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Multidisciplinary Research and Data Science for Advancing Air Quality and...
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Multidisciplinary Research and Data Science for Advancing Air Quality and Environmental Health

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 251

Special Issue Editors

Dr. Yuqing Dai

E-Mail Website
Guest Editor
School of Geography Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
Interests: air quality; data science; machine learning; environmental health; pollution intervention strategies; air quality modelling
Dr. Joe Acton

E-Mail Website
Guest Editor
School of Geography Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
Interests: air quality; atmospheric chemistry; clean air policies; sources of air pollution; eddy covariance; volatile organic compounds (VOCs)

Special Issue Information

Dear Colleagues,

Air pollution remains one of the most significant environmental risks to human health. According to the World Health Organization (WHO), approximately 4.2 million premature deaths annually are linked to ambient air pollution, with even lower pollutant levels proving harmful. As cities and regions seek to develop more effective strategies to mitigate these risks, there is an urgent need for multidisciplinary approaches that integrate cutting-edge research and innovative data science techniques.

This Special Issue, titled "Multidisciplinary Research and Data Science for Advancing Air Quality and Environmental Health", seeks contributions that address the complexities of air pollution and its impact on public health. We encourage submissions from diverse fields, including environmental science, public health, and data science, with a focus on the following topics:

  1. Air quality modelling: Investigating the chemical and physical processes of pollutants and simulating the behavior and interaction of air pollutants at various spatial scales.
  2. Health impact assessments: Evaluating the direct and indirect health effects of air pollution on vulnerable populations, including the impacts of particulate matter (PM2.5, PM10), nitrogen oxides (NOx), and ozone (O3).
  3. Exposure and risk assessments: Utilizing advanced data analytics, remote sensing, and machine learning to assess population exposure to pollution and develop risk models that inform public health strategies.
  4. Policy and intervention studies: Quantifying the effectiveness of air quality policies and interventions, including Clean Air Zones and urban traffic management, and examining the co-benefits of broader environmental strategies like Net Zero initiatives.

This Special Issue aims to integrate multidisciplinary insights and advanced analytical techniques, providing actionable research for cleaner, healthier urban environments. By bridging the gap between research, data science, and policy, we can develop effective strategies to protect public health and advance sustainable urban development.

We look forward to receiving your valuable contributions.

Dr. Yuqing Dai
Dr. Joe Acton
Guest Editors

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 quality
  • data science
  • multidisciplinary research
  • environmental health
  • pollution mitigation strategies

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

19 pages, 11697 KiB  
Article
Evaluating Policy Interventions for Air Quality During a National Sports Event with Machine Learning and Causal Framework
by Jing Guo, Ruixin Xu, Bowen Liu, Mengdi Kong, Yue Yang, Zongbo Shi, Ruiqin Zhang and Yuqing Dai
Atmosphere 2025, 16(5), 557; https://doi.org/10.3390/atmos16050557 - 7 May 2025
Viewed by 23
Abstract
Short-term control measures are often implemented during major events to improve air quality and protect public health. In preparation for the 11th National Traditional Games of Ethnic Minorities of China (denoted as “NMG”), held from 8 to 16 September 2019 in Zhengzhou, China, [...] Read more.
Short-term control measures are often implemented during major events to improve air quality and protect public health. In preparation for the 11th National Traditional Games of Ethnic Minorities of China (denoted as “NMG”), held from 8 to 16 September 2019 in Zhengzhou, China, the authorities introduced several air pollution control measures, including traffic restrictions and dust control. In the study presented herein, we applied automated machine learning-based weather normalisation combined with an augmented synthetic control method (ASCM) to evaluate the effectiveness of these interventions. Our results show that the impacts of the NMG control measures were not uniform, varying significantly across pollutants and monitoring stations. On average, nitrogen dioxide (NO2) concentrations decreased by 8.6% and those of coarse particles (PM10) decreased by 3.0%. However, the interventions had little overall effect on fine particles (PM2.5), despite clear reductions observed at the traffic site, where NO2 and PM2.5 levels decreased by 7.2 and 5.2 μg m−3, respectively. These reductions accounted for 56.3% of the NMG policy’s effect on NO2 concentration and 73.2% of its effect on PM2.5 concentration at the traffic site. Notably, the control measures led to an increase in ozone (O3) concentrations. Our results demonstrate the moderate effect of the short-term NMG intervention, emphasising the necessity for holistic strategies that address pollutant interactions, such as nitrogen oxides (NOX) and volatile organic compounds (VOCs), as well as location-specific variability to achieve sustained air quality improvements. Full article
(This article belongs to the Special Issue Multidisciplinary Research and Data Science for Advancing Air Quality and Environmental Health)
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