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Atmosphere
Special Issues
Data Analysis in Atmospheric Research
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Data Analysis in Atmospheric Research

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 988

Special Issue Editors

Dr. Zhaoxin Dai

E-Mail Website
Guest Editor
Chinese Academy of Surveying and Mapping, Beijing 100830, China
Interests: geospatial big data and machine learning; air quality simulation; spatial econometrics model; urban studies
Dr. Qi Zhou

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: multi-source data fusion; analysis of big data; spatial data deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Wang

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: aerosols; trace gas; satellite remote sensing; inversing modeling of emissions

Special Issue Information

Dear Colleagues,

Recently, with the advancement of technology, multi-source data applications in environment protection are becoming increasingly popular. We are pleased to announce a Special Issue dedicated to exploring the latest advancements in the field of multi-source data fusion and atmospheric research analysis.

The aim of this Special Issue is to showcase cutting-edge research, data science, methodologies, and practical applications related to the monitoring and assessment of atmospheric research. Original papers on statistical machine learning, spatial econometrics, data science, and time series analysis, including case studies on atmospheric data using both theoretical and empirical approaches, are welcome in this Special Issue. We encourage researchers in the fields of data analysis and atmospheric science to contribute their original work to this Special Issue, thereby promoting interdisciplinary collaboration and driving advancements in this domain.

Topics of interest include, but are not limited to, the following:

  • Multi-source data fusion for atmospheric research;
  • Big data analysis in atmospheric science;
  • Deep learning for predictive modeling in atmospheric science;
  • Air quality monitoring using big data;
  • Monitoring air quality techniques;
  • Simulation, modeling, and optimization;
  • Environmental data science;
  • Advanced methods;
  • Spatial data deep learning;
  • The application of satellite products;
  • Spatio-temporal analysis.

We look forward to receiving your contributions.

Dr. Zhaoxin Dai
Dr. Qi Zhou
Prof. Dr. Yi Wang
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. Atmosphere 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

  • aerosols
  • air quality
  • data science
  • geospatial and big data analysis
  • modeling

Published Papers (1 paper)

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Research

21 pages, 6706 KiB  
Article
Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation
by Hongyun Zhou, Zhaoxin Dai, Chuangqi Wu, Xin Ma, Lining Zhu and Pengda Wu
Atmosphere 2024, 15(3), 307; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15030307 - 29 Feb 2024
Viewed by 715
Abstract
PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and [...] Read more.
PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts. Full article
(This article belongs to the Special Issue Data Analysis in Atmospheric Research)
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