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Evaluation of Roughing Filtration for Pre-Treatment of Stormwater prior to Aquifer Storage and Recovery (ASR) Edwin Lin1, Declan Page2, Paul Pavelic2, Peter Dillon2, Stuart McClure2, and John Hutson1

1Flinders University of South Australia, School of Chemistry, Physics, and Earth Sciences 2CSIRO Land and Water

CSIRO Land and Water Science Report 03/06 February 2006

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Cover Photograph: Description: The Urrbrae Wetland during a winter storm, June 2005 Photographer: Edwin Lin © 2006 CSIRO

ISSN: 1833-4563

Evaluation of Roughing Filtration for Pre-Treatment of Stormwater prior to Aquifer Storage and Recovery (ASR) Page i

Evaluation of roughing filtration for pre-treatment of stormwater prior to aquifer storage and recovery (ASR) Edwin Lin1, Declan Page2, Paul Pavelic2, Peter Dillon2, Stuart McClure2, and John Hutson1

1Flinders University, School of Chemistry, Physics, and Earth Sciences, GPO Box 2100, Adelaide SA 5001, Australia 2CSIRO Land and Water, PMB2, Glen Osmond, SA 5064, Australia

CSIRO Land and Water Science Report 03/06 February 2006

Evaluation of Roughing Filtration for Pre-Treatment of Stormwater prior to Aquifer Storage and Recovery (ASR) Page ii

Acknowledgements

This study was financially supported by Australian Centre for International Agricultural Research (ACIAR) and Water for a Healthy Country.

The authors wish also to thank the following individuals for making a significant contribution to this study:

• Dr. Dzuy Nguyen (University of Adelaide Department of Chemical Engineering), Mr. Paul Barrett (ATA Scientific), Dr. Scott Abbott and Dr. Philip Moore (University of South Australia Ian Wark Research Institute) for their guidance and assistance with particle sizing and zeta potential methods;

• Dr. Mark Raven (CSIRO) for undertaking XRD analyses; • Ms. Karen Barry (CSIRO) for her guidance with the MFI method and overall support

in the laboratory; • Dr. Corrine Le Gal La Salle (formerly Flinders University of South Australia), Dr. John

Van Leeuwen (University of South Australia), and Dr. Mike Holmes (United Water International) for constructive feedback during the initial stages of the study;

• Dr. Allin Hodson (Urrbrae Wetland Educational Centre) for providing access to, and water quality data for, the Urrbrae Wetland;

• Dr. Michael Robin Collins (University of New Hampshire, USA) for providing valuable feedback on experimental materials and methods;

• Mr. John Winter (AWQC) for assisting with questions concerning water sample preparation and requested physico-chemical analyses

• Mr. Darrin Webb (Rasch Industries) for fabricating the filter columns; and • Mr. Toney Hirnyk (CSIRO) for constructing the column support structure.

Evaluation of Roughing Filtration for Pre-Treatment of Stormwater prior to Aquifer Storage and Recovery (ASR) Page iii

Executive Summary Multi-stage granular filtration, including roughing filtration and slow sand filtration, represents a promising method for improving the quality of stormwater retained in urban wetlands to allow for sustainable aquifer storage and recovery (ASR) in siliceous aquifers. The emphasis of this research is on the application of roughing filtration to remove suspended solids in stormwater prior to slow sand filtration, an area of research that has not been thoroughly investigated. Previous studies have demonstrated the importance of physico-chemical filtration (primarily the transport of suspended particles to filter media by sedimentation) for particle removal in roughing filtration. Surface and straining filtration may also contribute to particle removal, although the significance of these processes is not well understood. Using bench-scale column experiments, this study aimed to determine the suitability of roughing filtration for pre-treating stormwater retained at the Urrbrae Wetland intended for slow sand filtration and ASR and advance the understanding of particle removal mechanisms in roughing filters by applying recent advances in trajectory modelling. For this study, experimental waters were passed through four acrylic columns (each 6.3 cm diameter, 60 cm length) packed with filter media and connected in series to simulate a 2.4-m upflow roughing filter in series (URFS). The effects of filter design parameters and influent particle concentration on initial steady-state removals for a model inorganic (kaolinite clay) suspension were evaluated using three grades of media (2.18, 5.18, and 7.55 mm diameter), three hydraulic loads (0.5, 1.0 and 1.5 m/hr), four filter lengths (0.6, 1.2, 1.8, and 2.4 m), and three initial influent particle concentrations (100, 400, and 700 NTU). Two separate experiments were conducted using stormwater collected from the Urrbrae Wetland for comparison. After completion of experiments, selected water samples were characterised using total suspended solids (TSS), particle size distribution (PSD), physico-chemical, x-ray diffraction, scanning electron microscopy, and Membrane Filtration Index analyses. Results of a series of URFS experiments with kaolinite clay demonstrated that a systematic pattern for particle removal. In agreement with findings by previous researchers, improved removal efficiencies correlated to smaller media sizes, lower hydraulic loading rates, and longer filter lengths, and incremental removal efficiencies declined with increasing filter length due to the preferential removal of larger particles. However, initial steady-state removal efficiencies were worse (up to 18% after the first 60-cm filter segment) than predicted using existing empirical models developed for kaolinite clay suspensions in horizontal and downflow roughing filters due likely to differences in settling rates of kaolinite clay suspensions and filter orientation. Interestingly, increasing influent particle concentration resulted in systematic improvement in turbidity removal (up to 7% improvement for the first 60-cm filter segment) but had no measurable effect on TSS removal. This phenomenon was attributed to the curvilinear relationship between turbidity and TSS concentration for the kaolinite clay used in this study. There was no evidence for preferential flow along columns walls in the URFS. Errors introduced by biological activity inside the filter and filter packing procedures were shown to be relatively minor compared to accepted experimental errors for this study.

Experiments with kaolinite clay were evaluated using colloid filtration theory (CFT) taking into consideration close range forces, the results of which revealed:

1. Attachment factors for URFS experiments calculated over increasing filter length were relatively consistent (0.128 ± 0.027) substantiating the procedure used to apply CFT;

2. Consideration of hydrodynamic and van der Waals attractive forces improved the estimation of theoretical single collector efficiencies (SCE);

3. There is no conclusive evidence that surface or cake filtration occurs in URFS for particle concentrations up to 300 mg/L TSS.

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An empirical model (R2 = 0.94) capable of predicting initial steady-state TSS removal (for the kaolinite clay used in this study) for various URFS configurations was also developed and is shown below (where Ce/Co = effluent concentration divided by influent concentration):

Ce/Co = -0.245 + 0.0298*media(mm) + 0.171*rate(m/hr) + 0.206*Length-1 (m-1)

Although the empirical model is limited to the specific PSD of kaolinite clay used in these experiments, the experimental approach used in this study can be used to develop a similar model for any particular suspension of interest. When using any empirical model with filter length (or variation of filter length) as a design variable for filters packed with different media grades, removal efficiencies are best estimated by applying the average media size weighted to filter length.

Results of two URFS experiments with natural stormwater retained in the Urrbrae Wetland revealed poorer removal efficiencies (55-76%) relative to kaolinite clay (87%) for an intermediate filter configuration. These results were explained by the slower settling velocity of the organic-inorganic particle assemblages in the stormwater observed in suspension stability tests compare to the smaller, yet denser, kaolinite clay particles. Preferential removal of larger particle flocs and macro-organisms was observed. Because many macro-organisms identified were part of larger particle assemblages more likely to be retained in the filter, breakage of flocs in the roughing filter due to shear forces was likely minimal.

Overall, this study confirmed the suitability of roughing filtration for removing suspended solids in stormwater retained in the Urrbrae Wetland prior to slow sand filtration and ASR. Longer experiments to determine ultimate filter loads and the effect of biological activity on particle removal under field conditions are recommended. In addition, the procedure used to evaluate kaolinite clay experiments with CFT could be extended to establish a comprehensive database of attachment factors related to different water types, which would improve current guidelines in roughing filter design and application. Finally, additional research is needed to improve current source water guidelines for SSF clogging that rely solely on global particulate parameters. The Membrane Filtration Index could be used to relate the nature and concentration of suspended solids of source waters to SSF clogging rates.

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Table of Contents 1. Introduction..................................................................................................................... 1

1.1. Aquifer Storage and Recovery (ASR) with stormwater in siliceous aquifers .......................... 1 1.2. Well clogging and the need for stormwater pre-treatment prior to ASR ................................. 1 1.3. The Urrbrae Wetland ASR study site ...................................................................................... 1

1.3.1. Design and operational features ..................................................................................... 1 1.4. Stormwater pre-treatment by Multi-stage granular filtration.................................................... 3

1.4.1. Slow sand filtration .......................................................................................................... 4 1.4.2. Roughing filtration............................................................................................................ 4

1.5. Research aims and outline...................................................................................................... 4 1.6. Report Organisation ................................................................................................................ 5

2. Roughing Filtration Background and Literature Review ............................................ 6 2.1. Classification of granular filtration methods ............................................................................ 6 2.2. Roughing filter configurations.................................................................................................. 6 2.3. Roughing filter operation and maintenance ............................................................................ 7 2.4. Roughing filter design parameters .......................................................................................... 8

2.4.1. Filter media size .............................................................................................................. 8 2.4.2. Hydraulic loading rate...................................................................................................... 8 2.4.3. Filter length...................................................................................................................... 9

2.5. Important of suspension characteristics in roughing filtration ............................................... 10 2.5.1. Size and density distribution of solid matter .................................................................. 10 2.5.2. Influence of water chemistry on solid surface-chemical properties............................... 10

2.6. Particle removal mechanisms in roughing filters................................................................... 10 2.6.1. Physico-chemical filtration............................................................................................. 11 2.6.2. Surface and straining filtration....................................................................................... 14

2.7. Approaches to modelling deep-bed filter performance ......................................................... 14 2.7.1. Trajectory approach....................................................................................................... 14 2.7.2. Phenomenological approach......................................................................................... 15

2.8. Review of previous roughing filtration studies....................................................................... 16 2.9. Knowledge gaps.................................................................................................................... 18

3. Materials and Methods................................................................................................. 19 3.1. Experimental approach for kaolinite clay experiments.......................................................... 19 Experimental approach for Urrbrae Wetland water experiments ...................................................... 20 3.2. Column specifications and laboratory setup ......................................................................... 21 3.3. Filter media preparation and column packing procedures .................................................... 24 3.4. Passage of conservative tracer through column to confirm ideal plug flow conditions......... 24 3.5. Selection and preparation of experimental waters ................................................................ 25

3.5.1. Kaolinite clay suspension .............................................................................................. 25 3.5.2. Natural water ................................................................................................................. 25

3.6. Experimental Procedures ...................................................................................................... 26 3.7. Monitoring methods used during experiments ...................................................................... 26

3.7.1. Turbidity......................................................................................................................... 26 3.7.2. Electrical conductivity, temperature, and pH................................................................. 26

3.8. Analytical methods conducted after experiments.................................................................. 26 3.8.1. Total suspended solids (TSS) ....................................................................................... 26 3.8.2. Particle size distribution analysis by laser diffraction method ....................................... 27 3.8.3. Particle size distribution (PSD) analysis by particle counting method .......................... 27 3.8.4. Measurement of physico-chemical properties of Urrbrae Wetland water ..................... 28 3.8.5. Characterisation of particles by x-ray diffraction ........................................................... 28 3.8.6. Characterisation of particles by scanning electron microscopy (SEM) ......................... 30 3.8.7. Quantification of roughing filtration efficiency using Membrane Filtration Index........... 30

3.9. Other analytical methods....................................................................................................... 31 3.9.1. Zeta potential measurements........................................................................................ 31 3.9.2. Suspension stability tests .............................................................................................. 31

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3.10. Modelling steady-state particle removal of kaolinite clay in URFS using colloid filtration theory (CFT) ...................................................................................................................................... 31 3.11. Quality assurance / quality control .................................................................................... 31

4. Results and Discussion of Experiments with Kaolinite Clay Suspension.............. 33 4.1. Plug flow conditions verified by passage of conservative tracer........................................... 33 4.2. Effect of column packing on particle removal........................................................................ 34 4.3. Effect of biological activity on particle removal...................................................................... 34 4.4. Evaluation of steady-state particle removal in URFS experiments with kaolinite clay.......... 35

4.4.1. Preferential removal of large particles in URFS with kaolinite clay............................... 40 4.4.2. Suspension Stability Tests ............................................................................................ 46 4.4.3. Modelling steady-state particle removal of kaolinite clay in URFS using colloid filtration theory (CFT) .................................................................................................................................. 46 4.4.4. Comparison of experimental data with existing empirical models developed for steady-state kaolinite clay removal ........................................................................................................... 52 4.4.5. Development of a steady-state kaolinite clay removal model for URFS using multivariate regression analysis .................................................................................................... 54

5. Results and Discussion of Experiments with Urrbrae Wetland Waters.................. 56 5.1. Evaluation of steady-state particle removal in URFS experiments with Urrbrae Wetland waters 56

5.1.1. Preferential removal of large particles in URFS with Urrbrae Wetland waters ............. 56 5.1.2. Evaluation by X-ray diffraction (XRD) analysis ............................................................. 62 5.1.3. Evaluation by scanning electron microscopy (SEM) ..................................................... 62 5.1.4. Physico-chemical characterisation of untreated and roughing filter-treated stormwater 65 5.1.5. Evaluation of physical clogging potential of Urrbrae Wetland water using Membrane Filtration Index (MFI) method ........................................................................................................ 68 5.1.6. Suspension Stability Tests ............................................................................................ 70

6. Concluding Remarks.................................................................................................... 71 6.1. Assessment of laboratory set-up........................................................................................... 71 6.2. Key findings from URFS experiments with kaolinite clay suspension................................... 71 6.3. Key findings from URFS experiments with Urrbrae Wetland waters .................................... 71 6.4. Recommended additional research....................................................................................... 72

Glossary ............................................................................................................................... 73 References ........................................................................................................................... 75 Appendix A Water quality monitoring data for Urrbrae Wetland .................................... 78 Appendix B Attachment factor calculations using colloid filtration theory and zeta potential measurements ..................................................................................................... 83 Appendix C CXTFIT output files for breakthrough curve experiments .......................... 91 Appendix D Roughing filtration turbidity measurements and removal calculations.... 95 Appendix E Particle size distribution (PSD) results....................................................... 106 Appendix F Model regression statistics for steady-state kaolinite clay removal in URFS............................................................................................................................................. 127 Appendix G X-ray diffraction (XRD) results .................................................................... 129 Appendix H Scanning Electron Microscopy (SEM) micrographs and EDX spectra and analyses.............................................................................................................................. 131 Appendix I Membrane Filtration Index (MFI) results ...................................................... 213

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List of Figures Figure 1.1 Map of Urrbrae Wetland ......................................................................................... 2 Figure 1.2 Conceptual model for treatment of Urrbrae Wetland water prior to ASR ............... 4 Figure 2.1 Typical configurations used in roughing filtration.................................................... 7 Figure 2.2 Relationship between Reynolds number and different combinations of hydraulic

loading rate and collector (media) diameter ..................................................................... 9 Figure 2.3 Significance of filter length (and media size) in roughing filtration.......................... 9 Figure 2.4 Solid matter commonly found in natural surface waters....................................... 10 Figure 2.5 Primary particle removal mechanisms in granular filtration .................................. 11 Figure 2.6 Mechanisms of particle transport to a single collector surface ............................. 11 Figure 2.7 Contribution of transport processes to single-collector efficiency for a given

roughing filter design, showing the effect of particle size and density............................ 12 Figure 2.8 Comparison of SCE using equations developed by Yao et al. (1971) and Tufenkji

and Elimelech (2004) for representative inorganic (2.60 g/cm3) and organic (1.05 g/cm3) particles for one filter configuration................................................................................. 13

Figure 3.1 Upflow roughing filter in series (URFS) laboratory set-up .................................... 22 Figure 3.2 Schematic of laboratory upflow roughing filter column ......................................... 23 Figure 4.1 Results of calcium chloride breakthrough curves for one column. ....................... 33 Figure 4.2 Cumulative TSS removals (Ce/Co) of kaolinite clay in URFS experiments.......... 37 Figure 4.3 60-cm TSS removals (Ce/Ci) of kaolinite clay in URFS experiments................... 38 Figure 4.4 Calibration curve relating turbidity and TSS concentrations for the Glomax LL

kaolinite clay ................................................................................................................... 39 Figure 4.5 Additional experiment with continuous turbidity monitoring (5.18 mm, 0.5 m/hr,

700 NTU) ........................................................................................................................ 40 Figure 4.6 Particle Size Distribution of Glomax LL calcined kaolinite clay ............................ 41 Figure 4.7 Particle Size Distribution (2.18 mm media x 4 columns, 0.5 m/hr, 700 NTU)....... 42 Figure 4.8 Particle Size Distribution (7.55 mm media x 4 columns, 1.0 m/hr, 700 NTU)....... 43 Figure 4.9 TSS distributed by particle size (2.18 mm media x 4 columns, 0.5 m/hr, 700 NTU)

........................................................................................................................................ 44 Figure 4.10 TSS distributed by particle size (7.55 mm media x 4 columns, 1.0 m/hr, 700

NTU) ............................................................................................................................... 45 Figure 4.11 Relationship of remaining turbidity over time for samples collected from an

experiment with kaolinite clay ........................................................................................ 46 Figure 4.12 SCE values calculated for kaolinite clay using equation developed by Yao et al.

(1971) Equation 2.5. ....................................................................................................... 47 Figure 4.13 Comparison of SCE values using Equation 2.5 and 2.6 for kaolinite clay at 0.5,

1.0, and 1.5 m/hr ............................................................................................................ 47 Figure 4.14 Average attachment factor of kaolinite clay calculated over increasing filter length

using Yao et al. (1971) SCE equation ............................................................................ 49 Figure 4.15 Average attachment factor of kaolinite clay over increasing filter length using

Tufenkji and Elimelech (2004) SCE equation................................................................. 50 Figure 4.16 Observed vs. CFT-modelled TSS distribution for one experiment with kaolinite

clay (7.55 mm, 1.0 m/hr, and 298 mg/L TSS)................................................................. 51 Figure 4.17 Comparison between observed and predicted removals using existing empirical

models for kaolinite clay removal ................................................................................... 53 Figure 4.18 Comparison of actual vs. modelled removal efficiencies for kaolinite clay in

URFS for 24 data points used to develop model............................................................ 55 Figure 4.19 Comparison of actual vs modelled removal efficiencies for kaolintie clay in URFS

for 12 independent data points ....................................................................................... 55 Figure 5.1 PSD of main lagoon sample (20-Oct-05).............................................................. 56 Figure 5.2 PSD of ASR pond sample (18-Oct-05)................................................................. 57 Figure 5.3 Particle size distribution main lagoon (20-Oct-05) 1.0 m/hr, 34.0 NTU ................ 58 Figure 5.4 TSS distributed by particle size main lagoon (20-Oct-05) 1.0 m/hr, 34.0 NTU..... 60

Evaluation of Roughing Filtration for Pre-Treatment of Stormwater prior to Aquifer Storage and Recovery (ASR) Page viii

Figure 5.5 SEM micrographs showing kaolinite clay (Glomax LL) particles with Fe-Ti impurities ........................................................................................................................ 63

Figure 5.6 SEM micrographs showing typical particles in untreated ASR pond water .......... 63 Figure 5.7 SEM micrographs showing typical particles in roughing filter-treated ASR pond

water............................................................................................................................... 64 Figure 5.8 SEM micrographs showing typical particles in untreated main lagoon water....... 64 Figure 5.9 SEM micrographs showing typical particles in roughing filter-treated main lagoon

water............................................................................................................................... 65 Figure 5.10 Filter paper from MFI and TSS analyses of (A) untreated and (B) roughing filter-

treated ASR pond water (18-Oct-05).............................................................................. 69 Figure 5.11 Filter paper from MFI and TSS analyses of (A) untreated and (B) roughing filter-

treated main lagoon water (20-Oct-05)........................................................................... 69 Figure 5.12 Relationship of remaining turbidity over time for kaolinite clay and main lagoon

water............................................................................................................................... 70

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List of Tables Table 2.1 Classification of granular filters................................................................................ 6 Table 2.2 Typical media grades used in roughing filtration ..................................................... 8 Table 2.3 Summary of solids removal results from previous roughing filtration studies ........ 17 Table 3.1 36-test orthogonal array for kaolinite clay removal on clean media....................... 20 Table 3.2 Size distribution of media used in roughing filter experiments............................... 24 Table 3.3 Water quality analyses performed by Australian Water Quality Centre for untreated

and roughing filter-treated Urrbrae Wetland waters ....................................................... 29 Table 4.1 Results of experiments conducted to assess effect of column packing on removal

rates................................................................................................................................ 34 Table 4.2 Results of experiments conducted to assess column packing on particle removal

efficiency......................................................................................................................... 34 Table 4.3 Cumulative (Ce/Co) and 60-cm (Ce/Ci) removals for kaolinite clay in URFS........ 36 Table 4.4 Comparison of approaches for applying empirical models with filter length as a

parameter ....................................................................................................................... 54 Table 5.1 Removals (Ce/Co) of Urrbrae Wetland water and kaolinite clay experiments in

URFS experiments ......................................................................................................... 56 Table 5.2 Mineralogical and organic composition of main lagoon water ............................... 62 Table 5.3 Water quality analyses performed by AWQC for untreated and roughing filter-

treated Urrbrae Wetland waters ..................................................................................... 67 Table 5.4 MFI results for untreated and roughing-filter treated Urrbrae Wetland waters and

kaolinite clay suspension................................................................................................ 68

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1. Introduction

1.1. Aquifer Storage and Recovery (ASR) with stormwater in siliceous aquifers

In Adelaide, South Australia, where future water demand is projected to exceed expected supplies in dry years (Government of SA, 2004), stormwater runoff retained in urban wetlands represents a potentially valuable water supply resource for non-potable purposes, such as irrigation and industrial uses. One method that has been used successfully in Adelaide to capture and store stormwater for re-use is Aquifer Storage and Recovery (ASR) (Martin et al., 1999). ASR represents a specialised form of managed aquifer recharge, where surplus surface water is injected into an aquifer through a well during the wet season and recovered from the same well, when water is needed during the dry season (Pyne, 1995). ASR eliminates the need to construct large and expensive surface reservoirs (thereby protecting water supplies from evaporation and algal growth) and also provides potential water quality improvements during storage.

1.2. Well clogging and the need for stormwater pre-treatment prior to ASR

One of the most common operational challenges in ASR is the management of well clogging. Well clogging is caused by the interaction between recharge water (and its constituents), native groundwater, and porous media in the near-well environment that over time leads to decreased well efficiency (Pavelic and Dillon, 1997). Well clogging is ultimately a physical phenomenon but may be caused by physical, biological, and chemical processes (Osei-Bonsu, 1996; Perez-Paricio, 1998; Rinck-Pfeiffer, 2000). Common clogging processes in ASR include the filtration of suspended solids by porous media and microbial growth fed by available nutrients and biodegradable organic matter (BOM) in recharge water (Pavelic and Dillon, 1997). This study originated because an ASR operation proposed for the Urrbrae Wetland would confront wetland water that includes elevated levels of suspended solids, nutrients, and BOM.

Clogging may be remedied by back-flushing and other mechanical and chemical well re-development techniques. However, results are often variable, with success being highly dependent on the nature and degree of clogging that has occurred (Perez-Paricio, 1998; Martin and Dillon, 2005). In addition, frequent well maintenance incurs high costs, operational downtime, and increased risk of well damage. These operational challenges highlight the need to improve stormwater quality prior to well injection.

1.3. The Urrbrae Wetland ASR study site 1.3.1. Design and operational features The Urrbrae Wetland is located at the Urrbrae Agricultural High School adjacent to Cross Road in Urrbrae, South Australia. Figure 1.1 shows the major features of the wetland include the main lagoon, rubber-lined detention pond (herein also referred to as ASR pond) and two pre-settling ponds. Typical wetland conditions during a storm are captured in Photographs 1 through 3. The original wetland (comprising the main lagoon and detention pond) was built in 1996 primarily to mitigate local flooding and was engineered to handle peak stormwater flows associated with a 1-in-5-year storm event (Hodson, 1999). Estimated annual volumetric flow through the wetland is 300 to 400 mega litres (ML). Water depth in the main lagoon is typically greater than 1.5 m for the majority of the year (Hodson, 1999). In 2003, an earth- and rock-lined settling pond was constructed to reduce the large amount of debris that entered the main lagoon from the Cross Road inlet, particularly during storms following long dry periods.


Figure 1.1 Map of Urrbrae Wetland

3Evaluation of Roughing Filtration for Pre-Treatment of Stormwater prior to Aquifer Storage and Recovery (ASR) Page 3

The bottom of the main lagoon is clay-lined and needs to be filled during the dry season to protect the lining from shrinkage and cracking. The bottom of the detention pond is lined with 2.0-mm thick rubber and is filled during the wet season with passive flow from the main lagoon through an underground pipe located between the Cross Road inlet and eastern extent of the observation deck. During the wet season, flow from the main lagoon into the ASR pond is minimal due to the high surface water elevation maintained in the ASR pond after initial storms. During the dry season, water in the detention pond is used to replenish water lost to evaporation in the main lagoon. The Urrbrae wetland is directly underlain by sediments of the Hindmarsh Clay, a fluvial Quaternary unit comprised of stiff clay inter-bedded with thin layers of silt, sand, and gravel (Gerges, 1999). Beneath the Hindmarsh Clay are the Carisbrooke Sand and Port Willunga Formation, Tertiary aquifers comprised of fine-grained sand. Previous attempts to inject stormwater from the detention pond into the tertiary aquifers resulted in rapid well clogging (initial injection rates of 3 L/s) were reduced to 0.75 L/s) despite pre-treatment of water by rapid sand filtration. Clogging was attributed to inadequate removal of suspended solids and bacterial growth fed by dissolved organic carbon in the wetland water. A failure of a well screen joint inhibited normal procedures for unclogging the well. The ASR well and foundation for proposed pre-treatment works are located adjacent to the southern bank of the detention pond. The anticipated direction of surface water flow during well injection is also shown in the figure and indicates that source water for ASR will be derived from stormwater entering the main lagoon via the Cross Road inlet and overflowing into the detention pond and from direct rainfall (although direct rainfall volumes would be minimal in comparison to overflow volumes). If ASR proves to be successful at the Urrbrae Wetland, recovered wetland water will be used for landscape irrigation of adjacent school properties. Current annual irrigation demand for the school properties is estimated to be 100 ML (Hodson, 2005).

1.4. Stormwater pre-treatment by Multi-stage granular filtration Multi-stage granular filtration, including roughing filtration and slow sand filtration, has been identified as a promising method for pre-treating stormwater prior to well injection. Such treatment trains typically consist of a combination of shallow compartments filled with sand and gravel media of different grades through which water is filtered. Multi-stage granular filtration represents an attractive alternative to conventional coagulation treatment, which requires the constant and precise addition of chemical flocculants and is not designed to remove BOM.

The conceptual model for treatment for urban wetland water prior to ASR at the Urrbrae site is provided in and takes advantage of the primary treatment capabilities of roughing filtration and slow sand filtration as described below. In combination with water quality improvements expected in an urban wetland (e.g. settling of large debris and nutrient uptake by vegetation (Hodson, 2005)), the treatment train is engineered to systematically reduce the clogging potential of stormwater above-ground, where clogging may be more effectively monitored, characterised, and managed.


Wetland-treated Stormwater

Roughing Filtration

Slow Sand Filtration

ASR Well Injection

Stabilisation of Biological Activity

Removal of Suspended Solids

Figure 1.2 Conceptual model for treatment of Urrbrae Wetland water prior to ASR

1.4.1. Slow sand filtration Slow sand filtration is recognised as a well-established water treatment technology capable of removing viruses, cysts, and bacteria and reducing levels of BOM found in natural waters (Collins, 1991; Graham, 1988; Page et al., 2005). The technology associated with slow sand filtration is relatively simple, requiring no chemicals or sophisticated instrumentation. This is likely to be a robust treatment method applicable in developing countries.

A key limitation of slow sand filtration, however, is the strict requirements placed on source water quality to prevent premature filter clogging (Barrett 1991; Bellamy, et al., 1985; Collins, 1994; Graham, 1988; Wegelin, 1996). Reported maximum turbidity allowances for slow sand filters range from 5 to 50 NTU (Cleasby, 1991; Li and Du, 1996) with 10-20 NTU considered a conservative range to allow for reasonable filter run lengths (defined as the period during which treatment is uninterrupted by maintenance) (Collins, 1994). However, filter run lengths may be affected not only by particle concentration but also by the size and nature of particles in suspension, whereby organic particles may more easily clog slow sand filters (Cleasby, 1991). Additionally, maximum allowable concentrations of 1000 clump counts/ml for algae (Logsdon, et al., 2002) and 5 μg/L for chlorophyll-a (Collins, 1994) are recommended to prevent filter clogging and algal blooms, respectively. Because stormwater frequently has turbidity greater than 50 NTU and is also likely to contain elevated levels of algae and chlorophyll, pre-treatment of stormwater to remove suspended solids will be required prior to slow sand filtration.

1.4.2. Roughing filtration Previous studies have shown gravel roughing filtration to be an effective and reliable method for removing suspended solids (Clarke, et al., 1996; Collins, 1994; Galvis, et al., 1996; and Wegelin, 1987). For suspensions with particulates that do not readily settle, roughing filtration provides superior treatment to basic sedimentation methods (Wegelin, 1996) and represents an attractive alternative to more costly conventional coagulation methods. Roughing filters are classified as deep-bed filters, whereby proper filter design promotes particle removal throughout the depth of the filter bed, maximising the capacity of the filter to store removed solids. Particle removal efficiency in roughing filters is dependent on filter design, particulate, and water quality parameters (Boller, 1993; Collins,1994; Wegelin, 1987). However, additional research is necessary to improve current guidelines for filter design and to assess whether roughing filtration is an appropriate option for improving stormwater quality to satisfy source water requirements for slow sand filtration and ultimately ASR.

1.5. Research aims and outline The emphasis of this study is on the application of upflow roughing filtration in series (URFS) for the pre-treatment of stormwater retained in urban wetlands prior to ASR. This study was


undertaken to gain a better understanding of the parameters that influence the removal of suspended particles in roughing filters and specifically to determine the suitability of roughing filtration to treat stormwater retained in the Urrbrae Wetland.

The specific aims of this research were to:

(1) Establish methods to improve the process of modelling initial steady-state removal rates taking into consideration filter media size, hydraulic loading rate and filter length by conducting bench-scale column experiments simulating URFS using a model inorganic (kaolinite clay) suspension;

(2) Assess the performance of roughing filtration for a kaolinite clay suspension and stormwater retained in the Urrbrae Wetland; and

(3) Characterise the physico-chemical properties of untreated and roughing filter-treated Urrbrae Wetland water relevant to source water requirements for slow sand filtration and ASR.

1.6. Report Organisation The next section of this report presents the current understanding and knowledge gaps in roughing filtration. Section 3 describes the materials and methods used in this study to conduct and evaluate bench-scale column experiments simulating URFS using both a model inorganic (kaolinite clay) suspension and natural stormwater collected from the Urrbrae Wetland. Section 4 presents and discusses findings from URFS experiments with the kaolinite clay suspension. Section 5 presents and discusses findings from URFS experiments with Urrbrae Wetland water. Section 6 presents the key conclusions from this study and provides recommendations for future research.


2. Roughing Filtration Background and Literature Review This section summarises the current understanding in roughing filtration, including a description of 1) roughing filtration in comparison to other granular filtration methods, 2) the primary mechanisms by which suspended solids are removed in roughing filtration and the key parameters governing particle removal, and 3) the two approaches used to model particle removal in roughing filtration. Findings from recent studies in roughing filtration are also provided.

2.1. Classification of granular filtration methods The size of filter media, the hydraulic loading rate, and the length of the filter bed in the direction of flow are key design parameters in granular filters (Wegelin, 1987, Collins, 1994). Table 2.1 presents the typical design features of a roughing filter as well as those of other common granular filtration methods. Table 2.1 Classification of granular filters

Filter Type Filter Media Size

(mm) Hydraulic Loading Rate

(m/hr) Filter Length

(m)

Intake 6 – 40 2 – 5 1

Dynamic 4 – 12 0.6 – 1 0.5

Roughing 2 – 26 0.3 – 1.5a 3 – 4.5 ; 6 – 12b

Rapid Sand 0.5 – 4 5 – 15 –

Slow Sand 0.15 – 1c 0.04 – 0.4 0.6 – 1.2d

Compiled from Graham, N.J.D. (1988), Wegelin, M. (1996), and Collins (1991) arates above 1.0 are typically associated with horizontal roughing filters (HRFs) bshorter filter lengths are associated with vertical roughing filters, longer depths with HRFs csize typically between 0.35 - 0.15 mm ddoes not include underdrain gravel support, typically between 0.3 - 0.5 m in length

Intake and dynamic filters are located directly in a river or canal to improve water quality at the point of abstraction and, therefore, require relatively large media to reduce maintenance requirements. In contrast, roughing, rapid sand, and slow sand filters require separate engineered facilities and use finer grained filter media. A separation between rapid sand filters from other sand filters can be made based on the hydraulic loading rate (>5 m/hr).

2.2. Roughing filter configurations Roughing filters are generally either 1) a large compartment filled with successive layers of filter media decreasing in size in the direction of flow or 2) multiple compartments connected in series, each filled with one media size. Water flow through the filter can be either horizontal or vertical. Figure 2.1 shows three examples of roughing filters, including a horizontal roughing filter (HRF), a downflow roughing filter in series (DRFS), and an upflow roughing filter in series (URFS).


Figure 2.1 Typical configurations used in roughing filtration

Galvis, et al. (1996) compared the performance of HRF, HRFS, and URFS with natural waters in Cali, Columbia and found that removal efficiencies were better for URFS with little or no difference between the HRF and HRFS, despite the HRF having a filter length of 7.1 m compared to 4.3 m for the other two configurations. Also, less wash water was needed to restore efficiency in the URFS. The main advantage of the HRFs was the large silt storage capacity, aided by the downward drift of particles in HRFs (discussed further in Section 2.4). However, space requirements represented a disadvantage of HRFs. Collins (1994) found in laboratory experiments with kaolinite clay and kaolinite clay + algae that removal efficiencies in the first 60-cm of HRFs compared to URFs were better for larger media sizes (7.94 – 11.11 mm) but similar to worse for smaller media sizes (2.68 – 4.83 mm). Headloss is typically greater in VRFs than in HRFs. However, headloss in VRFs can be reduced by the use of URFs in series (URFS). In URFSs, secondary particle detachment prevents the build-up of solids at the filter surface and concentrates solids at the bottom of individual filters, where they can be more easily drained to restore hydraulic conductivity (Clarke, et al., 1996a).

2.3. Roughing filter operation and maintenance The treatment performance of a roughing filter over time can be divided into two phases (Collins, 1994). The first phase represents a period when the particle removal efficiency remains relatively constant (steady-state) with increasing solids deposition, whereas the second phase represents a period of decreasing removal efficiency due to increasing particle deposition in, and penetration through, the filter. Particle removal efficiency and particle penetration play a key role in determining filter run lengths. During a filter run, particles in a HRF drift deeper in the direction of flow and also downward by gravity Wegelin (1996). Unlike in a HRF, particle drift in VRFs occurs only in the direction of water flow allowing for deeper penetration of particles in the filter and generally shorter filter run lengths (Collins ,1994). The end of a filter run is typically determined when the quality of filter effluent deteriorates due to increasing solids deposit until minimum water treatment targets are exceeded. Drainage facilities located at the base of roughing filter compartments allow for rapid downflow drainage, a common maintenance procedure used to remove solids accumulated in the filter at the end of a filter run cycle (Galvis, et al., 1996). Because of the reliance on gravity drainage for filter cleaning, a DRF (in layers) configuration, wherein large media is

Source: Collins, 1994


placed over smaller media in the same filter compartment, is not recommended. As a general guide, filters should be drained when deposited solids reach ~10 g of solids/L of filter (Collins, 1994). Increases in filter resistance or head loss are typically small (<5 cm) in roughing filtration due to the relatively coarse filter media used and low hydraulic loading rates applied (Wegelin, 1996). A condition, whereby actual headloss exceeds the available headloss prior to decreases in removal efficiency (i.e. the filter storage capacity is not exhausted before filter cleaning is required) signals the need to re-evaluate the filter design.

2.4. Roughing filter design parameters 2.4.1. Filter media size Media types commonly used in roughing filtration are quartz sands and gravels but can be replaced by any clean, insoluble, and mechanically resistant material (Graham, 1988). Previous work by Wegelin (1987) showed that the effect of surface porosity and roughness of filter media on particle removal efficiency in roughing filtration was insignificant compared to the size and shape of macro-pores in the filter. Rooklidge and Ketchum (2002) studied the removal efficiencies in calcite limestone, basaltic river rock, and limestone-amended basalt horizontal roughing filters and found only marginally improved efficiency (7%) for calcite-amended basalt filters over unaltered filters. Improved removal efficiencies are generally correlated to smaller media sizes (Collins, 1994; Wegelin, 1987). The use of multiple grades of filter media in a roughing filter promotes the penetration of particles throughout the filter bed and takes advantage of the large storage capacities offered by larger media and high removal efficiencies offered by small media. The size of filter media decreases successively in the direction of water flow, and ideally the uniformity of filter media fractions is maximised to increase filter pore space (storage capacity) and aid in filter cleaning (Boller, 1993).

Common grades of media used in roughing filters are provided by Wegelin (1996) and shown in Table 2.2. Table 2.2 Typical media grades used in roughing filtration

Filter Media Size (mm) Roughing Filter Description 1st fraction 2nd fraction 3rd fraction

Coarse 24 – 16 18 – 12 12 - 8

Normal 18 – 12 12 – 8 4 - 8

Fine 12 – 8 8 – 4 4 - 2

2.4.2. Hydraulic loading rate Because sedimentation represent a key filtration mechanism in roughing filtration (Wegelin, 1987), operation of roughing filters under laminar flow conditions is essential to maximise removal efficiencies. Flow conditions are described by the Reynold’s number, which can be calculated through a porous medium by the following equation (Wegelin, 1996):

Re = (vdc) / ν Equation 2.1

where, v = hydraulic loading rate (m/s) dc = collector (media) diameter (m) ν = kinematic viscosity = 1.004 x 10-6 m2/s at 20°C

Laminar flow, characterized by consistent fluid motion, occurs at small Reynolds numbers (Re <10). Turbulent flow, characterized by random forces producing eddies and vortices, occurs at large Reynold’s numbers (Re >100). A transition zone occurs where the Reynold’s number is between 10 and 100. Figure 2.2 shows the different combinations of hydraulics loading rate and collector diameter (at 20°C) that result in a Reynold’s number equal to 10.


Source: Wegelin, 1996

0

20

40

60

80

100

120

140

160

0 1 2 3 4 5

hydraulic loading rate (m/hr)

colle

ctor

dia

met

er (m

m)

RE = 10 @ 20°C

v (m/hr) dc (mm) 0.5 72 1.0 36 1.5 24 2.0 18 2.5 14 3.0 12 3.5 10 4.0 9 4.5 8 5.0 7

Figure 2.2 Relationship between Reynolds number and different combinations of hydraulic loading rate and collector (media) diameter

Previous studies have shown that improved removal efficiencies are correlated to slower hydraulic loading rates when flow in laminar (Wegelin, 1987; Collins, 1996).

2.4.3. Filter length Improved cumulative removal efficiencies are typically correlated to longer filter lengths (Collins, 1994; Wegelin, 1987). However, incremental removal efficiencies tend to decrease with increasing filter length due to the preferential removal of larger particles early in the filter (Wegelin, 1996). The rate of decline is dependent on filter design variables and the size and nature of particles in suspension. The use of different media sizes may allow for treatment targets to be met by a shorter filter with multiple media sizes compared with long filter packed with one media size, as illustrated in Figure 2.3.

Figure 2.3 Significance of filter length (and media size) in roughing filtration

RE >10

RE <10


10-10 10-9 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1 1

1Å 1 μm 1 mm 1 cm

Particle Classification

ColloidsSuspended solids

Floating solids (leaves, wood)

BacteriaViruses

Algae

Solid Size (m)

Adapted from Wegelin, et al., 1991

2.5. Important of suspension characteristics in roughing filtration 2.5.1. Size and density distribution of solid matter Knowledge of the sizes and densities of solids in suspension is critical to predicting particle removal efficiencies in roughing filtration (Boller, 1991). Figure 2.4 shows the range of solid matter commonly found in natural surface waters.

Figure 2.4 Solid matter commonly found in natural surface waters

Prior to roughing filtration, large floating solids are typically removed by screening methods, and solids greater than about 20 μm can be separated from solution effectively using sedimentation methods (Wegelin, 1991). Remaining solids in suspension (suspended mineral and organic solids, algae, bacteria, viruses, and colloids) are thus the most commonly removed solids in roughing filtration.

2.5.2. Influence of water chemistry on solid surface-chemical properties The chemistry of the water in which particles are suspended effects particle removal efficiency in roughing filtration, particularly for particles <10 μm (Boller, 1991). Particles in solution will develop a charge due to 1) the adsorption of ions from solution, 2) dissolution of ions from the solid lattice into solution, and/or 3) ionisation of surface groups (Hunter, 1981). A particle’s charged surface attracts ions in solution of opposite charge and creates a charge (or potential) distribution into the bulk solution, the sign and magnitude of which is commonly measured as electrophoretic mobility or zeta potential of the particle. The distribution of charge on the solid surface is called the electrical double layer. When two particles approach, their electrical double layers cause the particles to repel one another. However, if the repulsion can be overcome, then particles will be attracted by van der Waals attractive forces, resulting in particle flocculation and increased particle settling rates (Hunter, 1981).

Increasing solution ionic strength increases particle flocculation rates and particle removal efficiency in granular filtration (Yao, et al, 1971; Fitzpatrick and Spielman, 1973; Tufenkji and Elimelech, 2004). In freshwater, particles (e.g. clay minerals and organic matter) develop a negative surface charge potential due to the lack of sufficient cations in solution to satisfy negatively charged surfaces (Olphen, 1963; Boller, 1991). The same condition applies to the surfaces of filter media in treatment systems (Fitzpatrick and Spielman, 1973). The association of particles with dissolved organic matter (e.g. humic and fulvic acids) further increase the negative surface charge density of particles (Narkis and Rebhun, 1975). Polyvalent cations and flocculants are commonly used to neutralise negative surface charges, promote flocculation and improve filter removal efficiencies.

2.6. Particle removal mechanisms in roughing filters Particles suspended in solution may be removed in roughing filters by one of three mechanisms (Figure 2.5). These include:

• surface (or cake) filtration, • straining filtration, and • physico-chemical filtration


Source: Yao, et al., 1971

Figure 2.5 Primary particle removal mechanisms in granular filtration

2.6.1. Physico-chemical filtration For particles much smaller than the size of filter media (the common case in roughing filtration), particle removal is dependent on the successful transport and attachment of a particle to a media (or collector) surface.

Transport Process

The three dominant mechanisms governing transport of particles to a single collector (diffusion, interception and sedimentation) are depicted in Figure 2.6.

Figure 2.6 Mechanisms of particle transport to a single collector surface

The sum of these three transport processes serve as the basis for the theoretical single-collector efficiency (SCE), which is the ratio of the rate at which a particle strikes a collector surface divided by the rate at which a particle flows toward that collector.

The analytical solution for each transport mechanism was first presented by Yao, et al. (1971) and is summarised below. For Equations 2.2 to 2.5, the following notation has been used: k = Boltzman constant v = hydraulic loading rate T = absolute temperature ρ p = particle density μ = fluid dynamic viscosity ρ f = fluid density dp = particle diameter g = gravitational constant dc = collector (media) diameter

Diffusion represents the dominant transport mechanism for small particles (generally less than 1-2 μm). The transport of a suspended particle out of its fluid streamline to a collector surface by diffusion, ηD, is described by the following equation:

ηD = 0.9 (kT/μdpdcv)2/3 Equation 2.2

Source: McDowell-Boyer, et al., 1986


1.0E-04

1.0E-03

1.0E-02

1.0E-01

0.01 0.1 1 10 100

Particle diameter ( m)

Sin

gle

colle

ctor

effi

cien

cy

Interception occurs when a particle following a fluid streamline comes in contact with a collector. Particle removal by interception, ηI, is described by the following equation:

ηI = 3/2(dp/dc)2 Equation 2.3

Sedimentation, ηG, occurs when a particle is transported out of its fluid streamline to a collector surface due to gravitational settling and is described by the ratio of settling velocity (as determined by Stokes Law) to hydraulic loading rate:

ηG = (ρ p – ρf)gdp2/18μv Equation 2.4

These sum of these three transport processes comprise the theoretical SCE, ηtotal, as shown below:

ηtotal = 0.9 (kT / μdpdcv)2/3 + 3/2(dp/dc)2 + (ρ p – ρf)gdp

2/18μv Equation 2.5

Figure 2.7 shows the contribution that each transport process contributes to the theoretical SCE for a given filter configuration.

Figure 2.7 Contribution of transport processes to single-collector efficiency for a given roughing filter design, showing the effect of particle size and density

Figure 2.7 reveals the following general relationships between particle size, particle density and the SCE:

• Smaller particles are transported to a collector surface primarily by diffusion. • Larger particles are transported to a collector surface primarily by sedimentation. • Particles in the range of 0.5-2 μm (depending on particle density) are the least likely

to be transported to a collector surface. • The importance of interception with respect to sedimentation is dependent on particle

density. For dense particles, interception may be considered negligible.

In this figure, a particle density of 2.60 g/cm3 is representative of soil mineral particulates, while a particle density of 1.05 g/cm3 is taken to represent organic particulates in suspension. The figure shows that for particles greater than 2 μm, SCE values for inorganic particles are an order of magnitude larger than for organic particles of the same size.

Fundamental transport theory as captured in Equation 2.5 has been refined to consider close range forces, including hydrodynamic interactions and universal van der Waals attractive forces, which affect the deposition of particles in a packed bed. A correlation equation for the refined SCE was first quantified by Rajagopolan and Tien (1976) and recently modified by

ηD + ηI + ηG(ρ p = 2.60 g/cm3)

ηD + ηI + ηG(ρ p = 1.05 g/cm3)

ηI

ηD

Adapted from Yao, et al., 1971

dc = 0.5 mm v = 4.8 m/hr T = 25°C ηG (ρ p = 2.60 g/cm3) ηG (ρ p = 1.05 g/cm)

Particle diameter (μm)


Tufenkji and Elimelech (2004) by regressing known dimensionless parameters governing particle deposition across a range of conditions common in granular filtration. The SCE correlation equation by Tufenkji and Elimelech (2004) and definition of parameters are provided below:

ηtotal = ηD + ηI + ηG = 2.4As1/3NR

-0.081NPe-0.715NvdW

-0.052 + 0.55 AsNR1.675NA

0.125

+ 0.22 NR-0.24NG

1.11NvdW-0.053 Equation 2.6

where, NR = dp/dc (aspect ratio)

NPe = vdc/(kT/3πdpμ) (Peclet number characterising ratio of convective to diffusive transport (latter described by Stokes-Einstein Equation))

NvdW = A/kT (van der Waals number characterising ratio of van der Waals interaction energy to the particle’s thermal energy; A equals Hamaker constant of the interacting media)

Ngr = 4/3π(dp/2)4(ρ p – ρf)g/kT (gravitational number; ratio of particle’s gravitational potential when located one particle radius from collector to particle’s thermal energy)

NA = A/12πμ(dp/2)2v (attraction number; combined influence of van der Waals attraction forces and fluid velocity on particle deposition by interception)

NG = (ρ p – ρf)*g*dp2/18uv (gravity number; ratio of Stokes particle settling velocity to

approach velocity of fluid

Figure 2.8 shows how incorporating hydrodynamic and van der Waals attractive forces affects the calculation of the SCE values for representative inorganic (2.60 g/cm3) and organic (1.05 g/cm3) particles. The figure shows that for inorganic particles >1 μm, a slightly smaller theoretical SCE value is calculated using Equation 2.6 compared to Equation 2.5. Whereas, for organic particles, a larger theoretical SCE value is calculated using Equation 2.6 compared to Equation 2.5, although the difference decreases with increasing particle size.

Figure 2.8 Comparison of SCE using equations developed by Yao et al. (1971) and Tufenkji and Elimelech (2004) for representative inorganic (2.60 g/cm3) and organic (1.05 g/cm3) particles for one filter configuration

dc = 0.5 mm v = 4.8 m/hr T = 25°C A = 1.0 E –20 As = 33.64

Particle diameter (μm)

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0.01 0.1 1 10 100

Particle diameter (⎠ m)

Sin

gle

colle

ctor

effi

cien

cy

Tufenjki and Elimelech (Eq 2.6) Yao et al. (Eq 2.5) ηG (ρ p = 2.60 g/cm3) ηG (ρ p = 1.05 g/cm)


Attachment Process

Physico-chemical conditions unfavourable to particle-collector attraction are common under natural conditions. In such cases, the actual SCE, η, will be less than the theoretical SCE, ηo. An empirical collision efficiency (or attachment) factor, α, accounts for the probability such that η = αηo. The attachment factor is determined experimentally from either 1) removal efficiencies of suspended particles through porous media and theoretical values of single-collector efficiency, or 2) flocculation rates of particles undergoing mixing in a batch reactor (Collins, 1994). When α = 0, the water is completely stabilised, and no contact between particle and collector will result in attachment. When α = 1, the water is completely destabilised, and only one contact between a suspended particle and collector grain surface is required for attachment.

2.6.2. Surface and straining filtration Surface (or cake) filtration results from the screening of particles at the surface of a porous media, resulting in the closing off of pore openings. Cake filtration is most likely to occur when the ratio of the collector diameter (dc) to particle diameter (dp) is less than 10 (McDowell-Boyer, et al., 1986). Straining filtration occurs when particles penetrate into porous media but are later lodged in the filter due to their large size. Straining filtration is likely to occur for the range of 10 ≤ dc/dp ≤ 20 (McDowell-Boyer, et al., 1986).

Surface and straining filtration are not likely to play a dominant role in roughing filtration for the following reasons:

• Large particles (>20 μm) are usually removed prior to roughing filtration by methods, such as sedimentation (Wegelin, 1987) assuming particle densities allow large particles to settle faster.

• Proper roughing filter design promotes the removal of larger particles earlier in the filter in the presence of larger media allowing progressively smaller particles to penetrate deeper into the filter, where they come into contact with smaller sized media.

• Filter cake development in horizontal roughing filters and vertical upflow roughing filters are limited by particle drift and secondary particle detachment, respectively. For all configurations, periodic filter maintenance limits filter cake development.

Surface filtration may become more significant in the latter stages of a filter run as particles retained in the filter act as strainers for smaller particles. A filter cake of up to 7 mm of kaolinite clay was observed at the completion of filter runs in DRF experiments with 2.68 mm diameter media (Collins, 1994). However, these experiments were conducted using very high particle concentrations (1,000 mg/L), which increased the potential for surface and straining filtration (Collins, 2005).

2.7. Approaches to modelling deep-bed filter performance Two approaches are used to model deep-bed filtration. These include the trajectory approach and the phenomenological approach, each of which is described below.

2.7.1. Trajectory approach The trajectory approach to filtration focuses on the physico-chemical processes in particle removal and does not consider the effect of surface or straining filtration. The trajectory approach to modelling also does not account for changes to filter conditions caused by solids accumulated in the filter. Clean-bed filter performance is described by colloid filtration theory (CFT) as developed by Yao et al. (1971) and is related to the SCE, η, and attachment factor, α, for a given particle size and density as follows:


ln(Ce/Co) = -3/2 (1 – e)ηαL/dc Equation 2.7

where, Ce = effluent particle concentration Co = influent particle concentration L = length of filter bed e = filter bed porosity

dc = collector diameter

Equation 2.7 indicates that improved removal efficiencies are expected with longer filters packed with finer grained media.

2.7.2. Phenomenological approach The phenomenological approach to modelling does not consider the mechanisms of particle transport or attachment. Instead, filter behaviour is described by a mass balance and empirical rate expression combined to describe the rate of particle removal as a function of filtration time and filter depth (Ohja and Graham, 1992). The mass balance equation (Equation 2.8) states that the mass of particles removed from suspension in the filter must result in an equal mass of accumulated solids in the pores.

v(∂C/∂L) + (∂σ/∂t) = 0 Equation 2.8

where, v = filtration velocity L = filter bed depth in the direction of flow t = time

σ = specific deposit (volume of deposited particles/filter volume) C = particle concentration in suspension (particle volume/liquid volume)

The rate equation, first proposed by Iwasaki (1937), describes a first-order removal with depth proportional to the local particle concentration in the fluid:

∂C/∂L = -λC (or Ce/Co = e-λL) Equation 2.9 where, λ = proportionality constant, called the filter coefficient (units of length-1) Ce = particle concentration of filter effluent Co = particle concentration of filter influent

Combining the mass balance and rate equations yields the following: ∂σ/∂t = vλC Equation 2.10

Equation 2.10 states that the filter coefficient varies depending on the volume of solids deposited in the filter, starting with an initial filter coefficient for a clean filter bed.

Empirical rate equations attempting to calculate the filter coefficient as a function of filter deposit have been proposed (Ives,1960; Tien and Payatakes, 1979). Ives (1960) stated that by starting with an initial particle size-specific filter coefficient, λo, the filter coefficient over time, λ, can be calculated using the following equation:

λ = λo + kσ - (φσ2) / (eo - σ ) Equation 2.11

where, kσ = constant accounting for increased filter surface area with progressive filter deposit eo = initial porosity φ = constant accounting for the influence of pore-size reduction and

increased pore fluid velocity

No substantial improvement in removal efficiency was observed with increasing filter deposit for kaolinite clay by Wegelin (1987). Therefore, Equation 2.11 can be conservatively simplified by removing the term, kσ, such that the filtration coefficient only decreases over time with increasing specific deposit (Wegelin 1987). The filter no longer retains particles when the quantity of pores attains its ultimate filter volume, σu, and λ = 0. Wegelin (1987) summarises that when λ = 0, φ can be solved as follows:

φi = λo (eo - σ u) / (σ u 2) Equation 2.12


Equation 2.11 can then be expressed as follows:

λ = λo (1 - (σ/σ u)2 (eo - σ u) / (eo - σ)) Equation 2.13

λo and σ u (in units of mass/volume) can be determined by conducting experiments. After identifying values for λo and σu and estimated a bulk density for deposited solids (in order to convert σ u into units of volume/volume), particle removal in roughing filters may be modelled as a function of time and space using Equation 2.13 by separating the filter into short filter layer segments and tracking specific deposit over time (Wegelin 1987).

Ohja and Graham (1992) assessed the application of phenomenological deep-bed filtration models and concluded that the major limitation of phenomenological modelling was the high uncertainty involved with the determination of an appropriate bulk density prior to applying an empirical rate equation for different suspensions.

2.8. Review of previous roughing filtration studies There have been several studies on the application of roughing filtration for both synthetic and natural waters using different filter configurations, media types and sizes, hydraulic loading rates, and filter lengths. Example design parameters and results are provided in Table 2.3. Of the studies presented in Table 2.3, only two have carefully evaluated particle removal processes to quantify the relationship between particle removal efficiency and filter design parameters over a broad range of design parameter values in roughing filtration.

Wegelin (1987) assessed particle removals over 20- to 40-cm horizontal filter segments using a model (kaolinite clay) suspension (200 mg/L initial influent concentration) and used multivariate regression analysis to develop empirical equations that related filter design parameters (media size and hydraulic loading rate) to the initial steady-state, particle size-specific filter coefficient (Equation 2.12) and the particle size-specific ultimate filter volume (Equation 2.13) assuming a constant density of 1.15 g/cm3 for deposited solids. Evaluation of particle straining and cake filtration was neglected in this study.

λi [cm-1] = 0.02v–0.88dc–0.85dp

1.0 Equation 2.14

σu [ml/l] = 10.0v–0.80dc–0.18dp

0.35 Equation 2.15

No improvement in removal efficiency with increasing filter deposit was observed in those experiments. Equations 2.14 and 2.15 were applied developed by Ives (1960) to model filter coefficient decline as a function of filter deposit in a pilot-scale HRF.

Collins (1994) extended the work of Wegelin and assessed particle removals over 90-cm downflow filter segments using kaolinite clay (1,000 mg/L TSS initial influent concentration) and algae (influent concentration not provided) suspensions. The influence of humic acid and calcium addition in kaolinite clay suspensions on removal efficiencies was also investigated. Equations relating filter design parameters (media size, hydraulic loading rate, and filter length (or depth)) to steady-state filter efficiency (Ce/Co) (Equation 2.14) and ultimate filter deposit for kaolinite clay (Equation 2.15) were developed using multivariate regression analysis. Since phenomenological modelling was not conducted, no estimated density for deposit solids was assumed to convert σv to units of volume/volume. Similar equations developed for algae on clean media and kaolinite clay on algae-ripened media were also developed.

Ce/Co = 0.188 + 0.0231dc + 0.136v – 0.101L Equation 2.16

σu (g/L) = 71.656 – 0.638dc – 34.993v – 3.822L Equation 2.17

Differences between observed removal rates and those predicted by Wegelin’s model were assumed to be due to orientation of filter configuration. Observation of a surface cake at the end of filter runs and a decrease in actual collector efficiency with increasing filter depth were attributed to cake and straining filtration. However, high influent concentrations may have contributed to increased cake and straining filtration (Collins, 2005).


Table 2.3 Summary of solids removal results from previous roughing filtration studies

Author Year Filter Design Filter SpecificationsMedia Sizes (mm)

Filter Length per media grade (m)

Hydraulic Loading Rate (m/hr) Test Duration

Influent Particle Concentration (NTU unless otherwise noted)

Clarke et al. 1996 URFS Cylinder - 3 x 1.5 m length x 1.3-m dia 40, 20, 10 0.5, 0.5, 0.5 0.6 568 days average 10-20, up to 400 (<2 days); unknown NTU for clay challenge

VRFS Cylinder - 14-cm dia x 0.5-3.5 m length 0.5 -1.2 0.5-3.5 N/A 10-1000 HRFS Box - two compartments 2.0 length x

2 5 idth1.2-5.0. 0.25-1.2 0.7, 1.0 >2 months 70-300

HRF Box - 7.1-m length x 1.2-m widthHRFS Box - 4.3-m length (divided into 3

compartments) x 1.2-m width

URFS Cylinder - 3 x 2.0-m dia. x 2.0-m length 20-10, 5-1020-6, 10-420-10, 10-4, 2-0.620-10, 10-5, 5-220-10, 10-2, 2-0.620-10, 10-2, 2-0.6

0.53 3840.42 393-8950.32 377

25.4-9.6 1.022x14x12 1.011x8x7, 6x4x3 0.05, 0.7511x8x7, 3-4 0.05, 0.7511x8x7, 6x4x3 0.1, 0.911x8x7, 3-4 0.1, 0.651.5-2.0, 3-5, 7-10, 15-25

0.5 - 4.0

3-5, 7-10, 15-25 0.5 65 days3-5, 7-10, 15-25 1 up to 38 days 3-5, 7-10, 15-25 2 up to 26 days

N/A

100-200

200 TSS

Ingallinella et al. 1998 Cylinder - 0.6-m dia. x 2.0-m length

0.420.3

20-40 cm1

~600 hrs

URF

Collins 1996

256-600 hrs0.560

Wegelin, et al. 1987 HRF

Box - four compartments, 0.5-m high x 0.20-m width x 4.0-m length

Reed and Kapranis

1998

1,000 TSS

1,000 TSS

1,000 TSS

URF Cylinder - 0.3-m dia. x 1.5-m length 0.75 40 days

5-7 days

Cylinder - 10-20-cm dia x 20-40-cm length 5.15 - 6.0

0.5-1.0VRF Cylinder - 15-20-cm dia x 90-cm length 2.68, 5.53, 7.94 90 cm

2.68, 4.83, 7.94, 11.11Cylinder - 15-20-cm dia x 60-cm lengthHRF

30-20, 20-12, 12-9 0.51.07, 0.71, 0.36 average <2, up to 7

URF 10 1.4

N/AEl-Taweel and Ali

1999 VRF (upflow or downflow?)

Cylinder - 0.4-m dia. x 2.0-m length

Galvis, et al. 1996 0.7 average 71-167, up to 420 NTU average 146-333, up to 881 mg/L TSS

N/A19-13, 13-6, 6-1.6 25 weeks

0.1-0.3Li and Du 1996

Rooklidge and Ketchum

2002 HRF Box - 2.14-m length x 0.9-m width 60 days

2.2-33

N/A

1 day 150 +/-20

Water Type Media TypeRemoval efficiency (%) (NTU only otherwise noted)

Basalt 24.5Calcite 18.8Calcite + Basalt 28.4Basalt 68.6Calcite 71.3Calcite + Basalt 75.4

River water and kaolin-clay

Gravel 60-75% average

Kaolin clay 85-98%Pond water 96-98%

68% turbidity, 93% TSS67% turbidity; 94% TSS

80% turbidity; 97% TSSBlast furnace slag 37%Basalt (2 layers) 61%Gravel + Basalt + Sand 66%Gravel 67%Gravel + Leca + Sand 51%Gravel + Gravel + Sand 86%

15%13.2-20%30%

Angular, crushed granite 41%42%<90%>90% for highly turbid wateraverage 45%; >60% for higher NTUaverage 45%; >60% for higher NTU

Quartz sand and gravel Filter coeff (0.003-0.01); Model developedGlass PumiceCharcoalQuartz sand and gravelQuartz sand and gravelQuartz sand and gravel

Kaolinite clay Quartz sand and gravel 56-97%; Model developedKaolinite clay S/A (algae ripened) 79-99%Algae Quartz sand and gravel 55-86%Kaolinite clay Quartz sand and gravel 64-77% (data for smallest media n/a)Kaolinite clay S/A (algae ripened) 82-91%Kaolinite clay Quartz sand and gravel 54-83%Kaolinite clay S/A (algae ripened) 75-97%

Kaolinite clay

Synthetic (unknown)

Polystyrene (s-shaped) + Beads

Filter coeff (0.01-0.02); no measureable difference between each media type

Variable as a function of filter deposit. Used to confirm model developed from parameter tests (above)

Polystyrene (s-shaped)

Kaolinite and montmorillonite clay

River water

GravelReservoir water

Natural surface water

GravelDynamic RF-treated Cauca River water

Quartz sand


2.9. Knowledge gaps Previous studies have advanced the understanding of particle removal mechanisms in roughing filtration and improved the guidelines for filter design and application. However, many knowledge gaps currently exist, including the following:

1. Application of roughing filtration to stormwater retained in urban wetlands prior to slow sand filtration has not been thoroughly investigated. Current guidelines are unable to predict the performance of roughing filtration applied to stormwater, in which particles are typically comprised of inorganic and organic assemblages (or flocs) of variable size, shape and density.

2. Establishing a database of attachment factors and surface charge potentials for various water types would create a knowledge base, upon which future advances may be added (Collins 1994). Colloid filtration theory (CFT) can be used to establish such a database. However, to date no study has applied CFT to roughing filtration to determine an attachment factor for a polydisperse suspension. Establishment of a reliable method to apply CFT would advance the application of trajectory modelling in roughing filtration as well as benefit phenomenological modelling of deep-bed filters, which rely on the determination of initial steady-state removal rates.

3. Although surface and straining filtration in DRFs for high influent concentrations of kaolinite clay have been observed, the significance of these processes at lower particle concentrations and in other roughing filter configurations (e.g. HRF and URF) has not been confirmed.


3. Materials and Methods

3.1. Experimental approach for kaolinite clay experiments A 36-test orthogonal array (Table 3.1) was used to evaluate the influence of key filter design variables and particle concentration on initial steady-state kaolinite clay removal in URFS. Individual experiments were conducted using one media size packed in each of the four columns. This provided the opportunity to evaluate simultaneously the filter performance for one media size, hydraulic loading rate and initial influent concentration over four filter lengths.

Three media sizes (2.18, 5.18, and 7.55 mm average grain diameter) were used to assess the influence of pore size and media density on particle removal efficiency. Selected media sizes are in the lower range of media sizes commonly used in field-scale roughing filters (2 to 20 μm). Media sizes smaller than 2.18 mm were not selected for evaluation in this study due to the likelihood of cake and straining filtration playing a significant role in removal efficiency.

Three hydraulic loading rates (0.5, 1.0, and 1.5 m/hr) were assessed in this study to determine the influence of interstitial fluid velocity on particle removal efficiency. The calculated Reynold’s numbers for experiments conducted in this study range from 0.3 (2.18 mm media at 0.5 m/hr) to 3.1 (7.55 mm media at 1.5 m/hr), well within the range of recommended operating conditions for roughing filtration (<10).

Four monitoring points (0.6, 1.2, 1.8, and 2.4 m) provided the opportunity to identify the influence of filter length on removal efficiency.

Three initial particle concentrations (100, 400, and 700 nephelometric turbidity units (NTU) equating to 52.6, 196 and 298 mg/L TSS) were used for experiments conducted at 0.5 and 1.0 m/hr to determine the influence of particle concentration on kaolinite removal efficiency. Initial particle concentrations for experiments conducted at 1.5 m/hr were maintained at 400 NTU (or 191 mg/L TSS).


Table 3.1 36-test orthogonal array for kaolinite clay removal on clean media

Initial Concentration (NTU) [mg/L TSS] Average Media size

(mm)

Hydraulic Loading Rate

(m/hr) Filter Length

(m) (100) [52.6] (400) [191] (700) [299]

2.18 0.5 0.6 2.18 0.5 1.2 2.18 0.5 1.8 2.18 0.5 2.4

5.18 0.5 0.6 5.18 0.5 1.2 5.18 0.5 1.8 5.18 0.5 2.4

7.55 0.5 0.6 7.55 0.5 1.2 7.55 0.5 1.8 7.55 0.5 2.4

2.18 1.0 0.6 2.18 1.0 1.2 2.18 1.0 1.8 2.18 1.0 2.4

5.18 1.0 0.6 5.18 1.0 1.2 5.18 1.0 1.8 5.18 1.0 2.4

7.55 1.0 0.6 7.55 1.0 1.2 7.55 1.0 1.8 7.55 1.0 2.4

2.18 1.5 0.6 2.18 1.5 1.2 2.18 1.5 1.8 2.18 1.5 2.4 5.18 1.5 0.6 5.18 1.5 1.2 5.18 1.5 1.8 5.18 1.5 2.4 7.55 1.5 0.6 7.55 1.5 1.2 7.55 1.5 1.8 7.55 1.5 2.4

Experimental approach for Urrbrae Wetland water experiments Due to the variable nature of suspended solids concentrations and character observed in the Urrbrae Wetland, roughing filtration experiments were conducted using water collected from two locations at the Urrbrae Wetland (refer to Section 3.4.2). Each test was conducted using a hydraulic loading rate of 1.0 m/hr and the following filter configuration:

Column 1: 7.55 mm media Column 2: 7.55 mm media Column 3: 5.18 mm media Column 4: 2.18 mm media

The selected filter configuration provided the opportunity to evaluate the influence of each media size available at a hydraulic loading rate that produced within a reasonable time


adequate sample volumes for desired water quality analyses. The same filter configuration was evaluated using a kaolinite clay suspension for comparison.

3.2. Column specifications and laboratory setup To simulate the processes observed in roughing filtration, experimental waters were continuously injected through laboratory columns packed with quartz sand and gravel media. A schematic of the experimental set-up is presented in Figure 3.1, and column specifications are shown in Figure 3.2. Four acrylic columns (each 67 cm in total length and 6.3 cm in internal diameter) were connected to simulate a 2.4-m vertical upflow roughing filter in series (URFS). Each column was sealed with end caps fitted with rubber o-rings to prevent leakage. Polyvinyl chloride fittings (1/2-inch threaded) were installed through the column wall for inflow, outflow, and drainage ports. A raised floor, positioned to support 60 cm of filter media, provided consistent hydraulic loading across each column and accommodated the influent and drainage ports. The floor was created by resting a perforated acrylic plate (25 perforations each 5 mm in diameter) on an installed ledge 3.5 cm above the base of the column. A 1-cm supporting layer of 9.5-mm diameter gravel was placed above the plate in all experiments. The 1-cm gravel base was not included in the total filter length.

Rubber o-rings (0.5 mm in annular width) were placed inside each column at 20 cm and 40 cm above the filter media base to prevent preferential flow along column walls, a phenomenon observed in previous studies (Collins, 1994). Additional discussion on the significance of the rubber o-rings is provided in the next section.

Although the experimental design represents an URFS, it also likely simulates closely a continuous 2.4-m upflow roughing filter (URF). Any potential cake or straining filtration was likely limited to experiments using the smallest media size (for this study, 2.18 mm) and was likely to occur only across the initial filter face. Larger particles in suspension, likely to settle on the top surface of filter media or base of the last three columns, were removed in the first filter. In summary, the URFS configuration provided the following advantages over URF and HRF:

• The potential for measurement errors resulting from particle accumulation and detachment in the vicinity of side sampling ports of a continuous filter were eliminated.

• Upward flow through the columns and downward flow through connector lines minimised the potential of particle settling at the base of each column and in connector lines (observed in preliminary experiments with a faster settling kaolinite clay suspension), thereby improving correlations between removal efficiency and design variables.

• At the conclusion of each experiment, particles retained in each filter column were removed by flushing each individual column in an upward direction (at an estimated hydraulic loading rate of 20 and 25 m/min) and allowing the water to gravity drain. This procedure was repeated on average 15 times and eliminated the need to repack the columns for each experiment. Rapid upward flushing was achieved by connecting the column drainage valve to a pressurised distilled water tap while holding a perforated plate across the top of the filter media. The perforated plate prevented mobilisation of the filter media and was removed prior to re-connecting the filter columns for the next experiment. Distilled water was passed through the columns at a minimum overnight prior to beginning a new experiment.


Figure 3.1 Upflow roughing filter in series (URFS) laboratory set-up


Figure 3.2 Schematic of laboratory upflow roughing filter column


3.3. Filter media preparation and column packing procedures The grain density of a filter bed is a key parameter in deep-bed filtration. Therefore, the influence of media size on treatment performance may be assessed only if the uniformity of different sized media is consistent and preferably high. A roughing filter packed with a media of low uniformity is likely to perform better than a filter packed with media of similar average size of high uniformity. This phenomenon occurs, because the pore spaces in the filter packed with media of low uniformity are smaller due to filling of pores spaces by smaller grains. Although removal efficiencies may be improved by using filter media of low uniformity, such design may promote undesired cake and straining filtration leading to shorter filter run lengths and increased filter cleaning difficulties.

For this study, laboratory columns were packed with quartz sand and gravel sourced from Filtsand Australia (Tanunda, SA). The media arrived in grades of 2.00-4.75 mm and 4.75-9.50 mm in diameter and was further segregated using 2.36 mm and 5.60 mm diameter sieves to provide three filter media grades of high uniformity (Uc<1.33). Table 3.2 summarises the characteristics of media sizes used in this study and also shows the ratio of the inner diameter of the laboratory columns (63 mm) to average media diameter. Porosity for each media grade was calculated by measuring the volume of water contained in a 1-litre graduated cylinder packed with media under saturated conditions.

Table 3.2 Size distribution of media used in roughing filter experiments

Average diameter (dave)

(mm)

Minimum diameter (dmin)

(mm)

Maximum diameter (dmax)

(mm) Uc

a

(d60/d10) Porosity

Column diameter divided by average

media diameter 2.18 2.00 2.36 1.09 0.42 26.9 5.18 4.75 5.60 1.09 0.41 12.2 7.55 5.60 9.50 1.33 0.40 8.3

aEstimated assuming linear grain size distribution. Uc likely lower than reported.

A column diameter to media diameter ratio greater than 30:1 is recommended to minimise variations in fluid velocity and particle-media collisions due to expected larger pore sizes along columns walls (Smith and Dillon, 1997; Collins, 2005). Collins (1994) installed rods in the interior side walls to address observed preferential flow conditions in selected HRF experiments evaluating 2.68 mm media. The water volume and pump capacity necessary to maintain hydraulic loading rates through columns satisfying this ratio for the 5.18 and 7.55 mm media sizes were deemed impractical for this bench-scale study. Therefore, to ensure the transport of particles through pore spaces unaffected by the column wall, rubber sealing rings were placed inside the columns 20 and 40 cm above the base of the media.

Prior to packing each column, filter media was washed thoroughly with distilled water. Each column was packed by first placing 1 cm of 9.5 mm media onto the perforated plate. The column was then filled with distilled water through the drainage port connected to a tap. Media was packed in the column in 5 cm increments and tamped down before pouring additional media. After 60-cm of filter media was poured in the column, a temporary perforated plate was placed on and pressed tightly against the top of the filter media while the column was repeatedly filled (to overflow) and drained through the drainage port. This procedure prevented media from fluidising during filling, allowed the media to settle into the tightest packing orientation, cleaned the interstitial water in the column of residual turbidity, and removed air bubbles from pore spaces. Additional media was added to make up 60 cm of filter media before connecting the water-filled column to the system.

3.4. Passage of conservative tracer through column to confirm ideal plug flow conditions

A calcium chloride solution (700 EC (μS/cm)) was used as a conservative tracer and passed through an individual filter-packed column for 7 minutes at a hydraulic loading rate of 0.50 m/hr to produce breakthrough curves and confirm ideal plug flow conditions through the filter


media. Experiments were conducted for both 2.18 and 7.55 mm media with no rubber rings and for 7.55 mm with rubber rings. EC measurements were recorded over time from the top of the column (55 cm from the injection point). The one-dimensional transport model, CXTFIT (Toride, et al., 1995) was used to quantify dispersivity through the filter column. Results of these tracer experiments are provided in Section 4.1.

3.5. Selection and preparation of experimental waters 3.5.1. Kaolinite clay suspension For this study, a calcined kaolinite clay (Glomax LL) sourced from the Georgia Kaolinite Company (USA) was used to evaluate the effect of filter design variables and influent particle concentration on removal efficiencies in URFS. The kaolinite clay provided the following advantages:

• The constant density of the kaolinite clay (2.60 g/cm3) provided the opportunity to evaluate results using CFT.

• The use of kaolinite clay provided an opportunity to expand knowledge and compare findings with previous studies using kaolinite clay (Collins, 1994; Wegelin, 1987).

• The consistent particle size distribution and refractive index of the kaolinite clay provided the opportunity to determine reliably the fate and transport of specific particles sizes through the filter using commonly available sizing techniques, such as laser diffraction.

• The non-swelling nature of the kaolinite clay allowed for reliable monitoring of turbidity during experiments.

Prior to each experiment, the kaolinite clay was hydrated in distilled water (initial pH between 5.6-5.8) overnight, dispersed in a blender, and mixed with additional distilled water to the desired initial turbidity. To replicate typical conditions observed in the Urrbrae Wetland (see Appendix A for water quality monitoring data), the pH of the solution was raised to 7 using diluted NaOH, and sodium chloride was added to maintain a 0.001 M NaCl suspension. The suspension was maintained at a constant electrical conductivity throughout the duration of each experiment.

3.5.2. Natural water A previous study by Massman, et al. (1999) revealed that particles in water collected from the main lagoon of the Urrbrae Wetland were comprised of a diverse assortment of inorganic and organic particles, the concentration and character of which were highly dependent on the energy of the system. A preliminary assessment of the ASR pond water revealed a consistent particle concentration generally unaffected by flow conditions in the main lagoon and a different particle composition compared to the main lagoon.

For this study, natural stormwater from both the main lagoon and ASR pond of the Urrbrae Wetland were evaluated. Samples were collected the day before planned roughing filtration experiments and kept in suspension overnight to allow for the water temperature equilibration.

Samples from the main lagoon were collected on 8 October 2005 and 19 October 2005 during stormy conditions as water from the settling pond was entering the main lagoon via the Cross Rd inlet (Location A in Figure 1.1). Considering the location of the passive overflow structure connecting the ASR pond and the main lagoon, samples were collected about 15 meters west of the Cross Road inlet along the southern banks. These samples represent a worst-case scenario for field-scale roughing filter design. The ASR pond sample was collected from the southern bank of the ASR pond near the foundation for proposed water treatment works (Location B in Figure 1.1) on 17 October 2005, which was a dry and sunny day.


3.6. Experimental Procedures Prior to each experiment, distilled water was continuously injected through the columns overnight at a rate of 20-25 ml/min. During each experiment, particles were suspended in primary feed and constant-head tanks using magnetic stirring and 450 L/hr aquarium pumps. Particle size analysis showed no significant change in particle size due to agglomeration and flocculation during experiments with kaolinite clay particles. For experiments with natural water, only magnetic stirring was used for mixing in the feed tank to reduce the chance of weakly flocculated particles breaking apart during the experiment. A multi-channel peristaltic pump (Masterflex), fitted with two to four mini-cartridges and PharMed® 3-stop tubing, was used in combination with the constant-head tank to inject water through the laboratory columns at constant hydraulic loading rates ranging from 26 to 78 ml/min (or 0.5 to 1.5 m/hr hydraulic loading rate). Initial trials revealed that hydraulic loading rates could vary up to 10% during an experiment using a peristaltic pump alone due primarily to water level changes in the feed tank. A 1-litre graduated cylinder was used to collect filtrate from the fourth column and monitor the volumetric flow rate through the filter.

The URFS configuration provided the opportunity to sample along the filter without interfering with hydraulic loading rates down gradient of sampling locations. This was accomplished by sampling filtrate from the fourth column after steady-state conditions were established, then carefully draining and detaching the fourth column to allow for sampling of the third column. The cycle was repeated to sample filtrate from the second and first column. Variations in filtrate quality during steady-state conditions were established by a 60-minute monitoring period after fourth column breakthrough (based on turbidity and electrical conductivity). Breakthrough times for the experiments measured after the fourth column varied from about 1.75 (1.5 m/hr) to 5.0 hours (0.5 m/hr), and effluent samples (minimum 250 ml each) from each column were collected over a period of between 1 and 1.5 hours after breakthrough.

Flow through the laboratory URFS was accomplished under pressurised conditions. To prevent the introduction of air bubbles through the system during experiments, it was necessary to fill the first, second, and third column with water above their respective outflow ports prior to sealing the columns. In addition, the fourth column was not sealed on top and was raised slightly above the other three columns. This prevented the creation of negative pressure and introduction of air bubbles through the system as effluent flowed from the fourth column down the final effluent line.

3.7. Monitoring methods used during experiments 3.7.1. Turbidity Steady-state conditions and removal efficiencies for all experiments were based initially on turbidity measurements (NTU). All turbidity measurements were directly measured using a Hach 2100P Turbidimeter. The instrument was calibrated every two weeks using STABLCAL™ Formazin Primary and GELEX® Secondary Turbidity Standards. The relative standard deviation of the turbidity measurements was 1.5% in the range of 5-15 NTU decreasing to about 0.5% in the range of 500-700 NTU. Turbidity values for ultra-pure Milli-Q water ranged from 0.15 to 0.17 NTU.

3.7.2. Electrical conductivity, temperature, and pH The electrical conductivity, temperature, and pH of water in the feed tank were monitored during each experiment using a TPS Field Analyser (Model 90-FLMV). The pH probe was calibrated biweekly using a two-point calibration with industry-standard pH 4 and 7 buffers. A mercury thermometer was used to confirm the temperature reading.

3.8. Analytical methods conducted after experiments 3.8.1. Total suspended solids (TSS) Selected water samples collected from experiments with kaolinite clay (those conducted at 1.5 m/hr) and Urrbrae Wetland water were analysed for total suspended solids (TSS)


according to Standard Methods 209 D - Total Nonfiltrable Residue at 103-105°C (APHA, 1981). Samples were filtered using a 250-ml Gooch crucible under vacuum through a standard 0.45-μm glass-fibre filter paper. After filtration, the filter paper with residue was oven-dried at 104°C for 8 hours. Each filter paper was weighed prior to sample filtering and after drying using a laboratory scale with an accuracy of ± 0.0001 mg. A “blank” filter paper was weighed prior to and after drying to account for water loss in the filter paper during drying for each sample batch. The weight of the dried blank filter paper relative to its original weight (between 98.5 and 99.5% for all three batches) was applied to all filter papers in the respective batch of analyses. Final TSS concentrations were calculated using the following formula:

TSS (mg/L) = (filter paper + residue) – (filter paper * (blank after drying / blank before drying)) (mg) sample volume (L)

Consistent with Standard Method 209 D, kaolinite clay samples were filtered to yield a residue mass greater than 10 mg to minimise sample error. Because a high percentage of particles in the Urrbrae Wetland water samples were organic in nature, residue mass collected prior to clogging of the filter paper was generally less than 5 mg. All analyses were conducted in duplicate on the same day of sample collection.

3.8.2. Particle size distribution analysis by laser diffraction method Water samples collected from roughing filter experiments were analysed for particle size distribution (PSD) using a 2600 Laser Diffraction Particle Sizer (Malvern Instruments). Samples were analysed according to standard procedures outlined in the operating manual (Malvern Instruments, 1991) on the same day of the roughing filter experiment to minimise errors due to biological activity and particle settling and agglomeration. The particle size measured by the Malvern 2600 represents the diameter of a spherical particle of equivalent volume. A 63 mm and 300 mm receiver lens were used for this study to size particles in the range of 0.5 to 118 μm and 5.64 to 524 μm, respectively.

Samples were introduced to the laser beam with a circulation system consisting of a flow-through cell connected to a dispersion tank. Prior to each analysis, the circulation system was flushed with distilled water to remove any foreign particles until the sample obscuration (a unit-less measure used by the Malvern 2600 corresponding to particle concentration) was 0.000. Signal noise caused by imperfections on the flow-through cell plates, dust on source and receiver lenses, and remaining particles was minimised by zeroing the instrument. Samples were then slowly added to the circulating distilled water to ensure that introduction of air bubbles was minimised. Concentrated samples were diluted with additional distilled water as necessary to minimise obscuration. This procedure reduced the potential for “multiple scattering”, a source of error caused by the laser beam being scattered by multiple particles within the flow-through cell. The obscuration for all analyses ranged from 0.010 to 0.030. A steady-state PSD was achieved after circulating the sample for about 2 minutes. Circulation and stirring speeds were kept at low setting throughout the analysis to ensure that particles in solution were introduced to the laser beam as a homogenous stream in a reproducible state of dispersion. All samples were analysed in triplicate.

3.8.3. Particle size distribution (PSD) analysis by particle counting method Water samples collected from one kaolinite clay test (2.18 mm media x 4 columns, 1.0 m/hr, and 700 NTU influent concentration) were analysed at the Ian Wark Institute using a NICOMP Particle Sizing Systems Accusizer 770A to confirm the particle size distribution provided by the Malvern 2600. The Accusizer employs the principle of single-particle optical sizing (SPOS), whereby the size of an individual particle is measured as it passes an optical sensor. In the absence of passing particles, a steady baseline voltage is received from the detector. Particles drawn by vacuum past the sensor cast a shadow over the detector, changing the output voltage. The magnitude of the change in voltage is related directly to


particle size, which is determined by comparing the pulse height with a calibration curve created by passing standard particles of known size through the sensor. The Accusizer 770A is able to count individual particles in the range of 0.5 μm to 484 μm.

3.8.4. Measurement of physico-chemical properties of Urrbrae Wetland water Water samples from the detention pond and main lagoon collected prior to and after passage through the laboratory roughing filter were submitted to the Australian Water Quality Centre (AWQC) to characterise the physical and chemical parameters of Urrbrae Wetland waters and the treatment benefits of roughing filtration. The list of parameters analysed is provided in Error! Reference source not found..

3.8.5. Characterisation of particles by x-ray diffraction X-ray diffraction analysis was conducted on untreated stormwater sample collected from the main lagoon to determine the mineralogical composition of suspended solids. In addition, a sample scrape of the bottom of the settling pond and the Glomax LL used in synthetic water URFS experiments was analysed for comparison.

A representative main lagoon sample was collected for XRD analysis by allowing suspended particles to settle in multiple vessels over two days. Settled solids were then collected from the bottom of the vessels by pipette and dried. This method was recommended (Raven, 2005) to eliminate the potential for sample contamination by filter paper material using traditional filtration methods.

Samples were ground in an agate mortar and pestle then pressed into aluminium sample holders for XRD analysis. XRD patterns were recorded with a Philips PW1800 microprocessor-controlled diffractometer using Co Kα radiation, variable divergence slit, and graphite monochromator. The diffraction patterns were recorded in steps of 0.05° 2θ with a 1.0 second counting time per step, and logged to data files on an IBM-compatible PC for analysis.


Table 3.3 Water quality analyses performed by Australian Water Quality Centre for untreated and roughing filter-treated Urrbrae Wetland waters

Data Type Analyte/Test Analysis Method/Description Reference Method Reporting UnitsPhysical pH Electrometric APHA 4500-H B 0.1 pH units

Conductivity (at 25oC) Electrical conductivity APHA 2520 B 2 uS/cmAlkalinity Potentiometric titration to end-point pH 4.5 APHA 2320 B. 0 mg/LBicarbonate Potentiometric titration from pH 8.3 to end-point pH 4.5 APHA 2320 B. 0 mg/LColour - True Filtered sample, spectrophotometric measurement at 456 Bennett and Drikas (1982) 1 HUSuspended Solids Total suspended solids dried at 103 - 105 oC APHA 2540 D 2 mg/LVolatile Suspended Solids Suspended solids lost due to ignition at 550 oC APHA 2540 E 1 mg/L

Derived Sodium Adsorption Ratio (SAR) Derived calculation -Total Hardness as CaCO3 Derived calculation mg/LIon Balance Derived calculation -Dissolved Solids by calculation Derived calculation mg/LTotal Dissolved Solids (by EC) Derived from conductivity (16-01) APHA 2520 B 1 mg/L

Major Cations Calcium 0.45um filtration, acidification to 1% HNO3, ICP-ES APHA ICP 3120 B 0.1 mg/LMagnesium Acid digestion, acidification to 1% HNO3, ICP-ES APHA ICP 3120 B 0.3 mg/LPotassium 0.45um filtration, acidification to 1% HNO3, ICP-ES APHA ICP 3120 B 1.0 mg/LSodium 0.45um filtration, acidification to 1% HNO3, ICP-ES APHA ICP 3120 B 0.5 mg/L

Major Anions Chloride Automated ferricyanide colorimetric method APHA 4500-Cl E. 4.0 mg/LFluoride Specific ion electrode APHA 4500-F- C 0.1 mg/LSulfate 0.45um filtration, acidification to 1% HNO3, ICP-ES APHA ICP 3120 B 1.5 mg/L

Metals Iron - Total Acid digestion, acidification to 1% HNO3. ICP-ES APHA ICP 3120 B 0.03 mg/LNutrients Nitrogen - Ammonia Automated colorimetric APHA 4500-NH3 H 0.005 mg/L

Nitrogen - (Nitrate & Nitrite) Automated colorimetric cadmium reduction APHA 4500-NO3- F. 0.005 mg/L

Nitrogen - Total Kjeldahl Kjeldahl digestion followed by automated colorimetric APHA 4500-NH3 G / 4500-Norg B 0.05 mg/LNitrogen - Soluble Kjeldahl Kjeldahl digestion followed by automated colorimetric APHA 4500-NH3 G / 4500-Norg B 0.5 mg/L

Nitrogen - Total Derived from total of (Nitrate & Nitrite) and Total Kjeldahl APHA 4500-NO3_ F . 0.05 mg/L

Phosphorus - Total H2SO4/K2SO4/HgO digestion, automated colorimetric APHA 4500-P E. 0.005 mg/LFilterable Reactive Phosphorus (FRP) Filtered, 0.45 um; automated colorimetric APHA 4500-P F. 0.005 mg/LSilica (Reactive) Automated heteropoly blue colorimetric APHA 4500-Si E. 1.0 mg/L

Organics Total Organic Carbon (TOC) Persulphate oxidation 0.3 mg/LDissolved Organic Carbon (DOC) Persulphate oxidation 0.3 mg/LBacterial Regrowth Potential (BRP) 12o Forward Scattering Turbidity, correlated to ACE ACE ug/L

Biodegradable Organic Carbon (BDOC) Biodegradation by surface fixed biofilm with DOC measurements over 10 days 0.2 mg/L mg/L

Biological Chlorophyll-a 95% cold ethanol extraction ISO10260 (1992); Winterman & deMots (1965) 0.1 ug/L ug/LICP-ES = Inductively Coupled Plasma-Emission SpectroscopyACE = Acetate Carbon Equivalents


3.8.6. Characterisation of particles by scanning electron microscopy (SEM) Raw and roughing filter-treated samples from main lagoon and ASR pond were analysed using scanning electron microscopy (SEM) to gain a better understanding of the compositional nature of suspended solids in Urrbrae Wetland water and the potential benefits of roughing filter treatment.

Samples were prepared on the day of sampling and analysed at the University of Adelaide Microscopy laboratory. Of the water samples collected, sub-samples were prepared by first filtering 5 ml of sample onto a 0.2-μm polycarbonate filter membrane (Millipore, Isopore Membrane filters Cat # GTTP01300). Secondly, a 1:200 dilution was made (using 10 ml of sample) and filtered onto membranes. Thirdly, 1 ml of 8% Gluteraldehyde was added to 50 ml of the diluted stock and allowed to stand overnight in a 4°C fridge before filtering through membranes. The filter membranes were allowed to air dry at room temperature and mounted onto aluminum specimen mounts using double-sided tape.

Where imaging of the surface topography was required, specimens were sputter-coated with 20 nm of gold to provide electrical conductivity and maximize Secondary Electron (SE) signal yield. Where imaging of the composition was required, specimens were evaporatively-coated with 30 nm of carbon to provide electrical conductivity and maximize Backscattered Electron (BE) phase contrast. Carbon coating also minimizes extraneous x-ray peaks from the characteristic X-ray spectrum. Where Energy Dispersive X-ray (EDX) analysis of carbon content was required, samples were sputter-coated with 20 nm of Copper to provide electrical conductivity. Each method employed an EmScope SC500 coating unit.

The specimens were placed into a “Phillips” XL30 FEG-SEM, with an attached “EDAX” DX4 energy-dispersive x-ray system examination, using primary electron beam energy of 10 KeV. Imaging was performed using the Secondary Electron (SE) signal, when information about surface topography was required. The secondary electrons originate from the interaction of the primary electron beam with the outer shell electrons and have energies less than 50eV. Due to the low energy of the secondary electrons they have a small escape depth of approximately 10 to 100 nm. The small escape-depth causes the SE signal strength to be dependent on the angle of incidence of the primary beam and, as such, the SE signal primarily carries information about the local topography. Imaging was also performed using the Backscattered Electron (BE) signal, when information about composition and phase were required. The backscattered electrons interact with the nucleus of the atom and have an escape-depth of approximately 0.5 to 10 μm, consequently the BE signal primarily carries information about the average atomic number and the density of the sample and is commonly called "atomic number contrast or Z contrast". When the elemental composition of the sample was required, characteristic x-ray signals were collected at selected locations for qualitative EDX analysis. The elemental x-ray signals are generated within the interaction volume of the primary electron beam when the incident electrons interact and cause ejection of the inner shell electrons from the atoms in the sample. The subsequent relaxation of the outer shell electrons generates characteristic x-rays. EDX analysis is possible within the volume over which the electron beam interacts (approximately 4 μm3) for all elements of atomic number greater than 6. Detection limits are in the order of 0.1 to 5.0 wt % depending on the energy of the characteristic x-ray line.

3.8.7. Quantification of roughing filtration efficiency using Membrane Filtration Index

The clogging potential of natural untreated and roughing filter-treated stormwater suspensions was analysed using the Membrane Filtration Index (MFI) developed by Shippers and Verdouw (1980). MFI analyses of kaolinite clay at initial concentrations of 20, 100, 400, and 700 NTU were also conducted for comparison. Analyses were conducted in triplicate according to procedures outlined in Dillon et al. (2001).


3.9. Other analytical methods 3.9.1. Zeta potential measurements The surface charge potential for the Glomax LL kaolinite clay was measured using a ZetaS (Malvern Instruments) to gain insight into the electrostatic qualities of kaolinite clay particles under a range of known water qualities. Measurements were performed across the pH range of 3 to 10 with electrical conductivity of 140 ±10.0 microSiemens per centimetre (µS/cm). The ZetaS converts the electrophoretic mobility of particles into zeta potential. Analysis of natural particles in Urrbrae Wetland water was not conducted, because a significant portion of particles were outside the range of the ZetaS (~30 µm).

3.9.2. Suspension stability tests Suspension stability tests were conducted to understand the significance of settling velocity in roughing filtration. Tests were conducted in a temperature-controlled room at 20°C to avoid temperature gradient-induced convective flow. For this analysis, a turbidimeter sample vial was placed inside the turbidimeter, and turbidity was measured over time to determine the stability of each sample. The distance from the top of the water in the sample vial to the light source in the Hach 2100P Turbidimeter is 3.5 cm. This method eliminated interference caused by manual sampling from graduated cylinders.

3.10. Modelling steady-state particle removal of kaolinite clay in URFS using colloid filtration theory (CFT)

Steady-state removal efficiencies for URFS experiments with kaolinite clay were evaluated using CFT developed by Yao et al. (1971). For this study, the following procedure was used to calculate values for the theoretical SCE, η, and attachment factor, α, for CFT modelling:

• A theoretical SCE was calculated for each particle size class (defined by the median particle diameter) provided by the Malvern 2600 laser diffraction unit using Equation 2.5 and the respective filter design variables for each experiment. Hydrodynamic and van der Waals attractive forces were considered by calculating SCE values in the same manner using Equation 2.6 (assuming a Hamaker constant of 1.0 x 10-20).

• The TSS concentration of the initial influent was partitioned to size classes weighted according to PSD results and matched to the respective SCE value.

• Using Equation 2.7, the attachment factor was used as a fitting parameter to match the sum of CFT-modelled TSS concentrations for each size class to the actual effluent TSS concentration. It should be noted that for in some instances (particularly for the small TSS values associated with particles <1.8 μm in diameter), the calculated TSS concentration of the effluent was greater than the influent TSS concentration for a particular size class (due to reasons discussed in Section 4.4.1). In these instances, the effluent TSS value was limited to the influent TSS value.

• Attachment factors over increasing filter length were calculated by comparing the TSS concentration and PSD results of the initial influent and effluent from each column.

A table summarising attachment factor calculations for one experiment is provided in Appendix B.

3.11. Quality assurance / quality control During experiments, every attempt was made to ensure that removal efficiencies accurately represented initial steady-state conditions. The following conditions were satisfied for this study:

• Initial influent was maintained at the targeted turbidity ± 3%.

• Flow in all experiments was maintained at the targeted hydraulic loading rate ± 2%.


• The electrical conductivity of kaolinite clay suspensions was maintained at 135 ±10 μS/cm.

• The pH of kaolinite clay suspensions was maintained at 7.0 ± 0.3.

• The temperature of all experimental waters was maintained at 20°C ± 1°C.

• Turbidity of effluent from fourth column ranged from 0.5 to 3.0 NTU prior to the commencement of each experiment.

• The repeatability of any given experiment, for a specific set of test conditions, was determined to be less than 6.2% (see Section 4.4 for discussion).


4. Results and Discussion of Experiments with Kaolinite Clay Suspension

4.1. Plug flow conditions verified by passage of conservative tracer Results of calcium chloride breakthrough curves for an individual, media-packed column are presented in Figure 4.1. The figure shows EC measurements recorded 55 cm from the raised perforated plate over incremental pore volume (defined as the ratio of fluid volume passing through the observation point starting from the midpoint time of tracer injection divided by the pore volume of the column. Filter pore volumes were calculated using porosities listed in Table 3.2 and assuming a porosity of 0.65 for the 3.5 cm space at the column base, which was packed with 9.5 mm media to minimise mixing. Since the tracer was injected into the column for 7 minutes, the midpoint time was 3.5 minutes for these experiments.

The figure shows that preferential flow in the media-packed column with and without rubber rings was unlikely, based on similar pore volumes observed for 5% breakthrough values (0.83 to 0.86). Variability in fluid velocity in the filter columns increased significantly with increasing media size (represented by the broad, flat breakthrough curve) and slightly with addition of rubber rings. The effect of media size was expected, as the ratio of column diameter to media size diameter for the 7.55 mm media (8.3:1) was smaller than the recommended 30:1 for column experiments (refer to Section 3.2). Some of the difference in observed peak and 90% breakthroughs and tracer recovery can be attributed to variations in the hydraulic loading rate (which was maintained at ±5%), likely departure from plug flow in the vicinity of the EC probe, and bulk densities. CXTFIT-modelling revealed that dispersivity increased from 0.86 for the test with 2.18 mm media test to 1.37 for the test with 7.55 mm media with rubber rings. CXTFIT output files are provided in Appendix C.

It should be noted that the importance of the rubber rings, in providing some assurance that particles travelled through pore spaces unaffected by the column wall, is not considered with these tracer tests.

5% Peak 90%2.18 mm no rings 0.83 1.01 1.59 58 1007.55 mm no rings 0.86 1.04 N/A 35 857.55 mm rings 0.84 1.09 1.92 30 95

Peak C/Co %

Recovery %

Observed breathrough (volume/pore volume)Media (mm)

Column packing

0.0000.0050.0100.0150.0200.0250.0300.0350.0400.0450.050

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

Pore volume

Incr

emen

tal E

C/E

Co =

(o

bser

ved

EC

*flu

id v

ol.)

/ (tr

acer

EC

*tra

cer v

ol.)

0.00.10.20.30.40.50.60.70.80.91.0

Cum

ulat

ive

EC

/EC

o

2.18 mm7.55 mm7.55 mm with rings

Figure 4.1 Results of calcium chloride breakthrough curves for one column.


4.2. Effect of column packing on particle removal Between URFS experiments evaluating the same media size, columns were drained and cleaned with the packed media left in place to eliminate errors associated with column re-packing. To evaluate the possible effect of column packing on removal efficiencies in URFS experiments, one experiment (5.18 mm media, 0.5 m/hr hydraulic loading rate, 100 NTU initial influent turbidity) was repeated after media from each column was removed, washed, and re-packed. Cumulative (Ce/Co) and 60-cm incremental (Ce/Ci) removals for the two experiments are shown in Table 4.1. Table 4.1 Results of experiments conducted to assess effect of column packing on removal rates

Original Experiment Repeated ExperimentCe/Co Ce/Ci Ce/Co Ce/Ci

Difference Ce/Co

Difference Ce/Ci

0.297 0.297 0.316 0.316 0.019 0.0190.161 0.542 0.186 0.589 0.025 0.0470.106 0.658 0.131 0.704 0.025 0.0460.082 0.774 0.103 0.786 0.021 0.013

The table shows that cumulative removal efficiencies for the repeated experiment were poorer by 1.9% to 2.5% compared to the original experiment. The results indicate that any error introduced by column re-packing procedures on cumulative removal efficiency in URFS experiments may be outweighed by error (average 2.5%, up to 6.2%) associated with variations in experimental conditions (see Section 4.4 for determination of experimental error).

4.3. Effect of biological activity on particle removal Biological “ripening” of filter media may improve particle removal efficiency in roughing filters due to increased stickiness of filter media (Collins, 1994). For this study, columns were emptied and re-packed with washed media on average every two weeks, and exposure of the media to light was limited to work hours to minimise potential errors introduced by biological activity in the columns. To evaluate the potential effect of biological activity on removal efficiency in URFS experiments, one experiment (2.18 mm media x 4 columns, 0.5 m/hr hydraulic loading rate, and initial influent turbidity of 100 NTU) was repeated 9 days after the original experiment with the same packed media to simulate the maximum opportunity for biological growth to have an effect. Cumulative (Ce/Co) and 60-cm incremental (Ce/Ci) removal efficiencies for the two experiments are provided in Table 4.2. Table 4.2 Results of experiments conducted to assess column packing on particle removal efficiency

Original Experiment Repeated Experiment Difference Ce/Co

Difference Ce/Ci

Ce/Co Ce/Ci Ce/Co Ce/Ci0.183 0.183 0.210 0.210 0.027 0.0270.095 0.519 0.124 0.590 0.029 0.0710.063 0.663 0.084 0.677 0.021 0.0140.046 0.730 0.070 0.833 0.024 0.103

The table shows that cumulative removal efficiencies for the repeated experiment were actually poorer compared to the original experiment, which would not be expected if biological activity had occurred in the columns. In addition, cumulative removal efficiencies between the two experiments differed by 2.1 to 2.7%, which are within the expected error range for URFS experiments (see Section 4.4). These results indicate that any biological activity in the columns had no significant effect on kaolinite clay removal efficiencies in URFS experiments.


4.4. Evaluation of steady-state particle removal in URFS experiments with kaolinite clay

Steady-state particle removals in URFS experiments with kaolinite clay based on turbidity and TSS concentrations are summarised in Table 4.3 and Figures 4.2 and 4.3. Particle removals over increasing filter length, expressed as Ce/Co, were calculated by dividing the effluent particle concentration from each column by the particle concentration maintained in the constant-head tank. Additionally, incremental particle removals, expressed as Ce/Ci, were calculated for each 60-cm filter segment. Tables summarising the turbidity measurements and removal efficiency calculations are provided in Appendix D.

Results revealed that the 2.4 m URFS was able to remove 75 to 96% turbidity (78 to 96% TSS) for the media sizes and hydraulic loading rates evaluated. In comparison, empty bed removal efficiencies for 0.5 and 1.0 m/hr were 26% and 18%, respectively. Consistent with deep-bed filtration theory, cumulative removal efficiencies improved with longer filter lengths, smaller media sizes, and slower hydraulic loading rates. The first 60-cm filter segment removed 39 to 84% turbidity (37 to 83% TSS), while the last 60 cm filter segment removed 20 to 27% turbidity and TSS. This observed decline in incremental removal efficiency with increasing filter length for all experiments can be attributed to the preferential removal of larger particles in the filter, which is supported by PSD results provided in Section 4.1.4.

Results showed that initial influent concentration had not effect on TSS removal rates. In contrast, turbidity removals improved systematically with increasing initial influent concentration, where removal efficiencies based on turbidity for experiments conducted at 700 NTU were up to 4.0% better than experiments conducted at 100 NTU. A close examination of the calibration curve relating turbidity and TSS for the Glomax LL (Figure 4.4) reveals an increasingly curvilinear relationship between 400 and 700 NTU, whereby turbidity increases at a slower rate at higher TSS concentrations. This phenomenon explains why removal efficiencies based on turbidity for the first 60-cm filter segment were between 0.1% and 5.7% better (average 3.2%) than those based on TSS concentrations for experiments conducted at 700 NTU. Results from an additional experiment (5.18 mm media x 4 columns, 0.5 m/hr, 700 NTU), where effluent turbidity from all columns was continuously monitored, showed no improvement in removal efficiency in the first 60-cm column after initial steady-state removal rates were established Figure 4.5. These results indicate that improved turbidity removals for higher initial influent concentrations were not due to higher filter deposits.


Table 4.3 Cumulative (Ce/Co) and 60-cm (Ce/Ci) removals for kaolinite clay in URFS

Ce/Co NTU

Ce/Co TSS

Ce/Co NTU

Ce/Co TSS

Ce/Co NTU

Ce/Co TSS

Ce/Co NTU

Ce/Co TSS

Ce/Ci NTU

Ce/Ci TSS

2.18 0.5 0.60 0.183 0.184 0.148 0.162 0.139 0.168 0.157 0.171 0.183 0.184

2.18 0.5 1.20 0.095 0.096 0.073 0.081 0.068 0.083 0.079 0.087 0.519 0.521

2.18 0.5 1.80 0.063 0.064 0.047 0.052 0.043 0.054 0.051 0.057 0.667 0.667

2.18 0.5 2.40 0.046 0.047 0.035 0.039 0.031 0.038 0.037 0.041 0.733 0.734

5.18 0.5 0.60 0.297 0.297 0.290 0.312 0.266 0.313 0.284 0.308 0.297 0.297

5.18 0.5 1.20 0.161 0.162 0.152 0.166 0.142 0.172 0.151 0.166 0.541 0.544

5.18 0.5 1.80 0.106 0.107 0.098 0.108 0.089 0.109 0.098 0.108 0.663 0.664

5.18 0.5 2.40 0.082 0.083 0.073 0.081 0.063 0.078 0.073 0.081 0.773 0.774

7.55 0.5 0.60 0.384 0.384 0.355 0.379 0.341 0.395 0.360 0.386 0.384 0.384

7.55 0.5 1.20 0.231 0.232 0.194 0.212 0.180 0.216 0.202 0.220 0.603 0.605

7.55 0.5 1.80 0.162 0.163 0.126 0.138 0.112 0.136 0.133 0.146 0.702 0.704

7.55 0.5 2.40 0.121 0.122 0.092 0.101 0.079 0.096 0.097 0.107 0.747 0.748

2.18 1.0 0.60 0.334 0.288 0.313 0.336 0.304 0.354 0.317 0.326 0.334 0.288

2.18 1.0 1.20 0.190 0.165 0.177 0.194 0.167 0.200 0.178 0.186 0.567 0.573

2.18 1.0 1.80 0.134 0.119 0.122 0.135 0.113 0.137 0.123 0.130 0.708 0.717

2.18 1.0 2.40 0.106 0.095 0.097 0.107 0.085 0.104 0.096 0.102 0.791 0.804

5.18 1.0 0.60 0.483 0.482 0.453 0.478 0.415 0.473 0.451 0.478 0.483 0.482

5.18 1.0 1.20 0.306 0.307 0.276 0.297 0.245 0.290 0.276 0.298 0.633 0.637

5.18 1.0 1.80 0.228 0.229 0.187 0.204 0.160 0.192 0.192 0.208 0.745 0.747

5.18 1.0 2.40 0.173 0.174 0.141 0.155 0.117 0.143 0.144 0.157 0.759 0.760

7.55 1.0 0.60 0.556 0.553 0.532 0.555 0.511 0.568 0.533 0.559 0.556 0.553

7.55 1.0 1.20 0.389 0.389 0.346 0.370 0.331 0.384 0.355 0.381 0.700 0.703

7.55 1.0 1.80 0.297 0.298 0.249 0.270 0.234 0.277 0.260 0.282 0.765 0.767

7.55 1.0 2.40 0.237 0.238 0.189 0.206 0.170 0.205 0.199 0.216 0.796 0.797

2.18 1.5 0.60 - - 0.380 0.404 - - 0.380 0.404 0.380 0.404

2.18 1.5 1.20 - - 0.219 0.238 - - 0.219 0.238 0.577 0.589

2.18 1.5 1.80 - - 0.159 0.173 - - 0.159 0.173 0.723 0.729

2.18 1.5 2.40 - - 0.124 0.137 - - 0.124 0.137 0.784 0.788

5.18 1.5 0.60 - - 0.519 0.542 - - 0.519 0.542 0.519 0.542

5.18 1.5 1.20 - - 0.355 0.379 - - 0.355 0.379 0.684 0.699

5.18 1.5 1.80 - - 0.255 0.275 - - 0.255 0.275 0.717 0.726

5.18 1.5 2.40 - - 0.196 0.213 - - 0.196 0.213 0.768 0.774

7.55 1.5 0.60 - - 0.610 0.630 - - 0.610 0.630 0.610 0.630

7.55 1.5 1.20 - - 0.423 0.448 - - 0.423 0.448 0.693 0.710

7.55 1.5 1.80 - - 0.319 0.342 - - 0.319 0.342 0.754 0.764

7.55 1.5 2.40 - - 0.255 0.276 - - 0.255 0.276 0.800 0.806

no media 0.5 0.60 - - 0.925 0.914 - - 0.925 0.914 0.925 0.914

no media 0.5 1.20 - - 0.875 0.876 - - 0.875 0.876 0.946 0.958

no media 0.5 1.80 - - 0.810 0.813 - - 0.810 0.813 0.926 0.929

no media 0.5 2.40 - - 0.738 0.748 - - 0.738 0.748 0.910 0.920

no media 1.0 0.60 - - 0.958 0.944 - - 0.958 0.944 0.958 0.944

no media 1.0 1.20 - - 0.915 0.909 - - 0.915 0.909 0.956 0.963

no media 1.0 1.80 - - 0.870 0.875 - - 0.870 0.875 0.951 0.962

no media 1.0 2.40 - - 0.815 0.820 - - 0.815 0.820 0.937 0.938

Average 100 NTU

(52.6 mg/L TSS)400 NTU

(196 mg/L TSS)700 NTU

(298 mg/L TSS) Average Media size

(mm)

Hydraulic Loading

Rate (m/hr)

Filter Length

(m)


0.0

0.2

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0.8

1.0C

e/C

o

0.6 1.2 1.8 2.4

2.18 mm

7.55 mm

Filter Length (m)

0.5 m/hr Tests

0.0

0.2

0.4

0.6

0.8

1.0

Ce/

Co

0.6 1.2 1.8 2.4

2.18 mm

7.55 mm

Filter Length (m)

1.0 m/hr Tests

0.0

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0.8

1.0

Ce/

Co

0.6 1.2 1.8 2.4

2.18 mm

7.55 mm

Filter Length (m)

1.5 m/hr Tests

Figure 4.2 Cumulative TSS removals (Ce/Co) of kaolinite clay in URFS experiments


Figure 4.3 60-cm TSS removals (Ce/Ci) of kaolinite clay in URFS experiments

0.0

0.2

0.4

0.6

0.8

1.0C

e/C

i

Col 1Col 2Col 3Col 4

2.18 mm

7.55 mm

Filter Length (m)

0.5 m/hr Tests

0.0

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Ce/

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2.18 mm

7.55 mm

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1.0 m/hr Tests

0.0

0.2

0.4

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0.8

1.0

Ce/

Ci


2.18 mm

7.55 mm

Filter Length (m)

1.5 m/hr Tests


Figure 4.4 Calibration curve relating turbidity and TSS concentrations for the Glomax LL kaolinite clay

URFS experiment TSS (mg/L) NTU2.18 mm x 4 columns 21.6 43.71.5 m/hr 29.7 60.5

40.9 87.771.5 151

193.7 400

5.18 x 4 columns 44.4 78.31.5 m/hr 57.4 102

74.5 142101.8 207198.4 400

7.55 mm x 4 columns 55.4 1021.5 m/hr 65.9 127

81.9 170118.3 244192.8 400

Confirmation run 29.14 51.57.55 mm x 2 columns 47.45 81.85.18 mm x 1 column 69.88 1342.18 mm x 1 column 102.72 2101.0 m/hr 195.6 400

700 NTU sample 295 700302 700300 700

Note: curve developed using the turbidity and TSS results collected from selected URFS experiments and an additional sample of 700 NTU (refer Appendix C). The figure reveals the linear relationship between the TSS and NTU between 0 and 400 NTU and the development of an increasingly non-linear relationship greater than 400 NTU, whereby the rate of change for average volume of light scattering (i.e. NTU) increases with increasing particle concentration.

NTU-TSS relationship for Glomax LL

y = -0.00016x2 + 0.54236xR2 = 0.99820

0

50

100

150

200

250

300

350

400

450

500

0 100 200 300 400 500 600 700

Turbidity [ ] (NTU)TS

S [ ]

(mg/

L)


0

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0 100 200 300 400 500 600Time since start (min)

Effl

uent

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idity

(NTU

)

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400

500

600

700

800

Initi

al in

fluen

t tur

bidi

ty (N

TU)

Co = 700 NTU

Ce1 197 NTU

Ce2 107 NTU

Ce3 67.5 NTU

Ce4 50.2 NTU

No improvement in removal efficiency after initial steady-state conditions established due to increasing filter deposit

Figure 4.5 Additional experiment with continuous turbidity monitoring (5.18 mm, 0.5 m/hr, 700 NTU)

4.4.1. Preferential removal of large particles in URFS with kaolinite clay Water samples collected from the constant-head tank and after passage through each of the four filter segments were analysed for PSD by the laser diffraction method. Each sample was subdivided and analysed in triplicate, with observed differences between each subdivided sample ranging from 0.0 to 0.2% volume for each size class. It should be noted that the Malvern 2600 automatically assigns a volumetric percentage to particles less than 0.5 µm in diameter based on an integral curve-fitting algorithm resulting in these data being largely unreliable (Barrett, 2005). Therefore, all volume percentages reported for this study were normalised to account for particles greater than 0.5 µm. PSD analysis of the Glomax LL kaolinite clay used in URFS experiments revealed particle sizes ranged from the lower detection limit of 0.5 μm to about 20 μm, coinciding with the upper size limit of suspended solids likely to enter a roughing filter (Wegelin, 1996) (Figure 4.6). The average particle diameter (by volume) for this kaolinite clay is 3.65 μm.


Glomax LL (Georgia Kaolin Company)

0

4

8

12

16

20

0.9

1.5

1.8

2.3

2.8

3.5

4.3

5.4

6.7

8.4

10.4

13.0

16.1

20.0

24.9

Particle Diameter (μm)

Volu

me

(%)

0

25

50

75

100

125

Cum

. Vol

ume

(%)

Figure 4.6 Particle Size Distribution of Glomax LL calcined kaolinite clay

PSD analysis of water samples collected after passage through each of the four filter segments revealed a remarkably consistent particle removal pattern. Figure 4.7 shows the PSD of water samples collected from one experiment (2.18 mm media, 0.5 m/hr hydraulic loading rate, 700 NTU initial concentration). The figure demonstrates the preferential removal of larger particles early in the filter leaving only progressively smaller particles to travel deeper through the filter, indicative of deep-bed filtration. The figure also confirms the theoretical difficulty in removing particles less than 2 μm, even with relatively small media sizes and slow hydraulic loading rates.

The PSD for water samples collected from another experiment (7.55 mm media, 1.0 m/hr hydraulic loading rate, 700 NTU initial concentration) is provided in Figure 4.8. A comparison between this figure and Figure 4.7 reveals that an increase in hydraulic loading rate and filter media size results in smaller PSD changes between initial and filtered samples. These observations can be attributed to the inverse relationship between hydraulic loading rate and successful particle transport to a single collector by sedimentation as well as the same relationship between collector diameter and particle removal efficiency as described by CFT (refer to Equations 2.4 and 2.7 in Section 2.7.1). A complete catalogue of PSD results for all experiments with kaolinite clay as well as tables showing the particle diameter representing the average settling velocity in all water samples is provided in Appendix E.

The relationship between removal efficiency and PSD change through the URFS is illustrated in Figures 4.9 and 4.10, which shows the TSS concentration for the same water samples shown in the previous two figures. TSS concentrations were partitioned to particle size classes according to PSD results for each water sample. Figure 4.9 shows that after passage through the first 60-cm of the filter packed with 2.18 mm media at a hydraulic loading rate of 0.5 m/hr, all particles greater than 6.26 μm in the initial suspension were completely removed, and removals greater than 50% were observed for particles down to about 2 μm. This explains the relatively high removal efficiency (83%) of the first 60-cm filter segment for this experiment. Figure 4.10 shows that after passage through the first 60-cm of the filter packed with 7.55 mm media at a hydraulic loading rate of 1.0 m/hr, all particles greater than 6.26 μm in the initial suspension were also completely removed. However, 60-cm removal efficiencies were less than 50% for particles less than 5.0 μm. This explains the relatively poor removal efficiency (43%) of the first 60-cm filter segment for this experiment.

3.65 μm


Figure 4.7 Particle Size Distribution (2.18 mm media x 4 columns, 0.5 m/hr, 700 NTU)


Figure 4.8 Particle Size Distribution (7.55 mm media x 4 columns, 1.0 m/hr, 700 NTU)


Figure 4.9 TSS distributed by particle size (2.18 mm media x 4 columns, 0.5 m/hr, 700 NTU)


Figure 4.10 TSS distributed by particle size (7.55 mm media x 4 columns, 1.0 m/hr, 700 NTU)


Interestingly, for particle size classes smaller than 1.8 μm, the TSS concentrations for the Column 1 effluent (Ce1) increased with respect to the initial influent (Co). This phenomenon was typical for most experiments and is likely attributable to a combination of errors introduced by 1) the process of normalising volume percentages to particles greater than 0.5 μm for the TSS partitioning method, 2) physical masking of small particles by large particles in the initial influent during PSD analysis (although PSD results for kaolinite clay were, confirmed by particle counting using an Accusizer 770A, the results of which are included in Appendix E) and 3) particle breakage during the experiment and PSD analysis.

4.4.2. Suspension Stability Tests The influence of sedimentation on particle removal in URFS experiments was evaluated by conducting suspension stability tests for selected water samples collected after passage through each 60-cm filter segment. Results of suspension stability test results for kaolinite clay samples collected from one experiment (7.55 mm media, 1.5 m/hr, and 400 NTU initial concentration) are provided in Figure 4.11. The figure shows the change in distribution of particle settling velocities in the kaolinite suspension as it travels through the roughing filter due to the preferential removal of larger particles (as predicted by CFT and confirmed by PSD results). The figure shows that the relative settling velocity of the influent suspension was the fastest correlating to the highest 60-cm removal efficiency. Smaller changes in PSD after the first 60-cm filter segment resulted in smaller changes to subsequent 60-cm removal efficiencies.

Figure 4.11 Relationship of remaining turbidity over time for samples collected from an experiment with kaolinite clay 4.4.3. Modelling steady-state particle removal of kaolinite clay in URFS using

colloid filtration theory (CFT) For this study, theoretical single collector efficiencies (SCEs) derived from Equations 2.5 and 2.6 were used in combination with PSD data to calculate an attachment factor representative of the entire kaolinite clay suspension. Figure 4.12 shows the SCEs calculated using Equation 2.5 for the three hydraulic loading rates tested. The figure depicts SCEs calculated specifically for 5.18 mm media. However, because the SCE value does not change significantly for different media sizes using Equation 2.5 (because sedimentation represents the primary particle removal

Suspension Stability Test Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000 10000

Time (minutes)

Rem

aini

ng T

urbi

dity

Ce4

Ce3

Ce2

Ce1

Co

Kaolinite clay samples collected from experiment evaluating 7.55 mm media, 1.5 m/hr, 400 NTU


mechanism for the relatively dense kaolinite clay particles), the SCE values in the figure also closely represent SCEs for 2.18 and 7.55 mm media.

Figure 4.13 shows SCEs calculated for each filter configuration using Equation 2.6 with SCEs in Figure 4.12 provided for comparison. The x and y scales are condensed in the figure to highlight the differences. The figure reveals that SCEs decrease with decreasing media size due to the increasing effect of hydrodynamic forces, which retard particle deposition. In addition, SCEs increase (relative to SCEs derived using Equation 2.5) with decreasing hydraulic loading rate due to the increasing effect of Van der Waals attractive forces (which increase particle deposition rates) and decreasing effect of hydrodynamic forces.

Figure 4.12 SCE values calculated for kaolinite clay using equation developed by Yao et al. (1971) Equation 2.5.

Figure 4.13 Comparison of SCE values using Equation 2.5 and 2.6 for kaolinite clay at 0.5, 1.0, and 1.5 m/hr

0.001

0.01

0.1

1

0.1 1 10 100Particle diameter (μm)

Sing

le c

olle

ctor

effi

cien

cy

0.5 m/hr1.0 m/hr1.5 m/hr

0.01

0.1

1 10Particle diameter (μm) (log scale)

Sing

le c

olle

ctor

effi

cien

cy

7.55 mm, 0.5 m/hr5.18 mm, 0.5 m/hr2.18 mm, 0.5 m/hr7.55 mm, 1.0 m/hr5.18 mm, 1.0 m/hr2.18 mm, 1.0 m/hr7.55 mm, 1.5 m/hr5.18 mm, 1.5 m/hr2.18 mm, 1.5 m/hr

ρp = 2.60 g/cm3

T = 20°CA = 1.0E-20

SCE 1.5 m/hr(Eq. 2.5)

SCE 1.0 m/hr(Eq. 2.5)

SCE 0.5 m/hr(Eq. 2.5)

SCE (Eq. 2.6)


Average attachment factors for each filter configuration tested using Equations 2.5 and 2.6 are provided in Figure 4.14 and 4.15, respectively. The average attachment factors for URFS experiments with kaolinite clay using Equation 2.5 (0.128 ± 0.027) and 2.6 (0.137 ± 0.023) were very similar. However, the relative standard deviation calculated for attachment factors grouped for each hydraulic loading rate was 20.3% using Equation 2.5 versus 11.3% using Equation 2.6, indicating that the inclusion of hydrodynamic and attractive van der Waals forces resulted in improved estimation of the theoretical SCE. This improvement is illustrated by the closer grouping of attachment factors observed in Figure 4.15 compared to Figure 4.14. The zeta potential measured for the kaolinite clay suspension was –35 mV, similar to the zeta potentials reported by Boller (1991) for kaolinite clay in freshwater. Attachment factors for all experiments and zeta potential results are provided in Appendix B.

A comparison of observed versus modelled TSS removal partitioned to particle sizes for one filter run is provided in Figure 4.16. The figure shows the generally close agreement between CFT-modelled versus observed removals for respective particle sizes. However, modelled removals are slightly underestimated for larger particles and overestimated for smaller particles. This trend was observed for all experiments and can be attributed to errors discussed at the end of Section 4.4.1.

In Collins’s study an attachment factor of 0.10 was assumed after measuring flocculation rates in batch tests, from which attachment factors ranging from 0.001 to 0.1 were estimated. An average SCE representing all particles in suspension was used as a fitting parameter to match CFT-modelled and actual TSS concentrations. This modelling method is adequate for the purpose of comparing relative collector efficiencies (which Collins used to compare the performance of unripened and algae-ripened media), but actual CFT variables derived using this method cannot be compared to CFT variables derived from other studies without the original PSD results from Collins study.

Collins (1994) observed a decrease in collector efficiency with increasing filter length for all media sizes evaluated, which he attributed to cake filtration. However, in all experiments conducted by Collins and for this study with kaolinite clay, collector efficiencies decreased with decreasing media size, opposite to what one would expect if significant cake or straining filtration had occurred. Also the ratio of collector diameter to particle diameter in all experiments conducted by Collins and this study were greater than 100:1, much higher than the 20:1 ratio, above which straining filtration is unlikely to occur (McDowell-Boyer, et al., 1986).

Decreasing attachment factors in URFS experiments are more likely to be attributed to the combined effect of 1) kaolinite clay and sand and gravel grains representing poorly the perfectly spherical particles and collectors assumed in CFT modelling, 2) a higher uniformity coefficient for the largest media size used in the experiments (7.55 mm) resulting in improved removals due to higher grain density, 3) minor particle settling at the base of each URFS column, especially in the first column, and 4) increased error associated with PSD results for particles less than 2 μm (refer to Section 3.9.2). In addition, CFT does not consider the effect of velocity fields created by settling particles. Large particles in suspension may have increased settling velocities (and, in turn, removals) of smaller adjacent particles (Allen, 1974), a process that is more likely to have occurred at the beginning of the filter when PSD was broader.


Figure 4.14 Average attachment factor of kaolinite clay calculated over increasing filter length using Yao et al. (1971) SCE equation

0.5 m/hr Tests

0.000.020.040.060.080.100.120.140.160.180.20

0.0 0.6 1.2 1.8 2.4 3.0 3.6

Filter Length (m)

Atta

chm

ent F

acto

r

7.55 mm5.18 mm2.18 mm

1.0 m/hr Tests

0.000.020.040.060.080.100.120.140.160.180.20

0.0 0.6 1.2 1.8 2.4 3.0 3.6

Filter Length (m)

Atta

chm

ent F

acto

r

7.55 mm5.18 mm2.18 mm

1.5 m/hr Tests

0.000.020.040.060.080.100.120.140.160.180.20

0.0 0.6 1.2 1.8 2.4 3.0 3.6

Filter Length (m)

Atta

chm

ent F

acto

r

7.55 mm5.18 mm2.18 mm


Figure 4.15 Average attachment factor of kaolinite clay over increasing filter length using Tufenkji and Elimelech (2004) SCE equation

0.5 m/hr Tests

0.000.020.040.060.080.100.120.140.160.180.20

0.0 0.6 1.2 1.8 2.4 3.0 3.6

Filter Length (m)

Atta

chm

ent F

acto

r

7.55 mm5.18 mm2.18 mm

1.0 m/hr Tests

0.000.020.040.060.080.100.120.140.160.180.20

0.0 0.6 1.2 1.8 2.4 3.0 3.6

Filter Length (m)

Atta

chm

ent F

acto

r

7.55 mm5.18 mm2.18 mm

1.5 m/hr Tests

0.000.020.040.060.080.100.120.140.160.180.20

0.0 0.6 1.2 1.8 2.4 3.0 3.6

Filter Length (m)

Atta

chm

ent F

acto

r

7.55 mm5.18 mm2.18 mm


Figure 4.16 Observed vs. CFT-modelled TSS distribution for one experiment with kaolinite clay (7.55 mm, 1.0 m/hr, and 298 mg/L TSS)

Ce1

0

5

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20

25

0 1 2 3 4 5 6 7 8 9 10Particle diameter (μm)

TSS

(mg/

L)

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50

100

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Cum

ulat

ive

TSS

(m

g/L)

CFT Ce1 TSS

Actual Ce1 TSS

CFT Cumulative Ce1 TSS

Actual Cumulative Ce1TSS

Ce2

0

5

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25


TSS

(mg/

L)

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Cum

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TSS

(m

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CFT Ce2TSS

Actual Ce2 TSS


Actual Cumulative Ce2 TSS

Ce3

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TSS

(mg/

L)

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(m

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CFT Ce3TSS

Actual Ce3 TSS



Ce4

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CFT Ce4TSS

Actual Ce4TSS




4.4.4. Comparison of experimental data with existing empirical models developed for steady-state kaolinite clay removal

Figure 4.17 shows the cumulative removal efficiencies in URFS experiments in comparison to predictions using empirical models developed by Wegelin (Equation 2.12) for HRF and Collins (Equation 2.14) for DRF. Because both empirical models were developed from TSS concentrations, average removal efficiencies for TSS from URFS experiments were used for comparison. Predicted removal efficiencies using Wegelin’s model were calculated by partitioning the TSS concentration of the initial influent into size classes and matching the size-specific filter coefficient to each size class. The sum of the remaining TSS concentrations for each size class was divided by the TSS concentration of the initial influent to calculate removal efficiencies. It should be mentioned that predicted removal efficiencies using Collins’s model were calculated for configuration beyond the original calibration limits for media size (2.68 - 7.94 mm) and hydraulic loading rate (0.5 - 1.0 m/hr). The figure shows that actual removal efficiencies were worse than predicted using the Collins or Wegelin model. The average difference for the first 60-cm filter segment was 18%.

Differences between actual removal efficiencies from URFS experiments and predicted removal efficiencies using Collins’s model may be attributed primarily to the type of kaolinite clay used for this study. In a vertical upflow empty bed test conducted by Collins (1994) at a hydraulic loading rate of 0.5 m/hr, only 29% of the kaolinite clay particles were removed after 0.6 m. In the same experiment conducted for this study was, only 9% of the kaolinite clay particles in this study were removed.

Differences between actual removal efficiencies from URFS experiments and predicted removal efficiencies using Wegelin’s model may be attributed to the fact that Wegelin based his removal efficiency on results using short filter lengths (20-40 cm). Over such short distances, removal efficiencies for smaller particles may be improved due to velocity fields generated by the settling of larger particles (see Section 4.1.6). In addition, Wegelin’s experiments were conducted using HRF where the bottom of the filter column serves as an additional collector surface resulting in improved removal efficiencies compared to VRFs. Additional research is necessary to confirm the basis for these discrepancies.


1.0 m/hr Tests

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0 0.6 1.2 1.8 2.4 3.0

Filter Length (m)

Ce/

Co

Actual 7.55 mmActual 5.18 mmActual 2.18 mmCollins 7.55 mmCollins 5.18 mmCollins 2.18 mmWegelin 7.55 mmWegelin 5.18 mmWegelin 2.18 mm

1.5 m/hr Tests

0.0

0.1

0.2

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0.7

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Co


0.5 m/hr Tests

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0 0.6 1.2 1.8 2.4 3.0

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Ce/

Co


Figure 4.17 Comparison between observed and predicted removals using existing empirical models for kaolinite clay removal


4.4.5. Development of a steady-state kaolinite clay removal model for URFS using multivariate regression analysis

Multivariate regression analysis was used to transform results from URFS experiments with kaolinite clay into a general equation capable of predicting kaolinite clay removals in URFS for numerous filter configurations. Media size (mm), hydraulic loading rate (m/hr), and filter length-1 (m-1) were regressed with average cumulative removals for TSS. An attempt to use filter length as a parameter was rejected as it resulted in non-normal distribution of residual error due to the curvilinear relationship between cumulative removal and filter length.

Regression was performed on 24 of the 36 data points (refer to Table 4.3). 12 data points associated with 5.18 mm media experiments were reserved to test the predictive capability of the model. The model developed for steady-state kaolinite clay removal is shown below:

Ce/Co = -0.245 + 0.0298*media(mm) + 0.171*rate(m/hr) + 0.206*Length-1 (m-1) Equation 4.1

Figure 4.18 shows modelled and observed results for the 24 data points, from which the model was developed. The data points are randomly scattered near the perfect line of agreement between actual and predicted removal efficiencies. The R2 for this correlation is 0.942, and a large and significant portion of removal efficiency can be explained by the three parameters (Fs = 108.7, see Appendix F). The difference between actual and modelled removal efficiency was on average 3.2% with a maximum difference of 7.7%.

Comparison of modelled and observed removals for the 12 data points associated with experiments with 5.18 mm is shown in Figure 4.19. The figure shows that the model was able to predict with good accuracy the removal efficiencies for experiments evaluating 5.18 mm media for all hydraulic loading rates. The difference between the actual and modelled removal efficiencies was on average 2.2% with a maximum difference of 5.4%.

It is important to recognise that the influence of filter length on removal efficiency is correlated to suspension PSD, which changes as the suspension travels through a roughing filter. Collins (1994) tested his model (Equation 2.16) on filters packed with multiple media sizes by using the effluent concentration after one media grade as the influent concentration for the next media grade (Approach A). However, because large particles are removed preferentially in the filter, application of the model developed by Collins and from this study using such a method will inevitably overestimate removal efficiencies for roughing filters packed with multiple media sizes beyond the first media grade. An improved approximation of removal efficiency after passage through two or more media sizes may be obtained by using the average media size weighted with respect to filter length (Model Approach B) as shown below in Table 4.4 for an additional test with kaolinite clay conducted for this study (modelled results obtained using Equation 4.1).

Table 4.4 Comparison of approaches for applying empirical models with filter length as a parameter

The new empirical model developed (Equation 4.1) is limited to predicting steady-state removals for the PSD of this kaolinite clay in URFS on clean filter beds. The expected decline in removal efficiency due to deposited solids is not considered. However, removal efficiency declines are not expected to occur until deposited solids reach at least 10 g TSS/L of filter (Collins, 1994). In these experiments, the maximum specific deposit reached (for the first 60-cm filter column) was less than 5 g TSS/L of filter.

Observed Modelled

Approach AModelled

Approach B1 0.6 7.55 7.55 0.534 0.494 0.4942 1.2 7.55 7.55 0.357 0.244 0.3233 1.8 5.18 6.76 0.234 0.104 0.2424 2.4 2.18 5.62 0.138 0.035 0.179

Approach A = effluent [ ] from preceeding filter segment used as influent [ ] for current filter segmentApproach B = initial influent [ ] with average media size over increasing filter length

Column

Ave. media size for filter length

(mm)

Ce/Co ( 1.0 m/hr, 196 mg/L TSS)Media size

(mm)

Filter length

(m)


Figure 4.18 Comparison of actual vs. modelled removal efficiencies for kaolinite clay in URFS for 24 data points used to develop model

Figure 4.19 Comparison of actual vs modelled removal efficiencies for kaolintie clay in URFS for 12 independent data points

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

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Actual Ce/Co

Mod

el C

e/C

o R2 = 0.942

0.5 m/hr

0.00.10.20.30.40.50.60.7

0 0.6 1.2 1.8 2.4 3

Length (m)

Ce/

Co

Actual 2.18 mmModel 2.18 mmActual 7.55 mmModel 7.55 mm

1.5 m/hr

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Length

Ce/

Co


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Length

Ce/

Co


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el C

e/C

o Actual 5.18 mm

0.5 m/hr

0.00.10.20.30.40.50.60.7

0 0.6 1.2 1.8 2.4 3

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Co

Actual 5.18 mm

Model 5.18 mm

1.5 m/hr

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Co

Actual 5.18 mm

Model 5.18 mm


Main Lagoon (19-october 2005)

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114.

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Volu

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(%)

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5. Results and Discussion of Experiments with Urrbrae Wetland Waters

5.1. Evaluation of steady-state particle removal in URFS experiments with Urrbrae Wetland waters

Water samples collected from the ASR pond and main lagoon of the Urrbrae Wetland were used to evaluate particle removal in URFS with urban wetland water. Table 5.1 shows the cumulative steady-state removals, Ce/Co, of stormwater particles for roughing filtration experiments with the two experimental waters. Removals for the kaolinite clay suspension using the same filter configuration are provided for comparison. Due to presence of particles floating at the top of first, second, and third columns at the end of the experiment with ASR pond water, reliable calculation of internal filter length removals was not possible. Table 5.1 Removals (Ce/Co) of Urrbrae Wetland water and kaolinite clay experiments in URFS experiments

Ce/Co ASR pond 18-Oct-05

Main lagoon 20-Oct-05

Kaolinite Clay (400 NTU/196 mg/L TSS)

Filter ID

Filter Length

(m) turbidity TSS turbidity TSS turbidity TSS Ce1 0.60 0.684 0.743 0.525 0.534 Ce2 1.20 0.605 0.623 0.343 0.357 Ce3 1.80 0.516 0.493 0.221 0.234 Ce4 2.40 0.243 0.245 0.454 0.403 0.129 0.138

Note: filter = 7.55 x 7.55 x 5.18 x 2.18 mm media (each 60 cm); hydraulic loading rate = 1.0 m/hr

Table 5.1 shows that removals were poorer for both Urrbrae Wetland waters than for the kaolinite clay suspension. Although initial particle concentration of the ASR pond water was lower than that of the main lagoon sample (10.6 mg/L TSS versus 30.0 mg/L TSS), the final removal efficiency was higher for the ASR pond water (76%) compared to the main lagoon water (54%). Interestingly, the initial 60-cm removal efficiency for the main lagoon experiment was only 15% less than for kaolinite clay.

5.1.1. Preferential removal of large particles in URFS with Urrbrae Wetland waters PSD analysis of the main lagoon water revealed a broad range in particle sizes from 0.5 to greater 118 μm with an average particle size (by volume) of 10 μm (Figure 5.1).

Figure 5.1 PSD of main lagoon sample (20-Oct-05)


ASR Pond (17 October 2005)

0

4

8

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16

205.

8

7.5

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17.9

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183.

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438.

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Volu

me

(%)

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The PSD results for the detention pond (hereafter referred to as ASR pond) sample are provided in Figure 5.2 and reveal much larger particles (50 to 300 μm, with an average particle size (by volume) of 130 μm) compared with the main lagoon sample.

Figure 5.2 PSD of ASR pond sample (18-Oct-05)

For the main lagoon experiment, samples were also collected from each column and analysed for PSD. The results are provided in Figure 5.3. The figure reveals the preferential removal of larger particles in the main lagoon sample through the roughing filter, similar to the pattern observed in experiments with kaolinite clay. Particles larger than 27 μm were removed within the first 60 cm of filter. Effluent from the fourth column contained particles less than about 10 μm. The relatively large particles in the untreated main lagoon water explain the relatively high removal efficiency observed for the first 60-cm filter segment.

The relationship between removal efficiency and PSD change through the URFS is illustrated in Figure 5.4, which shows the TSS concentration for the same water sample shown in the previous figure. Similar to the method used earlier for kaolinite clay, TSS concentrations were partitioned to particle size classes according to PSD results for each water sample assuming a constant particle density for the suspension. Overall, the figure confirms the preferential removal of TSS contributed by large particles. Interestingly, however, particles smaller than 6 μm in diameter contributed more TSS after passing through the first 60-cm filter segment. This phenomenon may be due to 1) large particles breaking due to shear stresses in the filter and/or 2) large particles being less dense than small particles (resulting in the underestimation of TSS contributed by smaller particles for the initial suspension). Further discussion on particle breakage is provided in Section 5.1.3.

Due to presence of visible particles floating near the top of first, second, and third columns at the end of the ASR pond experiment, samples from the columns for the ASR pond experiment were not analysed for PSD. However, visual observation of effluent showed that large particles penetrated through the first three columns (less penetration through the third column) but did not penetrate through the fourth column. Observation of drainage water from each column following the experiment indicated that large particles were removed throughout the depth of each column and not simply by surface (cake) filtration. Interception may have played a more significant role in removing buoyant particle aggregates in the ASR pond water than for other experimental waters.


Figure 5.3 Particle size distribution main lagoon (20-Oct-05) 1.0 m/hr, 34.0 NTU


Figure 5.3 continued

Low High Ave Co Ce1 Ce2 Ce3 Ce4 Low High Ave Coμm μm μm % % % % % μm μm μm %

0.50 1.22 0.86 0.10 0.32 0.37 0.98 2.84 27.70 29.80 28.75 1.511.23 1.31 1.27 0.10 0.21 0.32 0.98 3.18 29.80 32.00 30.90 1.411.32 1.41 1.37 0.10 0.32 0.32 1.09 3.25 32.00 34.40 33.20 1.411.42 1.51 1.47 0.10 0.42 0.48 1.19 3.10 34.40 37.00 35.70 1.311.52 1.63 1.58 0.20 0.58 0.70 1.52 2.84 37.00 39.80 38.40 1.311.64 1.75 1.70 0.30 0.85 1.07 1.85 2.54 39.80 42.80 41.30 1.201.76 1.88 1.82 0.40 1.27 1.50 2.17 2.47 42.80 46.10 44.45 1.201.89 2.03 1.96 0.60 1.80 1.98 2.61 2.62 46.10 49.50 47.80 1.102.04 2.18 2.11 0.80 2.33 2.57 3.15 3.07 49.50 53.30 51.40 1.002.19 2.34 2.27 1.10 2.96 3.16 3.58 3.93 53.30 57.30 55.30 1.002.35 2.52 2.44 1.41 3.54 3.69 4.13 4.90 57.30 61.60 59.45 0.902.53 2.71 2.62 1.81 4.18 4.17 4.56 5.91 61.60 66.20 63.90 0.902.72 2.91 2.82 2.01 4.60 4.60 4.89 6.54 66.20 71.20 68.70 0.902.92 3.13 3.03 2.11 4.87 4.81 5.10 6.84 71.20 76.60 73.90 0.803.14 3.37 3.26 2.21 5.03 4.92 5.10 6.62 76.60 82.40 79.50 0.803.38 3.62 3.50 2.31 4.97 4.81 4.99 6.17 82.40 88.60 85.50 0.803.63 3.89 3.76 2.31 4.81 4.65 4.89 5.38 88.60 95.20 91.90 0.703.90 4.19 4.05 2.41 4.50 4.39 4.67 4.34 95.20 102.00 98.60 0.704.20 4.50 4.35 2.51 4.23 4.17 4.45 3.59 102.00 110.00 106.00 0.704.51 4.84 4.68 2.61 4.18 4.01 4.23 3.36 110.00 118.00 114.00 0.704.85 5.21 5.03 2.71 4.13 3.90 4.02 3.405.22 5.60 5.41 2.71 4.07 3.80 3.80 3.365.61 6.02 5.82 2.81 3.92 3.58 3.58 3.076.03 6.48 6.26 2.71 3.49 3.37 3.26 2.176.49 6.97 6.73 2.61 3.07 3.10 2.82 1.276.99 7.49 7.24 2.51 2.75 2.78 2.39 0.757.44 8.05 7.75 2.41 2.54 2.46 2.06 0.458.06 8.66 8.36 2.41 2.43 2.14 1.74 0.568.67 9.31 8.99 2.31 2.33 1.87 1.41 0.649.32 10.00 9.66 2.31 2.17 1.66 1.19 0.52

10.01 10.80 10.41 2.31 1.90 1.50 1.09 0.3010.81 11.60 11.21 2.31 1.69 1.39 0.98 0.0411.61 12.50 12.06 2.31 1.48 1.28 0.8712.51 13.40 12.96 2.31 1.32 1.23 0.7613.41 14.40 13.91 2.31 1.22 1.18 0.7614.41 15.50 14.96 2.21 1.22 1.12 0.7615.51 16.70 16.11 2.11 1.11 1.07 0.6516.71 17.90 17.31 2.11 0.95 0.96 0.6517.91 19.30 18.61 2.01 0.79 0.86 0.4319.31 20.70 20.01 1.91 0.63 0.75 0.3320.71 22.30 21.51 1.81 0.42 0.64 0.2222.31 24.00 23.16 1.71 0.26 0.43 0.1124.01 25.80 24.91 1.61 0.11 0.2125.81 27.70 26.76 1.61 0.00 0.05


Figure 5.4 TSS distributed by particle size main lagoon (20-Oct-05) 1.0 m/hr, 34.0 NTU


Figure 5.4 continued

Low High Ave Co Ce1 Ce2 Ce3 Ce4 Low High Ave Coμm μm μm TSS TSS TSS TSS TSS μm μm μm TSS

0.50 1.22 0.86 0.03 0.07 0.08 0.17 0.44 27.70 29.80 28.75 0.451.23 1.31 1.27 0.03 0.05 0.07 0.17 0.49 29.80 32.00 30.90 0.421.32 1.41 1.37 0.03 0.07 0.07 0.19 0.50 32.00 34.40 33.20 0.421.42 1.51 1.47 0.03 0.10 0.10 0.21 0.48 34.40 37.00 35.70 0.391.52 1.63 1.58 0.06 0.13 0.14 0.27 0.44 37.00 39.80 38.40 0.391.64 1.75 1.70 0.09 0.20 0.22 0.32 0.39 39.80 42.80 41.30 0.361.76 1.88 1.82 0.12 0.29 0.31 0.38 0.38 42.80 46.10 44.45 0.361.89 2.03 1.96 0.18 0.42 0.40 0.45 0.40 46.10 49.50 47.80 0.332.04 2.18 2.11 0.24 0.54 0.52 0.55 0.47 49.50 53.30 51.40 0.302.19 2.34 2.27 0.33 0.68 0.64 0.62 0.60 53.30 57.30 55.30 0.302.35 2.52 2.44 0.42 0.82 0.75 0.72 0.75 57.30 61.60 59.45 0.272.53 2.71 2.62 0.54 0.97 0.85 0.80 0.91 61.60 66.20 63.90 0.272.72 2.91 2.82 0.60 1.06 0.94 0.85 1.00 66.20 71.20 68.70 0.272.92 3.13 3.03 0.63 1.12 0.98 0.89 1.05 71.20 76.60 73.90 0.243.14 3.37 3.26 0.66 1.16 1.00 0.89 1.02 76.60 82.40 79.50 0.243.38 3.62 3.50 0.69 1.15 0.98 0.87 0.95 82.40 88.60 85.50 0.243.63 3.89 3.76 0.69 1.11 0.95 0.85 0.83 88.60 95.20 91.90 0.213.90 4.19 4.05 0.72 1.04 0.90 0.81 0.67 95.20 102.00 98.60 0.214.20 4.50 4.35 0.75 0.98 0.85 0.78 0.55 102.00 110.00 106.00 0.214.51 4.84 4.68 0.78 0.97 0.82 0.74 0.52 110.00 118.00 114.00 0.214.85 5.21 5.03 0.81 0.95 0.80 0.70 0.525.22 5.60 5.41 0.81 0.94 0.78 0.66 0.525.61 6.02 5.82 0.84 0.90 0.73 0.62 0.476.03 6.48 6.26 0.81 0.81 0.69 0.57 0.336.49 6.97 6.73 0.78 0.71 0.63 0.49 0.206.99 7.49 7.24 0.75 0.64 0.57 0.42 0.117.44 8.05 7.75 0.72 0.59 0.50 0.36 0.078.06 8.66 8.36 0.72 0.56 0.44 0.30 0.098.67 9.31 8.99 0.69 0.54 0.38 0.25 0.109.32 10.00 9.66 0.69 0.50 0.34 0.21 0.08

10.01 10.80 10.41 0.69 0.44 0.31 0.19 0.0510.81 11.60 11.21 0.69 0.39 0.28 0.17 0.0111.61 12.50 12.06 0.69 0.34 0.26 0.1512.51 13.40 12.96 0.69 0.31 0.25 0.1313.41 14.40 13.91 0.69 0.28 0.24 0.1314.41 15.50 14.96 0.66 0.28 0.23 0.1315.51 16.70 16.11 0.63 0.26 0.22 0.1116.71 17.90 17.31 0.63 0.22 0.20 0.1117.91 19.30 18.61 0.60 0.18 0.17 0.0819.31 20.70 20.01 0.57 0.15 0.15 0.0620.71 22.30 21.51 0.54 0.10 0.13 0.0422.31 24.00 23.16 0.51 0.06 0.09 0.0224.01 25.80 24.91 0.48 0.02 0.0425.81 27.70 26.76 0.48 0.00 0.01


5.1.2. Evaluation by X-ray diffraction (XRD) analysis Qualitative x-ray diffraction (XRD) analysis was conducted for main lagoon water and settling pond (scrape). In addition, the Glomax LL used in synthetic water experiments was analysed for comparison. The relative composition of each sample is summarised in Table 5.2. XRD charts are provided in Appendix G Table 5.2 Mineralogical and organic composition of main lagoon water

Sample Mineralogical Composition

Main Lagoon Co-dominant quartz and amorphous material (organic matter), minor mica (muscovite), kaolin, probable smectite (montmorillonite), trace Na feldspar (albite), K-feldspar (microcline/orthoclase), dolomite, hematite, rutile and possible chlorite

Pre-Settling Pond (Cross Road)

Dominant quartz, minor mica (muscovite), kaolin, probable smectite (montmorillonite), trace Na feldspar (albite), K-feldspar (microcline/orthoclase), hematite and rutile

Glomax LL (kaolinite clay

Co-dominant amorphous and aluminium oxide, minor mullite, trace anatase and rutile

Note: Dominant (>60%), co-dominant (sum of phases >60%), sub-dominant (20-60%), minor (5-20%), trace (<5%)

XRD results revealed that the mineralogy of suspended particles in the main lagoon sample and the settled particles in the settling pond were comprised primarily of quartz with minor percentages of clay minerals, and titanium oxides. XRD analysis also revealed a high percentage of organic matter in the main lagoon sample. The results indicate that particles entering the settling pond and main lagoon have a similar composition. The high percentage of organic particulate matter in the main lagoon water suggests that organic matter does not readily settle in the settling pond during storm events. Re-suspension of particles in the settling pond is also likely due to the high energy observed in the system during storm events.

5.1.3. Evaluation by scanning electron microscopy (SEM) Five samples were analysed using scanning electron microscopy (SEM), including:

• Kaolinite Clay (Glomax LL)

• Urrbrae Wetland ASR pond water – untreated and roughing filter-treated

• Urrbrae Wetland main lagoon water – untreated and roughing filter-treated


Kaolinite Clay

Figure 5.5 is an SEM micrograph of the Glomax LL kaolinite clay. The figure reveals that individual particles are very crystalline and approximately 0.5 um to 1 um in diameter. The thickness of individual plates is about 10 to 50 nm, whereas kaolinite books varied in thickness up to about 2 μm. The platelike structure suggests that models regarding kaolinite clay particles as spherical are likely to be flawed. EDX spectra revealed a 1:1 ratio of aluminium to silica, indicative of kaolinite clay. Trace impurities were identified as iron-titanium oxides. The black dots in the background represent the filter membrane pores. The complete set of SEM micrographs and associated EDX spectra and analysis files for kaolinite clay are provided in Plates 01.01-01.06 and Figure 01.01 (Appendix H).

Figure 5.5 SEM micrographs showing kaolinite clay (Glomax LL) particles with Fe-Ti impurities

ASR Pond Untreated SEM analysis of the untreated ASR pond sample revealed a diverse assortment of discrete particles (mostly macro-organisms) and complex organic and inorganic particle assemblages (or flocs) bound by organic mucilage. Although difficult to delineate accurately in SEM micrographs, particle sizes generally ranged from 50 to 300 μm. Macro-organisms included algae, diatoms, amoebas, fungi and bacteria. Minerals included clay minerals, quartz, and iron oxides. The abundant amorphous mucilage was reflected in EDX spectra by the high C, O, P, S and K, while Al-Silicate peaks were associated with the minerals. Figure 5.6 shows particles typical of the untreated ASR pond sample. Unlike the SEM images of the kaolinite, the filter paper pores in these images (and for all images of natural water samples) have been blocked by organic mucilage. A complete set of SEM micrographs and associated EDX spectra and analysis files of the untreated ASR pond water sample are provided in Plates 02.01-02.27 and Figures 02.01-02.10 (Appendix H), respectively.

Figure 5.6 SEM micrographs showing typical particles in untreated ASR pond water

Fe-Ti

FilterPores

K-clay book

Algae

Clay and organics

Organic / diatom / clay

Algae

Clay and organicsBacteria

FilterPores


ASR Pond Treated SEM analysis of the roughing-filter treated ASR pond sample revealed a much more homogeneous particle structure compared to the untreated sample. Flocs of organic mucilage and some inorganic minerals were present, and no biological organisms were found, although some residual cellular material was observed. Particle size ranged from 5 to 40 μm. Figure 5.7 shows particles typical of the roughing filter-treated ASR pond sample. A complete set of SEM micrographs and associated EDX spectra and analysis files of the treated ASR pond water samples are provided in Plates 03.01-03.09 and Figures 03.01-03.03 (Appendix H), respectively.

Figure 5.7 SEM micrographs showing typical particles in roughing filter-treated ASR pond water

Main Lagoon Untreated SEM analysis of the untreated main lagoon sample revealed mostly inorganic and organic particle assemblages containing large organisms, some smaller organic remnants, diatoms, and bacteria. Particle sizes ranged from 10 to 100 μm. EDX spectra indicated Al-Silicates, iron oxides (F04.01 to F 04 .03, F 04.06, F 04.07), and organics. Compared to the ASR pond sample, macro-organisms were not as common (some algae was observed) and flocs were not as uniformly coated with mucilage indicating different biological population or environmental conditions. Figure 5.8 shows particles typical of the untreated main lagoon sample. A complete set of SEM micrographs and associated EDX spectra and analysis files of the untreated main lagoon water sample are provided in Plates 04.01-04.12 and Figures 04.01-04.05 (Appendix H), respectively.

Figure 5.8 SEM micrographs showing typical particles in untreated main lagoon water

Organics / clay / Fe Oxides right micrograph

Quartz

Fe oxides / organics

K – Al-Si

Biotite and organic

Organic

Kaolinite / mica

Monzanite

TiO2

Organic mucilage with some inorganic mineral

Organics / Fe Oxides


Main Lagoon Treated SEM analysis revealed mostly inorganic and organic flocs, similar in composition to the untreated sample. Particles sizes were generally smaller than in the untreated sample, but relatively large flocs (up to 50 μm) were still observed. Figure 5.9 shows particles typical of the untreated main lagoon sample. A complete set of SEM micrographs and associated EDX spectra and analysis files of the treated main lagoon water sample are provided in Plates 05.01-05.05 and Figures 05.01-05.02 (Appendix H), respectively.

Figure 5.9 SEM micrographs showing typical particles in roughing filter-treated main lagoon water

Summary of SEM analysis In combination with PSD and XRD results, SEM results helped to explain why removal efficiencies observed in roughing filter experiments with Urrbrae Wetland water were poorer than those observed for kaolinite clay. Poor cumulative removal efficiencies with Urrbrae Wetland water can be attributed to the low density of organic-inorganic particle flocs compared to discrete kaolinite clay particles. However, initial 60-cm removal efficiencies for experiments with main lagoon water (32-35%) were comparable to those with kaolinite clay (47%) due to the broad distribution in particle size of untreated main lagoon water. Particle flocs in the ASR pond water are larger than those in the main wetland water. This difference confirms PSD results and indicates different biological population or environmental conditions. SEM analysis also confirmed the preferential removal of larger particle flocs and macro-organisms. Because many macro-organisms identified were part of larger particle assemblages more likely to be retained in the filter, breakage of flocs in the roughing filter due to shear forces was likely minimal. Particle sizes observed in SEM generally confirm PSD results. However, particle sizes in SEM micrographs are likely inflated due to particle overlap and flattening on the filter membrane (particularly for organic particles).

5.1.4. Physico-chemical characterisation of untreated and roughing filter-treated stormwater

Untreated and roughing filter-treated Urrbrae Wetland samples were submitted to Australian Water Quality Centre (AWQC) for physical and chemical analyses. Results are provided in Table 5.3.

Results of physical and chemical analyses of the Urrbrae Wetland waters revealed the following:

• VSS and TSS measurements revealed that virtually all of the suspended solids in the untreated ASR pond sample (11 of the 12 mg/L TSS) were organic in nature. In contrast, only 11 of the 30 mg/L TSS in the untreated main lagoon sample were organic. The URFS preferentially removed inorganic matter in the main lagoon sample (based on roughing filter-treated VSS/TSS ratio of 6 to 9 mg/L).

• Roughing filtration was unable to reduce significantly BDOC or BRP levels in Urrbrae Wetland water. The ratio of biodegradable organic carbon (BDOC) to dissolved

Flocs of inorganic minerals and organic mucilage

right micrographOrganic

Kaolinite

Muscovite

Biotite


organic carbon (DOC) (3.2 to 4.1 mg/L BDOC to 3.9 to 4.1 mg/L DOC) was high for both untreated and roughing filter-treated samples. BDOC concentrations are more than one order of magnitude higher than the 0.15 mg/L BDOC limit reported by Servais et al. (1993) for biologically stable water. BRP concentrations for the untreated and roughing filter-treated samples (188 to 293 acetate carbon equivalent (ACE) μg/L) are more than four times the 40 ACE μg/L (equivalent to biological growth factor of 5) threshold for biologically stable waters (Werner and Hambsch, 1986).

• In addition to suspended solids, other guidelines for source water quality for slow sand filters include avoidance of high colour content, dissolved heavy metals, pesticides and herbicides (Logsdon, et al., 2002). Analytical results showed elevated levels of true colour for both the treated and untreated main lagoon (54 to 57 HU) and ASR pond (19 to 22 HU) samples. No analyses were conducted for heavy metals, pesticides or herbicides.

It is recognised that there are inherent temporal variations in water quality in the Urrbrae Wetland due to stormwater runoff and algal growth in the shallow nutrient-rich water. Residence time of water in the main lagoon is expected to be less compared to the ASR pond.


Table 5.3 Water quality analyses performed by AWQC for untreated and roughing filter-treated Urrbrae Wetland waters

ASR Pond Main Lagoon Data Type Analyte/Test Units Untreated RF-treated Untreated RF-treated

pH pH units 8.8 7.5 7.2 7Conductivity (at 25oC) μS/cm 183 181 72.0 74.0Alkalinity mg/L 42.6 41.0 23.8 23.8Colour - True HU 22 19 57 54Suspended Solids mg/L 12 3 30 9

Physical

Volatile Suspended Solids mg/L 11 2 11 6Sodium Adsorption Ratio (SAR) - 1.02 1.06 0.54 0.53Total Hardness as CaCO3 mg/L 46.1 45.2 22.8 22.6Ion Balance % 5.33 - -9.81 -7.64Dissolved Solids by calculation mg/L 96.2 98.1 48.0 46.5

Derived

Total Dissolved Solids (by EC) mg/L 100 99 39 41Calcium mg/L 13.5 13.3 7.0 6.9Magnesium mg/L 3.0 2.9 1.3 1.3Potassium mg/L 3.3 3.2 1.4 2.1

Major cations

Sodium mg/L 15.9 16.3 5.9 5.8Bicarbonate mg/L 52 50 29 29Chloride mg/L 23 23 11 11Fluoride mg/L 0.1 0.1 <0.1 <0.1

Major anions

Sulfate mg/L 7.6 7.6 4.9 4.1Metals Iron - Total mg/L 0.681 0.542 0.504 0.265

Nitrogen - Ammonia mg/L 2.39 4.73 0.029 0.026Nitrogen - (Nitrate & Nitrite) mg/L 0.09 0.073 0.223 0.097Nitrogen - Total Kjeldahl mg/L 0.77 0.47 0.65 0.7Nitrogen - Soluble Kjeldahl mg/L <2.0 <2.0 <2.0 <2.0Nitrogen - Total mg/L 0.86 0.54 0.87 0.8Phosphorus - Total mg/L 0.196 0.099 0.128 0.075Filterable Reactive Phosphorus (FRP) mg/L 0.085 0.024 0.055 0.007

Nutrients

Silica (Reactive) mg/L <1.0 <1 2 1Total Organic Carbon (TOC) mg/L 4.6 4.1 6.8 5.0Dissolved Organic Carbon (DOC) mg/L 3.9 4.1 4.1 3.9

Bacterial Regrowth Potential (BRP) ACE μg/L 190 188 293 200a

Organics

Biodegradable Organic Carbon (BDOC) mg/L 3.6 4.1 3.5 3.2b

Chlorophyll-a μg/L - 0.6 2.9 0.7Biological Chlorophyll-b μg/L - <0.1 1.0 <0.1Green algae Chlamydomonas cells/mL - - - Present Cryptomonas cells/mL - - 2 - Euglena cells/mL 12 7 - - Scenedesmus cells/mL Present 27 - -Diatoms (Total) Melosira cells/mL Present - - - Navicula cells/mL 10 Present - 3 Nitzschia cells/mL - - 2 -Blue-green algae Microsyctis cells/mL 195 - - -

Algae

Phormidium cells/mL - - 2 -

ACE = Acetate Carbon Equivalents aDuplicate sample BRP = 262 mg/L, giving a relative error of 31.0% for this analysis bDuplicate sample BDOC = 3.3 mg/L, giving a relative error of 3.1% for this analysis - = not found


5.1.5. Evaluation of physical clogging potential of Urrbrae Wetland water using Membrane Filtration Index (MFI) method

Because global parameters, such as turbidity and TSS, are poorly correlated with slow sand filter run lengths (Cleasby, 1991) and clogging rates in ASR injection wells (Dillon, et al., 2001), the clogging potential of untreated and roughing filter-treated Urrbrae Wetland water was quantified using the Membrane Filtration Index (MFI) method. MFI analyses of Glomax LL kaolinite clay suspensions of varying concentrations were also conducted for comparison. All samples were analysed three times with a relative standard deviation between 2.4 and 6.6%.

Results of the MFI analyses along with TSS and NTU measurements are provided below in Table 5.4. Turbidity and TSS measurements for natural waters are based on measurements conducted during MFI tests and, therefore, differ slightly from results reported by AWQC. MFI results and charts for all samples are provided in Appendix I. Table 5.4 MFI results for untreated and roughing-filter treated Urrbrae Wetland waters and kaolinite clay suspension

ASR Pond (18-Oct-05) Main Lagoon (20-Oct-05) Kaolinite Clay (400 NTU) Untreated RF-treated Ce/Co Untreated RF-treated Ce/Co Untreated RF-treated Ce/Co

Turbidity (NTU) 10.5 2.55 0.243 33.9 15.4 0.454 400 51.5 0.129TSS (mg/L) 10.6 2.6 0.245 30.0 12.1 0.403 196 28 0.143MFI (s/L2) 169.5 108.4 0.640 206.5 73.6 0.356 60.1 15.7a 0.261Note: Filter configuration is 7.55 x 7.55 x 5.18 x 2.18 mm media; hydraulic loading rate = 1.0 m/hr aBased on NTU-TSS-MFI correlation

The table shows that the physical clogging potential of Urrbrae Wetland water based on MFI values is much greater than a kaolinite clay suspension of 400 NTU (196 TSS mg/L). This phenomenon is attributable to the predominantly organic nature of suspended particles in natural stormwater, which more easily compresses and clogs the filter paper pores than the rigid kaolinite clay particles. Of the untreated Urrbrae Wetland samples, the MFI of the main lagoon water was only slightly higher than the ASR pond water, despite particle concentration in the main lagoon water being roughly 3 times greater than in the main lagoon water. The physical clogging potential of both untreated Urrbrae stormwater samples were higher than all ASR waters evaluated by Dillon, et al. (2001) with the exception of DAF/F (dissolved air flotation/filtration) water at the Bolivar Treatment Plant, which had an MFI value of 439 s/L2. The efficiency of the roughing filter in reducing MFI compared to turbidity and TSS varied considerably for each sample. For the ASR pond water, the roughing filter was capable of reducing the original MFI by only 36%, which is less efficient than turbidity and TSS removals (76% and 75%, respectively). Visual observation of filter papers used in MFI and TSS analyses of the ASR pond sample (Figure 5.10) showed that the URFS preferentially removed large particles in the ASR pond sample. However, a yellowish colour in the treated water indicated that fine colloidal matter in the ASR pond water was not as effectively removed by the roughing filter. In contrast, the roughing filter was capable of reducing by 64% the original MFI of the main lagoon water, which is slightly more efficient than turbidity and TSS removals (55% and 60%, respectively). The filter paper used for MFI and TSS analyses of the main lagoon sample (Figure 5.11) revealed a tan colour representative of mostly fine colloidal matter. For the kaolinite clay suspension, the roughing filter was capable of reducing by 74% the original MFI, which was slightly less efficient compared with turbidity and TSS removals (87% and 86%, respectively).

Results of MFI analyses indicate that consideration of the nature and concentration of suspended solids is essential for understanding the physical clogging potential of source waters intended for SSF. Additional research in this field is needed to improve current source water guidelines for SSF clogging that rely on global particulate parameters.


Figure 5.10 Filter paper from MFI and TSS analyses of (A) untreated and (B) roughing filter-treated ASR pond water (18-Oct-05)

Figure 5.11 Filter paper from MFI and TSS analyses of (A) untreated and (B) roughing filter-treated main lagoon water (20-Oct-05)

A B

Dark spots = large primarily-organic flocsYellow matrix = fine solids (w/ extracellular polymers)

Fine colloidal matter

A B

Primarily inorganic and organic particle flocs

Fine colloidal matter


5.1.6. Suspension Stability Tests Suspension stability test results for the untreated main lagoon water sample and for samples collected from one kaolinite clay experiment (same as presented in Section 4.1.5) are provided in Figure 5.12. The figure shows that a portion of the turbidity in the untreated main lagoon water settles more rapidly than the kaolinite clay effluent from column 1 (Ce1) due to the relatively large particles in the untreated main lagoon water, confirmed by PSD and SEM analyses. However, more than 50% of the turbidity in the main lagoon settles more slowly than the final kaolinite clay effluent from Column 4 (Ce4) due to the primarily organic nature of particles in the main lagoon water sample as confirmed by XRD and SEM analyses and VSS-TSS results. This explains the relatively high removals observed in the URFS experiment with main lagoon water for the first 60-cm filter segment (32-35%) and low removals (12-17%) for the last three 60-cm filter segments. Suspension stability tests with ASR pond water were not conducted due to the buoyancy of the large particle flocs in suspension.

Figure 5.12 Relationship of remaining turbidity over time for kaolinite clay and main lagoon water

Suspension Stability Test Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 10 100 1000 10000

Time (minutes)

Rem

aini

ng T

urbi

dity

Ce4

Ce3

Ce2

Ce1

Co

Main Lagoon (Co) (20-Oct-05)

Kaolinite clay samples collected from experiment evaluating 7.55 mm media, 1.5 m/hr, 400 NTU


6. Concluding Remarks

6.1. Assessment of laboratory set-up Experiments conducted to confirm that the laboratory set-up adequately simulated conditions of a clean bed roughing filter revealed no evidence for preferential flow along columns walls in the URFS. Errors introduced by biological activity within the filter columns were insignificant, and errors introduced by and filter packing were minor compared to overall experimental error accepted for any given experiment.

6.2. Key findings from URFS experiments with kaolinite clay suspension

Results of URFS experiments with kaolinite clay revealed that smaller media sizes, slower hydraulic loading rates, and longer filters resulted in improved steady-state removals. In addition, the preferential removal of larger particles (due primarily to sedimentation) resulted in decreasing incremental removal efficiencies with increasing filter length. These trends confirm findings reported by other researchers.

Interestingly, influent particle concentration (up to 300 mg/L TSS) had no measurable effect on TSS particle removal. Systematic improvement in turbidity removal with increasing influent particle concentrations can be attributed to the curvilinear relationship between TSS and turbidity for the kaolinite clay used in this study.

Removal efficiencies in URFS experiments were worse than predicted using empirical steady-state kaolinite clay models developed for HRF and DRF. Although differences may be attributed partly to filter configuration differences, differences in the PSD of kaolinite clay used in each study likely played a significant role.

Modelling steady-state kaolinite clay removals with colloid filtration theory (CFT) revealed that a single attachment factor can be reliably derived for a polydisperse suspension (of constant particle density) using PSD measurements and the procedures used in this study. Consideration of hydrodynamic and attractive van der Waals forces resulted in improved estimation of the SCE. Observed declines in attachment factor with increasing filter depth for some experiments could not be adequately explained by cake and/or straining filtration. Additional research is needed to confirm alternative explanations.

Multivariate regression analysis was used to develop an empirical model capable of simulating steady-state kaolinite clay removals for different filter configurations. The model best simulated the curvilinear relationship between cumulative removals and filter length by using filter length-1 as a design variable. Although the empirical model is limited to the specific PSD of kaolinite clay used in these experiments, the experimental approach used in this study can be used to develop a similar model for any particular suspension of interest. When using any empirical model with filter length (or variation of filter length) as a design variable for filters packed with different media grades, removal efficiencies are best estimated by applying the average media size weighted to filter length.

6.3. Key findings from URFS experiments with Urrbrae Wetland waters

Results of URFS experiments with Urrbrae Wetland waters revealed that removal efficiencies for both Urrbrae Wetland waters were worse compared to those for a kaolinite clay suspension. This was attributable to particles in the main lagoon and ASR pond being comprised of inorganic-organic aggregates comprised of algae, macro-organisms (including diatoms, cysts, and bacteria) and extracellular organic material, which were lower in density than kaolinite clay particles. Preferential removal of larger particle flocs and macro-organisms was observed. Because many macro-organisms identified were part of larger particle assemblages more likely to be retained in the filter, breakage of flocs in the roughing filter due to shear forces was likely minimal.


Maximum turbidity allowances for source waters intended for slow sand filtration (10-20 NTU) are exceeded in the main lagoon of the Urrbrae Wetland during storm events but are not exceeded in the ASR pond. Interestingly, however, the physical clogging potential (measured using the membrane filtration index (MFI) analysis) of untreated Urrbrae Wetland waters were similar and both higher than 1) a kaolinite clay suspension of 196 mg/L and 2) the majority of surface waters used or being considered for ASR in South and Western Australia. The URFS was able to remove a considerable portion of suspended solids in the main lagoon water to satisfy source water turbidity requirements and provide additional turbidity reduction in ASR pond water. However, MFI values for both roughing filter-treated Urrbrae Wetland waters were higher than the kaolinite clay suspension of 196 mg/L sue to the primarily organic nature of the particles in the Urrbrae Wetland waters. Results of MFI analyses indicate that consideration of the nature and concentration of suspended solids is essential for understanding the physical clogging potential of source waters intended for SSF.

The Urrbrae Wetland waters are considered biologically unstable. As expected, URFS is unable to reduce BDOC and BRP concentrations and additional pre-treatment (i.e. slow sand filtration) is likely to be necessary to minimise biological clogging during ASR injection of Urrbrae Wetland water.

6.4. Recommended additional research The following additional research is recommended to improve understanding in roughing filtration removal mechanisms and its application to stormwater:

• Bench-scale column studies with a model suspension resembling the particle size distribution and density found in urban stormwater should be used to determine the relationship between filter design variables and removal rates of such particles. Using the same methods used in kaolinite clay experiments for this study, CFT and multivariate regression analysis could be used to determine attachment factors and new empirical models for expected conditions in urban wetlands. Equations 3.5 and 3.6 could be used to confirm the significance of considering hydrodynamic and van der Waals forces in CFT modelling of bench-scale experiments for an organic particle suspension.

• Bench-scale experiments with natural stormwater should be conducted until ultimate filter loads are reached to determine the expected maintenance schedule for a field-scale roughing filter. Longer experiments may also be used to determine if expected biological ripening in the roughing filter measurably improves particle removals.

• Additional research is needed to improve current source water guidelines for SSF clogging that rely solely on global particulate parameters. Measurements, such as the Membrane Filtration Index could be used to relate the nature and concentration of suspended solids in source waters to SSF clogging rates.

• Experiments could be conducted with the same kaolinite clay suspension used in this study in a horizontal filter configuration to quantify the effect of flow direction on particle removals. It would be useful to determine if the base of an HRF, representing an additional collector surface, significantly improves filter collector efficiency.

• Column experiments using glass bead collectors and spherical particles could be used to test the application of CFT to roughing filtration and to quantify the error introduced by the non-ideal geometry of collectors and particles.


Glossary Symbol Definition Ce particle concentration of filter effluent

Ci particle concentration of respective filter influent

Co particle concentration of initial filter influent

Ce/Co cumulative removal

Ce/Ci incremental removal (in this study = 60 cm removal)

λ filter coefficient

λo initial filter coefficient σ specific filter deposit

σ u ultimate filter deposit

dc collector (filter media) diameter

dp particle diameter

v hydraulic loading rate

T absolute temperature

eo initial filter porosity

Uc uniformity coefficient (d60/d10)

ρ p particle density

ρ f fluid density

μ fluid dynamic viscosity

ν kinematic viscosity

Re Reynolds number

φ constant accounting for pore-size reduction and increased pore fluid velocity

k Boltzman constant

g gravitational constant

ηD theoretical single collector efficiency attributable to diffusion

ηI theoretical single collector efficiency attributable to interception

ηG theoretical single collector efficiency attributable to sedimentation

ηtoal theoretical total single collector efficiency (= ηD + ηI + ηG)

α empirical collision efficiency (or attachment) factor

NR dp/dc (aspect ratio)

NPe vdc/(kT/3πdpμ) (Peclet number characterising ratio of convective to diffusive transport (latter described by Stokes-Einstein Equation))

NvdW A/kT (van der Waals number characterising ratio of van der Waals interaction energy to the particle’s thermal energy; A equals Hamaker constant of the interacting media)

Ngr 4/3π(dp/2)4(ρ p – ρf)g/kT (gravitational number; ratio of particle’s gravitational potential when located one particle radius from collector to particle’s thermal energy)


Symbol Definition

NA A/12πμ(dp/2)2v (attraction number; combined influence of van der Waals attraction forces and fluid velocity on particle deposition by interception)

NG (ρ p – ρf)*g*dp2/18uv (gravity number; ratio of Stokes particle settling velocity to

approach velocity of fluid

Abbreviation Definition ASR Aquifer Storage and Recovery

CFT Colloid Filtration Theory

EDX Energy Dispersive X-ray

MFI Membrane Filtration Index

PSD Particle Size Distribution

SEM Scanning Electron Microscopy

SCE Single Collector Efficiency

XRD X-Ray Diffraction

HRF Horizontal Roughing Filter

HRFS Hrisontal Roughing Filter in Series

DRF Downflow Roughing Filter

DRFS Downflow Roughing Filter in Series

URF Upflow Roughing Filter

URFS Upflow Roughing Filter in Series


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Appendix A Water quality monitoring data for Urrbrae Wetland


Conductivity TDS Turbidity Water Temp Air Temp N as NO3 P as PO4

(% sat.) mg/L μS/cm mg/L JTU °C °C mg/L mg/L29-Mar-04 1 50 4.5 272 136 7.3 45 20.9 18.0 0.4 0.07

2 79 6.9 315 156 7.3 45 20.6 18.0 0.4 0.253 94 8.1 318 158 7.1 40 21.9 18.0 0.4 0.094 92 8 322 162 7.4 40 21.3 18.0 0.4 0.04

14-Apr-04 1 125 11.6 358 179 8.5 16 22.1 22.0 0.3 0.742 131.2 11.4 363 182 6 27 21.6 22.0 0.3 03 120 10.32 384 192 6 24 21.8 22.0 0.3 0.174 103.6 9.32 370 186 5.8 18 21.7 22.0 0.3 0.04

23-Apr-04 1 63 6.01 342 172 6.5 28 18.0 19.0 0.4 0.382 73.1 6.94 384 192 6.8 38 18.1 19.0 0.3 0.113 80 7.56 412 207 6.8 35 18.5 19.0 0.5 0.074 76 7.14 390 172 6.7 28 18.7 19.0 0.3 0.11

29-Apr-04 1 50 4.5 272 136 7.3 45 20.9 18.0 0.4 0.072 79 6.9 315 157 7.3 45 20.6 18.0 0.4 0.253 94.1 8.1 318 159 7.1 40 21.5 18.0 0.4 0.094 92 8 322 161 7.4 40 21.3 18.0 0.45 0.04

09-Jun-04 1 41 4.1 1350 680 5.8 26 13.7 17.0 0.4 0.092 47 4.8 1360 690 6.9 20 14.3 16.0 0.7 0.123 67 6.4 1020 500 5.7 60 14.9 16.0 0.6 0.14 66 6.4 1250 630 5.7 48 15.5 16.0 0.5 0.09

02-Jul-04 1 82 8.87 242 121 5.7 62 11.9 15.0 0.4 0.082 74.7 6.93 307 154 5.8 70 11.4 15.0 0.3 0.073 84.6 9.06 197 99 5.7 42 11.4 15.0 0.3 0.084 60 6.05 230 116 5.7 100 11.3 15.0 0.3 0.08

16-Jul-04 1 52.8 5.43 240 120 5.2 7 11.7 12.0 0.1 0.422 45.6 4.71 250 130 5.3 <5 11.3 12.0 0.1 0.343 51.2 5.34 240 120 5.5 <5 11.8 12.0 0.2 0.264 60 5.81 210 110 5.5 15 11.1 13.0 0.1 0.36

30-Jul-04 1 69.7 8.6 250 130 5.7 <5 12.2 16.5 0.4 0.612 55.5 5.1 210 100 5.7 <5 12.0 16.5 0.4 0.483 51.9 5.3 170 90 5.8 20 12.1 16.5 0.4 0.324 46.7 4.8 220 110 5.7 10 12.1 16.5 0.5 0.42

08-Aug-04 1 83 8.97 398 200 5.9 100 10.9 13.0 1.4 0.892 77.2 7.87 242 120 5.7 150 10.3 12.5 1.2 0.793 83.2 8.83 253 130 6.2 95 10.7 13.0 1.1 0.464 81 8.76 225 110 5.8 125 10.1 13.0 1.4 0.63

Dissolved Oxygen Date Site pH


Conductivity TDS Turbidity Water Temp Air Temp N as NO3 P as PO4

(% sat.) mg/L μS/cm mg/L JTU °C °C mg/L mg/L13-Aug-04 1 56.1 5.7 405 203 5.7 7 12.4 14.0 0.6 0.2

2 57 5.8 356 179 5.8 7 12.3 14.0 0.7 0.223 81 8.4 361 181 8.4 22 11.7 14.0 0.7 0.194 56 5.8 358 180 5.8 15 12.5 14.0 0.6 0.18

20-Aug-04 1 60 5.9 453 228 6.1 <5 12.4 19.5 0.9 0.532 66 6.1 427 214 6 <5 13.2 19.5 0.9 0.473 53 5.4 413 207 6.1 14 12.8 19.5 0.9 0.534 58 5.6 429 215 6.1 <5 14.9 19.5 0.9 0.4

28-Aug-04 1 55.9 4.81 438 220 5.5 <5 17.3 25.0 0.3 0.12 73.2 6.72 426 214 6.8 <5 17.8 25.0 0.2 0.073 55.7 4.89 416 208 6.8 <5 16.4 25.0 0.3 0.14 61 5.62 425 214 7.3 <5 17.3 25.0 0.3 0.09

03-Sep-04 1 45.4 4.4 384 192 6.8 <5 16.2 22.0 0.2 0.162 54.5 5.58 386 193 6.8 <5 14.0 22.0 0.3 0.093 58.2 5.78 372 187 6.3 <5 15.2 22.0 0.3 0.084 51.1 5 383 192 6.5 <5 15.9 22.0 0.3 0.09

17-Sep-04 1 59 5.7 204 102 6.5 <5 17.4 23.0 0.6 0.282 67 6.3 199 100 6 <5 17.7 23.0 0.5 0.313 68 6.4 198 100 6 <5 17.8 23.0 0.7 0.084 65 6.2 197 99 6.5 <5 17.2 23.0 0.5 0.91

15-Oct-04 1 38 3.4 230 120 6.8 <5 21.4 18.0 1.3 1.122 46.6 4.08 210 110 5.8 <5 21.3 18.0 1.5 0.133 50 4.4 210 110 6.2 <5 21.7 18.0 1.2 0.124 64 5.6 220 110 5.8 <5 21.3 18.0 0.6 0.13

29-Oct-04 1 38 3.2 232 116 6.5 <5 23.9 24.0 0.7 0.522 39 3.3 236 118 6.5 <5 22.9 24.0 0.7 0.293 33 2.9 235 118 6.5 <5 22.4 24.0 0.5 0.354 42 3.6 232 117 6.5 <5 23.7 24.0 0.5 0.25

29-Dec-04 1 77 5.95 225 113 6.8 6.5 28.6 N/A N/A 0.12 78.2 6.23 226 113 6.5 14 27.3 N/A N/A 0.13 74.4 5.85 222 112 6.5 12 29.5 N/A N/A 0.074 82.4 6.57 226 113 6.7 15 28.9 N/A N/A 0.1

04-Feb-05 1 10.6 0.65 230 116 6 10 19.0 20.0 0.6 0.132 13 1.11 240 120 7 5 19.0 20.0 0.5 0.153 12.5 1.05 240 130 7 <5 18.7 20.0 0.7 0.124 10.1 0.8 230 120 7 <5 19.1 20.0 0.5 0.13



Conductivity TDS Turbidity W ater Temp Air Temp N as NO3 P as PO 4

(% sat.) mg/L μS/cm mg/L JTU °C °C mg/L mg/L18-Feb-05 1 85.8 7.36 210 110 6.5 8 22.3 21.0 0.4 1.4

2 64 5.48 210 110 6 <5 22.4 21.0 0.2 0.923 55.8 4.79 210 110 6 <5 22.4 21.0 0.3 0.874 38.8 3.31 230 110 6 <5 22.4 21.0 0.3 0.97

04-Mar-05 1 88.8 8.65 220 110 7.64 8 20.8 19.0 0.6 0.092 85.1 7.51 220 110 7.48 <5 21.0 19.0 0.3 0.033 39 3.48 220 110 7.3 <5 20.7 19.0 0.3 0.034 41.6 3.66 220 110 7.37 <5 20.8 19.0 0.1 0.02

22-Apr-05 1 50.8 4.1 311 156 6.51 <5 21.7 25.0 0.5 1.42 34.5 3.2 274 138 6.59 <5 19.7 25.0 0.5 0.923 53.9 4.75 286 144 6.6 <5 20.7 25.0 0.4 0.874 63.6 5.43 280 141 6.64 <5 22.1 25.0 0.5 0.97

20-May-05 1 32.7 3.16 300 150 7.12 5 16.5 21.0 0.4 0.232 38.2 3.76 300 160 7.33 <5 15.7 21.0 0.4 0.173 34.9 3.33 310 160 7.41 <5 15.7 21.0 0.5 0.24 23.6 2.36 300 150 7.25 5 14.8 21.0 0.5 0.18

03-Jun-05 1 30 3.23 321 156 6.7 <5 14.1 21.0 0.5 0.62 50.3 4.89 326 159 6.7 <5 12.5 21.0 0.7 0.563 46.6 4.72 336 168 6.5 <5 12.2 21.0 0.4 0.484 51.1 5.14 314 158 6.5 <5 13.1 21.0 0.4 0.53

16-Jul-05 1 54.3 5.4 240 120 5.2 7 11.7 12.0 0.1 0.422 58.1 5.5 240 120 5.5 15 11.1 12.0 0.2 0.363 53.4 5.1 240 120 5.5 <5 11.8 12.0 0.1 0.264 47.1 4.7 250 130 5.3 <5 11.3 12.0 0.1 0.34

22-Jul-05 1 41.1 4.51 218 109 6.89 <5 10.4 19.0 0.6 0.532 44.1 5 254 127 6.9 <5 10.0 19.0 2.4 0.463 44 4.83 204 102 6.94 <5 10.0 19.0 0.5 0.514 46.1 4.82 215 109 6.93 <5 11.1 19.0 0.4 0.56

Cl- SO 42- NO 3 Ca Fe K Mg Na S

24-May-05 ASR Pond 46 5.6 <0.05 22.7 0.2 6.9 5.6 30.3 2.2

22-Jul-05

22-Jul-05

all conc. (mg/L)

13.4Settling

Pond (Kitch. Rd)

34.4 3.36 437 219 6.94 19.0 4.22 0.27

19.0 3.1 0.27

80

101 7.12 75 12.0Settling

Pond (Cross Rd)

44.8 4.7 201



Appendix B Attachment factor calculations using colloid filtration theory and zeta potential measurements


52.6 mg/L TSS

191 mg/L TSS

299 mg/L TSS Average

Standard Deviation

1 2.18 0.5 Ce1 0.60 0.097 0.118 0.108 0.108 0.0112 2.18 0.5 Ce2 1.20 0.080 0.097 0.090 0.089 0.0083 2.18 0.5 Ce3 1.80 0.069 0.083 0.078 0.077 0.0074 2.18 0.5 Ce4 2.40 0.062 0.072 0.070 0.068 0.0065 5.18 0.5 Ce1 0.60 0.142 0.138 0.135 0.139 0.0036 5.18 0.5 Ce2 1.20 0.131 0.129 0.126 0.129 0.0037 5.18 0.5 Ce3 1.80 0.120 0.119 0.120 0.120 0.0008 5.18 0.5 Ce4 2.40 0.107 0.108 0.112 0.109 0.0039 7.55 0.5 Ce1 0.60 0.145 - 0.135 0.140 0.00710 7.55 0.5 Ce2 1.20 0.134 - 0.145 0.139 0.00711 7.55 0.5 Ce3 1.80 0.124 - 0.146 0.135 0.01612 7.55 0.5 Ce4 2.40 0.117 - 0.142 0.130 0.01813 2.18 1.0 Ce1 0.60 0.122 0.107 0.083 0.104 0.02014 2.18 1.0 Ce2 1.20 0.108 0.098 0.092 0.099 0.00815 2.18 1.0 Ce3 1.80 0.094 0.089 0.086 0.090 0.00416 2.18 1.0 Ce4 2.40 0.083 0.079 0.079 0.080 0.00217 5.18 1.0 Ce1 0.60 0.137 0.138 0.159 0.145 0.01218 5.18 1.0 Ce2 1.20 0.136 0.129 0.157 0.141 0.01419 5.18 1.0 Ce3 1.80 0.126 0.119 0.160 0.135 0.02220 5.18 1.0 Ce4 2.40 0.122 0.108 0.154 0.128 0.02421 7.55 1.0 Ce1 0.60 0.142 0.161 0.159 0.154 0.01122 7.55 1.0 Ce2 1.20 0.139 0.160 0.159 0.153 0.01223 7.55 1.0 Ce3 1.80 0.134 0.156 0.160 0.150 0.01424 7.55 1.0 Ce4 2.40 0.131 0.154 0.164 0.149 0.01725 2.18 1.5 Ce1 0.60 - 0.107 - 0.107 -26 2.18 1.5 Ce2 1.20 - 0.120 - 0.120 -27 2.18 1.5 Ce3 1.80 - 0.108 - 0.108 -28 2.18 1.5 Ce4 2.40 - 0.099 - 0.099 -29 5.18 1.5 Ce1 0.60 - 0.154 - 0.154 -30 5.18 1.5 Ce2 1.20 - 0.164 - 0.164 -31 5.18 1.5 Ce3 1.80 - 0.158 - 0.158 -32 5.18 1.5 Ce4 2.40 - 0.148 - 0.148 -33 7.55 1.5 Ce1 0.60 - 0.154 - 0.154 -34 7.55 1.5 Ce2 1.20 - 0.163 - 0.163 -35 7.55 1.5 Ce3 1.80 - 0.164 - 0.164 -36 7.55 1.5 Ce4 2.40 - 0.161 - 0.161 -

Attachment factor of kaolinite clay in URFS experiments calculated from initial influent PSD using Yao et al. (1971) SCE equation (Eq. 2.5)

Calculated attachment factorTest No.

Media size

(mm)

Hydraulic Loading

Rate (m/hr)

Filter Length

(m)Filter

ID


52.6 mg/L TSS

191 mg/L TSS

299 mg/L TSS Average

Standard Deviation

1 2.18 0.5 Ce1 0.60 0.097 0.143 0.131 0.124 0.0242 2.18 0.5 Ce2 1.20 0.080 0.116 0.109 0.102 0.0193 2.18 0.5 Ce3 1.80 0.069 0.099 0.094 0.087 0.0164 2.18 0.5 Ce4 2.40 0.062 0.086 0.084 0.077 0.0145 5.18 0.5 Ce1 0.60 0.141 0.142 0.135 0.139 0.0046 5.18 0.5 Ce2 1.20 0.130 0.132 0.125 0.129 0.0047 5.18 0.5 Ce3 1.80 0.118 0.121 0.118 0.119 0.0028 5.18 0.5 Ce4 2.40 0.105 0.110 0.111 0.109 0.0039 7.55 0.5 Ce1 0.60 0.132 - 0.123 0.127 0.00710 7.55 0.5 Ce2 1.20 0.122 - 0.131 0.126 0.00611 7.55 0.5 Ce3 1.80 0.112 - 0.132 0.122 0.01412 7.55 0.5 Ce4 2.40 0.106 - 0.128 0.117 0.01613 2.18 1.0 Ce1 0.60 0.160 0.140 0.108 0.136 0.02614 2.18 1.0 Ce2 1.20 0.140 0.127 0.120 0.129 0.01015 2.18 1.0 Ce3 1.80 0.122 0.115 0.111 0.116 0.00616 2.18 1.0 Ce4 2.40 0.107 0.102 0.102 0.104 0.00317 5.18 1.0 Ce1 0.60 0.147 0.142 0.171 0.153 0.01618 5.18 1.0 Ce2 1.20 0.146 0.132 0.168 0.148 0.01819 5.18 1.0 Ce3 1.80 0.135 0.121 0.171 0.142 0.02620 5.18 1.0 Ce4 2.40 0.131 0.110 0.164 0.135 0.02721 7.55 1.0 Ce1 0.60 0.140 0.159 0.156 0.152 0.01022 7.55 1.0 Ce2 1.20 0.136 0.157 0.156 0.150 0.01223 7.55 1.0 Ce3 1.80 0.131 0.153 0.156 0.147 0.01424 7.55 1.0 Ce4 2.40 0.128 0.150 0.160 0.146 0.01625 2.18 1.5 Ce1 0.60 - 0.147 - 0.147 -26 2.18 1.5 Ce2 1.20 - 0.163 - 0.163 -27 2.18 1.5 Ce3 1.80 - 0.146 - 0.146 -28 2.18 1.5 Ce4 2.40 - 0.133 - 0.133 -29 5.18 1.5 Ce1 0.60 - 0.159 - 0.159 -30 5.18 1.5 Ce2 1.20 - 0.169 - 0.169 -31 5.18 1.5 Ce3 1.80 - 0.162 - 0.162 -32 5.18 1.5 Ce4 2.40 - 0.152 - 0.152 -33 7.55 1.5 Ce1 0.60 - 0.159 - 0.159 -34 7.55 1.5 Ce2 1.20 - 0.167 - 0.167 -35 7.55 1.5 Ce3 1.80 - 0.168 - 0.168 -36 7.55 1.5 Ce4 2.40 - 0.165 - 0.165 -

Attachment factor of kaolinite clay in URFS experiments calculated over increasing filter length using Tufenkji and Elimelech (2004) SCE equation (Eq. 2.6)

Calculated attachment factorTest No.

Media size (mm)

Hydraulic Loading

Rate (m/hr)

Filter Length

(m)Filter

ID


Zeta Potential of Glomax LL Kaolinite Clay (0.001 M NaCl)

R2 = 0.9609

-60

-50

-40

-30

-20

-10

0

0 2 4 6 8 10 12

pH

Zeta

Pot

entia

l (m

V)


Appendix C CXTFIT output files for breakthrough curve experiments


2.18 mm test CXTFIT output file INITIAL VALUES OF COEFFICIENTS ============================== NAME INITIAL VALUE V......... 1.9048 D......... 1.9048 R......... 1.0000 PULSE..... 7.0000 RX1....... 0.0000 RX0....... 0.0000 CI........ 4.5000 C0........ 700.0000 ITERATION SSQ V..... D..... PULSE. 0 0.45668E+06 1.90476 1.90476 7.00000 1 0.12285E+06 1.67753 1.14447 6.28625 2 0.57484E+05 1.74877 1.06501 6.44754 3 0.50203E+05 1.74508 0.88355 6.27075 4 0.49792E+05 1.74899 0.87551 6.24173 5 0.49766E+05 1.74913 0.86745 6.22814 6 0.49764E+05 1.74930 0.86570 6.22505 7 0.49764E+05 1.74933 0.86511 6.22400 8 0.49764E+05 1.74933 0.86511 6.22400 CORRELATION MATRIX ================== 1 2 3 1 1.0000 2 0.1236 1.0000 3 0.3978 0.5345 1.0000 RSQUARE FOR REGRESSION OF OBSERVED VS PREDICTED=0.96787402 1 NON-LINEAR LEAST SQUARES ANALYSIS, FINAL RESULTS ================================================ 95% CONFIDENCE LIMITS VARIABLE NAME VALUE S.E.COEFF. T-VALUE LOWER UPPER 1 V..... 1.74933 0.00713 245.26 1.73510 1.76357 2 D..... 0.86511 0.05108 16.94 0.76316 0.96707 3 PULSE. 6.22400 0.13597 45.77 5.95258 6.49542 Theta 0.4200E+00 Bulk density 0.1540E+01 Water flux 0.7347E+00 Dispersivity 0.4945E+00 Kd value 0.0000E+00 Pulse 0.6224E+01 1st order k 0.0000E+00 0 order k 0.0000E+00


7.55 mm test CXTFIT output file INITIAL VALUES OF COEFFICIENTS ============================== NAME INITIAL VALUE V......... 2.0000 D......... 2.0000 R......... 1.0000 PULSE..... 7.0000 RX1....... 0.0000 RX0....... 0.0000 CI........ 4.5000 C0........ 700.0000 ITERATION SSQ V..... D..... PULSE. 0 0.18093E+07 2.00000 2.00000 7.00000 1 0.36496E+06 1.57366 3.99174 4.82358 2 0.11427E+06 1.40363 2.39525 5.19002 3 0.58782E+05 1.45547 0.98015 5.53023 4 0.33148E+05 1.46984 1.27591 5.45627 5 0.32974E+05 1.46741 1.30576 5.46461 6 0.32954E+05 1.46649 1.31369 5.47490 7 0.32950E+05 1.46623 1.31680 5.47873 8 0.32950E+05 1.46614 1.31776 5.47994 9 0.32950E+05 1.46611 1.31809 5.48036 CORRELATION MATRIX ================== 1 2 3 1 1.0000 2 -0.0645 1.0000 3 0.0765 0.5952 1.0000 RSQUARE FOR REGRESSION OF OBSERVED VS PREDICTED=0.95209731 1 NON-LINEAR LEAST SQUARES ANALYSIS, FINAL RESULTS ================================================ 95% CONFIDENCE LIMITS VARIABLE NAME VALUE S.E.COEFF. T-VALUE LOWER UPPER 1 V..... 1.46611 0.00664 220.80 1.45292 1.47929 2 D..... 1.31809 0.06787 19.42 1.18334 1.45284 3 PULSE. 5.48036 0.11424 47.97 5.25356 5.70715 Theta 0.4000E+00 Bulk density 0.1540E+01 Water flux 0.5864E+00 Dispersivity 0.8990E+00 Kd value 0.0000E+00 Pulse 0.5480E+01 1st order k 0.0000E+00 0 order k 0.0000E+00


7.55 mm with rings test CXTFIT output file INITIAL VALUES OF COEFFICIENTS ============================== NAME INITIAL VALUE V......... 2.0000 D......... 2.0000 R......... 1.0000 PULSE..... 7.0000 RX1....... 0.0000 RX0....... 0.0000 CI........ 4.5000 C0........ 700.0000 ITERATION SSQ V..... D..... PULSE. 0 0.23331E+07 2.00000 2.00000 7.00000 1 0.59239E+06 1.57921 4.23143 4.27140 2 0.48272E+06 0.84729 5.51441 6.82480 3 0.29513E+06 1.33949 5.70432 6.22339 4 0.65587E+05 1.27702 2.85216 6.37828 5 0.23507E+05 1.31965 1.44235 6.51879 6 0.11767E+05 1.32440 1.76897 6.54859 7 0.11665E+05 1.32201 1.80082 6.56286 8 0.11660E+05 1.32149 1.80428 6.56779 9 0.11660E+05 1.32142 1.80514 6.56885 CORRELATION MATRIX ================== 1 2 3 1 1.0000 2 -0.1439 1.0000 3 -0.0337 0.6055 1.0000 RSQUARE FOR REGRESSION OF OBSERVED VS PREDICTED=0.98409904 1 NON-LINEAR LEAST SQUARES ANALYSIS, FINAL RESULTS ================================================ 95% CONFIDENCE LIMITS VARIABLE NAME VALUE S.E.COEFF. T-VALUE LOWER UPPER 1 V..... 1.32142 0.00359 368.45 1.31433 1.32852 2 D..... 1.80514 0.04493 40.17 1.71625 1.89402 3 PULSE. 6.56885 0.06700 98.05 6.43632 6.70138 Theta 0.4000E+00 Bulk density 0.1540E+01 Water flux 0.5286E+00 Dispersivity 0.1366E+01 Kd value 0.0000E+00 Pulse 0.6569E+01 1st order k 0.0000E+00 0 order k 0.0000E+00


Appendix D Roughing filtration turbidity measurements and removal calculations


Removal (Ce/Co) calculations (NTU) from kaolin clay roughing filtration experiments

2.18 mm media @ 0.5 m/hr

700 NTU CoLength NTU1 NTU2 NTU3 NTU4 NTU5 NTU6 NTU ave NTU S.E. Ce/Co ave Ce/Co S.E. Ce/Ci ave

0.6 96.9 97.4 97.3 97.4 97.6 97.7 97.4 0.114 0.139 0.0002 0.1391.2 46.2 48 48.6 47.2 47.5 47.7 47.5 0.330 0.068 0.0005 0.4881.8 30.3 30.1 30.7 30.3 30.5 30.2 30.4 0.089 0.043 0.0001 0.6382.4 21.5 21.2 21.4 21.8 21.5 21.3 21.5 0.085 0.031 0.0001 0.707

400 NTU CoLength NTU1 NTU2 NTU3 NTU4 NTU5 NTU6 NTU ave NTU S.E. Ce/Co S.E. Ce/Ci ave

0.6 59.0 59.2 59.0 58.9 59.3 59.1 59.1 0.060 0.148 0.0002 0.1481.2 28.8 29.0 29.3 28.6 29.9 29.8 29.2 0.217 0.073 0.0005 0.4951.8 18.7 18.7 18.9 19.1 19.0 18.9 18.9 0.065 0.047 0.0002 0.6462.4 14.0 13.9 14.0 14.2 14.1 14.1 14.1 0.043 0.035 0.0001 0.744

100 NTU CoLength NTU1 NTU2 NTU3 NTU4 NTU5 NTU6 NTU ave NTU S.E. Ce/Co S.E. Ce/Ci

0.6 17.8 18.5 18.3 18.5 18.3 18.4 18.3 0.106 0.183 0.0011 0.1831.2 9.48 9.54 9.47 9.51 9.49 9.54 9.51 0.0123 0.095 0.0001 0.5191.8 6.35 6.31 6.27 6.37 6.39 6.34 6.34 0.0176 0.063 0.0002 0.6672.4 4.49 4.50 4.63 4.81 4.79 4.67 4.65 0.056 0.046 0.0006 0.733



0.6 185 186 187 186 188 187 187 0.43 0.266 0.0006 0.2661.2 99.5 99.5 98.9 98.8 101 98.8 99.4 0.34 0.142 0.0005 0.5331.8 62.0 62.0 62.9 61.5 63.2 62.8 62.4 0.27 0.089 0.0004 0.6282.4 44.5 43.5 44.5 43.7 45.0 44.3 44.3 0.23 0.063 0.0003 0.709


0.6 114 118 118 114 116 116 116 0.73 0.290 0.0018 0.2901.2 60.5 59.8 61.8 60.6 60.4 60.8 60.7 0.27 0.152 0.0007 0.5231.8 39.9 39.3 39.8 39.8 38.0 39.2 39.3 0.29 0.098 0.0007 0.6492.4 29.1 28.5 29.6 29.9 29.5 29.3 29.3 0.20 0.073 0.0005 0.745


0.6 29.8 29.7 29.3 29.5 29.7 29.9 29.7 0.09 0.297 0.0009 0.2971.2 16.1 15.8 15.9 16.2 16.2 16.1 16.1 0.07 0.161 0.0007 0.5411.8 10.6 10.6 10.7 10.6 10.7 10.6 10.6 0.02 0.106 0.0002 0.6632.4 8.24 8.19 8.25 8.20 8.15 8.29 8.22 0.020 0.082 0.0002 0.773



0.6 238 239 241 239 238 239 239 0.447 0.341 0.0006 0.3411.2 126 127 123 126 127 127 126 0.6325 0.180 0.0009 0.5271.8 78.6 78.0 78.2 78.0 78.2 78.4 78.2 0.0955 0.112 0.0001 0.6212.4 55.9 54.1 55.1 55.2 55.0 54.9 55.0 0.236 0.079 0.0003 0.703


0.6 142 142 141 142 143 143 142 0.307 0.355 0.0008 0.3551.2 77.9 77.6 77.4 78.0 77.6 77.8 77.7 0.0910 0.194 0.0002 0.5471.8 50.3 50.2 50.6 51.0 50.3 50.2 50.4 0.1282 0.126 0.0003 0.6492.4 36.8 36.5 36.5 36.5 36.8 36.8 36.7 0.067 0.092 0.0002 0.727


0.6 37.8 38.5 38.5 38.5 38.2 38.7 38.37 0.131 0.384 0.0013 0.3841.2 23.3 23.3 23.0 22.9 23.0 23.2 23.1 0.0703 0.231 0.0007 0.6031.8 16.2 16.3 16.3 16.2 16.2 16.2 16.2 0.0211 0.162 0.0002 0.7022.4 12.3 12.1 12.0 12.2 12.0 12.2 12.1 0.049 0.121 0.0005 0.747





0.6 212 213 215 213 212 211 213 0.6 0.304 0.0008 0.3041.2 117 118 116 116 116 117 117 0.3 0.167 0.0005 0.5491.8 78.7 78.7 79.0 79.0 78.8 79.1 78.9 0.07 0.113 0.0001 0.6762.4 58.7 58.8 58.6 60.0 59.9 60.0 59.3 0.28 0.085 0.0004 0.752


0.6 126 125 126 125 125 125 125 0.211 0.313 0.0005 0.3131.2 70.8 71.1 69.5 71.5 71.6 71.4 71.0 0.320 0.177 0.0008 0.5661.8 48.0 48.8 49.0 49.2 49.2 49.7 49.0 0.232 0.122 0.0006 0.6902.4 38.8 38.7 38.8 39.0 38.9 38.7 38.8 0.048 0.097 0.0001 0.792


0.6 33.5 33.4 33.5 33.3 33.5 33.4 28.7 0.033 0.287 0.0003 0.2871.2 18.9 19.0 19.0 19.2 18.7 18.9 16.4 0.067 0.164 0.0007 0.5711.8 13.3 13.2 13.3 13.4 13.7 13.6 11.8 0.079 0.118 0.0008 0.7162.4 10.6 10.6 10.6 10.7 10.6 10.6 9.4 0.017 0.094 0.0002 0.803



0.6 292 291 291 289 291 291 291 0.401 0.415 0.0006 0.4151.2 170 171 174 170 172 173 172 0.667 0.245 0.0010 0.5901.8 112 112 109 114 111 112 112 0.667 0.160 0.0010 0.6502.4 83.3 81.8 82.4 82.0 81.9 81.4 82.1 0.268 0.117 0.0004 0.736


0.6 182 183 178 181 183 181 181 0.760 0.453 0.0019 0.4531.2 109 110 111 109 112 111 110 0.494 0.276 0.0012 0.6081.8 72.7 75.4 74.4 75.3 75.5 76.3 74.9 0.510 0.187 0.0013 0.6792.4 56.3 56.4 56.2 56.2 56.5 56.8 56.4 0.093 0.141 0.0002 0.753


0.6 49.2 48.3 47.8 48.7 48.0 48.0 48.3 0.216 0.483 0.0022 0.4831.2 30.9 31.0 31.0 30.9 30.0 29.8 30.6 0.224 0.306 0.0022 0.6331.8 21.9 24.1 22.0 24.7 22.3 21.8 22.8 0.516 0.228 0.0052 0.7452.4 16.8 17.4 17.8 17.1 17.3 17.4 17.3 0.137 0.173 0.0014 0.759



0.6 358 352 355 360 362 358 358 1.455 0.511 0.0021 0.5111.2 231 232 232 231 232 232 232 0.211 0.331 0.0003 0.6481.8 164 165 164 163 163 164 164 0.307 0.234 0.0004 0.7072.4 120 119 116 121 120 119 119 0.703 0.170 0.0010 0.727


0.6 212 213 212 214 213 212 213 0.333 0.532 0.0008 0.5321.2 137 139 138 139 139 138 138 0.333 0.346 0.0008 0.6501.8 99.8 99.7 100 100 99.4 99.8 99.78 0.091 0.249 0.0002 0.7212.4 75.9 75.1 75.9 75.5 76.1 76.2 75.78 0.168 0.189 0.0004 0.759


0.6 55.3 55.1 55.5 55.8 55.5 56.2 55.57 0.158 0.556 0.0016 0.5561.2 39 38.5 39 39.2 38.8 38.8 38.88 0.098 0.389 0.0010 0.7001.8 29.1 30.3 29.1 29.6 30.1 30.2 29.73 0.223 0.297 0.0022 0.7652.4 22.4 23.4 23.9 24 24.2 24.1 23.67 0.278 0.237 0.0028 0.796





0.6 152 152 151 152 153 152 152 0.3 0.380 0.0006 0.3801.2 88.1 87.5 87.3 87.8 88.1 87.7 87.8 0.13 0.219 0.0003 0.5771.8 63.2 62.8 63.5 63.3 64.3 63.6 63.5 0.20 0.159 0.0005 0.7232.4 50.0 50.0 49.6 49.7 49.5 49.8 49.8 0.08 0.124 0.0002 0.784



0.6 209 207 207 208 207 207 208 0.342 0.5188 0.0009 0.5191.2 142 142 142 143 142 141 142 0.258 0.3550 0.0006 0.6841.8 102 102 102 102 101 102 102 0.167 0.2546 0.0004 0.7172.4 78.6 78.0 78.0 78.3 77.8 78.5 78.2 0.129 0.1955 0.0003 0.768



0.6 242 242 242 243 248 247 244 1.125 0.6100 0.0028 0.6101.2 168 169 169 170 169 170 169 0.307 0.4229 0.0008 0.6931.8 130 128 129 125 126 127 128 0.764 0.3188 0.0019 0.7542.4 101 102 102 102 103 102 102 0.258 0.2550 0.0006 0.800



No media @ 0.5 m/hr


0.6 366 369 373 367 365 373 369 1.42 0.922 0.0036 0.9221.2 352 347 353 352 351 353 351 0.92 0.878 0.0023 0.9531.8 323 326 320 324 324 322 323 0.83 0.808 0.0021 0.9202.4 295 296 295 294 293 294 295 0.43 0.736 0.0011 0.911

No media @ 1.0 m/hr


0.6 384 385 377 384 383 382 383 1.18 0.956 0.0029 0.9561.2 365 366 365 366 371 366 367 0.92 0.916 0.0023 0.9581.8 348 354 356 349 350 348 351 1.38 0.877 0.0034 0.9572.4 326 325 325 329 327 326 326 0.61 0.816 0.0015 0.930



Confirmation Run 7.55 mm x 7.55 mm x 5.18 mm x 2.18 mm media @ 1.0 m/hr


0.6 208 210 211 211 210 209 210 0.5 0.525 0.0012 0.5251.2 137 139 136 137 138 136 137 0.5 0.343 0.0012 0.6541.8 88.8 88.4 88.4 88.5 88.8 88.6 88.6 0.07 0.221 0.0002 0.6462.4 51.5 51.3 51.6 51.2 51.9 51.5 51.5 0.10 0.129 0.0002 0.581

Main Lagoon Water Oct-20-05 - 7.55 mm x 7.55 mm x 5.18 mm x 2.18 mm media @ 1.0 m/hr


0.6 23.2 23.0 23.1 23.2 23.1 23.0 23.1 0.04 0.684 0.0011 0.6841.2 20.1 20.6 20.3 20.4 20.6 20.5 20.4 0.08 0.605 0.0023 0.8841.8 17.2 17.4 17.4 17.5 17.6 17.5 17.4 0.06 0.516 0.0017 0.8542.4 15.3 15.5 15.4 15.3 15.2 15.4 15.4 0.04 0.455 0.0013 0.880


Removal calculations (TSS) from kaolin clay roughing filtration experiments


298 TSS CoLength NTU1 NTU2 NTU3 NTU4 NTU5 NTU6 NTU ave TSS ave Ce/Co ave Ce/Ci ave

0.6 96.9 97.4 97.3 97.4 97.6 97.7 97.4 50.33 0.169 0.1691.2 46.2 48 48.6 47.2 47.5 47.7 47.5 24.94 0.084 0.496

1.8 30.3 30.1 30.7 30.3 30.5 30.2 30.4 16.01 0.054 0.6422.4 21.5 21.2 21.4 21.8 21.5 21.3 21.5 11.35 0.038 0.709

196 TSS CoLength NTU1 NTU2 NTU3 NTU4 NTU5 NTU6 NTU ave TSS ave Ce/Co Ce/Ci ave

0.6 59.0 59.2 59.0 58.9 59.3 59.1 59.1 30.90 0.158 0.1581.2 28.8 29.0 29.3 28.6 29.9 29.8 29.2 15.43 0.079 0.4991.8 18.7 18.7 18.9 19.1 19.0 18.9 18.9 10.00 0.051 0.6482.4 14.0 13.9 14.0 14.2 14.1 14.1 14.1 7.45 0.038 0.745

51.7 TSS CoLength NTU1 NTU2 NTU3 NTU4 NTU5 NTU6 NTU ave TSS ave Ce/Co Ce/Ci

0.6 17.8 18.5 18.3 18.5 18.3 18.4 18.3 9.69 0.187 0.1871.2 9.48 9.54 9.47 9.51 9.49 9.54 9.51 5.05 0.098 0.5211.8 6.35 6.31 6.27 6.37 6.39 6.34 6.34 3.37 0.065 0.6672.4 4.49 4.50 4.63 4.81 4.79 4.67 4.65 2.47 0.048 0.734


298 TSS CoLength NTU1 NTU2 NTU3 NTU4 NTU5 NTU6 NTU ave TSS ave Ce/Co Ce/Ci

0.6 185 186 187 186 188 187 187 93.72 0.314 0.3141.2 99.5 99.5 98.9 98.8 101 98.8 99.4 51.34 0.172 0.5481.8 62.0 62.0 62.9 61.5 63.2 62.8 62.4 32.60 0.109 0.6352.4 44.5 43.5 44.5 43.7 45.0 44.3 44.3 23.24 0.078 0.713


0.6 114 118 118 114 116 116 116 59.60 0.304 0.3041.2 60.5 59.8 61.8 60.6 60.4 60.8 60.7 31.70 0.162 0.5321.8 39.9 39.3 39.8 39.8 38.0 39.2 39.3 20.69 0.106 0.6532.4 29.1 28.5 29.6 29.9 29.5 29.3 29.3 15.47 0.079 0.748


0.6 29.8 29.7 29.3 29.5 29.7 29.9 29.7 15.64 0.303 0.3031.2 16.1 15.8 15.9 16.2 16.2 16.1 16.1 8.50 0.164 0.5441.8 10.6 10.6 10.7 10.6 10.7 10.6 10.6 5.64 0.109 0.6642.4 8.24 8.19 8.25 8.20 8.15 8.29 8.22 4.37 0.084 0.774



0.6 238 239 241 239 238 239 239 118.09 0.396 0.3961.2 126 127 123 126 127 127 126 64.54 0.217 0.5461.8 78.6 78.0 78.2 78.0 78.2 78.4 78.2 40.67 0.136 0.6302.4 55.9 54.1 55.1 55.2 55.0 54.9 55.0 28.81 0.097 0.708


0.6 142 142 141 142 143 143 142 72.45 0.370 0.3701.2 77.9 77.6 77.4 78.0 77.6 77.8 77.7 40.41 0.206 0.5581.8 50.3 50.2 50.6 51.0 50.3 50.2 50.4 26.44 0.135 0.6542.4 36.8 36.5 36.5 36.5 36.8 36.8 36.7 19.30 0.098 0.730


0.6 37.8 38.5 38.5 38.5 38.2 38.7 38.37 20.19 0.391 0.3911.2 23.3 23.3 23.0 22.9 23.0 23.2 23.1 12.22 0.236 0.6051.8 16.2 16.3 16.3 16.2 16.2 16.2 16.2 8.60 0.166 0.704





0.6 212 213 215 213 212 211 213 105.98 0.356 0.3561.2 117 118 116 116 116 117 117 59.93 0.201 0.565

1.8 78.7 78.7 79.0 79.0 78.8 79.1 78.9 41.00 0.138 0.6842.4 58.7 58.8 58.6 60.0 59.9 60.0 59.3 31.02 0.104 0.757


0.6 126 125 126 125 125 125 125 64.21 0.328 0.3281.2 70.8 71.1 69.5 71.5 71.6 71.4 71.0 36.98 0.189 0.5761.8 48.0 48.8 49.0 49.2 49.2 49.7 49.0 25.69 0.131 0.6952.4 38.8 38.7 38.8 39.0 38.9 38.7 38.8 20.42 0.104 0.795

51.7 TSS CoLength NTU1 NTU2 NTU3 NTU4 NTU5 NTU6 NTU ave TSS ave Ce/Co ave Ce/Ci ave

0.6 33.5 33.4 33.5 33.3 33.5 33.4 28.7 15.17 0.293 0.2931.2 18.9 19.0 19.0 19.2 18.7 18.9 16.4 8.70 0.168 0.5731.8 13.3 13.2 13.3 13.4 13.7 13.6 11.8 6.24 0.121 0.7172.4 10.6 10.6 10.6 10.7 10.6 10.6 9.4 5.01 0.097 0.804



0.6 292 291 291 289 291 291 291 141.29 0.474 0.4741.2 170 171 174 170 172 173 172 86.67 0.291 0.6131.8 112 112 109 114 111 112 112 57.45 0.193 0.6632.4 83.3 81.8 82.4 82.0 81.9 81.4 82.1 42.65 0.143 0.742


0.6 182 183 178 181 183 181 181 91.27 0.466 0.4661.2 109 110 111 109 112 111 110 56.79 0.290 0.6221.8 72.7 75.4 74.4 75.3 75.5 76.3 74.9 38.99 0.199 0.6872.4 56.3 56.4 56.2 56.2 56.5 56.8 56.4 29.52 0.151 0.757


0.6 49.2 48.3 47.8 48.7 48.0 48.0 48.3 25.36 0.490 0.4901.2 30.9 31.0 31.0 30.9 30.0 29.8 30.6 16.14 0.312 0.6371.8 21.9 24.1 22.0 24.7 22.3 21.8 22.8 12.05 0.233 0.7472.4 16.8 17.4 17.8 17.1 17.3 17.4 17.3 9.16 0.177 0.760



0.6 358 352 355 360 362 358 358 169.87 0.570 0.5701.2 231 232 232 231 232 232 232 114.74 0.385 0.6751.8 164 165 164 163 163 164 164 82.92 0.278 0.7232.4 120 119 116 121 120 119 119 61.17 0.205 0.738


0.6 212 213 212 214 213 212 213 105.98 0.541 0.5411.2 137 139 138 139 139 138 138 70.58 0.360 0.6661.8 99.8 99.7 100 100 99.4 99.8 99.78 51.53 0.263 0.7302.4 75.9 75.1 75.9 75.5 76.1 76.2 75.78 39.43 0.201 0.765


0.6 55.3 55.1 55.5 55.8 55.5 56.2 55.57 29.09 0.563 0.5631.2 39 38.5 39 39.2 38.8 38.8 38.88 20.46 0.396 0.7031.8 29.1 30.3 29.1 29.6 30.1 30.2 29.73 15.69 0.303 0.767





0.6 152 152 151 152 153 152 152 77.22 0.394 0.3941.2 88.1 87.5 87.3 87.8 88.1 87.7 87.8 45.48 0.232 0.589

1.8 63.2 62.8 63.5 63.3 64.3 63.6 63.5 33.13 0.169 0.7292.4 50.0 50.0 49.6 49.7 49.5 49.8 49.8 26.10 0.133 0.788



0.6 209 207 207 208 207 207 208 103.58 0.5284 0.5281.2 142 142 142 143 142 141 142 72.37 0.3692 0.6991.8 102 102 102 102 101 102 102 52.55 0.2681 0.7262.4 78.6 78.0 78.0 78.3 77.8 78.5 78.2 40.65 0.2074 0.774



0.6 242 242 242 243 248 247 244 120.37 0.6141 0.6141.2 168 169 169 170 169 170 169 85.48 0.4361 0.7101.8 130 128 129 125 126 127 128 65.27 0.3330 0.7642.4 101 102 102 102 103 102 102 52.64 0.2686 0.806



No media @ 0.5 m/hr


0.6 366 369 373 367 365 373 369 175 0.891 0.8911.2 352 347 353 352 351 353 351 167 0.854 0.958

1.8 323 326 320 324 324 322 323 155 0.793 0.9292.4 295 296 295 294 293 294 295 143 0.729 0.920

No media @ 1.0 m/hr


0.6 384 385 377 384 383 382 383 180 0.919 0.9191.2 365 366 365 366 371 366 367 174 0.886 0.9631.8 348 354 356 349 350 348 351 167 0.852 0.9622.4 326 325 325 329 327 326 326 157 0.799 0.938



Confirmation Run 7.55 mm x 7.55 mm x 5.18 mm x 2.18 mm media @ 1.0 m/hr


0.6 208 210 211 211 210 209 210 104.66 0.534 1.0711.2 137 139 136 137 138 136 137 70.01 0.357 0.654

1.8 88.8 88.4 88.4 88.5 88.8 88.6 88.6 45.90 0.234 0.6462.4 51.5 51.3 51.6 51.2 51.9 51.5 51.5 26.99 0.138 0.581

Main Lagoon Water Oct-20-05 - 7.55 mm x 7.55 mm x 5.18 mm x 2.18 mm media @ 1.0 m/hr


0.6 23.2 23.0 23.1 23.2 23.1 23.0 23.1 22.3 0.7433 0.7431.2 20.1 20.6 20.3 20.4 20.6 20.5 20.4 18.7 0.6233 0.8391.8 17.2 17.4 17.4 17.5 17.6 17.5 17.4 14.8 0.4933 0.7912.4 15.3 15.5 15.4 15.3 15.2 15.4 15.4 12.1 0.4033 0.818


Appendix E Particle size distribution (PSD) results


List of PSD Figures

Page media size (mm); hydraulic loading rate (m/hr); initial turbidity (NTU) A.1 2.18, 0.5, 100 A.2 2.18, 0.5, 400 A.3 2.18, 0.5, 700 A.4 2.18, 1.0, 100 A.5 2.18, 1.0, 400 A.6 2.18, 1.0, 700 A.7 2.18, 1.5, 400 A.8 5.18, 0.5, 100 A.9 5.18, 0.5, 400 A.10 5.18, 0.5, 700 A.11 5.18, 1.0, 100 A.12 5.18, 1.0, 400 A.13 5.18, 1.0, 700 A.14 5.18, 1.5, 400 A.15 7.55, 0.5, 100 A.16 7.55, 0.5, 400 A.17 7.55, 0.5, 700 A.18 7.55, 1.0, 100 A.19 7.55, 1.0, 400 A.20 7.55, 1.0, 700 A.21 7.55, 1.5, 400 A.22 no media, 0.5, 400 A.23 no media, 1.0, 400


Appendix F Model regression statistics for steady-state kaolinite clay removal in URFS


Appendix G X-ray diffraction (XRD) results


a

b

c

46- 1045 QUARTZ, SYN 14- 164 KAOLINITE-1A 6- 263 MUSCOVITE-2M1 13- 135 MONTMORILLONITE-15A 19- 932 MICROCLINE, INTERMEDIATE 9- 466 ALBITE, ORDERED 33- 664 HEMATITE, SYN 21- 1276 RUTILE, SYN 31- 966 ORTHOCLASE

File Name: c:\...\15273blk.102

Settling Pond

2-Theta Angle (deg)10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

10

20

30

40

50

60

70

Inte

nsity

(C

ount

s) X

100

10.049 7.1816.384 4.9834.476

4.253

4.0243.859

3.7763.6603.501

3.341

3.2443.214

3.189

3.1532.9902.9582.9262.8612.779

2.5712.490

2.456

2.391

2.280

2.235

2.1872.158

2.127

2.074

1.980

1.9251.8951.849

1.817

1.8021.784 1.7171.688

1.671

1.6601.6241.610

1.542

1.5051.4861.453

1.419

46- 1045 QUARTZ, SYN 14- 164 KAOLINITE-1A 6- 263 MUSCOVITE-2M1 13- 135 MONTMORILLONITE-15A 19- 932 MICROCLINE, INTERMEDIATE 9- 466 ALBITE, ORDERED 36- 426 DOLOMITE 33- 664 HEMATITE, SYN 21- 1276 RUTILE, SYN 29- 701 CLINOCHLORE-1MIIB, FE-RICH 31- 966 ORTHOCLASE


Main Lagoon

2-Theta Angle (deg)10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

8

16

24

32

40In

tens

ity (

Cou

nts)

X 1

00

14.405

10.0487.136 4.977

4.467

4.252

4.0223.775

3.6623.5723.5413.500

3.340

3.2453.196

3.0202.9912.9592.925

2.8862.858

2.7982.696

2.566

2.5222.490

2.456

2.4032.339

2.280

2.2342.189

2.128

2.0742.029

1.9941.980

1.8931.849

1.817

1.8031.7841.688

1.6711.660

1.6471.6231.604

1.542

1.5051.4871.4531.397

21- 1272 ANATASE, SYN 15- 776 MULLITE, SYN 21- 1276 RUTILE, SYN 50- 741 ALUMINUM OXIDE


Glomax LL

2-Theta Angle (deg)10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

1

2

3

4

5

Inte

nsi

ty (

Counts

) X

100

5.388

4.316

4.034

3.512

3.409

3.257

2.894

2.699

2.541

2.490

2.427

2.402

2.377

2.291

2.207

2.120

1.979

1.966

1.892

1.839

1.699

1.667

1.601

1.528

1.479

1.438

1.397

X-ray diffraction charts for main lagoon water (a) and settling pond (b) samples and Glomax LL calcined kaolin (c)


Appendix H Scanning Electron Microscopy (SEM) micrographs and EDX spectra and analyses Complete SEM micrographs and EDX spectra available by request from CSIRO


Appendix I Membrane Filtration Index (MFI) results

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