Options for Identifying & Quantifying Pollutant Loads

Options for
Identifying &
Quantifying
Pollutant Loads
1
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
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
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
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
Is a Data-driven Approach
Appropriate?
 Monitoring data
 Does it represent most conditions that occur
(low flow, storms, etc.)?
 Are spatial and source variability wellrepresented?
 Have all parameters of interest been
monitored?
 Is there a clear path to a management
strategy?
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
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
Allowable Total Suspended Solids Load (kg/day)
Observed Total Suspended Solids Load (kg/day)
Observed (Surface Flow > 50%)
Total Suspended Solids Load (kg/day)
1000000
100000
5
10
4
10000
1000
100
10
1
0%
10%
20%
30%
40%
50%
60%
70%
Observed Flow Exceedence at KAIN18
80%
90%
100%
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
(http://co.water.usgs.gov/sediment/bias.frame.html)
Regression Approach - Example
Total Nitrogen vs. Flow
4.5
4
Total Nitrogen (mg/L)
3.5
3
2.5
2
1.5
y = 0.0046x + 2.607
1
2
R = 0.0428
0.5
0
0
20
40
60
80
Flow (cfs)
100
120
140
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
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
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
Example Load Estimation Based on
Literature Values
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)
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
Nuts and Bolts of a Model
Input
Model
Algorithms
Output
Factor 1
Rainfall Event
Factor 2
Pollutant Buildup
Factor 3
Others
System
Land use
Soil
Stream
Pt. Source
Response
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
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)
Types of Models
 Landscape/Sitescale models
Crops
Pasture
 Receiving water
models
 Watershed models
Urban
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 (processbased)
Review of Commonly Used Models
 Landscape and Watershed models




Simple models
Mid-range models
Comprehensive watershed models
Field-scale models
Simple Models
 Minimal data preparation






Loading Rate
Simple Method
USLE / MUSLE
USGS Regression
PLOAD
STEPL
 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
Mid-range Models
 More detailed data
preparation




AGNPS
GWLF
P8
SWAT ( + receiving water)
 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
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.)
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
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
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
Process
Sources
Cropland
Runoff
Urban
Load before BMP
BMP
Load after BMP
Pasture
Forest
Erosion/
Sedimentation
Feedlot
Others
STEP 1
STEP 2
STEP 3
STEP 4
STEPL Web Site
Link to on-line
Data server
Link to download
setup program to
install STEPL program
and documents
Temporary URL: http://it.tetratech-ffx.com/stepl until moved to EPA server
STEPL Main Program
 Run STEPL executable program to create
and customize spreadsheet dynamically
 Go to demonstration
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 timeconsuming
 provides more insight into spatial and temporal
characteristics
 useful for future predictions and evaluation of
management options
 One size doesn’t fit all!