High Throughput Ecological

Exposure Modeling

High Throughput Ecological

Exposure Modeling

Presented by M. Craig Barber, Ph.D.

National Exposure Research Laboratory

Office of Research and Development

Presented by M. Craig Barber, Ph.D.

National Exposure Research Laboratory

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
Forward Prediction of Exp

Chemical Manufacture

Environmen

Release

Food 

Dietary Far‐Field

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE

PATHWAY

(MEDIA + RECEPTOR)

Media Samples

Waste

Data and

Models

Consumer

Building Materials

Products, Articles,

Building Materials

Air, Dust, Surfaces

Near‐Field

Direct

Near‐Field

Indirect

Human

Dietary

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

Biomarkers

of Exposure
Ecological Exposure Modeling

 Develop an integrated ecological exposure modeling system for assessing pers

bioaccumulative toxicants (PBTs) in aquatic ecosystems using existing peer revi

• Chemicals are characterized by their physico‐chemical properties not thei

action.

• Models include:

– ORD’s EFS chemical property calculator,

– ORD’s EXAMS chemical fate & transport model (steady‐state and dyn

– OPP’s KABAM steady‐state aquatic ecosystem bioaccumulation mode

– ORD’s BASS dynamic aquatic ecosystem bioaccumulation model.

 Challenges include:

• Develop a straightforward /well defined application architecture that can 

an automated, high throughput‐manner

• Develop high throughput methods for assessing multiple chemical proper

multiple environments and ecological communities.
Previously Identified Model

Ecological Component

 USEtox and RAIDAR were identified as HTE models by ExpoCast Model Challen

Blackwood et al., 2013)

 Both models predict fate and transport and bioaccumulation

7834 EDSP Chemicals

USEtox or RAIDAR

Predictions (kg / kg emitted)
EXAMS‐KABAM‐BASS Higher Tier E

Models

EFS

EXAMS

Project

Builder

(EXAMS_prj_*.exe)

Creates batch files for

project execution

Environments Chemical Properties

KowHLCMPSw

Projects

Environments +Chemicals +Loadings

Output

Builder

(EXAMS_report_ssv.

exe)

Generates a semicolon

separated value file for

import to an Excel

Workbook

EXAMS

UDB

Chem.exa

Env.exa

en

Loading

Generator Dietary &

Drinking Water

Assessment

SHEDS‐HT

BASS 
Adapting EXAMS‐KABAM‐BASS for High Throughp

Estimation

 EXAMS‐KABAM‐

system of integra

 To adapt these m

estimation, we hlinearized moderapidly parametethousands of chescenarios:

• We simulate

representativmultiple, rep

river environ

• We used the 

minimum, mloading rates SHEDS‐HT

• We then fit t

expected envbiological colinear model

EFS

EXAMS

Project

Builder

(EXAMS_prj_*.exe)

Creates batch files for project

execution

Environments

Chemical

Properties

KowHLCMPSw

BAF DB

Excel data

table of BAFs

Projects

Environments +Chemicals +Loadings

Output

Builder

(EXAMS_report_ssv.

exe)

Generates a semicolon

separated value file for import

to an Excel Workbook

EXAMS

UDB

Chem.exa

Env.exa

Environment

Builder

(EXAMS_river_

environments_v5_*.xlsx)

Facilitates creation of EXAMS *exv

files for input into the EXAMS UDB

Loading

Generator

Dietary &

Drinking Water

Assessment

SHEDS‐HT

BASS KABAM
Linearized EXAMS‐KABAM‐BASS HTE Mo

• Cx is the EEC or EBC of interest, e.g., dissolved water (mg/L), total 

(mg/L), total sediment (mg/kg dry wt), and whole‐body fish (mg/

concentrations,

• Q is the river’s mean annual stream flow (cfs),• L is the chemical’s loading rate (kg/h),

• Kow is the chemical octanol‐water partition coefficient,

• HLC is the chemical’s Henry Law constant (atm‐m3/mol), and

• Sw is the chemical’s aqueous solubility (mg/L).

 We can use this regression as:

• A HTE model for estimating EECs and EBCs without performing fu

KABAM‐BASS setups and simulations,

• To quantify the parameter sensitivities of the predicted EECs and 

• To back‐calculate unknown or poorly known loading rates to river

based on observed water, sediment, and/or fish concentrations, a

• To estimate priors for Bayesian and other advanced statistical mo

10 0 1 10 2 10 3 10 OW 4 10 

log log log log log HLC 

C a a Qa La K a 

x      

EEC: Expected Environmental Concentration

EBC: Expected Biological Concentrations
Chemicals and Environmental Releases for Linea

 Initial Evaluation Chemicals: 74 compounds from EDSP List 1 and 2, w

were selected

 Initial Environment Release:

• Discharges from publicly owned treatment works (POTWs) to wad

and large

• Loading rates to these waters were estimated for 27 of the above 

using

– mean down‐the‐drain discharges (g/person‐day) estimated b

– POTW removal efficiencies estimated by EPISUITE, and

– the assumption that an “average” POTW services approximat

people (e.g., http://css.snre.umich.edu/css_doc/CSS04‐14.pd

 Resulting loading rates varied from 2.23x10‐6 to 1.65 kg/hr (mean = 0.

0.419; median=4.18x10‐4)
Aquatic Environments for Linearized Mo

 Initial Aquatic Environments: nine existing EXAMS river environments develop

workplan chemicals [i.e., chloroparaffins (MCCP) and cyclic siloxanes (CS)].

• USGS_01335754_Hudson_River_NY.exv (OPPT CS site)

• USGS_01483700_St_Jones_River _DE.exv (OPPT MCCP site)

• USGS_02217500_Middle_Oconee_River_GA.exv (ORD development site)

• USGS_03277200_Ohio_River_KY.exv (OPPT CS site)

• USGS_04157000_Saginaw_River_MI.exv (OPPT CS site)• USGS_04176000_River_Rasin_MI.exv (OPPT CS site)• USGS_04196000_Sandusky_River_OH.exv (OPPT MCCP site)

• USGS_06710000_South_Platte_River_CO.exv (OPPT MCCP site)

• USGS_07174400_Caney_River_OK.exv (OPPT MCCP site)

 Initial Aquatic Communities:

• KABAM – fish represented by 3 trophic level compartments (fish_TL2, fish

fish_TL4)

• BASS – age‐structure fish community whose composition by total biomass 

approximately 67.4% redear sunfish (Lepomis microlophus), 19.2% largem

(Micropterus salmoides), 12.1% bluegill sunfish (Lepomis macrochirus), an

channel catfish (Ictalurus punctatus).
Statistical Modeling of Dynamic EXAMS‐BAS

 HTE model is behaving as expected

1.0E‐111.0E‐101.0E‐091.0E‐081.0E‐071.0E‐061.0E‐051.0E‐041.0E‐031.0E‐021.0E‐01

1.0E+00

0 2 4 6 8 10

EXAMSCwd

River ID

Dissolved water conc.

(Cwd mg/L)

‐12‐10

‐8‐6‐4‐2

0

‐12 ‐10 ‐8 ‐6 

EXAMS logCwd

Regression log Cwd (SE

Dissolved water co

(Cwd mg/L)
Statistical Modeling of Dynamic EXAMS‐BAS

‐10

‐8‐6‐4‐2

0246

0 2 4 6 8 10

BASS logC

f

River ID

Whole‐body fish conc.

(Cf mg/kg)

‐10

‐8‐6‐4‐2

0246

‐9 ‐41 

BASS logC

f

Regression log Cf (SE=0.2

Whole‐body fish conc

(Cf mg/kg)

 HTE model is behaving as expected
Comparison of ECHA (2005) MCCP Assessment w

KABAM‐BASS Steady‐state Modeling: Median 

Loading

0.01

0.1

1

10

100

ECHA Cf KABAM Cf BASS Cf ECHA Cs EXAMS Cs

C

f (mg/kg ww) or

C

s (mg/kg dw)

ECHA (2005) vs EXAMS‐KABAM‐BASS
Results of Statistical Analysis

 Investigated EXAMS‐KABAM‐BASS outputs for 27 chemicals acr

environments using loadings from POWT and SHEDS‐HT

 Environments of concern matter

• EECs and EBCs are inversely dependent on river streamflow.

 Loading rates matter

• EECs and EBCs are linearly dependent on loading rates.

 Water and sediment EECs have significantly different log Kow

dependencies.

 Given the same EDC loading rates, dynamic EECs are significant

than their steady‐state counterparts

• The ratio of dynamic EECs to their corresponding steady‐state counterpar

1.37 (median=1.29; SD=0.264; min=1.06, max=3.30).

EEC: Expected Environmental Concentration

EBC: Expected Biological Concentrations
Summary

 Adapting higher tier EPA models EXAMS‐KABAM‐BASS to augm

and USEtox ecological predictions

 HT EXAMS‐KABAM‐BASS linear regression model:

• Serves as HTE model for other chemicals, rivers, and loadin

whose parameters are bounded by those used in the curreprototype,

• Can be used to back‐calculate more refined loading rates fr

observed field data,

• Can be used to estimate priors for advanced statistical mod

• Can be used to quantify parameter sensitivities of predicte

EBCs.
 Broaden linear model with more chemicals and EXAMS envi Perform SEEM analysis with water concentration data

EstimateUncertainty

EDSPChemicals

Monitoring

Data

Measured

Ecological Concentrations

Model 1Model 2

...

Apply calibration and uncertaint

other chemicals

Evaluate Model Perfo

and Refine Mode

EXAMS‐KABAM‐BASS HTE Predi

Dataset 1Dataset 2

...

Data from National Water Quality

Monitoring Council USGS/EPA

portal

Next steps