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Sustainability
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Sustainable Urban Environments: Air Quality and Aerosol Particle Detection
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Sustainable Urban Environments: Air Quality and Aerosol Particle Detection

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

Deadline for manuscript submissions: closed (16 February 2025) | Viewed by 970

Special Issue Editors

Dr. Wenjie Wang

E-Mail Website
Guest Editor
Multiphase Chemistry Department, Max Planck Institute for Chemistry, 55128 Mainz, Germany
Interests: air pollution; atmospheric chemistry
Dr. Xiaobing Li

E-Mail Website
Guest Editor
Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
Interests: vertical observations of atmospheric boundary layer pollutants; ozone pollution studies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues:

Air quality of urban environments is closely related to the quality of life and physical health of human beings and aerosols and ozone have emerged as critical air pollutants in urban environments that threaten human health and affect climate change. The chemical composition and formation mechanisms of air pollutants are complicated, posing a grand challenge in terms of developing effective control strategies. The detection of air pollutants is a regular measure to quantify the air pollution level and understand the chemistry, sources, and health effects of air pollution. Therefore, this Special Issue aims to report the latest scientific progress in the scope of air quality and aerosol detection, with the aim of helping the community gain access to the newest research data on air pollutants and provide comprehensive analyses of the drivers behind the air pollution. The final purpose of this Special Issue is to develop effective control strategies for the mitigation of 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:

  • Improvements in the detection technologies for aerosols;
  • Measurement reports on aerosols, ozone, and precursors including volatile organic compounds (VOCs), nitrogen oxides (NOx), and so forth;
  • Sources and chemical processes of aerosol and ozone pollution;
  • Health risks of aerosol and ozone pollution;
  • Control strategies for air pollution.

I look forward to receiving your contributions.

Dr. Wenjie Wang
Dr. Xiaobing Li
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 quality
  • aerosols
  • ozone
  • detection of air pollutants

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Published Papers (1 paper)

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Research

30 pages, 6184 KiB  
Article
A New Hybrid Deep Sequence Model for Decomposing, Interpreting, and Predicting Sulfur Dioxide Decline in Coastal Cities of Northern China
by Guoju Wang, Rongjie Zhu, Xiang Gong, Xiaoling Li, Yuanzheng Gao, Wenming Yin, Renzheng Wang, Huan Li, Huiwang Gao and Tao Zou
Sustainability 2025, 17(6), 2546; https://doi.org/10.3390/su17062546 - 14 Mar 2025
Viewed by 586
Abstract
The recent success of emission reduction policies in China has significantly lowered sulfur dioxide (SO2) levels. However, accurately forecasting these concentrations remains challenging due to their inherent non-stationary tendency. This study introduces an innovative hybrid deep learning model, RF-VMD-Seq2Seq, combining the [...] Read more.
The recent success of emission reduction policies in China has significantly lowered sulfur dioxide (SO2) levels. However, accurately forecasting these concentrations remains challenging due to their inherent non-stationary tendency. This study introduces an innovative hybrid deep learning model, RF-VMD-Seq2Seq, combining the Random Forest (RF) algorithm, Variational Mode Decomposition (VMD), and the Sequence-to-Sequence (Seq2Seq) framework to improve SO2 concentration forecasting in five coastal cities of northern China. Our results show that the predicted SO2 concentrations closely align with observed values, effectively capturing fluctuations, outliers, and extreme events—such as sharp declines the Novel Coronavirus Pneumonia (COVID-19) pandemic in 2020—along with the upper 5% of SO2 levels. The model achieved high coefficients of determination (>0.91) and Pearson’s correlation (>0.96), with low prediction errors (RMSE < 1.35 μg/m3, MAE < 0.94 μg/m3, MAPE < 15%). The low-frequency band decomposing from VMD showed a notable long-term decrease in SO2 concentrations from 2013 to 2020, with a sharp decline since 2018 during heating seasons, probably due to the ‘Coal-to-Natural Gas’ policy in northern China. The input sequence length of seven steps was recommended for the prediction model, based on high-frequency periodicities extracted through VMD, which significantly improved our model performance. This highlights the critical role of weekly-cycle variations in SO2 levels, driven by anthropogenic activities, in enhancing the accuracy of one-day-ahead SO2 predictions across northern China’s coastal regions. The results of the RF model further reveal that CO and NO2, sharing common anthropogenic sources with SO2, contribute over 50% to predicting SO2 concentrations, while meteorological factors—relative humidity (RH) and air temperature—contribute less than 20%. Additionally, the integration of VMD outperformed both the standard Seq2Seq and Ensemble Empirical Mode Decomposition (EEMD)-enhanced Seq2Seq models, showcasing the advantages of VMD in predicting SO2 decline. This research highlights the potential of the RF-VMD-Seq2Seq model for non-stationary SO2 prediction and its relevance for environmental protection and public health management. Full article
(This article belongs to the Special Issue Sustainable Urban Environments: Air Quality and Aerosol Particle Detection)
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