The United States’ gulf region is home to a plethora of oil refineries, petroleum industrial plants, and other industrial plants. Such industry is a common source of Volatile Organic Compounds (VOCs), including benzene, toluene, ethylbenzene, and xylenes (BTEX) and styrene. Benzene is a known carcinogen, and work is under way to study the association of BTEX and styrene with novel neurologic health endpoints in the United States Gulf Region. However, information on exposure to these VOCs is limited, existing air quality models are not adequate, and observational data is sparse. The goal of this work is therefore to develop a Bayesian Maximum Entropy (BME) model to obtain geostatistical estimates of benzene, BTEX, and styrene across the US Gulf Region from 2006 through 2017 and assess the performance this BME model. The BME model developed uses a global offset that captures geographical trends at a coarse regional scale and temporal trends at fine temporal resolution. A covariance analysis of the offset-removed data revealed that a large proportion of the variability of these VOCs follow a nugget (i.e. purely random) model. The nugget proportion was 33%, 46% and 51% for BTEX, Benzene and Styrene. The r2 cross validation statistics was found to be 0.66, 0.53 and 0.65 for BTEX, Benzene and Styrene log-concentrations, and 0.29, 0.19 and 0.11 for BTEX, Benzene and Styrene concentrations. These results indicate that estimation performance is better for log concentrations and more moderate for raw concentrations. The performance for log concentrations may have been inflated by the procedure used to log transform non detect values, and by the limited ability of the global offset to capture fine scale geographical trends, so these issues should be explored further in future works.Master of Science in Environmental Engineerin
