Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool

To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori’s simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.

[1]  Antony J. Williams,et al.  Sourcing data on chemical properties and hazard data from the US-EPA CompTox Chemicals Dashboard: A practical guide for human risk assessment , 2021, Environment international.

[2]  J. Wambaugh,et al.  Bayesian inference of chemical exposures from NHANES urine biomonitoring data , 2021, Journal of Exposure Science & Environmental Epidemiology.

[3]  O. Jolliet,et al.  Exposure and toxicity characterization of chemical emissions and chemicals in products: global recommendations and implementation in USEtox , 2021, The International Journal of Life Cycle Assessment.

[4]  Peter Fantke,et al.  Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. , 2018, Environmental science & technology.

[5]  Jon A Arnot,et al.  A Model for Risk-Based Screening and Prioritization of Human Exposure to Chemicals from Near-Field Sources. , 2018, Environmental science & technology.

[6]  Tao Hong,et al.  The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products , 2018, Scientific Data.

[7]  Antony J. Williams,et al.  OPERA models for predicting physicochemical properties and environmental fate endpoints , 2018, Journal of Cheminformatics.

[8]  Peter Fantke,et al.  Stochastic modeling of near-field exposure to parabens in personal care products , 2017, Journal of Exposure Science and Environmental Epidemiology.

[9]  Daniel Vallero,et al.  Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals. , 2016, Environmental science & technology.

[10]  T. McKone,et al.  Volatilization of low vapor pressure--volatile organic compounds (LVP-VOCs) during three cleaning products-associated activities: Potential contributions to ozone formation. , 2016, Chemosphere.

[11]  Halûk Özkaynak,et al.  Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment , 2015, Environmental health perspectives.

[12]  Ord,et al.  Exposure Factors Handbook (2011 Edition) , 2015 .

[13]  Anna C. Salzberg,et al.  Assessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment: the challenge ahead , 2015, Carcinogenesis.

[14]  Peter Fantke,et al.  Exploring consumer exposure pathways and patterns of use for chemicals in the environment , 2015, Toxicology reports.

[15]  Daniel Vallero,et al.  SHEDS-HT: an integrated probabilistic exposure model for prioritizing exposures to chemicals with near-field and dietary sources. , 2014, Environmental science & technology.

[16]  D. Mackay,et al.  Correlation of chemical evaporation rate with vapor pressure. , 2014, Environmental science & technology.

[17]  C M Grulke,et al.  Development of a consumer product ingredient database for chemical exposure screening and prioritization. , 2014, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[18]  David M. Reif,et al.  High-throughput models for exposure-based chemical prioritization in the ExpoCast project. , 2013, Environmental science & technology.

[19]  David McCready,et al.  A Comparison of Screening and Refined Exposure Models for Evaluating Toluene Air Emissions from a Washing Machine , 2013 .

[20]  G. M. Richardson,et al.  Revisiting Dust and Soil Ingestion Rates Based on Hand-to-Mouth Transfer , 2013 .

[21]  W W Nazaroff,et al.  SVOC exposure indoors: fresh look at dermal pathways. , 2012, Indoor air.

[22]  Michael S. McLachlan,et al.  Prioritizing Chemicals and Data Requirements for Screening-Level Exposure and Risk Assessment , 2012, Environmental health perspectives.

[23]  D. Sarigiannis,et al.  Considering the cumulative risk of mixtures of chemicals – A challenge for policy makers , 2012, Environmental Health.

[24]  Donald D. Fontaine,et al.  Evaluation of Potential Exposure to Formaldehyde Air Emissions from a Washing Machine Using the IAQX Model , 2012 .

[25]  H. Buist,et al.  Predicting blood:air partition coefficients using basic physicochemical properties. , 2012, Regulatory toxicology and pharmacology : RTP.

[26]  Valerie G Zartarian,et al.  Modeled Estimates of Soil and Dust Ingestion Rates for Children , 2011, Risk analysis : an official publication of the Society for Risk Analysis.

[27]  Alexander Chuprina,et al.  Drug- and Lead-likeness, Target Class, and Molecular Diversity Analysis of 7.9 Million Commercially Available Organic Compounds Provided by 29 Suppliers , 2010, J. Chem. Inf. Model..

[28]  P. Switzer,et al.  Outdoor air pollution in close proximity to a continuous point source , 2009 .

[29]  Gurumurthy Ramachandran,et al.  Bayesian modeling of exposure and airflow using two-zone models. , 2009, The Annals of occupational hygiene.

[30]  Clive Matthew Earnest.  A two-zone model to predict inhalation exposure to toxic chemicals in cleaning products , 2009 .

[31]  R. Judson,et al.  The Toxicity Data Landscape for Environmental Chemicals , 2008, Environmental health perspectives.

[32]  Ann Richard,et al.  ACToR--Aggregated Computational Toxicology Resource. , 2008, Toxicology and applied pharmacology.

[33]  Mark A. J. Huijbregts,et al.  USEtox—the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment , 2008 .

[34]  D. Mackay,et al.  Policies for chemical hazard and risk priority setting: can persistence, bioaccumulation, toxicity, and quantity information be combined? , 2008, Environmental science & technology.

[35]  J. Ross,et al.  Relationship Between the Evaporation Rate and Vapor Pressure of Moderately and Highly Volatile Chemicals , 2008, Bulletin of environmental contamination and toxicology.

[36]  B C Singer,et al.  Cleaning products and air fresheners: emissions and resulting concentrations of glycol ethers and terpenoids. , 2006, Indoor air.

[37]  Marjo Yliperttula,et al.  Computational prediction of oral drug absorption based on absorption rate constants in humans. , 2006, Journal of medicinal chemistry.

[38]  Mark Nicas,et al.  Estimating Benzene Exposure at a Solvent Parts Washer , 2006, Journal of occupational and environmental hygiene.

[39]  Jon A Arnot,et al.  Screening level risk assessment model for chemical fate and effects in the environment. , 2006, Environmental science & technology.

[40]  William W. Nazaroff,et al.  Cleaning products and air fresheners: exposure to primary and secondary air pollutants , 2004 .

[41]  Mark Nicas,et al.  Predicting room vapor concentrations due to spills of organic solvents. , 2003, AIHA journal : a journal for the science of occupational and environmental health and safety.

[42]  J. E. Woodrow,et al.  Predicting pesticide emissions and downwind concentrations using correlations with estimated vapor pressures. , 2001, Journal of agricultural and food chemistry.

[43]  Cynthia Howard-Reed,et al.  Mass transfer of volatile organic compounds from drinking water to indoor air : The role of residential dishwashers , 1999 .

[44]  J. Sarver,et al.  Estimating xenobiotic half-lives in humans from rat data: influence of log P. , 1997, Environmental health perspectives.

[45]  J. E. Woodrow,et al.  Correlation techniques for estimating pesticide volatilization flux and downwind concentrations , 1997 .

[46]  M. Nicas,et al.  Estimating exposure intensity in an imperfectly mixed room. , 1996, American Industrial Hygiene Association journal.

[47]  Stephen W. Edwards,et al.  Biomarkers in computational toxicology , 2014 .

[48]  James Vail,et al.  The exposure data landscape for manufactured chemicals. , 2012, The Science of the total environment.

[49]  H. Frey,et al.  Modeling of the Penetration of Ambient PM2.5 to Indoor Residential Microenvironment , 2009 .

[50]  S E P T E M B,et al.  U.S.BUREAU OF LABOR STATISTICS , 2004 .

[51]  R. Corsi,et al.  Volatilization of Chemicals from Drinking Water to Indoor Air: The Role of Residential Washing Machines. , 1998, Journal of the Air & Waste Management Association.

[52]  R L Corsi,et al.  Volatilization of chemicals from drinking water to indoor air: the role of residential washing machines. , 1998, Journal of the Air & Waste Management Association.

[53]  K. Brubaker,et al.  Costs and air-quality impacts of alternative national ambient air-quality standards for particulate matter , 1983 .