Applications of Machine Learning and Data Modeling Techniques in Air Quality Monitoring and Control Mechanisms
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".
Deadline for manuscript submissions: 15 October 2025 | Viewed by 306
Special Issue Editors
Interests: UAV-based air quality monitoring system; machine learning; Internet of Things; wireless sensor networks; big data analysis
Interests: indoor air quality; environmental remediation; photocatalysis; antimicrobial materials
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid urbanization and industrialization of recent decades have significantly impacted global air quality, posing severe health and environmental risks. The increased prevalence of airborne pollutants such as particulate matter (PM), volatile organic compounds (VOCs), carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), polycyclic aromatic hydrocarbons (PAHs), and other hazardous pollutants has heightened the need for effective air quality monitoring and control mechanisms. Advancements in machine learning and data modeling have paved the way for innovative approaches to addressing these challenges, enabling precise pollutant detection, forecasting, and real-time intervention strategies.
This Special Issue on “Applications of Machine Learning and Data Modeling Techniques in Air Quality Monitoring and Control Mechanisms” seeks high-quality research and innovative solutions in this critical domain. Authors are invited to submit original research, case studies, or review articles on topics including, but not limited to, the following:
- IoT-based air quality monitoring systems and their seamless integration into smart environments;
- Ground vehicle-based sensor and unmanned aerial vehicle-based sensor monitoring systems;
- Data-driven approaches for real-time air quality assessment and monitoring;
- Development of machine learning models for air quality prediction and forecasting;
- Innovations in data fusion and visualization for air quality analytics;
- AI applications in designing effective pollution control strategies;
- Predictive models for assessing health risks associated with air pollution exposure;
- Case studies and practical implementations of air quality management systems.
Dr. Getaneh Berie Tarekegn
Dr. Abiyu Kerebo Berekute
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. Processes 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
- deep learning
- air quality sensing
- prediction
- smart city
- ship emissions
- drones
- cloud computing
- low-cost air quality sensors
- nanocatalysts
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