These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Estimation of chronic personal exposure to airborne polycyclic aromatic hydrocarbons.
    Author: Choi H, Zdeb M, Perera F, Spengler J.
    Journal: Sci Total Environ; 2015 Sep 15; 527-528():252-61. PubMed ID: 25965038.
    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.
    [Abstract] [Full Text] [Related] [New Search]