated sampling experiment. Collocated sampling was undertaken to determine what the uncertainties might be for similar sampling systems.

Figure 5-1 compares PM10 for the collocated hivol and OMNI filter samplers. Both samplers return the same results, on average, as indicated by slopes that are close to unity. There is more scatter in the OMNI data than in the hivol data, as reflected in the lower correlation coefficient (R, expressed as the variance, R2 in Figure 5-1). Hivol PM10 precisions, based on propagating the precisions of replicate filter weights and flow rate measurements (Watson et al., 2001), are on the order of ±3 µg/m3 and OMNI precisions are on the order of ±8 µg/m3 for five-hour sample durations. Most of the hivol and OMNI collocated values are within three precision intervals, but lower OMNI precisions for these short-duration samples resulted in a lower correlation. The hivols show two large deviations at high concentrations with differences of 14 µg/m3 on 10/23 and 28 µg/m3 on 10/27; the OMNIs showed a 25 µg/m3 difference on 10/23 and a 10 µg/m3 difference on 10/27 at Vulcan Site 1 during collocated sampling between 10/23 – 30.

Figure 5-2 shows 5-hour average PM10 derived from the collocated E-Samplers. PM10 levels are more comparable when the E-Sampler nephelometer output are normalized to the multiday filter mass, as indicated by the near-unity slopes and increased from 0.82 to 0.89. The factory nephelometer calibrations do not return the same results, but they are reasonably correlated. The higher concentrations are lower than those measured by the hivol and OMNI filter samplers, consistent with the lower sensitivities of nephelometers to larger particles in the PM10-2.5 fraction.

5-2

a)

b)

Figure 5-1. Collocated comparison of PM10 mass for collocated: a) Sierra Andersen hivol and b) BGI OMNI minivol integrated filter samplers at United Rock and Vulcan Materials. Trendlines are derived from unweighted ordinary linear regression with zero intercept.

HIVOL2 = 0.96 x HIVOL1

R2 = 0.90

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140

HIVOL1 (µg/m3)

HIV

OL

2 (µ

g/m

3 )

OMNI2 = 1.0 x OMNI1

R2 = 0.68

0

20

40

60

80

100

120

140

160

0 20 40 60 80 100 120 140 160

OMNI1 (µg/m3)

OM

NI2

(µ

g/m

3 )

5-3

a)

b)

Figure 5-2. Collocated comparison of PM10 mass for collocated MetOne E-Samplers: a) with factory calibration and b) normalized to filter mass measurements at United Rock and Vulcan Materials. Trendlines are derived from unweighted ordinary linear regression with zero intercept.

E-Sampler2 = 0.71 x E-Sampler1

R2 = 0.82

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

E-Sampler1 (µg/m3)

E-S

amp

ler2

(µ

g/m

3 )

E-Sampler2 = 0.94 x E-Sampler1

R2 = 0.89

0

10

20

30

40

50

60

0 10 20 30 40 50 60

E-Sampler1 (µg/m3)

E-S

amp

ler2

(µ

g/m

3 )

5-4

Figure 5-3 compares different TSI Model 8250 DustTrak nephelometers under two different situations. In each case the DustTraks were located in cabinets at opposite ends of the sampler array; at United Rock Site 1, Unit 2 was on the west end and Unit 4 was on the east end of the sampler array. At Vulcan Site 1, Unit 4 was on the west end and Unit 3 was on the east end of the array. The Vulcan Site 1 array (Figure 4-5) was more compact than the United Rock Site 1 array (Figure 4-3).

Even though the units had been factory re-calibrated prior to the experiment, Units 3 and 4 show a ~30% difference, similar to that for the E-Samplers; the results are highly correlated at Vulcan Site 1, consistent with a reasonably homogeneous distribution across the array, even on 10/23 and 10/27 when the hivols showed the largest discrepancies. At United Rock Site 1, however, the correlation between Units 2 and 3 is low and the data are scattered, consistent with substantial concentration variability across the sampler array. Unit 2 had a much larger calibration difference with respect to Unit 3 than did Unit 4.

5.2 Inter-Sampler Comparisons

As described in Section 4, the sampling sites represent four different micro-environments within which to compare measurements from the different sampling systems. Time series of 5-hour PM10 equivalents for each site are shown in Figures 5-4 and 5-5. PM10 concentration levels and day-to-day variations are consistent with the site descriptions. Except for the 9/25 and 9/26 fugitive dust generation events at United Rock Site 1, United Rock Site 2 shows higher PM10 levels than any of the other sites. Several of the hivol FRM PM10 concentrations exceeded the 150 µg/m3 NAAQS limit. Sundays fell on 9/14 and 9/21, when United Rock was inoperative and there was no traffic on Arrow Highway; these Sundays experienced the lowest PM10 levels. Aggregate materials were picked up by customers on Saturdays and there was moderate traffic on Arrow Highway, but minerals were not processed; this is reflected in lower PM10 levels on 9/13 and 9/20.

A similar situation is evident at Vulcan Site 1, where Saturday sampling occurred on 10/4, 10/11, and 10/18, while Sunday samples were taken on 10/5, 10/12, and 10/19. Vulcan Site 2 shows the lowest PM10 concentrations and the least day-to-day variability, consistent with its isolation from most routine activities. A few excursions are evident at this site.

PM10 using factory calibrations for the Model 8250, DRX, E-BAMs and E-Samplers are also plotted in Figures 5-4 and 5-5. The Grimm OPCs provide number counts in the different size bins. PM10 mass was estimated from these number counts by: 1) calculating particle volumes assuming spherical particles with diameters equal to the size bin specification; 2) assigning particles densities of 1.5 g/cm3 for diameters <3µm and 2.5 g/cm3 for diameters >3µm; and 3) summing the resulting mass estimates for all size bins up to 10 µm.

The highest concentrations, with the hivol PM10 exceeding 250 µg/m3, occurred at United Rock Site 1 on 9/26 during the non-controlled fugitive dust demonstration experiments. The roadway was left unswept, watering systems were turned off, material was lifted from and dumped into the storage piles, and trucks were run along the unpaved turnout just south of the monitors. TSI Model 8250 DustTrak Unit 2 was removed from the sampling array on 9/25 and 9/26, and the TSI DRX DustTrak was removed on 9/26, for location within the observable plumes from the different activities. The distance from the sample array was not more

5-5

a)

b)

Figure 5-3. Collocated comparison of 5-hour PM10 mass for collocated DustTraks with factory calibration for: a) Units 2 and 3 at United Rock Site 1 from 9/8 – 27, excluding 9/25 – 26 during dust generation events and b) Units 3 and 4 at Vulcan Site 1 from 10/23 – 30. Trendlines are derived from unweighted ordinary linear regression with zero intercept.

DustTrak2 = 1.6 x DustTrak3

R2 = 0.46

0

20

40

60

80

100

120

140

160

180

0 20 40 60 80 100 120 140 160 180

DustTrak3 (µg/m3)

Du

stT

rak2

(µ

g/m

3 )

DustTrak4 = 0.72 x DustTrak3

R2 = 0.95

0

10

20

30

40

50

60

0 10 20 30 40 50 60

DustTrak3 (µg/m3)

Du

stT

rak4

(µ

g/m

3 )

5-6

a)

b)

Figure 5-4. Five-hour average PM10 concentrations at United Rock for: a) downwind Site 1 and b) upwind Site 2 for the period from 9/11 – 27. Fugitive dust episodes were simulated near United Rock Site 1 on 9/25 and 9/26.

0

50

100

150

200

250

300

350

400

9/11

9/12

9/13

9/14

9/15

9/16

9/17

9/18

9/19

9/20

9/21

9/22

9/23

9/24

9/25

9/26

9/27

Sampling Date at United Rock Site 1

PM

10

Co

nce

ntr

atio

n (

µg

/m3 )

Hivol PQ200OMNI E-SamplerE-BAM DustTrak3DustTrak2 DustTrak DRXGrimm OPC

0

50

100

150

200

250

300

350

400

9/11

9/12

9/13

9/14

9/15

9/16

9/17

9/18

9/19

9/20

9/21

9/22

9/23

9/24

9/25

9/26

9/27

Sampling Date at United Rock Site 2

PM

10

Co

nce

ntr

atio

n (

µg

/m3 )

Hivol OMNI

E-Sampler E-BAM

DustTrak4 Grimm OPC

5-7

a)

b)

Figure 5-5. Five-hour average PM10 concentrations at Vulcan Materials for: a) downwind Site 1 and b) upwind Site 2 for the period from 10/2 – 20.

than 20 m, yet the PM10 levels from these units were more than double those of most of the other samplers. The generated plumes had localized impacts. Minor wind shifts placed some samplers completely outside of the visible plume.

0

50

100

150

200

250

10/1

10/2

10/3

10/4

10/5

10/6

10/7

10/8

10/9

10/1

010

/11

10/1

210

/13

10/1

410

/15

10/1

610

/17

10/1

810

/19

10/2

0

Sampling Date at Vulcan Site 1

PM

10 C

on

cen

trat

ion

(µ

g/m

3 )

Hivol PQ200OMNI E-SamplerE-BAM DustTrak3DustTrak DRX Grimm OPC

0

50

100

150

200

250

10/1

10/2

10/3

10/4

10/5

10/6

10/7

10/8

10/9

10/1

010

/11

10/1

210

/13

10/1

410

/15

10/1

610

/17

10/1

810

/19

10/2

0

Sampling Date at Vulcan Site 2

PM

10

Co

nce

ntr

atio

n (

µg

/m3 )

Hivol OMNI

E-Sampler E-BAM

DustTrak4 Grimm OPC

5-8

PM10 concentrations from different instruments generally rose and fell with each other for the five-hour averages, but there are exceptions outside of the 9/25 and 9/26 dust events. The hivol and PQ200 lovol FRM tracked each other most closely, which is reasonable since both are designated PM10 FRMs and have previously demonstrated equivalence in ambient environments. However, their PM10 concentrations differ from each other by more than that found in their equivalence experiments, as highlighted in Figure 5-6. On average, the PQ200 yields ~82% of the hivol PM10 concentrations as indicated by the slope of the regression equation. The measurement scatter is beyond the ±3 µg/m3 precisions of both concentration measurements.

DustTrak Unit 2 shows the highest PM10 concentrations, consistent with the bias indicated in Figure 5-3a with respect to DustTrak Unit 3. The calibration for this unit differs substantially from DustTrak Units 3 and 4. The other instruments typically show lower PM10 levels than the hivol and PQ200 lovol FRMs, with some exceptions. The E-Samplers yielded the lowest PM10 levels at all four sites and did not track day-to-day variations as well as the other measurement systems.

Inter-sampler correlation coefficients in Table 5-1 demonstrate good relationships among the hivol, PQ200, OMNI, and DRX measurements at downwind sites, even though Figures 5-4 and 5-5 demonstrate that the absolute values are different. The higher correlation of the DRX with respect to the DustTrak indicates the value of adding the OPC function for coarse particles, even though the factory calibration does not represent the aerosol sampled in this experiment. The lower DustTrak and E-Sampler nephelometer correlations are consistent with their expected response to smaller particles. Correlations at the upwind sites are higher, with the OMNI and E-BAM PM10 being highly correlated with the hivol PM10.

Figure 5-6. Comparison between five-hour average PM10 from collocated FRMs, PQ200 lovol and hivol samplers at United Rock and Vulcan Materials downwind sites. Samples during dust generation events at United Rock Site 1 on 9/25 and 9/26 are excluded.

PQ200 = 0.82 x HIVOL1

R2 = 0.78

0

50

100

150

200

250

0 50 100 150 200 250

HIVOL1 PM10 Concentration (µg/m3)

PQ

20

0 P

M1

0 C

on

cen

tra

tio

n (

µg

/m3 )

5-9

Table 5-1. PM10 correlation coefficients (R) for downwind and upwind sites. Downwind data include 9 samples from United Rock Site 1 and 18 samples from Vulcan Materials Site 1, excluding dust generation events on 9/25 and 9/26. Upwind data include 13 samples from United Rock Site 2 and 11 samples from Vulcan Materials Site 2. Insufficient Grimm OPC data corresponded with that from the other samplers for inclusion.

Downwind Sites

Hivol PQ200 OMNI E-Sampler

E-BAM DustTrak3 DustTrak DRX

Hivol 1.00 PQ200 0.86 1.00 OMNI 0.85 0.94 1.00 E-Sampler 0.20 0.49 0.49 1.00 E-BAM 0.36 0.58 0.47 0.35 1.00 DustTrak3 0.29 0.49 0.51 0.89 0.28 1.00 DustTrak DRX

0.90 0.83 0.91 0.47 0.33 0.55 1.00

Upwind Sites

Hivol OMNI E-Sampler E-BAM DustTrak4

Hivol 1.00 OMNI 0.95 1.00 E-Sampler 0.50 0.52 1.00 E-BAM 0.86 0.82 0.71 1.00 DustTrak4 0.77 0.79 0.71 0.80 1.00

5.3 Particle Size distributions

Figures 5-7 to 5-9 show the daily particle size distributions from the DRX and Grimm OPCs. Since the DRX PM10 is reasonably correlated with the hivol PM10 (see Table 5-1), even though the absolute concentrations are not comparable, the relative fractions in each size range provides some insight into the causes of differences between sampling systems.

The Grimm OPCs in Figures 5-8 and 5-9 provide greater size resolution than the DRX, though this detail is probably unnecessary for this application. The bottom five bars in the Grimm figures are approximately equivalent to the bottom bar in the DRX figures, while the top three bars in the Grimm figures are approximately equivalent to the top bar in the DRX figures. The bottom four bars in the DRX figures and the bottom 12 bars in the Grimm figures approximate the PM10 concentrations.

PM1.0 is a small portion of the total mass, averaging 8 µg/m3 for the DRX at United Rock Site 1 and 7 µg/m3 at Vulcan Materials Site 1. The highest PM1.0 concentration was 31 µg/m3 when the DRX was within the fugitive dust episode plume on 9/26. The plumes were visible to the naked eye, indicating that they scattered a large amount of light. The highest PM1.0 concentration of 25 µg/m3 at Vulcan Materials Site 1 occurred on 10/9. The one-minute resolution offers insights into the causes of these high concentrations, as illustrated in Figure 5-10. At United Rock, PM1.0 is clearly related to the fugitive dust plumes as indicated by the corresponding rapid increases in concentrations for all of the DRX size fractions between 1400 and 1530 PDT. The sharpness of the spikes reflects the narrowness and short durations of the plumes, yet the concentrations over these short time periods are so large that they dominate the 5-hr average concentrations in each size fraction, as seen in Figure 5-7a.

5-10

a)

b)

Figure 5-7. DRX mass size distributions at: a) United Rock downwind Site 1 and b) Vulcan Materials downwind Site 1.

0

50

100

150

200

250

300

350

400

9/11

9/12

9/13

9/14

9/15

9/16

9/17

9/18

9/19

9/20

9/21

9/22

9/23

9/24

9/25

9/26

9/27

Sampling Date at United Rock Site 1

DR

X M

ass

Co

nce

ntr

atio

n (

µg

/m3)

DRX0-1 DRX1-2.5

DRX2.5-4 DRX2.5-10

DRX10-TSP

635 1524

0

50

100

150

200

250

300

350

400

10/2

10/3

10/4

10/5

10/6

10/7

10/8

10/9

10/1

010

/11

10/1

210

/13

10/1

410

/15

10/1

610

/17

10/1

810

/19

10/2

0

Sampling Date at Vulcan Site 1

DR

X M

ass

Co

nce

ntr

atio

n (

µg

/m3 )

DRX0-1 DRX1-2.5

DRX2.5-4 DRX2.5-10

DRX10-TSP

484

5-11

a)

b)

Figure 5-8. Grimm OPC mass size distributions at: a) United Rock downwind Site 1 and b) United Rock upwind Site 2.

0

50

100

150

200

250

9/11

9/12

9/13

9/14

9/15

9/16

9/17

9/18

9/19

9/20

9/21

9/22

9/23

9/24

9/25

9/26

9/27

Sampling Date at United Rock Site 1

Gri

mm

Mas

s C

on

cen

trat

ion

(µ

g/m

3 )

GRIM.3-.4 GRIM.4-.5GRIM.5-.65 GRIM.65-.8GRIM.8-1 GRIM1-1.6GRIM1.6-2 GRIM2-3GRIM3-4 GRIM4-5GRIM5-7.5 GRIM7.5-10GRIM10-15 GRIM15-20GRIM>20

0

50

100

150

200

250

9/11

9/12

9/13

9/14

9/15

9/16

9/17

9/18

9/19

9/20

9/21

9/22

9/23

9/24

9/25

9/26

9/27

Sampling Date at United Rock 2

Gri

mm

Mas

s C

on

cen

trat

ion

(µ

g/m

3 )

GRIM.3-.4 GRIM.4-.5GRIM.5-.65 GRIM.65-.8GRIM.8-1 GRIM1-1.6GRIM1.6-2 GRIM2-3GRIM3-4 GRIM4-5GRIM5-7.5 GRIM7.5-10GRIM10-15 GRIM15-20GRIM>20

5-12

a)

b)

Figure 5-9. Grimm OPC mass size distributions at: a) Vulcan Materials downwind Site 1 and b) Vulcan Materials upwind Site 2.

0

10

20

30

40

50

60

70

80

90

100

10/2

10/3

10/4

10/5

10/6

10/7

10/8

10/9

10/1

010

/11

10/1

210

/13

10/1

410

/15

10/1

610

/17

10/1

810

/19

10/2

0

Sampling Date at Vulcan Site 1

Gri

mm

Mas

s C

on

cen

trat

ion

(µ

g/m

3 )

GRIM.3-.4 GRIM.4-.5GRIM.5-.65 GRIM.65-.8GRIM.8-1 GRIM1-1.6GRIM1.6-2 GRIM2-3GRIM3-4 GRIM4-5GRIM5-7.5 GRIM7.5-10GRIM10-15 GRIM15-20GRIM>20

0

10

20

30

40

50

60

70

80

90

100

10/2

10/3

10/4

10/5

10/6

10/7

10/8

10/9

10/1

010

/11

10/1

210

/13

10/1

410

/15

10/1

610

/17

10/1

810

/19

10/2

0

Sampling Date at Vulcan Site 2

Gri

mm

Mas

s C

on

cen

trat

ion

(µ

g/m

3 )

GRIM.3-.4 GRIM.4-.5GRIM.5-.65 GRIM.65-.8GRIM.8-1 GRIM1-1.6GRIM1.6-2 GRIM2-3GRIM3-4 GRIM4-5GRIM5-7.5 GRIM7.5-10GRIM10-15 GRIM15-20GRIM>20

5-13

a)

b)

Figure 5-10. One-minute DRX size variations for highest 5-hour PM1.0 concentrations for: a) United Rock Site 1 on 9/26/08 and b) Vulcan Site 1 on 10/9/08.

1

10

100

1000

10000

100000

11:0

0

11:3

0

12:0

0

12:3

0

13:0

0

13:3

0

14:0

0

14:3

0

15:0

0

15:3

0

16:0

0

Sampling Time at United Rock Site 1 on 9/26/2008

DR

X P

M C

on

cen

trat

ion

(µ

g/m

3 )

PM1 PM2.5 PM4

PM10 PMTSP

1

10

100

1000

10000

11:0

0

11:3

0

12:0

0

12:3

0

13:0

0

13:3

0

14:0

0

14:3

0

15:0

0

15:3

0

16:0

0

Sampling Time at Vulcan Site 1 on 10/9/2008

DR

X P

M C

on

cen

trat

ion

(µ

g/m

3 )

PM1 PM2.5 PM4

PM10 PMTSP

5-14

The 10/9 one-minute measurements illustrate a completely different type of event. PM1.0, PM2.5, and PM4 are relatively constant throughout the 5-hour period, with a slight tail-off near the end. This is consistent with secondary aerosol formation that often affects the eastern SoCAB during autumn. Several wildfires had also started, and high PM1.0 levels are consistent with transported smoke that would be well-mixed and constant by the time it reached Vulcan Site 1. Wildfires are probably the cause of the higher concentrations of fine particle fractions seen in Figures 5-7b and 5-9 on 10/18, 10/19, and 10/20 at the Vulcan sites. The similarity of fine particle levels at Vulcan upwind and downwind sites indicates a more regional than local event.

Engine exhaust contributions would manifest themselves as PM1.0 unaccompanied by increases in the other size fractions. These would evidence themselves as spikes in the time series with wind directions from the source areas. However, more detailed chemical speciation would be needed to confirm this. Engine exhaust is composed primarily of organic and elemental carbon that distinguishes it from mineral matter. Outside of the episodes, PM1.0 levels are <10 µg/m3 and are somewhat comparable at the upwind and downwind sites. Fine particle concentration appear to be slightly higher at United Rock Site 2 in Figure 5-8, possibly due to the large amount of diesel traffic on Arrow Highway within 20 m of the sampling inlets.

Effects of the 9/25 and 9/26 fugitive dust events are evident in Figure 5-7a, but they are not as pronounced of those in Figure 5-8a, even though the DRX was not separated from the Grimm OPC by more than 20 m. Figure 5-8b shows higher levels at the upwind site which was not affected by the generated dust at the north end of the facility. These examples illustrate the large spatial gradients in fugitive dust concentrations which can easily vary over distances of 20 m or less. Integrated filter samples do not provide information on these short-duration and high-concentration events.

Figure 5-7 shows the DRX detecting most of the PM in sizes larger than 10 µm, much more than that detected by the Grimm OPC. This detection difference might be caused by the different sample introduction methods in both instruments. DRX has a vertical probe of 1.6 mm diameter that enters directly to the sensing cell. The Grimm has a ~1 mm diameter probe that makes a right angle bend to the sensing cell. Some of the largest particles may be lost in the bend. As shown in Section 3, PM10 inlets collect larger particles, but with varying efficiencies. Even small changes in inlet cut-points and slopes could have a recognizable effect on the material collected. This is one explanation for the differences found between the hivol and PQ200 lovol FRMs which have shown equivalent data in previous comparison tests with ambient, rather than source-oriented, aerosols. As shown in Section 2, 10 µm and larger particles have short residence times, especially when they are only a few meters above ground level. These size distributions, coupled with the differences between DustTrak measurements at both ends of the United Rock Site 1 array, indicate that the different units were not necessarily sampling the same aerosol. This is another potential cause of differences between the hivol and PQ200 lovol FRMs.

5.4 Wind Direction and Wind Speed Variability

The terms “upwind” and “downwind” have been used somewhat loosely to describe the sampling locations. The tower measurements at all four locations can be examined to determine the extent to which such terms are applicable during the five-hour sampling periods. Tables 5-2 through 5-5 summarize the frequencies of winds within the sampling periods at each site.

5-15

Table 5-2. Daily wind direction and wind speed frequencies (% of time) at United Rock Site 1 from 1100 to 1600 PDT.

Date (2008) Dir 9/11 9/12 9/13 9/14 9/15 9/16 9/17 9/18 9/19 9/20 9/21 9/22 9/23 9/24 9/25 9/26 9/27 Total N 0.0 87.6 7.0 0.3 0.0 0.0 0.3 0.3 0.3 1.0 0.3 0.0 0.3 0.0 0.0 0.0 0.0 5.7 NNE 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.7 0.0 0.0 0.0 0.0 0.0 0.1 NE 0.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 ENE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 E 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ESE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 SE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 1.3 2.3 0.3 0.3 0.0 0.0 0.0 0.3 SSE 0.3 0.0 0.3 1.0 0.0 0.0 0.7 0.7 0.0 1.0 3.3 5.0 1.3 0.0 0.0 0.7 0.7 0.9 S 1.0 0.7 1.3 9.0 1.0 2.7 8.0 9.7 12.4 16.4 16.4 14.0 14.4 5.0 4.3 5.7 4.0 7.4 SSW 11.0 2.7 16.7 12.4 12.0 11.4 11.0 13.0 19.7 18.4 14.0 16.4 15.7 13.7 9.4 13.0 10.0 13.0 SW 19.4 2.7 18.7 27.1 29.4 26.8 22.7 24.1 30.1 27.4 22.1 17.4 22.1 25.8 25.4 34.1 24.7 23.5 WSW 8.7 1.7 14.0 12.7 21.7 13.4 19.4 17.1 14.0 13.0 13.7 10.7 17.1 18.7 21.1 19.7 19.4 15.1 W 31.8 2.3 18.1 23.4 21.7 33.8 24.7 17.1 16.1 10.7 13.0 17.1 19.7 21.7 26.4 16.7 26.8 20.1 WNW 21.1 1.0 13.0 11.0 12.4 12.0 11.0 14.7 6.4 9.7 10.4 12.7 9.0 13.7 13.0 9.4 14.0 11.4 NW 5.0 0.7 4.7 2.7 1.3 0.0 2.0 2.3 0.7 2.3 3.0 3.0 0.0 1.0 0.3 0.7 0.3 1.8 NNW 1.7 0.7 0.3 0.0 0.3 0.0 0.0 1.0 0.0 0.0 1.0 0.7 0.0 0.0 0.0 0.0 0.0 0.3 Speed (m/s) 9/11 9/12 9/13 9/14 9/15 9/16 9/17 9/18 9/19 9/20 9/21 9/22 9/23 9/24 9/25 9/26 9/27 Total 0-0.5 0.0 87.6 7.0 0.3 0.0 0.0 0.3 0.3 0.3 1.0 0.3 0.0 0.3 0.0 0.0 0.0 0.0 5.7 0.5-1 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.7 0.0 0.0 0.0 0.0 0.0 0.1 1-1.5 0.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 1.5-2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2-2.5 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.5-3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 3-3.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 1.3 2.3 0.3 0.3 0.0 0.0 0.0 0.3 3.5-4 0.3 0.0 0.3 1.0 0.0 0.0 0.7 0.7 0.0 1.0 3.3 5.0 1.3 0.0 0.0 0.7 0.7 0.9 4-4.5 1.0 0.7 1.3 9.0 1.0 2.7 8.0 9.7 12.4 16.4 16.4 14.0 14.4 5.0 4.3 5.7 4.0 7.4 4.5-5 11.0 2.7 16.7 12.4 12.0 11.4 11.0 13.0 19.7 18.4 14.0 16.4 15.7 13.7 9.4 13.0 10.0 13.0 5-5.5 19.4 2.7 18.7 27.1 29.4 26.8 22.7 24.1 30.1 27.4 22.1 17.4 22.1 25.8 25.4 34.1 24.7 23.5 5.5-6 8.7 1.7 14.0 12.7 21.7 13.4 19.4 17.1 14.0 13.0 13.7 10.7 17.1 18.7 21.1 19.7 19.4 15.1 6-6.5 31.8 2.3 18.1 23.4 21.7 33.8 24.7 17.1 16.1 10.7 13.0 17.1 19.7 21.7 26.4 16.7 26.8 20.1

5-16

Table 5-3. Daily wind direction and wind speed frequencies (% of time) at United Rock Site 2 from 1100 to 1600 PDT.

Date (2008) Dir 9/11 9/12 9/13 9/14 9/15 9/16 9/17 9/18 9/19 9/20 9/21 9/22 9/23 9/24 9/25 9/26 9/27 Total N 0.3 1.0 0.0 0.0 0.3 0.3 0.0 1.0 1.3 0.3 0.0 0.3 0.0 0.3 0.4 NNE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ENE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 E 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ESE 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.1 SE 0.0 0.0 0.0 0.0 0.0 0.0 0.3 2.0 3.0 0.0 0.0 0.0 0.7 0.0 0.4 SSE 0.3 0.0 0.0 0.7 2.3 2.3 1.0 3.7 5.7 1.7 0.7 0.0 0.0 0.0 1.3 S 1.0 0.0 0.7 1.7 4.7 4.3 5.0 7.0 10.4 4.3 0.3 1.0 3.0 1.0 3.2 SSW 12.0 10.0 6.0 6.4 13.0 21.4 24.4 17.4 14.7 22.7 20.1 11.4 10.4 10.4 14.3 SW 73.2 70.9 84.9 73.2 56.5 65.2 52.5 52.2 38.1 55.9 67.2 76.6 71.6 71.6 65.0 WSW 6.7 11.7 4.7 11.0 9.7 4.0 8.0 6.4 10.7 6.4 7.7 8.0 11.4 10.4 8.3 W 4.3 5.7 3.3 6.7 7.4 1.0 4.0 5.4 7.4 6.0 3.7 2.3 3.0 3.0 4.5 WNW 1.7 0.3 0.3 0.0 4.0 0.0 1.3 2.3 3.7 1.3 0.3 0.3 0.0 2.3 1.3 NW 0.3 0.3 0.0 0.0 1.3 1.0 3.0 2.3 3.3 1.3 0.0 0.0 0.0 1.0 1.0 NNW 0.0 0.0 0.0 0.0 0.7 0.3 0.3 0.3 0.7 0.0 0.0 0.0 0.0 0.0 0.2 Speed (m/s) 9/11 9/12 9/13 9/14 9/15 9/16 9/17 9/18 9/19 9/20 9/21 9/22 9/23 9/24 9/25 9/26 9/27 Total 0-0.5 15.1 12.7 0.7 9.7 19.4 3.3 12.4 17.4 31.4 15.1 8.4 10.7 14.4 9.0 12.8 0.5-1 20.1 15.7 3.0 15.1 20.4 4.7 16.1 20.7 26.4 19.1 20.1 14.0 12.0 13.7 15.8 1-1.5 26.1 16.1 10.4 22.1 19.1 6.7 19.1 26.1 26.4 27.1 27.4 19.4 17.4 17.7 20.1 1.5-2 19.4 26.1 33.4 26.8 13.7 12.4 17.7 24.1 11.7 26.4 21.7 17.4 18.4 24.7 21.0 2-2.5 13.4 17.7 29.1 20.7 13.7 22.1 18.1 9.4 4.0 10.7 15.4 15.4 20.7 17.7 16.3 2.5-3 6.0 9.4 15.7 4.7 7.7 25.1 13.7 2.3 0.0 1.7 6.4 14.4 12.0 12.7 9.4 3-3.5 0.0 2.3 6.0 1.0 5.0 20.7 3.0 0.0 0.0 0.0 0.7 6.0 5.0 4.0 3.8 3.5-4 0.0 0.0 1.7 0.0 1.0 3.7 0.0 0.0 0.0 0.0 0.0 2.7 0.0 0.3 0.7 4-4.5 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 4.5-5 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5-5.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.5-6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6-6.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5-17

Table 5-4. Daily wind direction and wind speed frequencies (% of time) at Vulcan Materials Site 1 from 1100 to 1600 PDT.

Date (2008) Dir 10/2 10/3 10/4 10/5 10/6 10/7 10/8 10/9 10/10 10/11 10/12 10/13 10/14 10/15 10/16 10/17 10/18 10/19 10/20 Total N 0.0 41.5 1.0 1.7 0.0 0.7 1.0 0.3 2.0 3.7 5.0 1.0 0.3 1.3 1.7 1.3 0.7 0.7 0.0 3.4 NNE 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 1.3 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.2 NE 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 1.0 1.3 0.0 1.3 0.0 0.3 0.0 0.0 0.0 0.2 ENE 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.7 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 E 0.0 0.0 0.0 0.0 0.0 0.0 2.7 0.0 0.0 0.3 1.0 0.3 0.0 0.3 0.7 1.7 0.0 0.0 0.0 0.4 ESE 0.3 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 1.3 0.3 0.3 0.0 0.0 2.0 3.0 0.0 0.0 0.0 0.5 SE 0.3 0.0 0.0 0.0 0.0 0.0 4.3 0.3 0.0 2.0 0.3 1.0 0.0 0.3 0.0 4.0 0.0 0.0 0.0 0.7 SSE 4.0 0.0 0.0 0.3 0.0 0.0 8.4 0.3 0.0 2.7 0.7 3.3 0.0 0.3 0.7 9.7 0.0 0.0 0.0 1.6 S 4.3 0.0 0.0 1.7 0.0 4.3 11.7 1.7 0.0 6.0 1.3 1.7 0.0 0.3 1.0 17.7 0.7 0.0 0.0 2.8 SSW 4.7 0.0 0.0 5.0 0.0 4.7 13.4 3.7 0.3 10.7 4.0 13.7 0.0 1.0 6.7 26.4 2.0 0.0 0.0 5.1 SW 4.3 0.7 0.0 11.0 3.0 11.7 10.7 6.7 4.0 11.4 2.7 12.0 0.0 3.7 11.4 11.4 3.0 0.0 2.7 5.8 WSW 23.4 5.4 1.0 21.7 18.7 19.7 11.7 10.0 10.0 10.0 9.4 16.7 1.0 7.0 17.4 8.7 4.3 6.4 5.4 10.9 W 41.5 32.8 42.1 45.8 63.2 36.8 28.1 55.5 62.2 21.7 29.1 21.1 20.7 15.4 21.1 7.7 27.1 35.5 37.1 33.9 WNW 11.0 6.0 16.1 5.7 8.4 10.0 2.0 12.4 14.0 12.7 23.4 18.7 54.2 49.2 27.8 6.7 50.5 50.5 50.2 22.6 NW 6.0 12.4 23.1 5.4 6.0 9.7 1.0 8.4 5.7 8.7 7.4 4.7 16.7 12.0 3.7 0.7 7.0 3.3 3.3 7.6 NNW 0.0 1.3 16.7 1.3 0.7 2.3 1.0 0.7 1.7 6.7 12.4 3.0 7.0 7.7 5.0 0.7 4.7 3.7 1.3 4.1 Speed (m/s) 10/2 10/3 10/4 10/5 10/6 10/7 10/8 10/9 10/10 10/11 10/12 10/13 10/14 10/15 10/16 10/17 10/18 10/19 10/20 Total 0-0.5 2.0 42.1 3.7 5.0 0.0 10.4 22.4 2.3 1.7 16.4 10.4 6.4 0.3 17.4 10.4 9.7 4.0 0.0 1.7 8.7 0.5-1 3.0 1.0 11.0 8.0 3.0 20.4 11.0 5.4 1.0 10.7 13.4 8.7 1.3 24.4 13.0 13.4 8.4 3.7 3.3 8.6 1-1.5 9.4 5.4 23.7 17.7 5.4 27.1 13.0 11.4 4.7 19.4 19.4 8.4 13.7 25.1 25.1 13.0 14.0 7.7 21.1 15.0 1.5-2 14.0 11.4 24.4 16.1 22.1 22.4 10.4 16.1 8.7 16.7 19.1 17.1 20.1 16.1 22.7 18.7 18.7 19.4 24.4 17.8 2-2.5 13.0 13.7 18.1 20.1 23.1 11.7 9.7 20.4 12.4 13.4 16.4 18.4 27.8 7.7 16.4 19.1 18.1 24.4 17.4 16.9 2.5-3 15.7 11.4 11.0 13.0 18.7 5.0 10.4 13.0 19.4 11.0 11.0 12.4 16.7 4.0 8.4 9.7 11.0 21.7 15.7 12.6 3-3.5 12.0 7.0 5.0 11.0 16.4 2.3 7.0 9.7 16.4 7.7 6.4 13.4 11.0 4.7 2.7 8.7 15.1 11.4 12.0 9.5 3.5-4 11.4 4.3 2.0 5.0 7.7 0.3 6.7 8.7 12.4 2.7 2.7 9.4 7.4 0.3 0.7 5.4 6.0 8.4 3.3 5.5 4-4.5 4.0 2.3 0.7 2.3 2.3 0.3 4.0 5.0 9.4 1.7 1.0 2.3 0.7 0.3 0.7 0.7 4.0 3.0 1.0 2.4 4.5-5 11.4 1.0 0.3 1.7 1.3 0.0 4.7 8.0 13.0 0.3 0.3 3.3 1.0 0.0 0.0 1.7 0.7 0.3 0.0 2.6 5-5.5 2.3 0.3 0.0 0.0 0.0 0.0 0.7 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 5.5-6 1.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 6-6.5 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5-18

Table 5-5. Daily wind direction and wind speed frequencies (% of time) at Vulcan Materials Site 2 from 1100 to 1600 PDT.

Date (2008) Dir 10/2 10/3 10/4 10/5 10/6 10/7 10/8 10/9 10/10 10/11 10/12 10/13 10/14 10/15 10/16 10/17 10/18 10/19 10/20 Total N 0.0 0.0 0.0 0.0 0.0 0.7 1.0 0.0 0.0 0.3 0.0 0.0 1.0 2.3 0.0 0.0 0.0 0.3 NNE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 NE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.1 ENE 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.0 0.1 E 0.0 0.0 0.0 0.0 0.0 0.0 3.7 0.0 0.0 0.3 0.0 0.0 0.0 7.7 0.0 0.0 0.0 0.7 ESE 0.0 0.0 0.0 0.0 0.0 0.0 4.3 0.0 0.0 4.7 0.0 0.0 0.0 20.4 0.0 0.0 0.0 1.7 SE 0.7 0.0 0.0 0.0 0.0 0.3 10.7 0.0 0.0 4.3 0.0 0.0 0.0 17.4 0.3 0.0 0.0 2.0 SSE 0.0 0.0 0.0 0.0 0.0 2.0 14.0 0.3 0.0 7.4 0.0 0.0 0.3 8.7 0.0 0.0 0.0 1.9 S 6.0 0.0 0.0 5.4 0.0 4.3 6.0 1.7 0.0 7.4 0.0 1.7 7.4 10.7 2.3 0.0 1.0 3.2 SSW 4.0 2.0 0.0 7.7 0.3 6.4 6.7 5.7 4.0 15.4 0.0 9.4 11.4 7.7 5.0 0.3 2.0 5.2 SW 7.4 11.4 0.0 21.7 12.4 24.1 21.4 14.7 25.4 20.4 1.3 20.4 23.4 6.7 11.0 15.1 20.1 15.1 WSW 44.1 36.8 22.7 32.1 44.1 27.4 21.7 35.5 39.8 18.7 47.5 31.1 28.1 7.7 48.2 50.8 46.8 34.3 W 34.4 39.8 45.5 26.1 40.8 21.7 7.4 36.5 27.8 20.1 47.5 27.1 19.1 5.4 30.1 33.1 28.4 28.9 WNW 3.3 8.4 22.1 6.4 2.3 9.0 2.0 5.7 3.0 0.7 3.7 8.0 7.4 2.7 3.0 0.7 1.7 5.3 NW 0.0 1.0 9.7 0.7 0.0 3.7 0.3 0.0 0.0 0.3 0.0 2.0 1.3 0.0 0.0 0.0 0.0 1.1 NNW 0.0 0.7 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.3 0.7 0.0 0.0 0.0 0.0 0.1 Speed (m/s) 10/2 10/3 10/4 10/5 10/6 10/7 10/8 10/9 10/10 10/11 10/12 10/13 10/14 10/15 10/16 10/17 10/18 10/19 10/20 Total 0-0.5 0.7 0.3 0.0 2.3 0.0 4.7 9.4 0.7 0.0 5.0 0.0 7.7 6.0 1.7 1.3 0.0 0.0 2.3 0.5-1 1.7 2.0 0.0 3.3 0.3 6.4 9.7 3.0 0.3 5.0 0.0 8.4 5.4 4.7 2.3 0.0 0.3 3.1 1-1.5 1.7 1.0 0.0 5.0 2.0 14.7 14.7 3.7 0.7 6.4 0.0 14.7 12.7 7.4 5.4 0.3 5.7 5.6 1.5-2 4.7 3.0 2.0 10.4 2.3 24.7 8.7 8.0 1.7 7.0 0.7 24.7 15.7 11.0 7.4 1.3 7.7 8.3 2-2.5 3.0 5.7 13.4 14.7 8.0 17.7 8.4 11.4 1.7 8.0 2.3 14.0 19.7 11.7 10.0 5.7 21.4 10.4 2.5-3 5.7 9.0 28.1 18.7 11.0 17.4 10.4 8.0 5.4 11.0 5.7 15.1 17.1 15.1 8.7 20.4 13.7 13.0 3-3.5 7.0 17.4 32.1 18.7 22.7 7.7 6.7 10.4 12.0 16.1 18.7 11.0 10.4 19.4 19.1 22.1 19.4 15.9 3.5-4 9.4 19.4 18.1 13.7 29.4 4.0 6.0 17.1 17.4 11.7 33.1 2.7 9.4 13.7 17.1 21.1 17.7 15.3 4-4.5 15.7 18.1 5.7 10.0 17.7 2.3 5.0 13.7 17.7 11.0 27.8 1.3 3.0 9.4 17.7 17.4 9.7 12.0 4.5-5 33.8 20.1 0.7 3.0 6.4 0.3 13.0 21.1 25.4 15.7 11.4 0.3 0.7 6.0 10.4 11.4 4.3 10.8 5-5.5 11.4 3.0 0.0 0.0 0.0 0.0 4.3 2.3 8.4 2.0 0.3 0.0 0.0 0.0 0.7 0.3 0.0 1.9 5.5-6 5.0 1.0 0.0 0.0 0.0 0.0 3.0 0.3 4.7 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 6-6.5 0.3 0.0 0.0 0.0 0.0 0.0 0.7 0.3 3.7 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3

5-19

Figure 5-11 compares the wind directions from the upwind/downwind masts at each facility for the each sampling period, summarizing the frequencies in the final columns of the tables. United Rock Site 2 experienced the most consistent winds over the monitoring period, with winds mostly from the southwest for 65% of the measurement periods. The wind vane was unobstructed by trees or structures and the terrain to the south and southeast also lacked buildings and structures. This contrasts with the more variable wind directions at “downwind” United Rock Site 1. While these are still generally from the southwest, directions are spread out more among the neighboring sectors. As seen in Figure 4-1, there are many tall obstructions between Sites 2 and 1 that can cause flows to vary. The 5 m wind mast at Site 1 was also lower than the trees located to the north; this natural windbreak also probably affects the wind measurement by blocking the flow. Frequencies for the individual days reflect the frequencies for the entire period, except for 9/12 at United Rock Site 1 where winds were predominantly from the north.

Wind direction distributions at the two Vulcan Materials sites were more similar than those at United Rock. Flows were primarily from the west, typical of the well-documented SoCAB transport patterns. As a result, Vulcan Materials Site 2 is not really an “upwind” site, even though its measurements appear to be regionally representative and only occasionally affected by local source contributions. The individual days experienced wind direction distributions similar to those of Figure 5-11, except for 10/3 at Vulcan Materials Site 1 which showed a northerly flow for part of the period. This northerly flow did not register at Vulcan Materials Site 2, however, and may be an anomaly.

Wind speed distributions at the upwind and downwind sites were also more similar at Vulcan Materials than at United Rock. The United Rock upwind site experienced a greater fraction of calm conditions (< 1 m/s), nearly 30% for the sampling period, compared to the downwind site, which had less than 6%. This difference can be attributed to siting of the upwind meteorological tower, which was immediately adjacent to tall conveyors that might have attenuated wind speeds. The downwind monitor was further away from similar obstructions, but as noted earlier, the fetch was insufficient for more than a local-scale characterization. The situation was reversed at the Vulcan Materials upwind site, which was surrounded by few obstructions and offered a good fetch. Calms were recorded <6% of the time during the study period. The Vulcan downwind site, however, which was closer to obstructions and often blocked by large trucks passing through the scales, recorded ~17% calms during the afternoon sampling periods.

The differences between United Rock Sites 1 and 2 show the importance of placing the wind vane in an exposed area above nearby obstructions to obtain an accurate transport direction. The 1 m above ground level (agl) wind directions from the E-BAM wind sensors were not correlated with those on the mast, being mostly affected by very local flows, including passing vehicles and other mechanical processes.

5.5 Downwind/Upwind PM10 Differences

Figure 5-12 summarizes the differences between the downwind and upwind sites at the two facilities. These differences further indicate the uncertainty of the “downwind” and “upwind” site designations. Nearly all of the differences are negative, and by large amounts for the United Rock sites.

a)

5-20

b)

Figure 5-11. Wind direction frequencies from 1100 to 1600 PDT at: a) United Rock Sites 1 and 2 from 9/11 through 9/27 and b) Vulcan Materials Sites 1 and 2 from 10/2 through 10/20. Scale is percent of time from the indicated direction.

0

10

20

30

40

50

60

70

N

NNE

NE

ENE

E

ESE

SE

SSE

S

SSW

SW

WSW

W

WNW

NW

NNW

United Rock Site 1

United Rock Site 2

0

5

10

15

20

25

30

35N

NNE

NE

ENE

E

ESE

SE

SSE

S

SSW

SW

WSW

W

WNW

NW

NNW

Vulcan Site 1

Vulcan Site 2

5-21

a)

b)

Figure 5-12. Downwind/upwind PM10 differences for hivol and OMNI minivol filter samplers at: a) United Rock and b) Vulcan Materials.

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

9/11

9/12

9/13

9/14

9/15

9/16

9/17

9/18

9/19

9/20

9/21

9/22

9/23

9/24

9/25

9/26

9/27

Sampling Date at United Rock

Do

wn

win

d-U

pw

ind

PM

10

(µg

/m3 )

HIVOL Difference

OMNI Difference

-50

0

50

100

150

200

10/1

10/2

10/3

10/4

10/5

10/6

10/7

10/8

10/9

10/1

0

10/1

1

10/1

2

10/1

3

10/1

4

10/1

5

10/1

6

10/1

7

10/1

8

10/1

9

10/2

0

Sampling Date at Vulcan Materials

Do

wn

win

d-U

pw

ind

PM

10

(µg

/m3 )

HIVOL Difference

OMNI Difference

5-22

The hivol and OMNI minivol differences are not the same, with 9/16 and 9/26 differences showing both positive and negative values. It is evident that United Rock Site 2 is affected by more local sources than United Rock Site 1 and does not represent the neighborhood-scale PM10 concentrations that should be subtracted from the levels transported downwind of the United Rock facility. Given the abundance of industrial activities in Irwindale, especially other sand and gravel operations, and the heavy diesel traffic that serves these industries, it unlikely that a suitable neighborhood-scale location would be found in the upwind direction. A more neighborhood representative site might be located in the Santa Fe Dam Recreation Area to the northeast (downwind) of the facility. However, this would not well represent contributions from local sources being transported into the facility, some of which probably deposit within the United Rock facility prior to reaching the United Rock Site downwind Site 1.

Figure 5-12b shows downwind/upwind PM10 at Vulcan that are more realistic than those at United Rock, even though Vulcan Site 2 is not strictly “upwind”. Again, the hivol and OMNI minivol differences do not match, with the hivol usually measuring a higher increment. There are several examples of PM10 difference exceeding 50 µg/m3, but this is expected given the proximity of the sampling site to dust generating activities. Given the particle size distributions’ weighting toward extra-coarse particles, it is unlikely that such high differences would be measured near the property fence lines that are distant from most of the dust generating activities. Only one negative PM10 difference was measured on 10/5, a Sunday, and the difference was <2 µg/m3, within the hivol measurement uncertainty. The remaining Sundays (10/12 and 10/19) also show insignificant differences between Vulcan Materials Site 1 and Vulcan Materials Site 2 hivol PM10, demonstrating that both sites are capable of characterizing neighborhood-scale PM10 levels in the absence of mineral processing activities.

6-1

6. SUMMARY AND CONCLUSIONS Study results are classified by the study objectives of: 1) identifying, describing, and

selecting PM10 and meteorological measurement systems; 2) developing sampling configurations and procedures; 3) completing a field study; and 4) evaluating measurement methodologies for implementing Rule 403. Rule 403 requires upwind and downwind monitoring at near facility fence lines. Downwind PM10 levels are to be less than 50 µg/m3 over upwind intervals.

6.1 PM10 Measurement Systems

Literature and manufacturer descriptions were identified and surveyed for PM10 samplers and meteorological instruments. PM10 can be measured by filter sampling with laboratory weighing, the attenuation of electrons (beta rays) through PM10 deposits on a paper tape, changes in the frequency of a vibrating element as aerosol loadings increase, particle light scattering, and optical counting and sizing of individual particles. Wind monitors consist of mechanical wind vanes and anemometers and sonic anemometers. Published PM10 comparison studies show mixed results, with good comparability for non-volatile particles with a low PM10-2.5 fraction relative to PM2.5. Collocated measurements near fugitive dust sources with high spatial gradients and high PM10-2.5 fractions show moderate to low comparability.

Samplers were selected for inclusion in the study based on their availability within time and budget constraints, but these represented a broad range of manufacturers and measurement principles. Integrated filter samplers included: 1) the Sierra-Andersen high-volume (1130 L/min) sampler (hivol), a PM10 federal reference method (FRM); 2) the BGI low-volume (16.7 L/min) PQ200, a PM10 FRM; and 3) the BGI OMNI mini-volume (5 L/min) sampler, which is not a FRM. Light scattering was measured with the Met-One E-Sampler and TSI Model 8250 DustTrak nephelometers. Particles were counted with Grimm Model 1.108 and TSI Model 8533 DustTrak DRX optical particle counters (OPCs). The Met-One E-BAM used the beta attenuation principle.

Both mechanical and sonic wind measurements were implemented, but the TacMAT sonic anemometer yielded physically unreasonable data soon after developing a dust coating. This type of sonic anemometer may be more suitable for less dusty environments.

6.2 Sampling Configurations and Procedures

Equipment manuals were only marginally useful for setting up, calibrating, and operating instruments. More complete and practical standard operating procedures (SOPs) were developed for each instrument and are included in Appendix A of this report.

Sampling sites were configured within the fence lines at United Rock in Irwindale, CA, and at Vulcan Materials in Upland, CA. This was necessary to provide for security. As a result, the measurements taken do not represent concentrations outside of facility property lines. The instruments were closer to fugitive dust emitters than sites implemented as part of Rule 403 would be.

Inlets were uniformly located at 2 m above ground level. Most of the samplers are adjustable to this height, but the hivol samplers required leg extensions. Samplers were placed at 2 m intervals, but concentration gradients across the array caused differences in the measurements, especially at the United Rock downwind Site 1. Mechanical anemometers were located on 5 m masts, the tallest that is accessible without a guyed tower. The E-BAM has a mechanical wind vane and anemometer integrated with its support stand, but 1 m above ground level measurements did not represent the general transport flows. Good exposure, away from

6-2

obstructions, for determining wind measurements was accentuated by variable winds at the United Rock downwind Site 1. Site 1 wind directions were more variable than those at Site 2 owing to the nearby presence of tall structures (crushers, dryers, conveyors, storage piles) and a tree line on the north side of the property.

6.3 Field Study Completion

Collocated sampling was carried out at the United Rock downwind site from 9/8 through 9/10/08 and at the Vulcan downwind site from 10/23–30/08. Owing to lack of equipment availability, only the hivols, OMNIs, DustTraks, and E-Samplers had sufficient collocated measurement for comparison. Upwind/downwind sampling was conducted at United Rock from 9/11 through 9/27/2008 and at Vulcan Materials from 10/2 through 10/20/2008.

Continuous data were acquired at one-minute intervals for 24 hours each day. Filters were sampled between 1100 and 1600 PDT on each day of the study because consistent winds have previously been observed during this period. Data from each instrument were edited for instrument down time, and compiled into a data base in Microsoft Excel. The one-minute data was averaged for the 1100 to 1600 PDT filter sampling periods for comparison purposes.

6.4 PM10 and Meteorological Monitoring Relevant to Rule 403

The hivol samplers were comparable to each other during collocated sampling, even under high dust loading conditions. The PQ200 lovol FRM measured ~18% lower PM10 levels than the collocated hivols, even though tests in ambient environments show they are equivalent. The particle size measurements showed large concentrations with sizes >10 µm, and previous comparisons have shown large differences among hivol samplers with different inlets under these conditions. Rule 403 requires “…high-volume particulate matter samplers or other U.S. EPA-approved equivalent method for PM10 monitoring…” so as an FRM, the PQ200 could be used with no modification to the rule. The PQ200 is much smaller and lighter, has its own support stand, and operates on a rechargeable 12V battery, so it would be much easier to deploy for temporary monitoring.

None of the other instruments correlated well with the hivol PM10. The OMNI minivols do not acquire sufficient mass over the five hour sampling period to be comparable with each other or with the other samplers. The factory calibrations for the nephelometers do not represent the light scatting to mass relationships for a fugitive dust environment. Light scattering is sensitive to the PM2.5 fraction, but less sensitive to larger particles. While the DRX PM10 concentration did not equal the hivol, it was highly correlated (R = 0.9) with hivol PM10 at United Rock and moderately correlated (R = 0.7) at Vulcan Materials. Multiple size ranges allow different sources to be identified. A 10/9 PM2.5 episode at Vulcan showed consistently high PM2.5 that was not associated with larger particle sizes. This probably represents secondary aerosol formation and/or wildfire contributions. On the other hand, high PM2.5 at United Rock during simulated fugitive dust episodes showed PM2.5 increasing over periods of a few minutes along with the larger particle sizes, indicating a local dust source. This experiment emphasized the constrained nature of nearby plumes, as the Grimm OPC that was within 20 m of the DRX did now register the event.

The concept of upwind and downwind in Rule 403 may be refined. The upwind site at United Rock consistently measured higher PM10 than the downwind site, even though they were along the prevailing wind vector. The Vulcan “upwind” site was isolated from nearby sources and provided a reasonable representation of neighbor-hood-scale concentrations, as indicated by similar PM10 levels at both sites on Sunday when the facility was not in operation.

7-1

7. REFERENCES Adams, B.; Boyum, J.; Griffin, G.; Yoshida, C. (1994). Oakridge, Oregon 1994 PM10 saturation

monitoring study. prepared by Lane Regional Air Pollution Authority, Monitoring & Data Analysis Section, Springfield, OR,

Ahuja, M.S.; Paskind, J.J.; Houck, J.E.; Chow, J.C. (1989). Design of a study for the chemical and size characterization of particulate matter emissions from selected sources in California. In Transactions, Receptor Models in Air Resources Management, Watson, J. G., Ed.; Air & Waste Management Association: Pittsburgh, PA, 145-158.

Airmetrics (2009). MiniVol MRI (Mini reference impactor). prepared by Airmetrics Inc., Eugene, OR, http://www.airmetrics.com/downloads/MRI_Flyer.pdf

Aklilu, Y.; Mozurkewich, M.; Prenni, A.J.; Kreldenweis, S.M.; Alfarra, M.R.; Allan, J.D.; Anlauf, K.; Brook, J.R.; Leaitch, W.R.; Sharma, S.; Boudries, H.; Worsnop, D.R. (2006). Hygroscopicity of particles at two rural, urban influenced sites during Pacific 2001: Comparison with estimates of water uptake from particle composition. Atmos. Environ., 40(15): 2650-2661.

Amato, J.A. (2000). A History of the Small and the Invisible: Dust. University of California Press: Berkeley, CA.

Armstrong, J.A.; Russell, P.A. (1980). Particle production from surface mining Part 1. Vertical measurements. NTIS: Springfield, VA.

Axetell, K.; Cowherd, C. (1984). Improved emission factors for fugitive dust from western surface coal mining sources. prepared by PEDCo Environmental Inc., Kansas City, MO, for U.S. Environmental Protection Agency, Research Triangle Park, NC;

Bachmann, J.D. (2007). 2007 critical review summary - Will the circle be unbroken: A history of the U.S. National Ambient Air Quality Standards. EM, 13(June): 27-34.

Badr, T.; Harion, J.L. (2005). Numerical modelling of flow over stockpiles: Implications on dust emissions. Atmos. Environ., 39(30): 5576-5584.

Badr, T.; Harion, J.L. (2007). Effect of aggregate storage piles configuration on dust emissions. Atmos. Environ., 41(2): 360-368.

Balentine, H.W.; Williamson, H.J.; Steinmetz, J.I. (1985). Thermal influence of a large coal pile on a nearby meteorological tower. American Meteorological Society: Portland, OR.

Barnard, W.R.; Stensland, G.J.; Gatz, D.F. (1986). Alkaline materials flux from unpaved roads: Source strength, chemistry and potential for acid rain neutralization. Water Air and Soil Pollution, 30: 285-293.

Baxter, R.A. (1983). Quantifying fugitive emissions from mining and material handling operations using gas trace techniques. 19 June 83 A.D.; Atlanta, GA.

7-2

Becker, S.; Soukup, J.M.; Sioutas, C.; Cassee, F.R. (2003). Response of human alveolar macrophages to ultrafine, fine, and coarse urban air pollution particles. Experimental Lung Research, 29(1): 29-44.

Belnap, J.; Phillips, S.L.; Herrick, J.E.; Johansen, J.R. (2007). Wind erodibility of soils at Fort Irwin, California (Mojave Desert), USA, before and after trampling disturbance: implications for land management. Earth Surface Processes and Landforms, 32(1): 75-84.

BGI (2009a). PQ100 Portable PM10 / TSP / PM2.5. prepared by BGI Incorporated, Waltham, MA, http://www.bgiusa.com/aam/portable.htm

BGI (2009b). PQ200 Ambient Fine Particulate Sampler. prepared by BGI Incorporated, Waltham, MA, http://www.bgiusa.com/aam/pq200.htm

BGI (2009c). frmOMNITM Ambient Air Sampler (Filter Reference Method). prepared by BGI Incorporated, Waltham, MA, http://www.bgiusa.com/aam/frmomni.htm

Billman, B.J.; Arya, S.P.S. (1985). Windbreak effectiveness for storage-pile fugitive-dust control: A wind tunnel study. Report Number EPA-600/S3-85/059; prepared by U.S. Environmental Protection Agency, Research Triangle Park, NC,

Blanchard, C.L.; Carr, E.L.; Collins, J.F.; Smith, T.B.; Lehrman, D.E.; Michaels, H.M. (1999). Spatial representativeness and scales of transport during the 1995 Integrated Monitoring Study in California's San Joaquin Valley. Atmos. Environ., 33(29): 4775-4786.

Bradford, J.M.; Grosman, R.B. (1982). In-situ measurements of near-surface soil strength by the fall-cone device. Soil Science Society of America Journal, 46: 685-688.

Burnett, R.T.; Goldberg, M.S. (2003). Size-fractionated particulate mass and mortality in eight Canadian cities. In Revised Analysis of Air Pollution and Health. Special Report, Health Effects Institute: Boston, MA, 85-90.

Burton, R.M.; Lundgren, D.A. (1987). Wide-Range Aerosol Classifier - A size selective sampler for large particles. Aerosol Sci. Technol., 6(3): 289-301.

Buser, M.D.; Wanjura, J.D.; Whitelock, D.P.; Capareda, S.C.; Shaw, B.W.; Lacey, R.E. (2008). Estimating FRM PM10 sampler performance characteristics using particle size analysis and collocated TSP and PM10 samplers: Cotton gins. Transactions of the Asabe, 51(2): 695-702.

Cai, S.; Chen, F.F.; Soo, S.L. (1983). Wind penetration into a porous storage pile and use of barriers. Enivron. Sci. Technol., 17: 298-305.

CARB (2009). ARB's emissions inventory. prepared by California Air Resources Board, Sacramento, CA, http://www.arb.ca.gov/ei/ei.htm

7-3

Castillejos, M.; Borja-Aburto, V.H.; Dockery, D.W.; Gold, D.R.; Loomis, D. (2000). Airborne coarse particles and mortality in Mexico City. Inhal. Toxicol., 12(Suppl 1): 61-72.

Chakraborty, M.K.; Ahmad, M.; Singh, R.S.; Pal, D.; Bandopadhyay, C.; Chaulya, S.K. (2002). Determination of the emission rate from various opencast mining operations. Environ. Modelling & Software, 17(5): 467-480.

Chang, C.T. (2006). Characteristics and emission factors of fugitive dust at gravel processing sites. AAQR, 6(1): 15-29. www.aaqr.org.

Chang, Y.M.; Chang, T.C.; Chen, W.K. (1999). An estimation on overall emission rate of fugitive dust emitted from road construction activity. Environ. Eng. Sci., 16(5): 375-388.

Chao, C.Y.; Wong, K.K. (2002). Residential indoor PM10 and PM2.5 in Hong Kong and the elemental composition. Atmos. Environ., 36(2): 265-277.

Chaulya, S.K. (2006). Emission rate formulae for surface iron ore mining activities. Environmental Modeling & Assessment, 11(4): 361-370.

Cheng, Y.; Li, S.M.; Leithead, A.; Brickell, P.C.; Leaitch, W.R. (2004). Characterizations of cis-pinonic acid and n-fatty acids on fine aerosols in the Lower Fraser Valley during Pacific 2001 Air Quality Study. Atmos. Environ., 38(34): 5789-5800.

Cheng, Y.; Li, S.M. (2005). Nonderivatization analytical method of fatty acids and cis-pinonic acid and its application in ambient PM2.5 aerosols in the greater Vancouver area in Canada. Environ. Sci. Technol., 39(7): 2239-2246.

Cheng, Y.H. (2008). Comparison of the TSI model 8520 and Grimm Series 1.108 portable aerosol instruments used to monitor particulate matter in an iron foundry. Journal of Occupational and Environmental Hygiene, 5(3): 157-168.

Chepil, W.S. (1959). Equilibrium of soil grains at the threshold of movement by wind. Proc. Soil Sci. Soc. Am., 23(6): 422-428.

Choudhury, A.H.; Gordian, M.E.; Morris, S.S. (1997). Associations between respiratory illness and PM10 air pollution. Arch. Environ. Health, 52(2): 113-117.

Chow, J.C.; Watson, J.G. (1992). Fugitive emissions add to air pollution. Environ. Protect., 3: 26-31.

Chow, J.C.; Watson, J.G.; Houck, J.E.; Pritchett, L.C.; Rogers, C.F.; Frazier, C.A.; Egami, R.T.; Ball, B.M. (1994). A laboratory resuspension chamber to measure fugitive dust size distributions and chemical compositions. Atmos. Environ., 28(21): 3463-3481.

Chow, J.C. (1995). Critical review: Measurement methods to determine compliance with ambient air quality standards for suspended particles. J. Air Waste Manage. Assoc., 45(5): 320-382.

7-4

Chow, J.C.; Watson, J.G. (1997). Imperial Valley/Mexicali Cross Border PM10 Transport Study. Report Number 4692.1D1; prepared by Desert Research Institute, Reno, NV, for U.S. Environmental Protection Agency, Region IX, San Francisco, CA;

Chow, J.C.; Watson, J.G.; Green, M.C.; Lowenthal, D.H.; DuBois, D.W.; Kohl, S.D.; Egami, R.T.; Gillies, J.A.; Rogers, C.F.; Frazier, C.A.; Cates, W. (1999). Middle- and neighborhood-scale variations of PM10 source contributions in Las Vegas, Nevada. J. Air Waste Manage. Assoc., 49(6): 641-654.

Chow, J.C.; Watson, J.G.; Green, M.C.; Lowenthal, D.H.; Bates, B.A.; Oslund, W.; Torres, G. (2000). Cross-border transport and spatial variability of suspended particles in Mexicali and California's Imperial Valley. Atmos. Environ., 34(11): 1833-1843.

Chow, J.C.; Watson, J.G. (2001). Zones of representation for PM10 measurements along the U.S./Mexico border. Sci. Total Environ., 276(1-3): 49-68.

Chow, J.C.; Watson, J.G.; Edgerton, S.A.; Vega, E.; Ortiz, E. (2002). Spatial differences in outdoor PM10 mass and aerosol composition in Mexico City. J. Air Waste Manage. Assoc., 52(4): 423-434.

Chow, J.C.; Watson, J.G.; Lowenthal, D.H.; Chen, L.-W.A.; Tropp, R.J.; Park, K.; Magliano, K.L. (2006). PM2.5 and PM10 mass measurements in California's San Joaquin Valley. Aerosol Sci. Technol., 40(10): 796-810.

Chow, J.C.; Watson, J.G.; Feldman, H.J.; Nolan, J.; Wallerstein, B.R.; Bachmann, J.D. (2007). 2007 Critical review discussion - Will the circle be unbroken: A history of the U.S. National Ambient Air Quality Standards. J. Air Waste Manage. Assoc., 57(10): 1151-1163.

Chow, J.C.; Watson, J.G. (2008). New directions: Beyond compliance air quality measurements. Atmos. Environ., 42(21): 5166-5168. doi:10.1016/j.atmosenv.2008.05.004.

Chow, J.C.; Doraiswamy, P.; Watson, J.G.; Chen, L.-W.A.; Ho, S.S.H.; Sodeman, D.A. (2008). Advances in integrated and continuous measurements for particle mass and chemical composition. J. Air Waste Manage. Assoc., 58(2): 141-163.

Claiborn, C.S.; Mitra, A.; Adams, G.; Bamesberger, W.L.; Allwine, L.; Kantamaneni, R.; Lamb, B.; Westberg, H.H. (1995). Evaluation of PM10 emission rates from paved and unpaved roads using tracer techniques. Atmos. Environ., 29(10): 1075-1089.

Climatronics Corp. (2009a). Basic weather systems. prepared by Climatronics Corp., Bohemia, NY, http://www.climatronics.com/Products/Weather-Station-Systems/basic_weather_stations_(bws).php

Climatronics Corp. (2009b). TACMET II sonic weather sensor systems. prepared by Climatronics Corp., Bohemia, NY, http://www.climatronics.com/Products/Weather-Station-Systems/tacmet_ll_sonic_weather_station.php

7-5

Climatronics Corp. (2009c). All-In-One (AIO) compact weather station. prepared by Climatronics Corp., Bohemia, NY, http://www.climatronics.com/Products/Weather-Station-Systems/AIO_compact_weather_station.php

Code of Federal Regulations (2007). Appendix G to Part 50-Reference Method of the determination of lead in suspended particulate matter collected from ambient air. CFR, 40(50): 57-63. http://frwebgate1.access.gpo.gov/cgi-bin/PDFgate.cgi?WAISdocID=05434191166+1+1+0&WAISaction=retrieve.

Cowherd, C.; Cuscino, T.A.; Gillette, D.A. (1979). Development of emission factors for wind erosion of aggregate storage piles. Cincinnati, OH.

Cowherd, C. (1981). Control of windblown dust from storage piles. Environ. Int., 6: 307-311.

Cowherd, C. (1982). Emission factors for wind erosion of exposed aggregates at surface coal mines. 20 June 82 A.D.; New Orleans, LA.

Cowherd, C. (1983). New approach to estimating wind-generated emissions from coal storage piles. In Fugitve Dust Issues in the Coal Use Cycle, Air Pollution Control Association: Pittsburgh, PA.

Cowherd, C. (1988). A refined scheme for calculation of wind generated PM10 emissions from storage piles. In Transactions, PM10: Implementation of Standards, Mathai, C. V., Stonefield, D. H., Eds.; Air Pollution Control Association: Pittsburgh, PA, 314-325.

Cowherd, C.; Englehart, P.J. (1988). Modeling of wind erosion of asbestos tailing piles in Arizona. In Proceedings, Particulate Matter, Fugitive Dusts, Measurement and Control in Western Arid Regions, Air Pollution Control Association: Pittsburgh, PA, 164-175.

Cowherd, C. (2001). Fugitive dust emissions. In Aerosol Measurement: Principles, Techniques, and Applications, Second Edition, 2nd; Baron, P., Willeke, K., Eds.; John Wiley & Sons: New York, NY, 845-857.

Davis Instruments (2009). Vantage Pro2 weather stations. prepared by Davis Instruments, Vernon Hills, IL, www.davis.com/catalog/product_view.asp?sku=8640303

de Faveri, D.M.; Converti, A.; Vidili, A.; Campidonico, A.; Ferraiolo, G. (1990). Reduction of the environmental impact of coal storage piles: A wind tunnel study. Atmos. Environ., 24A(11): 2787-2793.

Delfino, R.J.; Sioutas, C.; Malik, S. (2005). Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health. Environ. Health Perspect., 113(8): 934-946.

Delfino, R.J.; Staimer, N.; Tjoa, T.; Polidori, A.; Arhami, M.; Gillen, D.L.; Kleinman, M.T.; Vaziri, N.D.; Longhurst, J.; Zaldivar, F.; Sioutas, C. (2008). Circulating biomarkers of inflammation, antioxidant activity, and platelet activation are associated with primary

7-6

combustion aerosols in subjects with coronary artery disease. Environ. Health Perspect., 116(7): 898-906.

Dietrich, D.L.; Marlatt, W.E.; Fox, D.G. (1980). Particle production from surface mining: Part 2 - Surface particulate and meteorological measurements. In Proceedings: Fourth Symposium on Fugitive Emissions: Management and Control, New Orleans, LA.

Dyck, R.I.; Stukel, J.J. (1976). Fugitive dust emissions from trucks on unpaved roads. Enivron. Sci. Technol., 10(10): 1046-1048.

Eatough, D.J.; Long, R.W.; Modey, W.K.; Eatough, N.L. (2003). Semi-volatile secondary organic aerosol in urban atmospheres: Meeting a measurement challenge. Atmos. Environ., 37(9-10): 1277-1292.

Ecotech (2009a). Hivol 3000 particulate sampler. prepared by Ecotech, Knoxfield, VIC, Australia, http://www.americanecotech.com/Libraries/Particulate_Brochure_Library/HiVol_3000_High_Volume_Air_Sampler.sflb.ashx

Ecotech (2009b). Microvol 1100 particulate sampler. prepared by Ecotech, Knoxfield, VIC, Australia, http://www.americanecotech.com/Libraries/Particulate_Brochure_Library/MicroVol_1100_Air_Sampler.sflb.ashx

Englehart, P.J.; Kinsey, J.S. (1983). Study of construction related mud/dirt carryout. prepared by Midwest Research Institute, Kansas City, MO, for U.S. Environmental Protection Agency, Region V, Chicago, IL;

Etyemezian, V.; Kuhns, H.D.; Gillies, J.A.; Green, M.C.; Pitchford, M.L.; Watson, J.G. (2003a). Vehicle-based road dust emission measurement I. Methods and calibration. Atmos. Environ., 37(32): 4559-4571.

Etyemezian, V.; Kuhns, H.D.; Gillies, J.A.; Chow, J.C.; Hendrickson, K.; McGown, M.; Pitchford, M.L. (2003b). Vehicle-based road dust emissions measurement (III): Effect of speed, traffic volume, location, and season on PM10 road dust emissions in the Treasure Valley, ID. Atmos. Environ., 37(32): 4583-4593.

Etyemezian, V.; Kuhns, H.D.; Nikolich, G. (2006). Precision and repeatability of the TRAKER vehicle-based paved road dust emission measurement. Atmos. Environ., 40(16): 2953-2958.

Flocchini, R.G.; Cahill, T.A.; Matsumura, R.T.; Carvacho, O.F.; Lu, Z. (1994a). Study of fugitive PM10 emissions from selected agricultural practices on selected agricultural soils. prepared by University of California, Davis, CA, for California Air Resources Board, Sacramento, CA;

Flocchini, R.G.; Cahill, T.A.; Matsumura, R.T.; Carvacho, O.F.; Lu, Z. (1994b). Evaluation of the emission of PM10 particulates from unpaved roads in the San Joaquin Valley. Report

7-7

Number San Joaquin Valley Grant File #20960; prepared by University of California, Davis, CA, for California Air Resources Board, Sacramento, CA;

Freeman, D.L.; Watson, J.G.; Chow, J.C.; Pritchett, L.C.; Lu, Z. (1990). PM10 source apportionment study for Gold Quarry Mine. Report Number DRI 8682.1F1; prepared by Desert Research Institute, Reno, NV, for Newmont Gold Co., Carlin, NV;

Gehrig, R.; Hueglin, C.; Schwarzenbach, B.; Seitz, T.; Buchmann, B. (2005). A new method to link PM10 concentrations from automatic monitors to the manual gravimetric reference method according to EN12341. Atmos. Environ., 39(12): 2213-2223.

Gertler, A.W.; Coulombe, W.G.; Watson, J.G.; Bowen, J.L.; Egami, R.T.; Marsh, S. (1993). Comparison of PM10 concentrations in high- and medium-volume samplers in a desert environment. Environ. Mon. Assess., 24(1): 13-25.

Gillette, D.A. (1978). Tests with a portable wind tunnel for determining wind erosion threshold velocities. Atmos. Environ., 12: 2309-2313.

Gillette, D.A.; Adams, J.B.; Endo, A.; Smith, D. (1979). Threshold friction velocities on typical Mojave Desert soils, undisturbed and disturbed by off-road vehicles. In Proceedings, Int. Powder and Bulk Solids Handling and Processing, Industrial and Scientific Conference Management: Chicago, IL.

Gillette, D.A.; Adams, J.B.; Endo, A.; Smith, D.; Kihl, R. (1980). Threshold velocities for input of soil particles into the air by desert soils. J. Geophys. Res., 85(C10): 5621-5630.

Gillette, D.A.; Adams, J.B.; Muhs, D.R.; Kihl, R. (1982). Threshold friction velocities and rupture moduli for crusted desert soils for the input of soil particles into the air. J. Geophys. Res., 87(C11): 9003-9015.

Gillette, D.A. (1983). Threshold velocities for wind erosion on natural terrestrial arid surfaces. In Precipitation Scavenging, Dry Deposition, and Resuspension. Vol. 2 - Dry Deposition and Resuspension, Pruppacher, H. R., Semonin, R. G., Slinn, W. G. N., Eds.; Elsevier: New York, 1047-1058.

Gillette, D.A. (1988). Threshold friction velocities for dust production for agricultural soils. J. Geophys. Res., 93(D10): 14,645-14,662.

Gillette, D.A.; Adams, J.B.; Endo, A.; Smith, D.; Kihl, R. (1990). Threshold velocities for input of soil particles into the air by desert soils. J. Geophys. Res., 85: 5621-5630.

Gillette, D.A.; Hardebeck, E.; Parker, J. (1997). Large-scale variability of wind erosion mass flux rates at Owens Lake 2. Role of roughness change, particle limitation, change of threshold friction velocity, and the Owen effect. J. Geophys. Res., 102(D22): 25989-25998.

7-8

Gillette, D.A.; Chen, W.A. (2001). Particle production and aeolian transport from a "supply-limited" source area in the Chihuahuan desert, New Mexico, United States. J. Geophys. Res., 106(D6): 5267-5278.

Gillette, D.A.; Niemeyer, T.C.; Helm, P.J. (2001). Supply-limited horizontal sand drift at an ephemerally crusted, unvegetated saline playa. J. Geophys. Res., 106(D16): 18085-18098.

Gillette, D.A.; Lawson, R.E.; Thompson, R.S. (2004). A "test of concept" comparison of aerodynamic and mechanical resuspension mechanisms for particles deposited on field rye grass (Secale cercele). Part 2. Threshold mechanical energies for resuspension particle fluxes. Atmos. Environ., 38(28): 4799-4803.

Gillies, J.A.; Watson, J.G.; Rogers, C.F.; DuBois, D.W.; Chow, J.C.; Langston, R.; Sweet, J. (1999). Long term efficiencies of dust suppressants to reduce PM10 emissions from unpaved roads. J. Air Waste Manage. Assoc., 49(1): 3-16.

Gillies, J.A.; Kuhns, H.D.; Engelbrecht, J.P.; Uppapalli, S.; Etyemezian, V.; Nikolich, G. (2007). Particulate emissions from US Department of Defense artillery backblast testing. J. Air Waste Manage. Assoc., 57(5): 551-560.

Greeley, R.; Iversen, J.D. (1985). Wind as a Geological Process. Cambridge University Press: Cambridge, 1-333.

Green, D.C.; Fuller, G.W.; Baker, T. (2009). Development and validation of the volatile correction model for PM10 - An empirical method for adjusting TEOM measurements for their loss of volatile particulate matter. Atmos. Environ., 43(13): 2132-2141.

Grimm (2009). Aerosol spectrometer and dust monitor Model 1.108. prepared by Grimm Labortechnik GmbH & Co, Ainring, Germany, http://www.grimm-aerosol.com/Indoor-Air-Quality/1108-standard.html

Grimm, H.; Eatough, D.J. (2009). Aerosol measurement: The use of optical light scattering for the determination of particulate size distribution, and particulate mass, including the semi-volatile fraction. J. Air Waste Manage. Assoc., 59(1): 101-107.

Grover, B.D.; Eatough, N.L.; Woolwine, W.R.; Cannon, J.P.; Eatough, D.J.; Long, R.W. (2008). Semi-continuous mass closure of the major components of fine particulate matter in Riverside, CA. Atmos. Environ., 42(2): 250-260.

Hall, D.J.; Upton, S.L.; Marsland, G.W.; Waters, R.A. (1988). Wind tunnel measurements of the collection efficiency of two PM10 samplers: The Sierra-Andersen Model 321A hi-volume sampler and the EPA prototype dichotomous sampler. prepared by Warren Spring Laboratory, Hertfordshire, England, http://www.bgiusa.com/cau/PM10_sampler_testing_for_EPA.pdf

7-9

Heal, M.R.; Beverland, I.J.; Mccabe, M.; Hepburn, W.; Agius, R.M. (2000). Intercomparison of five PM10 monitoring devices and the implications for exposure measurement in epidemiological research. J. Environ. Monit., 2: 455-461.

Heim, M.; Mullins, B.J.; Umhauer, H.; Kasper, G. (2008). Performance evaluation of three optical particle counters with an efficient "multimodal" calibration method. J. Aerosol Sci., 39(12): 1019-1031.

Hering, S.V.; Friedlander, S.K. (1982). Origins of aerosol sulfur size distributions in the Los Angeles basin. Atmos. Environ., 16(11): 2647-2656.

Hinds, W.C. (1999). Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles, 2nd Ed. John Wiley and Sons, Inc.: New York, NY.

Hodkinson, J.R.; Greenfield, J.R. (1965). Response calculations for light-scattering aerosol counters and photometers. Appl. Opt., 4(11): 1463-1474.

Hoffmann, C.; Funk, R.; Sommer, M.; Li, Y. (2008). Temporal variations in PM10 and particle size distribution during Asian dust storms in Inner Mongolia. Atmos. Environ., 42(36): 8422-8431.

Houck, J.E.; Chow, J.C.; Ahuja, M.S. (1989). The chemical and size characterization of particulate material originating from geological sources in California. In Transactions, Receptor Models in Air Resources Management, Watson, J. G., Ed.; Air & Waste Management Association: Pittsburgh, PA, 322-333.

Houck, J.E.; Goulet, J.M.; Chow, J.C.; Watson, J.G.; Pritchett, L.C. (1990). Chemical characterization of emission sources contributing to light extinction. In Transactions, Visibility and Fine Particles, Mathai, C. V., Ed.; Air and Waste Management Association: Pittsburgh, PA, 437-446.

Hu, S.; Polidori, A.; Arhami, M.; Shafer, M.M.; Schauer, J.J.; Cho, A.; Sioutas, C. (2008). Redox activity and chemical speciation of size fractioned PM in the communities of the Los Angeles-Long Beach harbor. Atmos. Chem. Phys., 8(21): 6439-6451.

Huang, C.H.; Tai, C.Y. (2008). Relative humidity effect on PM2.5 readings recorded by collocated beta attenuation monitors. Environmental Engineering Science, 25(7): 1079-1089. ISI:000259125600013.

Hubbard, S.J. (1976a). Evaluation of fugitive dust emissions from mining, Task 1 Report. prepared by PEDCo- Environmental Specialists, Inc, Cincinnati, OH, Industrial Environmental Research Laboratory, Resource Extraction and Handling Division, U S Environmental Protection Agency;

Hubbard, S.J. (1976b). Evaluation of fugitive dust emissions from mining, Task 2 Report - Assessment of the current status of the environmental aspects of fugitive dust source associated with mining. prepared by PEDCo-Environmental Specialists, Inc, Cincinnati,

7-10

OH, Industrial Environmental Research laboratory, Resource Extraction and Handling Division, U S Environmental Protection Agency;

Hubbard, S.J. (1976c). Identification of fugitive dust sources associated with mining. prepared by PEDCo-Environmental Specialists, Inc., Cincinnati, OH, for Industrial Environmental Research Laboratory, Resource Extraction and Handling Division, U.S. Environmental Protection Agency, Cincinnati, OH;

Ibrahim, A.H.; Dunn, P.F.; Brach, R.M. (2004). Microparticle detachment from surfaces exposed to turbulent air flow: Effects of flow and particle deposition characteristics. J. Aerosol Sci., 35(7): 805-821.

Ishizuka, M.; Mikami, M.; Yamada, Y.; Zeng, F.J.; Gao, W.D. (2005). An observational study of soil moisture effects on wind erosion at a gobi site in the Taklimakan Desert. J. Geophys. Res. -Atmospheres, 110(D18)

Ito, K. (2003). Associations of particulate matter components with daily mortality and morbidity in Detroit, Michigan. Special report. In Revised Analyses of Time-Series Studies of Air Pollution and Health, Health Effects Institute: Boston, MA, 143-156.

Jie, X. (2004). Turbulence factors for threshold velocity and emission rate of atmospheric mineral dust. Atmos. Environ., 38(12): 1777-1783.

John, W.; Wall, S.M.; Wesolowski, J.J. (1983). Validation of samplers for inhaled particulate matter. Report Number EPA-600/S4-83-010; prepared by U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Research Triangle Park, NC, for Grant Number R806414-02,

John, W.; Wall, S.M.; Ondo, J.L.; Winklmayr, W. (1990). Modes in the size distributions of atmospheric inorganic aerosol. Atmos. Environ., 24A(9): 2349-2359.

John, W.; Wang, H.C. (1991). Laboratory testing method for PM10 samplers: Lowered effectiveness from particle loading. Aerosol Sci. Technol., 14: 93-101.

John, W.; Winklmayr, W.; Wang, H.C. (1991). Particle deagglomeration and reentrainment in a PM10 sampler. Aerosol Sci. Technol., 14: 165-176.

Kasahara, M. (1999). Characterization of atmospheric aerosols and aerosol studies applying PIXE analysis. In Analytical Chemistry of Aerosols, Spurny, K. R., Ed.; CRC Press LLC: Boca Raton, FL, 145-171.

Kemp, M. (1990). El Paso/Juarez Saturation PM10 Study. Report Number EPA 906-R-92-001; prepared by EPA, U.S., Triangle Research Park, NC,

Kenny, L.C.; Beaumont, G.; Gudmundsson, A.; Thorpea, A.; Ko.W. (2005). Aspiration and sampling efficiencies of the TSP and louvered particulate matter inlets. J. Environ. Mon., 7: 481-487.

7-11

http://www.rsc.org/delivery/_ArticleLinking/DisplayArticleForFree.cfm?doi=b419001g&JournalCode=EM.

Keywood, M.D.; Ayers, G.P.; Gras, J.L.; Gillett, R.W.; Cohen, D. (1999). An evaluation of PM10 and PM2.5 size selective inlet performance using ambient aerosol. Aerosol Sci. Technol., 30(4): 401-407.

King, J.; Nickling, W.G.; Gillies, J.A. (2005). Representation of vegetation and other nonerodible elements in aeolian shear stress partitioning models for predicting transport threshold. J. Geophys. Res. -Atmospheres, 110(F4)

Kingham, S.; Durand, M.; Aberkane, T.; Harrison, J.; Wilson, J.G.; Epton, M. (2006). Winter comparison of TEOM, MiniVol and DustTrak PM10 monitors in a woodsmoke environment. Atmos. Environ., 40(2): 338-347.

Kinsey, J.S.; Cowherd, C. (1992). Fugitive emissions. In Air Pollution Engineering Manual, Buonicore, A. J., Davis, W. T., Eds.; Van Nostrand Reinhold: New York, 133-146.

Kjelgaard, J.; Sharratt, B.; Sundram, I.; Lamb, B.; Claiborn, C.; Saxton, K.; Chandler, D. (2004). PM10 emission from agricultural soils on the Columbia Plateau: comparison of dynamic and time-integrated field-scale measurements and entrainment mechanisms. Agricultural and Forest Meteorology, 125(3-4): 259-277.

Kolak, N.P.; Visalli, J.P. (1981). Comparison of three methods for measuring suspended particulate concentrations. Enivron. Sci. Technol., 15(2): 219-224.

Kuhns, H.D.; Etyemezian, V.; Landwehr, D.; Macdougall, C.S.; Pitchford, M.L.; Green, M.C. (2001). Testing Re-entrained Aerosol Kinetic Emissions from Roads (TRAKER): A new approach to infer silt loading on roadways. Atmos. Environ., 35(16): 2815-2825.

Kuhns, H.D.; Gillies, J.A.; Etyemezian, V.; DuBois, D.W.; Ahonen, S.; Nikolic, D.; Durham, C. (2005). Spatial variability of unpaved road dust PM10 emission factors near El Paso, Texas. J. Air Waste Manage. Assoc., 55(1): 3-12.

Labban, R.; Veranth, J.M.; Watson, J.G.; Chow, J.C. (2006). Feasibility of soil dust source apportionment by the pyrolysis-gas chromatography/mass spectrometry method. J. Air Waste Manage. Assoc., 56(9): 1230-1242.

Lai, C.Y.; Chen, C.C. (2000). Performance characteristics of PM10 samplers under calm air conditions. J. Air Waste Manage. Assoc., 50(4): 578-587.

Lee, C.H.; Tang, L.W.; Chang, C.T. (2001). Modeling of fugitive dust emission for construction sand and gravel processing plant. Environ. Sci. Technol., 35(10): 2073-2077.

Lee, J.H.; Hopke, P.K.; Holsen, T.M.; Lee, D.W.; Jaques, P.A.; Sioutas, C.; Ambs, J.R.L. (2005). Performance evaluation of continuous PM2.5 mass concentration monitors. J. Aerosol Sci., 36(1): 95-109.

7-12

Lehrsch, G.A.; Jolley, P.M. (1992). Temporal changes in wet aggregate stability. p. 493.

Li, L.; Pomeroy, J.W. (1997). Estimates of threshold wind speeds for snow transport using meteorological data. J. Appl. Meteorol., 36(3): 205-213.

Li, N.; Sioutas, C.; Cho, A.; Schmitz, D.; Misra, C.; Sempf, J.; Wang, M.Y.; Oberley, T.; Froines, J.; Nel, A. (2003). Ultrafine particulate pollutants induce oxidative stress and mitochondrial damage. Environ. Health Perspect., 111(4): 455-460.

Lillienfeld, P. (1970). Beta-absorption-impactor aerosol mass monitor. J. Am. Ind. Hyg. Assoc., 31: 722-729.

Lowenthal, D.H.; Kumar, N.; Hand, J.L.; Kreidenweis, S.; Collett, J.L., Jr.; Lee, T.; Day, D. (2003). Hygroscopic organic aerosols during BRAVO? J. Air Waste Manage. Assoc., 53(10): 1273-1279.

Lundgren, D.A.; Hausknecht, B.J.; Burton, R.M. (1984). Large particle size distribution in 5 United States cities and the effect on a new ambient particulate matter standard (PM10). Aerosol Sci. Technol., 3(4): 467-473.

Lyles, L.; Krauss, R.K. (1971). Threshold velocities and initial particle motion as influenced by air turbulence. Trans. Am. Soc. Agric. Eng., 17: 134-139.

Magliano, K.L.; Hughes, V.M.; Chinkin, L.R.; Coe, D.L.; Haste, T.L.; Kumar, N.K.; Lurmann, F.W. (1999). Spatial and temporal variations in PM10 and PM2.5 source contributions and comparison to emissions during the 1995 Integrated Monitoring Study. Atmos. Environ., 33(29): 4757-4773.

Marple, V.A.; Willeke, K. (1976). Impactor design. Atmos. Environ., 10: 891-896.

Marticorena, B.; Bergametti, G.; Gillette, D.A.; Belnap, J. (1997). Factors controlling threshold friction velocity in semiarid and arid areas of the United States. J. Geophys. Res., 102(D19): 23277-23288.

Mathai, C.V.; Estes, R.T.; Belknap, K.L.; Hicks, D.B. (1988). Comparison of PM10 data collected using Wedding and Sierra-Anderson high volume samplers from an area with high soil dust contribution. In Transactions, PM10: Implementation of Standards, Mathai, C. V., Stonefield, D. H., Eds.; Air Pollution Control Association: Pittsburgh, PA, 93-103.

Mathai, C.V.; Watson, J.G.; Rogers, C.F.; Chow, J.C.; Tombach, I.H.; Zwicker, J.O.; Cahill, T.A.; Feeney, P.J.; Eldred, R.A.; Pitchford, M.L.; Mueller, P.K. (1990). Intercomparison of ambient aerosol samplers used in western visibility and air quality studies. Enivron. Sci. Technol., 24(7): 1090-1099.

Mauderly, J.L.; Chow, J.C. (2008). Health effects of organic aerosols. Inhal. Toxicol., 20(3): 257-288. DOI: 10.1080/08958370701866008.

7-13

McFarland, A.R.; Ortiz, C.A.; Bertch, R.W. (1978). Particle collection characteristics of a single-stage dichotomous sampler. Enivron. Sci. Technol., 12: 679-682.

McFarland, A.R.; Ortiz, C.A.; Rodes, C.E. (1980). Characterization of sampling systems. In Proceedings, The Technical Basis for A Size Specific Particulate Standard, Part I & II, Cowherd, C., Ed.; Air Pollution Control Association: Pittsburgh, PA, 59-76.

McFarland, A.R.; Ortiz, C.A. (1984a). Wind tunnel characterization of Wedding IP10 and 10 m inlet for hiVol samplers. Report Number 4716/01/06; prepared by Texas A&M Air Quality Laboratory, College Station, TX,

McFarland, A.R.; Ortiz, C.A. (1984b). Characterization of Sierra-Andersen Model 321A 10 m size selective inlet for Hi-Vol samplers. Report Number 4716/01/02/84/ARM; prepared by Texas A&M University, College Station, TX, for Sierra-Andersen, Inc., Atlanta, GA;

McFarland, A.R.; Ortiz, C.A.; Bertch, R.W., Jr. (1984). A 10 µm cutpoint size selective inlet for Hi-Vol samplers. J. Air Poll. Control Assoc., 34(5): 544-547.

McFarland, A.R.; Ortiz, C.A. (1985). Transmission of large solid particles through PM10 inlets for the Hi-Vol sampler. Report Number 4716/02/07; prepared by Texas A&M University, College Station, TX, for Andersen Samplers, Inc., Atlanta, GA;

Motallebi, N.; Taylor, C.A.; Turkiewicz, K.; Croes, B.E. (2003). Particulate matter in California: Part 1 - Intercomparison of several PM2.5, PM10-2.5, and PM10 monitoring networks. J. Air Waste Manage. Assoc., 53(12): 1509-1516.

McLaughlin, S.B. (1985). Critical review: Effects of air pollution on forests. J. Air Poll. Control Assoc., 35(5): 512-534.

McMurry, P.H. (2000). A review of atmospheric aerosol measurements. Atmos. Environ., 34(12-14): 1959-1999.

Meng, Z.; Seinfeld, J.H. (1994). On the source of the submicrometer droplet mode of urban and regional aerosols. Aerosol Sci. Technol., 120: 253-265.

Met One Instruments (2009a). BAM-1020 continuous particulate monitor. prepared by Met One Instruments, Grants Pass, OR, http://metone.com/documents/BAM-1020_6-08.pdf

Met One Instruments (2009b). E-BAM continuous particulate monitor. prepared by Met One Instruments, Grants Pass, OR, http://metone.com/documents/E-BAM_Brochure.pdf

Met One Instruments (2009c). E-Sampler. prepared by Met One Instruments, Grants Pass, OR, http://metone.com/documents/E-SAMPLER_Brochure.pdf

Met One Instruments (2009d). AWS Automatic Weather Monitoring System. prepared by Met One Instruments, Grants Pass, OR, http://www.metone.com/meteorology.php

7-14

Midwest Research Institute (1991). Unpaved road emission impact. Report Number 9525-L; prepared by Midwest Reseach Institute, Kansas City, MO, for Arizona Dept. of Environmental Quality, Phoenix, AZ;

Moosmüller, H.; Gillies, J.A.; Rogers, C.F.; DuBois, D.W.; Chow, J.C.; Watson, J.G.; Langston, R. (1998). Particulate emission rates for unpaved shoulders along a paved road. J. Air Waste Manage. Assoc., 48(5): 398-407.

Mueller-Anneling, L.; Avol, E.; Peters, J.M.; Thorne, P.S. (2004). Ambient endotoxin concentrations in PM10 from Southern California. Environ. Health Perspect., 112(5): 583-588.

Muleski, G.E.; Stevens, K.M. (1992). PM10 emissions from public unpaved roads in rural Arizona. In Transactions, PM10 Standards and Nontraditional Particulate Source Controls, Chow, J. C., Ono, D. M., Eds.; Air & Waste Management Association: Pittsburgh, PA.

Nalpanis, P.; Hunt, J.C.R. (1986). Suspension, transport and deposition of dust from stockpiles. prepared by Warren Spring Laboratory, United Kingdom,

Nicholson, K.W.; Branson, J.R.; Giess, P.; Cannell, R.J. (1989). The effects of vehicle activity on particle resuspension. J. Aerosol Sci., 20(8): 1425-1428.

Nicholson, K.W.; Branson, J.R. (1990). Factors affecting resuspension by road traffic. Sci. Total Environ., 93: 349-358.

Nickling, W.G.; Ecclestone, M. (1981). The effect of soluble salts on the threshold shear velocity of fine sand. Sedimentology, 28: 505-510.

Okin, G.S. (2005). Dependence of wind erosion and dust emission on surface heterogeneity: Stochastic modeling. J. Geophys. Res. -Atmospheres, 110(D11): D11208. doi:10.1029/2004JD005288.

Ono, D.M.; Hardebeck, E.; Parker, J.; Cox, B.G. (2000). Systematic biases in measured PM10 values with U.S. Environmental Protection Agency-approved samplers at Owens Lake, California. J. Air Waste Manage. Assoc., 50(7): 1144-1156.

Ono, D. (2006). Application of the Gillette model for windblown dust at Owens Lake, CA. Atmos. Environ., 40(17): 3011-3021.

Ostro, B.D.; Broadwin, R.; Lipsett, M.J. (2000). Coarse and fine particles and daily mortality in the Coachella Valley, California: a follow-up study. J. Expo. Anal. Environ. Epidemiol., 10(5): 412-419.

Pall Life Sciences (2009a). PTFE membrane disc filters. prepared by Pall Corporation, East Hills, NY, http://labfilters.pall.com/catalog/laboratory_20061.asp

7-15

Pall Life Sciences (2009b). Pallflex Filters: Emfab™, Fiberfilm™, and Tissuquartz™ filters. prepared by Pall Corporation, East Hills, NY, http://www.pall.com/pdf/02.0601_Pallflex_LR.pdf

Pang, Y.; Eatough, N.L.; Eatough, D.J. (2002). PM2.5 semivolatile organic material at Riverside, California: Implications for the PM2.5 Federal Reference Method sampler. Aerosol Sci. Technol., 36(3): 277-288.

Park, C.W.; Lee, S.J. (2002). Verification of the shelter effect of a windbreak on coal piles in the POSCO open storage yards at the Kwang-Yang works. Atmos. Environ., 36(13): 2171-2185.

Park, J.M.; Rock, J.C.; Wang, L.J.; Seo, Y.C.; Bhatnagar, A.; Kim, S. (2009). Performance evaluation of six different aerosol samplers in a particulate matter generation chamber. Atmos. Environ., 43(2): 280-289.

Pasquill, F. (1961). The estimation of the dispersion of windborne material. Met. Mag., 90(1063): 33-49.

Patashnick, H.; Rupprecht, E.G. (1991). Continuous PM10 measurements using the tapered element oscillating microbalance. J. Air Waste Manage. Assoc., 41(8): 1079-1083.

Patra, A.; Colvile, R.; Arnold, S.; Bowen, E.; Shallcross, D.; Martin, D.; Price, C.; Tate, J.; ApSimon, H.; Robins, A. (2008). On street observations of particulate matter movement and dispersion due to traffic on an urban road. Atmos. Environ., 42(17): 3911-3926.

Peters, T.M.; Ott, D.; O'Shaughnessy, P.T. (2006). Comparison of the Grimm 1.108 and 1.109 portable aerosol spectrometer to the TSI 3321 aerodynamic particle sizer for dry particles. Ann. Occup. Hyg., 50(8): 843-850.

Pope, C.A., III; Dockery, D.W. (2006). Critical Review: Health effects of fine particulate air pollution: Lines that connect. J. Air Waste Manage. Assoc., 56(6): 709-742.

Purdue, L.J.; Rodes, C.E.; Rehme, K.A.; Holland, D.M.; Bond, A.E. (1986). Intercomparison of high-volume PM10 samplers at a site with high particulate concentrations. J. Air Poll. Control Assoc., 36: 917-920.

Rabinowitz, M.B. (2005). Lead isotopes in soils near five historic American lead smelters and refineries. Sci. Total Environ., 346(1-3): 138-148.

Rappen, A. (1972). Dust suppression: How to control stockpile & materials handling dust emission. Rock Products, 137-156.

Raupach, M.R.; Gillette, D.A.; Leys, J.F. (1993). The effect of roughness elements on wind erosion thresholds. J. Geophys. Res., 98(D2): 3023-3029.

Ringler, E.S.; Shrieves, V.X.; Berg, N.J. (1993). The Summer 1992 PM10 Saturation Monitoring Study in the Ashland, Kentucky area. 13 June 93 A.D.; Denver, CO.

7-16

Rodes, C.E.; Evans, E.G. (1985). Preliminary assessment of 10 µm particulate samples at right locations in the United States. Atmos. Environ., 19: 293.

Rodes, C.E.; Holland, D.M.; Purdue, L.J.; Rehme, K.A. (1985). A field comparison of PM10 inlets at four locations. J. Air Poll. Control Assoc., 35(4): 345-354.

Rogge, W.F.; Medeiros, P.M.; Simoneit, B.R.T. (2006). Organic marker compounds for surface soil and fugitive dust from open lot dairies and cattle feedlots. Atmos. Environ., 40(1): 27-49.

Rogge, W.F.; Medeiros, P.M.; Simoneit, B.R.T. (2007). Organic marker compounds in surface soils of crop fields from the San Joaquin Valley fugitive dust characterization study. Atmos. Environ., 41: 8183-8204.

Root, R.A. (2000). Lead loading of urban streets by motor vehicle wheel weights. Environ. Health Perspect., 108(10): 937-940.

Rosbury, K.D.; Zimmer, R.A. (1983). Cost-effectiveness of dust controls used on unpaved haul roads. Volume I - Results, analysis, and conclusions. prepared by PEDCo Environmental, Inc., Golden, CO, for Bureau of Mines, U.S. Dept. of the Interior, Washington, DC;

Salminen, K.; Karlsson, V. (2003). Comparability of low-volume PM10 sampler with beta-attenuation monitor in background air. Atmos. Environ., 37(26): 3707-3712. ISI:000184353300010.

SCAQMD (2000). Multiple air toxics exposure study in the South Coast Air Basin MATES-II - final report. prepared by South Coast Air Quality Management District, Diamond Bar, CA,

SCAQMD (2005). Rule 403, Fugitive dust. prepared by South Coast Air Quality Management District, Diamond Bar, CA, http://www.aqmd.gov/rules/reg/reg04/r403.pdf

SCAQMD (2007). Final 2007 AQMP Appendix V: Modeling and attainment demonstrations. prepared by South Coast Air Quality Management District, Diamond Bar, CA, http://www.aqmd.gov/aqmp/07aqmp/aqmp/Appendix_V.pdf

Schwab, J.J.; Felton, H.D.; Rattigan, O.V.; Demerjian, K.L. (2006). New York state urban and rural measurements of continuous PM2.5 mass by FDMS, TEOM, and BAM. J. Air Waste Manage. Assoc., 56(4): 372-383.

Schwartz, J. (1996). Air pollution and hospital admissions for respiratory disease. Epidemiology, 7(1): 20-28.

Schwartz, J.; Neas, L.M. (2000). Fine particles are more strongly associated than coarse particles with acute respiratory health effects in schoolchildren. Epidemiology, 11(1): 6-10.

7-17

Shimp, D.R. (1988). Field comparison of beta attenuation PM10 sampler and high-volume PM10 sampler. In Transactions, PM10: Implementation of Standards, Mathai, C. V., Stonefield, D. H., Eds.; Air Pollution Control Association: Pittsburgh, PA, 171-178.

Slinn, W.G.N. (1982). Predictions for particle deposition to vegetative canopies. Atmos. Environ., 16(7): 1785-1794.

Smith, R.L.; Spitzner, D.; Kim, Y.; Fuentes, M. (2000). Threshold dependence of mortality effects for fine and coarse particles in Phoenix, Arizona. J. Air Waste Manage. Assoc., 50(8): 1367-1379.

Solomon, P.A.; Sioutas, C. (2008). Continuous and semicontinuous monitoring techniques for particulate matter mass and chemical components: A synthesis of findings from EPA's particulate matter supersites program and related studies. J. Air Waste Manage. Assoc., 58(2): 164-195.

Song, X.H.; Hopke, P.K.; Bruns, M.A.; Graham, K.; Scow, K. (1999). Pattern recognition of soil samples based on the microbial fatty acid contents. Environ. Sci. Technol., 33(20): 3524-3530.

Stevens, K. (1991). Unpaved road emission impact. prepared by Midwest Research Institute, Kansas City, MO, for Arizona Department of Environmental Quality, Phoenix, AZ;

Stunder, B.J.B.; Arya, S.P.S. (1988). Windbreak effectiveness for storage pile fugitive dust control: A wind tunnel study. J. Air Poll. Control Assoc., 38(2): 135-143.

Sweitzer, T.W. (1985). A field evaluation of two PM10 inlets in an industrialized area of Illinois. J. Air Poll. Control Assoc., 35(7): 744-746.

Takahashi, K.; Minoura, H.; Sakamoto, K. (2008). Examination of discrepancies between beta-attenuation and gravimetric methods for the monitoring of particulate matter. Atmos. Environ., 42(21): 5232-5240.

Thermo Scientific (2009a). GMW PM10 high volume air sampler - Volumetric. prepared by Thermo-Scientific, Inc., Franklin, MA, http://www.thermo.com/com/cda/product/detail/0,1055,23297,00.html

Thermo Scientific (2009b). Partisol-Plus, Model 2025. prepared by Thermo-Scientific, Inc., Franklin, MA, http://www.thermo.com/com/cda/product/detail/1,,10122677,00.html

Thermo Scientific (2009c). Partisol FRM, Model 2000. prepared by Thermo-Scientific, Inc., Franklin, MA, http://www.thermo.com/com/cda/product/detail/0,1055,10122676,00.html

Thermo Scientific (2009d). Continuous particulate TEOM monitor, series 1400ab. prepared by Thermo-Scientific, Inc., Franklin, MA, http://www.thermo.com/com/cda/product/detail/0,1055,10122682,00.html

7-18

Thermo Scientific (2009e). FH 62 C14, Continuous Particulate Monitor. prepared by Thermo-Scientific, Inc., Franklin, MA, www.thermo.com/com/cda/product/detail/0,1055,20102,00.html

Tisch (2009a). TE-5170 mass flow controlled total suspended particulate monitor. prepared by Tisch Environmental Inc., Cleves, OH, http://www.tisch-env.com/tisch/te-5170.asp

Tisch (2009b). SPM-613D beta gauge method PM2.5/PM10/OBC dichotomous monitor. prepared by Tisch Environmental Inc., Cleves, OH, www.tisch-env.com/tisch/SPM613D.asp

Tolocka, M.P.; Peters, T.M.; Vanderpool, R.W.; Chen, F.L.; Wiener, R.W. (2001). On the modification of the low flow-rate PM10 dichotomous sampler inlet. Aerosol Sci. Technol., 34(5): 407-415.

Tsai, C.J. (1995). A field study of 3 collocated ambient PM10 samplers. Particle & Particle Systems Characterization, 12(1): 10-15. ISI:A1995QQ74100002.

Tsai, C.J.; Cheng, Y.H. (1995). Atmospheric aerosol sampling by an annular denuder system and a high-volume PM10 sampler. Environ. Int., 21: 283-291.

Tsai, C.J.; Chang, C.T. (2002). An investigation of dust emissions from unpaved surfaces in Taiwan. Separation and Purification Technology, 29(2): 181-188. ISI:000178274800011.

TSI, I. (2009). DUSTTRAK™ aerosol monitors. prepared by TSI, Inc., Shoreview, MN, http://www.tsi.com/en-1033/products/14000/dusttrak%E2%84%A2_aerosol_monitors.aspx

Turner, J.R. (1998). Laboratory and field evaluation of the Minivol PM2.5 sampler. Report Number 97A-DIR-1; prepared by Washington University, St. Louis, MO, for U.S. Environmental Protection Agency, Research Triangle Park, NC;

U.S.EPA (1976). Evaluation of fugitive dust emissions from mining, Task 1 Report. Identification of fugitive dust sources associated with mining. prepared by PEDCo-Environmental Specialists, Inc., Cincinnati, OH, for U.S. Environmental Protection Agency, Research Triangle Park, NC;

U.S.EPA (1978). Survey of fugitive dust from coal mines. Report Number EPA-908/1-78-003; prepared by PEDCo-Environmental, Inc., Cincinnati, OH, Environmental Protection Agency, Region VIII;

U.S.EPA (1987). Ambient Air Quality Standards for Particulate Matter; Final Rule. Federal Register, 52(126): 24634-24669.

U.S.EPA (2006a). AP-42, Volume I: Compilation of air pollution emission factors. Report Number Fifth Edition; prepared by U.S. Environmental Protection Agency, Washington, D.C.,

7-19

U.S.EPA (2006b). National ambient air quality standard for particulate matter: Final rule. Federal Register, 71(200): 61144-61233. http://frwebgate.access.gpo.gov/cgi-bin/getpage.cgi?dbname=2006_register&position=all&page=61144.

U.S.EPA (2008). National ambient air quality standards for lead: Final rule . Federal Register, 73(219): 66964-67062.

U.S.EPA (2009). List of designated reference and equivalent methods. prepared by U.S. Environmental Protection Agency, Research Triangle Park, NC, http://www.epa.gov/ttn/amtic/files/ambient/criteria/reference-equivalent-methods-list.pdf

van Eimern, J.; Karschon.R.; Razumova, L.A.; Robertson, G.W. (1964). Windbreaks and shelterbelts. Report Number WMO No. 147.TP.70, Technical Note No. 59; prepared by World Meteorological Organization, Geneva, Switzerland,

van Osdell, D.W.; Chen, F.L. (1990). Wind tunnel test report No. 28. Test of the Sierra-Andersen 246b dichotomous sampler inlet at 2, 8, and 24 km/hr. prepared by Research Triangle Institute, Research Triangle Park, NC, Atmospheric Research and Exposure Assessent Laboratory, U. S. Environmental Protection Agency;

Varma, R.; Moosmüller, H.; Arnott, W.P. (2003). Toward an ideal integrating nephelometer. Opt. Lett., 28(12): 1007-1009.

Vedal, S. (1997). Critical review - Ambient particles and health: Lines that divide. J. Air Waste Manage. Assoc., 47(5): 551-581.

Vincent, J.H.; Ramachandran, G.; Kerr, S.M. (2001). Particle size and chemical species 'fingerprinting' of aerosols in primary nickel production industry workplaces. J. Environ. Monit., 3(6): 565-574.

Vinitketkumnuen, U.; Kalayanamitra, K.; Chewonarin, T.; Kamens, R. (2002). Particulate matter, PM 10 & PM 2.5 levels, and airborne mutagenicity in Chiang Mai, Thailand. Mutation Research-Genetic Toxicology and Environmental Mutagenesis, 519(1-2): 121-131.

Vutukuru, S.; Griffin, R.J.; Dabdub, D. (2006). Simulation and analysis of secondary organic aerosol dynamics in the South Coast Air Basin of California. J. Geophys. Res. -Atmospheres, 111(D10)

Wang, X.L.; Chancellor, G.; Evenstad, J.; Farnsworth, J.E.; Hase, A.; Olson, G.M.; Sreenath, A.; Agarwal, J.K. (2009). A novel optical instrument for estimating size segregated aerosol mass concentration in real time. Aerosol Sci. Tehnol., 43: 939-950.

Watson, J.G.; Chow, J.C.; Shah, J.J.; Pace, T.G. (1983). The effect of sampling inlets on the PM10 and PM15 to TSP concentration ratios. J. Air Poll. Control Assoc., 33(2): 114-119.

Watson, J.G.; Chow, J.C.; Moosmüller, H.; Green, M.C.; Frank, N.H.; Pitchford, M.L. (1998). Guidance for using continuous monitors in PM2.5 monitoring networks. Report Number

7-20

EPA-454/R-98-012; prepared by U.S. Environmental Protection Agency, Research Triangle Park, NC, http://www.epa.gov/ttn/amtic/pmpolgud.html

Watson, J.G.; Chow, J.C.; Frazier, C.A. (1999). X-ray fluorescence analysis of ambient air samples. In Elemental Analysis of Airborne Particles, Vol. 1, Landsberger, S., Creatchman, M., Eds.; Gordon and Breach Science: Amsterdam, 67-96.

Watson, J.G.; Chow, J.C. (2000). Reconciling urban fugitive dust emissions inventory and ambient source contribution estimates: Summary of current knowledge and needed research. Report Number 6110.4D2; prepared by Desert Research Institute, Reno, NV, for U.S. Environmental Protection Agency, Research Triangle Park, NC; http://www.epa.gov/ttn/chief/efdocs/fugitivedust.pdf

Watson, J.G.; Chow, J.C.; Pace, T.G. (2000). Fugitive dust emissions. In Air Pollution Engineering Manual, Second Edition, 2nd; Davis, W. T., Ed.; John Wiley & Sons, Inc.: New York, 117-135.

Watson, J.G.; Turpin, B.J.; Chow, J.C. (2001). The measurement process: Precision, accuracy, and validity. In Air Sampling Instruments for Evaluation of Atmospheric Contaminants, Ninth Edition, 9th; Cohen, B. S., McCammon, C. S. J., Eds.; American Conference of Governmental Industrial Hygienists: Cincinnati, OH, 201-216.

Watson, J.G. (2002). Visibility: Science and regulation. J. Air Waste Manage. Assoc., 52(6): 628-713.

Watson, J.G.; Chow, J.C.; Chen, L.-W.A.; Kohl, S.D. (2007). Non-destructive XRF and SEM analyses on beta attenuation filters for elemental concentrations at the Craig Road monitor. prepared by Desert Research Institute, Reno, NV, for Clark County Department of Air Quality and Environmental Management, Las Vegas, NV;

Watson, J.G.; Chow, J.C.; Lowenthal, D.H.; Magliano, K.L. (2008a). Estimating aerosol light scattering at the Fresno Supersite. Atmos. Environ., 42(6): 1186-1196.

Watson, J.G.; Chen, L.-W.A.; Chow, J.C.; Lowenthal, D.H.; Doraiswamy, P. (2008b). Source apportionment: Findings from the U.S. Supersite Program. J. Air Waste Manage. Assoc., 58(2): 265-288.

Watson, J.G.; Chow, J.C. (2009). Ambient aerosol sampling. In Aerosol Measurement: Principles, Techniques and Applications, Willeke, K., Baron, P., Eds.; accepted.

Watson, J.G.; Chow, J.C.; Chen, L.-W.A. (2009). Methods to assess carbonaceous aerosol sampling artifacts for IMPROVE and other long-term networks. J. Air Waste Manage. Assoc., 59(8): 898-911.

Wedding, J.B.; McFarland, A.R.; Cermak, J.E. (1977). Large particle collection characteristics of ambient aerosol samplers. Enivron. Sci. Technol., 11(4): 387-390.

7-21

Wedding, J.B.; Weigand, M.A.; John, W.; Wall, S.M. (1980). Sampling effectiveness of the inlet to the dichotomous sample. Enivron. Sci. Technol., 14(11): 1367-1370.

Wedding, J.B.; Carney, T.C. (1983). A quantitative technique for determining the impact of non-ideal ambient sampler inlets on the collected mass. Atmos. Environ., 17: 873-882.

Wedding, J.B.; Weigand, M.A.; Kim, Y.J. (1985a). Evaluation of the Sierra-Anderson 10-m inlet for the high-volume sampler. Atmos. Environ., 19(3): 539-542.

Wedding, J.B.; Lodge, J.P.; Kim, Y.J. (1985b). Comments on 'A field comparison of PM10 inlets at

four locations'. J. Air Poll. Control Assoc., 35(6): 649-651.

Wedding, J.B.; Lodge, J.P.; Kim, Y.J. (1985c). Author's reply to "Response to comment on 'A field comparison of PM10 inlets at four locations' ". J. Air Poll. Control Assoc., 35(9): 953-956.

Whatman (2009a). PM2.5 air monitoring membrane . prepared by Whatman, Part of GE Healthcare, Kent, UK, http://www.whatman.com/PRODPM25AirMonitoringMembrane.aspx

Whatman (2009b). Air sampling filters and quartz filters. prepared by Whatman, Part of GE Healthcare, Kent, UK, http://www.whatman.com/AirSamplingandQuartzFilters.aspx

Wiener, R.W.; Vanderpool, R.W. (1992). Evaluation of the Lane Regional PRO-1A and PRO-2 saturation monitor. prepared by U.S. Environmental Protection Agency, Research Triangle Park, NC,

Williams, D.S.; Shukla, M.K.; Ross, J. (2008). Particulate matter emission by a vehicle running on unpaved road. Atmos. Environ., 42(16): 3899-3905.

Williams, R.; Suggs, J.; Rodes, C.; Lawless, P.; Zweidinger, R.; Kwok, R.; Creason, J.; Sheldon, L. (2000). Comparison of PM2.5 and PM10 monitors. J. Expo. Anal. Environ. Epidemiol., 10(5): 497-505.

Xuan, J.; Robins, A. (1994). The effects of turbulence and complex terrain on dust emissions and depositions from coal stockpiles. Atmos. Environ., 28(11): 1951-1960.

Zhao, T.L.; Gong, S.L.; Zhang, X.Y.; Mawgoud, A.A.; Shao, Y.P. (2006). An assessment of dust emission schemes in modeling east Asian dust storms. J. Geophys. Res. -Atmospheres, 111(D5): Dd05S90. doi:10.1029/2004JD005746.

Zhu, Y.F.; Hinds, W.C.; Kim, S.; Sioutas, C. (2002). Concentration and size distribution of ultrafine particles near a major highway. J. Air Waste Manage. Assoc., 52(9): 1032-1042.