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.
Highlights
PM2.5, fine particulate matter with aerodynamic diameter less than 2.5 μm, is a complex heterogeneous mixture of chemical constituents originating from a variety of sources (Bressi et al, 2013; He et al, 2019; Kelly and Fussell, 2012)
The results showed a strong correlation with years: cluster 1 (C1) consisted of most sites in 2018 and 2019; sites in 2016 and 2017 were classified as cluster 2 (C2); cluster 3 (C3) included all the sites in 2015; and 2016DJY3, the only site far from the other sites, was separated as cluster 4 (C4)
As a typical background site in Chengdu, DJY3 is surrounded by plants and agricultural activities, so it is featured by the distinctive PM2.5 compositions with markedly high NH+4 and crustal elements
Summary
PM2.5, fine particulate matter with aerodynamic diameter less than 2.5 μm, is a complex heterogeneous mixture of chemical constituents originating from a variety of sources (Bressi et al, 2013; He et al, 2019; Kelly and Fussell, 2012). The link between urbanization and the spatiotemporal variability of PM2.5 has been studied (Zhang et al, 2015; Li et al, 2016). A thorough understanding of the spatiotemporal and policy-related variations of PM2.5 is necessary to investigate the relationship between urbanization and PM2.5. The spatiotemporal variability of PM2.5 with the impact of urbanization has been reported in previous studies (Li et al, 2016; Timmermans et al, 2017; Zhang et al, 2019; Yang et al, 2020; Seto et al, 2017), among which a small number of publications focused on the analysis of PM2.5 composition and sources (Lin et al, 2014; Yan et al, 2018). Related studies are critical for promulgating targeted control policies from the perspective of urbanization
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
