Assessment of Alternative Bayesian Hierarchical Models for Estimating Gas Emissions

This study focused on the comparison of different Bayesian models developed for prediction of two primary air pollutants, ozone and PM2.5 emissions. Three alternate models were developed to incorporate different correlation structures: 1) univariate model which served as reference for comparison; 2) univariate spatial model which incorporated the spatial random effects to account for the spatial correlation structures among the TAZs; and 3) multivariate model which addressed the potential correlation among the dependent variables and allowed the simultaneous prediction to generate more precise estimates. Many socioeconomic variables were observed to be influential such as household density, population, education, and poverty. Such phenomenon indicates the disproportionate impact of Ozone and PM2.5 emissions on the specific areas which requires the efforts to emphasize social equity and environmental justice. In terms of factors pertaining to traffic conditions, traffic density was observed to be statistically significant which served as an indicator of vehicular emissions. The univariate spatial model revealed the influence of space for ozone prediction as a significant positive correlation was recorded, which reflected the large amount of variability explained by spatial random effects that may have escaped the explanatory variables. This finding highlighted that, relative to PM2.5, ozone emission models benefit with the inclusion of spatial correlations as such dependency may be more profound. In terms of model performance at goodness-of-fit, the multivariate model significantly outperformed the others by demonstrating lowest posterior deviance without a notable increase in model complexity suggesting the implementation of joint modeling for PM2.5 and ozone prediction. However, the spatial model was observed to be superior based on predictive accuracy which indicated the importance of accommodating the spatial correlation to account for unobserved heterogeneity and obtain more precise posterior estimates with minimum deviation from observed data.

Author(s): Gurdiljot Singh Gill, Wen Cheng

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