Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

Standard

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. / Ring, Caroline L.; Arnot, Jon; Bennett, Deborah H.; Egeghy, Peter; Fantke, Peter; Huang, Lei; Isaacs, Kristin K.; Jolliet, Olivier ; Phillips, Katherine; Price, Paul S.; Shin, Hyeong-Moo; Westgate, John N; Setzer, R. Woodrow; Wambaugh, John F.

In: Environmental Science and Technology, Vol. 53, No. 2, 2019, p. 719-732.

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

Harvard

Ring, CL, Arnot, J, Bennett, DH, Egeghy, P, Fantke, P, Huang, L, Isaacs, KK, Jolliet, O, Phillips, K, Price, PS, Shin, H-M, Westgate, JN, Setzer, RW & Wambaugh, JF 2019, 'Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways', Environmental Science and Technology, vol. 53, no. 2, pp. 719-732. https://doi.org/10.1021/acs.est.8b04056

APA

CBE

Ring CL, Arnot J, Bennett DH, Egeghy P, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips K, Price PS, Shin H-M, Westgate JN, Setzer RW, Wambaugh JF. 2019. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. Environmental Science and Technology. 53(2):719-732. https://doi.org/10.1021/acs.est.8b04056

MLA

Vancouver

Author

Ring, Caroline L. ; Arnot, Jon ; Bennett, Deborah H. ; Egeghy, Peter ; Fantke, Peter ; Huang, Lei ; Isaacs, Kristin K. ; Jolliet, Olivier ; Phillips, Katherine ; Price, Paul S. ; Shin, Hyeong-Moo ; Westgate, John N ; Setzer, R. Woodrow ; Wambaugh, John F. / Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. In: Environmental Science and Technology. 2019 ; Vol. 53, No. 2. pp. 719-732.

Bibtex

@article{15b8ae6a747d40ef8dfcb6c572fa219b,
title = "Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways",
abstract = "Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources – consumer (near-field), dietary, far-field industrial, and far-field pesticide – to the general population. The balanced accuracies of these source-based exposure pathway models range from 73-81{\%}, with the error rate for identifying positive chemicals ranging from 17-36{\%}. We then used exposure pathways to organize predictions from thirteen different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models (SEEM) framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R2 of ~0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479,926 chemicals, mostly with minimal exposure information. This approach identifies 1,880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95{\%} confidence that the median intake rate is below 1 µg/kg BW/day for 478,046 compounds.",
author = "Ring, {Caroline L.} and Jon Arnot and Bennett, {Deborah H.} and Peter Egeghy and Peter Fantke and Lei Huang and Isaacs, {Kristin K.} and Olivier Jolliet and Katherine Phillips and Price, {Paul S.} and Hyeong-Moo Shin and Westgate, {John N} and Setzer, {R. Woodrow} and Wambaugh, {John F.}",
year = "2019",
doi = "10.1021/acs.est.8b04056",
language = "English",
volume = "53",
pages = "719--732",
journal = "Environmental Science & Technology (Washington)",
issn = "0013-936X",
publisher = "American Chemical Society",
number = "2",

}

RIS

TY - JOUR

T1 - Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

AU - Ring, Caroline L.

AU - Arnot, Jon

AU - Bennett, Deborah H.

AU - Egeghy, Peter

AU - Fantke, Peter

AU - Huang, Lei

AU - Isaacs, Kristin K.

AU - Jolliet, Olivier

AU - Phillips, Katherine

AU - Price, Paul S.

AU - Shin, Hyeong-Moo

AU - Westgate, John N

AU - Setzer, R. Woodrow

AU - Wambaugh, John F.

PY - 2019

Y1 - 2019

N2 - Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources – consumer (near-field), dietary, far-field industrial, and far-field pesticide – to the general population. The balanced accuracies of these source-based exposure pathway models range from 73-81%, with the error rate for identifying positive chemicals ranging from 17-36%. We then used exposure pathways to organize predictions from thirteen different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models (SEEM) framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R2 of ~0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479,926 chemicals, mostly with minimal exposure information. This approach identifies 1,880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 µg/kg BW/day for 478,046 compounds.

AB - Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources – consumer (near-field), dietary, far-field industrial, and far-field pesticide – to the general population. The balanced accuracies of these source-based exposure pathway models range from 73-81%, with the error rate for identifying positive chemicals ranging from 17-36%. We then used exposure pathways to organize predictions from thirteen different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models (SEEM) framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R2 of ~0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479,926 chemicals, mostly with minimal exposure information. This approach identifies 1,880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 µg/kg BW/day for 478,046 compounds.

U2 - 10.1021/acs.est.8b04056

DO - 10.1021/acs.est.8b04056

M3 - Journal article

VL - 53

SP - 719

EP - 732

JO - Environmental Science & Technology (Washington)

JF - Environmental Science & Technology (Washington)

SN - 0013-936X

IS - 2

ER -