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Atmosphere
Special Issues
Remote Sensing Applications in Particulate Matter
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Remote Sensing Applications in Particulate Matter

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

Deadline for manuscript submissions: closed (9 May 2022) | Viewed by 2445

Special Issue Editors

Dr. Tongwen Li

E-Mail Website
Guest Editor
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, China
Interests: environmental remote sensing; geospatial artificial intelligence; multisource data fusion; atmospheric environment
Dr. Chao Zeng

E-Mail Website
Guest Editor
School of Resources and Environmental Sciences, Wuhan University, Wuhan 430072, Hubei, China
Interests: environmental monitoring; multisource information fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Jingan Wu

E-Mail Website
Guest Editor
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, Guangdong, China
Interests: fine-scale particulate matter (PM) mapping; gap filling in remote sensing observations; multisource remote sensing data fusion; deep learning

Special Issue Information

Dear Colleagues,

Particulate matter (PM) pollution (including PM1, PM2.5, and PM10) is harmful to human health, and it is of great significance to carry out fine-scale monitoring and assessment. Satellite remote sensing has been vigorously used in PM monitoring and analysis in the last decade (spatial mapping of PM, long-term trend analysis, health effect assessment, etc.). There is still more to be studied regarding the application of remote sensing in PM in the aspects of spatial and temporal resolution, data coverage, estimation accuracy, and so on. With the development of satellite remote sensing (e.g., the new generation of geostationary satellites) and retrieval and analysis methods (e.g., deep learning), remote sensing of PM research has ushered in new opportunities. As a result, the purpose of this Special Issue, “Remote Sensing Applications in Particulate Matter”, is to showcase the most recent papers on the application of remote sensing in PM research, in order to further promote the understanding of PM pollution. All aspects of remote sensing research on PM, including observation, modeling, mapping, and analysis, are welcome.

Topics of interest may include, but are not limited to:

  • New satellite observation and its retrieval methods for PM
  • High-resolution remote sensing retrieval and mapping of PM
  • Joint retrieval and analysis of PMs
  • Missing remote sensing PM data processing
  • Spatiotemporally continuous monitoring of PM
  • Remote sensing data assimilation for PM estimation and prediction
  • Health effect assessment and analysis
  • Applications.

Dr. Tongwen Li
Dr. Chao Zeng
Dr. Jingan Wu
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

  • particulate matter (PM)
  • satellite remote sensing
  • new-generation satellite observation
  • retrieval methods
  • spatial mapping
  • missing data processing
  • spatiotemporally continuous analysis
  • health effect

Published Papers (1 paper)

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Research

29 pages, 4679 KiB  
Article
A Suitable Model for Spatiotemporal Particulate Matter Concentration Prediction in Rural and Urban Landscapes, Thailand
by Pirada Tongprasert and Suwit Ongsomwang
Atmosphere 2022, 13(6), 904; https://doi.org/10.3390/atmos13060904 - 02 Jun 2022
Cited by 4 | Viewed by 1687
Abstract
Spatiotemporal particulate matter (PM) concentration prediction using MODIS AOD with significant PM factors in rural and urban landscapes in Thailand is necessary for public health and has been complicated by the limitations of PM monitoring stations. The research objectives were (1) to identify [...] Read more.
Spatiotemporal particulate matter (PM) concentration prediction using MODIS AOD with significant PM factors in rural and urban landscapes in Thailand is necessary for public health and has been complicated by the limitations of PM monitoring stations. The research objectives were (1) to identify significant factors affecting PM10 concentrations in rural landscapes and PM2.5 in urban landscapes; (2) to predict spatiotemporal PM10 and PM2.5 concentrations using geographically weighted regression (GWR) and mixed-effect model (MEM), and (3) to evaluate a suitable spatiotemporal model for PM10 and PM2.5 concentration prediction and validation. The research methodology consisted of four stages: data collection and preparation, the identification of significant spatiotemporal factors affecting PM concentrations, the prediction of spatiotemporal PM concentrations, and a suitable spatiotemporal model for PM concentration prediction and validation. As a result, the predicted PM10 concentrations using the GWR model varied from 50.53 to 85.79 µg/m3 and from 36.92 to 51.32 µg/m3 in winter and summer, while the predicted PM10 concentrations using the MEM model varied from 50.68 to 84.59 µg/m3 and from 37.08 to 50.81 µg/m3 in both seasons. Likewise, the PM2.5 concentration prediction using the GWR model varied from 25.33 to 44.37 µg/m3 and from 16.69 to 24.04 µg/m3 in winter and summer, and the PM2.5 concentration prediction using the MEM model varied from 25.45 to 44.36 µg/m3 and from 16.68 and 23.75 µg/m3 during the two seasons. Meanwhile, according to Thailand and U.S. EPA standards, the monthly air quality index (AQI) classifications of the GWR and MEM were similar. Nevertheless, the derived average corrected Akaike Information Criterion (AICc) values of the GWR model for PM10 and PM2.5 predictions during both seasons were lower than that of the MEM model. Therefore, the GWR model was chosen as a suitable model for spatiotemporal PM10 and PM2.5 concentration predictions. Furthermore, the result of spatial correlation analysis for GWR model validation based on a new dataset provided average correlation coefficient values for PM10 and PM2.5 concentration predictions with a higher than the expected value of 0.5. Subsequently, the GWR model with significant monthly and seasonal factors could predict spatiotemporal PM 10 and PM2.5 concentrations in rural and urban landscapes in Thailand. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Particulate Matter)
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