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Advances in Air Pollution Detection and Air Quality Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 887

Special Issue Editors


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Guest Editor
Department of Physics, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania
Interests: sensors; networks; air quality monitoring; techniques of measurement; air quality modelling

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Guest Editor
Department of Management, Marketing and Business Administration, Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania
Interests: social impact of air pollution; smart and green cities; sustainable communities; SmartPLS; statistics

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Guest Editor
National Meteorological Administration, Sos. Bucuresti-Ploiesti 97, 013686 Bucharest, Romania
Interests: statistical approaches to air pollution; climatology; social impact of air pollution

Special Issue Information

Dear Colleagues,

Air pollution threatens human health, the environment, and biodiversity; therefore, in this context, the evolution of air quality monitoring sensors and the development of innovative methods for measuring and extracting knowledge from measurements collected by such sensors become imperative.

The measurement datasets provided by these sensors can be processed by taking advantage of recent trends in artificial intelligence, leading to better the prediction, modelling, and forecasting of air quality or improved identification of local sources of air pollution.

Last but not least, the development of independent air quality monitoring networks reflects the desire of local communities to live in a clean environment and draws the attention of decision-makers who can make decisions for the benefit of their communities.

We are therefore interested in attracting articles that investigate advanced measurement methods and air quality research. Potential topics include, but are not limited to:

  • Innovative measurement principles;
  • Dynamic real-time capabilities in monitoring air quality;
  • New sensors infrastructures;
  • Independent low-cost sensor networks;
  • Knowledge extraction from the measurement datasets, based on artificial intelligence;
  • Novel findings about the air quality predictions, modelling and forecasting;
  • IoT, big data, and machine learning;
  • Application case studies;
  • Sustainable environments;
  • The social aspects of air pollution;
  • Smart cities;
  • Apps for air quality monitoring.

Dr. Mihaela Tinca Udriștioiu
Dr. Silvia Puiu
Dr. Liliana Velea
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • sensors
  • measurement techniques
  • sensor networks
  • air quality monitoring
  • community
  • air quality predictions
  • datasets

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Published Papers (2 papers)

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Research

17 pages, 4211 KiB  
Article
Effects of Airborne Particulate Matter in Biomass Treatment Plants on the Expression of DNA Repair and IL-8 Genes
by Noemi Zanchi, Elena Franchitti and Deborah Traversi
Appl. Sci. 2025, 15(9), 4904; https://doi.org/10.3390/app15094904 - 28 Apr 2025
Viewed by 55
Abstract
Biogas plants for sewage and organic waste treatment are rapidly expanding. While these facilities provide valuable benefits, such as renewable energy production and the promotion of circular economy practices, they also emit airborne particles of biological origin, which may pose potential health risks. [...] Read more.
Biogas plants for sewage and organic waste treatment are rapidly expanding. While these facilities provide valuable benefits, such as renewable energy production and the promotion of circular economy practices, they also emit airborne particles of biological origin, which may pose potential health risks. This study aims to evaluate, by in vitro assay, the cytotoxic and genotoxic potential of PM10 sub-fractions (0.49–10 µm and <0.49 µm) generated in eight different plants, also assessing the endotoxin component using the Limulus Amebocyte Lysate (LAL) assay. Human embryonic lung fibroblasts (HELF) were exposed to organic extracts of particulate matter (PM). Cytotoxic effects (XTT assay) were analyzed, along with the modulation of gene expression involved in DNA repair (ERCC1, XRCC1, XPA, and XPF) and IL-8 production as a marker of inflammatory response. PM10 and endotoxin concentrations varied significantly among the plants (ANOVA, p < 0.01), with PM10 levels ranging from 14 to 18,000 µg/m3 and endotoxin content from 1 to 138 EU/m3. Exposure significantly increased ERCC1 and IL-8 expression by 25% and 53%, respectively (paired t-test, p < 0.01). IL-8 expression correlated with endotoxin exposure (Spearman’s rho = 0.35; p < 0.01). A deeper understanding of the biological component of airborne PM10 can enhance risk assessments for occupational and nearby resident communities’ safety. Full article
(This article belongs to the Special Issue Advances in Air Pollution Detection and Air Quality Research)
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17 pages, 5664 KiB  
Article
Explanation of Air Quality Data Using Takagi–Sugeno Fuzzy Inference System
by Alžbeta Michalíková
Appl. Sci. 2025, 15(7), 3461; https://doi.org/10.3390/app15073461 - 21 Mar 2025
Viewed by 224
Abstract
The explainability of system behaviour is one of the most important concepts of modern data science. If a system is described by using rules that are clearly readable and understandable, then it is possible to model various problems arising from real life. In [...] Read more.
The explainability of system behaviour is one of the most important concepts of modern data science. If a system is described by using rules that are clearly readable and understandable, then it is possible to model various problems arising from real life. In this paper, we present a way to create the so-called IF-THEN rules for urban air quality modelling by using the Takagi–Sugeno fuzzy inference system. The presented research study builds on previous work where such a problem was modelled by using a Takagi–Sugeno fuzzy inference system with linear membership functions. Such functions are difficult for the average person to interpret. Therefore, we replaced the output linear functions with constant functions and subsequently optimised the system to achieve the lowest approximation error. From the point of view of data analysis, this approach allows us to obtain a system with a comparatively smaller approximation error. From the point of view of model explainability, we obtain a rule base that describes the influence of individual input variables on the overall output in human terms. Finally, based on the obtained rules, we can evaluate the impact of traffic data and weather conditions on the selected air pollution parameter. Full article
(This article belongs to the Special Issue Advances in Air Pollution Detection and Air Quality Research)
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