Hyunok Choi1, Michael Zdeb2, Frederica Perera3, John Spengler4. 1. Department of Environmental Health Sciences, State University of New York at Albany, School of Public Health, United States; Department of Epidemiology and Biostatistics, State University of New York at Albany, School of Public Health, United States. Electronic address: hchoi@albany.edu. 2. Department of Epidemiology and Biostatistics, State University of New York at Albany, School of Public Health, United States. 3. Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, 722 W 168th St, 12th Floor, New York, NY 10032, United States; Columbia Center for Children's Environmental Health, Columbia University Mailman School of Public Health, 722 W 168th St, 12th Floor, New York, NY 10032, United States. Electronic address: fpp1@columbia.edu. 4. Harvard School of Public Health, 401 Park Drive, Landmark Center 4th Floor West, Room 406A, Boston, MA 02215, United States. Electronic address: spengler@hsph.harvard.edu.
Abstract
BACKGROUND: Polycyclic aromatic hydrocarbons (PAH) exposure from solid fuel burning represents an important public health issue for the majority of the global population. Yet, understanding of individual-level exposures remains limited. OBJECTIVES: To develop regionally adaptable chronic personal exposure model to pro-carcinogenic PAH (c-PAH) for the population in Kraków, Poland. METHODS: We checked the assumption of spatial uniformity in eight c-PAH using the coefficients of divergence (COD), a marker of absolute concentration differences. Upon successful validation, we developed personal exposure models for eight pro-carcinogenic PAH by integrating individual-level data with area-level meteorological or pollutant data. We checked the resulting model for accuracy and precision against home outdoor monitoring data. RESULTS: During winter, COD of 0.1 for Kraków suggest overall spatial uniformity in the ambient concentration of the eight c-PAH. The three models that we developed were associated with index of agreement approximately equal to 0.9, root mean square error < 2.6 ng/m(3), and 90th percentile of absolute difference ≤ 4 ng/m(3) for the predicted and the observed concentrations for eight pro-carcinogenic PAH. CONCLUSIONS: Inexpensive and logistically feasible information could be used to estimate chronic personal exposure to PAH profiles, in lieu of costly and labor-intensive personal air monitoring at wide scale. At the same time, thorough validation through direct personal monitoring and assumption checking are critical for successful model development.
BACKGROUND:Polycyclic aromatic hydrocarbons (PAH) exposure from solid fuel burning represents an important public health issue for the majority of the global population. Yet, understanding of individual-level exposures remains limited. OBJECTIVES: To develop regionally adaptable chronic personal exposure model to pro-carcinogenic PAH (c-PAH) for the population in Kraków, Poland. METHODS: We checked the assumption of spatial uniformity in eight c-PAH using the coefficients of divergence (COD), a marker of absolute concentration differences. Upon successful validation, we developed personal exposure models for eight pro-carcinogenic PAH by integrating individual-level data with area-level meteorological or pollutant data. We checked the resulting model for accuracy and precision against home outdoor monitoring data. RESULTS: During winter, COD of 0.1 for Kraków suggest overall spatial uniformity in the ambient concentration of the eight c-PAH. The three models that we developed were associated with index of agreement approximately equal to 0.9, root mean square error < 2.6 ng/m(3), and 90th percentile of absolute difference ≤ 4 ng/m(3) for the predicted and the observed concentrations for eight pro-carcinogenic PAH. CONCLUSIONS: Inexpensive and logistically feasible information could be used to estimate chronic personal exposure to PAH profiles, in lieu of costly and labor-intensive personal air monitoring at wide scale. At the same time, thorough validation through direct personal monitoring and assumption checking are critical for successful model development.
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