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Atmospheric Pollution and Microenvironmental Air Quality

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Air, Climate Change and Sustainability".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 505

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Special Issue Editors

Dr. Hui Chen

E-Mail Website
Guest Editor

Institute of Environmental Pollution and Health, Shanghai University, Shanghai, China
Interests: atmospheric chemistry; novel PM_2.5/O₃ source apportionment techniques; heterogeneous reaction mechanisms in polluted atmospheres; policy-relevant studies on pollution mitigation

Dr. Lan Yao

E-Mail Website
Guest Editor

School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
Interests: black carbon (BC) aerosols; chemical speciation and source apportionment; single aerosol particles and microscopic morphology; mobile environmental monitoring technologies; indoor air quality
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Special Issue Information

Dear Colleagues,

Atmospheric pollution remains one of the most pressing global challenges, directly threatening human health and accelerating climate change. Pollutants such as particulate matter (PM), nitrogen oxides (NO_x), ozone (O₃), and volatile organic compounds (VOCs) are linked to respiratory diseases, cardiovascular disorders, and premature mortality. Simultaneously, these pollutants contribute to global warming through radiative forcing and atmospheric chemistry interactions. While outdoor air pollution has traditionally dominated research agendas, mounting evidence highlights the critical role of microenvironmental air quality—that is, of spaces where individuals spend over 90% of their time, including indoor settings (e.g., homes, offices, schools) and outdoor micro-scale urban spaces (e.g., street canyons, transportation hubs, parks).

Urban areas, home to more than half of the global population, face compounded risks due to dense populations, overlapping pollution sources (e.g., traffic, industrial emissions, indoor combustion), and prolonged exposure in confined microenvironments. These localized spaces exhibit unique pollution dynamics shaped by factors such as ventilation efficiency, human activity patterns, and microclimate conditions. Understanding both outdoor atmospheric pollution and microenvironmental air quality is essential for designing equitable mitigation strategies, reducing health disparities, and advancing climate-resilient urban development.

This Special Issue emphasizes the equal importance of studying outdoor and microenvironmental pollution systems. By bridging scales—from city-wide emissions to hyper-localized exposures—we aim to foster interdisciplinary solutions that align with the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities), and SDG 13 (Climate Action).

Scope of Contributions

We invite original research, reviews, and case studies addressing the following themes, with a focus on integrating outdoor and microenvironmental perspectives:

Outdoor Atmospheric Pollution
- Formation mechanisms and source apportionment of pollutants (e.g., PM_2.5, O₃, NO_x) in urban and regional contexts;
- Interactions between air pollution and climate change (e.g., feedback loops involving black carbon, methane, and urban heat islands);
- Impacts of extreme weather events (e.g., heatwaves, stagnant air) on pollution dispersion and public health.
Microenvironmental Air Quality
- Pollution characterization in indoor spaces (residential, commercial, industrial) and outdoor micro-environments (street canyons, bus stops, urban parks);
- Role of human activities (e.g., cooking, commuting, energy use) and building design (e.g., ventilation, green walls) in exposure risks;
- Socioeconomic disparities in microenvironmental pollution burdens.
Health and Climate Co-Benefits
- Quantitative exposure–risk models linking pollution levels (indoor/outdoor) to health outcomes;
- Strategies with dual health and climate benefits (e.g., electrification of transport, clean cooking technologies).
Innovative Monitoring and Modeling
- Low-cost sensor networks for real-time, high-resolution air quality mapping;
- Machine learning, Computational Fluid Dynamics modeling, or satellite remote sensing to predict pollution hotspots.
Policy and Sustainable Solutions
- Urban planning interventions (e.g., green infrastructure, low-emission zones) to reduce emissions and exposure;
- Behavioral and technological innovations for pollution mitigation in microenvironments;
- Case studies of cities successfully integrating outdoor and microenvironmental data into climate action plans.

Dr. Hui Chen
Dr. Lan Yao
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 250 words) can be sent to the Editorial Office for assessment.

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

atmospheric pollution
microenvironmental air quality
health exposure assessment
climate change interactions
urban sustainability
indoor and outdoor pollution
pollution mitigation strategies
sustainable urban design

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Published Papers (1 paper)

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22 pages, 3829 KB

Open AccessArticle

Air Pollutant Concentration Prediction Using a Generative Adversarial Network with Multi-Scale Convolutional Long Short-Term Memory and Enhanced U-Net

by Jiankun Zhang, Pei Su, Juexuan Wang and Zhantong Cai

Sustainability 2025, 17(24), 11177; https://doi.org/10.3390/su172411177 - 13 Dec 2025

Viewed by 312

Abstract

Accurate prediction of air pollutant concentrations, particularly fine particulate matter (PM_2.5), is essential for controlling and preventing heavy pollution incidents by providing early warnings of harmful substances in the atmosphere. This study proposes a novel spatiotemporal model for PM_2.5 concentration prediction based on a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP). The framework incorporates three key design components: First, the generator employs an Inception-style Convolutional Long Short-Term Memory (ConvLSTM) network, integrating parallel multi-scale convolutions and hierarchical normalization. This design enhances multi-scale spatiotemporal feature extraction while effectively suppressing boundary artifacts via a map-masking layer. Second, the discriminator adopts an architecturally enhanced U-Net, incorporating spectral normalization and shallow instance normalization. Feature-guided masked skip connections are introduced, and the output is designed as a raw score map to mitigate premature saturation during training. Third, a composite loss function is utilized, combining adversarial loss, feature-matching loss, and inter-frame spatiotemporal smoothness. A sliding-window conditioning mechanism is also implemented, leveraging multi-level features from the discriminator for joint spatiotemporal optimization. Experiments conducted on multi-source gridded data from Dongguan demonstrate that the model achieves a 12 h prediction performance with a Root Mean Square Error (RMSE) of 4.61 μg/m³, a Mean Absolute Error (MAE) of 6.42 μg/m³, and a Coefficient of Determination (R²) of 0.80. The model significantly alleviates performance degradation in long-term predictions when the forecast horizon is extended from 3 to 12 h, the RMSE increases by only 1.84 μg/m³, and regional deviations remain within ±3 μg/m³. These results indicate strong capabilities in spatial topology reconstruction and robustness against concentration anomalies, highlighting the model’s potential for hyperlocal air quality early warning. It should be noted that the empirical validation is limited to the specific environmental conditions of Dongguan, and the model’s generalizability to other geographical and climatic settings requires further investigation. Full article

(This article belongs to the Special Issue Atmospheric Pollution and Microenvironmental Air Quality)

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