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Air
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Air Pollution Exposure and Its Impact on Human Health
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Air Pollution Exposure and Its Impact on Human Health

A special issue of Air (ISSN 2813-4168).

Deadline for manuscript submissions: 1 July 2026 | Viewed by 4464

Special Issue Editor

Dr. Nedim Durmus

E-Mail Website
Guest Editor
Department of Medicine, New York University Grossman School of Medicine, New York, NY 10016, USA
Interests: asthma; lung cancer; pharmacology; diseases associated with the World Trade Center disaster (particularly chronic respiratory diseases and cancer)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Air pollution remains one of the most significant environmental health risks worldwide, contributing to a range of acute and chronic diseases. Exposure to pollutants such as particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO₂), volatile organic compounds (VOCs), and ozone (O₃) has been linked to respiratory disorders, cardiovascular diseases, neurodevelopmental impacts, and adverse pregnancy outcomes. Additionally, climate change is intensifying air pollution through rising temperatures, altering atmospheric chemistry, and increased frequency of wildfires, exacerbating its health effects.

With rapid urbanization, industrialization, excessive drug waste, and climate change-driven environmental changes, there is an urgent need for multidisciplinary research to understand the mechanisms, risks, and mitigation strategies associated with air pollution. This Special Issue aims to bring together the latest research on air pollution, climate change, and human health, including epidemiological studies, toxicological assessments, exposure science, and policy-driven interventions. By fostering collaboration between environmental scientists, public health experts, policymakers, and clinicians, this collection of articles will provide valuable insights into mitigating the adverse effects of air pollution and climate change while protecting public health.

You may choose our Joint Special Issue in IJERPH.

Dr. Nedim Durmus
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 250 words) can be sent to the Editorial Office for assessment.

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. Air is an international peer-reviewed open access quarterly 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 1000 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
  • particulate matter (PM)
  • human health
  • respiratory diseases
  • cardiovascular diseases
  • environmental exposure
  • urban air quality
  • epidemiology
  • public health policy
  • pollution mitigation
  • climate&ndash
  • health interactions
  • wildfire smoke
  • extreme weather and air quality
  • drug pollution

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

Published Papers (2 papers)

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20 pages, 1831 KB  
Article
Urban Air Pollution and Cardiovascular Health: A Study of PM2.5 and CVD Morbidity in a Metropolitan City, Karachi (Pakistan)
by Omosehin D. Moyebi, Azhar Siddique, Mirza M. Hussain, David O. Carpenter and Haider A. Khwaja
Air 2026, 4(1), 5; https://doi.org/10.3390/air4010005 - 28 Feb 2026
Viewed by 739
Abstract
Ambient air pollution, particularly fine particulate matter (PM2.5), poses significant health risks, especially concerning cardiovascular diseases (CVDs). This study assesses the association between PM2.5 exposure and CVD hospital admissions (HAs) and emergency room (ER) visits in Karachi, Pakistan. Daily PM [...] Read more.
Ambient air pollution, particularly fine particulate matter (PM2.5), poses significant health risks, especially concerning cardiovascular diseases (CVDs). This study assesses the association between PM2.5 exposure and CVD hospital admissions (HAs) and emergency room (ER) visits in Karachi, Pakistan. Daily PM2.5 samples were collected from four Karachi sites (Makro, Karachi University, Keamari, and Malir) between October 2009 and June 2011. CVD morbidity data, including HAs and ER visits, were gathered from major hospitals. A single-pollutant model was employed to evaluate associations between PM2.5 levels and CVD outcomes, adjusting for meteorological variables and other potential confounders. PM2.5 concentrations and CVD morbidity were significantly associated across all sites Stratification by age and gender revealed stronger associations among males and individuals aged 40 and above. Exposure to elevated levels of PM2.5 in Karachi was significantly associated with increased CVD HAs and ER visits, with the highest association found between PM2.5 exposure and arrhythmias. The study underscores the need for effective air quality management policies and interventions to reduce PM2.5 levels. Karachi’s high PM2.5 levels demand urgent attention from regulatory agencies and public health professionals to implement interventions that mitigate air pollution and protect vulnerable populations. Full article
(This article belongs to the Special Issue Air Pollution Exposure and Its Impact on Human Health)
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20 pages, 1686 KB  
Article
Spatiotemporal Graph Neural Networks for PM2.5 Concentration Forecasting
by Vongani Chabalala, Craig Rudolph, Karabo Mosala, Edward Khomotso Nkadimeng, Chuene Mosomane, Thuso Mathaha, Pallab Basu, Muhammad Ahsan Mahboob, Jude Kong, Nicola Bragazzi, Iqra Atif, Mukesh Kumar and Bruce Mellado
Air 2026, 4(1), 2; https://doi.org/10.3390/air4010002 - 13 Jan 2026
Cited by 1 | Viewed by 1990
Abstract
Air pollution, particularly fine particulate matter (PM2.5), poses significant public health and environmental risks. This study explores the effectiveness of spatiotemporal graph neural networks (ST-GNNs) in forecasting PM2.5 concentrations by integrating remote-sensing hyperspectral indices with traditional meteorological and pollutant [...] Read more.
Air pollution, particularly fine particulate matter (PM2.5), poses significant public health and environmental risks. This study explores the effectiveness of spatiotemporal graph neural networks (ST-GNNs) in forecasting PM2.5 concentrations by integrating remote-sensing hyperspectral indices with traditional meteorological and pollutant data. The model was evaluated using data from Switzerland and the Gauteng province in South Africa, with datasets spanning from January 2016 to December 2021. Key performance metrics, including root mean squared error (RMSE), mean absolute error (MAE), probability of detection (POD), critical success index (CSI), and false alarm rate (FAR), were employed to assess model accuracy. For Switzerland, the integration of spectral indices improved RMSE from 1.4660 to 1.4591, MAE from 1.1147 to 1.1053, CSI from 0.8345 to 0.8387, POD from 0.8961 to 0.8972, and reduced FAR from 0.0760 to 0.0719. In Gauteng, RMSE decreased from 6.3486 to 6.2319, MAE from 4.4891 to 4.4066, CSI from 0.9555 to 0.9560, and POD from 0.9699 to 0.9732, while FAR slightly increased from 0.0154 to 0.0181. Error analysis revealed that while the initial one-day ahead forecast without spectral indices had a marginally lower error, the dataset with spectral indices outperformed from the two-day ahead mark onwards. The error for Swiss monitoring stations stabilized over longer prediction lengths, indicating the robustness of the spectral indices for extended forecasts. The study faced limitations, including the exclusion of the Planetary Boundary Layer (PBL) height and K-index, lack of terrain data for South Africa, and significant missing data in remote sensing indices. Despite these challenges, the results demonstrate that ST-GNNs, enhanced with hyperspectral data, provide a more accurate and reliable tool for PM2.5 forecasting. Future work will focus on expanding the dataset to include additional regions and further refining the model by incorporating additional environmental variables. This approach holds promise for improving air quality management and mitigating health risks associated with air pollution. Full article
(This article belongs to the Special Issue Air Pollution Exposure and Its Impact on Human Health)
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