CAN A CONSTRUCTED STORMWATER FACILITY REMOVE FINE PARTICLES
FROM URBAN RUNOFF? by
ADRIENNE ROCHELLE AIONA
B.S. (Swarthmore College, Swarthmore, PA) 2000
Submitted in partial satisfaction of the requirements for the degree of
MASTER OF SCIENCE in
Civil Engineering in the
OFFICE OF GRADUATE STUDIES of the
UNIVERSITY OF CALIFORNIA
DAVIS
Approved:
Dr. S. Geoffrey Schladow
____________________________
Dr. John E. Reuter
____________________________
Dr. Stefan Wuertz
____________________________
Committee in Charge
2013 i

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Acknowledgements
Thank you to my advisor Geoff Schladow for the opportunity to do this research. My committee members John Reuter and Stefan Wuertz for helpful guidance and comments during the project and for the thesis report. Much appreciation to the El Dorado County Department of
Transportation and Russ Weigart for use of the site and for meteorological data. This research was supported by an agreement from the USDA Forest Service Pacific Southwest Research
Station. It was supported in part using funds provided by the Bureau of Land Management through the sale of public lands as authorized by the Southern Nevada Public Land Management
Act.
My deepest gratitude goes to Raph Townsend for his expert, super-human assistance with stormwater field sampling. Raph wrestled job boxes into place, moved rip-rap, installed flow sensors and much more. He lent expertise on Tahoe weather prediction, started my autosamplers when I could not beat the storm, and met me in the snow in the middle of the night to make sure everything was working. This project would not have been possible without his help.
This project was made more enjoyable because of my research partner, Yujie Jin, who took care of the parallel biofilm study expertly and was an invaluable colleague and friend. My labmates: Dan Nover, Steve Andrews, Kristen Eastman-Reardon, Courtney Siu and Bridget Tracy provided support, friendship and much needed advice. Special thanks to Dan for his humor and data analysis assistance. Thanks to Amelia Holmes for her friendship, encouragement and hospitality.
The staff at the Tahoe Environmental Research Center and UC Davis were indispensible in the completion of this project. Above and beyond serving on my committee I appreciated John
Reuter's expertise on stormwater in Tahoe and for calling to make sure I survived driving over
ii

the pass in a snow storm at midnight. Collin Strasenburgh sent storm updates, hosted me in
Tahoe and showed me the ropes in the stormwater lab. Anne Liston's encouraging words and laboratory guidance made tedious hours in the lab more bearable. Barbara Bellieu helped keep my paperwork in order and was always a friendly face in the office. Professor Tom Young and
Mike Johnson generously lent sampling equipment and Henry Calanchini helped organize it. Bill
Sluis and Brant Allen designed and constructed biofilm sampling equipment and flume modifications.
Finally, I would like to thank my family. My parents, Michael and Debbie Aiona, for being great parents and for teaching me the value of education. My in-laws, Michael and Margaret
Sherraden, for hosting my three-week long thesis writing retreat and for always being welcoming and encouraging. To my wife, Catherine, for drawing my site plan, worrying about me driving in the snow, her constant encouragement and support, and her love. iii

Abstract
This study measured the ability of a stormwater detention basin and associated floodplain to remove sediment from stormwater runoff. Primary constituents measured were: flow, total suspended solids (TSS), the inorganic fraction of TSS, fine particle concentration (FSP) for particles 16 μ m, and turbidity. Fine particles are the primary pollutant of concern for the Lake
Tahoe basin because they decrease the optical clarity of the lake. The study was performed at
Cattlemans detention basin, a constructed stormwater facility, in South Lake Tahoe, CA.
Samples were taken at three points: the inflow and overflow of the basin, and the bottom of the portion of the floodplain that the basin overflows into. Eight storm and snowmelt events were sampled during 2011 and 2012. The basin showed significant reductions, for a level of significance = 0.05, for concentrations of TSS, FSP and turbidity. The floodplain often acts as a load source and did not significantly reduce concentrations for any constituents. Median inflow, basin outflow and floodplain event mean concentrations were: TSS (mg/L): 54.4, 9.27,
21.6; FSP (#/mL): 2.57 x 10 6 , 7.34 x 10 5 , 6.56 x 10 5 ; inorganics (mg/L): 39.22, 3.94, 12.46; and turbidity (NTU): 82.05, 15.22, 29.95, respectively. The basin provided significant reductions of total loads of all constituents. Through the entire system, basin and floodplain, only fine particle concentrations showed a significant decrease. The basin removed fine particles at similar rates, by mass, as TSS and the floodplain coarsened the particle size distribution. The percent-by-mass of fine particles was 29 for the influent, 31 for the basin effluent and 21 for the floodplain. Timebased analysis of constituent concentrations, pollutographs, show first-flush behavior for the two storms monitored in this detail. Inflow concentrations of FSP had the greatest first flush behavior. Basin outflows show increased concentrations over the course of the event, most likely due to flushing of cleaner water from previous events and arrival of dirtier water from the current
iv

event. Floodplain outflows varied little with flow indicating dominance of local sources and background load. Power law relationships relating turbidity to TSS and fine particle mass show good fits for the basin inflow and outflow (0.87 R 2 0.94). These can be used to convert turbidity to TSS and fine particle mass in the future. Floodplain fits were low and are not recommended for use. The basin is successfully removing fine particles from stormwater runoff.
Extrapolating from the data collected, the basin is estimated to remove a total of 4.4 x 10 16 particles annually; a little over 2% of the reductions El Dorado County is required to meet in the
TMDL. Considering this facility is only treating an 11.2-acre drainage basin, this is a significant amount of removal. v

Table of Contents
1.
Introduction ................................................................................................................................... 1
1.1.
Characteristics of Lake Tahoe and Water Quality Limitations ............................................... 1
1.2.
Cattlemans Detention Basin Study ........................................................................................... 1
1.3.
Decline in Lake Tahoe Transparency ...................................................................................... 4
1.4.
TMDL ....................................................................................................................................... 6
1.5.
Stormwater Characteristics ..................................................................................................... 9
1.6.
Sediment processes ................................................................................................................. 10
1.7.
Floodplain processes: deposition, flocs and particle size distribution .................................. 13
1.8.
Stormwater BMP performance .............................................................................................. 16
2.
Methods and Site Description .................................................................................................... 21
2.2.
Water quality .......................................................................................................................... 27
2.3.
Particle Size Distribution ....................................................................................................... 27
3.
Results and Discussion ................................................................................................................ 28
3.1.
Hydrology and Meteorology .................................................................................................. 28
3.2.
Monitored Events ................................................................................................................... 31
3.3.
Event mean concentrations .................................................................................................... 32
3.4.
Fine particles by mass ............................................................................................................ 44
3.5.
Event Particle Size Distribution ............................................................................................. 46
3.6.
Event pollutographs ............................................................................................................... 54
3.7.
Event Loads ............................................................................................................................ 61
3.8.
Correlations of FSP and TSS to Turbidity, ............................................................................ 69
4.
Conclusion .................................................................................................................................... 85
4.1.
Treatment performance .......................................................................................................... 85
4.2.
Pollutographs ......................................................................................................................... 87
4.3.
Turbidity as a surrogate ......................................................................................................... 87
4.4.
Floodplains as treatment ........................................................................................................ 88
4.5.
Treatment of fines from stormwater runoff ............................................................................ 89
Appendix A: Monitored events – flow, samples, meteorological data ........................................... 90
Appendix B: Test for normal distribution ........................................................................................ 94
Appendix C: Test statistics for significant differences .................................................................... 99
Appendix D: Removal of fine particles by biofilm ........................................................................ 102
References .......................................................................................................................................... 109
Figures
Figure 1-1: Lake Tahoe annual average Secchi depth (TERC 2011). ............................................ 3
Figure 1-2: Light scattering and adsorption by particles (Swift 2004). .......................................... 4
Figure 1-3: Effect of particle size on light scattering (Swift et al. 2006). ...................................... 5
Figure 1-4: Modeled contribution to light scattering by particle type (Swift et al. 2006). ............. 6
Figure 1-5: Fine particle loading by source category (Lahontan and NDEP 2009). ...................... 7
Figure 2-1: Location of Cattlemans detention basin in the Cold Creek watershed at Lake Tahoe
(Green et al. 2006). ............................................................................................................... 22
Figure 2-2: Site plan for field equipment set-up. Modified from Green et al. 2004. .................... 25
Figure 2-3: Site photos: a) View of basin from Pioneer Trail near inflow site one. b) Pioneer
Trial sampling equipment housing. c) Cattlemans Trail (foreground) and basin outflow
vi

(background) sampling equipment housing. d) Basin overflow weir and flow monitoring flume during an overflow event. e) Floodplain below basin with biolog and equipment housing. f) Biologs routing flow to H-flume before entering Cold Creek. ........................... 26
Figure 3-1: Meteorology for WY2011 sampling period. a) Hourly precipitation cm/hr, b) hourly average air temperature °C. Meteorological data collected at Sierra House Elementary
School, 1709 Remington Trail, South Lake Tahoe by El Dorado County. .......................... 29
Figure 3-2: Floodplain on 4/29/11 showing standing water on the floodplain despite lack of precipitation. ......................................................................................................................... 30
Figure 3-3: Volumes for inflow, basin outflow and floodplain outflow for monitored events. ... 32
Figure 3-4: Event mean concentrations from sampled events: (a) total suspended solids (TSS);
(b) fine particles (<16 μ m); (c) turbidity; and (d) inorganic fraction of TSS. ...................... 34
Figure 3-5: Box plots of EMC data for a) TSS, b) fine particles, c) turbidity and d) inorganics.
The y-axes are presented in log(e), boxes show median, 1st and 3rd quartiles, and whiskers show minimum and maximum values. ................................................................................. 35
Figure 3-6: Quantile plots for monitored constituents: (a) TSS (mg/L), (b) FSP (#/mL), (c) inorganics (mg/L) and (d) turbidity (NTU). ......................................................................... 41
Figure 3-7: Inorganic fraction of TSS. Earlier events are not included due to laboratory errors. 44
Figure 3-8: Particle size distribution (#/mL) for a) 03/15/11 and b) 3/16/11. .............................. 47
Figure 3-9: Particle size distribution (#/mL) for a) 04/16/11; b) 04/17/11; and c) 04/18/11. ....... 48
Figure 3-10: Particle size distribution (#/mL) for a) 06/06/11; b) 03/15/12, and c) 03/16/12. ..... 49
Figure 3-11: Linear regression coefficients for PSD data through the system, by event. (a)
1 slope and (b)
0
concentration (#/mL). ................................................................................. 51
Figure 3-12: Pollutographs for 06/06/11 for: a. TSS (mg/L); b. FSP (#/mL); and c. turbidity
(NTU). Shown with d. flow. Arrows above the flow graph indicate the samples included in the EMC composite. .............................................................................................................. 57
Figure 3-13: Pollutographs for 06/06/11 for fine particles <16 μ m by size class: a. inflow (#/mL); b. basin (#/mL); and c. floodplain (#/mL). Shown with d. flow. FSP concentrations are normalized by bin width. ...................................................................................................... 58
Figure 3-14: Pollutographs for 03/15/12 for: a. TSS (mg/L); b. FSP (#/mL); and c. turbidity
(NTU). Shown with d. flow. Arrows above the flow graph indicate samples included in the
EMC composite. ................................................................................................................... 59
Figure 3-15: Pollutographs for 06/06/11 for fine particles <16 μ m by size class: a. inflow (#/mL); b. basin (#/mL); and c. floodplain (#/mL). Shown with d. flow. FSP concentrations are normalized by bin width. ...................................................................................................... 60
Figure 3-16: Estimated load of total sediment (a), fine particles (b), inorganics (c), and event volumes (d) for sampled events. ........................................................................................... 63
Figure 3-17: Box plots of event loads for (a) TSS, (b) fine particles, and (c) inorganics. The yaxes are presented in log(e), boxes show median, 1st and 3rd quartiles, notches indicate 95 percent confidence interval for the median, and whiskers show minimum and maximum values. ................................................................................................................................... 64
Figure 3-18: Probability plots for loads: (a) TSS (kg), (b) FSP (#), and (c) inorganics (kg). ...... 68
Figure 3-19: Power law fits for inflow site data. (a) TSS (mg/L) versus turbidity (NTU); (b) fine particle concentration <16 μ m (#/mL); (c) fine particle mass <16 μ m (mg/L). ................... 71
Figure 3-20: Power law fits for basin outflow data. (a) TSS (mg/L) versus turbidity (NTU); (b) fine particle concentration <16 μ m (#/mL); (c) fine particle mass <16 μ m (mg/L). ............ 72 vii

Figure 3-21: Power law fits for floodplain site data. (a) TSS (mg/L) versus turbidity (NTU); (b) fine particle concentration <16 μ m (#/mL); (c) fine particle mass <16 μ m (mg/L). ............ 73
Figure 3-22: Power law fits for inflow site data versus turbidity (NTU). (a) FSP 0.5 - 1.0 μ m
(#/mL/ μ m); (b) FSP 1.0 - 2.0 μ m (#/mL/ μ m); (c) FSP 0.5 – 1.0 μ m (mg/L); (d) FSP 1.0 –
2.0 μ m (mg/L). ...................................................................................................................... 76
Figure 3-23: Power law fits for inflow site data versus turbidity (NTU). (a) FSP 2.0 - 4.0 μ m
(#/mL/ μ m); (b) FSP 4.0 - 8.0 μ m (#/mL/ μ m); (c) FSP 2.0 – 4.0 μ m (mg/L); (d) FSP 4.0 –
8.0 μ m (mg/L). ...................................................................................................................... 77
Figure 3-24: Power law fits for inflow site data versus turbidity (NTU). (a) FSP 8.0 - 16.0 μ m
(#/mL/ μ m); (b) FSP 8.0 – 16.0 μ m (mg/L). .......................................................................... 78
Figure 3-25: Power law fits for basin site data versus turbidity (NTU). (a) FSP 0.5 - 1.0 μ m
(#/mL/ μ m); (b) FSP 1.0 - 2.0 μ m (#/mL/ μ m); (c) FSP 0.5 – 1.0 μ m (mg/L); (d) FSP 1.0 –
2.0 μ m (mg/L). ...................................................................................................................... 79
Figure 3-26: Power law fits for basin site data versus turbidity (NTU). (a) FSP 2.0 - 4.0 μ m
(#/mL/ μ m); (b) FSP 4.0 - 8.0 μ m (#/mL/ μ m); (c) FSP 2.0 – 4.0 μ m (mg/L); (d) FSP 4.0 –
8.0 μ m (mg/L). ...................................................................................................................... 80
Figure 3-27: Power law fits for basin site data versus turbidity (NTU). (a) FSP 8.0 - 16.0 μ m
(#/mL/ μ m); (b) FSP 8.0 – 16.0 μ m (mg/L). .......................................................................... 81
Figure 3-28: Power law fits for floodplain site data versus turbidity (NTU). (a) FSP 0.5 - 1.0 μ m
(#/mL/ μ m); (b) FSP 1.0 - 2.0 μ m (#/mL/ μ m); (c) FSP 0.5 – 1.0 μ m (mg/L); (d) FSP 1.0 –
2.0 μ m (mg/L). ...................................................................................................................... 82
Figure 3-29: Power law fits for floodplain site data versus turbidity (NTU). (a) FSP 2.0 - 4.0 μ m
(#/mL/ μ m); (b) FSP 4.0 - 8.0 μ m (#/mL/ μ m); (c) FSP 2.0 – 4.0 μ m (mg/L); (d) FSP 4.0 –
8.0 μ m (mg/L). ...................................................................................................................... 83
Figure 3-30: Power law fits for floodplain site data versus turbidity (NTU). (a) FSP 8.0 - 16.0 μ m
(#/mL/ μ m); (b) FSP 8.0 – 16.0 μ m (mg/L). .......................................................................... 84
Tables
Table 1-1: Fine particle loading by source category (Lahontan and NDEP 2009). ........................ 7
Table 1-2: Pollutant reductions needed to reach water quality standards (Lahontan and NDEP
2011). ...................................................................................................................................... 8
Table 1-3: Fine sediment reductions by source category to reach the Clarity Challenge (Lahontan and NDEP 2011). .................................................................................................................... 8
Table 1-4: Fine sediment reduction requirements for South Lake Tahoe jurisdictions. In total number of particles (Lahontan 2011). ..................................................................................... 9
Table 1-5: Influent and effluent medians for detention basins, retention ponds, wetland basins and wetland channels from the International Stormwater BMP Database (Geosyntec 2011b).
............................................................................................................................................... 17
Table 1-6: Stormwater BMP performance in the Tahoe basin for TSS. Blank values were either not given in the report or were not easily calculated from data provided. ............................ 19
Table 2-1: Water quality monitoring equipment. All sites have an ISCO 6700 auto sampler. Area velocity sensors are ISCO model 750 and bubbler sensors are model 730. ......................... 25
Table 2-2: Recommended analytes and processing methods. From Heyvaert et al. (2009) p. 11.27
Table 3-1: Monthly meteorological data. Data for July, August and September 2011 from
Weather Underground, station KTVL South Lake Tahoe. ................................................... 30
viii

Table 3-2: Summary of meteorology and hydrology for sampled events. “Basin” data serves as both the basin effluent and floodplain influent. .................................................................... 32
Table 3-3: Event mean concentrations (EMC) of TSS, fine particles, turbidity and inorganics for each event. ............................................................................................................................. 36
Table 3-4: Summary statistics for event mean concentration (EMC) of TSS, fine particles, turbidity and inorganics for each observation location. ........................................................ 37
Table 3-5: EMC reductions and summary statistics through the basin, floodplain and entire system (basin and floodplain) for TSS, fine particles, turbidity, and inorganics for each event. ..................................................................................................................................... 38
Table 3-6: Inorganic fraction (%) for each observation location and event. ................................ 44
Table 3-7: Fine sediment EMC (mg/L) for each event and percent by mass of fine sediment as a proportion of TSS. ................................................................................................................ 46
Table 3-8: Linear regression slope coefficients, their R 2 values and summary statistics for PSD data by event. Summary statistics for differences are based on actual values not percentages.
............................................................................................................................................... 50
Table 3-9: Linear regression slope and intercept coefficients, their R 2 values and summary statistics for PSD data by event. ........................................................................................... 51
Table 3-10: Comparison of summary statistics of linear regression coefficients for PSD data for
Cattlemans detention basin, 1 (Sunman 2004), and 2 (Rabidoux 2002). ................................. 52
Table 3-11: Event loads of TSS, fine particles, and inorganics for each observation location. ... 65
Table 3-12: Summary statistics for event loads of TSS, fine particles, and inorganics for each observation location. ............................................................................................................. 66
Table 3-13: Load reductions and summary statistics through the basin, floodplain and entire system (basin and floodplain) for TSS, fine particles, and inorganics for each event. ......... 67
Table 3-14: Power law relationships for TSS (mg/L), FSP concentration for particles 16 μ m
(#/mL), and FSP mass for particles 16 μ m (mg/L), for all basin influent, basin effluent and floodplain outflow data points. ............................................................................................. 70
Table 3-15: Power law relationships for FSP concentration (#/mL) by size class for all basin influent, basin effluent and floodplain outflow data points. ................................................. 74
Table 3-16: Power law relationships for FSP mass (#/mL) by size class for all basin influent, basin effluent and floodplain outflow data points. ................................................................ 75
Table 4-1: Median EMC reductions, effluent concentrations, and load reductions for the basin, floodplain and entire system for each constituent. ................................................................ 86 ix

1.1.
Characteristics of Lake Tahoe and Water Quality Limitations
Lake Tahoe is known for its clarity and stunning beauty. Located in the Sierra Nevada
Mountains on the border of California and Nevada, Lake Tahoe is a sub-alpine oligotrophic lake.
It is the eleventh deepest lake in the world and second deepest in the United States with an average depth of 305 meters and a maximum depth of 501 meters. The Tahoe basin is 800 km 2 nearly two-fifths of which is the lake 495 km 2 . The hydraulic residence time is approximately
600 years. Secchi disk measurements performed with a 25 cm disk by UC Davis researchers show a decline in deep water transparency since measurements began in 1967; see Figure 1-1
(TERC 2011). The annual average deep-water transparency in Lake Tahoe has decreased from
31.2 m in 1968 to 19.6 m in 2010 (TERC 2011).
Research indicates that the decline in clarity is due to increased inputs of fine sediments and nutrients driven by urbanization in the basin. Loss of clarity led to the listing of Lake Tahoe as water quality limited under Section 303(d) of the Clean Water Act for deep water transparency by input of nitrogen, phosphorus and fine sediment ( 16 m dia.) (Lahontan and NDEP 2010).
1.2.
Cattlemans Detention Basin Study
In response to declining lake clarity, agencies in the Tahoe basin are constructing stormwater treatment facilities, and implementing stormwater best management practices to reduce the input of fine sediment and nutrients to the lake. These efforts include restoration of stream environment zones (areas including stream corridors and floodplains), construction of stormwater treatment facilities, and street sweeping, among others.
1

This study was conducted at Cattlemans detention basin, a constructed stormwater treatment facility capturing urban runoff from a single-family residential neighborhood in South Lake
Tahoe. At this facility, stormwater enters a detention basin, which fills and overflows onto a portion of the Cold Creek floodplain during events with more than one inch of runoff (the equivalent volume of one-inch of rain over the entire drainage area). The area of the floodplain that receives the basin overflow is isolated from the creek with a bio-log to increase the flow distance, and treatment, before entering the creek.
The data presented in this thesis are part of a larger study at the site undertaken by researchers at the UC Davis Tahoe Environmental Research Center and UC Davis Department of
Civil Engineering. The goals of the overall Cattlemans basin study included: quantifying the gravitational settling and biofilm processes in the floodplain and to determine the elemental composition of sediment in stormwater to determine impacts of stormwater on floodplain biota and food webs. The overall study will be used to help refine a two-dimensional fine sediment transport floodplain model.
Typically stormwater studies consider removal of total suspended solids (TSS) (Heyvaert et al. 2006, Fassman 2012). Less effort has been placed on treatment processes for and removal performance of fine sediment. The focus of this study is to confirm findings for removal of TSS
(2ND Nature 2010, Geosyntec and WWE 2011a and b, and others), but more specifically to quantify the removal of fine particles through the study site. In this study two treatment mechanisms were measured: the treatment provided by the basin and floodplain and the potential for biofilm to capture and remove fine particles. Large particles will readily settle out of the water column in the basin. However, settling times for fine particles are much longer so additional experiments were conducted to determine the ability of biofilm to capture fine
2

1.4.2.
Findings & approach to improvement
The TMDL process compiled data from many sources to determine the sources of pollutants in the Tahoe Basin. Estimated fine sediment particle (FSP) loads from several source categories, as determined by the TMDL process are shown in Table 1-1 and Figure 1-5. The largest contributor of fine particles is urban runoff (comprised of both stormwater and snowmelt) at 72 percent. In the TMDL documents this is referred to as the contribution by the "urban uplands."
Earlier work correlating stormwater runoff pollution concentrations with land use showed increasing TSS (of which FSP is a portion) with increasing development (Reuter et al. 2001).
Table 1-1: Fine particle loading by source category (Lahontan and NDEP 2009).
Major Source
Total # of FSP
0.5-16 m
Percent contribution
Upland Runoff
Urban 34.8 x 10
19
72
Non-Urban 4.11 x 10
19
9
Stream Channel Erosion 1.67 x 10
19
4
Atmospheric Deposition 7.45 x 10
19
15
Shoreline Erosion
Total
0.108 x 10
19
<1
48.1 x 10
19
Shoreline
Erosion
0.2%
Stream
Channel
Erosion
3.5%
Non-
Urban
8.5%
Atmosphr.
Deposition
16%
Urban
72.3%
Figure 1-5: Fine particle loading by source category (Lahontan and NDEP 2009).
7

The water clarity target for Lake Tahoe is based on the annual average Secchi depth from
1967 to 1971, 29.7 meters. The Lake Tahoe Clarity Model (Sahoo et al. 2006, 2010) was developed and used to determine allowable loads of pollutants to reach this clarity goal. An interim 20-year transparency goal, the Clarity Challenge, was set at 24.9 meters of Secchi depth
(Lahontan and NDEP 2010). The TMDL Pollutant Reduction Opportunity Report outlines the specific reductions necessary to meet the transparency goals (Lahontan and NDEP 2008).
Table 1-2: Pollutant reductions needed to reach water quality standards (Lahontan and NDEP 2011).
Pollutant
Reductions
FSP
Interim Secchi Depth
24.9 m
Clarity Challenge
32 %
Phosphorus 14 %
Nitrogen 4 %
Target Secchi Depth
29.7 m
Transparency Standard
65 %
35 %
10 %
Pollutant load reductions are allocated by source category in the Recommended Strategy.
This calls for a 25 percent load reduction from urban upland areas to meet the Clarity Challenge.
The load reductions by source category are included in Table 1-3. Urban uplands are expected to contribute 76.9 percent of the 32 percent reduction in fine sediment particles necessary to reach the Clarity Challenge.
Table 1-3: Fine sediment reductions by source category to reach the Clarity Challenge (Lahontan and
NDEP 2011).
Pollutant Source
Forest Upland
Stream Channel Erosion
Atmospheric Deposition
Urban Uplands
Total FSP Reduction
Clarity Challenge
Load Reduction
1.0 %
1.8 %
4.6 %
24.6 %
32 %
To meet these load reductions local municipalities are installing stormwater best management practices (BMPs) to treat urban runoff. The City of South Lake Tahoe’s MS4
8

permit requires capture and infiltration of the 20 year 1-hour storm from urban runoff, approximately equivalent to one inch of rain during a one-hour period. By September 2016, the
City of South Lake Tahoe, El Dorado and Placer Counties must reduce the fine particle load by
10 percent, see Table 1-4 (Lahontan 2011). Cattlemans detention basin is located in the City of
South Lake Tahoe in El Dorado County. It is also located in the Trout Creek watershed, the watershed with the second highest fine particle flux (# particles/ second) on the order of 1 x 10 11 after the Upper Truckee river which is around an order of magnitude higher (Heyvaert et al.
2010).
Table 1-4: Fine sediment reduction requirements for South Lake Tahoe jurisdictions. In total number of particles (Lahontan 2011).
Jurisdiction
El Dorado County
Placer County
City of South Lake Tahoe
FSP allowable load FSP reduction
2.0 x 10
19
2 x 10
18
2.3 x 10
19
1.7 x 10
19
3 x 10
2 x 10
18
18
1.5.
Stormwater Characteristics
Removing sediment from stormwater is one of the primary goals of stormwater treatment.
Not only does sediment negatively impact downstream ecosystems but other contaminants such as nutrients and metals exist in particulate form and sorb onto sediment particles (McKenzie et al. 2008). Cumulative annual precipitation is well correlated with average annual particle concentration, R 2 =0.73, underscoring the importance of stormwater and stream inputs to the lake
(Heyvaert et al. 2010).
Sediments in stormwater runoff are composed of a range of particle sizes. Due to flow conditions this often includes a large percentage of fines; shallow surface flow preferentially washes off the finest particles from impervious surfaces. Vaze and Chiew (2002) found all sampled washoff sediment to be finer than 100 μ m as compared to only 15 percent of dry swept
9

samples. Li et al. (2005) found more than 97 percent of particles in highway runoff were <30 m and particle size and concentration decreased throughout the storm. At Tahoe, extensive stormwater sampling shows particle size distributions with peaks, as measured in percent by volume, ranging from about 9 to 70 μ m. Additional Tahoe monitoring shows that fine particles
0.49 to 22 μ m made up a median of 54 percent of TSS (Heyvaert et al. 2007 & 2010, Geosyntec and WWE 2009). Monitoring of inflow at the Park Avenue stormwater ponds in South Lake
Tahoe showed that 19 percent of the TSS mass was <10 μ m (2 ND Nature 2008). These studies show that a substantial proportion of suspended sediment in stormwater falls in the smaller size classes. While data is typically presented in mass of FSP, the TMDL regulates the total number of fine particles, not the mass.
1.6.
Sediment processes
The primary mechanisms for removal of solids in stormwater ponds and wetlands are sedimentation and filtration (Geosyntec 2011a). Sedimentation is the gravitational settling of particles in the water column. In a surface water system filtration is provided by vegetation and the biofilm associated with vegetation. The efficiency of both mechanisms is improved by coagulation and flocculation by increasing effective particle size.
Sedimentation is controlled by the settling velocity of a particle. A theory for this process was first described by Stokes (1851). Stokes’ Law, given below balances gravitational, drag and buoyancy forces (Metcalf and Eddy 2003). It is applicable to spherical particles in laminar conditions with low Reynolds numbers (<0.3).
10

g( s
)d
2 v c
=
18
Where v c
= particle terminal velocity g = acceleration due to gravity s
= particle density
= fluid density d = particle diameter
μ = dynamic viscosity
Since settling velocity is strongly dependent on particle diameter long detention times or shallow depths are required for fine particles to gravitationally settle independently. In still water a sand particle with a diameter of 63 μ m will settle 1000 times faster than a clay particle with a diameter of 2 μ m. Sand (63 μ m - 2 mm) will fall 10 cm in seconds, fine silt (16 μ m) in a week, and clay (<2 μ m) in years. However, Stokes’ Law assumes spherical particles and no turbulence.
Natural particles often have non-spherical shapes including flat clay particles, or are complex flocs, and can have variable densities (Droppo 2001, Nicholas and Walling 1996, Krishnappan et al. 1999). Also, conditions in stormwater facilities during storm events are often turbulent increasing settling times for all particle sizes.
Despite long theoretical settling times for silt and clay particles, deposits in stormwater facilities and floodplains are often composed of fines. Marsalek and Marsalek (1997) found up to
43 percent silt and 55 percent clay in sediment deposited in an in-line stormwater pond with coarser particles concentrated around the inlet.
1.6.1.
Flocculation
While flocculation does not directly remove particles from the water column the formation of larger particles can facilitate sedimentation. This process is key to the removal of fine particles in
11

natural systems. Flocculation has been observed in both stormwater facilities and floodplains.
Fine particles increase their settling potential when they coalesce into larger flocs.
Nicholas and Walling (1987) define the terms effective
and ultimate
particle size distribution.
This distinction considers the difference between the particle size distribution of suspended sediment where smaller particles form flocs increasing their effective
size and that of their ultimate constituent particles. These two terms are helpful in looking at the degree of flocculation, the settling potential of suspended sediment and the fate of fine particles.
The formation of flocs is complex interaction of sediment and biological activity. Droppo
(2001) investigated the structure of fluvial flocs from rivers and lakes in Ontario, Canada using correlative microscopy. Microscope images show heterogeneous makeup including, diatoms, bacteria, organic, inorganic and extracellular polymeric substances (EPS). Organic and inorganic components interact through organic nutrient uptake by organics and development of EPS by bacteria trapping additional inorganic materials. The bacteria produce EPS that contributes to the incorporation of additional materials by increasing external and internal surface area. The EPS takes the form of fibrils that increase the porosity and the adsorption of nutrients and contaminants.
Flow dynamics are important to the formation of flocs. Turbulence and shear need to be high enough to promote particle interactions but low enough to prevent breaking up already formed flocs. Krishnappan and Marsalek (2002) studied deposition and erosion characteristics of deposited sediments from a wet stormwater pond in a rotating flume. The sediment was made up solely of fine particles with 45 percent silt and 55 percent clay. Bed shear stress controlled floc size, the maximum floc size of 55 m occurred at bed shear of 0.213 N/m2 with smaller floc sizes for both lower and higher bed shear conditions. Haralampides et al. (2003) found similar
12

results for fine St. Clare river sediment, <75 m. A maximum D50 occurred at a bed shear of
~0.17 N/m2 with a representative settling velocity for flocs estimated to be 0.08 – 0.1 mm/s.
Few studies characterize floc formation and behavior within stormwater facilities.
Krishnappan et al. (1999) used an in-situ laser diffraction particle size analyzer to measure PSD in an in-line stormwater pond during different seasons and then compared those to the ultimate
PSD of sampled water. They found flocculation in every measurement. Mean floc size ranged from 6.62 m in February to 17.49 m in September. In contrast, the ultimate mean particle size ranged from 3.58 m in November to 6.16 m in September. They observed larger flocs in the summer, which are most likely due to higher temperatures and nutrient concentrations that increase microbial activity.
The settling potential for flocs is controlled by more than just size, other factors including porosity, and density were important in predicting settling velocity (Williams et al. 2008, Thonon et al. 2005). Thonon et al. (2005) used a LISST-ST to measure PSD and settling velocity in floodplains along the Rhine River in the Netherlands. They found an effective median grain size
2-5 times larger in-situ than the ultimate median grain size and greater variability in settling velocity with increasing floc size. Williams et al. (2008) found that generally, Stoke’s Law overpredicts settling velocity of natural particles, however, the mean settling velocity of flocs was greater than dispersed sediment with larger and more porous particles settling faster.
1.7.
Floodplain processes: deposition, flocs and particle size distribution
1.7.1.
Floodplain processes
Floodplains play an important role in the sediment dynamics of river systems contributing to the removal of suspended sediment from floodwaters. Floodplain processes are relevant to this
13

study because secondary filtration is provided by a floodplain area after water overflows the detention basin. High suspended sediment loads carried by rivers during floods are deposited on floodplains when the river spills out of its banks. Particle size and settling velocity control the ability of a particular particle to deposit. While much of the total suspended sediment carried by rivers is made up of small silt and clay particles, shallow flow depths, development of flocs, vegetation and complex topography facilitate the deposition of these smallest particles.
1.7.2.
Floods and suspended sediment quantities
During flood events, rivers carry large quantities of suspended sediment. When floods are large enough to overtop the banks of the river and spread out over the floodplain this sediment can deposit. A 25 km reach of the River Adour in southwest France retained 10 to 20 percent of suspended sediment entering it during two flood events and 36,000 tons of sediment over a water year. The 10 to 50 m wide vegetated riparian zone retained 15 to 22 percent of the sediment entering the reach, averaging 28 kg/m 2 (1 cm/year) (Brunet et al. 1994; Brunet and Astin 2008).
The River Clum in the UK captures 30 percent of suspended sediment during flood events for an estimated accumulation of 0.49 mm/year and a total removal of 1750 tons/year (Lambert and
Walling 1987).
1.7.3.
Ultimate PSD
Nicholas and Walling (1987) measure the differences between effective (in-situ suspended sediment) and ultimate (dried, dispersed) particle size distributions in their study of floods in the
River Culm, Devon, UK. Both suspended sediment and deposited sediment exhibited aggradation with ultimate particle size distributions containing larger numbers of clay particles than effective particle size distribution. Suspended sediment particles >63 μ m were 35 percent by mass particles <8 μ m, with increasing fractions of <8 μ m in smaller size bins. Deposited
14

sediment particles in all size bins were all composed of at least 10 percent by mass particles <8
μ m (Nicholas and Walling 1987). A related study found more fine particles < 20 μ m in suspended sediment, 95 percent, than deposited sediment, 82 percent (Lambert and Walling
1987). Smaller particles are incorporated into larger flocs increasing the number of larger particles in effective as compared to ultimate size distributions. These results show the importance of flocculation in facilitating the deposition of small particles during flood events.
1.7.4.
PSD and Spatial Variation in Deposition
Flow depths, topography and vegetation all control the quantity and size of deposited sediment. Shallow flow depths have low shear velocities that allow deposition of fine particles.
Backwater and isolated topographic depressions reduce velocities, increase retention times and trap water that then evaporates or infiltrates. Vegetation increases surface area sediment can impact, and decreases flow velocities encouraging deposition.
Gradations in particle size are seen in vertical and horizontal axes along floodplain deposits.
He and Walling (1997) found increasing proportions of clay from 13 to 20 percent and decreasing proportions of sand from 30 to 5.8 percent farther from river channels where flow depths are shallower. Moody and Meade (2008) observed coarsening upward sequences of deposited sediment from a large flood event on the Powder River, Montana, USA. As floodplain areas are initially inundated shallow flows with low shear velocities allow for deposition of silt and clay. Then as water depth increases shear velocity increases so the size of deposited particles increases.
Along the River Adour, while most retained sediment was captured in the riparian zone, the sediment retained in a backwater area was 98 percent silt and clay (Brunet and Astin, 2008).
Backwater areas trap water and have low flow velocities, which encourage sedimentation of
15

smaller particles. Additionally, lower flow velocities and shallower flows do not have the energy to transport larger particles.
Studies have shown a correlation between presence of vegetation and deposition of sediment.
The presence of vegetation and other obstacles on the floodplain encouraged deposition during flood events along the River Adour, vegetated sites showed 18.0 kg/m 2 of deposition compared to 1.26 kg/m 2 for less vegetated sites averaged over two flood events (Brunet and Astin 2008).
The presence of grass encouraged the deposition of silt and clay in the Powder River (Moody and Meade 2008). Gully bed vegetation was the most important factor in capturing sediment deposition in the short term (1-15 years) on steep (slopes >40 percent), deforested hillslopes in the Ecuadoran Andes. In well vegetated gullies (>=30 percent ground vegetation) ~0.035 m 3 /m of sediment is deposited in the gully bed annually. Vegetation cover explained 49 percent of observed variance in sediment deposition (Molina et al. 2009).
1.8.
Stormwater BMP performance
1.8.1.
International Stormwater BMP Database
While the ability of constructed stormwater facilities to remove TSS is well understood, research is less available for finer size fractions. The International Stormwater BMP Database provides a repository for performance studies facilitating evaluation of BMPs by type and constituent (BMPDB 2012). A 2011 summary of solids constituents: TSS, turbidity and total dissolved solids (TDS) evaluates data of BMPs in the database for each of these constituents.
Too few studies in the database report particle size distribution data to include in summaries of facility performance. Detention basins, retention ponds, wetland basins and wetland channels reduced all constituents; a summary of inflow and outflow medians is given in Table 1-5.
16

Table 1-5: Influent and effluent medians for detention basins, retention ponds, wetland basins and wetland channels from the International Stormwater BMP Database (Geosyntec 2011b). basin pond
Wetland basin
Wetland channel
TSS (mg/l)
Influent
(95% conf. interval)
Effluent
(95% conf. interval)
Turbidity (NTU)
Influent
(95% conf. interval)
Effluent
(95% conf. interval)
64.0
(47.0, 76.0)
24.0
(19.0, 27.0)
39.0
(27.0, 50.0)
19.0
(15.0, 26.0)
60.0
(49.0, 70.0)
12.0
(10.0, 12.0)
17.0
(10.0, 20.0)
1.0
(1.0, 1.0)
20.0
(16.0, 26.0)
8.0
(6.0, 9.0) n/a n/a
31.0
(22.0, 42.0)
14.0
(8.0, 16.0) n/a n/a
Retention ponds, wetland basins and wetland channels reduced effluent concentrations for
TSS to < 15 mg/l, detention basins had the worst performance with an effluent median of 24 mg/l. Only detention basins and retention ponds have data for turbidity, both reduced concentrations but retention ponds had effluent concentrations of 1.0 NTU while detention basins only achieved 19 NTU (Geosyntec 2011b). Cattlemans basin is most similar to a retention pond or a wetland basin. Retention ponds are designed to have a permanent pool of water.
Wetland basins are similar to retention ponds but most of the surface area is planted with wetland vegetation. During most of the rainy season, Cattlemans basin has standing water and most of the pond bottom and banks are covered with vegetation.
A few studies have measured the removal of fine sediment by stormwater treatment facilities.
Karamelegos et al. (2005) found a volume reduction for particles < 75 m from 15 ppm v
to 3 ppm v
through an extended detention basin. Greb and Bannerman (1997) studied a detention basin for TSS removal and particle size distribution. Influent PSD was 50.5 percent clay (<4 μ m), 40.2 percent silt and 9.3 percent sand with a D
50
of 2 μ m. A majority of effluent particles were < 2 μ m
(the smallest measurable size). Despite a finining of the PSD the pond still removed a large
17

majority (74 percent) of the clay fraction and 87 percent of TSS. Despite long settling times these studies found removal of fine particles.
1.8.2.
Tahoe basin BMP studies
Numerous studies of stormwater BMP effectiveness have been performed in the Tahoe basin.
A summary of several are collected in the
Lake Tahoe BMP Monitoring Evaluation Process
(2 ND
Nature 2006). Of these, four detention basins: Coon Street Basin, Northwood Ditch, Eloise
Basin, and Cattlemans Basin; and one wetland: Tahoe City Wetland, were sampled for sediment removal and are summarized in the report. Additional Tahoe specific BMP studies cover sediment removal these include: Park Avenue Ponds (2 ND Nature 2008), Cattlemans basin
(Green et al. 2006) and an experimental wetland (Reuter et al. 1992). Event mean concentrations
(EMC) for TSS into and out of the facilities along with removal percentages for concentration and load are given in Table 1-6. EMCs are flow-weighted composites representing a mean concentration of a constituent over the course of the event.
18

Table 1-6: Stormwater BMP performance in the Tahoe basin for TSS. Blank values were either not given in the report or were not easily calculated from data provided.
TSS in
(mg/L)
TSS out
(mg/L)
% load reduction
% EMC reduction
Park Ave. Ponds*
(2
ND
Nature 2008)
41.2 20 83%
Coon St. Basin
(2
ND
Nature 2006)
481 15 91% 94%
Northwood Basin
(2
ND
Nature 2006)
Eloise basin
(2
ND
Nature 2006)
Tahoe City Wetland*
(Heyvaert et al. 2006)
Cattlemans Basin*
(Greene et al. 2006)
105
239
34
74
25
66
9
23
74%
83%
88%
72%
68%
74%
Experimental Wetland
(Reuter et al. 1992)
80%
*values reported are EMC medians, others are EMC means.
88%
All facilities reduced TSS concentrations with removal efficiencies of 74 to 91 percent. Load efficiencies had greater variation with values between 68 and 94 percent. These are higher than removal efficiencies reported for earlier Tahoe studies which were in the range of 45 to 50%
(Reuter et al. 2001). Reported EMCs vary, partly because some are EMC means and others are medians. These concentrations also varied depending on season, land use and other study variables.
The experimental wetland study also measured turbidity (Reuter et al. 1992). The inflow turbidity ranged from <10 to nearly 60 NTU while outflow measurements were all <10 and mostly <5. The calculated efficiencies were an 83 percent concentration reduction, and 65 percent load reduction.
19

Only the Park Avenue Pond study measured fine particle concentrations (2 ND Nature 2008).
However, the results of this study are difficult to extrapolate for the TMDL criteria because fine particle concentrations were measured as mg/l and only looked at particles <10 μ m. The threshold of 10 μ m was used because of instrument availability. Median fine particle (<10 μ m) concentration reduced from 31 mg/l to 3 mg/l through the upper pond giving a 64 percent reduction in load by mass.
1.8.3.
USGS Cattlemans study
Prior to and after construction of Cattlemans detention basin the United States Geological
Survey (USGS); the El Dorado County Department of Transportation, Tahoe Engineering Unit and; the California Tahoe Conservancy undertook a multi-year study to determine the effectiveness of the basin in removing sediments and nutrients and the impact of the detention basin on groundwater flows and chemistry (Green et al. 2006). The study ran from 2000 through
2005, the basin was constructed in 2003. During the study samples from 12 to 15 groundwater monitoring wells throughout the vicinity of the basin were analyzed for dissolved nutrients, trace elements, and dissolved organic carbon. No substantial change was found in the water quality or geochemical processes due to the construction of the basin.
Stormwater runoff was monitored throughout the study. There was very little overflow from the basin during the study and 32 inflow and 6 overflow samples were collected. TSS for the inflow ranged from 0 to 1540 mg/L with a mean of 165 and median of 74. TSS for the outflow had lower concentrations from 6 to 45 mg/L with a mean of 26 and median of 23. This showed a reduction of sediment load through the basin during the study period with most of the reductions coming from capture and infiltration of runoff (Green et al. 2006).
20

This study was conducted from October 2011 through March 2012 at Cattlemans detention basin in South Lake Tahoe, CA, Figure 2-1. The detention basin is a constructed stormwater
BMP that captures runoff from a suburban neighborhood. It has a natural bottom with some wetland vegetation to provide additional treatment and infiltration between storms. When the basin reaches its maximum capacity it overflows through a portion of the Cold Creek floodplain before entering the main channel of the creek. It was built in 2003 by the El Dorado County
Transportation Department to improve stormwater quality.
This study investigates the ability of the basin and connected floodplain to remove fine sediment from urban runoff. Constituents measured and methods used are based on standard operating procedures developed by the Desert Research Institute and the UC Davis Tahoe
Environmental Research Center (Heyvaert et al. 2009). Samples were analyzed for total suspended sediment (TSS), turbidity, particle size distribution, and inorganic fraction.
21

of rain over the catchment. It has a capacity of 22,000 cubic-feet with a maximum pool depth of
2.5-feet and was designed to infiltrate most of the runoff it receives throughout the year without overflowing. The design achieves much of its treatment by removing flow of stormwater from the creek (Weigart 2010).
Stormwater enters the basin through a bubbler inlet. The bubbler has two inputs a 30-inch pipe from Pioneer Trail that carries runoff from a majority of the catchment and an 18-inch pipe that drains Cattleman’s Trail. A broad-crested concrete weir at the basin outlet overflows to a portion of the Cold Creek floodplain that is separated from the creek with a bio-log. The bio-log is an erosion control measure made of natural fibers, it is approximately 6 inches high and 18 inches wide and was placed in the floodplain as part of the original construction to lengthen the flow path of stormwater overflowing from the basin before it enters the creek. See site plan,
Figure 2-2 and site photos, Figure 2-3.
Stormwater flow through this system was monitored with ISCO 6700 auto samplers. Intake lines and flow sensors were installed in the two inflow pipes (inflow 1 and 2), at the overflow weir (basin) and at the low point in the bio-log where water in the floodplain overflows to Cold
Creek (floodplain). These sampling locations allow for measurements of the change in water quality through the basin and across the floodplain to determine their relative effectiveness. The basin outflow serves as both the effluent for the basin and the influent to the floodplain. Flumes were installed below the overflow weir and at the outlet of the floodplain to accurately measure flow at these two locations. See Table 2-1 for a summary of locations and measuring devices.
Auto samplers were powered with deep cycle gel cell batteries charged with 12 V solar panels.
They are housed in steel job boxes with vents. Each sampler held 24 – 1000 ml sample bottles.
23

The contribution of biofilm in the removal of fine sediment was studied in-situ with plates holding an array of vertical slides covered in pre-grown biofilm. These plates were installed in the floodplain before storm events and removed afterward. Due to winter ice cover, plates could not be installed in the basin for any events or in the floodplain for some events. Particle capture was measured in the laboratory by measuring the inorganic fraction of material scraped off the slides. Results of this portion of the project were led by another researcher are included in a different report (Jin et al. 2011).
Precipitation data were collected by the El Dorado County Transportation Department with a rain gage at Sierra House Elementary School located across Pioneer Trail from the site.
24

Road slope
Basin Outflow autosampler
2”-45° trapezoidal flume Cold Cr eek basin bubbler inlet
Pioneer T rail
Basin
Biolog
Spillway
Floodplain
Outflow autosampler
0.75 H flume
Floodplain
Inflow 1
A/V sensor & autosampler in pipe
Cattlemans Ct.
Inflow 2
A/V sensor & autosampler in pipe
LEGEND surface water flow direction stormwater drain biofilm sampling location, see Jin et al. 2011.
stormwater sampling location
Figure 2-2: Site plan for field equipment set-up. Modified from Green et al. 2004.
Table 2-1: Water quality monitoring equipment. All sites have an ISCO 6700 auto sampler. Area velocity sensors are ISCO model 750 and bubbler sensors are model 730.
Site ID in 1 in 2 basin
Location
Pioneer Trail inlet pipe
Cattlemans Trail inlet pipe
Flow sensor
Area Velocity
Area Velocity
Flow measuring device
30” reinforced concrete pipe
18” reinforced concrete pipe
Bubbler (pressure) 0.75 H-flume
25

(a) (b)
(c) (d)
(e) (f)
Figure 2-3: Site photos: a) View of basin from Pioneer Trail near inflow site one. b) Pioneer Trial sampling equipment housing. c) Cattlemans Trail (foreground) and basin outflow (background) sampling equipment housing. d) Basin overflow weir and flow monitoring flume during an overflow event. e) Floodplain below basin with biolog and equipment housing. f) Biologs routing flow to H-flume before entering Cold Creek.
26

2.2.
Water quality
Samples of stormwater runoff collected by auto samplers were analyzed for turbidity, total suspended solids, inorganic solids, and particle size distribution. Event mean concentrations
(EMC) were calculated for each event as flow-weighted composites (these were combined either in the laboratory by physically mixing sample aliquots or mathematically weighted individual sample results). Standard methods recommended for stormwater monitoring at Lake Tahoe by the Lake Tahoe Interagency Monitoring Program were used, Table 2-2 (Heyvaert et al. 2009).
Table 2-2: Recommended analytes and processing methods. From Heyvaert et al. (2009) p. 11.
Description
Quantitation limit
Preferred holding conditions
Total suspended solids
Turbidity
Particle size distribution
EPA 160.2; or
SM 2540D
EPA 180.1; or
SM 2130B
SM 2560 Laser backscattering N/A up to 7 days up to 7 days
In dark at 4°C, up to 7 days
2.3.
Particle Size Distribution
Methods for particle size distribution measurements are based on those developed by researchers with the Desert Research Institute and the Tahoe Environmental Research Center as described in Heyvart et al. (2010). Measurements of particle size distribution were performed using two different laser backscattering instruments: the Beckman Coulter LS-13320 laser diffraction particle size analyzer, and the Particle Measuring Systems LiQuilaz . Particle size distribution was measured for size classes 0.5, 0.63, 0.794, 1.0, 1.414, 2.0, 2.828, 4.0, 4.757,
5.657, 6.727, 8.0, 11.314, 16.0 and 20.0 μ m. Measurements from the LiQuilaz are presented in this report because it provided a complete data set. Cleaner samples are below the detection limit of the Beckman Coulter.
27

3.1.
Hydrology and Meteorology
The basin was monitored from October 1 st , 2010 through June 30 th , 2011; additional data were collected from March 15 through March 17, 2012. Figure 3-1 shows the hourly precipitation and air temperature during the monitoring period. Monthly summary data are shown in Table 3-1. The events were mostly sampled during a wet water year (WY2011) with a total precipitation of 88 cm compared to a mean annual precipitation in South Lake Tahoe of 51 cm (Lahontan and NDEP 2007). The monitoring period had several large storms in the fall, a period of spring snowmelt from March through May and a final storm in early June. Additional data were collected in the spring of 2012; the meteorology data for this event are included in
Appendix A.
28

(a)
(b)
Figure 3-1: Meteorology for WY2011 sampling period. a) Hourly precipitation cm/hr, b) hourly average air temperature °C. Meteorological data collected at Sierra House Elementary School, 1709 Remington Trail,
South Lake Tahoe by El Dorado County.
29

Table 3-1: Monthly meteorological data. Data for July, August and September 2011 from Weather
Underground, station KTVL South Lake Tahoe.
Total
Precipitation
(cm)
October 2010
November 2010
December 2010
January 2011
14.8
10.1
20.6
2.0
February 2011
March 2011
April 2011
May 2011
9.2
18.9
1.7
4.0
June 2011
July 2011
August 2011
September 2011
5.9
0
0
0.9
Total 88.1
Average
Temperature
(°C)
7.5
0.3
-0.1
-1.2
-2.7
0.6
2.7
5.2
10.8
16.1
16.1
15.0
During spring snowmelt, much of the floodplain was inundated with water due to high flows in Cold Creek, overland snowmelt, and beaver activity, Figure 3-2. Some of the flows measured at the floodplain site may be the result of backwater conditions due to downstream beaver activity and flood conditions in the creek rather than event driven. For monitored events, baseflow was removed when calculating event volumes.
Figure 3-2: Floodplain on 4/29/11 showing standing water on the floodplain despite lack of precipitation.
30

3.2.
Monitored Events
Water quality data for eight events, both storm and snowmelt, were collected during the study (with two events from 2012). Sampling was limited to events where the basin overflowed so water quality improvements through the basin (basin inflow and basin outflow) and across the floodplain (basin outflow and floodplain outflow) could be compared. The basin overflowed about a dozen times during WY2011 and only did so if the basin was mostly full before a substantial rain or snowmelt event. Volumes for sampled storms are shown in Figure 3-3 and summary event data is given in Table 3-2. The largest inflow volume of 1200 cubic meters occurred during the 3/16/11 rain event. Basin outflow volumes were largest during the snowmelt period in April 2011. The basin overflowed more during this time because of continual inputs from snowmelt. Inflow samples were not obtained for the 4/16/11 snowmelt event due to equipment problems, for this reason it is included only in determination of floodplain performance. Events missing data from inflow site two are analyzed with only inflow site one because site two only contributed 5-percent of the total flow volume for events where both inflow sites were monitored. Inflow site two did not take samples for some events because it has a small drainage area so flows in the pipe were often too shallow to measure flow or to submerge the intake line. Samples were composited based on observations of the hydrograph, the events on
3/15/11 through 3/17/11 and 3/15/12 through 3/17/12 were each divided into two events at clear low points in the flow hydrographs. For the 4/16/11 through 4/18/11 events, samples were separated over approximately 24 hour periods at slope breaks.
31

(PPCC) and all failed to reject the null hypothesis that the data were log-normally distributed to the 5 percent significance level and are thus assumed have a log-normal distribution (Helsel and
Hirsch 1992). Plots and test statistics for this are given in Appendix B. Data were logtransformed before performing test statistics. Applicability of the log-normal distribution to water quality data is well established (Helsel and Hirsch 1992; Geosyntec 2009; Strecker et al.
2001; Van Buren et al. 1997).
All analyte EMCs were significantly lower after traveling through the basin, except for the inorganic fraction of TSS, which had a p-value of 0.0748. Similar reductions were not seen through the floodplain where no analyte EMCs were found to have a significant difference.
Through the entire system, basin and floodplain combined, only fine particle concentrations showed a significant decrease. Significance was determined using a one-sided, paired sample ttest. All data were first log-transformed, using the natural log, to improve normality. A null hypothesis of no difference between the means was tested at the 5 percent significance level.
Additionally, the signed-rank non-parametric test was performed with the data. The conclusions of the signed-rank test agreed with the paired t-test in all instances (Helsel and Hirsch 1992). The p-values for these tests are provided in Appendix C. The differences can be visually seen in boxplots presented in Figure 3-5.
33

Table 3-3: Event mean concentrations (EMC) of TSS, fine particles, turbidity and inorganics for each event.
36

Table 3-4: Summary statistics for event mean concentration (EMC) of TSS, fine particles, turbidity and inorganics for each observation location.
37

Table 3-5: EMC reductions and summary statistics through the basin, floodplain and entire system (basin and floodplain) for TSS, fine particles, turbidity, and inorganics for each event.
38

3.3.1.
EMC: Quantile plots and effectiveness
Performance studies for stormwater BMPs often base effectiveness on percent removal.
However, this approach can be misleading because percent removal is typically higher when inflow concentrations are higher and reaches a limit for cleaner inflows. For this reason, the
International Stormwater BMP Database recommends using the effluent probability method where differences in inflow and outflow EMCs are analyzed for statistical differences and also graphically presented on standard parallel quantile plots (Geosyntec and WWE 2009).
Quantile plots are constructed by ranking the data points and then plotting each data point on the x-axis against a calculated quantile representing a normal distribution on the y-axis. The plotting positions are calculated using various formulae; for this analysis the Cunnae formula was used (Helsel and Hirsch 2002, Geosyntec and WWE 2009). This graphical method can be used to determine if the data are normally distributed and to compare the distributions of two data sets. For example, comparing data from basin inputs to basin outputs can show the ability of the basin to remove sediment over a range of concentrations. If the influent data set is to the right of the effluent data set concentrations are reducing through the system If the two data sets are parallel, removal rates are similar for all influent concentrations, as is seen for basin inflow and outflow data for FSP and turbidity in Figure 3-6 (b) and (c). If the sets are closer together on one end, removal rates are different for different concentrations, as is seen for basin inflow and outflow data for TSS and inorganics in Figure 3-6 (a) and (b). These plots can show effluent limits where concentrations cannot reduce any further. This approach for evaluating effectiveness is used here.
Quantile plots for EMC data are presented in Figure 3-6 below. The basin is effective in removing FSP and turbidity across all concentrations, although FSP may be better removed at
39

lower influent concentrations than higher, the lines are closer together for FSP concentrations greater than about 1.2 x 10 6 (#/mL), with a natural log of 14. TSS and inorganics are poorly removed at low concentrations but removal increases at higher concentrations. The floodplain has similar concentrations of FSP to the basin effluent. The floodplain has higher concentrations of TSS, inorganics and turbidity than the basin effluent for lower effluent concentrations and similar concentrations for higher effluent concentrations.
40

soil and other materials from the floodplain most likely contributed to large TSS values, see
Figure 3-7.
Snowmelt runoff measured from 4/17/11 through 4/18/11 had the lowest inflow EMCs for
TSS, fine particles, and turbidity: 2.3 to 6.4 mg/L, 6.08 x 10 5 to 6.91 x 10 5 #/mL, and 12.7 to
14.4 NTU, respectively. The other events, including rain and rain-on-snow events had higher
TSS, fine particle and turbidity concentrations. The median values for these events were much higher: 54.4 mg/L, 2.57 x 10 6 #/mL, 82.1 NTU, respectively. Each of the non-snowmelt events lasted over twelve hours and had peak flows above 0.07 cms. The June 6 th , 2011 rain event was the first major runoff event after spring snowmelt and had the highest concentration of TSS,
292.2 mg/L. The March 16 th , 2012 rain event had the highest concentration of fine particles, 5.24 x 10 6 #/mL.
Basin influent and effluent concentrations of TSS are in the range of those compiled in the
International Stormwater BMP Database and in Lake Tahoe studies. Reported influent values in
Tahoe range from 41.2 to 239 mg/L (2 ND Nature 2008, 2 ND Nature 2006), median values in the
BMP Database were 60 mg/L for retention ponds (Geosyntec 2011b), and the median from the
USGS Cattlemans detention basin study was 74 mg/L (Green et al. 2006); influent values from this study had a median value of 54.4 mg/L. Effluent values in Tahoe range from 9 to 66 mg/L
(Heyvaert et al. 2006, 2 ND 2006), the median value in the BMP Database was 12 mg/L for retention ponds (Geosyntec 2011b), and the median from the USGS Cattlemans detention basin study was 23 mg/L (Green et al. 2006); effluent values from this study had a median value of 8.3 mg/L.
The turbidity values found in this study were somewhat higher than those found in stormwater treatment internationally and in Tahoe. In Tahoe, only Reueter et al. (1992) reported
42

turbidity with inflow values ranging from <10 to nearly 60 NTU and outflow values generally <5
NTU for an experimental wetland. The BMP Database reported median influent values of 39
NTU for detention basins and 17 NTU for retention ponds and effluent values of 19 NTU for detention basins and 1.0 NTU for retention ponds. This study had median turbidity values of 82
NTU and 15 NTU for influent and effluent and maximum values of 139 and 62 NTU from
Cattlemans basin, respectively.
The Park Avenue Pond study performed in South Lake Tahoe is the only study to quantify removal of fine particles through a stormwater facility. This study measured the mass of fine particles <10 μ m into and out of a stormwater pond and found a median reduction of 31 mg/L to
3 mg/L through the pond (2 ND Nature 2008). This study, at Cattlemans basin found median reductions from 2.57 x 10 6 #/mL to 7.34 x 10 5 #/mL through the basin; these values convert to
33.5 mg/L and 3.02 mg/L, respectively, see Table 3-7. The numbers reported for fine sediment here are very similar as those from the Park Avenue study despite including particles between 10 and 16 μ m.
3.3.2.
Inorganic fraction
The inorganic fraction of TSS was measured for all events starting in April 2011. Evaluation of the inorganic fraction shows decreases in the percent of TSS that is inorganic through the basin and the floodplain for all events except for 4/18/11. The median inorganic content through the system was 72 percent for the influent, 65 percent out of the basin and 58 percent at the bottom of the floodplain, see Table 3-6. This shows an increase in the ratio of organics (TSS minus inorganics) to inorganics as water moves through the system. Floodplain sediment had generally, lower inorganic content than inflows most likely due to recruitment of organic material on the floodplain.
43

calculated by multiplying density, assumed to be 2.56 g/cm 3 , similar to quartz (Heyvaert et al.
2010), with the mean particle volume for each bin and summing all bins 16 μ m. Using this number, the percent of TSS made up of FSP was calculated. These numbers are given below in
Table 3-7.
For all storms, the mass of fines was reduced through the basin; median concentrations were
33.5 mg/L and 3.02 mg/L for influent and effluent, respectively. The median percentage of total mass made up of FSP was similar for basin inflows and outflows, 29 and 31 percent respectively.
The similar percent by mass of FSP for the basin influent and effluent measurements indicate that the basin is removing FSP and larger particles at similar rates (as measured by mass not by number). This is unexpected because, according to Stoke’s Law (1851), larger particles should settle out more readily than fine particles leading to a greater proportion of fines in the effluent than the influent. The data do not show this, indicating removal of fines through the basin.
The median percent-by-mass of FSP for the floodplain outflow, 21 percent, was less than the medians measured for either the basin outflow or inflow. This lower percentage indicates a greater percentage of mass being contributed by large particles in water exiting the floodplain than water exiting the basin. Coarsening of particles through the floodplain could be due to sources of large particles in the floodplain and/or flocculation of fine particles coming out of the basin, both of these mechanisms could be at work in this system. For events with higher (>11 mg/L) FSP concentrations leaving the basin, the floodplain removed FSP mass; for events with cleaner basin effluent the floodplain added FSP mass. The median FSP mass off the floodplain was 5.13 mg/L with a minimum of 1.29 mg/L and maximum of 18.9 mg/L. This addition of mass is likely due to background loads of organic material and sediment recruitment from the floodplain. Even though the floodplain added fine sediment mass for some events it will be
45

presented later, that fine sediment loads are the only constituent with significant reductions through the floodplain.
Table 3-7: Fine sediment EMC (mg/L) for each event and percent by mass of fine sediment as a proportion of TSS.
FSP concentration (mg/L)
3/15/11 44.94
3/16/11 12.39
4/16/11
4/17/11 1.69
4/18/11 0.80
6/6/11 45.56
3/15/12 33.48
3/16/12 57.38
Median 33.48
24.72
3.61
0.53
1.20
0.39
2.44
11.65
26.42
3.02
3.5.
Event Particle Size Distribution
18.88
5.12
6.01
4.53
6.44
3.39
1.29
5.14
5.13
Fines (% by mass) basin floodplain
28%
29%
52%
35%
54%
44%
26%
22%
23%
11%
21%
30%
20%
89%
50%
14%
27%
52%
60%
17%
22%
9%
28%
29% 31% 21%
Particle size distributions were measured for each event for particles between 0.5 and 20 μ m.
These data illustrate the changes in the actual particle size distribution of FSP as sediment moves through the system. Concentrations are measured by size class channels defined by: 0.5, 0.63,
0.794, 1, 1.414, 2, 2.828, 4, 4.757, 5.657, 6.727, 8, 11.314, 16, and 20 μ m. Particle concentrations for sizes between 0.5 and 0.63 μ m are presented in the 0.5 μ m size class and so on. To display particle size distributions (PSD) concentrations are normalized by the width of the channel, for example, the concentration of particles between diameters 0.5 and 0.63 μ m (the 0.5
μ m size class) is divided by 0.13 resulting in the units of #/ml/ μ m. Normalizing the data looses the measurement within the >20 μ m channel because it is unbounded and there is no way of knowing the size of the largest particles measured or the width of the channel. PSD data have a hyperbolic distribution and thus present a linear relationship on a log-log plot. Characteristics of the PSD are described by the slope and intercept of a two-parameter linear regression of the data.
This approach was used by Coker 2000. The form of the linear equation is (Rabidoux 2002):
46

Table 3-8: Linear regression slope coefficients, their R
2
values and summary statistics for PSD data by event. Summary statistics for differences are based on actual values not percentages.
1
(slope/ distribution) Differences
1
R
2
3/15/11 -2.21 -2.38
3/16/11 -2.69 -2.91
4/16/11 -3.11
4/17/11 -2.98 -2.61
4/18/11 -3.25 -3.16
6/6/11 -2.06 -2.19
3/15/12 -2.47 -2.49
3/16/12 -2.33 -2.37 n minimum
1 st
quarter median
3 rd
quarter maximum mean std. dev.
CV
7.00 8.00
-3.25 -3.16
-2.83 -2.96
-2.47 -2.55
-2.27 -2.38
-2.06 -2.19
-2.57 -2.65
0.43 0.36
-0.17 -0.14
-2.43 -7.9%
-2.60 -8.1%
-2.52
-2.52 12.2%
-2.39 3.0%
-2.37 -6.3%
-2.51 -1.0%
-2.23 -1.7%
8.00 7
-2.60 -0.36
-2.52 -0.04
-2.47 0.04
-2.39
-2.23
-2.45
0.12
0.15
0.22
0.02
0.20
-0.05 11.27
8
-0.77
-0.38
-0.11
0.03
0.19
-0.20
0.33
-1.61
-2.1% -10.2% 0.986 0.996
10.8% 3.6% 0.991 0.985
19.0% 0.996
3.4% 15.2% 0.998 0.991
24.3% 26.6% 0.998 0.997
-8.6% -15.5% 0.996 0.997
-0.8% -1.8% 0.991 0.990
5.9% 4.3% 0.994 0.994
7
-0.86
-0.28
-0.10
0.13
0.32
-0.13
0.41
-3.09 slopes, μ m. Less negative values of
1
indicate more particles in larger size bins; negative percent-difference values (shaded in
Table 3-8) indicate fining. Figure 3-11a shows
1 values at the basin inflow, basin outflow and floodplain outflow for each event. In all but two events, 4/16/11 and 4/18/11, PSD fined as it passed through the basin. This is expected, as larger particles are more likely to settle out. The
0.995
0.993
0.982
0.991
0.991
0.987
0.979
0.981 floodplain did not consistently make PSD more fine or more coarse. Instead, PSD slopes off the floodplain converged regardless of the PSD slope out of the basin. This could indicate the dominance of local sediment sources present on the floodplain.
50

Intercepts,
0
, are a measure of concentration, larger intercepts indicate higher concentrations.
These data offer another way to demonstrate changes in FSP concentrations previously discussed. Generally, concentrations (
0
) decrease as stormwater passes through the detention basin; the median difference in the intercepts was 0.89, see Table 3-9. This can been seen in the graphs and by comparing the
0
values. For snow melt events, 4/16/11 though 4/18/11, particle concentrations increased through the floodplain, median
0
differences ranged from -0.96 to -
1.09 for these events. For the entire system, all values of
0
for the inflow were larger than the outflow of the floodplain except 4/18/11, with a median difference of 1.15, indicating concentration reductions through the entire system.
Coker (2000) and Sunman (2004) presented
1
and
0 values for the Lake Tahoe Index and
Midlake stations; Rabidoux (2002) performed the same analysis with stream data. Summary statistics for
1
and
0 values at each monitoring point for this study are presented along with those for Lake Tahoe, by Sunman (2004), and Tahoe streams by Rabidoux (2002), see Table 3-
10.
Table 3-10: Comparison of summary statistics of linear regression coefficients for PSD data for
Cattlemans detention basin,
1
(Sunman 2004), and
2
(Rabidoux 2002).
Cattlemans in
Cattlemans basin
1
(slope)
-3.25
-3.16
-2.57 -2.06
-2.65 -2.19
0
(intercept)
12.12 13.54 14.54
11.03 12.35 13.85
Cattlemans floodplain -2.60 -2.45 -2.23 10.77 12.18 13.69
Mean of Cattlemans -3.00 -2.56 -2.16 11.31 12.69 14.03
Lake Tahoe Index
Station
1
Lake Tahoe Midlake
Station
1
Tahoe streams
2
-3.91
-3.24
-3.5
8.02
8.06
-2.77 -1.92 7.15 10.16 18.19
*Mean is presented as best fit line for all data for Lake Tahoe points.
52

The mean
1
values for this study are less negative than Lake Tahoe values, by 26 and 37 percent, indicating higher concentrations of very fine particles in the Lake as compared to samples in this study. The mean
1
values for this study, -3.0 to -2.2, are similar to those of the stream data, -3.9 to -1.92, and have a smaller range. It is expected for stormwater and stream runoff to have more large particles than Lake Tahoe because they have more energy and can mobilize more sediment. The larger data sets for Lake Tahoe and Tahoe streams may contribute to greater variability in the data. The
0
values show the much larger concentrations of fine particles in stormwater, even after treatment, than lake water; the means were 12.69 and 8.0 for stormwater and Lake Tahoe respectively. Again, the stormwater values fall within the range measured in stream samples.
53

3.6.
Event pollutographs
Pollutographs show concentrations of pollution at different times throughout a storm event and are used to illustrate relationships between time, flow and concentration. Samples for June 6,
2011 and March 15 through March 17, 2012 were processed as more than one sample and then used to arithmetically calculate flow-weighted EMCs. Pollutographs of the data from these two time periods are presented in this section to illustrate temporal variations in constituent concentrations. Analysis of the data in a temporal framework provides some insight into the constituent trends throughout the course of a storm and can show the presence of a first flush effect. First flush is a process where the peak concentration of a pollutant precedes the peak flow and larger proportions of accumulated pollution are washed off during the rising limb of the storm than the falling limb. This provides the theoretical reasoning for stormwater management regulations requiring treatment of the first one-inch of rain, such as the design size of Cattlemans basin (Weigart 2010). Figures 3-12 and 3-14 show pollutographs for TSS, fine particle concentration, and turbidity for the monitored events. Figures 3-13 and 3-15 show pollutographs for FSP concentrations by size class for the basin influent and effluent, and floodplain outlet.
All constituents in the inflow show correlations to flow with varying degrees of first-flush behavior. Concentrations for TSS, turbidity and FSP track with flow magnitudes at this sampling location, peaking at similar times. For the 6/6/11 event, see Figure 3-12, TSS had concentrations of 250, 128, 1037, and 26.8 mg/L, initially, at the base of the rising limb, at the peak and at the last measurement, respectively. Turbidity concentrations were 180, 90.7, 449, and 17.5 NTU, initially, at the base of the rising limb, the peak and at the last sample. FSP concentrations were
3.32 x 10 6 , 1.99 x 10 6 , 3.96 x 10 6 , 5.11 x 10 6 #/mL for the same sample times. For the events from 3/15/12 through 3/17/12, see Figure 3-14, values were: TSS: 45.6, 84.4, 30.4, 231.6, and
54

39.2 mg/L; turbidity: 75.8, 114, 53, 202, and 51.7 NTU; and FSP 3.58 x 10 6 , 5.41 x 10 6 , 7.08 x
10 6 , and 3.15 x 10 6 #/mL, for the initial measurement, first peak, end of first event, second peak, and final value. Turbidity and fine particle concentrations show the greatest first flush trend with higher concentrations measured on the rising than on the falling limb of the hydrograph, this is most evident in the 6/6/11 graphs.
The outflow from the basin does not exhibit first flush and trends less with flow. For the
6/6/11 event, see Figure 3-12, concentration values were: TSS: 26.4, 3.0, and 8.4 mg/L; turbidity: 9.97, 4.36, and 12.4 NTU; and FSP: 8.72 x 10 4 , 7.91 x 10 4 , and 3.74 x 10 5 #/mL for the initial measurement, the lowest point, and the final measurement, respectively. For the events from 3/15/12 through 3/17/12, see Figure 3-14, concentration values were: TSS: 22.8, 24.8, and
45.2 mg/L; turbidity: 32.3, 43.2, and 61.6 NTU; and FSP: 1.37 x 10 6 , 1.72 x 10 6 , and 2.80 x 10 6
#/mL for the initial measurement, inflection point, and the final measurement, respectively.
Lower concentrations in earlier basin outflows, lack of substantial peaks, and increasing concentrations throughout the course of the event are most likely due to initial flushing of water remaining in the basin from previous storm events. Long holding times between storms allow sediment to settle out reducing concentrations. Increasing concentrations later in the storm are most likely runoff from the event reaching the outflow during the course of the event.
Floodplain concentrations are less variable and less correlated with flow. During the June 6 th ,
2011 event the floodplain samples show lower concentrations after the basin begins overflowing indicating that the water from the detention basin is cleaner than the baseflow present on the floodplain. The concentration values for this data set were: TSS: 188.8, 58.4, and 14.2 mg/L; turbidity: 69.2, 56.1, and, 10.5 NTU; and FSP: 5.86 x 10 5 , 6.62 x 10 5 , and 2.92 x 10 5 #/mL, for the initial measurement, last point before the drop in concentration, and the final point,
55

respectively. During the March 15 th , 2012 event, floodplain concentrations changed little throughout the course of the event, see Figure 3-14. Minima and maxima for these data were:
TSS: 10.0 and 69.9 mg/L; turbidity: 18.4 and 37 NTU; and FSP: 1.52 x 10 5 and 6.51 x 10 5 , respectively. The contribution of background loads from local floodplain sediment sources are illustrated by this data where concentrations in the floodplain are minimally impacted by stormwater runoff.
Fine particle data for particle number by size class are presented as pollutographs in Figures
3-13 and 3-15. These graphs are shown on a log-scale to better differentiate the size classes unfortunately, this presentation dampens peaks and differences. Generally, the size class data follows similar patterns to that of all FSP 16 μ m. A few differences are apparent and can be described by comparing concentrations for the smallest size class, 0.5 – 1.0 μ m, to the largest size class, 8.0 – 16.0 μ m. At the falling limb of the influent, the concentration in the largest class decreases more than the smallest size class. The peak and final measurements for the smallest class are 3.64 x 10 6 , and 7.05 x 10 5 ; the same points for the largest size class are 2.52 x 10 4 , and
646 #/mL, respectively. For the basin outflow during the 6/6/11 event the concentration of particles in the smallest size class is 1.33 x 10 5 , 1.02 x 10 5 , and for the largest size class is 48.7,
224, and 335 #/mL, for the initial point, minimum and first peak in the smallest size class data.
The floodplain data diverge most at the end with measurements for the smallest class rising from
2.06 x 10 5 to 4.79 x 10 5 ; the same points for the largest size class decrease from 411 to 198
#/mL.
56

3.7.
Event Loads
Event loads were estimated for TSS and fine particles by multiplying EMCs with total runoff volumes. Calculating loads is important when discussing the performance of a stormwater facility. Load numbers quantify the benefits of reductions in flow volume as well as reductions in concentration. Also, the Lake Tahoe TMDL for fine particles is measured as a load making this analysis more relevant to the regulatory context.
Events with large quantities of rain had higher loads than snowmelt events. Rain and rain on snow events with the highest volumes had the largest inflow loads, the 6/6/11 event had the largest sediment load of 147 kg in a flow volume of 504 m 3 ; the 3/16/12 event had the highest
FSP loads of 3.66 x 10 15 in a flow volume of 699 m 3 . Snowmelt events (April 2011) had low loads of TSS and fine particles: the lowest TSS value was 2.5 kg on 4/18/11 despite a large flow volume of 1068 m 3 ; the lowest concentration of FSP was 5.85 x 10 14 on 4/17/11 which had a flow volume of 847 m 3 . These data are presented in Figure 3-16, with measured values, summary statistics and reductions are given in Tables 3-11, 3-12, and 3-13.
All analyte loads were significantly lower after traveling through the basin. Median load reductions through the basin for TSS and FSP were 49.8 kg and 7.56 x 10 14 , respectively. Only fine particle loads were significantly lower through the floodplain with a median reduction of
8.79 x 10 13 . The median load reduction across the floodplain for TSS was only 0.99 kg. Loads of all constituents showed a significant difference through the entire system with median reductions of TSS and FSP of 38.8 kg and 7.81 x 10 14 , respectively. Significance was determined with a one-sided, paired sample t-test was used to determine whether there were significant differences in loads through the basin, the floodplain and the entire system (basin and the floodplain).
Additionally, the signed-rank non-parametric test was performed with the data. The conclusions
61

of the signed-rank test agreed with the paired t-test at the 5 percent significance level in all instances. The p-values for these tests are provided in Appendix C. Box plots presented in Figure
3-17 visually indicate these findings.
62

Table 3-11: Event loads of TSS, fine particles, and inorganics for each observation location.
65

Table 3-12: Summary statistics for event loads of TSS, fine particles, and inorganics for each observation location.
66

Table 3-13: Load reductions and summary statistics through the basin, floodplain and entire system
(basin and floodplain) for TSS, fine particles, and inorganics for each event.
67

3.8.
Correlations of FSP and TSS to Turbidity,
In the Tahoe basin and elsewhere, studies quantifying sediment concentrations in water have attempted to correlate turbidity to both TSS and fine sediment particle (FSP) concentrations (2 ND
Nature 2010, Heyvaert et al. 2010, Reuter et al. 2010, Thonon et al. 2005). Using turbidity as a surrogate for TSS or FSP allows for continuous in-situ measurements and avoids expensive, labor intensive, field sampling and laboratory analysis. Developing site-specific relationships is recommended as sediment dynamics can vary by site.
A correlation between FSP concentration and turbidity for hundreds of stormwater runoff samples collected as part of the Tahoe TMDL Stormwater Monitoring program gave this equation (Heyvaert et al. 2010):
FSP (#/mL) = 10.19(NTU) 2 + 114,214(NTU) - 136,074 (R 2 =0.83)
However, there was fair amount of scatter in the data. 2nd Nature (2010) applied a power law relationship to correlate TSS (mg/L) and FSP (mg/L) to turbidity (NTU). In this study FSP was calculated using particles 16 μ m. These relationships were:
TSS (mg/L) = 2.5(NTU) 0.96
(R 2 =0.89)
FSP (mg/L) = 1.1(NTU) 0.97
(R 2 =0.87)
3.8.1.
Cattlemans basin turbidity power law correlations
Figures 3-19 through 3-21 relate TSS (mg/L), FSP concentration (#/mL) and FSP mass
(mg/L) to turbidity (NTU). Power law relationships are used for the correlations as was done by
2 nd Nature (2010). Linear fits were also tested and were better in a few instances but overall the power law provided better R 2 values. Coefficients, exponents and R 2 values are given in Table 3-
14. For this analysis, each individual sample analyzed is included, thus events that were analyzed
69

as multiple samples are more heavily represented than those where the EMC was manually mixed before analysis. Fewer data points are presented for storms 3/15/11 through 3/16/11 and
4/16/11 through 4/17/11. The mass of fine particles was calculated by multiplying density with the mean particle volume for each bin and summing relevant bins. Density was assumed to be the same as the density of quartz: 2.56 g/cm 3 (Heyvaert et al. 2010).
Correlations for TSS and turbidity are generally good with R 2 values of 0.93, 0.89 and 0.77 for the basin inflow, basin outflow and floodplain, respectively. The exponents for the same power law functions were 1.62, 0.997 and 1.13, respectively. The basin outflow exponent is most similar to the 0.96 presented by 2 nd Nature (2010).
There is more scatter in the FSP concentration data (#/mL) leading to worse R 2 values of
0.78, .073 and 0.60 for the basin inflow, basin outflow and floodplain respectively. The exponents for the same power law were 0.735, 1.03 and 0.959, respectively. Converting FSP concentration data to FSP mass (mg/L) improved R 2 values for the basin influent and effluent to
0.94 and 0.92, respectively. This conversion had little effect on the R 2 value for the floodplain resulting in a value of 0.57.
Table 3-14: Power law relationships for TSS (mg/L), FSP concentration for particles 16 m (#/mL), and
FSP mass for particles 16 m (mg/L), for all basin influent, basin effluent and floodplain outflow data points.
TSS (mg/L)
Coeff. R
2
FSP Concentration (#/mL)
Coeff. Expon.
FSP mass (mg/L)
Coeff. Expon. R
2 in 0.0637 1.62 0.93 1.05E+05 0.735 0.78 0.0199 1.62 0.94 basin 0.710 0.997 0.89 3.05E+04 floodplain 0.0602 1.13 0.77 1.92E+04
1.03
0.959
0.73
0.60
0.0562
0.199
1.48
0.918
0.92
0.57
70

3.8.2.
Cattlemans basin turbidity power law correlations by size class
Fine sediment particle number and mass data were divided into different size classes to provide more detail in the analysis. Figures 3-22 through 3-30 show the power law relationships between FSP number (#/mL) and FSP mass (mg/L) for the basin influent and effluent and floodplain outflow for the particle size bins used in the Lake Tahoe Clarity Model (Sahoo et al.
2006).
The power law fits for FSP number for each size class had lower R 2 values than those using all particles 16 μ m: between 0.3 and 0.67 as compared to 0.6 to 0.78, see Table 3-15. Summing particles across more size classes may mask errors in assigning particles to specific size classes.
Basin influent FSP number data show inconsistencies between storms with higher turbidity values dominated by two storm periods: 6/6/11 and 3/15/12. Basin effluent data are dominated by the March 2011 and 2012 events at higher concentrations. This divergence is reflected in the low R 2 values. R 2 values decrease with increasing particle size, this could be due to better correlations between the finest particles and turbidity because they are more efficient at scattering light (Swift 2004). The exponent for influent data increased from 0.590 to 0.775 for influent data from the 0.5 to 1.0 μ m, to the 8.0 to 16.0 μ m size classes.
Table 3-15: Power law relationships for FSP concentration (#/mL) by size class for all basin influent, basin effluent and floodplain outflow data points.
FSP Number
(#/mL) Coeff. Expon. R
2
Coeff. Expon.
4.0 to 8.0 m 3.50E+03 0.724 0.34
8.0 to 16.0 m 695 0.775 0.30
Coeff. Expon.
0.5 to 1.0 m 2.44E+05 0.590 0.63 4.58E+04 0.991 0.65 2.80E+04 0.962 0.58
1.0 to 2.0 m 5.83E+04 0.639 0.47 8.99E+03 1.10 0.57 1.92E+03 1.27 0.67
2.0 to 4.0 m 1.37E+04 0.697 0.41 2.00E+03 1.17 0.51 251 1.48 0.64
438
81.0
1.23 0.44
1.29 0.39
48.7
7.97
1.60 0.60
1.72 0.56
74

Converting fine particle number concentrations to fine particle mass improved R 2 values to a minimum of 0.66 and maximum of 0.93 among basin influent and effluent data. With improving
R 2 values for larger class sizes. Influent R 2 values improved from 0.66 to 0.93 from the 0.5 to 1.0
μ m, to the 8.0 to 16.0 μ m size classes, respectively. Basin effluent R 2 values improved from 0.67 to 0.91 from the 0.5 to 1.0 μ m, to the 8.0 to 16.0 μ m size classes, respectively. This conversion compresses the spread in the data by increasing the relative weight of each particle in the largest bin and decreasing the weight of the smallest bin. This conversion allows for easier comparison with TSS because it is in the same units but is difficult for regulatory compliance which is in number of particles.
Data from the floodplain samples had the worst correlations. The R 2 values were 0.56 to 0.67 for FSP number. Conversion to FSP mass did not improve correlations as it did for the inflow and outflow data with values ranging from 0.52 to 0.58. These poor correlations may be because the floodplain samples generally had the lowest percentage mass of fine particles. The presence of larger particles, > 16.0 μ m, may add more variability to turbidity readings and could impact particle size distribution measurements.
Table 3-16: Power law relationships for FSP mass (#/mL) by size class for all basin influent, basin effluent and floodplain outflow data points.
FSP mass In Basin
(mg/L) Coeff. Expon. R
2
Coeff. Expon.
0.5 to 1.0 m
1.0 to 2.0 m
2.0 to 4.0 m
4.0 to 8.0 m
8.0 to 16.0 m
0.0106 0.62 0.66 0.0021
0.0048 1.05 0.76 0.0031
0.0075 1.19 0.92 0.0081
0.0057 1.48 0.92 0.0137
0.0052 1.73 0.93 0.0192
1.00 0.67
1.12 0.81
Floodplain
Coeff. Expon.
0.0014
0.002
0.945 0.58
0.956 0.58
1.18 0.85 0.0058 0.998 0.52
1.32 0.87 0.0205 0.964 0.55
1.54 0.91 0.0808 0.934 0.56
75

4.1.
Treatment performance
The goals of this study were to measure the ability of Cattlemans detention basin and the adjacent floodplain to remove fine particles from stormwater runoff. The results presented in this thesis focus on quantifying sediment removal provided by the basin and floodplain. Constituents measured were: TSS (mg/L), inorganic fraction (mg/L), fine particles (#/mL) and turbidity
(NTU). The results show significant removal of sediment through the basin, however, the floodplain only provides treatment for fine particles.
As stormwater flowed through the basin, EMCs for TSS, FSP and turbidity were significantly ( =0.05) reduced. A combination of flow reductions and treatment lead to significant load reductions through the basin for all constituents, see Table 4-1. Median inflow
TSS was 29 percent fine particles by weight nearly the same as basin outflow, which was 31 percent, so the basin is removing fine sediment at similar rates by mass as all sediment. Particle size distribution data corroborates this by showing uniform reductions for all size-classes 16 μ m through the basin. Inflow concentrations impacted treatment for TSS with poor removal at lower concentrations indicating a lower limit for removal. Fines and turbidity were removed at all inflow concentrations. Pollutograph analysis indicates a first flush effect, most evident for fines and turbidity. For the 6/6/11 event, individual samples with high concentrations during first flush were not included in EMCs. If these had been included improvements in EMCs and loads would have been larger.
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Table 4-1: Median EMC reductions, effluent concentrations, and load reductions for the basin, floodplain and entire system for each constituent.
EMC reduction Effluent concentration Load reduction
TSS
(mg/L), (kg)
FSP
(#/mL), (#)
32.3
1.42 x 10
6
Turbidity
(NTU)
62.9
Inorganics
(mg/L), (kg)
23.5
-1.4
-0.9
-7.4
38.8
62.3
28.7
9.27
15.2
3.94
21.6
30.0
12.46
49.8
4.43
1.15 49.9
3.7 x 10
3
1.68 x 10
6
2.57 x 10
6
7.34 x 10
6
7.56 x 10
14
1.29 x 10
14
7.81 x 10
14
0.82 5.54
No constituents showed significant reductions across the floodplain and in most instances the floodplain increased concentrations and loads above those leaving the basin. Only loads of FSP were significantly reduced through the floodplain and this was primarily due to flow reductions rather than concentration reductions. The floodplain maintained similar concentrations of FSP to the basin effluent at all concentrations. It had higher concentrations of TSS and turbidity at lower concentrations of basin effluent and similar concentrations for higher basin effluents.
Through the entire system (basin and floodplain), floodplain sources negate concentration reductions achieved in the basin for TSS and turbidity; FSP is the only constituent that maintains significant concentration reductions through the entire system. All constituent loads were reduced through the entire system.
The impact of the floodplain on particle size distribution is important to note The percentage of TSS made up of fines was 21 percent by mass as compared to 31 percent from the basin indicating smaller proportions of FSP in floodplain water. Particle size distribution slopes for the floodplain indicate minimal fining for the three large rain events not immediately preceded by another event: 3/15/11, 6/6/11 and 3/15/12. For the other events the PSD from the floodplain was coarser than the basin effluent. Generally, the PSDs slopes for the floodplain converged with a
86

standard deviation of 0.12 as compared to 0.43 for the inflow and 0.36 for the basin outflow.
This could indicate a dominance of floodplain sources driving PSD. Additional flows on and through the floodplain from creek overbank flow, groundwater upwelling, and overland flow complicate data analysis for this portion of the study.
4.2.
Pollutographs
Pollutographs for two monitored periods, 6/6/11 and 3/15-3/17/12, show the relationship between concentrations of TSS, turbidity, fines and flow throughout the course of the storm. For inflows, constituent concentrations track with flow magnitudes with a strong first flush evident for fines and a moderate first flush for turbidity and TSS. Floodplain and basin concentrations through the storms were less associated with flow magnitudes. Basin concentrations were generally low and showed small increases through events perhaps this trend is indicating water from the previous storm flushing from the basin and new stormwater from the current storm reaching the outlet. Floodplain concentrations changed little throughout the course of the events, and even showed reduced concentrations as cleaner water from the basin reached the outlet.
These data also validate treatment strategies designed to capture and store the initial runoff volume from events. This is particularly important for the treatment of the fine particles of interest in Tahoe.
4.3.
Turbidity as a surrogate
Correlations between turbidity and TSS, FSP and fine particle mass were calculated. These relationships can provide a way to convert turbidity, a constituent that is relatively easy to measure, to sediment concentrations. The fits for TSS and fine particle mass at the basin inflow and outflow were good (0.87 R 2 0.94) and could be used to calculate TSS and FSP.
Additional data in mid-concentrations would improve the confidence of these relationships.
87

Relationships for the floodplain samples were poor and are not recommended for use.
Converting FSP concentrations by number of particles to FSP by mass improved correlations for inflow and basin values, 0.94 and 0.92 respectively; it did not improve correlations for the floodplain.
The same regression was performed on each particle size class: 0.5, 1, 2, 4, 8 and 16 μ m. The fits for FSP number were less good than those for all particles 16 μ m, all R 2 less than 0.65.
Conversion to FSP concentrations by mass improved correlations for basin inflow and outflow
(0.62 R 2 0.93) but not for the floodplain (R 2 0.58).
Overall, these fits seem promising but would need more data before implementation. The higher turbidity values are dominated by two storm periods, 6/6/11 and 3/15-3/17/12, which diverge. While, conversions for FSP by mass are more promising application to regulatory benchmarks for removal of total numbers of particles is difficult.
4.4.
Floodplains as treatment
While a primary goal of this study is to determine the ability of floodplains to treat sediment and fine particles in stormwater, the data collected through the floodplain are inconclusive. It is clear that the floodplain coarsens particle size distributions but it does not decrease concentrations of TSS, FSP or turbidity. Coarsening could be due to removal of fine particles, flocculation of small particles into larger flocs, or the addition of larger particles. Low effluent concentrations out of the basin and small differences between basin and floodplain samples make it difficult to determine which process is dominant. These limitations mean this system may not be representative of the ability of a floodplain to remove sediment and fines from stormwater. To further study this question, a location should be chosen where untreated stormwater is discharged directly onto a floodplain. In this case it was difficult to measure the contribution of the
88

floodplain because water leaving the basin was often cleaner than standing water on the floodplain.
4.5.
Treatment of fines from stormwater runoff
The basin is removing fine particles from stormwater runoff. In the Lake Tahoe TMDL, El
Dorado County is required to reduce discharges of fine particles by 2 x 10 18 particles (Lahontan and NDEP 2011). Over the course of the study 1.13 x 10 16 particles were removed during 7 events totaling 13.1 cm of precipitation. Extrapolating this out to an annual average precipitation of 51 cm gives a total annual removal of 4.4 x 10 16 ; a little over 2% of the reductions required in the TMDL and equaling 4.4 clarity credits (one credit = 1 x 10 16 particles). Considering this facility is only treating an 11.2-acre drainage basin, this is a significant amount of removal. Also, this estimated annual load reduction is most likely lower than the actual annual removal rate. For this study, only events that overflowed the basin were sampled but over the course of a year, the basin captures and infiltrates all runoff for many events, these events have 100 percent treatment.
While the ability of stormwater BMPs to remove TSS is well documented there is little literature on the removal of fine sediment, the primary pollutant of concern in Lake Tahoe.
Jurisdictions in the Tahoe basin have been constructing facilities like Cattlemans detention basin to improve stormwater quality, particularly fines. These results confirm the effectiveness of detention basins for removing fine sediments. Additional studies at this site or other stormwater facilities measuring treatment of the fine sediment fraction in stormwater would help confirm this finding.
89

EMC and load data were checked for normal and log-normal distributions using the probability plot correlation coefficient (PPCC) as outlined by Hensel and Hirsch. Test statistics for the log-normal distribution are presented below (Table B-1). Analysis for the normal distribution are not included because the agreements were poor.
The applicability of the log-normal distribution to stormwater data is well accepted (Helsel and Hirsch 1992; Geosyntec 2009; Strecker et al. 2001; Van Buren et al. 1997). All data sets fail to reject the null hypothesis that the data is normally distributed at the =0.05 level except for the inflow turbidity data which fail at the =0.025 level.
R 2 is the least-squares correlation coefficient of a probability plot of the log(e) transformed data plotted against normal quantiles.
R is the square-root of R.
R* is the test statistic.
Hypothesis: H
0
= data are normally distributed
Reject if R<R*
94

Table B-1: PPCC test statistics for EMC data.
EMC R
2
TSS in 0.915 basin 0.942 floodplain 0.969
FSP in 0.901 basin 0.886 floodplain 0.961
Turbidity in* 0.779 basin 0.896 floodplain 0.905
Inorganics in 0.946 basin 0.954 floodplain 0.865
*fail to reject H0 for =0.025.
R
0.957
0.971
0.984
0.949
0.941
0.980
0.882
0.947
0.951
0.973
0.977
0.930
R*
( =0.05) Conclusion
0.898 fail to reject H
0
0.906 fail to reject H
0
0.906 fail to reject H
0
0.898 fail to reject H
0
0.906 fail to reject H
0
0.906 fail to reject H
0
0.898 reject H
0
0.906 fail to reject H
0
0.906 fail to reject H
0
0.880 fail to reject H
0
0.888 fail to reject H
0
0.888 fail to reject H
0
95

Table B-2: PPCC test statistics for load data.
Load R
2
TSS in 0.866 basin 0.932 floodplain 0.874
FSP in 0.813 basin 0.962 floodplain 0.920
Inorganics in 0.918 basin 0.920 floodplain 0.934
R
0.941
0.965
0.935
0.902
0.981
0.959
0.958
0.959
0.967
R*
( =0.05) Conclusion
0.898 fail to reject H
0
0.906 fail to reject H
0
0.906 fail to reject H
0
0.898 fail to reject H
0
0.906 fail to reject H
0
0.906 fail to reject H
0
0.880 fail to reject H
0
0.888 fail to reject H
0
0.888 fail to reject H
0
96

Data for concentrations and loads were compared for significant differences across
Cattlemans Basin, the floodplain and for the entire system. Since the log-normal distribution was found to be applicable to the data both parametric and non-parametric were used to compare transformed data. The parametric test used was the paired t-test. The non-parametric test used was the Wilxocon signed-rank test. Both tests are outlined in Helsel and Hirsch (1992) and their decision rules are provided below.
Calculated p-values for both tests for all constituents are included below in Tables C-1 and
C-2. In all instances both tests agreed on the conclusion for significance for = 0.05.
Paired t-test decision rule
μ = sample mean t = test statistic
H
0
: μ x
= μ y
1.
H
1
: μ x
μ y
2-sided: The two groups have different mean values, but there is no prior knowledge which of x or y might be higher.
Reject H
0
if t p
< -t
(1/2),(n-1)
or t p
> t
(1/2),(n-1)
2.
H
2
: μ x
> μ y
1-sided: prior to seeing data, x is expected to be greater than y.
Reject H
0
if t p
> t
(1),(n-1)
3.
H
3
: μ x
< μ y
1-sided: prior to seeing data, y is expected to be greater than x.
Reject H
0
if t p
< -t
(1),(n-1)
Wilcoxon signed-rank test decision rule
D = differences between pairs
W + = test statistic
H
0
: median[D] = 0
1.
H
1
: median[D] 0
2-sided: the x measurement tends to be either larger or smaller than the y measurement.
Reject H
0
if W + x
/2,n
or W + x
/2,n
99

2.
H
2
: median[D] > 0
1-sided: the x measurement tends to be larger or smaller than the y measurement.
Reject H
0
if W + x
/2,n
3.
H
3
: median[D] < 0
1-sided: the x measurement tends to be smaller than the y measurement.
Reject H
0
if W + x
/2,n
EMC
Basin t-test p-value
TSS 0.0195 fines 0.00553 turbidity 0.00141 inorganics 0.0748
Floodplain
TSS 0.157 fines 0.869 turbidity 0.202 inorganics 0.162
Entire System signed-rank p-value Significant for = 0.05
0.016 fail to reject H
0
0.008 fail to reject H
0
0.008 fail to reject H
0
0.062 reject H
0
0.156 reject H
0 x reject H
0
0.191 reject H
0
0.109 reject H
0
TSS 0.198 fines 0.0190 turbidity 0.0766
0.188 reject H
0
0.023 fail to reject H
0 xx reject H
0 inorganics 0.338 xx reject H
0
Table C-1: p-values for paired t-test and signed-rank test for constituent EMCs and the determination of significance for = 0.05.
100

Loads
Basin t-test p-value
TSS 0.000679 fines 0.00198 inorganics 0.00498
Floodplain
TSS 0.307 fines 0.0198 inorganics 0.482
Entire System signed-rank p-value Significant = 0.05
0.016 fail to reject H
0
0.008 fail to reject H
0
0.031 fail to reject H
0
0.098 reject H
0
0.020 fail to reject H
0
0.219 reject H
0
TSS 0.000430 fines 2.43e-5
0.008 fail to reject H
0
0.008 fail to reject H
0 inorganics 0.00881 0.031 fail to reject H
0
Table C-2: p-values for paired t-test and signed-rank test for constituent loadings and the determination of significance for = 0.05.
101

This is the content of a poster originally presented at the IWA biofilm conference, Shanghai, China, Oct
26th-29th 2011 as:
Jin, Y., Aiona, A.R., Schladow, S.G., Wuertz, S. (2011) Biofilms in floodplain retain inorganic fine particles present in stormwater, IWA biofilm conference, Shanghai, China, Oct 26th-
29th 2011.
Y. Jin, A. Aiona, G. Schladow, S. Wuertz
Department of Civil and Environmental Engineering, University of California Davis, USA
(yjjin@ucdavis.edu; aaiona@ucdavis.edu; gschladow@ucdavis.edu; swuertz@ucdavis.edu
• Lake Tahoe is a major tourist attraction in both Nevada and California.
• Its Secchi depth measured an average of 68.1 feet in 2009, down from 102 feet in 1968.
• Inorganic soil particles (< 20 microns diameter) are the primary cause of this decline.
• Sources of these fine particles: urban uplands, nonurban uplands, stream channel erosion, atmospheric deposition, and shoreline erosion
• The largest contribution is from urban uplands (72%)
• Biofilm on the vegetation present in the floodplain may have the ability to remove fine particles because of 3 possible properties: 1) internal water channels and voids, 2) other architectural elements like roughness, and 3) multiply charged sites on the biofilm surface.
• The objective was to study the propensity of biofilms to remove inorganic particles from storm water under controlled conditions in the laboratory and under field conditions.
102

Table 1: Capacity of biofilm to remove fine inorganic particles
Exposure time
8 hours
25 hours
Specific inorganic particle removal, mg/mg biomass
Inorganic particles, mg
(±SD)
Biofilm, mg (±SD)
1.81
2.17 75.1 (41.1) 34.57 (8.73)
6 days 5.81
Effect of dehydration on removal of kaolinite clay by pregrown biofilm
• Dehydrated biofilm removed 11.35 mg inorganic particles which was three-fold less than the 38.89 mg removed by nondehydrated biofilm (Table 2).
• As a result of the biofilm biovolume not changing while the thickness decreased by half the dehydrated biofilm had a higher density, which was verified microscopically (Figure
2).
Table 2: Characteristics of nondehydrated and dehydrated biofilms
Parameter
TSS, mg/ plate
Inorganic particles, mg/ plate
Specific inorganic particle removal, mg /mg biomass
Roughness
Mean Thickness ( μ m)
Nondehydrated biofilm
(±SD)
57.75 (5.03)
Dehydrated biofilm (±SD)
39.83 (2.19)
38.89 (1.35) 11.35 (1.89)
2.06 0.40
0.55 (0.06)
66.6 (14.29)
0.49 (0.01)
32.13 (2.8)
Biovolume ( μ m3) 9.8
105 (3.06
105) 9.8
105 (1.56
105)
104

• Growth of biofilm on plates.
Biofilm was grown in 10 times diluted LB medium on
PVC plates stacked with 60 Rinzyl Plastic Micro Slides (Electron Microscopy Sciences,
PA) for 6-7 days. The medium was recycled and w replaced every day. The HRT of medium was 2.5 h. The flow rate was 6 L/h which was in the laminar flow range. The inoculum was Davis arboretum water.
• Analysis of biofilm using confocal laser scanning microscope
•
Biofilm grown on Rinzyl microscope slides was collected and stained for 15 min with
1000 times diluted Syto 9 (Invitrogen, CA, USA). Three locations were scanned at 488 nm wavelength for every slide and
• the CLSM images were quantified with the biofilm analysis software PHLIP to determine biovolume, thickness and roughness.
• Treatment of artificial fine particle water . Clay Kaolinite ( size~2 m) was suspended in overnight tap water at a concentration of 300 ppm.. The artificial storm water was pumped through the tray at a flow rate of 6 L/h for 12 h.
• Quantification of norganic particle removed by biofilm
•
Biofilm was removed with a rubber spatula and resuspended in DI water. The resuspended biofilm was vacuumed through a glass-fibre filter (Millipore, U.S.A.) and dried at 103°C -105 o C until a constant
• weight was recorded. The dry filters were moved into a 550° C muffle furnace for 15 min
(or until constant weight).
107

This research was supported by an agreement from the USDA Forest Service Pacific
Southwest Research Station. It was supported in part using funds provided by the Bureau of
Land Management through the sale of public lands as authorized by the Southern Nevada Public
Land Management Act.
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