Artificial Intelligence for Atmospheric Pollution and Hazard Research: Advances in Detection, Estimation, Forecasting, and Risk Assessment

Dear Colleagues,

Atmospheric pollution and hazardous environmental events, including aerosol, wildfire smoke, dust storms, haze, extreme heat, and harmful trace gas accumulation, have profound impacts on air quality, weather, climate, ecosystems, human health, and socioeconomic activities. Atmospheric components such as aerosols, clouds, greenhouse gases, and air pollutants interact with radiation, cloud microphysics, and atmospheric circulation, influencing both short-term meteorological conditions and long-term climate variability. At the same time, hazardous atmospheric events can intensify environmental risks, disrupt transportation and agriculture, reduce visibility, and threaten public safety and health. Therefore, accurate, timely, and high-resolution analysis of atmospheric pollution and hazards is essential for scientific understanding, operational forecasting, and effective decision-making.

Recent advances in artificial intelligence (AI), including machine learning, deep learning, computer vision, and spatiotemporal modeling, are transforming the way atmospheric pollution and hazards are monitored, analyzed, and predicted. These approaches offer powerful tools for extracting information from complex and large-volume datasets, such as satellite remote sensing observations, ground-based monitoring networks, numerical model outputs, and atmospheric reanalysis products. AI technologies can improve the retrieval and detection of atmospheric constituents, enhance data fusion from multiple observing systems, identify subtle patterns and anomalies, and support more accurate forecasts of pollution episodes and hazardous events. As AI methods continue to mature, they provide new opportunities to bridge scientific research and practical applications in environmental monitoring, hazard warning, public health protection, and climate resilience.

This Special Issue aims to bring together high-quality original research, reviews, and application-oriented studies that explore innovative AI technologies for atmospheric pollution and hazard research. This issue will focus on advances in AI-based methods for the detection, retrieval, monitoring, forecasting, and risk assessment of atmospheric pollutants, hazardous events, and related environmental processes. We particularly welcome interdisciplinary contributions at the intersection of artificial intelligence, atmospheric science, remote sensing, environmental health, geospatial analysis, and hazard research.

Suggested Themes

• AI and deep learning methods for atmospheric pollution monitoring and hazard detection;
• Machine learning retrievals of aerosols, clouds, trace gases, and other atmospheric compositions;
• AI-enhanced analysis of wildfire smoke, dust storms, haze, and extreme pollution episodes;
• Spatiotemporal forecasting of air quality, pollutant transport, and atmospheric hazards;
• Data fusion methods integrating satellite, ground-based, airborne, and model datasets;
• AI applications in exposure assessment, environmental health, and pollution-related risk analysis;
• Detection and characterization of atmospheric anomalies and extreme events using AI;
• Explainable AI, uncertainty quantification, and model interpretability in atmospheric applications;
• Super-resolution, downscaling, gap-filling, and reconstruction of atmospheric datasets;
• AI-assisted hazard mapping, early warning, and decision-support systems;
• Integration of GIS, remote sensing, and AI for atmospheric pollution and environmental hazard studies;
• AI applications for climate-related hazard monitoring and long-term atmospheric change analysis;
• Benchmarking, validation, and comparison of AI methods with traditional physical or statistical approaches;
• Review articles on recent progress, challenges, and future directions in AI for atmospheric pollution and hazard research.

Dr. Qian Liu
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-anonymized 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.

• artificial intelligence
• atmospheric pollution
• hazardous