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Atmosphere
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Air Pollution in China (3rd Edition)
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Air Pollution in China (3rd Edition)

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: 17 March 2025 | Viewed by 4178

Special Issue Editor

Prof. Dr. Xiao-San Luo

E-Mail Website
Guest Editor
International Center for Ecology, Meteorology, and Environment, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: environmental geochemistry and health; air pollution; atmospheric particulate matters; bioaerosols; emerging contaminants; nano-plastics; heavy metals; toxicology; risk assessments; climate change and health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of the Special Issue, titled ‘Air Pollution in China (2nd Edition)’ (https://www.mdpi.com/journal/atmosphere/special_issues/Air_Pollution_in_China_2nd) published in Atmosphere in 2023 and will cover all aspects of Chinese atmospheric-pollution issues.

In China, serious air pollution, caused by human activities and partly natural factors, has been apparent since around the 1990s. It is worth mentioning that local air pollution has greatly improved in the past 5 years, mainly due to progress in institutional and technical measures since the 2010s. However, the trajectory of air pollution in China is changing at present due to the compound event of photochemical and aerosol pollution, and air pollution control has thus entered a new phase.

 This Special Issue, ‘Air Pollution in China (3rd Edition)’ invites submissions of innovative papers that will help with the development of the Chinese atmospheric environment and the implementation of effective air pollution control strategies in the future.

Prof. Dr. Xiao-San Luo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • air pollution and health in China
  • atmospheric fine particulate matters
  • aerosols
  • bioaerosols
  • micro/nano-plastics
  • ozone
  • emerging contaminants
  • toxicology and risk assessments
  • air pollution and climate change
  • air pollution observation in China
  • remote sensing of air pollution in China
  • numerical simulation of air pollution in China
  • air pollution prediction method in China
  • air quality management and pollution control in China

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Published Papers (4 papers)

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Research

24 pages, 3886 KiB  
Article
De-Carbonisation Pathways in Jiangxi Province, China: A Visualisation Based on Panel Data
by Shun Li, Jie Hua and Gaofeng Luo
Atmosphere 2024, 15(9), 1108; https://doi.org/10.3390/atmos15091108 - 11 Sep 2024
Viewed by 779
Abstract
Environmental degradation remains a pressing global concern, prompting many nations to adopt measures to mitigate carbon emissions. In response to international pressure, China has committed to achieving a carbon peak by 2030 and carbon neutrality by 2060. Despite extensive research on China’s overall [...] Read more.
Environmental degradation remains a pressing global concern, prompting many nations to adopt measures to mitigate carbon emissions. In response to international pressure, China has committed to achieving a carbon peak by 2030 and carbon neutrality by 2060. Despite extensive research on China’s overall carbon emissions, there has been limited focus on individual provinces, particularly less developed provinces. Jiangxi Province, an underdeveloped province in southeastern China, recorded the highest GDP (Gross Domestic Product) growth rate in the country in 2022, and it holds significant potential for carbon emission reduction. This study uses data from Jiangxi Province’s 14th Five-Year Plan and Vision 2035 to create three carbon emission reduction scenarios and predict emissions. The extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology), along with various visualisation techniques, is employed to analyse the impacts of population size, primary electricity application level, GDP per capita, the share of the secondary industry in fixed-asset investment, and the number of civilian automobiles owned on carbon emissions. The study found that there is an inverted U-shaped curve relationship between GDP per capita and carbon emissions, car ownership is not a major driver of carbon emissions, and the development of primary electricity has significant potential as a means for reducing carbon emissions in Jiangxi Province. If strict environmental protection measures are implemented, Jiangxi Province can reach its peak carbon target by 2029, one year ahead of the national target. These findings provide valuable insights for policymakers in Jiangxi Province to ensure that their environmental objectives are met. Full article
(This article belongs to the Special Issue Air Pollution in China (3rd Edition))
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22 pages, 4005 KiB  
Article
Assessing PM2.5 Dynamics and Source Contributions in Southwestern China: Insights from Winter Haze Analysis
by Hui Guan, Ziyun Chen, Jing Tian and Huayun Xiao
Atmosphere 2024, 15(7), 855; https://doi.org/10.3390/atmos15070855 - 19 Jul 2024
Viewed by 615
Abstract
Despite enhancements in pollution control measures in southwestern China, detailed assessments of PM2.5 dynamics following the implementation of the Clean Air Action remain limited. This study explores the PM2.5 concentrations and their chemical compositions during the winter haze period of 2017 [...] Read more.
Despite enhancements in pollution control measures in southwestern China, detailed assessments of PM2.5 dynamics following the implementation of the Clean Air Action remain limited. This study explores the PM2.5 concentrations and their chemical compositions during the winter haze period of 2017 across four major urban centers—Chengdu, Chongqing, Guiyang, and Kunming. Significant variability in mean PM2.5 concentrations was observed: Chengdu (71.8 μg m−3) and Chongqing (53.3 μg m−3) recorded the highest levels, substantially exceeding national air quality standards, while Guiyang and Kunming reported lower concentrations, suggestive of comparatively milder pollution. The analysis revealed that sulfate, nitrate, and ammonium (collectively referred to as SNA) constituted a substantial portion of the PM2.5 mass—47.2% in Chengdu, 62.2% in Chongqing, 59.9% in Guiyang, and 32.0% in Kunming—highlighting the critical role of secondary aerosol formation. The ratio of NO3−/SO42− and nitrogen oxidation ratio to sulfur oxidation ratio (NOR/SOR) indicate a significant transformation of NO2 under conditions of heavy pollution, with nitrate formation playing an increasingly central role in the haze dynamics, particularly in Chengdu and Chongqing. Utilizing PMF for source apportionment, in Chengdu, vehicle emissions were the predominant contributor, accounting for 33.1%. Chongqing showed a similar profile, with secondary aerosols constituting 36%, followed closely by vehicle emissions. In contrast, Guiyang’s PM2.5 burden was heavily influenced by coal combustion, which contributed 46.3%, reflecting the city’s strong industrial base. Kunming presented a more balanced source distribution. Back trajectory analysis further confirmed the regional transport of pollutants, illustrating the complex interplay between local and distant sources. These insights underscore the need for tailored, region-specific air quality management strategies in southwestern China, thereby enhancing our understanding of the multifaceted sources and dynamics of PM2.5 pollution amidst ongoing urban and industrial development. Full article
(This article belongs to the Special Issue Air Pollution in China (3rd Edition))
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13 pages, 1820 KiB  
Article
A Graph Attention Recurrent Neural Network Model for PM2.5 Prediction: A Case Study in China from 2015 to 2022
by Rui Pan, Tuozhen Liu and Lingfei Ma
Atmosphere 2024, 15(7), 799; https://doi.org/10.3390/atmos15070799 - 3 Jul 2024
Viewed by 836
Abstract
Accurately predicting PM2.5 is a crucial task for protecting public health and making policy decisions. In the meanwhile, it is also a challenging task, given the complex spatio-temporal patterns of PM2.5 concentrations. Recently, the utilization of graph neural network (GNN) models [...] Read more.
Accurately predicting PM2.5 is a crucial task for protecting public health and making policy decisions. In the meanwhile, it is also a challenging task, given the complex spatio-temporal patterns of PM2.5 concentrations. Recently, the utilization of graph neural network (GNN) models has emerged as a promising approach, demonstrating significant advantages in capturing the spatial and temporal dependencies associated with PM2.5 concentrations. In this work, we collected a comprehensive dataset spanning 308 cities in China, encompassing data on seven pollutants as well as meteorological variables from January 2015 to September 2022. To effectively predict the PM2.5 concentrations, we propose a graph attention recurrent neural network (GARNN) model by taking into account both meteorological and geographical information. Extensive experiments validated the efficiency of the proposed GARNN model, revealing its superior performance compared to other existing methods in terms of predictive capabilities. This study contributes to advancing the understanding and prediction of PM2.5 concentrations, providing a valuable tool for addressing environmental challenges. Full article
(This article belongs to the Special Issue Air Pollution in China (3rd Edition))
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12 pages, 8950 KiB  
Article
Improvement of PM2.5 Forecast in China by Ground-Based Multi-Pollutant Emission Source Inversion in 2022
by Lili Zhu, Xiao Tang, Wenyi Yang, Yao Zhao, Lei Kong, Huangjian Wu, Meng Fan, Chao Yu and Liangfu Chen
Atmosphere 2024, 15(2), 181; https://doi.org/10.3390/atmos15020181 - 31 Jan 2024
Viewed by 1229
Abstract
This study employs an ensemble Kalman filter assimilation method to validate and update the pollutant emission inventory to mitigate the impact of uncertainties on the forecasting performance of air quality numerical models. Based on nationwide ground-level pollutant monitoring data in China, the emission [...] Read more.
This study employs an ensemble Kalman filter assimilation method to validate and update the pollutant emission inventory to mitigate the impact of uncertainties on the forecasting performance of air quality numerical models. Based on nationwide ground-level pollutant monitoring data in China, the emission inventory for the entire country was inverted hourly in 2022. The emission rates for PM2.5, CO, NOx, SO2, NMVOCs, BC, and OC updated by the inversion were determined to be 6.6, 702.4, 37.2, 13.4, 40.3, 3, and 18.2 ng/s/m2, respectively. When utilizing the inverted inventory instead of the priori inventory, the average accuracy of all cities’ PM2.5 forecasts was improved by 1.5–4.2%, especially for a 7% increase on polluted days. The improvement was particularly remarkable in the periods of January–March and November–December, with notable increases in the forecast accuracy of 12.5%, 12%, and 6.8% for the Northwest, Northeast, and North China regions, respectively. The concentration values and spatial distribution of PM2.5 both became more reasonable after the update. Significant improvements were particularly observed in the Northwest region, where the forecast accuracy for all preceding days was improved by approximately 15%. Additionally, the underestimated concentration of PM2.5 in the priori inventory compared to the observation value was notably alleviated by the application of the inversion. Full article
(This article belongs to the Special Issue Air Pollution in China (3rd Edition))
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