Photochemical oxidation of VOCs and their source impact assessment on ozone under de-weather conditions in Western...

The application of statistical models has excellent potential to provide crucial information for mitigating the challenging issue of ozone (O₃) pollution by capturing its associations with explanatory variables, including reactive precursors (VOCs and NO_X) and meteorology. Considering the large contribution of O₃ in degrading the air quality of western Taiwan, three-year (2019-2021) hourly concentration data of VOC, NO_X and O₃ from 4 monitoring stations of western Taiwan: Tucheng (TC), Zhongming (ZM), Taixi (TX) and Xiaogang (XG), was evaluated to identify the effect of anthropogenic emissions on O₃ formation. Owing to the high-ambient reactivity of VOCs on the underestimation of sources, photochemical oxidation was assessed to calculate the consumed VOC (VOC_cons) which was followed by the source identification of their initial concentrations. VOC_cons was observed to be highest in the summer season (16.7 and 22.7 ppbC) at north (TC and ZM) and in the autumn season (17.8 and 11.4 ppbC) in southward-located stations (TX and XG, respectively). Results showed that VOCs from solvents (25-27%) were the major source at northward stations whereas VOCs-industrial emissions (30%) dominated in south. Furthermore, machine learning (ML): eXtreme Gradient Boost (XGBoost) model based de-weather analysis identified that meteorological factors favor to reduce ambient O₃ levels at TC, ZM and XG stations (-67%, -47% and -21%, respectively) but they have a major role in accumulating the O₃ (+38%) at the TX station which is primarily transported from the upwind region of south-central Taiwan. Crucial insights using ML outputs showed that the finding of the study can be utilized for region-specific data-driven control of emission from VOCs-sources and prioritized to limit the O₃-pollution at the study location-ns as well as their accumulation in distant regions.