Air Pollution Monitoring, AI-Based Modeling, and Health
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality and Health".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 47
Special Issue Editors
Interests: air quality; data science; machine learning; health science; atmospheric science
Interests: air quality; tropospheric composition; tropospheric change; secondary organic aerosol; ultrafine particles; volatile organic compounds; atmospheric reactivity
Special Issues, Collections and Topics in MDPI journals
2. School of Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
Interests: air quality; environmental science; machine learning and deep learning; data science and big data analytics; multimodal data integration; synthetic data and simulation; trustworthy and responsible AI; AI for digital twins
Special Issue Information
Dear Colleagues,
Air pollution remains one of the most significant environmental challenges affecting human health worldwide. Advances in air quality monitoring, atmospheric measurements, and sensing technologies have led to rapidly growing and increasingly complex datasets describing pollutant concentrations, sources, and spatiotemporal variability. At the same time, developments in data science and machine learning provide powerful tools to extract meaningful insights from these data, enabling improved assessment of air pollution dynamics, exposure patterns, and associated health impacts.
This Special Issue, titled “Air Pollution Monitoring, AI-Based Modeling, and Health”, aims to bring together interdisciplinary research that integrates observational air quality data with modelling approaches, statistical methods, and machine learning techniques. Contributions may focus on atmospheric measurements, air quality monitoring networks, exposure assessment, and air quality modelling across different spatial and temporal scales, as well as on data-driven approaches for prediction, source characterisation, and risk analysis. Studies linking air pollution data with human health outcomes, environmental health indicators, epidemiology, or toxicology are particularly encouraged, alongside other relevant interdisciplinary studies.
By fostering collaboration between atmospheric scientists, data scientists, and health researchers, this Special Issue seeks to advance understanding of the relationships between air pollution and human health. The collected contributions aim to support evidence-based decision-making, improve exposure and risk assessments, and inform strategies and policies for protecting public and environmental health.
Dr. Balendra Vir Singh Chauhan
Dr. Kevin P. Wyche
Dr. Sneha Verma
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 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. 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
- data science and machine learning
- atmospheric measurements
- air quality modelling
- human health impacts
- exposure assessment
- environmental health
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