Recent Advances in Research on Air Pollution and Health Effects
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).
Deadline for manuscript submissions: 30 June 2024 | Viewed by 4922
Interests: atmospheric environment; air pollution control technology; air cleaning devices; personal exposure
Interests: aerosol; air pollution; biomass burning; carbon; emission inventory; forest fires; health risks; particulate matter; remote sensing; wildfire hazard
Special Issues, Collections and Topics in MDPI journals
Air pollution has been one of the main environmental issues in the recent decade. The World Health Organization (WHO) reports that 99% of the world population is exposed to annual health-damaging particles with a diameter of 2.5 microns or less (≤PM2.5) levels over the WHO air quality guidelines of 5 μg·m−3. Exposure to PM as well as to ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), micro- and nano plastics (MPs), and naturally radioactive elements (e.g., radon) pose serious health risks for several populations. This air pollution is also associated with urbanization and rapid economic development. Recent advanced research in air pollution linked to human health will be essential to address the air pollution problem through the development of public health and environmental policies, considering the spatio-temporal variation of each pollutant. This Special Issue invites original studies, reviews, and perspective articles that aim to study air pollution and its health effects in advance. Subject areas may include, but are not limited to, the following:
- Sub-microns and nano-particles linked to health risks;
- Gaseous pollutants and radioactive related health effects;
- Atmospheric micro- and nano-plastics and human health;
- Physical, optical, and chemical characteristics of air pollutants;
- Trans-boundary air pollution on air quality and human health;
- Long-term and short-term effects from exposure to air pollutants;
- Multipollutant exposures and changes in environnemental conditions;
- Measurement techniques, instruments, and modeling in air pollution;
- Influence of meteorological conditions on air quality and human health;
- Data sciences, machine learning, and artificial intelligence of air pollutants.
Prof. Dr. Masami Furuuchi
Dr. Worradorn Phairuang
Dr. Narongchai Autsavapromporn
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. International Journal of Environmental Research and Public Health 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 2500 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.
- air monitoring
- Artificial Intelligence (AI)
- atmospheric microplastics
- atmospheric modeling
- atmospheric nanoparticles
- biomass burning
- gaseous pollutants
- health risks
- machine learning
- ultrafine particles