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Sustainability
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Air Quality Characterisation and Modelling—2nd Edition
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Air Quality Characterisation and Modelling—2nd Edition

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (1 July 2025) | Viewed by 3578

Special Issue Editors

Dr. José Carlos Magalhães Pires

E-Mail Website
Guest Editor
LEPABE—Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Interests: CO2 capture; wastewater treatment; microalgal biofuels; process modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Álvaro Gómez-Losada

E-Mail Website
Guest Editor
Department of Quantitative Methods, Loyola Andalucía University, 41704 Seville, Spain
Interests: air pollution; environmental data science; knowledge discovery from databases; spatial and temporal forecasting; statistics data mining methods; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Air pollution is a mixture of particles and gases, which can reach unsafe concentrations for human health, the environment, vegetation, and materials. It has become one of the main sustainability issues and a concerning topic in atmospheric science. According to the World Health Organization (WHO), 90% of the world’s population lives in highly polluted environments, and about seven million premature deaths are caused every year by outdoor and indoor air pollution. The combination of fast-growing populations, transport, fossil fuels, and biomass burning is leading to pollution levels being especially high in some urban areas. Agriculture and natural phenomena are also an important source of pollution, underscoring the multi-faceted and transboundary nature of air pollution. The monitoring and understanding of the temporal and spatial behaviours of air pollutant concentrations are essential for both the implementation of air quality policies and the definition of effective measures to mitigate air pollution and its effects. Quantifying and monitoring exposure to air pollution in terms of public health are also critical components in policy discussion.

Continuing the success of the first edition of the Special Issue “Air Quality Characterisation and Modelling”, the second edition will present recent research activities concerning the characterization of air pollution and the applied modelling approaches.

Dr. José Carlos Magalhães Pires
Dr. Álvaro Gómez-Losada
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. Sustainability 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

  • particulate matter
  • African dust
  • nitrogen oxides
  • ground-level ozone
  • development, evaluation and application of models
  • statistical models
  • data mining and machine-learning-based models
  • integrated modelling and assessment

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Published Papers (2 papers)

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Research

33 pages, 59097 KiB  
Article
Street Canyon Vegetation—Impact on the Dispersion of Air Pollutant Emissions from Road Traffic
by Paulina Bździuch, Marek Bogacki and Robert Oleniacz
Sustainability 2024, 16(23), 10700; https://doi.org/10.3390/su162310700 - 6 Dec 2024
Cited by 2 | Viewed by 1486
Abstract
Roadside vegetation helps to retain air pollutants emitted by road traffic. On the other hand, its presence makes it difficult to ventilate street canyons. The paper examines the influence of vegetation on the dispersion of air pollution generated by road traffic, using the [...] Read more.
Roadside vegetation helps to retain air pollutants emitted by road traffic. On the other hand, its presence makes it difficult to ventilate street canyons. The paper examines the influence of vegetation on the dispersion of air pollution generated by road traffic, using the example of two street canyons—both-sided and one-sided street canyons. The study was conducted taking into account the actual emission conditions occurring on the analyzed road sections estimated using the HBEFA methodology. Subsequently, a three-dimensional pollution dispersion model named MISKAM was employed to simulate the air pollutant dispersion conditions in the analyzed street canyons. The modelling results were compared with the measurement data from air quality monitoring stations located in these canyons. The obtained results indicated that the presence of vegetation can significantly impact on the air dispersion of traffic-related exhaust and non-exhaust emissions. The impact of vegetation is more pronounced in the case of a street canyon with dense, high-rise development on both sides than in the case of a street canyon with such development on only one side. The results for the both-sided street canyon demonstrate that the discrepancy between the scenario devoid of vegetation and the scenario with vegetation was approximately 5 µg/m3 (10%) for PM10 and approximately 54 µg/m3 (45%) for NOx, with the former scenario showing lower values than the latter. Nevertheless, the scenario with the vegetation exhibited a lesser discrepancy with the air quality measurements. Vegetation functions as a natural barrier, reducing wind speed in the street canyon, which in turn limits the spread of pollutants in the air, leading to pollutant accumulation near the building walls that form the canyon. Consequently, atmospheric dispersion modelling must consider the presence of vegetation to accurately evaluate the effects of road traffic emissions on air quality in urban areas, particularly in street canyons. The results of this study may hold importance for urban planning and decision-making regarding environmental management in cities aimed at improving air quality and public health. Full article
(This article belongs to the Special Issue Air Quality Characterisation and Modelling—2nd Edition)
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17 pages, 6102 KiB  
Article
Improving Air Quality Data Reliability through Bi-Directional Univariate Imputation with the Random Forest Algorithm
by Filip Arnaut, Vladimir Đurđević, Aleksandra Kolarski, Vladimir A. Srećković and Sreten Jevremović
Sustainability 2024, 16(17), 7629; https://doi.org/10.3390/su16177629 - 3 Sep 2024
Cited by 3 | Viewed by 1432
Abstract
Forecasting the future levels of air pollution provides valuable information that holds importance for the general public, vulnerable populations, and policymakers. High-quality data are essential for precise and reliable forecasts and investigations of air pollution. Missing observations arise when the sensors utilized for [...] Read more.
Forecasting the future levels of air pollution provides valuable information that holds importance for the general public, vulnerable populations, and policymakers. High-quality data are essential for precise and reliable forecasts and investigations of air pollution. Missing observations arise when the sensors utilized for assessing air quality parameters experience malfunctions, which result in erroneous measurements or gaps in the dataset and hinder the data quality. This research paper presents a novel approach for imputing missing values in air quality data in a univariate approach. The algorithm employs the random forest (RF) algorithm to impute missing observations in a bi-directional (forward and reverse in time) manner for air quality (particulate matter less than 2.5 μm (PM2.5)) data from the Republic of Serbia. The algorithm was evaluated against simple methods, such as the mean and median imputation methods, for missing observations over durations of 24, 48, and 72 h. The results indicate that our algorithm yielded comparable error rates to the median imputation method for all periods when imputing the PM2.5 data. Ultimately, the algorithm’s higher computational complexity proved itself as not justified considering the minimal error decrease it achieved compared with the simpler methods. However, for future improvement, additional research is needed, such as utilizing low-code machine learning libraries and time-series forecasting techniques. Full article
(This article belongs to the Special Issue Air Quality Characterisation and Modelling—2nd Edition)
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