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Atmosphere
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Revolutionizing Air Quality Research: Unlocking New Insights through Cutting-Edge Artificial Intelligence...
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Revolutionizing Air Quality Research: Unlocking New Insights through Cutting-Edge Artificial Intelligence Techniques

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2153

Special Issue Editors

Dr. Khalid Mehmood

E-Mail Website
Guest Editor
Institute of Environmental Health and Ecological Security, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: air quality; modelling; emissions; health risk assessment; climate change; data mining
Prof. Dr. Jaroslaw Krzywanski

E-Mail Website
Guest Editor
Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland
Interests: modeling; adsorption chillers; CFB boilers; oxy-fuel combustion; CLC; CaL; biomass; machine learning; artificial neural networks; fuzzy logic; genetic algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Globally, air pollution is a major environmental challenge that affects the health and well-being of millions of people. More than seven million premature deaths are estimated to be caused by poor air quality every year. A variety of factors cause poor air quality, including industrial activities, transportation, and energy production. Pollutants released from these activities can cause respiratory problems, heart disease, and cancer. It is necessary to use advanced modeling and data analysis tools to identify pollution sources, estimate emissions, and inform policymakers. In recent years, artificial intelligence approaches have revolutionized air quality research, providing new insights into how air pollution affects human health and the environment. This Special Issue of the journal Atmosphere is dedicated to "Revolutionizing Air Quality Research: Unlocking New Insights through Cutting-Edge Artificial Intelligence Techniques". We seek submissions of original research articles, reviews, and perspectives following international hotspot-based air quality research and artificial intelligence/machine learning approaches. The scope of this issue mainly includes but is not limited to:

  • Novel artificial intelligence/machine learning algorithms for air quality modeling, forecasting, and data analysis;
  • The integration of machine learning with air quality monitoring data to identify sources of pollution and estimate emissions;
  • Machine-learning-based approaches for air quality management and policymaking;
  • Applications of machine learning in assessing the health impacts of air pollution;
  • Deep learning, ensemble learning, and transfer learning approaches in air quality research;
  • Visualization and interpretation of machine learning results for air quality research.

Dr. Khalid Mehmood
Prof. Dr. Jaroslaw Krzywanski
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. 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

  • artificial intelligence
  • air quality
  • predictive modeling
  • deep learning
  • ensemble learning
  • big data
  • source identification
  • environmental health

Published Papers (2 papers)

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Research

12 pages, 1288 KiB  
Article
Modelling Smell Events in Urban Pittsburgh with Machine and Deep Learning Techniques
by Andreas Gavros, Yen-Chia Hsu and Kostas Karatzas
Atmosphere 2024, 15(6), 731; https://doi.org/10.3390/atmos15060731 - 19 Jun 2024
Viewed by 358
Abstract
By deploying machine learning (ML) and deep learning (DL) algorithms, we address the problem of smell event modelling in the Pittsburgh metropolitan area. We use the Smell Pittsburgh dataset to develop a model that can reflect the relation between bad smell events and [...] Read more.
By deploying machine learning (ML) and deep learning (DL) algorithms, we address the problem of smell event modelling in the Pittsburgh metropolitan area. We use the Smell Pittsburgh dataset to develop a model that can reflect the relation between bad smell events and industrial pollutants in a specific urban territory. The initial dataset resulted from crowd-sourcing citizen reports using a mobile phone application, which we categorised in a binary matter (existence or absence of smell events). We investigate the mapping of smell data with air pollution levels that were recorded by a reference station located in the southeastern area of the city. The initial dataset is processed and evaluated to produce an updated dataset, which is used as an input to assess various ML and DL models for modelling smell events. The models utilise a set of air quality and climate data to associate them with a smell event to investigate to what extent these data correlate with unpleasant odours in the Pittsburgh metropolitan area. The model results are satisfactory, reaching an accuracy of 69.6, with ML models mostly outperforming DL models. This work also demonstrates the feasibility of combining environmental modelling with crowd-sourced information, which may be adopted in other cities when relevant data are available. Full article
(This article belongs to the Special Issue Revolutionizing Air Quality Research: Unlocking New Insights through Cutting-Edge Artificial Intelligence Techniques)
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15 pages, 26882 KiB  
Article
Convolutional Forecasting of Particulate Matter: Toward a Data-Driven Generalized Model
by Luca Ferrari and Giorgio Guariso
Atmosphere 2024, 15(4), 398; https://doi.org/10.3390/atmos15040398 - 24 Mar 2024
Viewed by 803
Abstract
Air pollution poses a significant threat to human health and ecosystems. Forecasting the concentration of key pollutants like particulate matter can help support air quality planning and prevention measures. Deep learning methods are becoming increasingly popular for predicting air pollution and particulate matter [...] Read more.
Air pollution poses a significant threat to human health and ecosystems. Forecasting the concentration of key pollutants like particulate matter can help support air quality planning and prevention measures. Deep learning methods are becoming increasingly popular for predicting air pollution and particulate matter concentration. Architectures like Convolutional Neural Networks can effectively account for the geographical features of the study domain. This work tests a Feed-Forward, a Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN) on a polluted geographical domain in northern Italy. The best convolutional architecture was then implemented in two other quite different regions. The results show that the same CNN architecture provides remarkably accurate forecasts in all applications and that a network trained on PM10 data can accurately forecast PM2.5 concentrations up to 10 days ahead. These results suggest that the proposed CNN has high generalization capabilities and can thus be reliably used as a forecasting model for different areas. Full article
(This article belongs to the Special Issue Revolutionizing Air Quality Research: Unlocking New Insights through Cutting-Edge Artificial Intelligence Techniques)
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