Given the critical roles of nitrates and sulfates in fine particulate matter (PM2.5) formation, we examined spatiotemporal associations between PM2.5 and sulfur dioxide (SO2) as well as nitrogen dioxide (NO2) in China by taking advantage of the in situ observations of these three pollutants measured from the China national air quality monitoring network for the period from 2015 to 2018. Maximum covariance analysis (MCA) was applied to explore their possible coupled modes in space and time. The relative contribution of SO2 and NO2 to PM2.5 was then quantified via a statistical modeling scheme. The linear trends derived from the stratified data show that both PM2.5 and SO2 decreased significantly in northern China in terms of large values, indicating a fast reduction of high PM2.5 and SO2 loadings therein. The statistically significant coupled MCA mode between PM2.5 and SO2 indicated a possible spatiotemporal linkage between them in northern China, especially over the Beijing-Tianjin-Hebei region. Further statistical modeling practices revealed that the observed PM2.5 variations in northern China could be explained largely by SO2 rather than NO2 therein, given the estimated relatively high importance of SO2. In general, the evidence-based results in this study indicate a strong linkage between PM2.5 and SO2 in northern China in the past few years, which may help to better investigate the mechanisms behind severe haze pollution events in northern China.
Keywords: NO2; PM2.5 pollution; SO2; maximum covariance analysis; spatiotemporal association.