Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
Changes in Atmospheric Environment
sustainability-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Changes in Atmospheric Environment

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

Deadline for manuscript submissions: 1 October 2024 | Viewed by 3179

Special Issue Editors

Prof. Dr. Baozhu Ge

E-Mail Website
Guest Editor
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: atmospheric deposition; air pollution; regional transport; model simulation; atmospheric chemistry; source indentification; air quailty prediction
Prof. Dr. Xuejun Liu

grade E-Mail Website
Guest Editor
Department of Environmental Science and Engineering, College of Resources and Environmental Sciences, China Agricultural University (CAU), Beijing 100193, China
Interests: atmospheric environment; wet and dry deposition; nitrogen cycling; ammonia emission reduction
Special Issues, Collections and Topics in MDPI journals
Dr. Syuichi Itahashi

E-Mail Website
Guest Editor
Sustainable System Research Laboratory (SSRL), Central Research Institute of Electric Power Industry (CRIEPI), Abiko 2701194, Japan
Interests: numerical modeling simulation; air pollution; deposition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Anthropogenic activities strongly influence the atmospheric environment, which in turn alters human lives. In recent years, the atmospheric environment has changed a great deal due to rapid development in economics, energy consumption and global climate change. For example, China’s GDP has increased by almost 6% in the past few decades; this has been associated with the emergence of serious environmental problems such as heavy haze pollution, ozone exposure and acid deposition. These serious atmospheric environmental problems degrade the quality of human lives, hinder the development of society and even change the global climate, which feedback with damaging disasters such as global warming, super rainstorms, and hurricanes. China’s government has implemented a series of measures to reduce the air pollutant emissions—it reformed the energy structure, eliminated backward production capacity, and halted or cut back on the production levels since the 2010s. The air quality improved substantially. China’s measures, which have taken less than 10 years, have proven to be successful. During these periods, long-term observation networks for air pollutants, comprehensive field measurements, and model simulations for the analysis of air pollution improved our scientific knowledge of the atmospheric environment. However, the connection between the changes in the social and environmental sciences remains unclear, and investigations into these changes are urgent and important to expand the knowledge of this research area.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Air pollution and climate change.
  • Long-term measurement and simulation on air pollution.
  • Relations between the changes in the atmospheric environment and human beings.
  • Prediction of future changes in the atmospheric environment.
  • Changes in the atmospheric environment, from Asia to worldwide.

We look forward to receiving your contributions.

Dr. Baozhu Ge
Prof. Dr. Xuejun Liu
Dr. Syuichi Itahashi
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

  • changes
  • atmospheric environment
  • prediction
  • human beings
  • air pollution
  • Asia
  • long-term measurement
  • modeling and simulation
  • deposition

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 8269 KiB  
Article
Long-Term Variation Patterns of Precipitations Driven by Climate Change in China from 1901 to 2022
by Jing Han, Rui Zhang, Bing Guo, Baomin Han, Tianhe Xu and Qiang Guo
Sustainability 2024, 16(17), 7283; https://doi.org/10.3390/su16177283 - 24 Aug 2024
Viewed by 1124
Abstract
Studying long-term precipitation trends is crucial for sustainable development, as the proper utilization of water resources is essential for maintaining a sustainable water supply. The objective and novelty of this paper was to reveal the gradual mutation process of precipitation in China over [...] Read more.
Studying long-term precipitation trends is crucial for sustainable development, as the proper utilization of water resources is essential for maintaining a sustainable water supply. The objective and novelty of this paper was to reveal the gradual mutation process of precipitation in China over a century. This study utilized monthly precipitation data from 1901 to 2022 (at a century scale) to analyze and explore the spatiotemporal variability in precipitation across different time scales and regions with a trend analysis, an abrupt change analysis, and gravity center models. The findings indicate that (1) from 1901 to 2022, the precipitation in China generally decreased from the southeast coastal areas toward the northwest inland regions. (2) There were significant differences in the migration of precipitation gravity centers among the different study regions, with the least dispersion being observed in the Liao River basin, while the Hai River basin, various river basins in the northwest, and the Pearl River basin exhibited certain regularities in gravity center movement, and other regions showed periodic variations. (3) Over the period from 1901 to 2022, there was a trend of transitioning from lower to higher precipitation levels. (4) According to continuous long-term abrupt change tests, the timing of precipitation shifts varied across different basins. Precipitation, as a crucial component of natural resources, directly impacts various aspects of socio-economic life. Research findings provide decision support for regional flood control and disaster reduction and offer scientific decisions for ecological security. Full article
(This article belongs to the Special Issue Changes in Atmospheric Environment)
►▼ Show Figures

Figure 1

19 pages, 4655 KiB  
Article
Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments
by Edgar Tello-Leal, Ulises Manuel Ramirez-Alcocer, Bárbara A. Macías-Hernández and Jaciel David Hernandez-Resendiz
Sustainability 2024, 16(16), 7062; https://doi.org/10.3390/su16167062 - 17 Aug 2024
Viewed by 1261
Abstract
Air pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an [...] Read more.
Air pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an air quality index (AQI). On the other hand, accurate future predictions of air pollutant concentration levels can provide valuable information for data-driven decision-making to reduce health risks from short- and long-term exposure when indicators exceed permissible limits. In this paper, five deep learning architectures are evaluated to predict the concentration of particulate matter pollutants (in their fractions PM2.5 and PM10) and carbon monoxide (CO) in consecutive hours. The proposed prediction models are based on recurrent neural networks (RNNs), long short-term memory (LSTM), vanilla LSTM, Stacked LSTM, Bi-LSTM, and encoder–decoder LSTM networks. Moreover, a methodology is presented to guide the construction of the prediction model, encompassing raw data processing, model design and optimization, and neural network training, testing, and evaluation. The results underscore the precision and reliability of the Stacked LSTM model in predicting the hourly concentration level for PM2.5, with an RMSE of 3.4538 μg/m3. Similarly, the encoder–decoder LSTM model accurately predicts the concentration level for PM10 and CO, with an RMSE of 3.2606 μg/m3 and 2.1510 ppm, respectively. These evaluations, with their minimal differences in error metrics and coefficient of determination, validate the effectiveness and superiority of the deep learning models over other reference models, instilling confidence in their potential. Full article
(This article belongs to the Special Issue Changes in Atmospheric Environment)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop