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 1574

Special Issue Editors


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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

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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
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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

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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 (1 paper)

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Research

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 685
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
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