Evaluation of Bayesian Maximum Entropy Data Fusion Approaches to Estimate Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes and to Inform Epidemiological Analyses in the US Gulf States

Abstract

The Gulf States are home to industries emitting styrene, benzene, toluene, ethylbenzene, and xylenes (SBTEX). Presently, adverse health effects of ambient SBTEX exposure in highly polluted regions, such as the Gulf States, must be evaluated. Epidemiologists, however, are limited by inadequate estimates of ambient SBTEX. Using Bayesian Maximum Entropy, SBTEX estimation methods of varying resource intensity were evaluated, including simple kriging (least intense), incorporation of observational and emissions data trends (moderately intense), and data fusion of observed and Comprehensive Air quality Model with extensions (CAMx) data (most intense). Generally, as resource intensity increased, so did SBTEX estimation performance, where SBTEX Spearman R values increased by 0.48 on average from the least to most intense methods. Data fusion of observed and CAMx data was identified as the best ambient SBTEX estimation method in the Gulf States. Exposure estimates revealed that Gulf States residences within commuting distance of high industrial activity experienced 1.64 times higher 97.5th percentile daily exposures to SBTEX on average than those living in less industrialized areas, which could contribute to total occupational and ambient exposure disparities. Furthermore, ambient benzene exposure was greater than the acceptable one-in-a-million excess cancer risk threshold for 75% of estimated residence locations in the Gulf States.