DWQ-2024-004886

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______________________________________________________________________________ Utah Lake Bioassay Work Action Plan Bioassays to investigate nutrient limitations in Utah Lake Version 1.4 Sept 2019
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Prepared by the Utah Lake Bioassay Team
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List of Contributors PI: Dr. Zachary Aanderud, Brigham Young University Co-PI: Dr. Ben Abbott, Brigham Young University Co-PI: Dr. Michelle Baker, Utah State University Rachel Buck, Utah State University (PhD candidate) Erin Jones, Brigham Young University (PhD candidate) Gabriella Lawson, Brigham Young University (MS candidate) I. Revision History June 6, 2019 Initial release within the Utah Lake Bioassay Team June 7, 2019 Initial release to the Utah Department of Environmental Quality-Water Quality June 9, 2019 Second release to the Utah Department of Environmental Quality-Water Quality July 24, 2019 Third release to the Utah Department of Environmental Quality-Water Quality with a defined SAP Sept 27, 2019 Fourth release to the Utah Department of Environmental Quality-Water Quality with a SAP including N2 fixation, carbonate additions to alleviate photosynthesis limitations, nutrient removal treatment, and zooplankton filtering II. Acknowledgements The work was supported by the Utah Department of Environmental Quality-Water Quality. Any recommendations expressed here are those of the author(s) and do not necessarily reflect the views of the Utah Department of Environmental Quality.
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III. Contents List of Contributors and Revision History.......................................................................... 2 Acknowledgements ...................................................................................................................... 2 Acronyms/Abbreviations .......................................................................................................... 4 1. Introduction ............................................................................................................................... 5 2. Problem Statement .................................................................................................................. 5 3. Study Objectives ....................................................................................................................... 6 4. Research Outcomes and Deliverables ............................................................................. 6
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5. Task 1 - SAP ................................................................................................................................ 7 5. Task 1 - SAP ................................................................................................................................ 7 5. Task 1 - SAP ................................................................................................................................ 7 5. Task 1 - SAP ................................................................................................................................ 7 6. Task 2 - Conduct Bioassay Experiment .......................................................................... 8 7. Task 3 - Prepare Technical Report ................................................................................ 13 8. Approach for Science Panel Collaboration and Data Sharing ............................ 15 9. Project Milestones and Deliverables ............................................................................. 15 10. Level of Effort ....................................................................................................................... 16 11. Cost of Proposal .................................................................................................................. 16 12. References ............................................................................................................................. 18
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IV. V. Acronyms/Abbreviations BYU Brigham Young University USU Utah State University UT-DWQ Utah Department of Environmental Quality-Water Quality ULWQS Utah Lake Water Quality Study Science Panel iUTAH Innovative Urban Transitions and Aridregion Hydro-sustainability SAP Sample and Analysis Plan HAB Harmful algal bloom P phosphorus N nitrogen PAR Photosynthetically active radiation CUWCD Central Utah Water Conservancy District SOP Standard operating procedures SAP Sample action plan DIN Dissolved inorganic nitrogen SRP Soluble reactive phosphorus or orthophosphate TN Total nitrogen (mg/L) TP Total phosphorus (mg/L) TSS Total suspended solids VSS Volatile suspended
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1. Introduction Excess nutrients from human activity trigger toxic cyanobacterial and algal blooms creating expansive hypoxic dead zones in lakes damaging ecosystems, hurting local economies, undermining food and water security, and directly harming human health (Brooks et al 2016). The inception of a blooms is linked to the appropriate conditions allowing photosynthetic organisms to break dormancy and become abundant (Aanderud et al 2016). Cyanobacteria and algae become dominant under specific conditions physiochemical water conditions generally connected to total phosphorus availability, but many other phosphorus and nitrogen chemical forms and lake physiochemical conditions influence blooms (Descy et al 2016; Song et al 2017). Additionally, blooms may be dominated by a single species or a cohort of species responding to a cadre of environmental factors and interacting one with another (Wood et al 2017; Randall et al 2019). Besides chemistry and biology, weather fluctuations (e.g., temperature, wind speed, and solar irradiance) may facilitate different species to contribute the bloom (Wu et al 2016). Algae and diatoms species are part of blooms and are sensitive to chemistry and weather; however, deleterious effects of HAB are due to cyanobacteria producing cyanotoxins. Regardless, both organisms are degrading the ecological function of water bodies across the United States. Human activities are causing blooms. Economic damage from nitrate (NO3-) contamination alone is estimated to cost 0.2 to 2.3 trillion USD annually—up to 3% of the global gross domestic product (Boirsky et al 2014). Over the past 50 years, global fertilizer use increased by over 500%4, and nitrogen and phosphorus pollution are expected to keep pace with population growth, meat consumption, and wastewater discharge from sewage treatment facilities (Foley et al. 2011). Further, agriculture has disturbed approximately three-quarters of the Earth's ice-free land surface7, reducing the capacity of ecosystems to buffer and process nutrient inputs and contaminating waters through poorly-defined non-point sources (Seitzinger et al 2010; Pinay et al 2015). Freshwater ecosystems along the Wasatch Front are, in particular, being hit hard due to concentrated and growing human development. Utah Lake, the largest naturally occurring freshwater lake in the western U.S., and many of the diversions (e.g., Deer Creek and Jordanelle Reservoir) along its tributaries, are experiencing frequent and extensive HABs leading to lake impairment due to nutrient overloading, altered hydrology, and climate (PSOMAS 2007; Randall et al. 2019). 2. Problem Statement Understanding which nutrients limit cyanobacteria and algal primary production in Utah Lake will help describe the current state of the lake with respect to nutrients, trophic state, and ecology. Often, shallow lake systems transition from P limitations early in the growing season to N limitations later in the season, providing opportunities for algal dominated waters to transition to late-season Cyanobacterial dominance due to their N-fixing capabilities. In other lakes, non-N fixing Cyanobacteria may dominate throughout the bloom season, or N-fixing species may increase but may not be actively fixing N. Little is known about nutrient limitation in Utah Lake, including which
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nutrients are limiting, and whether there are seasonal and spatial dynamics in nutrient limitation among algal or different species of cyanobacteria. 3. Study Objectives The objective of this research is to address the following question identified by the ULWQS as critical to understanding the current state of Utah Lake with respect to nutrients and ecology, specifically, which nutrients are actually controlling primary production and HABs and when? Special attention is given to cyanobacteria due to their ability to generate cyanotoxins dramatically impacting Utah Lake designated uses. The study is designed to address the following topics: 3.1. Nutrient Limitation and HAB Primary Production Determine the potential for N, P, and/or N+P limitations to influence the primary production/growth of algal and cyanobacterial species. 3.2. Seasonal Nutrient Limitations of HABs Determine if there is a seasonal component (i.e., spring, summer, and fall) driving the potential nutrient limitations and algal and cyanobacteria species growth. 3.3. Spatial Nutrient Limitations of HABs Determine whether there is a spatial aspect to the nutrient limitations of algal or cyanobacterial growth (i.e., Provo Bay; main body of lake, east; main body of lake, west). 4. Outcomes and Deliverables Our study will provide baseline information on nutrient limitation in Utah Lake via bioassay experiments. The data may serve to inform a follow-up project investigating nutrient limitation in more detail. When this study is completed, the ULWQS will be able to answer the study objectives listed above and provide the following deliverables:
• We will identify the potential for N, P, and N+P to limit/co-limit the growth of individual species of algae and Cyanobacteria.
• We will identify spatial and seasonal water chemistry and nutrient triggers that facilitate blooms of individual HAB species.
• We will be able to predict the influence of N and P chemical species on HAB severity
• We will generate relationships between common Cyanobacterial species and the level of cyanotoxins they produce.
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5. Task 1 - SAP 5.1. SAP Introduction The SAP was created by Aanderud, Baker, and graduate students in accordance with the UT-DWQ’s Assurance Program Plan for Environmental Data Operations. All essential elements outlined in Appendix A of the Quality Assurance Program Plan were included. Baker has partnered extensively with UT-DWQ and is familiar with DWQ’s Quality Assurance Program Plan. Both Aanderud and Baker have created multiple SAPs. Section 8.1 contains an example SAP created for the iUTAH biweekly sampling of water quality/chemistry in three watersheds across the Wasatch Front that was followed by multiple investigators over two years. Baker was the lead of iUTAH and Aanderud was the lead for Research Focal Area 1: Ecohydrology, that evaluated water quality and quantity. The SAP for bioassays will follow the same template as outlined in the iUTAH SAP with the iUTAH SAP serving as a starter document for the SAP. The SAP will also include field and laboratory handling procedures, QA/QC documentation for all samples, and standard operating procedures (SOPs) for each analytical analysis adopted from the Baker’s Aquatic Biogeochemistry Laboratory (ABL) analytical procedures (attached 8.2 ABL analytical lab manual, attached pdf) and from Aanderud SOP documents (SOPs listed on box.com; an example SOP is provided in 8.3 SOP-total suspended solids and volatile suspended solids). All QA/QC and SOPs will be clearly outlined in the SAP and approved by UT-DWQ to reach a final SAP. 5.2. Nutrient Additions, Carbonate Additions, and Stock Solutions We are testing four nutrient treatments mirroring potential future nutrient requirements to wastewater effluent. P amendments will equal 0.10 mg-P/L added as K2HPO4, and N amendments will equal 0.72 mg-N/L added as NH4NO3. Within all nutrient additions, NaHCO3 (221.8 mg C-NaHCO3/L) will be added to alleviate any possible CO2 substrate limitations to photosynthesis of algae and cyanobacteria. CO2 is added at the rate required for 100 µg/L chlorophyll. Further, based on the high level of carbonates and buffering capacity of Utah Lake waters, we don’t expect CO2 to limit photosynthesis. The molar ratio for N:P, or more specifically DIN:SRP, additions will equal 16:1. Specific stock solutions for each addition is as follows (all additions = 1mL):
• Control (C only) – 1 mL at 221.8 mg C-NaHCO3/L (1 mL per cubitainer)
• N (N+C) – 1 mL at 2170.5 mg N- NH4NO3/L, 1 ml at 221.8 mg C-NaHCO3/L
• P (P+C) – 1 mL at 300 mg P- K2HPO4/L, 1 mL at 221.8 mg C-NaHCO3/L
• N+P (N+P+C) – 1 mL at 2170.5 mg N- NH4NO3/L, 1 mL at 300 mg P- K2HPO4/L, 1 ml at 221.8 mg C-NaHCO3/L
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5.3. Specific Sampling Times and Locations Samples will be as follows: mid-summer (22-26 July 2019), late summer (26-30 August 2019), fall (7-11 October 2019), spring (4-8 May 2020), and early summer (15-19 June 2020) to assess the seasonal component to HAB-nutrient interactions. The specific locations for each of the bioassays are as follows: Provo Bay (Latitude: 40°10’42”N, Longitude: 111°42’41”W), Main Body East (Latitude: 40°14’16”N, Longitude: 111°45’56”W), and Main Body West (Latitude:40°15’33”N, Longitude: 111°50’22”W, Figure 1). The time series is proposed to occur over a relatively brief time frame with sampling time points at 0- (T0), 24-, 48-, and 92-hours (T1, T2, and T3, respectively). The time course will only be performed for the first sampling date in mid-summer. After the first sampling, T1 will be set at either 2 days or 3 days based on the photosynthetic growth in the summer bioassays. The bioassays will be allowed to run for 2 days if an active bloom is present at the lake sampling location, and 3 days if a there is no bloom present at the lake sampling location. The identification of the bloom will be informed by a combination of sources including: UT-DWQ bulletins, a secchi depth < 10 cm, and high levels of phycocyanin and chlorophyll-a content measured on the EXO sondes (chlorophyll-a > 5 µg/L and BGA-PC > 0.10). 5.4. Zooplankton Removal and Zooplankton Plus Treatment For all bioassays, we will remove the zooplankton using a Wisconsin net during the original water collection. By removing zooplankton, we alleviate any grazing potential that will curb photosynthetic growth. We will also conduct a plus zooplankton treatment during the spring bioassay. We will create a second full set of nutrient additions and control bioassays but with unfiltered lake water. We will measure growth responses and all response variables in these bioassays at the T1 time point. The spring plus zooplankton treatment will allow the us to measure the effect of grazers on photosynthetic growth. 5.5. Nutrient Removal Treatment During the early summer bioassay, we will create a second set of nutrient additions and control bioassays where the lake water is diluted in half with distilled water to create a nutrient removal treatment. The diluted waters will still receive the nutrient additions and we will include control bioassays as well. The distilled water will be raised to a similar temperature as the lake water before the addition. We will measure growth responses and all response variables in these bioassays at the T0 and T1 time point. The spring plus zooplankton treatment will allow us to measure the effect of even lower nutrient levels on photosynthetic growth.
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5.6. Supplies List 5.6.1. Deployment Day (Time 0, T0)
Boat
Three buckets*
Three 55 gal drums*
Three paddles* (Rachel)
Three funnels*
Wisconsin net to filter out zooplankton
PAR sensor wand
Three 1-L graduated cylinder*
Four nutrient additions
Six corals with five lengths of rope
Three 1-mL pipettor
108 cubitainers – labeled*
EXO, cable, and handheld
Power generator,
Vacuum pumps, aspirator, six 1-L bottles*
Three Nalgene filter towers*
12 filters & cryovials, tweezers, ethanol, dewar, liquid N
9 pee cups and Lugols for microscopy
18 amber vials for toxins
9 1-L bottles for nutrients/pigments*
Cooler with wet ice
Sharpies
DI 5.6.2. Sampling Bioassays on the Dock (Time T0 and 24-[T1], 48-[T2], and 96-hours [T3] for Mid-summer Bioassay)
EXO, cable, and handheld
Six corals
Six screen covers
Zip ties
PAR sensor wand
36 1-L bottles for nutrients/pigments*
Generator etc.,
24 Nalgene filter towers, 48 filters & cryovials
48 amber vials for toxins
24 sampling cups and Lugols for microscopy
Cooler with wet ice
Sharpies
DI
Figure 1. Map of the three Utah Lake locations: Provo Bay, Main Body West, and Main Body East
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5.6.3. Sampling in the Lab (Time T0 and 24-[T1], 48-[T2], and 96-hours [T3] for Mid-summer Bioassays
Lab supplies (T0)
3 Nalgene filter towers*
12 0.2-µm filters
cryovials
tweezers
70% ethanol
vacuum pump
18 250-mL acid-wash bottles
18 GF/F filters,
Aluminum foil
9 TSS/VSS filters
9 15-mL tubes phosphate buffer for phycocyanin
Lab supplies (T1-3)
36 Nalgene filter towers*,
72 0.2-um filters
cryovials
tweezers
70% ethanol
vacuum pump
72 250-mL bottles
72 GF/F filters
Aluminum foil
36 TSS/VSS filters
36 15-mL tubes phosphate buffer for phycocyanin 5.7. Tasks Deployment Day (T0) On the Boat 5.7.1. Measurements 5.7.1.1. Measure lake water with EXO and record parameters on data sheet 5.7.1.2. Measure Secchi depth and total depth and record on data sheet 5.7.2. Collect Water 5.7.2.1. Rinse buckets, 55-gallon barrel, and paddle with (dump unused water on opposite side of boat) 5.7.2.2. Collect water from the top 20 cm with orange 5-gallon buckets and pour into another bucket covered with the Wisconsin net. 5.7.2.3. Gently pour the filtered water in the barrel 5.7.2.4. Make sure to fill barrel with at least 40 gallons (36+ extra for sampling pre-deployment) using buckets. 5.7.2.5. Repeat for each of the three sites
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5.7.3. Baseline Samples 5.7.3.1. Complete full sampling sets for three subsamples from the barrel (9 total reps). The lake barrel data serves as a baseline prior to nutrient additions 5.7.3.2. Begin filtering on the boat for RNA (three filters, up to 250 mL each) and record amounts of water filtered 5.7.3.3. Place all samples on ice 5.8. Tasks Deployment Day (T0) At the Dock, Corals, Cubitainers 5.8.1. Measurements 5.8.1.1. Measure barrel water and record all parameters on data sheet 5.8.1.2. Measure PAR and record all parameters on data sheet 5.8.1.3. Measure EXO parameters and record on data sheet to see how much values change from lake to dock and record on data sheet 5.8.2. Dispense water into cubitainers 5.8.2.1. Gently mix barrel water 5.8.2.2. Using a graduated cylinder and funnel dispense 3L into each cubitainers 5.8.2.3. Add 1 mL of C to all cubitainers 5.8.2.4. Add nutrient additions based on treatment 5.8.2.5. Cap tightly 5.8.2.6. Color code cubitainers to quickly discern sampling times and site 5.8.3. Corals set-up 5.8.3.1. Place one replicate from each site and treatment in each coral (Figure 2; Note: place only one replicate by treatment combination in each cubitainers; corals function as experimental blocks) 5.8.3.2. Zip tie shade cover onto the coral 5.8.3.3. Tie the corals together with rope to limit the movement of the coral in the boat slip (see Figure 3) Figure 2. Corals for cubitainers
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5.8.4. Nutrient Sampling Correction 5.8.4.1. Complete nutrient sampling for 2 additional T0 for all sites to assess changes in nutrients immediately following the chemical additions. 5.8.4.2. Place samples on ice until transported back to the lab 5.9. Tasks Sampling Day at the Dock (T1, T2, and T3, no T2 or T3 Except for Mid-summer Bioassay) 5.9.1. Measurements 5.9.1.1. Measure PAR and record all parameters on data sheet 5.9.2. Retrieve cubitainers and cubitainer water chemistry 5.9.2.1. Retrieve cubitainers and homogenize by gently inverting the cubitainer 3 to 5 times 5.9.2.2. Replace shade covering 5.9.2.3. Carefully open the cubitainer 5.9.2.4. Rinse EXO and EXO storage cup with 200 mL of water from the cubitainer 5.9.2.5. Pour sample from cubitainer for EXO (300 mL into storage cup) 5.9.2.6. Measure EXO parameters and record on data sheet 5.9.3. Cubitainers splitting for Analyses (see schematic Figure 4) 5.9.3.1. Pour 1L of water for RNA filtering into 1L acid-washed bottle 5.9.3.1.1. Place immediately on ice 5.9.3.2. Pour 1L of water for nutrient, pigments, and algal biomass estimates into 1L acid washed bottle 5.9.3.2.1. Place immediately on ice 5.9.3.3. Pour 100 mL into new 100 mL sampling cup for microscopy evaluation of Cyanobacteria and algae 5.9.3.3.1. Add 2 mL of Lugol’s reagent to the water in sampling cup 5.9.3.4. Pour approximately 30 mL of water into a 60 mL acid-washed, amber vial for microcystin and cylindrospermopsin evaluation 5.9.3.4.1. Place immediately on ice 5.9.3.5. Pipette, with 5 mL pipettor, 4.5 mL of water into 60 mL acid-washed, amber vial containing anatoxin-a preservative 5.9.3.5.1. Place immediately on ice

Figure 3. Corals and cubitainers deployed in boat slip at Utah Lake State Park
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5.10. Tasks Sampling Day at the Lab 5.10.1. Sample Transport 5.10.1.1. Immediately bring samples on ice to the lab 5.10.2. Storing: Cyanotoxin Storing samples 5.10.2.1. Place both 60 mL cyanotoxin amber vials in freezer at -20°C 5.10.2.2. For future processing see SOPs 5.10.3. Storing: Microscopy of algae and Cyanobacteria 5.10.3.1. Place microscope sample in refrigerator 5°C 5.10.3.2. For future processing see SOPs 5.10.4. Processing: Filtering RNA for community composition, Cyanobacterial biomass, and nifH 5.10.4.1. Filter RNA samples using an acid-washed Nalgene tower filter apparatus, vacuum line, and 0.2 µm polystyrene filters 5.10.4.2. Filter 100-250 mL onto each filter, based on the amount of algal biomass in the sample 5.10.4.3. Record the amount of water filtered onto each filter 5.10.4.4. With ethanol rinsed tweezers carefully role filter with the filtered-side facing in the center of the role into a pre-labeled 3.5 mL cryovial 5.10.4.5. Discard filtrate 5.10.4.6. Repeat the process for at least three filters 5.10.4.7. Place the three filters into the ultra-low freezer at -80°C 5.10.4.8. For future processing see SOPs 5.10.5. Processing: Analysis to relate nifH with N2 fixation rates 5.10.5.1. In one full replicate of all nutrient treatments and the control cubitainers for each harvest in the fall, spring, and early summer, we will measure N2 fixation potential. We will follow the N2 fixation method described by Marcarelli and Wurtsbaugh (2009) and define the relationship between nifH gene abundance and N2 fixation rates. The relationship will allow all nifH gene abundance date from all replicates to be converted to N2 fixation rates. 5.10.5.2. We will measure N2 fixation rates using acetylene reduction assays. Briefly, 50-mL aliquots of lake water will be incubated in 62-mL glass serum vials. We will inject the vials with 4 mL of acetylene generated from calcium carbide to create a serum head-space atmosphere of 20% acetylene gas. We will incubate assays for 2 hours during midday in the corals at the depth of water collection. Standards will be incubated with the assays. The resulting ethylene and acetylene in the assays and standards will be measured using a SRI 8610 gas chromatograph equipped with a Poropak T column, He carrier gas, and a flame ionization detector. Concentrations of ethylene in the samples will be compared to
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the known concentrations in the standards and then converted to the amount of N2 fixed using an assumed 3:1 ethylene:N2 conversion ratio.
5.10.6. Processing: Nutrients-TN and TP 5.10.6.1. Invert the 1L nutrient sampling bottle to mix
Figure 4. Cubitainer sample splitting for water chemistry and lab analyses
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5.10.6.2. Subsample 100 mL for TN and TP analysis 5.10.6.3. Freeze water in freezer at -20°C 5.10.6.4. For future processing see SOPs 5.10.7. Processing: total suspended solids (TSS), volatile suspended solids (VSS), chlorophyll-a 5.10.7.1. TSS and VSS: Filter 100 mL from the 1L nutrient sample using an acid-washed Nalgene tower filter apparatus, vacuum line, and 1.5 µm glass fiber filter 5.10.7.1.1. Place the filter on tin foil and dry in drying oven 5.10.7.1.2. For future processing see SOPs 5.10.7.2. Chlorophyll-a: Filter 100 mL from the 1L nutrient sample using an acid-washed Nalgene tower filter apparatus, vacuum line, and 0.7 µm glass fiber filter 5.10.7.2.1. Place filter in tin foil and freeze at -20°C 5.10.7.2.2. For future processing see SOPs 5.10.7.3. Phycocyanin: Filter 100 mL from the 1L nutrient sample using an acid-washed Nalgene tower filter apparatus, vacuum line, and 0.7 µm glass fiber filter 5.10.7.3.1. Place filter into 15 mL of buffer 5.10.7.3.2. Freeze filter and buffer at -20°C in the freezer 5.10.7.3.3. For future processing see SOPs 5.10.8. Processing: Soluble Reactive P (SRP) and Dissolved Inorganic N (DIN = ammonium + nitrate) 5.10.8.1. SRP and DIN: filter 200 mL from the 1L nutrient sample using an acid-washed Nalgene tower filter apparatus, vacuum line, and 1.5 µm glass fiber filter 5.10.8.1.1. Place filtrate into the freeze at -20°C 5.10.8.1.2. There is no need to set-up a unique sampling tower but use the effluent from the TSS and VSS sampling 5.11. Standard Operating Procedures for Analyses 5.11.1. Hyperlinks for SOPs For Utah Lake Bioassay in Box (email Zach Aanderud for access permission to box folder containing SOPs)
SRP  https://byu.box.com/s/esjvwkdr5dsiu0mti163r2woknqc2qbc
TN/TP  https://byu.box.com/s/gpxbiw0t6rr0ldfba8c1s468b7h5x3zj
NH4/NO3  https://byu.box.com/s/tv1xi46uzmjlwq6owgttp4ikxoytrqox
Phycocyanin  https://byu.box.com/s/hajhh6v3x3kl94qezzxggcyyvj1k3b8q
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Chlorophyll-a ethanol extraction  https://byu.box.com/s/aiw8nd2zfs2tjrsayz1aimbz5b2s2epg
TSS/VSS https://byu.box.com/s/24v6gadexd0zx0xo9p5jqdfo8uucmnvw
Microscopy https://byu.box.com/s/aep0t5wbilhm80jfj0a8nlsfxf19bfxf
RNA filtering  https://byu.box.com/s/a6v0y9pmzgzi21u0v2ib96z8j13uq4e1
RNA extraction https://byu.box.com/s/ex73ups9v8dvcax6o6a9eamuek9lt027
cDNA library prep  https://byu.box.com/s/hn7voj40u534vykltkaclp4ys2gxmypn
RT-qPCR(total cyano, nifH, toxin)  https://byu.box.com/s/c6u70qftivy5j5kquohqhubpbwkhrk3z
Toxin ELISA 
Mycrocystin 
Cylindrospermopsin 
Annatoxin  5.12. Key Team Members: Aanderud, Baker, Jones, Lawson 5.13. Approach discussion 5.13.1. Approach for required Scope of Work deliverables Aanderud and Baker are budgeted a ½ month summer salary to expedite the development of SAP and SOPs in the summer of 2019. Aanderud will be the point meeting with Baker and UT-DWQ to quickly develop a suitable SAP. 5.13.2. Supplemental approach None for Task 1 5.13.3. Task milestones and deliverables The draft of the SAP will be delivered to the UT-DWQ and ULWQS) 14 days after the approved grant agreement, with the final SAP delivered 16 days after receiving feedback from the UT-DWQ and ULWQS (approximate duration = 30 days after the approved grant agreement). 6. Task 2 - Conduct Bioassay Experiments 6.1. Introduction We propose to follow much of the design outlined in the Scope of Work: Bioassays to Investigate Nutrient Limitations in Utah Lake prepared by UT-DWQ and the ULWQS. The scope of work outlines two experiments. The first experiment, will be conducted in the summer of 2019 (Bioassay Experiment 1) to determine to the time necessary to measure the rate of increase in cyanobacterial biomass between nutrient amendments. The second set of experiments, will be completed through the year, including the late summer, fall
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(2019), spring, and early summer (2020) time points (Bioassay Experiment 2) to assess the whether there is a seasonal component to HAB-nutrient interactions. The number of experimental units vary between the two experiments: Bioassay Experiment 1 = three locations × three endpoints (including time zero for five replicates) × four treatments (control, N, P, N+P) × three replicates = 113 experimental units); Bioassay Experiment 2 = three locations × one time (including time zero for five replicates) × four treatments (control, N, P, N+P) × three seasonal times × three replicates = 113 experimental units, but the two experiments share 36 replicates so the total number of experimental units = 190. Further, we have an opportunity to preform monthly evaluations of Utah Lake, see section 2.2.4. 6.2. Bioassay Design 6.2.1. Utah Lake Sampling Locations We propose to conduct the bioassays at three locations in Utah Lake: Provo Bay, main body east, and main body west. Nearly all urban development and anthropogenic nutrient inputs border the east side of Utah Lake, providing an opportunity to evaluate HABs in relation to a gradient of N and P concentrations in the water column and legacy sediments between the east and west sides of the lake (Randall et al. 2019). Provo Bay is also a unique area of the lake (Collins 2019). The bay is highly impacted by urbanization and is shallow leading to anaerobic conditions and potentially alterations in N and P availability. The locations will be located as close as possible to a UT-DWQ sampling buoy at the main body east and Provo Bay site. The main body west site will be located west of the Provo Marina and south of Goose Point where there are few anthropogenic inputs to the lake. 6.2.2. Nutrient Treatments To determine the rate of increase in cyanobacterial species biomass in response to nutrients, we propose to create three nutrient treatments (N, P, and N+P) and a control. P amendments will equal 0.10 mg-P/L added as K2HPO4, and N amendments will equal 0.83 mg-N/L added as NH4NO3. The molar ratio for N:P, or more specifically DIN:SRP, additions will equal 16:1. Based on water chemistry in 2017 across the three proposed sampling locations, DIN concentrations were roughly higher in spring than summer and fall, while SRP concentrations were higher in fall and summer than spring, suggesting that N-fixing cyanobacteria may be favored in fall and summer while non-heterocystous cyanobacteria may be favored in spring (Figure 5). Across the three sites, N availability was relatively lower in DIN and higher in SRP at the mouth of Provo Bay, and SRP was relatively higher in the east- compared to the west-side of the lake. The N amendments will roughly increase DIN availability by a factor of two in spring and a factor of four in fall and summer. The amendments of P are more intensive, with P availability increasing roughly by a factor of five in fall and summer and a factor o 10 in spring.
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6.2.3. Bioassay Experiment 1-Time Selection The time series is proposed to occur over a relatively brief time frame with sampling time points at 24-, 48-, and 92-hours. The four-day time point will ensure that we capture growth and allow time for cyanobacteria to recover from any disturbance during the set-up of the treatments. The most sensitive time point with earliest significant Cyanobacterial growth will be selected as the evaluation time for Bioassay Experiment 2. 6.2.4. Possible Monthly HAB Evaluation in Utah Lake We may be able to leverage existing research to help determine the seasonal and spatial dynamics of HAB-nutrient relationships in Utah Lake. In collaboration with the CUWCD, we are proposing to create an early detection network to predict the interactions among nutrients, water chemistry, and weather influencing cyanobacteria and algae in the Provo River’s reservoirs and identify the whether or not the cyanobacteria are transported downstream into the Provo River. Specifically, starting in June and continuing for the next five years, we will identify relationships among nutrient species, water
Figure 5. The concentrations of N and P species in Utah Lake across seasons and between locations differing in anthropogenic nutrient inputs
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chemistry, and cyanobacteria in Deer Creek Reservoir, Jordanelle Reservoir, and Olmstead Diversion along the Provo in the reservoirs and immediately following the impoundments. We will further generate relationships between common cyanobacterial species and the level of cyanotoxins they produce using RT-qPCR cyanotoxin gene evaluation. There is no evidence to suggest harmful levels of cyanobacteria have ever existed in the Provo River, but because of the critical nature of the river as a drinking water source, CUWCD wants to closely monitor trends in cyanobacteria in the River (which historically are extremely low). The network will provide insights into the minimal information necessary to accurately predict temporal patterns of cyanobacteria within the Provo River, especially in reaches managed by the CUWCD. The creation of our cyanobacteria detection network will be accomplished through three focal areas: 1. Ecohydrology and Bloom Sensor Network (EBS Network)-three sondes measuring a suite of water chemistry, water quality, and phycocyanin (surrogate for cyanobacteria biomass) and chlorophyll a (surrogate for cyanobacteria and algal biomass) downstream of the reservoirs; 2. biweekly nutrient analyses-grab samples to check EBSN sondes and measure the availability of multiple forms of P and N in outflows and reservoirs; and 3. Cyanobacteria detection-biweekly direct counts and molecular identification of cyanobacteria and algae, quantitative PCR of cyanobacteria species and determinations of major cyanotoxin downstream and within reservoirs. We have asked CUWCD if we may also sample in Utah Lake as part of the network. For this research, CUWCD isn’t interested in Utah Lake, but we are proposing to measure the same suite of analyses in Utah Lake at the three buoys managed by UT-DWQ. We don’t have funds for Utah Lake and we are currently going to conduct an abbreviated suite of analyses. We are open to discussing the possibility of adding/altering sites or performing the same suite of analyses monthly if some more funds are made available to help with the costs. 6.2.5. HAB Conditions During Bioassay The scope of work focuses on alleviating nutrient limitations to better understand cyanobacterial species growth. We believe that the ideal water conditions to track nutrient-HABs relationships are not necessarily an active bloom. If there is already a bloom present when we run the trial, we are identifying if nutrients intensify the bloom instead of if excess nutrients are causing blooms. With the monthly HAB detection along with time zero measurement we will at least know a general HAB history prior to the bioassay and be able to explain the differences in our results. 6.3. Methods 6.3.1. Cubitainers We propose to conduct all experimental units in 3.8 L (1 gallon) cubitainers. Surface waters from top 20 cm will be carefully collected in a 200 L (55 gallon) drum from each site to ensure that the sample water is well mixed. While being
23
gently homogenized, 3 L will be placed in each cubitainers and each cubitainers will receive their respective nutrient treatments in a liquid dose of 10 mL. Every day, all cubitainers will be opened to allow the headspace to equilibrate with the atmosphere to lessen the potential for changes in oxygen or carbon dioxide to impact cyanobacterial growth. Surface waters will be collected with our 24' Sunchaser pontoon boat with a 115 HP Mercury outboard motor. The boat is modified to provide 112V AC power in several protected outlets, the inclusion of a work table, and the addition of deployment points for instruments/cubitainers. 6.3.2. Common Garden in Utah Lake State Park, Provo Marina We propose to conduct all experiments in common water garden in the Provo Marina. We will place cubitainers in floating corals (diameter 1.5 M) covered with shade cloth of reduce incoming solar radiation by ≈30% to prevent any solar inhibition occurring within Cyanobacteria photosystem. The cubitainers will be readily accessible and will remain at a common depth and exposed to similar light and temperature conditions. We will measure PAR daily at the dock to evaluate differences between radiation levels across seasons. We will are paying to use a slip at the Utah Lake State Park, Provo Marina, contact Joshua Holt, [e-mail], 801-420-1227. 6.4. Analyses We will conduct analyses in three general areas: in-situ lake chemistry, nutrients, and biology as outlined in the SOW to identify if N and/or P are controlling the primary production of HABs. SOPs for all analyses will be included in the SAP. Further, the SAP will include a detailed discussion of the procedures related to the bioassays: deployment of experiment, sampling timeline, sample handling, and sample QA/QC. 6.4.1. In-situ lake chemistry
In-situ lake chemistry analyses will be conducted immediately after opening the cubitainers. For in-situ lake chemistry, we will use a YSI EXO2 probe to capture multiple water chemistry parameters, see Table 1 for EXO2 details (Jones et al. 2017). The sonde will allow an immediate estimate of chlorophyll-a and phycocyanin. We will still perform the chemical analyses for chlorophyll-a and phycocyanin to provide a more accurate estimate of cyanobacteria pigments. Table 1. YSI EXO2 water quality sonde sensors to measure a suite of in-situ lake chemistry. Sensors Specifications pH 0 to 14 unit measurement range/±0.1 pH unit accuracy Temp/Electrical Conductivity 0 to 200 mS/cm measurement range/±0.5% of reading or 0.001 mS
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Dissolved oxygen 0 to 50 mg/L measurement range/±0.1 mg/L Turbidity 0 to 4000 FNU measurement range/0.3 FNU or ±2% of reading accuracy Dissolved organic matter 0 to 300 ppb Quinine Sulfate equivalent (QSE) measurement range Blue Green Algae-phycocyanin Range: ~0 to 280,000 cells/mL; 0 to 100 RFU/Detection Limit: ~220 cells/mL Chlorophyll-a Range: ~0 to 400 ug/L; 0 to 100 RFU/Detection Limit: ~0.1 ug/L
6.4.2. Nutrient Analyses Nutrient analyses include: TP, TN, NH4-N, NOx-N (nitrate and nitrite), SRP as outlined in multiple pubs by the PI and Co-PIs and outlined in the ABL Manual (8.2). 6.4.3. Biology: cyanobacteria pigments, direct counts, and cyanotoxins The biology evaluations will help determine the growth of individual cyanobacteria during the bioassay. Chlorophyll-a will be measured via an ethanol extraction and evaluated on an Aqualog (Horiba Scientific, NJ USA). Phycocyanin, a major phycobiliprotein pigment produced by cyanobacteria, will be analyzed with fluorometry (Kasinak et al 2014). The growth of cyanobacterial species (biovolume, cells per mL) will be analyzed by direct microscopy (quantitative cyanobacteria identification and enumeration between time points), but we will only perform a general quantitative evaluation of algae to the division level. All microscopy will be conducted on a Zeiss Axioplan2 upright fluorescent microscope (Ziess, SD USA) with an Axiocam SO3 color camera and a PhotoFluor LM-75 light source in the Aanderud Lab. We will only conduct direct microscopy on two of the three replicates for all treatment and season combinations. We will measure three cyanotoxins (i.e., microcystin, anatoxin-a, and cylindrospermopsin) using ELISA (Abraxis Inc., PA USA) immediately after opening the cubitainers based on the dominant cyanobacteria found in Utah lake during 2017 (i.e., Aphanizomenon, Microcystis, and Dolichospermum
sp; Collins 2019). Toxins will be measure in only two of the three replicates due to the high cost of the analyses. There is the potential to evaluate cyanotoxin expression with the same RNA extraction detailed in 2.4.4. Along with CUWCD, we are developing primers to evaluate gene expression of cyanotoxins by the three dominant cyanobacteria in Utah Lake. One benefit to the genetic approach is that it quantifies the expression of the gene and not the cyanotoxin itself which may be difficult to detect.
25
6.4.4. Biology: cyanobacterial molecular identification and quantification, N fixation, and TSS For biology, we propose to more fully evaluate cyanobacteria form and function. We will extract RNA from cubitainers using a DNEASY Powerwater kit (Qiagen, MD USA) and characterize the active cyanobacterial community by sequencing the rRNA transcripts of the 16s rRNA gene on MiSeq Illumina Sequencer at USU. Transcripts will identify all Cyanobacterial species including picocyanobacterial species, which are neglected in microscopy due to their small cell size but may constitute upwards of 30% of the cyanobacteria in Utah Lake (Collins 2019). Further, picocyanobacteria may also produce microcystin (Sliwinska-Wilczewska et al. 2018) and obscure relationships between other microcystin-producing species. The same RNA extractions will be used in reverse-transcriptase PCR (RT-qPCR) analyses (Eppendorf RealPlex, NY USA) to: 1) capture the total biomass of cyanobacteria, including picocyanobacteria, that will serve as a biomass estimate of all cyanobacterial species from the sequencing effort, and 2) estimate N fixation rates using nitrogenase (nifH) expression (Turk et al. 2011). We included N fixation estimates since we are already extracting RNA and nitrogenase activity occurs in-situ in the cubitainers and it provides valuable information about the nutrient activity of cyanobacteria. Alternatively, other N fixation proxies, such as acetylene reduction assays, requires an additional set of samples and analysis pathway (acetylene reduction uses gas chromatography). The RT-qPCR nifH expression will be expressed as the number of
nifH transcripts per L and further divided by time and the cells counts of N2-fixing species from molecular or microscopy techniques, but not as an amount of N2 fixed. We have conducted acetylene techniques often but the gene approach is more conducive to what we do best in our lab. RT-qPCR and sequencing are common analyses performed in the Aanderud Lab and will be conducted on the same two replicates measured by direct microscopy and cyanotoxins. We will generate regressions for each species relating the direct microscopy cell counts to the number of genes. It is our hope that we will be able to replace molecular HAB detection with microscopic HAB detection that is labor intensive and often cost prohibitive. We will also measure TSS and VSS for another estimate of photosynthetic biomass. 6.5. Key Team Members: Aanderud, Buck, Jones, Lawson, and Abbott Aanderud will help advise the bioassay deployment and biology analyses. Erin Jones, Rachel Buck, Gabriella Lawson, and BYU undergraduate students will perform the bioassays on the lake. Erin Jones will oversee/conduct the field research. Rachel Buck will advise/perform the nutrient analyses and Erin Jones
26
will advise perform the in-situ lake chemistry and cyanotoxin evaluations, and biological analyses. Abbott will also help advise the nutrient analyses. 6.6. Task milestones and deliverable The data for the fall and spring bioassays will be provided to the Utah DWQ and ULWQS 45 days. We are requesting to provide the first bioassay data 75 days after the sampling effort. The summer bioassay is composed of 72 experimental units, shorlty proceeds another bioassay run in the fall, and requires lab set-up to perform analyses. The only exception to the data delivery is the sequencing of cyanobacterial composition that will be run at the end of all three seasons to save costs. However, all other metrics biological metrics, RT-qPCR of nifH and cyanobacterial biomass and microscopy determinations, will be delivered on schedule. 7. Task 3 - Prepare Technical Report The technical report will describe the primary production limitations of cyanobacteria and algae in Utah Lake across seasons and from east to west and in the Provo Bay. Specifically, the technical report will focus on determining the nutrient limitations/co-limitations regarding P, N, and NP influencing individual cyanobacterial and algal species; discerning if there is a seasonal signature to primary production nutrient limitations; and identifying the spatial intensity of nutrient limitations on photosynthetic biomass. Further, the technical report will provide baseline information on nutrient limitation in Utah Lake and serve to inform follow-up project investigating nutrient limitation in more detail. The PI and Co-PI are professors and are prolific science writers, each with an outstanding publication record, averaging 3-5 publications a year for the last five years. The technical report will follow the same outline as a standard scientific publication: introduction, methods, results, figures, tables, and discussion. The technical report will be more extensive than a standard publication providing baseline data and quantitative analyses to address the questions. Further, nutrient and cyanobacterial abundance data from the Aanderud Lab’s extensive 2017 sampling effort (1.1.1.4. Tracking Utah Lake
cyanobacteria blooms) will also be provided to identify multiple years of spatial and temporal data on Utah Lake (for example of data, see Figure 6). The technical report, with the consent of UT-DWQ, will be submitted to a scientific journal with Aanderud as the lead and all other participants co-authors
27
. 7.1. Key Team Members: Aanderud, Baker, and Abbott 7.2. Approach discussion 7.2.1. Approach for required Scope of Work deliverables Aanderud is budgeted for ½ month summer salary in year 2 to draft and complete the technical report. Abbott will also receive 1/4th month summer salary and will also contribute 20 hours to the preparation and editing of the technical report. Both Baker and Abbott will consult on data analysis and the interpretation of the results, but Aanderud will be primarily responsible for the report and interacting with the DWQ and ULWQS. 7.2.2. Supplemental approach None for Task 3
Figure 6. The seven locations sampled for eater chemistry and Cyanobacterial species in 2017 spring, summer, and winter (A), the population dynamics of Aphanizomenon flos-aquae over time (B), and the concentrations of SRP across the lake.
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7.2.3. Task milestones and deliverables The technical report will be extensively reviewed by the PI and Co-PIs. A draft of the report will be provided to the UT-DWQ and ULWQS Science Panel on June 12th 2020 and the final technical report will be submitted on June 30th 2020. 8. Approach for Science Panel Collaboration and Data Sharing 8.1. Data sharing plan (DSP) All team members are committed to openly sharing data with the UT-DWQ and ULWQS in a timely manner as possible. The team will create data spreadsheets, which will be shared online, with the minimum parameters of: analysis date, analyst name, sample identification, concentration of each analyses, calibration information, reagent blanks, check standards. The team will also create a series of figures demonstrating the growth of cyanobacteria, algae, and specific species for bioassays in a given season that will also be provided. The team will work with UT-DWQ to provide data in the optimal format for their needs. 8.2. Sample management and data availability
Each physical sample (consisting of sampling bottles and a cubitainer) collected will be
assigned a globally unique identifier before the sample is collected in the field. These
identifiers will be affixed to the sampling containers and will accompany the samples in
the field and in the laboratory. Data will be immediately entered into the shared google
document-excel spreadsheets. 9. Project Milestones and Deliverables Table 2. The milestones and attending dates for bioassay deliverables to the UT-DWQ and ULWQS Task # Deliverable Due date 1 Deliver first draft SAP, SOPs, DSP to DWQ and ULWQS 14 days after award
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1 Deliver complete SAP, SOPs ,DSP to DWQ and ULWQS 30 days after award 2 Set-up bioassays summer, fall, spring 2 Conduct bioassays summer, fall, spring 2 Analyze physiochemical characteristics summer, fall, spring 2 Analyze cyanobacteria and algae summer, fall, spring 2 Deliver raw data and interpretation of data to DWQ and ULWQS 45 days after each bioassay 3 Deliver draft technical report June 12th, 2020 3 Incorporate edits and deliver final technical report June 30th 2020
10. Level of Effort Table 3. The time allocation of proposed hours for project management, support, and completion. One semester of graduate stipends is equivalent to approximately 600 hours Team Member (hours) Task # Deliverable Aanderud (PI) Baker (Co-PI) Abbott (Co-PI)
Jones (GS) Buck (G)
1 Develop SAP 40 30 × × × Edit SAP with DWQ/ ULWQS 20 10 × × × 2 Set-up bioassays 10 × × 120 40 Conduct bioassays × × × 440 180 2 Analyze physiochemical characteristics × × 10 300 360 2 Analyze cyanobacteria and algae × × × 360 × 2 Manage data 10 × × 40 20 Interpret data 30 10 20 × × 3 Prepare technical report 40 10 × × 3 Edit technical report 10 10 10 × ×
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11. References Aanderud, Z.T. et al. Bacterial dormancy is more prevalent in freshwater than hypersaline lakes. Frontiers in Micro., vol. 7, 2016 Bodirsky, B.L. et al. Reactive nitrogen requirements to feed the world in 2050 and potential to mitigate nitrogen pollution. Nat. Commun. 5, (2014) Brooks, B.W. et al. Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems? Environ. Toxicol. Chem. 35, 6–13 (2016) Collins, S. 2019. More biologically available phosphorus, less eukaryotic grazing, and warmer temperatures may intensify harmful algal blooms. PhD Dissertation, BYU Descy, J.P. et al. Identifying the factors determining blooms of cyanobacteria in a set of shallow lakes. Ecol. Informatics, vol. 34, pp. 129-138, 2016 Foley, J.A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011) Kasinak, J.E., B.M. Holt, M.F. Chislock, A.E. Wilson. 2015. Benchtop fluorometry of phycocyanin as a rapid approach for estimating cyanobacterial biovolume. Journal of Plankton Research 37:248-257 Pinay, G. et al. Upscaling Nitrogen Removal Capacity from Local Hotspots to Low Stream Orders’ Drainage Basins. Ecosystems 18, 1101–1120 (2015) PSOMAS. Utah Lake TMDL: Pollutant Loading Assessment and Designated Beneficial Use Impairment Assessment. 88 (State of Utah Division of Water Quality, 2007) Seitzinger, S.P. et al. Global river nutrient export: A scenario analysis of past and future trends. Glob. Biogeochem. Cycles 24, GB0A08 (2010)
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Sliwinska-Wilczewska, S., J. Maculewicz, A.B. Felpeto, A. Latala. 2018. Allelopathic and bloom-forming picocyanobacterial in a changing world. Toxins 10:48 Steffen, W. et al. Planetary boundaries: Guiding human development on a changing planet. Science 347, 1259855 (2015) Song, H. et al. Biological and chemical factors driving the temporal distribution of cyanobacteria and heterotrophic bacteria in a eutrophic lake (West Lake, China). Applied Micro. and Biotech., vol. 101, no. 4, pp. 1685-1696, 2017 Randall, M.C., G.T. Carling, D.B Dastrup, T. Miller, et al. 2019. Sediment potentially controls in-lake phosphorus cycling and harmful cyanobacteria in shallow, eutrophic Utah Lake. PLOS ONE 14(2):e0212238 Turk, K.A., A.P. Rees, J.P. Zehr, N. Pereira, et al. 2011. Nitrogen fixation and nitrogenase (nifH) expression in tropical waters of the eastern North Atlantic. ISME Journal 5:1201-1212 Wood, A. et al. Contrasting cyanobacterial communities and microcystin concentrations in summers with extreme weather events: Insights into potential effects of climate change. Hydrobiologia, vol. 785, no. 1, pp. 71-89, 2017 Wu, Y. et al. Patterns of succession between bloom-forming cyanobacteria Aphanizomenon flos-aquae and Microcystis and related environmental factors in large, shallow Dianchi Lake, China. Hydrobiologia, vol. 765, no. 1, pp. 1-13, 2016
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12. Related Case Study 12.1. SAP for iUTAH bi-weekly water quality sampling
______________________________________________________________________________

Sample and Analysis
Plan

Stream Water Quality and Quantity for
GAMUT

Version 1.1 April 2014
33

Prepared by the iUTAH Water Quality Team

34

35
VI. List of Contributors

Zachary Aanderud, Brigham Young University
Michelle Baker, Utah State University
Jeff Horsburgh, Utah State University
Beth Neilson, Utah State Univeristy
Nancy Mesner, Utah State University
Matt Miller, USGS
Amber Spackman-Jones, Utah State University
VII.

VIII. Revision History
December 10, 2013 Initial release within Water Quality Team
April 11, 2014 Version 1.1 release within RFA1

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37

IX. Acknowledgements

This plan was developed by the iUTAH Water Quality Team, a subgroup of Research Focus Area 1
(Ecohydrology) in support of the GAMUT monitoring network. This work was supported by NSF EPSCoR
award EPS 12-08732 to Utah State University, as part of the State of Utah Research Infrastructure
Improvement Award. Any recommendations expressed here are those of the author(s) and do not
necessarily reflect the views of the National Science Foundation.

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X. Contents

XI. List of Contributors and Revision History....................................................................... 35 Acknowledgements ................................................................................................................... 37
39
Contents ......................................................................................................................................... 38 Acronyms/Abbreviations ....................................................................................................... 40 1. Introduction ........................................................................................................................... 42 2. Data Quality Objectives ..................................................................................................... 44 3. Sampling Plan ......................................................................................................................... 50 4. Laboratory Analyses ............................................................................................................ 52 5. Literature Cited ...................................................................................................................... 55

40
XII.
XIII.
XIV. Acronyms/Abbreviations

BYU Brigham Young University
EPSCoR Experimental Program to Stimulate Experimental Research
GAMUT Gradients Along Mountain to Urban Transitions
iUTAH Innovative Urban Transitions and Arid-region Hydro-sustainability
MDF Modeling and Data Federation
NSF National Science Foundation
NWIS National Water Information System
TN Total nitrogen (mg/L)
TP Total phosphorus (mg/L)
USGS United States Geological Survey
USU Utah State University
UU University of Utah

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______________________________________________________________________________

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XV.

XVI. 1. Introduction

This Sample and Analysis Plan was prepared by the Water Quality sub-group of iUTAH’s Research Activity
1 (Ecohydrology). Team members include: Zach Aanderud (BYU), Michelle Baker (USU), Dave Bowling
(UU), Greg Carling (BYU), Joe Crawford (BYU), Dylan Dastrup (BYU), Jim Ehleringer (UU), Timothy Goodsell
(BYU), Steven Hall, (UU), Erin Jones (BYU), Julie Kelso (USU), Matt Miller (USGS), Elizabeth Ogata (USU),
Jeff Horsburgh (USU), Amber Jones (USU), Tony Melcher (USU), Nancy Mesner (USU). The purpose of
this document is to provide justification of and a plan for collection of environmental samples in order to
establish streamflow rating curves, baseline water chemistry, and proxies of continuous sensor data in
association with established sites of the iUTAH Gradients Along Mountain to Urban Transitions (GAMUT)
surface water observatory network.

1.1 Background

In August 2012, the National Science Foundation’s EPSCoR program awarded a Track 1 Research
Infrastructure Award to Utah State University on behalf of the state of Utah. The purpose of the award is
to increase research capacity and enhance research competitiveness in STEM (science, technology,
engineering, math) fields across the state. With this award, the State chose to enhance capacity related
to water sustainability. Part of the award was aimed at building an environmental observatory focused
on gradients along mountain to urban transitions (GAMUT). The instrumented watersheds (Logan River,
Red Butte Creek, and Middle Provo River) differ in current urban footprint, rate and type of land use
conversion to urban area, size, and elevation. Details of the GAMUT design and installation are provided
elsewhere.

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The GAMUT includes 14 stream monitoring stations, aimed at providing real-time data collection on
stream flow and water quality, much like USGS NWIS monitoring stations found across the USA. There
are two classes of stream GAMUT stations; basic and advanced (Table 1).

Table 1. Sensors deployed at fundamental and enhanced GAMUT stations in Utah.
Sensor Sensor type Units
Stream stage Pressure transducer cm All stations*
Temperature Thermistor oC All stations
Electrical Conductivity Electrode (nickel cell) mS/cm All stations
pH Electrode pH All stations
Dissolved Oxygen Optical (lifetime luminescence) % sat; mg/L All stations
Turbidity Optical (laser diode) NTU All stations
Chlorophyll Optical (fluorescence) RFU Enhanced
Phycocyanin Optical (fluorescence) RFU Enhanced
fDOM Optical (fluorescence) QSE Enhanced
Nitrate Optical (MBARI-ISUS) mg/L Enhanced
* 3 stations in Middle Provo, and 1 station in Red Butte obtain stage measurements from USGS gauges

1.2 Objectives

Sensors at GAMUT stations generally make continuous measurements of indicators of water quantity and
quality (Table 1), rather than direct measurements of stream discharge and water chemistry. Thus,
discrete environmental samples must be collected in order to interpret sensor output. At the same time,
such baseline data should provide iUTAH investigators and partners with important information to guide
research questions and study designs, and calibration data for models of water quality and water balance.
Data collection outlined in this plan will be used to:

A. Establish and verify rating curves in order to transform continuous measures of stream
depth to volumetric flow (discharge)

B. Provide baseline measurements of standard water quality parameters

C. Enable development of proxies between discrete water quality data and continuous data
from real-time sensors at GAMUT stations

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1.3 Site Descriptions

Aquatic stations are installed in three watersheds:
1. Provo River at Soapstone, Below Jordanelle Reservoir, at Lower Midway and at
Charleston (http://data.iutahepscor.org/mdf/provo-river)
2. Red Butte Creek at Knowlton Fork, Above Red Butte Reservoir, at Red Butte Gate, at
Cottom’s Grove, and at Foothill Drive (http://data.iutahepscor.org/mdf/red-butte-creek)
3. Logan River at Franklin Basin, at Tony Grove, at the USU Water Lab, At Main Street, and
at Mendon Road (http://data.iutahepscor.org/mdf/logan-river)

1.4 Project Organization

Todd Crowl (USU) is the iUTAH Project Director. Co-Principal Investigator Jim Ehleringer (UU) leads major
facilities for iUTAH, under which the GAMUT facility falls. There are three watershed leads and associated
technicians: Logan River (USU) = Scott Jones, Chris Cox, Jobie Carlisle; Red Butte (UU) = Dave Bowling,
Dave Eiriksson; Middle Provo (BYU) = Zach Aanderud, Joe Crawford. Technicians oversee the day-to-day
operations of the GAMUT stations, with assistance from graduate students and other student employees
as needed. GAMUT technicians also conduct QA/QC of incoming data streams and work closely with the
iUTAH Data and Modeling Federation (Jeff Horsburgh (USU) and Amber Jones (USU)).
XVII. 2. Data Quality Objectives

Data quality requirements and design rationale presented here are informed by the US EPA’s seven-step
data quality objective (DQO) process (EPA 2006). This process defines the type, quantity, and quality of
data required so that the data collected support the goals of the study in such a way that is consistent
with professional and nationally accepted standards.

2.1 Problem Statement

Problem
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Most GAMUT stations do not have established rating curves (relationship between stage (cm) and
volumetric flow rate (m3/s)). Water quality sensors can record parameters such as electrical conductivity
and turbidity, but we have little sense of how these indicators of water quality relate to actual water
quality metrics such as total dissolved solids (in the case of conductivity) or total suspended solids or total
phosphorus (in the case of turbidity). Enhanced water quality sensors that bracket urban sections in each
watershed are also fairly novel. For example the fluorometric sensors for chlorophyll and fDOM are
calibrated to a standard fluorophore (rhodamine in the case of chlorophyll and quinine sulfate in the case
of fDOM). Thus while these sensors measure fluorescence properties of the water, the output needs to
be calibrated to discrete water samples to translate sensor output to extracted chlorophyll and in the
case of fDOM sensors, DOC concentration and absorbance at 254 nm (Downing et al. 2012). Finally the
nitrate sensors are calibrated to nitrate standards and do read nitrate concentration, but fDOM in the
water may cause an interference, thus environmental samples will be required to better interpret sensor
output.

This problem will be addressed by coordinated sampling of stream flow (rating curve) and water
chemistry at each GAMUT station in all seasons in order to capture a variety of hydrochemical conditions.

Project Team
Implementation of this Plan will require a collaborative effort across iUTAH institutions. The team
consists of members from the following:
• Utah State University – Design standard operating procedures (SOPs) for collection and
analysis of stream water samples
• Brigham Young University – Design SOPs collection and analysis of stream water E. coli
and Total Coliform samples from streams
• GAMUT technicians – Maintain GAMUT aquatic sensors following SOPs; Perform quality
control (QC) checks of sensor data; Coordinate with RFA1 graduate students and other
research personnel to plan and implement sample collection following established
SOPs
• RFA1 Graduate student at USU – Act as liaison between field and lab (water chemistry);
Maintain water chemistry lab data base and coordinate with iUTAH Data and Modeling
Federation; Perform QC checks of lab data per established SOP.

Available Resources
The iUTAH award provides for personnel and resources at BYU, USU, and UU to collect environmental
samples at approximately biweekly intervals (20-25 samples per site), roughly about as often as GAMUT
sensors will require field checking and maintenance. Biweekly sampling is not necessarily the best
46
sampling interval for establishing chemical proxies. Sampling at advanced aquatic stations will also
include samples collected every 1-3 days during snowmelt, as well 10+ samples during opportune storms.
Costs for chemical analyses of routine water samples (anions, cations, filtered and unfiltered nutrients,
DOC, chlorophyll, TSS and VSS) were included in the USU award under the facility 1 budget category.

Relevant Deadlines
It is important that data collection for rating curve development begin soon after GAMUT stations are
installed so that stream depth can be converted to volumetric flow. This data product is critical for iUTAH
researchers interested in understanding and modeling water balance. Water chemistry sampling should
occur at the same time as stream flow is measured for the rating curve. All GAMUT stations should be
installed and recording data by June 30, 2014, and regular sampling as outlined in this plan will be
expected to be in place at that time.

2.2 Research Goals

Objective
The objective of this plan is to collect environmental data that will accomplish the following:
1. Establish a rating curve (relationship between stream stage and discharge) at each
GAMUT station.
2. Allow interpretation of GAMUT sensor output using chemical proxies collected as
discrete environmental data.
3. Allow calibration of water quality and quantity models being developed and tested
under the iUTAH research umbrella
4. Provide background information describing chemical and microbiological water quality
at different times of the year at each GAMUT station that can serve as context for and
support a variety of research projects as they are launched under the iUTAH umbrella.

Key Questions
The overarching goal of Research Focus Area 1 of the iUTAH project is to strengthen Utah’s capacity to
monitor and understand the ecohydrologic system of the Wasatch Range Megapolitan Area (WRMA).
This will be accomplished by improving watershed-scale measurement capacities, and using this
instrumentation to conduct research aimed at better understanding ecohydrologic processes that
47
influence Utah’s water resources. Guiding research questions related to water quality include (Crowl et
al. 2013):

• What is the spatial and temporal variability of biogeochemical transformations that
affect water quality?
• What is the relationship between snowpack chemistry and runoff chemistry?
• What are major nitrogen (and other chemical) sources and loading rates to WRMA
streams and how do they vary along mountain-to-urban gradients? How do these vary
seasonally, with storm/runoff events, and with varying antecedent conditions?
• What are Escherichia coli sources across the WRMA and how do they relate to water
quality indices and overall microbial community structure in streams?

Underlying these questions is a need to understand the ambient water quality in the study
watersheds. Toward that end, the iUTAH GAMUT network was designed in part to provide real-time
data on stream flow and indicators of water quality. GAMUT design was completed in late 2012, and
site instrumentation began in spring 2013. While some stations are at or near USGS monitoring
stations, for the most part there are few accessible environmental data with which we can put the
current monitoring data in context.

Possible Outcomes
1. The desired outcome is that sufficient data are collected to create reliable rating
curves and to develop chemical proxies of water quality at each GAMUT station.
The Water Quality team will evaluate data collected and make recommendations
as to whether or not, and to what extent further sample collection should
continue.

2. Potential risks to achieve desired outcome include a lack of resources (especially
travel and personnel) available on a given day to complete necessary sampling.
Suggested remediation measures include quarterly planning meetings at a
minimum to ensure resource availability, and a shared calendar with specific
personnel identified for data collection.

3. Sensor failure is another risk. The Water Quality team recommends that iUTAH invest
in at least one spare sensor so that duplicate measurements can be made as part
of the field quality control process (see Data QA/QC plan) and that could be used
to replace failed sensors.

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2.3 Variables/Characteristics to be measured

Each regular, bi-weekly site visit will collect the following (see separate SOP for field collection; see
section 4 below for laboratory analyses to be conducted):
1. Water surface elevation (cm) on the stage plate installed at each GAMUT site
2. Filtered water chemistry; grab sample from thalweg
3. Unfiltered water chemistry; grab sample from thalweg
4. TSS and VSS filter; grab sample from thalweg
5. Chlorophyll filter; grab sample from thalweg
6. Unfiltered bacteria sample for Total Coliform/E. coli analysis

In addition to regular, bi-weekly site visits, technicians will plan and execute stream discharge
measurements at strategic locations and times aimed at capturing a wide range of discharges at each site.
These discharge sampling events may be coordinated with regular, bi-weekly sampling events, but may
have to be more or less frequent depending on streamflow conditions at the sites. Each discharge
measurement visit will collect the following (see separate SOP):

1. Surveyed water surface elevations within the established cross section at each site at the
beginning and end of the discharge measurement
2. Water surface elevation on the stage plate installed at each GAMUT site at the beginning
and end of the discharge measurement
3. Discharge (m3/s) at the site

Special studies are described in detail below. One special case of special study is synoptic sampling
wherein unfiltered water is collected from ~20 locations in each GAMUT watershed and analyzed for E.
coli, water isotopes, and total N and P.

2.4 Study Boundaries

Temporal
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Streamflow and water chemistry will be measured as described in this plan through at least year 5 of the
iUTAH project. Data will be made available to all iUTAH investigators through the Data and Modeling
Federation website. The Water Quality team will prepare a short report of the data annually to provide
information to and receive feedback from the iUTAH research community.

Practical Constraints on Data Collection
1. Property access is being negotiated and confirmed
2. Availability of staff, vehicular, and sampling equipment may limit some activities
3. Weather, and winter access may limit ability to collect samples, some sites may not
have year-round flow
4. iUTAH research personnel are not to sample alone
5. Sample numbers. Between 20-25 measurements at each GAMUT station across the
entire range of flow are recommended per year, especially including snowmelt and
storms. Spackman Jones et al. (2011) used considerably more samples (148-175) to
develop water quality surrogates for sensor outputs using TP and TSS in the Little
Bear River; however, existing iUTAH resources may not allow that quantity. To
optimize resource availability with data collection needs, it is recommended that a
minimum of 20 samples/GAMUT/year be collected at all stations, and that data from
2-3 storms (15-30 samples each) be collected at each enhanced GAMUT station
during the next 3-4 years.
6. Types of analyses. Resources limit the types of analyses in the USU budget to facile
and inexpensive measures of water quality. Samples can be archived for additional
investigator-driven analyses using other resources. Requests for additional sample
collections can be considered on a case by case basis, and will require approval of
iUTAH research personnel and their supervisor(s). The Water Quality Team
recommends a separate plan be developed for sample archival.

2.5 Decision Rules

Each year, the Water Quality Team will analyze available data and will make recommendations as to
future data collection. If sufficient information has been collected to generate reliable rating curves and
chemical surrogates of sensor outputs, then the recommendation will be to stop making detailed
measurements.

Tolerance limits for laboratory water quality analyses are defined in the SOPs for each analyte. Generally
EPA methods will be followed, and QA/QC lab protocol will include spikes, sample duplicates, blanks, and
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certified reference materials – results above or below a 20% threshold will be considered invalid, and will
require reanalysis of the sample(s). All samples will be run blind.

It is assumed that these data quality objectives, sampling, analyses, and QA/QC methods will be reviewed
and revised as needed on an ongoing basis.

XVIII. 3. Sampling Plan

This sampling plan attempts to address fundamental data requirements for the GAMUT network. It is not
meant to describe the many investigator-driven research activities that may require similar data
collection and laboratory analyses. Note that GAMUT sensor calibration and QA/QC objectives are
described in a separate GAMUT QA/QC plan. We outline three broad categories of work to be completed
in support of the GAMUT network:

1. Environmental sample collection: These activities will provide necessary input to
analyses aimed at establishing rating curves and water chemistry surrogates of sensor
outputs. Environmental samples also will provide important context and background
information for research related to surface water quantity and quality across the iUTAH
project.

2. Developing water quality proxies to sensor outputs: These are ways of analyzing the data
from environmental samples to better interpret GAMUT sensor data.

3. Special studies: These include special sample collections aimed at providing baseline
data, context or to stimulate research questions.

3.1 Environmental Sample Collection

Baseline environmental sampling is required to optimize GAMUT station output. While this may be
considered “monitoring,” there are important scientific outcomes possible as described in section 3.2.
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Furthermore the effort will provide important data to be incorporated into planned RFA1 graduate
student and postdoctoral research projects.
1. Measuring stream flow
2. Water quality sampling
3. Sampling particulate constituents

3.2 Developing water quality proxies to sensor outputs

Analyzing and interpreting real-time sensor data is a research challenge in hydrologic science. iUTAH has
the opportunity to lead this challenge in analyzing data from the GAMUT network. We envision several
research outcomes from this effort including improvements to establishing surrogates of water quality
from sensor output (Spackman Jones et al. 2011), assessing nutrient constraints on ecosystem
metabolism (Cohen et al. 2013), and developing loading estimates and yields that are important in
guiding water quality policy (note that Utah has insufficient data to contribute to national estimates of
nutrient loads and yields in surface water http://www2.epa.gov/nutrient-policy-data/estimated-total-
nitrogen-and-total-phosphorus-loads-and-yields-generated-within).

3.3 Special Studies

Synoptic Sampling
As the GAMUT was being designed and installed, the iUTAH leadership made the decision to allocate
GAMUT sampling and analysis resources to synoptic sample collection at ~ 20 locations in each of the 3
study watersheds. Sampling began in June 2013 and will continue every 4-6 weeks for 1 year.
Parameters to be measured include isotopic composition of water, E. coli, and total N and P
concentrations. UU will lead isotope sampling and provided resources for analysis of samples. BYU will
lead E. coli sampling and provided resources for analysis of samples. USU will lead TN/TP sampling and
provided resources for analysis of samples. Initially, personnel from across the 3 watersheds
collaborated to sample at all locations. Over time as part of this plan, resident personnel will conduct
these sampling efforts in order to more efficiently use travel resources. Responsible persons for
interfacing with the Data and Modeling Federation are as follows: Jim Ehleringer (isotopes); Erin Jones (E.
coli); Julie Kelso (TN/TP).
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Baseline Sampling Additions
iUTAH recruited postdoctoral researcher Steven Hall (UU) to conduct biogeochemical studies and analysis
of nitrate sources to the GAMUT watersheds. In that vein additional filtered samples will be collected as
part of routine baseline samples for analysis of isotopic composition of NO3 at the UU SIRFER lab
(resources for analysis of these samples will be provided by UU).

In fall 2013 the USU-ABL lab installed a new instrument to measure fluorometeric properties of DOM in
stream water. These measurements allow one to characterize the DOM as being of microbial or
terrestrial origin. iUTAH graduate student Julie Kelso’s (USU) dissertation focuses on dynamics of
anthropogenic organic matter in streams, and additional filtered and acidified samples will be collected as
part of routine baseline samples for this purpose (resources for analysis of these samples will be provided
by USU).

Given that both Hall and Kelso are actively engaged in field sampling associated with the GAMUT, these
additional sample collections will not be overly burdensome additions to routine sampling over the next
1-2 years.

Sample Archiving
It is not possible to foresee all of the interesting potential uses for water samples after the major analyses
have been completed. A decision was made by the iUTAH leadership to archive synoptic and baseline
water samples whenever possible. USU purchased two upright freezers for that purpose. These are
located in the College of Agriculture and Applied Science’s Biotechnology Center where iUTAH has lab
space. The iUTAH Water Quality team recommends that iUTAH Leadership Team develop a set of
decision rules describing the number and types of samples that can be archived, who decides when a
sample can be removed from archive, and who decides when a sample can be used or consumed.

XIX. 4. Laboratory Analyses

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Detailed SOPs and QA/QC information will be available by contacting the laboratory responsible for
conducting the specific analysis. Where analysis of the same constituent is to be conducted by more than
one lab, a round-robin analysis of certified reference material and sample splits will be conducted to
ensure inter-lab comparability. The USGS conducts a semi-annual interlab comparison study using a
standard reference material that could be useful in this regard.

Filtered samples will be analyzed for the following constituents:
1. Major anions and cations (ion chromatography)
2. NO3-N + NO2-N (colorimetric)
3. NH4-N (colorimetric)
4. Soluble Reactive Phosphorus (SRP, colorimetric)
5. NP-DOC (catalytic oxidation) and total dissolved N
6. Absorbance at 254nm

Solids collected on filters should be analyzed for the following constituents:
1. Total Suspended Solids (TSS)
2. Volatile Suspended Solids (VSS)
3. Chlorophyll and phycocyanin
4. Total N (TN) and total P (TP) via persulfate oxidation followed by colorimetry
5. Total Coliforms
XX. 5. Data Management

All of the data resulting from sampling under this plan will be published via the iUTAH Modeling & Data
Federation (MDF). Once entered into the MDF, the data will be freely available to all iUTAH researchers
and partners. The time between sample collection and the availability of analytical results online will
depend on the workload of each analytical laboratory; however, all results will be posted within timelines
specified within the iUTAH Data Management Policy.

5.1 Sample Identifiers
Each physical sample (bottle or container) to be collected will be assigned a globally unique identifier
before the sample is collected in the field. These identifiers will be affixed to the sampling containers and
will accompany the samples in the field, in the laboratory, and eventually in the physical sample archive.

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5.2 Sample Data Workflow

The following figure shows the sample data workflow for the samples collected as part of this effort. As
described above, unique identifiers will be assigned by the technicians or the laboratories before the
samples are collected in the field. The technicians then collect samples in the field and submit them to
the respective laboratories for analysis. Metadata describing each of the samples will be entered by the
technicians using online forms provided by the iUTAH MDF. Each laboratory has a single point of contact
that will enter the data corresponding with each sample analysis into online forms provided by the iUTAH
MDF. The data will then be reviewed by the iUTAH MDF data manager and entered into the operational
databases for each of the GAMUT watersheds for publication online.

Figure 1. iUTAH water quality sample data workflow.

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XXI. 5. Literature Cited

Cohen, M.J., M. J. Kurz, J. B. Heffernan, J.B. Martin, R. L. Douglass, C. R. Foster, and R. G. Thomas. 2013.
Diel phosphorus variation and the stoichiometry of ecosystem metabolism in a large spring-fed river.
Ecological Monographs 82:155-176.

Crowl, T. A., J. Ehleringer, D. Pataki, M. Baker and D. Jackson-Smith. 2013. Utah EPSCoR NSF EPSCoR RII
Track 1 innovative Urban Transitions and Arid-region Hydro-sustainability (iUTAH). Strategic Plan for
2012-2017.

Downing, B.D., B. A. Pellerin, B.A. Bergamaschi, J.F., Saraceno, and T.E.C. Kraus. 2012. Seeing the light: the
effects of particles, dissolved materials and temperature on in site measurements of DOM fluorescence in
rivers and streams. Limnology and Oceanography Methods 10: 767-775.

Spackman Jones, A., D.K. Stevens, J. S. Horsburgh, and N. O. Mesner. 2011. Surrogate measures for
providing high frequency estimates of total suspended solids and total phosphorus concentrations.
Journal of the American Water Resources Association 47: 239-253.

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12.2. Aquatic Biogeochemistry Laboratory (ABL) analytical lab manual (see attached PDF)
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12.3. SOP: Total Suspended Solids, Volatile Suspended Solids Prepared By: Zach Aanderud Date Prepared: 2/25/2019 Modified By: Date Last Modified: Contents
• Overview
• Definitions
• Precautions
• Sampling
• Sample Preservation
• Materials Needed
• Procedure
• Calculations
• Disposal
• References Overview The method determines non-filterable residue in drinking, surface, and saline waters; domestic and industrial wastes. The sample matrix is aqueous and applicable to samples with greater than 4 mg/L total suspended solids. The sample size should be limited to a volume that yields no more than 200 mg residue. Definitions TSS=residue, non-filterable, defined as solids retained by a glass fiber filter and dried to a constant weight at 105°C for 24 hours. VSS=residue, non-filterable, defined as loss of solids retained by a glass fiber filter after combustion at 550°C for 4 hours. TFS=residue, non-filterable, weight of the ash content of the matter (mass of the combusted filter minus the initial weight of the filter.
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Method Blank: aliquot of reagent water treated exactly as a sample including exposure to all glassware, equipment, solvents, reagents, internal standards, and surrogates used. Used to determine if interferences or analytes are present in the lab environment. Duplicate Sample: sample analyzed second time exactly the same way as the first analysis. Used to calculate precision expressed as Relative Percent Difference (RPD). Precautions Standard laboratory practices must be followed (see procedure 0010) Sampling
• Samples may be collected in either glass or plastic containers.
• Samples to be analyzed for TSS, VSS, and TFS are unpreserved and cooled to 4°C.
• Samples must be analyzed within seven (7) days of sampling. Sample Preservation Preservation of this sample is not practical; analysis should begin as soon as possible. Refrigeration or icing to 4°C, to minimize microbiological decomposition of solid, is recommended. Supplies
• Glass fiber filters (pre-weighed) 1.5 microns VWR: serial number
• Tin weigh boats
• Desiccator with recharged dessicant
• Nalgene vacuum filtration apparatus
• Vacuum pump
• Drying oven, set at 105°C
• Muffle furnace, set at 550°C
• Desiccator
• Analytical balance
• 500 mL Graduated cylinder
• 100 mL Graduated cylinder Procedure
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TSS Procedure 1. Record initial weight and filter paper 2. Label filter tray or tin weigh boat with laboratory number of the sample to be filtered. 3. Assemble filtration apparatus. Note: The filter apparatus only needs to be rinsed not autoclaved between samples. Make sure to have all gaskets in place on the apparatus following autoclaving or re-assembly to ensure the best seal. Multiple samples may be run in tandem by linking multiple apparatuses. 4. Transfer sample, after mixing the sample for approximately 15 seconds by vigorously shaking sample , to apparatus 5. Turn on vacuum pump and wet filter with approximately 10mL ultra pure water. 6. Filter all media or approximately 500 mL of sample Note: Determine an appropriate sample volume to be filtered, usually 500 ml. For samples with a high concentration of suspended material, reduce the sample size so that the residue mass no more than 200 mg. 7. Rinse filter with solids with approximately 10 mL ultra pure water. Allow filter vacuum to dry disk for 1-2 minutes under vacuum. Note: Excessive residue on the filter may form a water entrapping crust. For samples high in dissolved solids, rinse the filter thoroughly to ensure removal of the dissolved material prolonged filtration times resulting from clogging of the filter may produce results that are high, because increased colloidal materials are captured on the clogged filter. 8. Remove filter from support, place in labeled tin. Place tin and filter in drying oven for at least 24 hours at 105°C.
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9. Remove to desiccator to cool with recharged desiccant 10. After cooling for 10 minutes, weigh the filter and solids and record weight. Return filter to desiccator. 11. After 10 more minutes, reweigh filter and record weight in notebook, continue reweighing until a stable weight is shown. ( ± 4% or ± 1.0mg ) TVS Procedure 1. Place tin and filter in muffle furnace in the Environmental Analytical Lab for at least 4 hours at 550°C. 2. Remove to desiccator to cool with recharged desiccant 3. Remove to desiccator to cool with recharged desiccant 4. After cooling for 10 minutes, weigh the filter and solids and record weight. Return filter to desiccator. 5. After 10 more minutes, reweigh filter and record weight in notebook, continue reweighing until a stable weight is shown. ( ± 4% or ± 1.0mg ) VSS Procedure Calculations mg/L Total Suspended Solids (TSS) = (A-B) X 1000
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C Where: A = Final weight of filter in grams B = Tare weight of filter in grams C = Volume of sample in liters Report results <100 mg/L to 2 significant figures. Example; 56.7 mg/L would be reported as 57 mg/L. Report results >100 mg/L to 3 significant figures. Example; 123.4 mg/L sould be 123 mg/L. Disposal Dispose of used filters in solid waste drum. The filtrate should be disposed of down the drain with running water. Quality Control
• Each analytical batch analyzed for TSS must be accompanied by a method blank consisting of 1000ml ultra pure water and a standard of 50mg Kaolin.
• All weights, volumes, comments etc. for each batch must be entered in notebook form for data review.
• Every tenth sample shall be run in duplicate References Methods for Chemical Analysis of Water and Wastes EPA 600/4-79-020 March 1983 Method 160.2 pp. 160.2 - 1 through 160.2 - 2
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Standard Methods for The Examination of Water and Waste Water 18th Edition 1992 Method 2540D pp. 2-56 National Environmental Methods Index. April 1994. Method 160.2. 5 April 2004. <http://www.nemi.gov>