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Options for Identifying & Quantifying Pollutant Loads PowerPoint Presentation
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Options for Identifying & Quantifying Pollutant Loads

Options for Identifying & Quantifying Pollutant Loads

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Options for Identifying & Quantifying Pollutant Loads

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  1. Options for Identifying & Quantifying Pollutant Loads

  2. Presentation Overview • Goals of pollutant load estimation • Options for quantifying current loads or conditions • Data-driven approaches • Models • Modeling, as it relates to environmental systems • Types of models • Models typically used for load estimation • Data needs • Example Application of Simple Model

  3. Why is Pollutant Load Estimation Necessary? • Identify relative magnitude of contributions from different sources • Determine whether locations of sources are critical • Evaluate timing of source loading • Target future management efforts • Plan restoration strategies • Project future loads under changing conditions • Develop a mechanism for quantifying potential improvement

  4. Pollutant Load Estimation Approaches • Has it already been done? • Total Maximum Daily Loads (TMDLs) • Clean Lakes Studies • Other local and regional studies • If not… • Data-driven approaches • Best when detailed monitoring data available • Models • Provide greater insight into impact of sources (temporally and spatially) • Readily allow for evaluation of future conditions

  5. Data-driven Approaches • Estimate source loads using: • Monitoring data • Periodic water quality concentrations and flow gauging data • Facility discharge monitoring reports • Literature • Loading rates, often by landuse (e.g., lbs/acre/year) • Typical facility concentrations and flow

  6. Is a Data-driven Approach Appropriate? • Monitoring data • Does it represent most conditions that occur (low flow, storms, etc.)? • Are spatial and source variability well-represented? • Have all parameters of interest been monitored? • Is there a clear path to a management strategy?

  7. Load Estimates – Monitoring Data • In simplest terms… load = flow x concentration • Load duration curves • Flow-based presentation • Statistical techniques • Relationships between flow and concentration to “fill in the blanks” when data aren’t available • Examples include: • Regression approach • FLUX

  8. Load Duration Curves • Rank daily flow and generate flow duration curve • Multiply water quality concentrations by corresponding flow values • Flow “curve” represents water quality target

  9. Regression Approach • Develop a regression equation by plotting flow vs. corresponding water quality concentration • Use the relationship to predict water quality concentration for days when flow data exist • Note: limited applicability to data that is heavily storm-driven and spans orders of magnitude (e.g., sediment) • should consider log transform regression approach • Minimum Variance Unbiased Estimator (MVUE) recommended by USGS for bias correction (

  10. Regression Approach - Example

  11. FLUX • Interactive computer program • Developed for U.S. Army Corps of Engineers • “Maps” flow-concentration relationship from available data onto entire flow record • Calculates total mass, streamflow, and error statistics • Can stratify data into groups based on flow • Six available estimation algorithms

  12. FLUX – Data Requirements • Constituent concentrations, ideally collected weekly to monthly for at least a year • Date each sample was collected • Corresponding flow measurements (instantaneous or daily mean) • Complete flow record (daily mean) for the period of interest

  13. Load Estimates – Literature • Landuse-specific loading rates (typically annual) • Multiply loading rate by area: loadall = (arealu1 x loading ratelu1)+ (arealu2 x loading ratelu2) +… • Generally for landuse or watershed-wide analysis • Many sources: Lin (2004); Beaulac and Reckhow (1982), etc. • Use with caution (need correct representation for your local watershed) • Pollution sources • Climate • Soils

  14. Example Load Estimation Based on Literature Values

  15. Limitations of Data-driven Approaches • Monitoring data • Reflect current/historical conditions (limited use for future predictions) • Insight limited by extent of data (usually water quality data) • Often not source-specific • May reflect a small range of flow conditions • Literature • Not reflective of local conditions • Wide variation among literature • Often a “static” value (e.g., annual)

  16. If a Data-driven Approach Isn’t Enough…Models are Available What is a Model? • A theoretical construct, • together with assignment of numerical values to model parameters, • incorporating some prior observations drawn from field and laboratory data, • and relating external inputs or forcing functions to system variable responses * Definition from: Thomann and Mueller, 1987

  17. Nuts and Bolts of a Model Input Model Algorithms Output Factor 1 Rainfall Event System Response Land use Factor 2 Soil Pollutant Buildup Stream Pt. Source Factor 3 Others

  18. Is a Model Necessary? It depends what you want to know… Probably Not • What are the loads associated with individual sources? • Where and when does impairment occur? • Is a particular source or multiple sources generally causing the problem? • Will management actions result in meeting water quality standards? • Which combination of management actions will most effectively meet load targets? • Will future conditions make impairments worse? • How can future growth be managed to minimize adverse impacts? Probably Models are used in many areas… TMDLs, stormwater evaluation and design, permitting, hazardous waste remediation, dredging, coastal planning, watershed management and planning, air studies

  19. Types of Models • Landscape models • Runoff of water and materials on and through the land surface • Receiving water models • Flow of water through streams and into lakes and estuaries • Transport, deposition, and transformation in receiving waters • Watershed models • Combination of landscape and receiving water models • Site-scale models • Detailed representation of local processes, for example Best Management Practices (BMPs)

  20. Types of Models Crops Crops Pasture Pasture Urban Urban • Landscape/Site-scale models • Landscape/Site-scale models • Receiving water models • Receiving water models • Watershed models • Watershed models

  21. Model Basis • Empirical formulations • mathematical relationship based on observed data rather than theoretical relationships • Deterministic models • mathematical models designed to produce system responses or outputs to temporal and spatial inputs (process-based)

  22. Review of Commonly Used Models • Landscape and Watershed models • Simple models • Mid-range models • Comprehensive watershed models • Field-scale models

  23. Simple Models • Loading Rate • Simple Method • USLE / MUSLE • USGS Regression • PLOAD • STEPL • Minimal data preparation • Landuse, soil, slope, etc. • Good for long averaging periods • Annual or seasonal budgets • No calibration • Some testing/validation is preferable • Comparison of relative magnitude Limitations: • Limited to waterbodies where loadings can be aggregated over longer averaging periods • Limited to gross loadings

  24. Mid-range Models • AGNPS • GWLF • P8 • SWAT ( + receiving water) • More detailed data preparation • Meteorological data • Good for seasonal/event issues • Minimal or no calibration • Testing and validation preferable • Application objectives • Storm events, daily loads Limitations: • Daily/monthly load summaries • Limited pollutants simulated • Limited in-stream simulation and comparison with standards

  25. Comprehensive Watershed Models • HSPF/LSPC • SWMM • Accommodate more detailed data input • Short time steps and finer configuration • Complex algorithms need state/kinetic variables • Ability to evaluate various averaging periods and frequencies • Calibration is required • Addresses a wide range of water and water quality problems • Include both landscape and receiving water simulation Limitations: • More training and experience needed • Time-consuming (need GIS help, output analysis tools, etc.)

  26. Source of Additional Information on Model Selection • EPA 1997, Compendium of Models for TMDL Development and Watershed Assessment. EPA841-B-97-007 • Review of loading and receiving water models • Ecological assessment techniques and models • Model selection

  27. Example of Simple Model Application • Spreadsheet Tool for Estimating Pollutant Load (STEPL) • Employs simple algorithms to calculate nutrient and sediment loads from different land uses • Also includes estimates of load reductions that would result from the implementation of various BMPs • Data driven and highly empirical • A customized MS Excel spreadsheet model • Simple and easy to use

  28. STEPL Users? • Basic understanding of hydrology, erosion, and pollutant loading processes • Knowledge (use and limitation) of environmental data (e.g., land use, agricultural statistics, and BMP efficiencies) • Familiarity with MS Excel and Excel Formulas

  29. Process Sources Cropland Runoff Urban BMP Load before BMP Load after BMP Pasture Erosion/ Sedimentation Forest Feedlot Others STEP 1 STEP 2 STEP 3 STEP 4

  30. STEPL Web Site Link to on-line Data server Link to download setup program to install STEPL program and documents Temporary URL: until moved to EPA server

  31. STEPL Main Program • Run STEPL executable program to create and customize spreadsheet dynamically • Go to demonstration

  32. Conclusions • Many tools are available to quantify pollutant loads • Approach depends on intended use of predictions • Simplest approaches are data-driven • Watershed modeling is more complex and time-consuming • provides more insight into spatial and temporal characteristics • useful for future predictions and evaluation of management options • One size doesn’t fit all!