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Home My WebLink AboutDWQ-2025-000397Process Commitments •Seek to learn and understand each other’s perspective •Encourage respectful, candid, and constructive discussions •Seek to resolve differences and reach consensus •As appropriate, discuss topics together rather than in isolation •Make every effort to avoid surprises Steering Committee Operating Principles Ground Rules •Focus on the task at hand •Have one person speaking at a time •Allow for a balance of speaking time by providing succinct statements and questions •Listen with respect •Be civil •Keep side conversations to a minimum •Turn off cell phones or put them in the non-ring mode during formal meeting sessions Steering Committee Operating Principles UTAH LAKE WATER QUALITY STUDY Steering Committee Meeting January 9, 2025 3 ULWQS Phases Phase 1: Data Gathering and Characterization Phase 2: Development of Numeric Nutrient Criteria Phase 3: Implementation Planning ULWQS Phase 2 Overview Numeric Nutrient Criteria (NNC) DevelopmentPhase 2 •Steering Committee charged the Science Panel with two main responsibilities in Phase 2: •Respond to charge questions posed by the Steering Committee •Develop and implement a framework to recommend NNC for Utah Lake that will protect its designated uses CHARGE QUESTION UPDATE AND TIMELINE 6 ULWQS Phase 2 Charge Question Status Update Jun 2019 Steering Committee develops charge questions and tasks the Science Panel with developing responses. Aug 2020 Science Panel develops the Strategic Research Plan (SRP) to fill data gaps and address uncertainty in responding to the charge questions and developing NNC recommendations. 2020-2024 Science Panel oversees the development of SRP studies. Aug-Oct 2021 Science Panel develops interim charge question reports. Jan 2022 Steering Committee and Science Panel jointly meet to review and discuss the interim charge question responses. Studies Conducted from 2020-2024 •Sediment Nutrient Interactions Study •Carbon, Nitrogen, and Phosphorus Budget Study •Bioassay Study •Littoral Sediment Study •Phosphorus-Binding Study •Paleolimnology Study •TSSD Limnocorral Study* •Atmospheric Deposition Study* ULWQS Phase 2 Charge Question Status Update Evaluate Existing Information •Empirical analysis •Utah Lake literature and technical reports •Literature from similar lakes ULWQS Phase 2 Charge Question Status Update January 2025 Steering Committee and Science Panel jointly meet to review and discuss the charge question responses. Apr-May 2024 Science Panel members meet in subgroups to update the charge question responses using new available data from completed studies. NNC RECOMMENDATION UPDATE 10 Technical Support Document Purpose of the Technical Support Document •Provide the technical basis for the development of numeric nutrient criteria (NNC) to protect designated uses •Recreation •Aquatic Life •Others (Agriculture, Downstream) •Conduct analyses to support multiple lines of evidence in the NNC framework Technical Support Document Lines of Evidence 12 •Reference-based •Results from paleolimnological studies •Utah Lake Nutrient Model prediction/extrapolation of reference conditions •Stressor-response analysis •Utah Lake Nutrient Model output •Statistical models •Scientific literature •Scientific studies of comparable/related lake ecosystems •Support/supplement other lines of evidence Current focus of the Science Panel •Science Panel –Phase 2: Finalize charge question responses –Phase 2: Continue work to implement the NNC framework to generate NNC recommendations •Steering Committee –Phase 3: Begin implementation planning ULWQS Next Steps Meeting Objectives •Review the Science Panel updated charge question responses •Better understand Science Panel discussion on atmospheric deposition research and estimates and hear from differing scientific perspectives on the topic •Lay the scientific groundwork to help inform future Steering Committee discussions on implementation planning ULWQS Today’s Meeting QUESTIONS? 15 UTAH LAKE WATER QUALITY STUDY Steering Committee Meeting January 9, 2025 1 ULWQS Phases Phase 1: Data Gathering and Characterization Phase 2: Development of Numeric Nutrient Criteria Phase 3: Implementation Planning ULWQS Phase 2 Overview Numeric Nutrient Criteria (NNC) DevelopmentPhase 2 •Steering Committee charged the Science Panel with two main responsibilities in Phase 2: •Respond to charge questions posed by the Steering Committee •Develop and implement a framework to recommend NNC for Utah Lake that will protect its designated uses CHARGE QUESTION UPDATE AND TIMELINE 4 ULWQS Phase 2 Charge Question Status Update Jun 2019 Steering Committee develops charge questions and tasks the Science Panel with developing responses. Aug 2020 Science Panel develops the Strategic Research Plan (SRP)to fill data gaps and address uncertainty in responding to the charge questions and developing NNC recommendations. 2020-2024 Science Panel oversees the development of SRP studies. Aug-Oct 2021 Science Panel develops interim charge question reports. Jan 2022 Steering Committee and Science Panel jointly meet to review and discuss the interim charge question responses. Studies Conducted from 2020-2024 •Sediment Nutrient Interactions Study •Carbon, Nitrogen, and Phosphorus Budget Study •Bioassay Study •Littoral Sediment Study •Phosphorus-Binding Study •Paleolimnology Study •TSSD Limnocorral Study* •Atmospheric Deposition Study* ULWQS Phase 2 Charge Question Status Update Evaluate Existing Information •Empirical analysis •Utah Lake literature and technical reports •Literature from similar lakes ULWQS Phase 2 Charge Question Status Update January 2025 Steering Committee and Science Panel jointly meet to review and discuss the charge question responses. Apr-May 2024 Science Panel members meet in subgroups to update the charge question responses using new available data from completed studies. NNC RECOMMENDATION UPDATE 8 Technical Support Document Purpose of the Technical Support Document •Provide the technical basis for the development of numeric nutrient criteria (NNC) to protect designated uses •Recreation •Aquatic Life •Others (Agriculture, Downstream) •Conduct analyses to support multiple lines of evidence in the NNC framework Technical Support Document Lines of Evidence 10 •Reference-based •Results from paleolimnological studies •Utah Lake Nutrient Model prediction/extrapolation of reference conditions •Stressor-response analysis •Utah Lake Nutrient Model output •Statistical models •Scientific literature •Scientific studies of comparable/related lake ecosystems •Support/supplement other lines of evidence Current focus of the Science Panel •Science Panel –Phase 2: Finalize charge question responses –Phase 2: Continue work to implement the NNC framework to generate NNC recommendations •Steering Committee –Phase 3: Begin implementation planning ULWQS Next Steps Meeting Objectives •Review the Science Panel updated charge question responses •Better understand Science Panel discussion on atmospheric deposition research and estimates and hear from differing scientific perspectives on the topic •Lay the scientific groundwork to help inform future Steering Committee discussions on implementation planning ULWQS Today’s Meeting Process Commitments •Seek to learn and understand each other’s perspective •Encourage respectful, candid, and constructive discussions •Seek to resolve differences and reach consensus •As appropriate, discuss topics together rather than in isolation •Make every effort to avoid surprises Steering Committee Operating Principles Ground Rules •Focus on the task at hand •Have one person speaking at a time •Allow for a balance of speaking time by providing succinct statements and questions •Listen with respect •Be civil •Keep side conversations to a minimum •Turn off cell phones or put them in the non-ring mode during formal meeting sessions Steering Committee Operating Principles QUESTIONS? 15 Charge Questions Update Steering Committee & Science Panel Meeting | January 9, 2025 Overarching Charge Questions 1.What was the historical condition of Utah Lake with respect to nutrients and ecology pre-settlement and along the historical timeline with consideration of trophic state shifts and significant transitions since settlement? 2.What is the current state of the lake with respect to nutrients and ecology? 3.What additional information is needed to define nutrient criteria that support existing beneficial uses? (addressed as part of strategic research plan) 4.Can the lake be improved given current management constraints? Charge Question Responses 1.Evidence Evaluation Focus: technical Detailed analysis of studies that inform the question (Utah Lake and related) Figures from cited studies 2.Synthesis Focus: plain-language, non-technical Overall response to the question Includes assessment of SP confidence in the response Confidence Evaluation Confidence Evaluation •High confidence Direct evidence in Utah Lake Well-established methods Consistent behavior of Utah Lake compared to lakes in the literature If multiple studies/lines of evidence, findings were consistent •“Big picture” conclusions more often resulted in high confidence •Sometimes very specific items or interacting drivers resulted in lower confidence •Questions needing analysis from mass balance and/or mechanistic models were not assessed for confidence yet Question 1: Historical Condition •1.1. What does the diatom community and macrophyte community in the paleo record tell us about the historical trophic state and nutrient regime of the lake? i. Can diatom (benthic and planktonic) and/or macrophyte extent or presence be detected in sediment cores? And if so, what are they? ii. What were the environmental requirements for diatoms and extant and locally extirpated macrophyte species? iii. How have environmental conditions changed over time? •1.2. What were the historic phosphorus, nitrogen, and silicon concentrations as depicted by sediment cores? (add calcium, iron, and potentially N and P isotopes) •1.3. What information do paleo records (eDNA/scales) provide on the population trajectory/growth of carp over time? What information do the paleo records provide on the historical relationship between carp and the trophic state and nutrient regime of the lake? •1.4. What do photopigments and DNA in the paleo record tell us about the historical water quality, trophic state, and nutrient regime of the lake? Summary of Chronology 1.Utah Lake has experienced major phase shifts, including European settlement & carp introduction: late 1800s/early 1900s shift to cyanobacteria dominance:1950s increases in population changes in wastewater loads associated with population growth & treatment technologies 2.Trophic state has increased to more eutrophic conditions from pre-European settlement to present day, as supported by multiple indicators. 3.Climate change has impacted temperature and precipitation in the basin, resulting in changes in the hydrologic and thermal regime of Utah Lake. Brahney et al. 2024 Diatoms & Macrophytes •3 independent diatom sediment core studies showed (high confidence): Historical dominance of benthic & epiphytic species Shift to dominance of planktonic species Change from more sensitive taxa to more nutrient-tolerant taxa over time •Body of evidence supporting historical presence of submerged & emergent macrophytes (high confidence generally, medium in geographic specifics) Brahney et al. 2024 Williams et al. 2023 Elemental Concentrations & Isotopes •2 major phase transitions in lake biogeochemistry: European settlement & carp introduction (late 1800s) Timing of eutrophic conditions & cyanobacteria dominance (1950s) •↑ P, N, and C over time in Utah Lake sediments •C and N isotopes: Shift from macrophyte-dominated to phytoplankton-dominated state Increasing prevalence of wastewater in lake N supply. •Metals: changes in redox, variable by location •High confidence (multiple lines of evidence) deepwater Goshen Bay Provo Bay Brahney et al. 2024 Williams et al. 2023 Paleo record for carp •Carp introduced ~1881 aligns with lake phase transition •Elemental concentrations suggest sediments were more stable and capable of Fe & Mn reduction pre-carp •eDNA attempted, was inconclusive •Medium confidence: difficult to parse specific magnitude & mechanisms due to carp vs. other human drivers Brahney et al. 2024 Historical water quality, trophic state •Photosynthetic pigments have increased from baseline to present •Phase transitions: late 1800s/early 1900s & mid-1900s (see below) •Degradation has increased due to sediment destabilization •High confidence King et al. 2024 Questions about Historical Condition? Question 2: Current Condition •2.1. What are the impacts of carp on the biology/ecology and nutrient cycling of the lake and how are those impacts changing with ongoing carp removal efforts? i. What contribution do carp make to the total nutrient budget of the lake via excretion rates and bioturbation? How much nutrient cycling can be attributed to carp? ii. What is the effect of carp removal efforts on macrophytes, nutrients, secchi depth, turbidity, and primary productivity? iii. How much non-algal turbidity and nutrient cycling is due to wind action versus carp foraging? How much does sediment resuspension contribute to light limitation, and does wind resuspension contribute substantially in the absence of carp? Contributions of carp to nutrient cycling •Carp decreases from ~50 million kg to < 20 million kg (2018-2019), with compensatory responses to lake level increases to ~35 million (2021) •Lakewide estimates for carp excretion: TP: 51,100 -117,000 kg/y (mean: 71,500) TN: 496,000-1,140,000 kg/y (mean: 694,000) Note: represents recycling Medium confidence Landom et al. 2022 Impact of carp removal •Carp have a negative impact on macrophytes Bioturbation & herbivory Removal relief of negative pressure •Removal efforts have a positive impact on: Macroinvertebrate abundance & diversity Water clarity Green algae abundance (no impact on HABs) Large-bodied zooplankton abundance •High confidence Landom et al. 2022 Question 2: Current Condition •2.2 What are the environmental requirements for submerged macrophytes currently present at Utah Lake? i. What is the role of lake elevation and drawdown in macrophyte recovery? Are certain species more resilient to drawdowns and nutrient related impacts? Can some species establish/adapt more quickly? ii. What is the relationship between carp, wind, and macrophytes on non-algal turbidity and nutrient cycling in the lake? What impact could macrophyte reestablishment have? Environmental requirements for submerged macrophytes •Current absence of macrophytes attributed to: Phytoplankton primary production Carp bioturbation Sediment resuspension •Increased water clarity is needed to re-establish macrophytes Literature review Monitoring data Scenario modeling •High confidence King et al. 2023 Role of lake elevation on macrophyte recovery •Fluctuating lake levels are a barrier to macrophyte recovery •Some emergent species are more resilient than submerged species •Lower lake elevations also associated with larger HABs •High confidence utahlake.gov/water-levels/Landom and Walsworth 2024 Interactions between carp, wind, macrophytes •Non-algal turbidity makes up majority of turbidity •Wind & carp have negative impact on macrophytes Wind is primary hypothesized driver Carp exclosures also impacted wind & waves •Macrophytes could have a positive feedback on sediment stabilization and water clarity •Medium-High confidence Tetra Tech 2021 Questions about Carp and Macrophytes? Question 2: Current Condition •2.3. What are the linkages between changes in nutrient regime and Harmful Algal Blooms (HABs)? i. Where do HABs most frequently start/occur? Are there hotspots and do they tend to occur near major nutrient sources? ii. Which nutrients are controlling primary production and HABs and when? iii. If there are linkages between changes in nutrient regime and HABs, what role if any does lake elevation changes play? iv. How do other factors affect HAB formation in Utah Lake (e.g., climate change; temperature; lake stratification; changes in zooplankton and benthic grazers and transparency) v. What is the role of calcite “scavenging” in the phosphorus cycle? vi. What is the relationship between light extinction and other factors (e.g., algae, TSS, turbidity)? Where do HABs occur? •Most common in: Provo Bay Goshen Bay Eastern portion of main basin •Direct monitoring & remote sensing •HABs generally associated with areas near watershed loading with consistent nutrient enrichment •High confidence Tetra Tech 2021 Cardall et al. 2021 Which nutrients control HABs, and when? •Utah Lake displays typical pattern of phytoplankton seasonal succession •Phytoplankton growth limited by N and P; variable over time & space •High confidence Experimental bioassays Mechanistic & statistical modeling Literature Utah Lake Data Explorer Aanderuud et al. 2021 Other factors controlling HABs •Climate change expected to worsen HABs (high confidence) Higher temperatures More variable precipitation •Grazing pressure from zooplankton (medium confidence) Question 2: Current Condition •2.4. How do sediments affect nutrient cycling in Utah Lake? i. What are current sediment equilibrium P concentrations (EPC) throughout the lake? What effect will reducing inputs have on water column concentrations? If so, what is the expected lag time for lake recovery after nutrient inputs have been reduced? ii. What is the sediment oxygen demand of, and nutrient releases from, sediments in Utah Lake under current conditions? iii. Does lake stratification [weather patterns] play a result in anoxia and phosphorus release into the water column? Can this be tied to HAB formation? Role of calcite scavenging in the P cycle •Process by which P binds to calcite minerals •2/3 of sediment P is in calcite mineral Non-bioavailable One-way process at observed pH Subject to burial •Sediments are capable of retaining more P than they currently hold •Spatially variable: Provo Bay has higher P concentrations and storage capacity •High confidence 0 10 20 30 40 50 60 70 80 90 100 Total P-fraction (%) BI GB PB PP PV SS VY NH 4Cl BD NaOH HCl Residual Sequential P-Fraction A) LeMonte et al. 2023 Randall et al. 2019 Equilibrium P concentrations (EPC) •Definition: P conc. at which sediments switch between uptake and release Batch sorption experiments: 0.30-1.07 mg/L P EPC is lower than influent P concentrations EPC is higher than water column P sediments are an overall sink for P but display dissolved P release LeMonte et al. 2023 Effect and lag time of reducing P concentrations •Reducing influent P concentrations below EPC sediment P release until a new equilibrium is reached •Predicted impact: water column P ↓, w/lag time to reach new equilibrium Shorter lag time if internal P release and hydraulic flushing rates are maintained Longer lag time if P retention decreases with decreasing external load Sediment oxygen and nutrient fluxes •Sediments consume oxygen (positive SOD) Positively correlated with sediment organic matter Highest in Provo Bay •Sediments are overall a sink for nutrients (net flux) •Sediments release dissolved nutrients (gross flux) Soluble reactive P, ammonia, nitrate Dependent on temperature, supply of organic matter, drying/rewetting, sediment resuspension, carp, algae Seasonal release of nitrate indicates surface of sediments is oxic •Med-high to high confidence Hogsett et al. 2019 Rivers et al. 2022 Impact of lake stratification on nutrient fluxes •Thermal stratification doesn’t occur on a widespread, seasonal scale •Transient stratification has the capacity to alter redox gradients •Release of nutrients via redox-driven pathways (e.g., iron reduction) is not likely a major pathway •High confidence “Classic” dimictic lake (Wetzel 2001) Question 2: Current Condition •2.5. For warm water aquatic life, waterfowl, shorebirds, and water- oriented wildlife: i. Where and when in Utah Lake are early life stages of fish present? ii. Which species are most sensitive and need protection from nutrient-related impacts? Nutrient-related impacts on aquatic life •TSD is evaluating exceedances of DO and pH relevant to early (and other) life stages •PSOMAS and SWCA (2007) evaluated: Spawning & rearing requirements for fish species % of time spawning conditions are met •Medium confidence Draft TSD PSOMAS and SWCA (2007) Questions about HABs, Sediment, Aquatic Life? Question 4: Future Conditions •4.1. What would be the current nutrient regime of Utah Lake assuming no nutrient inputs from human sources? This question may require the identification of primary sources of nutrients. •4.2. Assuming continued carp removal and current water management, would nutrient reductions support a shift to a macrophyte-dominated state within reasonable planning horizons (i.e., 30-50 years)? •4.3. If the lake stays in a phytoplankton-dominated state, to what extent can the magnitude, frequency, and extent of harmful and nuisance algal blooms be reduced through nutrient reductions? Note: confidence statements not generated due to ongoing/upcoming work Nutrient regime under no human sources •Utah Lake expected to be less eutrophic under minimal nutrient loading •Paleo studies showed: Historical N and P concentrations were lower than present day Algal communities were historically dominated by less nutrient-tolerant species, and cyanobacteria were not abundant Historical water clarity was higher and macrophyte cover was greater •Mass balance modeling and mechanistic modeling will be used to infer reference conditions for Utah Lake Would nutrient reductions support macrophyte-dominated state? •Nutrient reduction alone is not sufficient to restore macrophytes •Restoration efforts take a combination of active management: Nutrient and algal reductions Habitat modification Active planting Carp reduction Sediment stabilization Invasive phragmites reduction •Lake level fluctuations are a significant barrier Landom and Walsworth 2024 To what extent can HABs be reduced through nutrient reductions? •Linkages between nutrients and HABs: Nutrient bioassay experiments Stressor-response analysis Paleolimnological analysis Literature •Ongoing work: Mass balance analysis Technical Support Document Implementation planning Draft TSD Questions about Future Conditions? Overarching Discussion & Zoom Poll for Follow-up Information •Each presenter will have 20 minutes of uninterrupted speaking time. •A 20-minute timer will be set, and speakers are expected to stop presenting at the end of their 20 minutes. •We will go through all presentations before taking questions and discussion. Presentation Format •Overview of the Atmospheric Deposition Subgroup process •Presentation from majority and minority perspectives on key points of divergence: –Whether to consider insects as contamination –The attenuation rate of atmospheric deposition over Utah Lake/Bird Island data Presentation Outline UTAH LAKE WATER QUALITY STUDY Science Panel Atmospheric Deposition Update January 9, 2024 3 UTAH LAKE ATMOSPHERIC DEPOSITION BACKGROUND 4 •October 2019: Dr. Janice Brahney writes the white paper Estimating Total and Bioavailable Nutrient Loading to Utah Lake from the Atmosphere •December 2019: Science Panel approves temporary atmospheric deposition loading rate recommendation while additional research is conducted. •April 2019: Wasatch Front Water Quality Council submits proposal to measure atmospheric deposition in Utah Lake with input from the National Atmospheric Deposition Program. Science Panel provides recommendations to the sampling plan. Atmospheric Deposition Timeline •May 2020: Science Panel provides recommendations for the Wasatch Front Water Quality Council Atmospheric Deposition program and an update to the Steering Committee on engaging with all potential sources of information. •April-August 2022: Science Panel members discuss atmospheric deposition rates. The Science Panel decides to form a subgroup to discuss assumptions, aggregate available data, and discuss atmospheric deposition results. Atmospheric Deposition Timeline Reason for Forming: The in-lake model requires a numeric input for nitrogen and phosphorus atmospheric deposition rates. •Members •Dr. Mike Brett, University of Washington •Dr. Mitch Hogsett, Unaffiliated •Dr. Theron Miller, Wasatch Front Water Quality Council •Dr. Hans Paerl, University of North Carolina •Timeline: Met 20 times from August 18, 2022, to February 23, 2023 Atmospheric Deposition Subgroup Overview Subgroup Objectives •Analyze available information and data to improve understanding of atmospheric deposition to Utah Lake •Work collaboratively toward a recommendation for atmospheric loading, ideally achieved through consensus •Document the SP’s decision-making process for analyzing and evaluating evidence and working toward an atmospheric deposition recommendation Atmospheric Deposition Subgroup Scope of Work Subgroup Tasks •Develop and agree to the analysis plan •Review and summarize raw data from G. Williams (Olsen 2018, Reidhead 2019, and Barrus 2021) and W. Miller datasets •Evaluate “outlier” samples for potential explanations •Evaluate spatial interpolation among sites and attenuation of fluxes •Evaluate speciation •Compare direct estimates of atmospheric deposition to other constraining analyses •Determine loading for inclusion in the Utah Lake Nutrient Model Atmospheric Deposition Subgroup Scope of Work DATA SUMMARY AND STANDARDIZATION 10 Data Summary and Review Study Year(s)Number of stations Constituents Sample type Metadata availability Williams 2017 5 TP, DIN, nitrate, ammonium, SRP Bulk Yes Williams 2018 5 TP, DIN, nitrate, ammonium, SRP Bulk No Williams 2019 5 TP, DIN, nitrate, ammonium, SRP Bulk No Williams 2020 5 TP, DIN, nitrate, ammonium, SRP Bulk Yes W. Miller 2017-2020 9 TP, TN, orthophosphate Bulk No •Decision Point: Assigning non-detect values as 0 mg/m2 –No method to convert non-detect concentrations to area- based fluxes –Very few values listed as 0 mg/m2 •Decision Point: Converting W. Miller volume-based fluxes (mg/L) to area-based fluxes (mg/m2 ) –Area-based fluxes based on W. Miller dataset was estimated using precipitation values from a single precipitation gauge –All subgroup members agreed to calculate area-based fluxes from W. Miller dataset using data from the nearest precipitation sampler Data Summary and Review Processed and visualized TP (and SRP) time series Data Summary and Review Williams W. Miller Processed and visualized DIN (nitrate + ammonium) time series Data Summary and Review Williams W. Miller WILLIAMS DATASET OUTLIER ANALYSIS AND RECOMMENDATIONS 15 •Outliers identified as 75th percentile + 1.5*IQR –Exploratory approach –No low outliers found (25th – 1.5*IQR) –Simply identified as “high deposition events,” not removed •Potential explanations for high outliers: –Weather event –Local deposition source –Contamination •Decision Point: Identifying outliers –All subgroup members agreed to use the IQR approach to identify outliers due to the distribution of the dataset Evaluating Outlier Samples for Potential Explanations Decision Point: Should insects be considered AD or contamination? •Three subgroup members agreed that insects in sampling buckets should be considered contamination. •One subgroup member did not support this decision. Perspectives from majority and minority to come later in this presentation. Evaluating Outlier Samples for Potential Explanations Decision Point: How to handle data without metadata •Insofar as insects are considered contamination, subgroup members supported including: –Data collected from screened samplers –Data where metadata indicated the samples did not contain insects •Data from unscreened samplers without metadata or where metadata indicated the presence of insects were not used •Insect metadata available for 2017 and 2020 data Evaluating Outlier Samples for Potential Explanations Study Year(s)Number of stations Constituents Sample type Metadata availability Williams 2017 5 TP, DIN, nitrate, ammonium, SRP Bulk Yes Williams 2018 5 TP, DIN, nitrate, ammonium, SRP Bulk No Williams 2019 5 TP, DIN, nitrate, ammonium, SRP Bulk No Williams 2020 5 TP, DIN, nitrate, ammonium, SRP Bulk Yes W. Miller 2017-2020 9 TP, TN, orthophosphate Bulk No •To compute cumulative annual load, need to fill in gaps in sampling dates •Options –Impute via linear interpolation –Impute via relationships with weather •Decision Point: Imputing fluxes for missing samples due to contamination –All subgroup members agreed to use statistical relationships with weather –Linear interpolation assumes a predictable and consistent pattern, but AD in the basin is episodic Imputing Data MILLER DATASET ANALYSIS AND RECOMMENDATIONS 20 •TP and DIN fluxes were significantly lower in W. Miller dataset than Williams dataset •Comparison included several stations that were consistent between studies Evaluating Outlier Samples for Potential Explanations Decision Point: Interpreting W. Miller dataset •All subgroup members agreed to use the Williams data as the primary line of evidence for calculating loading to Utah Lake •Several caveats with the W. Miller dataset that impact confidence: –Evaporation from sampling tube between sampling events -> fluxes were concentration-based, so evaporation would lead to overestimate in flux –Overflow from funnel-shaped collector -> precipitation event of >0.5 in would exceed sampler volume –Loss of dry deposition from dust blowing off shallow pan collector –Sampler cleaning between samples only conducted “now and then” by weather service •Several analyses conducted to evaluate impact of precipitation and evaporation, but no conclusive evidence for degree of impact •Miller used modified precipitation samplers, not atmospheric deposition-specific samplers Evaluating Outlier Samples for Potential Explanations SHORELINE FLUXES AND ATTENUATION RATES 23 •Previous studies assumed some flux decreased moving away from shore •Sampler installed on Bird Island to quantify potential attenuation •Hypotheses: 1.Attenuation occurs moving away from shore  Bird Island fluxes lower than shoreline fluxes 2.Attenuation does not occur  Bird Island fluxes equivalent to shoreline fluxes Bird Island Data •Alternative hypotheses: –Higher land-based flux not captured by current sampling array -> Bird Island fluxes higher than shoreline fluxes –Lake-based source of deposition to Bird Island sampler (e.g., bird droppings, aerosolized materials, lake spray) -> Bird island fluxes higher than shoreline fluxes •Decision Point: Bird Island data –Three subgroup members supported not using Bird Island from load calculations –One subgroup member did not support this decision Perspectives from majority and minority to come later in this presentation. Bird Island Data 25 Estimating Local and Regional Sources •Both regional and local sources are depositing on Utah Lake •Samplers collect the combined local and regional deposition •Independent estimates for each need to be made and added together Attenuation Estimates 26 Estimating Local Sources Assumed to Attenuate •Local large particles are expected to attenuate, small regional particles are not •Use attenuation rates by grain size from VanCuren et al. (2012a) •Use sampled bulk dust grain sizes from Goodman et al. (2019) Estimating Regional Sources Assumed to Not Attenuate •Use Goodman et al. (2019), which estimated that 91% of urban dust was regional •Use Carling (2022), which estimated phosphorus content in regional dust •TP regional flux = 79.0 mg TP/m2/yr •DIN regional flux = 575 mg DIN/m2/yr Estimating Local and Regional Sources Attenuation Estimates 27 Decision Point: Attenuation Scenarios •Three subgroup members supported applying an attenuation rate to shoreline sampler fluxes and apply a regional flux beyond the attenuation distance •One subgroup member did not support attenuation and supported using Bird Island fluxes instead Perspectives from majority and minority to come later in this presentation. Attenuation Estimates 28 Step 1: Create a raster layer of shoreline fluxes around the edge of Utah Lake Step 2a: Assign decay rate and regional flux under different attenuation distances, as identified in literature Step 2b: Create a scenario that assumes no attenuation ATMOSPHERIC DEPOSITION LOADING ESTIMATES 29 Determining Loading to Utah Lake Scenario DIN (metric tons/yr)TP (metric tons/yr) Attenuation @ 100 m 218 31 Attenuation @ 200 m 220 32 Attenuation @ 2000 m 249 45 No attenuation 351 93 Carling 2022 (dust conversion, no attenuation)57.5 Brahney et al. 2019 153-288 2-21 Brahney (mass balance)33 Brett (mass balance)60 Miller 2021 (assumed no attenuation)257-409 50-104 Olsen et al. 2018 (low: uncontaminated, high: contaminated)57-570 10-430 Reidhead et al. 2019 (unscreened)637 193 Barrus et al. 2021 (low: partially screened, high: unscreened)482-1052 133-262 Determining Loading to Utah Lake Decision Point: Load recommendations Modeling team requested one primary recommendation and a range for sensitivity analysis •Three subgroup members recommended: •32 metric tons TP (31-45 range) •220 metric tons DIN (218-249 range) •Based on 200-m attenuation scenario, with range based on 100-2000-m attenuation scenarios •One subgroup member recommended: •150 metric tons TP (93-200 range) •Based on Williams data in its entirety with no samples removed due to contamination or Bird Island •Additional studies and comments provided Evaluating Chemical Speciation Study Site NO3/DIN NH4/DIN SRP/TP Williams data 2020 Orem 0.35 0.65 0.46 Lakeshore 0.37 0.63 0.48 Mosida 0.10 0.90 0.24 Pump Station 0.39 0.61 0.27 Brahney 2019 Urban dust 0.75 Regional dust 0.34 Reidhead 2019 Utah Lake shoreline sites 0.37 W. Miller 2021 Utah Lake shoreline sites 0.32 •DIN constituents •Avg 30.25% nitrate •Avg 69.75% ammonium •Consistent among sites except Mosida •TP constituents •Avg 37.5% SRP •More consistent with regional dust than urban dust •Decision Point: Speciation •All subgroup members supported these proportions •Additional specifics (org N and P) to be determined by the modeling team Utah Lake Water Quality Study Majority Perspective on Atmospheric Deposition Michael T. Brett* Professor of Limnology Dept. of Civil & Environmental Engineering University of Washington *I have served on the Independent Science Panel for the Utah Lake Water Quality Study for over six years https://health.utah.gov/enviroepi/appletree /HAB/Utah_Lake_2016_1.JPG Steps of the AD Decision Support Analysis 1. Review and summarize available data. 2. Evaluate potential explanations of high magnitude (outlier) results. 3. Evaluate fluxes among shoreline sampling sites and potential attenuation of fluxes moving into Utah Lake. 4. Evaluate the chemical speciation of atmospheric deposition nutrient loads. 5. Compare direct estimates of atmospheric deposition to other constraining analyses. 6. Determine atmospheric deposition loading estimates to Utah Lake. 7. Have a transparent AD data vetting process Outline •Points of agreement •How were the Atmospheric Deposition (AD) data vetted? The influence of contaminated samples on AD estimates •AD attenuation “first principles” Particle transport in the atmosphere Using particle size to predict AD attenuation •Is Bird Island a “representative” location in Utah Lake for AD sampling? •The David Gay “reality check” •Other constraining estimates •How was the minority AD Phosphorus loading rate calculated? Points of agreement (“All subgroup members agreed”) 1. . . “with assigning non-detect values at 0 mg/m2”. 2. . . “that calculating area-based flux values for the W. Miller dataset would be more representative of local conditions when the sampling stations were paired with the data from the nearest possible precipitation sampler”. 3. . . “to identify outliers using the defined IQR approach”. 4. . . to “using the atmospheric deposition data [from the Williams (2017-2020) dataset ] if one of the following conditions were true: • The atmospheric deposition data were collected from a sampler with a screen installed • The atmospheric deposition data were collected from a sampler without a screen installed and metadata were available to indicate insect or insect parts were not in the sample”. 5. . . “to use the results of the weather regression analysis to impute missing values within the dataset”. 6. . . to “using the Williams uncontaminated dataset as primary data in calculating the cumulative annual flux and loading to Utah Lake”. 7. . . to “calculating a regional flux to be applied across the entire surface of Utah Lake”. 8. . . to “apportioning the N load as an unknown proportion organic, 30.25% of DIN as nitrate, and 69.75% DIN as ammonium”. Sample inclusion/exclusion decision tree Was the sample screened? Sample inclusion/exclusion decision tree Was the sample screened? YES NO Sample inclusion/exclusion decision tree Was the sample screened? USE Was meta-data available? YES NO Sample inclusion/exclusion decision tree Was the sample screened? USE Was meta-data available? YES NO YES NO Sample inclusion/exclusion decision tree Was the sample screened? USE Was meta-data available? EXCLUDEWas the sample contaminated? YES NO YES NO Sample inclusion/exclusion decision tree Was the sample screened? USE Was meta-data available? EXCLUDEWas the sample contaminated? YES NO YES NO YES NO Sample inclusion/exclusion decision tree Was the sample screened? USE Was meta-data available? EXCLUDEWas the sample contaminated? USEEXCLUDE YES NO YES NO YES NO Summary •If a sample was screened, it was used •If a sample was unscreened and meta-data indicated the sample was not contaminated, it was used •If a sample was unscreened and meta-data was not available, it was not used •If a sample was unscreened and meta-data indicated the sample was contaminated, it was not used The same process was used for both the N and P samples We did not look at how this affected the final estimates as we vetted these data Were the samples contaminated? Were the samples contaminated? n = 175 n = 316 n = 102 Were the samples contaminated? •The high average for the contaminated samples was mostly due to a few EXTREMELY high values Were the samples contaminated? •The high average for the contaminated samples was mostly due to a few EXTREMELY high values •For example, six insect contaminated samples collected at Saratoga Springs in June and July of 2017 had an average value of 247 mg TP m-2 d-1 Were the samples contaminated? •The high average for the contaminated samples was mostly due to a few EXTREMELY high values •For example, six insect contaminated samples collected at Saratoga Springs in June and July of 2017 had an average value of 247 mg TP m-2 d-1 •The Olsen et al. (2019) paper states these samples were contaminated by sweat bees (Lasioglossum) Were the samples contaminated? •The high average for the contaminated samples was mostly due to a few EXTREMELY high values •For example, six insect contaminated samples collected at Saratoga Springs in June and July of 2017 had an average value of 247 mg TP m-2 d-1 •The Olsen et al. (2019) paper states these samples were contaminated by sweat bees (Lasioglossum) •The average for the contaminated Saratoga Springs samples was ≈ 800 times larger than the median for the uncontaminated samples Were the samples contaminated? •The high average for the contaminated samples was mostly due to a few EXTREMELY high values •For example, six insect contaminated samples collected at Saratoga Springs in June and July of 2017 had an average value of 247 mg TP m-2 d-1 •The Olsen et al. (2019) paper states these samples were contaminated by sweat bees (Lasioglossum) •The average for the contaminated Saratoga Springs samples was ≈ 800 times larger than the median for the uncontaminated samples •Removing just these six samples from the overall dataset reduced the overall estimated average AD by 50% The most common method employed by entomologists to sample bees and other pollinators is very similar to the method used by Olsen et al. (2018) to collect AD samples “we identified large amounts of insects, with the great majority being a terrestrial bee species Halictidae Lasioglossum, mostly at the Mosida location (Figure 5). For example, during the 2019 sampling year, from July to August, we counted approximately 100+ bugs per sample at the Mosida location in samples taken over a 4 week period.” Barrus et al. (2021) From Barrus et al. (2021) “we recommend using screens on sample buckets” Barrus et al. (2021) From Barrus et al. (2021) The majority recommendation with regard to insects and AD loading to Utah Lake The majority recommendation with regard to insects and AD loading to Utah Lake Why insects are not counted as atmospheric deposition •Insects represent a sources that is separate from small particulate AD •AD samplers are not designed to systematically quantify insects •AD samplers attract insects (white color & water) •Insects represent both an import and export of nutrients •2020 Science Panel Recommendation to install screens to exclude insects AD Attenuation: First Principles Particle settling velocity is geometrically related to particle size! Raw data taken from Table 3.1 of page 12 of the Williams (2023) memo AD Attenuation: AD Attenuation: •The smallest particles transport AD Attenuation: •The smallest particles transport •The largest particles attenuate AD Attenuation: •The smallest particles transport •The largest particles attenuate “The way particulates settle in the atmosphere depends on their size and weight, with larger particulates settling faster than smaller particulates. For example, dust particles may settle within a few hours, while smaller particles, like PM2.5, can stay in the atmosphere for days to weeks.” G. Williams (2023) AD Attenuation: From: Jassby et al. (1994) SRP attenuation from shoreline (WL) to mid lake (MID) 23% attenuation in Buoy Buckets mostly dry deposition (summer) AD Attenuation: From: Jassby et al. (1994) SRP attenuation from shoreline (WL) to mid lake (MID) 23% attenuation in Buoy Buckets mostly dry deposition (summer) 89% attenuation in Snow Tubes mostly wet deposition (winter) Attenuation vs. particle size, and Utah Lake AD size distribution Goodman et al. (2019)VanCuren et al. (2012) Attenuation vs. particle size, and Utah Lake AD size distribution Goodman et al. (2019)VanCuren et al. (2012) 67% > 14 µm Is Bird Island a representative location in Utah Lake for AD monitoring? There was considerable debate with regard to whether Bird Island is an active rookery. For example, in a memo summarizing the minority view it was claimed a “panel member stated that there are ‘thousands and thousands of birds on Bird Island’.This was apparently accepted as fact by the rest of the Subcommittee without evidence and is highly conjectural. . . while the panel believes that there is some type of point source emanating from Bird Island, there is no scientific support for such an assumption, or for removing this data.” https://www.youtube.com/shorts/EfatDEflJ8s “Hypothesis 4: The fluxes observed at Bird Island represent a lake-based or island- based local source of nutrient flux. Possibilities for a lake-based or island-based source could include contamination from bird droppings, volatilized material from the island, and spray from lake water.” Decision Point: Bird Island Data “The majority of the Subgroup members supported retaining the Bird Island data as a representation of local lake-based or island-based source nutrient input but not using it to estimate external atmospheric deposition [attenuation] influx into Utah Lake.” • “they were concerned that birds could have deposited droppings into the samplers due to the number of birds visiting the Island. These droppings would increase the N and P values in the sampler.” • They acknowledged that bird droppings are a nutrient source to Utah Lake but that the fluxes calculated at Bird Island may not be representative of atmospheric deposition inputs across all of Utah Lake since the sampler is stationed at a bird rookery. • Furthermore, since birds may be eating organisms from Utah Lake, their droppings may not necessarily represent a net influx of nutrients to Utah Lake.” “Hypothesis 4: The fluxes observed at Bird Island represent a lake-based or island- based local source of nutrient flux. Possibilities for a lake-based or island-based source could include contamination from bird droppings, volatilized material from the island, and spray from lake water.” Decision Point: Bird Island Data “The majority of the Subgroup members supported retaining the Bird Island data as a representation of local lake-based or island-based source nutrient input but not using it to estimate external atmospheric deposition [attenuation] influx into Utah Lake.” •bird droppings don't necessarily have to land in the sampler to increase the concentrations. • birds flying around would increase the deposition of fine particles even if droppings don't go directly into the bucket. “Past assumptions were that AD rates decrease significantly from the shore samplers to the middle of the lake . . . Data collected at Bird Island show that these assumptions were incorrect, that mid-lake deposition rates are similar to those measured by shoreline samplers.” Barrus et al. (2021) 181% higher TP loading at Bird Is! 52% higher DIN loading “Past assumptions were that AD rates decrease significantly from the shore samplers to the middle of the lake . . . Data collected at Bird Island show that these assumptions were incorrect, that mid-lake deposition rates are similar to those measured by shoreline samplers.” Barrus et al. (2021) Having 181% higher TP loading at Bird Island than the adjoining source areas is NOT POSSIBLE from a mass balance perspective UNLESS there is an additional unaccounted-for source of nutrients. Figure 35. Cumulative TP fluxes for the Williams dataset, starting on the date when the Bird Island sampler was installed. Having 181% higher TP loading at Bird Island than the adjoining source areas is NOT POSSIBLE from a mass balance perspective UNLESS there is an additional unaccounted-for source of nutrients. Figure 35. Cumulative TP fluxes for the Williams dataset, starting on the date when the Bird Island sampler was installed. 3X Figure 35. Cumulative TP fluxes for the Williams dataset, starting on the date when the Bird Island sampler was installed. Having 181% higher TP loading at Bird Island than the adjoining source areas is NOT POSSIBLE from a mass balance perspective UNLESS there is an additional unaccounted-for source of nutrients. Barrus et al. (2021) suggested this high AD loading comes from the “northwest shore of Utah Lake”, BUT this area did not have an AD sampler so no AD data exist to support this conjecture. 3X Are the Bird Island values higher? Paired T-test P-value = 0.08 (n = 5) Figure 35. Cumulative TP fluxes for the Williams dataset, starting on the date when the Bird Island sampler was installed. Having 181% higher TP loading at Bird Island than the adjoining source areas is NOT POSSIBLE from a mass balance perspective UNLESS there is an additional unaccounted-for source of nutrients. Barrus et al. (2021) suggested this high AD loading comes from the “northwest shore of Utah Lake”, BUT this area did not have an AD sampler so no AD data exist to support this conjecture. 3X The Bird Island Conundrum only applies to the attenuation estimates since the majority of the AD sub-committee recognizes the samples collected at that site are a valid representation of conditions at that specific location. But the majority of the AD sub-committee did not believe that this site is a “random location” in Utah Lake that could be used to extrapolate the nature of AD attenuation in Utah Lake. In fact, Bird Island may be the LEAST representative location for general conditions of Utah Lake. Table 9 in the report shows a 3X difference in AD estimates for various attenuation assumptions The reality check suggested by David Gay •“The US EPA estimates total (wet and dry) deposition of N through modeling.” •“I calculated [using the EPA model] what total N deposition was for Utah Lake based on these modeling estimates for 2021.” •“I get the following 384 total N in [metric] tons (as N) [per year]” •“My idea is to do the following. Compare the 384 tons of deposition as N . . versus the Utah Board’s preferred number for atmospheric N deposition. Does it compare well?” The reality check suggested by David Gay •“The US EPA estimates total (wet and dry) deposition of N through modeling.” •“I calculated [using the EPA model] what total N deposition was for Utah Lake based on these modeling estimates for 2021.” •“I get the following 384 total N in [metric] tons (as N) [per year]” •“My idea is to do the following. Compare the 384 tons of deposition as N . . versus the Utah Board’s preferred number for atmospheric N deposition. Does it compare well?” The estimated Science Panel DIN loading to Utah Lake was 351 metric tons per year for the without attenuation scenario. This is very comparable to David Gay’s TN loading estimate since the EPA model does not account for dry matter attenuation across large lakes and DIN is likely the large majority of TN loading (but not the entirety). The reality check suggested by David Gay •“The US EPA estimates total (wet and dry) deposition of N through modeling.” •“I calculated [using the EPA model] what total N deposition was for Utah Lake based on these modeling estimates for 2021.” •“I get the following 384 total N in tons (as N)” •“My idea is to do the following. Compare the 384 tons of deposition as N . . versus the Utah Board’s preferred number for atmospheric N deposition. Does it compare well?” •“If you have data that says the mass ratio of N to P in your samples is X, then you can estimate TP deposition from the EPA TN deposition values.” •“Then if this estimate of TP is much greater than the Utah Board estimate, then you have a logical estimate to argue that the TP values they want to use are also probably low.” The reality check suggested by David Gay •“I get the following 384 total N in tons (as N)” •“If you have data that says the mass ratio of N to P in your samples is X, then you can estimate TP deposition from the EPA TN deposition values.” •“Then if this estimate of TP is much greater than the Utah Board estimate, then you have a logical estimate to argue that the TP values they want to use are also probably low.” The reality check suggested by David Gay •“I get the following 384 total N in tons (as N)” •“If you have data that says the mass ratio of N to P in your samples is X, then you can estimate TP deposition from the EPA TN deposition values.” •“Then if this estimate of TP is much greater than the Utah Board estimate, then you have a logical estimate to argue that the TP values they want to use are also probably low.” The reality check suggested by David Gay •“I get the following 384 total N in tons (as N)” •“If you have data that says the mass ratio of N to P in your samples is X, then you can estimate TP deposition from the EPA TN deposition values.” •“Then if this estimate of TP is much greater than the Utah Board estimate, then you have a logical estimate to argue that the TP values they want to use are also probably low.” The reality check suggested by David Gay •“I get the following 384 total N in tons (as N)” •“If you have data that says the mass ratio of N to P in your samples is X, then you can estimate TP deposition from the EPA TN deposition values.” •“Then if this estimate of TP is much greater than the Utah Board estimate, then you have a logical estimate to argue that the TP values they want to use are also probably low.” The approach suggested by David Gay results in somewhat lower TP loading estimates than the Science Panel majority conclusion! Constraining estimates Observed Sediment Phosphorus Accrual (SPA) Constraining estimates Poorly constrained Well constrained Well constrained Constraining estimates Used a hybrid bootstrap & Monte Carlo simulation approach to represent uncertainty (N = 1000) Constraining estimates Used a hybrid bootstrap & Monte Carlo simulation approach to represent uncertainty (N = 1000) Constraining estimates Used a hybrid bootstrap & Monte Carlo simulation approach to represent uncertainty (N = 1000) Constraining estimates Greg Carling (2022) memo “Atmospheric deposition of total phosphorus to Utah Lake”, 3/21/2022” Estimated dust loading to Utah Lake to be 30 g/m2/yr, and that “urban dust” has a phosphorus content of 5 g/kg These assumed values give an estimated AD to Utah Lake of 57.5 MT TP/yr Carling (2022) also noted that playa dust had a phosphorus content of ≈ 2 g/kg It is commonly argued that long distance transport of playa dust is the main source of AD to Utah Lake. If this were the case, then the Carling estimate would decrease to 23 MT/yr