Air Pollution: Health Risks and Mitigation Strategies

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2767

Special Issue Editors


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Guest Editor
1. Institute of Clinical Physiology of the National Research Council (CNR-IFC), 56124 Pisa, Italy
2. Research Group in Social and Nutritional Epidemiology, Pharmacoepidemiology and Public Health, Department of Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine, Faculty of Pharmacy, Universitat de València, Av. Vicent Andrés Estelles s/n, 46100 Burjassot, Valencia, Spain
Interests: environmental epidemiology; human health; environmental pollution; epidemiological studies; biostatistics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Research Group in Social and Nutritional Epidemiology, Pharmacoepidemiology and Public Health, Department of Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine, Faculty of Pharmacy, Universitat de València, Av. Vicent Andrés Estelles s/n, 46100 Burjassot, Valencia, Spain
2. Biomedical Research Center in Epidemiology and Public Health Network (CIBERESP), Carlos III Health Institute, Av. Monforte de Lemos 3-5 Pabellón 11 Planta 0, 28029 Madrid, Spain
Interests: environmental epidemiology; human health; environmental pollution; epidemiological studies; biostatistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The detrimental health effects of major air pollutants, particularly on the cardiovascular and respiratory systems, are well-documented through robust epidemiological and toxicological studies. However, research is increasingly exploring the broader health impacts, including potential effects on reproductive health and childhood neurological development.

A critical barrier to fully elucidating the health burden of air pollution lies in accurately quantifying individual exposure. Unlike single-source contaminants, air pollution presents a complex mixture with spatiotemporal variability due to factors like traffic patterns, meteorology, and even building layouts. Additionally, individual factors such as age, respiratory health, and activity patterns significantly influence exposure.

Traditional methods, such as relying on central monitoring stations or residence location, often fail to capture these intricate variations, potentially underestimating individual health risks. This Special Issue addresses this gap by focusing on studies that utilized advanced exposure assessment techniques. We encourage submissions that employ the following:

  • Sophisticated air quality models: these models incorporate diverse data sets on air quality, meteorology, and human behavior to estimate individual exposure with greater precision.
  • Personal exposure monitors: worn by participants, these devices directly measure an individual's exposure to air pollutants throughout their daily routines.

We welcome research investigating any health effects associated with these advanced exposure assessments, alongside literature reviews that summarize the current state of knowledge in overcoming the challenges of individual exposure assessment in air pollution research.

Dr. Gabriele Donzelli
Prof. Dr. María M. Morales Suárez-Varela
Guest Editors

Manuscript Submission Information

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Keywords

  • environmental epidemiology
  • air pollution
  • environmental health
  • human health
  • burden of disease

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Published Papers (3 papers)

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Research

22 pages, 974 KiB  
Article
Environmental Information Disclosure and Firms’ Green Total Factor Productivity: Evidence from New Ambient Air Quality Standards in China
by Jiemei Hu, De Xiao, Baoxi Li and Lv Peng
Atmosphere 2025, 16(2), 155; https://doi.org/10.3390/atmos16020155 - 31 Jan 2025
Viewed by 283
Abstract
Green total factor productivity (GTFP) is a key factor for achieving sustainable development and enhancing economic competitiveness. Environmental information disclosure plays a significant role in improving the corporate GTFP. Using A-share-listed company data in China from 2009 to 2019, this study employs the [...] Read more.
Green total factor productivity (GTFP) is a key factor for achieving sustainable development and enhancing economic competitiveness. Environmental information disclosure plays a significant role in improving the corporate GTFP. Using A-share-listed company data in China from 2009 to 2019, this study employs the Ambient Air Quality Standards (GB3095-2012) promulgated by China in 2012 as a quasi-natural experiment. This study employs difference-in-differences (DID) to examine the impact of environmental information disclosure on corporate GTFP. The findings reveal that on average, environmental information disclosure positively affects firms’ GTFP. Mechanism analyses show that environmental information disclosure promotes GTFP by increasing total corporate costs, alleviating corporate financing constraints, and promoting green technological innovation. Environmental information disclosure mainly affects non-state-owned smaller, and young enterprises. These conclusions provide theoretical support and empirical evidence for governments to leverage environmental information disclosure to promote green and sustainable development, thereby achieving high-quality economic growth. Full article
(This article belongs to the Special Issue Air Pollution: Health Risks and Mitigation Strategies)
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14 pages, 2120 KiB  
Article
Improving Air Quality and Childhood Respiratory Health in Mongolia: The Impact of the Raw Coal Ban and COVID-19 Restrictions—An Interrupted Time-Series Analysis
by Emma Dickinson-Craig, Terkhen Turbat, Karla Hemming, Francis D. Pope, Suzanne E. Bartington, Suvdaa Anjaa, Sumiya Davaa, Rosie Day, Damdindorj Boldbaatar, Chimedsuren Ochir, David Warburton, Graham Neil Thomas, Jargalsaikhan Badarch and Semira Manaseki-Holland
Atmosphere 2025, 16(1), 46; https://doi.org/10.3390/atmos16010046 - 3 Jan 2025
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Abstract
Background: In May 2019, Ulaanbaatar, Mongolia introduced a city-wide raw coal ban (RCB) to improve ambient air quality. Air pollution exposure particularly affects child health and is associated with acute respiratory disease. We assessed the effects of the RCB on air quality and [...] Read more.
Background: In May 2019, Ulaanbaatar, Mongolia introduced a city-wide raw coal ban (RCB) to improve ambient air quality. Air pollution exposure particularly affects child health and is associated with acute respiratory disease. We assessed the effects of the RCB on air quality and child respiratory health. Methods: An interrupted time-series analysis was conducted on air quality (PM2.5, PM10, SO2, CO) and child (<5 y) severe acute respiratory disease, adjusting for coinciding co-events (COVID-19) where necessary. The subgroup analysis focussed on peak pollution months (November–February). Results: PM10 significantly decreased by −34.3 µg/m3 (95% CI −62.9, −5.8, p = 0.02) and PM2.5 by −17.1 µg/m3 (95% CI −26.3, −7.9, p < 0.01), immediately post-intervention. The subgroup analysis showed SO2 increased by 81.6 µg/m3 (95% CI 45.2, 117.9, p < 0.01) in the first month and by 4.9 µg/m3 (95% CI 2.6, 7.3, p < 0.01) monthly afterwards. CO increased by 56.1 µg/m3 (95% CI 13.5, 98.7, p = 0.01). Child respiratory health showed no significant change after adjusting for the COVID-19 effect. Discussion: The RCB successfully reduced key pollutants (PM), almost instantly. Long-term increases in SO2 and CO and limited health effects highlight the need for further air quality improvements. Outputs from Mongolia’s continuous air quality mitigation efforts provide useful insights for countries facing similar challenges. Full article
(This article belongs to the Special Issue Air Pollution: Health Risks and Mitigation Strategies)
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20 pages, 2516 KiB  
Article
Rapid PM2.5-Induced Health Impact Assessment: A Novel Approach Using Conditional U-Net CMAQ Surrogate Model
by Yohan Lee, Junghyun Park, Jinseok Kim, Jung-Hun Woo and Jong-Hyeon Lee
Atmosphere 2024, 15(10), 1186; https://doi.org/10.3390/atmos15101186 - 2 Oct 2024
Viewed by 1148
Abstract
There is a pressing need for tools that can rapidly predict PM2.5 concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting [...] Read more.
There is a pressing need for tools that can rapidly predict PM2.5 concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting practical scenario analysis. To address this, we propose a novel method integrating a conditional U-Net surrogate model with health impact assessments, enabling swift estimation of PM2.5 concentrations and related health effects. The U-Net model was trained with 2019 South Korean PM2.5 data, including precursor emissions and boundary conditions. Our model showed high accuracy and significant efficiency, reducing processing times while maintaining reliability. By combining this surrogate model with the EPA’s BenMAP-CE tool, we estimated potential premature deaths under various emission reduction scenarios in South Korea, extending projections to 2050 to account for demographic changes. Additionally, we assessed the required PM2.5 emission reductions needed to counteract the increase in premature deaths due to an aging population. This integrated framework offers an efficient, user-friendly tool that bridges complex air quality modeling with practical policy evaluation, supporting the development of effective strategies to reduce PM2.5-related health risks and estimate economic benefits. Full article
(This article belongs to the Special Issue Air Pollution: Health Risks and Mitigation Strategies)
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