Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity

Abstract

Abstract. A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a rapidly developing Chinese megacity, Chengdu, as the study area to investigate the spatiotemporal and policy-related variations of PM2.5 composition and sources based on long-term observation at multiple sites. A total of 836 samples were collected from 19 sites in winter 2015–2019. According to the specific characteristics, 19 sampling sites were assigned to three layers. Layer 1 was the most urbanized area and referred to the core zone of Chengdu, layer 2 was located in the outer circle of layer 1, and layer 3 belonged to the outermost zone with the lowest urbanization level. The average PM2.5 concentrations for 5 years were in the order of layer 2 (133 µg m−3) > layer 1 (126 µg m−3) > layer 3 (121 µg m−3). Spatial clustering of the chemical composition at the sampling sites was conducted for each year. The PM2.5 composition of layer 3 in 2019 was found to be similar to that of the other layers 2 or 3 years ago, implying that urbanization levels had a strong effect on air quality. During the sampling period, a decreasing trend was observed for the annual average concentration of PM2.5, especially at sampling sites in layer 1, where the stricter control policies were implemented. The SO42-/NO3- mass ratio at most sites exceeded 1 in 2015 but dropped to less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu, and these values can be further supported by temporal variations of the SO42- and NO3- concentrations. The positive matrix factorization (PMF) model was applied to quantify PM2.5 sources. A total of five sources were identified, with average contributions of 15.5 % (traffic emissions), 19.7 % (coal and biomass combustion), 8.8 % (industrial emissions), 39.7 % (secondary particles), and 16.2 % (resuspended dust). From 2015 to 2019, a dramatic decline was observed in the average percentage contributions of coal and biomass combustion, but the traffic emission source showed an increasing trend. For spatial variations, the high coefficient of variation (CV) values of coal and biomass combustion and industrial emissions indicated their higher spatial difference in Chengdu. High contributions of resuspended dust occurred at sites with intensive construction activities, such as subway and airport construction. Combining the PMF results, we developed the source-weighted potential source contribution function (SWPSCF) method for source localization. This new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was evaluated.

Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021

“…The decreasing rates of deweathered NO 2 (17.3%) and SO 2 (30.0%) during 2013–2017 were slightly higher or comparable to those during 2018–2021 (9.08% and 29.0%) in BTH, while they displayed opposite characteristics in YRD and PRD. Such characteristic confirmed that emission reduction measures in YRD and PRD have been further strengthened after 2018 (Feng et al., 2021; Yang et al., 2022) and the emission control measures in these regions might be more efficient than BTH. In fact, BTH still suffered from severe NO 2 pollution compared with other regions in China, and thus it is urgent to further decrease NO 2 level in this region.…”

Chemical characteristics and sources of PM2.5 in Hohhot, a semi-arid city in northern China: insight from the COVID-19 lockdown

“…The result suggests that the contribution of chemical composition related to secondary formation has increased in recent years. However, the proportion of MD was still substantially higher than those of other cities in South China (Huang et al, 2013a); southwest China (Feng et al, 2021); southeast China (Li et al, 2017b); and the Central Plains Urban Agglomeration (Liu et al, 2019), which is close to the cities in northern China (Liu et al, 2021;Xie et al, 2019) and northwest China (Zhou et al, 2021). The lower RH, higher WS and larger area of uncovered surface soil lead to frequent dust storms in semi-arid regions, resulting in a higher contribution of MD than in the humid area (Liang et al, 2019).…”

Quantifying the seasonal variations and regional transport of PM2.5 in the Yangtze River Delta region, China: Characteristics, sources, and health risks

“…CC BY 4.0 License. (Feng et al, 2021). In this study, the meteorological data used for the HYSPLIT model and the CWT method remained the same.…”