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

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 NOX) 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, NOX 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 (VOCcons) which was followed by the source identification of their initial concentrations. VOCcons 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.

The influence of COVID-19 pandemic on PM2.5 air quality in Northern Taiwan from Q1 2020 to Q2 2021.

The study aimed to investigate the PM2.5 variations in different periods of COVID-19 control measures in Northern Taiwan from Quarter 1 (Q1) 2020 to Quarter 2 (Q2) 2021. PM2.5 sources were classified based on long-range transport (LRT) or local pollution (LP) in three study periods: one China lockdown (P1), and two restrictions in Taiwan (P2 and P3). During P1 the average PM2.5 concentrations from LRT (LRT-PM2.5-P1) were higher at Fuguei background station by 27.9% and in the range of 4.9-24.3% at other inland stations compared to before P1. The PM2.5 from LRT/LP mix or pure LP (Mix/LP-PM2.5-P1) was also higher by 14.2-39.9%. This increase was due to higher secondary particle formation represented by the increase in secondary ions (SI) and organic matter in PM2.5-P1 with the largest proportion of 42.17% in PM2.5 from positive matrix factorization (PMF) analysis. A similar increasing trend of Mix/LP-PM2.5 was found in P2 when China was still locked down and Taiwan was under an early control period but the rapidly increasing infected cases were confirmed. The shift of transportation patterns from public to private to avoid virus infection explicated the high correlation of the increasing infected cases with the increasing PM2.5. In contrast, the decreasing trend of LP-PM2.5-P3 was observed in P3 with the PM2.5 biases of ∼45% at all the stations when China was not locked down but Taiwan implemented a semi-lockdown. The contribution of gasoline vehicle sources in PM2.5 was reduced from 20.3% before P3 to 10% in P3 by chemical signatures and source identification using PMF implying the strong impact of strict control measures on vehicle emissions. In summary, PM2.5 concentrations in Northern Taiwan were either increased (P1 and P2) or decreased (P3) during the COVID-19 pandemic depending on control measures, source patterns and meteorological conditions.