Characterization of Atmospheric Pollutants over Industrial Areas: Source Apportionment and Transport Modeling



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The high concentration of air pollutants in the atmosphere presents a threat to human health and the ecosystem. This dissertation proposes an application of source apportionment and atmospheric transport modeling for studying the prevalent air pollutants over industrial metropolitan areas. In the first chapter, we applied a deep convolutional neural network model and examined the underlying nonlinearity between meteorological variables and surface ozone variations over three regions of Texas. Results showed that specific humidity (26% and 23%) and temperature (19% and 21%) contributed the most to ozone formation over Houston and Dallas, respectively. Solar radiation (%38) impacted ozone variations over West Texas. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on MDA8 ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. In the second chapter, we performed a source characterization analysis to identify the dominant sources affecting the PM2.5 in Houston metropolitan area. The results showed that gas-phase and aqueous-phase oxidation processes contributed to 39.1% of the measured PM2.5 and biomass burning was associated with 19.2% and gasoline combustion with 14.5%. In the third chapter, to investigate the influences of seasonal variations on ozone precursors, we analyzed the summertime and wintertime Volatile Organic Compounds (VOCs) measured in Houston industrial area. We found alkane-linked sources constitute a significant part of VOCs measured in an industrial area in both seasons, but alkene compounds have a higher potential for the formation of ozone. Ambient concentrations of VOC in both seasons revealed compounds that were measured at the HSC were influenced by the emissions from the petrochemical sectors and industrial complexes, especially from the Baytown refinery and Bayport industrial district next to the HSC and Galveston Bay refineries at the south of the study area. In the last chapter, we introduced a developed model of convective mixing of atmospheric pollutants in the presence of clouds. We developed a mass-flux-based convection module based on Kain and Fritsch (KF) method. The method is consistent with the convection parametrization of the meteorology model, and the test results in the chemical transport model CMAQ showed the developed model could be employed for studying ozone in large domains around the world.



Surface Ozone, Air quality, Dispersion Transport, Fine Particulate Matter


Portions of this document appear in: Sadeghi, B., Choi, Y., Yoon, S., Flynn, J., Kotsakis, A., & Lee, S. (2020). The characterization of fine particulate matter downwind of Houston: Using integrated factor analysis to identify anthropogenic and natural sources. Environmental Pollution, 262, 114345; and in: Sadeghi, B., Pouyaei, A., Choi, Y., & Rappenglueck, B. (2022). Influence of seasonal variability on source characteristics of VOCs at Houston industrial area. Atmospheric Environment, 119077; and in: Pouyaei, A., Sadeghi, B., Choi, Y., Jung, J., Souri, A. H., Zhao, C., & Song, C. H. (2021). Development and implementation of a physics‐based convective mixing scheme in the Community Multiscale Air quality modeling framework. Journal of Advances in Modeling Earth Systems, 13(6), e2021MS002475.