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Atmosphere
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Advances in Air Pollution Data Analysis: From Classical Geostatistics to...
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Advances in Air Pollution Data Analysis: From Classical Geostatistics to Big Data and Artificial Intelligence

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1408

Special Issue Editors

Dr. Mateusz Zareba

E-Mail Website
Guest Editor
Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Krakow, 30-059 Krakow, Poland
Interests: air pollution measurements; air quality monitoring; artificial intelligence, anthropogenic emission; spatio-temporal geostatistics; geophysics; smart cities
Dr. Elżbieta Węglińska

E-Mail Website
Guest Editor
Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Krakow, 30-059 Krakow, Poland
Interests: geostatistics; spatial data analysis; machine learning; air pollution measurements; air quality monitoring; geophysics
Prof. Dr. Yunhua Chang

E-Mail Website
Guest Editor
Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: atmospheric chemistry; reactive nitrogen; ammonia; isotopic analysis; haze; secondary aerosol formation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite researchers and practitioners to contribute to this Special Issue focusing on the evolving landscape of air pollution data analysis, bridging traditional geostatistics with groundbreaking advancements in big data and artificial intelligence (AI). This Special Issue aims to capture the latest innovations and foster interdisciplinary dialog on leveraging advanced analytical techniques to address air quality challenges. Air pollution, a critical environmental and public health concern, demands increasingly sophisticated tools to handle the complexity and scale of modern data. From satellite imagery and ground-based sensors to citizen science and IoT networks, the availability of vast, high-resolution datasets opens new frontiers for exploration. However, extracting actionable insights from these data sources requires a fusion of traditional methods and emerging technologies.

This Special Issue seeks contributions across a broad spectrum of topics, including, but not limited to, the following:

  • Applications of geostatistics for spatial and temporal modeling of air quality;
  • Big data techniques for managing and analyzing large-scale pollution datasets;
  • AI and machine learning models for predictive analysis, anomaly detection, and source apportionment;
  • Integrating heterogeneous data sources (satellite, sensor, and citizen science) for comprehensive air quality assessments;
  • Uncertainty quantification, explainable AI, and ethical considerations in air pollution analysis;
  • Real-time applications in pollution forecasting, urban planning, and policymaking.

By submitting to this Special Issue, you will showcase your research at the forefront of this dynamic field, contributing to innovative solutions for global air quality management. Together, let us push the boundaries of air pollution science and technology.

Dr. Mateusz Zareba
Dr. Elżbieta Węglińska
Prof. Dr. Yunhua Chang
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 pollution
  • air quality monitoring
  • machine learning
  • big data
  • spatial analysis
  • artificial intelligence

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

Published Papers (2 papers)

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Research

23 pages, 3742 KB  
Article
Emergency Medical Interventions in Areas with High Air Pollution: A Case Study from Małopolska Voivodeship, Poland
by Ewa Szewczyk, Michał Lupa, Mateusz Zaręba, Elżbieta Węglińska, Tomasz Danek and Amit Kumar Mishra
Atmosphere 2025, 16(8), 983; https://doi.org/10.3390/atmos16080983 - 18 Aug 2025
Viewed by 571
Abstract
Air pollution poses a significant threat to public health, particularly in urban and industrialized regions. This study investigates the relationship between air quality and the frequency of Emergency Medical Service (EMS) calls in the Małopolska Voivodeship of Poland between 2020 and 2023. Data [...] Read more.
Air pollution poses a significant threat to public health, particularly in urban and industrialized regions. This study investigates the relationship between air quality and the frequency of Emergency Medical Service (EMS) calls in the Małopolska Voivodeship of Poland between 2020 and 2023. Data from over 190 air quality sensors (PM10) were spatially aggregated using both hexagonal grids and administrative boundaries, while EMS call records were filtered to focus on cardiovascular and respiratory incidents. During 2020–2023, a total of 305,142 EMS calls were analyzed, and months with PM10 exceedances showed an average of 1.50 respiratory calls per 1000 residents compared to 1.19 in months without exceedances. Statistical analyses, including Kolmogorov-Smirnov tests and Pearson correlation, were applied to explore temporal and spatial associations. Results indicate a statistically significant increase in EMS calls during periods of elevated air pollution, with the strongest correlation observed for respiratory-related incidents. Comparative analyses between high- and low-pollution municipalities supported the observed relationships. Further analysis indicated that the COVID-19 pandemic may have partially confounded these associations, particularly for respiratory cases, though significant patterns remained even after accounting for pandemic peaks. While limitations related to data gaps and seasonal biases exist, the findings suggest that real-time air pollution data could inform better EMS resource allocation. This research highlights the potential of integrating environmental data into public health strategies to improve emergency response and reduce health risks in polluted regions. Full article
(This article belongs to the Special Issue Advances in Air Pollution Data Analysis: From Classical Geostatistics to Big Data and Artificial Intelligence)
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27 pages, 15404 KB  
Article
Machine-Learning Models for Surface Ozone Forecast in Mexico City
by Mateen Ahmad, Bernhard Rappenglück, Olabosipo O. Osibanjo and Armando Retama
Atmosphere 2025, 16(8), 931; https://doi.org/10.3390/atmos16080931 - 1 Aug 2025
Viewed by 635
Abstract
Mexico City frequently experiences high near-surface ozone concentrations, and exposure to elevated near-surface ozone causes harmful effects to the inhabitants and the environment of Mexico City. This necessitates developing models for Mexico City that predict near-surface ozone levels in advance. Such models are [...] Read more.
Mexico City frequently experiences high near-surface ozone concentrations, and exposure to elevated near-surface ozone causes harmful effects to the inhabitants and the environment of Mexico City. This necessitates developing models for Mexico City that predict near-surface ozone levels in advance. Such models are crucial for regulatory procedures and can save a great deal of near-surface ozone detrimental effects by serving as early warning systems. We utilize three machine-learning models, trained on seven-year data (2015–2021) and tested on one-year data (2022), to forecast the near-surface ozone concentrations. The trained models predict the next day’s 24-h near-surface ozone concentrations for up to one month; before forecasting the following months, the models are trained again and updated. Based on prediction results, the convolutional neural network outperforms the rest of the models on a yearly scale with an index of agreement of 0.93 for three stations, 0.92 for nine stations, and 0.91 for one station. Full article
(This article belongs to the Special Issue Advances in Air Pollution Data Analysis: From Classical Geostatistics to Big Data and Artificial Intelligence)
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