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Predicting polycyclic aromatic hydrocarbons using a mass fraction approach in a geostatistical framework across North Carolina


Currently in the United States there are no regulatory standards for ambient concentrations of polycyclic aromatic hydrocarbons (PAHs), a class of organic compounds with known carcinogenic species. As such, monitoring data are not routinely collected resulting in limited exposure mapping and epidemiologic studies. This work develops the log-mass fraction (LMF) Bayesian maximum entropy (BME) geostatistical prediction method used to predict the concentration of nine particle-bound PAHs across the US state of North Carolina. The LMF method develops a relationship between a relatively small number of collocated PAH and fine Particulate Matter (PM2.5) samples collected in 2005 and applies that relationship to a larger number of locations where PM2.5 is routinely monitored to more broadly estimate PAH concentrations across the state. Cross validation and mapping results indicate that by incorporating both PAH and PM2.5 data, the LMF BME method reduces mean squared error by 28.4% and produces more realistic spatial gradients compared to the traditional kriging approach based solely on observed PAH data. The LMF BME method efficiently creates PAH predictions in a PAH data sparse and PM2.5 data rich setting, opening the door for more expansive epidemiologic exposure assessments of ambient PAH.

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This research was supported in part by the National Institute on Aging (NIA) under award number R01AG033078, the National Institute of Occupational Safety and Health (NIOSH) under grant 2T42/OH-008673, the National Institute of Environmental Health Sciences (NIEHS) under grant T32ES007018 and an appointment of Jeanette Reyes to the Postdoctoral Research Program at the National Center for Environmental Assessment, Office of Research and Development, USEPA administered by the Oak Ridge Institute for Science and Education through Interagency Agreement No. DW-89-92298301 be- tween the U.S. Department of Energy and the USEPA.

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Correspondence to Marc L. Serre.

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Reyes, J.M., Hubbard, H.F., Stiegel, M.A. et al. Predicting polycyclic aromatic hydrocarbons using a mass fraction approach in a geostatistical framework across North Carolina. J Expo Sci Environ Epidemiol 28, 381–391 (2018).

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  • Ambient exposures
  • PAHs
  • Bayesian maximum entropy
  • Mass fraction
  • Geostatistics


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