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Predicting polycyclic aromatic hydrocarbons using a mass fraction approach in a geostatistical framework across North Carolina
Citation:
Reyes, J., H. Hubbard, M. Stiegel, J. Pleil, AND M. Serre. Predicting polycyclic aromatic hydrocarbons using a mass fraction approach in a geostatistical framework across North Carolina. Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 28:381–391, (2018). https://doi.org/10.1038/s41370-017-0009-6
Impact/Purpose:
Polycyclic Aromatic Hydrocarbons (PAHs) are a class of organic compounds containing 2 or more fused aromatic rings created through incomplete fuel combustion from a variety of sources including biofuel burning, wildfires, coal production, etc. Several species of PAHs have been designated by the US Environmental Protection Agency (EPA) as being probable human carcinogens. Currently the EPA only has PAH regulatory standards for drinking water and the National Institute for Occupational Safety and Health (NIOSH) has established occupational exposure limits to coal tar pitch volatiles. International organizations and other countries have established ambient concentration guidelines for one of the more toxic PAHs, benzo(a)pyrene. However, currently in the US there are no regulatory standards for ambient concentrations of PAHs. Compared to regulated ambient air pollutants, there are few epidemiologic studies that have utilized observed data or explored ambient exposures to different PAHs, which can be costly to measure. From a geostatistical perspective, limited ambient observed data have resulted in few studies creating maps of PAHs concentrations across space/time. Others have used Chemical Transport Models (CTMs) to predict PAH concentrations. However, these studies are also limited in number. As a result, there is a gap in the literature exploring ambient PAH exposures and their associations with various health endpoints. Short-term health effects include eye and skin irritation, nausea and vomiting while long-term health effects include increased risk to skin, lung and bladder cancer and well has cardiopulmonary mortality. While many of these health effects are associated with either occupational exposures or drinking water, the relationship between ambient concentrations of PAH to their associated health effects have not been well explored.
Description:
Currently in the United States there are no regulatory standards for ambient concentrations of Polycyclic Aromatic Hydrocarbons (PAHs), organic compounds with known carcinogenic species. Limited monitoring and observed data haveresulted in limited exposure mapping and epidemiologic studies. This work develops the log-­‐Mass Fraction (LMF) BayesianMaximum entropy (BME) geostatistical prediction method used to predict 9 particle-­‐bound PAHs across the US state ofNorth Carolina in 2005. The LMF method develops a relationship between a relatively small number of collocated PAH andfine Particulate Matter (PM2.5) and applies that relationship to PM2.5 observed locations to estimate PAH. Through aPAH-­‐specific neighborhood optimization, the LMF method requires comparatively less observed PAH values. The LMFBME method reduces mean squared error by over 39% compared with kriging as seen through a cross validation. PAHconcentrations predicted through the LMF BME method show more realistic spatial gradients and more of an associationbetween known fire locations compared with more traditional geostatistical prediction methods. The LMF BME methodallows to efficiently create PAH predictions in a PAH data sparse and PM2.5 data rich setting, opening the doors for moreexpansive epidemiologic exposure assessments for ambient PAH.6465Keywords:ambientexposures,PAH,wildfires,BayesianMaximumEntropy,massfraction,geostatistics6667:
URLs/Downloads:
DOI: Predicting polycyclic aromatic hydrocarbons using a mass fraction approach in a geostatistical framework across North Carolina
https://doi.org/10.1038/s41370-017-0009-6