Severe particulate matter (PM, including PM and PM) pollution frequently impacts many cities in the Yangtze River Delta (YRD) in China, which has aroused growing concern. In this study, we examined the associations between relative humidity (RH) and PM pollution using the equal step-size statistical method. Our results revealed that RH had an inverted U-shaped relationship with PM concentrations (peaking at RH = 45-70%), and an inverted V-shaped relationship (peaking at RH = 40 ± 5%) with PM, SO, and NO. The trends of polluted-day number significantly changed at RH = 70%. The very-dry (RH < 45%), dry (RH = 45-60%) and low-humidity (RH = 60-70%) conditions positively affected PM and exerted an accumulation effect, while the mid-humidity (RH = 70-80%), high-humidity (RH = 80-90%), and extreme-humidity (RH = 90-100%) conditions played a significant role in reducing particle concentrations. For PM, the accumulation and reduction effects of RH were split at RH = 45%. Moreover, an upward slope in the PM/PM ratio indicated that the accumulation effects from increasing RH were more intense on PM than on PM, while the opposite was noticed for the reduction effects. Secondary transformations from SO and NO to sulfate and nitrate were mainly responsible for PM pollution, and thus, controlling these precursors is effective in mitigating the PM pollution in the YRD, especially during winter. The conclusions in this study will be helpful for regional air-quality management.

Recent studies in PM2.5 sources show that anthropogenic emissions are the main contributors to haze pollution. Due to their essential roles in establishing policies for improving air quality, socioeconomic drivers of PM2.5 levels have attracted increasing attention. Unlike previous studies focusing on the annual PM2.5 concentration (Cyear), this paper focuses on the accumulation phase of PM2.5 during the pollution episode (PMAE) in the Yangtze River Delta in China. This paper mainly explores the spatial variations of PMAE and its links to the socioeconomic factors using a geographical detector and simple linear regression. The results indicated that PM2.5 was more likely to accumulate in more developed cities, such as Nanjing and Shanghai. Compared with Cyear, PMAE was more sensitive to socioeconomic impacts. Among the twelve indicators chosen for this study, population density was an especially critical factor that could affect the accumulation of PM2.5 dramatically and accounted for the regional difference. A 1% increase in population density could cause a 0.167% rise in the maximal increment and a 0.214% rise in the daily increase rate of PM2.5. Additionally, industry, energy consumption, and vehicles were also significantly associated with PM2.5 accumulation. These conclusions could serve to remediate the severe PM2.5 pollution in China.