Investigating the characteristics and source analyses of PM2.5 seasonal variations in Chengdu, Southwest China

Kong, Liuwei; Tan, Qinwen; Feng, Miao; Yu, Qian; An, Junling; Liu, Xingang; Cheng, Ning; Deng, Yijun; Zhai, Ruixiao; Wang, Zheng

doi:10.1016/j.chemosphere.2019.125267

Cited by 84 publications

(38 citation statements)

References 73 publications

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“…In particular, it was higher than 60%, a threshold that is considered uncomfortable for many people. Moreover, it is accepted that PMs descend when the wind velocity increases [ 14 , 40 , 47 , 48 , 49 , 50 ]. Minimum PM values occur when the wind velocity reaches 1.0–2.0 m/s [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

PM2.5 Pollution Strongly Predicted COVID-19 Incidence in Four High-Polluted Urbanized Italian Cities during the Pre-Lockdown and Lockdown Periods

Kotsiou

Kotsios

Lampropoulos

et al. 2021

IJERPH

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Background: The coronavirus disease in 2019 (COVID-19) heavily hit Italy, one of Europe’s most polluted countries. The extent to which PM pollution contributed to COVID-19 diffusion is needing further clarification. We aimed to investigate the particular matter (PM) pollution and its correlation with COVID-19 incidence across four Italian cities: Milan, Rome, Naples, and Salerno, during the pre-lockdown and lockdown periods. Methods: We performed a comparative analysis followed by correlation and regression analyses of the daily average PM10, PM2.5 concentrations, and COVID-19 incidence across four cities from 1 January 2020 to 8 April 2020, adjusting for several factors, taking a two-week time lag into account. Results: Milan had significantly higher average daily PM10 and PM2.5 levels than Rome, Naples, and Salerno. Rome, Naples, and Salerno maintained safe PM10 levels. The daily PM2.5 levels exceeded the legislative standards in all cities during the entire period. PM2.5 pollution was related to COVID-19 incidence. The PM2.5 levels and sampling rate were strong predictors of COVID-19 incidence during the pre-lockdown period. The PM2.5 levels, population’s age, and density strongly predicted COVID-19 incidence during lockdown. Conclusions: Italy serves as a noteworthy paradigm illustrating that PM2.5 pollution impacts COVID-19 spread. Even in lockdown, PM2.5 levels negatively impacted COVID-19 incidence.

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Section: Discussionmentioning

confidence: 99%

PM2.5 Pollution Strongly Predicted COVID-19 Incidence in Four High-Polluted Urbanized Italian Cities during the Pre-Lockdown and Lockdown Periods

Kotsiou

Kotsios

Lampropoulos

et al. 2021

IJERPH

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“…Compared with other seasons, PM 2.5 pollution was the most severe in winter. Toxic and hazardous substances such as organic matter, heavy metal (i.e., Cr, Cu, Zn, As, and Pb), and water-soluble ions (i.e., SO 4 2− and NO 3 − ) adsorbed on PM 2.5 exerted adverse effects on bacteria [41]. Moreover, low temperature in winter (Xi'an: −3-4 • C; Linfen: −1-2 • C) was not conducive to the activity of enzymes and metabolism of substances in cell membrane fluidity [42], thus inhibiting the growth and reproduction of bacteria.…”

Section: Microbial Richness and Diversitymentioning

confidence: 99%

Seasonal Variation Characteristics of Bacteria and Fungi in PM2.5 in Typical Basin Cities of Xi’an and Linfen, China

Wang

Liu

et al. 2021

Atmosphere

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Microorganisms existing in airborne fine particulate matter (PM2.5) have key implications in biogeochemical cycling and human health. In this study, PM2.5 samples, collected in the typical basin cities of Xi’an and Linfen, China, were analyzed through high-throughput sequencing to understand microbial seasonal variation characteristics and ecological functions. For bacteria, the highest richness and diversity were identified in autumn. The bacterial phyla were dominated by Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. Metabolism was the most abundant pathway, with the highest relative abundance found in autumn. Pathogenic bacteria (Pseudomonas, Acinetobacter, Serratia, and Delftia) were positively correlated with most disease-related pathways. Besides, C cycling dominated in spring and summer, while N cycling dominated in autumn and winter. The relative abundance of S cycling was highest during winter in Linfen. For fungi, the highest richness was found in summer. Basidiomycota and Ascomycota mainly constituted the fungal phyla. Moreover, temperature (T) and sulfur dioxide (SO2) in Xi’an, and T, SO2, and nitrogen dioxide (NO2) in Linfen were the key factors affecting microbial community structures, which were associated with different pollution characteristics in Xi’an and Linfen. Overall, these results provide an important reference for the research into airborne microbial seasonal variations, along with their ecological functions and health impacts.

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“…On the basis of the results from the EOF analysis, we selected cities with high pollution levels as the starting point and used the length of time of severe pollution as the pushback time. The air mass movement path at 500 m of altitude (the wind field at 500 m of altitude) can reflect the characteristics of the average flow field in the boundary layer [16,17,19]. Therefore, the height of the simulation was selected as 500 m, and we calculated the 48 h backward trajectory every 2 h.…”

Section: Backward Trajectory Analysis and Concentration-weighted Trajmentioning

confidence: 99%

“…Backward trajectory analysis is an important method for analyzing the sources of air pollution. Scholars have studied the regional transportation characteristics of atmospheric particles by analyzing their air mass trajectories [16][17][18][19]. Existing research has focused on descriptions of long-term pollution sources in a single region, which cannot distinguish between the air mass trajectory during a heavily polluted period and the air mass trajectory during a slightly polluted period.…”

Section: Introductionmentioning

confidence: 99%

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

Zhao

et al. 2020

Sustainability

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China has made some progress in controlling PM2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) pollution, but there are still some key areas that need further strengthening. Considering that excessive prevention and control efforts affect economic development, this paper combined an empirical orthogonal function, a continuous wavelet transform, and a concentration-weighted trajectory method to study joint regional governance during key pollution periods to provide suggestions for the efficient control of PM2.5. The results from our panel of data of PM2.5 in China from 2016 to 2018 could be decomposed into two modes. In the first mode, the pollution center was in central Shaanxi Province, and the main eruption period was from November to January of the following year. As the center of this region, Xi’an should cooperate with the four cities in eastern Sichuan (Nanchong, Guangan, Bazhong, and Dazhou) to control PM2.5, since the eruption occurred in this area. Moreover, governance should last for at least two cycles, where one cycle is at least 23 days. The pollution center of the second mode was in the western part of Xinjiang. Therefore, after the prevention and control efforts during the first mode are completed, the regional city of Kashgar should continue to build a joint governance zone for PM2.5 along the Tianshan mountains in the east, focusing on prevention and control over two cycles (where one cycle is 28 days).

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Investigating the characteristics and source analyses of PM2.5 seasonal variations in Chengdu, Southwest China

Cited by 84 publications

References 73 publications

PM2.5 Pollution Strongly Predicted COVID-19 Incidence in Four High-Polluted Urbanized Italian Cities during the Pre-Lockdown and Lockdown Periods

PM2.5 Pollution Strongly Predicted COVID-19 Incidence in Four High-Polluted Urbanized Italian Cities during the Pre-Lockdown and Lockdown Periods

Seasonal Variation Characteristics of Bacteria and Fungi in PM2.5 in Typical Basin Cities of Xi’an and Linfen, China

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

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