Source Identification, Monitoring, and Modelling of Ambient and Indoor Air Pollutants with Novel Techniques in Urban and Rural Areas
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".
Deadline for manuscript submissions: 10 July 2026 | Viewed by 17
Special Issue Editors
Interests: air-quality monitoring; air-quality modeling; machine learning; indoor air quality; climate change
Interests: air quality; climate change
Interests: air quality; air-quality modelling; tropospheric ozone; urban air quality; indoor air quality
Special Issues, Collections and Topics in MDPI journals
Interests: air pollution; ceramic filters; chemical-process control
Special Issue Information
Dear Colleagues,
Air pollution remains a severe threat to public health and sustainable development in urban and rural areas across the world. The World Health Organization (WHO) estimates that the combined effect of ambient air pollution and household air pollution is associated with approximately 6.7 million premature deaths annually. Addressing this challenge requires moving beyond conventional methods. A new paradigm is emerging, centred on the integration of novel techniques for source identification, monitoring and modelling for both ambient and indoor environments.
Air pollution is a critical environmental and public health crisis driven by rapid industrialization, urbanization and energy demand. A complex mix of air pollutants is present in the environment, including PM10, PM2.5, ozone (O3), nitrogen oxides (NOx), sulfur dioxide (SO2) and carbon monoxide (CO). Efforts to tackle this issue rely on a two-integrated approach, such as identifying pollution sources to target control measures and monitoring air quality to assess trends and exposure.
The future of air quality management lies in the application of novel monitoring technologies (low-cost sensors and drones), advanced data analytics (artificial intelligence and machine learning), and sophisticated integrated models. This multidisciplinary approach provides the high-resolution, evidence-based foundation needed to design targeted policies, resolve transboundary disputes and, ultimately, mitigate the profound health and economic impacts of air pollution in both ambient and indoor environments.
This Special Issue invites contributions describing new methodological aspects of source apportionment of heavy metals in outdoor and indoor dust, monitoring of outdoor and indoor air quality with advanced innovative techniques and modelling of ambient and indoor air pollution using novel methodologies.
Dr. Thomas Lei
Dr. Francisco Ferreira
Dr. Nelson Barros
Prof. Dr. Murilo D.M. Innocentini
Dr. Mohd Shahrul Mohd Nadzir
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-quality monitoring
- air-quality modelling and forecasting
- source apportionment
- machine learning and deep learning
- artificial intelligence
- low-cost sensors
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