Impact of variable emissions on ozone formation in the Houston area
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Ground level ozone is one of the most ubiquitous air pollutants in urban areas, and is generated by photochemical reactions of oxides of nitrogen (NOx) and volatile organic compounds (VOCs). The effectiveness of emission reduction strategies for ozone precursors is typically evaluated using gridded, photochemical air quality models. One of the underlying assumptions in these models is that industrial emissions are nearly constant, since many industrial facilities operate continuously at a constant rate of output. However, recent studies performed in the Houston-Galveston-Brazoria area indicate that some industrial emission sources exhibit high temporal emission variability that can lead to very rapid ozone formation, especially when emissions are composed of highly reactive volatile organic compounds. This work evaluates the impact of variable emissions from industrial sources on ground-level ozone formation in Houston area, utilizing a unique hourly emission inventory, known as the 2006 Special Inventory, created as a part of the second Texas Air Quality Study. Comparison of the hourly emissions inventory data with ambient measurements indicated that the impact of the variability of industrial source emissions on ozone can be significant. Photochemical modeling predictions showed that the variability in industrial emissions can lead to differences in local ozone concentrations of as much as 27 ppb at individual ozone monitor locations. The hourly emissions inventory revealed that industrial source emissions are highly variable in nature with diverse temporal patterns and stochastic behavior. Petrochemical and chemical manufacturing flares, which represent the majority of emissions in the 2006 Special Inventory, were grouped into categories based on industrial process, chemical composition of the flared gas, and the temporal patterns of their emissions. Stochastic models were developed for each categorization of flare emissions with the goal of simulating the characterized temporal emission variability. The stochastic models provide representative temporal profiles for flares in the petrochemical manufacturing and chemical manufacturing sectors, and as such serve as more comprehensive input for photochemical air quality modeling.