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

Jeanette M. Reyes, Heidi F. Hubbard, Matthew A. Stiegel, Joachim D. Pleil, Marc L. Serre
2018 Journal of Exposure Science and Environmental Epidemiology  
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 9 particle-bound PAHs across the US
more » ... 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. Graphical abstract Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:
doi:10.1038/s41370-017-0009-6 pmid:29317739 pmcid:PMC6013350 fatcat:hsqqlrnzsragdfpaqo64xzvffa