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Predicting Indoor Air and Dust Concentrations and Interpreting Measured Urinary Biomarkers
Citation:
Jacketti, M., R. Dodson, R. Rudel, J. Wambaugh, Woody Setzer, AND K. Isaacs. Predicting Indoor Air and Dust Concentrations and Interpreting Measured Urinary Biomarkers. SOT, Nashville, TN, March 19 - 23, 2023. https://doi.org/10.23645/epacomptox.22285627
Impact/Purpose:
This SOT presentation describes the development of new predictive models for indoor air and dust concentrations and their use in interpreting urine biomarkers in a cohort of women.
Description:
US EPA, under its ExpoCast program, is developing high-throughput (HT) near-field modeling methods to estimate human chemical exposure and to provide real-world context to HT screening hazard data. These novel modeling methods include reverse methods to infer parent chemical exposures from biomonitoring measurements and forward models to predict multi-pathway exposures from chemical use information and/or residential media concentrations. Here, both forward and reverse modeling methods were used to characterize the relationship between matched near-field environmental (air and dust) and biomarker measurements. Indoor air, dust, and urine samples from 120 females (aged 60 to 80 years) were collected and analyzed for concentrations of 89 chemicals identified as endocrine disrupting compounds (EDCs), including pesticides, flame retardants, and consumer product chemicals. In the measured data, 78% of the residential media measurements (across 89 chemicals) and 54% of the urine measurements (across 21 chemicals) were censored, i.e. below the limit of detection (LOD). Due to the degree of censoring, a partitioning model was applied to the available air and dust measurements to infer concentrations below the limit of detection, thus providing estimates for all monitored analytes in each household. Next, these data were used to train support vector regression machine learning models to predict air and dust concentrations from chemical use and structure descriptors (R2=0.98 for air, R2=0.95 for dust); the models were validated against available air and dust measurements from the literature. The air and dust models were then applied to chemicals with parent exposures inferred from biomonitoring measurements. The predicted air and dust concentrations were used to estimate associated near-field exposures using a high-throughput (HT) model. These near-field exposures and existing HT exposure predictions for food contact pathways were combined and compared with the inferred exposures from the biomonitoring data (R2=0.4). These results indicate that the forward and reverse methods being developed in ExpoCast can aid in the identification of exposure pathways associated with measured urinary biomarkers.
URLs/Downloads:
DOI: Predicting Indoor Air and Dust Concentrations and Interpreting Measured Urinary Biomarkers
PRESENTATION.PDF (PDF, NA pp, 1123.886 KB, about PDF)