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Atmosphere
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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: 17 April 2025 | Viewed by 2580

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

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

Published Papers (2 papers)

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17 pages, 5177 KiB  
Article
A Branched Convolutional Neural Network for Forecasting the Occurrence of Hazes in Paris Using Meteorological Maps with Different Characteristic Spatial Scales
by Chien Wang
Atmosphere 2024, 15(10), 1239; https://doi.org/10.3390/atmos15101239 - 17 Oct 2024
Viewed by 498
Abstract
A convolutional neural network (CNN) has been developed to forecast the occurrence of low-visibility events or hazes in the Paris area. It has been trained and validated using multi-decadal daily regional maps of many meteorological and hydrological variables alongside surface visibility observations. The [...] Read more.
A convolutional neural network (CNN) has been developed to forecast the occurrence of low-visibility events or hazes in the Paris area. It has been trained and validated using multi-decadal daily regional maps of many meteorological and hydrological variables alongside surface visibility observations. The strategy is to make the machine learn from available historical data to recognize various regional weather and hydrological regimes associated with low-visibility events. To better preserve the characteristic spatial information of input features in training, two branched architectures have recently been developed. These architectures process input features firstly through several branched CNNs with different kernel sizes to better preserve patterns with certain characteristic spatial scales. The outputs from the first part of the network are then processed by the second part, a deep non-branched CNN, to further deliver predictions. The CNNs with new architectures have been trained using data from 1975 to 2019 in a two-class (haze versus non-haze) classification mode as well as a regression mode that directly predicts the value of surface visibility. The predictions of regression have also been used to perform the two-class classification forecast using the same definition in the classification mode. This latter procedure is found to deliver a much better performance in making class-based forecasts than the direct classification machine does, primarily by reducing false alarm predictions. The branched architectures have improved the performance of the networks in the validation and also in an evaluation using the data from 2021 to 2023 that have not been used in the training and validation. Specifically, in the latter evaluation, branched machines captured 70% of the observed low-visibility events during the three-year period at Charles de Gaulle Airport. Among those predicted low-visibility events by the machines, 74% of them are true cases based on observation. Full article
(This article belongs to the Special Issue Data Analysis in Atmospheric Research)
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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://doi.org/10.3390/atmos15030307 - 29 Feb 2024
Viewed by 1418
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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