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Towards Sustainability: Advanced Research on Environmental Analysis and Air Pollution
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Towards Sustainability: Advanced Research on Environmental Analysis and Air Pollution

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Pollution Prevention, Mitigation and Sustainability".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 4827

Special Issue Editors

Dr. Ling Huang

E-Mail Website
Guest Editor
School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200000, China
Interests: air quality modeling; formation mechanism of secondary air pollutants; development of emission inventory; interaction between climate change and air quality
Dr. Jiani Tan

E-Mail Website
Guest Editor
School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200000, China
Interests: reactive nitrogen; atmospheric deposition; carbon sink and climate change; machine learning
Dr. Kun Zhang

E-Mail Website
Guest Editor
School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200000, China
Interests: formation mechanism of secondary air pollutants; box models; field observations
Dr. Qing Mu

E-Mail Website
Guest Editor
Department of Health and Environmental Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China
Interests: numerical simulation of bioaerosols; air quality model development; study of pollution sources; transport; physiochemical processes of air pollutants

Special Issue Information

Dear Colleagues,

In recent years, the pressing challenges posed by environmental degradation and air pollution have become increasingly evident, underscoring the critical need for advanced research focused on environmental analysis and air pollution. The scientific community has long recognized the detrimental effects of air pollutants on both the environment and human health, making it imperative to delve deeper into an understanding of these complex issues and to develop sustainable solutions.
Environmental analysis plays a pivotal role in assessing the quality issues of our natural surroundings, helping us to identify the sources of pollution, to monitor environmental changes, and to evaluate the effectiveness of mitigation measures. Air pollution, in particular, stands out as a significant environmental concern with far-reaching consequences; from industrial emissions and vehicular exhausts to agricultural practices and urban development, the sources of air pollutants are diverse and widespread. 
Against this backdrop, advanced research on environmental analysis and air pollution holds immense importance in shaping a sustainable future. By exploring new avenues of inquiry, embracing cutting-edge technologies, and fostering interdisciplinary collaboration, researchers can advance our understanding of environmental dynamics and develop strategies to mitigate pollution effectively. As we strive to achieve sustainable development goals and combat the adverse impacts of climate change, investing in advanced research on environmental analysis and air pollution emerges as a critical pathway towards building resilient and environmentally conscious societies.

In light of these considerations, this Special Issue aims to showcase the latest advancements in research, highlight innovative approaches to environmental analysis, and underscore the importance of collective efforts to tackle air pollution. In this Special Issue, original research articles and reviews are welcome and research areas may include, but are not limited to, the following: 

  • New technologies to monitor or measure atmospheric composition;
  • New improvement in air quality modelling;
  • Formation mechanism of secondary air pollutants;
  • Application of machine learning in addressing air pollution issues;
  • Emission inventory of greenhouse gases and air pollutants;
  • New practices in air quality management;
  • Impact of air pollution on human health;
  • Interactions between climate change and air pollution;
  • New findings on air–surface exchange of pollutants.

We look forward to receiving your contributions. 

Dr. Ling Huang
Dr. Jiani Tan
Dr. Kun Zhang
Dr. Qing Mu
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

  • air pollution monitoring
  • emission inventory
  • air quality modeling
  • air pollution management
  • climate change
  • secondary air pollutants
  • machine learning
  • remote sensing
  • atmospheric deposition

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Published Papers (5 papers)

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20 pages, 4224 KiB  
Article
Enhancing Environmental Policy Decisions in Korea and Japan Through AI-Driven Air Pollution Forecast
by Yushin Kim, Jungin Kim, Sunghyun Cho, Hyein Sim and Ji-Young Kim
Sustainability 2024, 16(23), 10436; https://doi.org/10.3390/su162310436 - 28 Nov 2024
Viewed by 294
Abstract
(1) Background: Although numerous artificial intelligence (AI)-based air pollution prediction models have been proposed, research that links key pollution drivers, such as regional industrial facilities, to actionable policy recommendations is required. (2) Methods: This study employs the radial basis function (RBF) and spatial [...] Read more.
(1) Background: Although numerous artificial intelligence (AI)-based air pollution prediction models have been proposed, research that links key pollution drivers, such as regional industrial facilities, to actionable policy recommendations is required. (2) Methods: This study employs the radial basis function (RBF) and spatial lag features to capture spatial interactions among regions, utilizing a transformer model for analysis. The model was trained on air quality and industrial data from South Korea (2010–2022) and Japan (2017–2020). (3) Results: The transformer model achieved a mean squared error of 0.045 for the Korean dataset and 0.166 for the Japanese dataset, outperforming benchmark models, including Support Vector Regression, neural networks, and the AutoRegressive Integrated Moving Average model. (4) Conclusions: By capturing complex spatial dynamics, the proposed model provides valuable insights that can assist policymakers in developing effective, data-driven strategies for air pollution reduction at the national and regional levels, thereby supporting the broader goals of sustainability through informed, equitable environmental interventions. Full article
(This article belongs to the Special Issue Towards Sustainability: Advanced Research on Environmental Analysis and Air Pollution)
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20 pages, 16741 KiB  
Article
The Effect of Diesel Vehicle Regulation on Air Quality in Seoul: Evidence from Seoul’s Low Emission Zone
by Dongkyu Park and Nori Tarui
Sustainability 2024, 16(21), 9573; https://doi.org/10.3390/su16219573 - 3 Nov 2024
Viewed by 835
Abstract
This study investigates the effect of the low emission zone (LEZ), designed to restrict old diesel vehicles, on air quality in Seoul, Republic of Korea, using the regression discontinuity in time (RDiT) approach. While previous studies have examined LEZ impacts using traditional econometric [...] Read more.
This study investigates the effect of the low emission zone (LEZ), designed to restrict old diesel vehicles, on air quality in Seoul, Republic of Korea, using the regression discontinuity in time (RDiT) approach. While previous studies have examined LEZ impacts using traditional econometric models such as time series and panel data approaches, our research uniquely integrates high-frequency daily weather data to better control for confounding environmental variables and captures time-of-day effects on pollutant concentrations. Our findings reveal that the LEZ policy effectively reduced NO2 and SO2 concentrations by 4.7% and 11.6%, respectively. Notably, during daytime hours, when traffic is heaviest, NO2, SO2, and PM10 concentrations decreased by 7.1%, 14.8%, and 13.6%, respectively. These results suggest that the observed improvements can be attributed not only to reduced diesel vehicle registrations but also to significant declines in overall traffic volume. Full article
(This article belongs to the Special Issue Towards Sustainability: Advanced Research on Environmental Analysis and Air Pollution)
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21 pages, 3356 KiB  
Article
Indoor Environmental Quality in Portuguese Office Buildings: Influencing Factors and Impact of an Intervention Study
by Fátima Felgueiras, Zenaida Mourão, André Moreira and Marta F. Gabriel
Sustainability 2024, 16(21), 9160; https://doi.org/10.3390/su16219160 - 22 Oct 2024
Viewed by 693
Abstract
Office workers spend a considerable part of their day at the workplace, making it vital to ensure proper indoor environmental quality (IEQ) conditions in office buildings. This work aimed to identify significant factors influencing IEQ and assess the effectiveness of an environmental intervention [...] Read more.
Office workers spend a considerable part of their day at the workplace, making it vital to ensure proper indoor environmental quality (IEQ) conditions in office buildings. This work aimed to identify significant factors influencing IEQ and assess the effectiveness of an environmental intervention program, which included the introduction of indoor plants, carbon dioxide (CO2) sensors, ventilation, and printer relocation (source control), in six modern office buildings in improving IEQ. Thirty office spaces in Porto, Portugal, were randomly divided into intervention and control groups. Indoor air quality, thermal comfort, illuminance, and noise were monitored before and after a 14-day intervention implementation. Occupancy, natural ventilation, floor type, and cleaning time significantly influenced IEQ levels. Biophilic interventions appeared to decrease volatile organic compound concentrations by 30%. Installing CO2 sensors and optimizing ventilation strategies in an office that mainly relies on natural ventilation effectively improved air renewal and resulted in a 28% decrease in CO2 levels. The implementation of a source control intervention led to a decrease in ultrafine particle and ozone concentrations by 14% and 85%, respectively. However, an unexpected increase in airborne particle levels was detected. Overall, for a sample of offices that presented acceptable IEQ levels, the intervention program had only minor or inconsistent impacts. Offices with declared IEQ problems are prime candidates for further research to fully understand the potential of environmental interventions. Full article
(This article belongs to the Special Issue Towards Sustainability: Advanced Research on Environmental Analysis and Air Pollution)
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15 pages, 5646 KiB  
Article
Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals
by Thomas M. T. Lei, Jianxiu Cai, Altaf Hossain Molla, Tonni Agustiono Kurniawan and Steven Soon-Kai Kong
Sustainability 2024, 16(17), 7477; https://doi.org/10.3390/su16177477 - 29 Aug 2024
Viewed by 852
Abstract
To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be developed to construct a sustainable, safe, and resilient city and mitigate climate change for [...] Read more.
To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be developed to construct a sustainable, safe, and resilient city and mitigate climate change for a double win. Machine learning (ML) and deep learning (DL) models have been applied to datasets in Macau to predict the daily levels of roadside air pollution in the Macau peninsula, situated near the historical sites of Macau. Macau welcomed over 28 million tourists in 2023 as a popular tourism destination. Still, an accurate air quality forecast has not been in place for many years due to the lack of a reliable emission inventory. This work will develop a dependable air pollution prediction model for Macau, which is also the novelty of this study. The methods, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), were applied and successful in the prediction of daily air pollution levels in Macau. The prediction model was trained using the air quality and meteorological data from 2013 to 2019 and validated using the data from 2020 to 2021. The model performance was evaluated based on the root mean square error (RMSE), mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and Kendall’s tau coefficient (KTC). The RF model best predicted PM10, PM2.5, NO2, and CO concentrations with the highest PCC and KTC in a daily air pollution prediction. In addition, the SVR model had the best stability and repeatability compared to other models, with the lowest SD in RMSE, MAE, PCC, and KTC after five model runs. Therefore, the results of this study show that the RF model is more efficient and performs better than other models in the prediction of air pollution for the dataset of Macau. Full article
(This article belongs to the Special Issue Towards Sustainability: Advanced Research on Environmental Analysis and Air Pollution)
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19 pages, 891 KiB  
Article
Indoor Environmental Quality and Effectiveness of Portable Air Cleaners in Reducing Levels of Airborne Particles during Schools’ Reopening in the COVID-19 Pandemic
by Florentina Villanueva, Fátima Felgueiras, Alberto Notario, Beatriz Cabañas and Marta Fonseca Gabriel
Sustainability 2024, 16(15), 6549; https://doi.org/10.3390/su16156549 - 31 Jul 2024
Cited by 1 | Viewed by 1152
Abstract
Educational buildings tend to fail in the contagion containment of airborne infectious diseases because of the high number of children, for several hours a day, inside enclosed environments that often have inadequate indoor air quality (IAQ) conditions. This study aimed to assess indoor [...] Read more.
Educational buildings tend to fail in the contagion containment of airborne infectious diseases because of the high number of children, for several hours a day, inside enclosed environments that often have inadequate indoor air quality (IAQ) conditions. This study aimed to assess indoor environmental quality and test the effectiveness of portable air cleaners (PACs) in alleviating airborne particle levels in schools of Central–Southern Spain during the period of reopening after the lockdown due to the COVID-19 outbreak. To accomplish this, three sampling campaigns were organized from September to December 2020 to consistently monitor temperature and relative humidity, carbon dioxide, and particulate matter in nineteen classrooms (seven school buildings). Results showed that although the recommendation of maintaining the windows open throughout the day seemed to be effective in promoting, in general, proper ventilation conditions (based on CO2 levels). For the colder campaigns, this practice caused notorious thermal comfort impairment. In addition, a great number of the surveyed classrooms presented levels of PM2.5 and PM10, attributable to outdoor and indoor sources, which exceeded the current WHO guideline values. Moreover, considering the practice of having the windows opened, the installation of 1 unit of PACs per classroom was insufficient to ensure a reduction in particle concentration to safe levels. Importantly, it was also found that children of different ages at different education levels can be exposed to significantly different environmental conditions in their classrooms; thus, the corrective measures to employ in each individual educational setting should reflect the features and needs of the target space/building. Full article
(This article belongs to the Special Issue Towards Sustainability: Advanced Research on Environmental Analysis and Air Pollution)
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