Evaluation Data and Statistical

Framework for Ecological

Exposure Predictions

Evaluation Data and Statistical

Framework for Ecological

Exposure Predictions

Presented by R. Woodrow Setzer

National Center for Computational Toxicology

Office of Research and Development

Presented by R. Woodrow Setzer

National Center for Computational Toxicology

Office of Research and Development
Presentation Roadma

Introduction

High Throughput Exposure

(HTE) Estimation

High Throughput Toxicokinetics

(HTTK) and Reverse

Toxicokinetics (RTK)

Ongoing Research

• A

Ec

• Th

An

Ex

Future Appli
Goals

 To develop as extensive a dataset as po

to evaluate models predicting surface w

concentrations of environmentally relev

chemicals

 To develop methods for comparing that 

set to model predictions
EstimateUncertainty

EDSPChemicals

Monitoring

Data

Measured

Ecological Concentrations

Model 1Model 2

...

Calibratemodels

Apply calibration and uncertainty to

other chemicals

Evaluate Model Performance

and Refine Models

Weighted HTE Model Predictions

Dataset 1Dataset 2

...

SEEM for Ecological Expo

 Ecological modeling is different from human modeling in that the expo

(concentration in environmental media) can be more readily directly a

rather than indirectly inferred from biomonitoring (e.g., urine) data.
Ecological Monitorin

Chemical Manufacture

Consumer

Building Materials

Products, Articles,

Building Materials Environmen

Release

Air, Dust, Surfaces Food 

Near‐Field

Direct

Near‐Field

Indirect

Human

Dietary Far‐Field

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE

PATHWAY

(MEDIA + RECEPTOR)

Biomarkers

of Exposure

Media Samples

Waste

Data and

Models
Ecological Monitorin

Chemical Manufacture

Consumer

Building Materials

Products, Articles,

Building Materials Environmen

Release

Air, Dust, Surfaces Food 

Near‐Field

Direct

Near‐Field

Indirect

Human

Dietary Far‐Field

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE

PATHWAY

(MEDIA + RECEPTOR)

Biomarkers

of Exposure

Media Samples

Waste

Data and

Models
National Water Quality Mon

Council USGS/EPA porta
Data Structure
Data Structure

• Spatial Structure:

• Sampling intensity can be quite heterogeneous, because sites wer

for different purposes

• Site location is not random

• Site location NOT analogous to NHANES probability sampling

• For example, sites established, e.g., to evaluate compliance and so 

downstream of release point

• Spatial variation of sampling intensity can cause bias in estimates 

are lots more samples in New Jersey than in Wyoming)

• Temporal Structure:

• Sampling frequency and times are heterogeneous

• Temporal heterogeneity will be difficult to generalize

• It is notoriously difficult to capture peak concentrations with this s

generally low‐frequency sampling.

• Many < LOD values

• > 700 organics with multiple sample sites and times
Surface Water Sampling Sites

US

> 600,000 surface water sites in lower 48

> 700 individual chemicalsGPS, date, and time stampsLOD indication

Plotted w/R and packages rgda
Outline of Analysis Strat

 Choose chemicals with extensive geographic and tempo

 Stratify data temporally: e.g., seasonally, monthly

 Group data for single chemicals geographically, using nest

hydrologic units:

 Estimate national‐ and lower‐ level geometric means and 

variance.

• Use a hierarchical random effects model for effects at huc2 

levels (maybe even to huc12)

• Handle LODs with censored likelihoods

 Evaluate relationship between HT model predictions and 

national‐ or lower‐level geometric means of water conce

 Calibrate HT models and/or use additional information ab

chemicals to improve prediction and quantify prediction 
Outline of Analysis Strat

 Choose chemicals with extensive geographic and tempor Stratify data temporally: e.g., seasonally, monthly

 Group data for single chemicals geographically, using nest

hydrologic units:

 Estimate national‐ and lower‐ level geometric means and 

variance.

• Use a hierarchical random effects model for effects at huc2 

levels (maybe even to huc12)

• Handle LODs with censored likelihoods

 Evaluate relationship between HT model predictions and 

national‐ or lower‐level geometric means of water conce

 Calibrate HT models and/or use additional information ab

chemicals to improve prediction and quantify prediction

uncertainty.
Outline of Analysis Strat

 Choose chemicals with extensive geographic and tempor Stratify data temporally: e.g., seasonally, monthly

 Group data for single chemicals geographically, using ne

hydrologic units:

 Estimate national‐ and lower‐ level geometric means and 

variance.

• Use a hierarchical random effects model for effects at huc2 

levels (maybe even to huc12)

• Handle LODs with censored likelihoods

 Evaluate relationship between HT model predictions and 

national‐ or lower‐level geometric means of water conce

 Calibrate HT models and/or use additional information ab

chemicals to improve prediction and quantify prediction

uncertainty.
Nested Hydrologic Un
03

Level 1: HUC2
Level 2: HUC4
0

Level 2: HUC4
030201

030202

Level 3: HUC6
Level 4 – HUC8
Sampling Sites are Located w

Hydrologic Units
Outline of Analysis Strat

 Choose chemicals with extensive geographic and tempor Stratify data temporally: e.g., seasonally, monthly

 Group data for single chemicals geographically, using nest

hydrologic units:

 Estimate national‐ and lower‐ level geometric means an

variance.

• Use a hierarchical random effects model for effects at huc

levels (maybe even to huc12)

• Handle LODs with censored likelihoods

 Evaluate relationship between HT model predictions and 

national‐ or lower‐level geometric means of water conce

 Calibrate HT models and/or use additional information ab

chemicals to improve prediction and quantify prediction

uncertainty.
Hierarchical Nested Statistica

 is the national level log geometric mean: μ ~ N(M, Σ) (across

chemicals)
Hierarchical Nested Statistica

 is the national level log geometric mean: μ ~ N(M, Σ) (across

chemicals)R, S, A, W are effects at different spatial scales:Ri : ith region: R ~ N(0, R2)Sij : jth subregion of the ith region: S ~ N(0, S2)

Aijk : kth accounting unit in the ... in the ith region: A ~ N(0, A2)

Wijkl : lth watershed ... : W ~ N(0, W2)
Hierarchical Nested Statistica

 is the national level log geometric mean: μ ~ N(M, Σ) (across

chemicals)R, S, A, W are effects at different spatial scales:Ri : ith region: R ~ N(0, R2)Sij : jth subregion of the ith region: S ~ N(0, S2)

Aijk : kth accounting unit in the ... in the ith region: A ~ N(0, A2)

Wijkl : lth watershed ... : W ~ N(0, W2)

 : deviations of samples due to local spatial and temporal variati

 ~ N(0, 2)
(simulated dat

Estimate mean concentra
Outline of Analysis Strat

 Choose chemicals with extensive geographic and tempor Stratify data temporally: e.g., seasonally, monthly

 Group data for single chemicals geographically, using nest

hydrologic units:

 Estimate national‐ and lower‐ level geometric means and 

variance.

• Use a hierarchical random effects model for effects at huc2 

levels (maybe even to huc12)

• Handle LODs with censored likelihoods

 Evaluate relationship between HT model predictions and

estimated national‐ or lower‐level geometric means of w

concentrations.

 Calibrate HT models and/or use additional information ab

chemicals to improve prediction and quantify prediction

uncertainty.
(simulated dat

Evaluate Model Predictions v
Outline of Analysis Strat

 Choose chemicals with extensive geographic and tempor Stratify data temporally: e.g., seasonally, monthly

 Group data for single chemicals geographically, using nest

hydrologic units:

 Estimate national‐ and lower‐ level geometric means and 

variance.

• Use a hierarchical random effects model for effects at huc2 

levels (maybe even to huc12)

• Handle LODs with censored likelihoods

 Evaluate relationship between HT model predictions and 

national‐ or lower‐level geometric means of water conce

 Calibrate HT models and/or use additional information a

chemicals to improve prediction and quantify prediction

uncertainty.
(simulated dat

Variance unexplained by mo

Step 2: Regress on Model Pre
EstimateUncertainty

EDSPChemicals

Monitoring

Data

Measured

Ecological Concentrations

Model 1Model 2

...

Calibratemodels

Apply calibration and uncertainty to

other chemicals

Evaluate Model Performance

and Refine Models

Weighted HTE Model Predictions

Dataset 1Dataset 2

...

SEEM for Ecological Exposure: N

Steps
Summary

• The goal is to evaluate and calibrate eco exposure 

for surface water chemical concentrations against e

water sample data.

• We can take advantage of the hierarchical nature o

hydrology unit to estimate geographic variation on 

spatial scales.

• Temporal variation is proposed to be handled by

stratification on time.

• Observations less than their limit of detection are t

censored, assuming a lognormal likelihood.

• The goal is achieved by using regression to compar

predictions to estimates of water concentrations at 

appropriate level.
Craig Barber, ORD

Peter Eghehy, ORD

Kristin Isaacs, ORD

Woody Setzer, ORD

John Wambaugh, ORD

ExpoCast Ecological Team

Elaine Hubal

CSS Deputy National 

Program Director

Tina Bahadori

CSS National Program 

Director

Acknowledgemen