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Applied Sciences
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Advanced Technologies for Air Quality Monitoring, Assessment and Control
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Advanced Technologies for Air Quality Monitoring, Assessment and Control

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

Deadline for manuscript submissions: 25 October 2026 | Viewed by 1272

Special Issue Editors

Dr. Alfred Micallef

E-Mail Website
Guest Editor
Department of Geosciences, Faculty of Science, University of Malta, Msida, Malta
Interests: general atmospheric physics; atmospheric aerosols; indoor and outdoor air quality modelling; climate science
Special Issues, Collections and Topics in MDPI journals
Dr. Adam Gauci

E-Mail Website
Guest Editor
Faculty of Science, University of Malta, Room 205, Maths & Physics Building, Msida, Malta
Interests: image processing; machine learning; artificial intelligence; pattern recognition; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is a well-established fact that the quality of the air that we breathe has an impact on our overall health, either directly or indirectly. Polluted air in outdoor urban areas is having its toll on the physical and mental well-being of city dwellers, which constitutes most of the world’s population. The same can be attributed to indoor air quality. Air pollution in general is affecting our environment and the climate, which, in turn, leaves a negative impact on human health and lifestyle. Indeed, the issue pertaining to air quality is no longer limited to cities—suburban and rural areas are also experiencing the problem and its negative effects.

Both short- and long-term air quality monitoring, using various techniques and protocols, have become of paramount importance, forming part of a comprehensive assessment of the environmental quality. Such assessments are a necessary first step in tackling the problem and seeking ways of limiting and controlling it. Both monitoring campaigns and modeling efforts are essential in this complex endeavor. The outcomes of data analysis are also proving to be essential in finding a meaningful solution to such a large-scale and persisting problem. Novel techniques, such as those classified as remote sensing, are aiding researchers in their experimental work. Overall, a multidisciplinary approach is proving to be more suitable for the investigation of air quality both within microenvironments and on a local scale, as well as both regionally and globally.

It is hoped that this Special Issue will encourage and attract substantial interest from seasoned experts in the field, as well as younger scientific researchers. Additionally, we welcome contributions that explore the integration of Artificial Intelligence (AI) and Machine Learning to enhance air quality monitoring and analysis.

To this end, we are pleased to invite submissions to this Special Issue, which aims to showcase the outcome of high-quality scientific research pertaining to air quality monitoring, assessment, and control for the enhancement of the relevant scientific knowledge and benefit to society at large.

In this Special Issue, articles, short communications, and technical notes pertaining to original research, as well as reviews, are all welcome. Research areas may include (but are not limited to) the following:

  • Urban air pollution episodes and trends;
  • Urban air quality modelling and monitoring;
  • Street canyon air quality modelling;
  • Air pollution data analysis;
  • Transboundary air pollution and impacts;
  • Air pollution in relation to other environmental issues, e.g., global warming and climate change;
  • Use of artificial intelligence and machine learning techniques in air quality studies;
  • Application of remote sensing (including the use of drones) in air quality studies;
  • Emission and dispersion of air pollutants;
  • Indoor air quality (monitoring and modelling);
  • Technology and protocols for the control of air quality;
  • Machine Learning for predicting air pollution episodes;
  • AI-driven air quality monitoring systems;
  • Transboundary air pollution mapping with Machine Learning;
  • Integrating air pollution and climate change models using AI;
  • Remote sensing for air quality studies;
  • AI in emission and dispersion modeling of air pollutants;
  • Smart indoor air quality management.

Dr. Alfred Micallef
Dr. Adam Gauci
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. 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

  • air quality
  • air pollution
  • modeling
  • monitoring campaigns
  • emission
  • dispersion
  • transboundary pollution
  • indoor air
  • ambient air
  • instrumentation
  • sensors
  • remote sensing
  • artificial intelligence
  • machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 3572 KB  
Article
Position-Aware Coupling Between Ozone and Welding Fume Peaks Under Local Exhaust Ventilation
by Yuxiong Xia, Satoshi Yamane, Weixi Wang, Hiroki Ihara, Jidong Lu and Yuxi Luo
Appl. Sci. 2026, 16(10), 4814; https://doi.org/10.3390/app16104814 - 12 May 2026
Viewed by 192
Abstract
Real-time management of short-term ozone peaks during arc welding remains challenging because ventilation- and enclosure-defined transport boundaries can create strong position-dependent peak behavior, even under fixed process settings. This study establishes a coordinate-referenced, event-level monitoring and analysis framework to quantify ozone–fume peak coupling [...] Read more.
Real-time management of short-term ozone peaks during arc welding remains challenging because ventilation- and enclosure-defined transport boundaries can create strong position-dependent peak behavior, even under fixed process settings. This study establishes a coordinate-referenced, event-level monitoring and analysis framework to quantify ozone–fume peak coupling under a controlled local exhaust ventilation (LEV) suction boundary during CO2 arc welding. A controlled process–environment testbed with a defined suction condition was implemented, and synchronized ozone and fume signals were acquired at three sampling points referenced to the arc position and the LEV inlet direction. The particulate channel was anchored to a PM4 gravimetric reference, yielding a condition-specific traceable CPM-to-mass conversion factor K1 of 1.76 × 10−2 mg/(m3·CPM) and enabling standardized peak-fume endpoints on a mass-concentration scale. The primary inferential analysis used the curtain-on dataset, comprising 21 sessions and 42 event-level records balanced across three sampling points. Under the same suction boundary, peak coupling was strongly monitoring-coordinate dependent: LEV-aligned locations showed statistically supported ln–ln scaling between peak ozone and peak fume, whereas the opposite-side location did not exhibit statistically supported scaling; a pooled point-parameterized ln–ln model achieved an adjusted R2 of 0.777. As a descriptive control-relevant contrast, adding a curtain enclosure under continuous LEV produced strong event-level ozone peak suppression at LEV-aligned locations, with a maximum reduction of 87.8%, while attenuation at the opposite-side location remained limited. Overall, the results provide a ventilation-boundary-consistent, coordinate-specific basis for monitoring placement and control evaluation, identifying where peak translation is supported and where direct ozone monitoring remains necessary. Full article
(This article belongs to the Special Issue Advanced Technologies for Air Quality Monitoring, Assessment and Control)
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31 pages, 19415 KB  
Article
Integration of Multi-Gas Sensors and Aerial Thermography into UAVs for Environmental Monitoring of a Landfill
by Juan Francisco Escudero-Villegas, Macaria Hernández-Chávez, Bertha Nelly Cabrera-Sánchez, Gilgamesh Luis-Raya, Josué Daniel Rivera-Fernández and Diego Adrián Fabila-Bustos
Appl. Sci. 2026, 16(8), 3970; https://doi.org/10.3390/app16083970 - 19 Apr 2026
Viewed by 448
Abstract
Landfills are a significant source of atmospheric emissions associated with the decomposition of organic waste; however, conventional monitoring methods typically have limited spatial coverage. This study evaluates the use of an UAV-based system for the spatial characterization of gases associated with biogas emissions [...] Read more.
Landfills are a significant source of atmospheric emissions associated with the decomposition of organic waste; however, conventional monitoring methods typically have limited spatial coverage. This study evaluates the use of an UAV-based system for the spatial characterization of gases associated with biogas emissions at a municipal landfill. A DJI Matrice 350 RTK platform equipped with a Sniffer4D Mini2 multi-gas station and a Zenmuse H20T thermal camera were used. Four flight campaigns were conducted at an altitude of 20 m, with an acquisition frequency of approximately 1 Hz, recording total hydrocarbons (CxHy) as an indirect indicator of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), oxygen (O2), temperature, and relative humidity. The results showed a marked transition around 13:10 h, characterized by a simultaneous increase in CH4 equivalent and CO2, along with a decrease in NO2, O3, and SO2. Furthermore, CH4 equivalent and CO2 showed the highest positive correlation among the variables (r = 0.96). Spatial maps generated using ordinary kriging revealed more heterogeneous patterns, while the qualitative thermal orthophoto confirmed the site’s surface variability. Overall, the results demonstrate that the integration of multi-gas sensors and aerial thermography on UAVs is viable for the spatial monitoring of landfills. Full article
(This article belongs to the Special Issue Advanced Technologies for Air Quality Monitoring, Assessment and Control)
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16 pages, 3754 KB  
Article
Novel Spatiotemporally Dependent Diffusion Coefficient Models for PM Removal by Passive Air Purifiers: A Theoretical and Experimental Study
by Zhentao Li, Xinlei Pan, Bin Yang, Xiaochuan Li and Tao Wei
Appl. Sci. 2026, 16(8), 3824; https://doi.org/10.3390/app16083824 - 14 Apr 2026
Viewed by 348
Abstract
Fine particulate matter (PM)-induced pollution is one of the major causes of indoor air quality deterioration. Passive air purification technologies offer advantages of structural simplicity and low energy consumption, yet their spatiotemporal mass transfer characteristics remain poorly understood. This study presents a theoretical [...] Read more.
Fine particulate matter (PM)-induced pollution is one of the major causes of indoor air quality deterioration. Passive air purification technologies offer advantages of structural simplicity and low energy consumption, yet their spatiotemporal mass transfer characteristics remain poorly understood. This study presents a theoretical and experimental investigation of PM spatiotemporal mass transfer under the sink effect induced by an electro-convective passive air purifier. The apparent mass transfer coefficient (Dapp) and PM concentration prediction models based on Fick’s second law were established, and then the space-and-time-dependent mass transfer coefficient (Dst) was determined by using the Boltzmann–Matano method. The results revealed that the absolute values of Dst quantified local migration intensity, while its sign provided directional information unattainable from conventional averaged parameters. The logarithmic values of Dapp showed a consistent logarithmic relationship with distance at fixed time windows, and the validated prediction model maintained errors within ±15%, enabling accurate reconstruction of full-field concentration distributions from limited measurement points. The complementary nature of these two coefficients offers a comprehensive evaluation framework. This work advances both the theoretical understanding and practical application of passive air purification technology, offering new tools for indoor PM exposure control and purifier performance optimization. Full article
(This article belongs to the Special Issue Advanced Technologies for Air Quality Monitoring, Assessment and Control)
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