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Processes
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Applications of Machine Learning and Data Modeling Techniques in Air...
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Applications of Machine Learning and Data Modeling Techniques in Air Quality Monitoring and Control Mechanisms

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 774

Special Issue Editors

Dr. Getaneh Berie Tarekegn

E-Mail Website
Guest Editor
Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 300093, Taiwan
Interests: UAV-based air quality monitoring system; machine learning; Internet of Things; wireless sensor networks; big data analysis
Dr. Abiyu Kerebo Berekute

E-Mail Website
Guest Editor
Institute of Environmental and Occupational Health Sciences, National Yang Ming Chiao Tung University, Taipei City 112304, Taiwan
Interests: indoor air quality; environmental remediation; photocatalysis; antimicrobial materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid urbanization and industrialization of recent decades have significantly impacted global air quality, posing severe health and environmental risks. The increased prevalence of airborne pollutants such as particulate matter (PM), volatile organic compounds (VOCs), carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), polycyclic aromatic hydrocarbons (PAHs), and other hazardous pollutants has heightened the need for effective air quality monitoring and control mechanisms. Advancements in machine learning and data modeling have paved the way for innovative approaches to addressing these challenges, enabling precise pollutant detection, forecasting, and real-time intervention strategies.

This Special Issue on “Applications of Machine Learning and Data Modeling Techniques in Air Quality Monitoring and Control Mechanisms” seeks high-quality research and innovative solutions in this critical domain. Authors are invited to submit original research, case studies, or review articles on topics including, but not limited to, the following:

  • IoT-based air quality monitoring systems and their seamless integration into smart environments;
  • Ground vehicle-based sensor and unmanned aerial vehicle-based sensor monitoring systems;
  • Data-driven approaches for real-time air quality assessment and monitoring;
  • Development of machine learning models for air quality prediction and forecasting;
  • Innovations in data fusion and visualization for air quality analytics;
  • AI applications in designing effective pollution control strategies;
  • Predictive models for assessing health risks associated with air pollution exposure;
  • Case studies and practical implementations of air quality management systems.

Dr. Getaneh Berie Tarekegn
Dr. Abiyu Kerebo Berekute
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. Processes 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

  • deep learning
  • air quality sensing
  • prediction
  • smart city
  • ship emissions
  • drones
  • cloud computing
  • low-cost air quality sensors
  • nanocatalysts

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Published Papers (1 paper)

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Research

18 pages, 6278 KiB  
Article
Application of Deep Learning Techniques for Air Quality Prediction: A Case Study in Macau
by Thomas M. T. Lei, Jianxiu Cai, Wan-Hee Cheng, Tonni Agustiono Kurniawan, Altaf Hossain Molla, Mohd Shahrul Mohd Nadzir, Steven Soon-Kai Kong and L.-W. Antony Chen
Processes 2025, 13(5), 1507; https://doi.org/10.3390/pr13051507 - 14 May 2025
Viewed by 392
Abstract
To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI [...] Read more.
To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM10 and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Data Modeling Techniques in Air Quality Monitoring and Control Mechanisms)
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