A better knowledge of surface ozone variations and the relevant influential factors is of great significance for controlling frequent ozone pollution events. In this study, we first examined the primary variation patterns of surface ozone in space and time across China via a clustering analysis on the basis of daily maximum 8h average surface ozone (MDA8) between 2015 and 2018. Statistical models were then established between MDA8 and a set of influential factors to pinpoint dominant factors contributing to regional MDA8 variations. The clustering results revealed four typical variation patterns of MDA8 in China given distinct pollution levels, seasonality, and long-term trends. Statistical modeling results indicated that the seasonal variability of MDA8 was closely associated with UV radiation and meteorological factors like boundary layer height, temperature and relative humidity. In contrast, the long-term trends of MDA8 were largely linked to ozone precursors and meteorological variables including temperature, relative humidity, and total cloud cover. Moreover, the phenomenal increasing trends of MDA8 in North China were found to be statistically associated with the depletion of nitrogen dioxide (NO2) and carbon monoxide (CO). Specifically, substantial increases in volatile organic compounds (VOCs) along with depletions in NO2 and CO significantly boosted the photochemical ozone formation chain process in a VOC-limited regime like the North China plain. Overall, the inferred linkage in this study provides evidence and clues to help control increasing ozone pollution events in North China.
Keywords: Air quality management; Ozone pollution; Spatial clustering; Statistical modeling; Surface ozone.
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