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Remote Sensing of Air Pollution
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Remote Sensing of Air Pollution

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 11042

Special Issue Editors

Dr. Ziyue Chen

E-Mail Website
Guest Editor
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
Interests: anthropogenic aerosols; air pollution monitoring; deep learning modeling
Dr. Xing Yan

E-Mail Website
Guest Editor
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
Interests: satellite-based anthropogenic aerosol; atmospheric environment pollution; deep learning modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Zhen Wang

E-Mail Website
Guest Editor
School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Interests: atmospheric environment pollution; point cloud object recognition and deep learning modeling

Special Issue Information

Dear Colleagues,

The World Health Organization (WHO) indicates that 12.6 million deaths are associated with unhealthy environments each year across the globe, particularly in South-East Asia and Western Pacific regions, where the majority of air-pollution-linked deaths have been recorded. Meanwhile, the urbanization process has a significant negative effect on air pollutant concentrations. Thus, the accurate monitoring of air pollution with continuous spatiotemporal coverage is urgently required. Spaceborne remote sensing has been employed widely for the retrieval of information on various air pollutants, especially particulate matter. However, there are still limited studies on retrieving data on trace gases (e.g., O3, NO2, SO2, CO) and other aerosols (e.g., organic carbons) which significantly affect the ecosystem and climate. The spatiotemporal distribution of air pollutants and how they are affected by urbanization require still more research. Advanced techniques such as machine learning provide unprecedented opportunities to aggregate multi-source data for air pollution monitoring and estimation, which benefits further studies of air pollution exposure and deepens the understanding of the spatiotemporal characteristics of air pollutants.

This Special Issue aims to discuss the satellite-based monitoring and estimation of air pollution at urban, national or global scales for trace gases and aerosols and the interaction between pollutants and human activities or urbanization. Authors are encouraged to use multi-source data and advanced techniques such as machine learning models to improve the retrieval accuracy.

The potential topics include but are not limited to the following:

  • Improving air pollution retrieval techniques by artificial intelligence and machine learning algorithms.
  • Investigating the variables, relations of pollutions and spatiotemporal characteristics for improving air pollution retrieval accuracy.
  • Synergizing multi-source data for air pollution retrieval.
  • Long-term historical air pollution data reconstruction.
  • Air pollution near-real-time monitoring.
  • Investigating the relation between pollution and human activity or landscape patterns.
  • Analysis of effect of urbanization on spatiotemporal changes of air pollutants.

Dr. Ziyue Chen
Dr. Xing Yan
Dr. Zhen 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. Remote Sensing 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 2700 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

  • satellite-based monitoring
  • air pollution monitoring and estimation
  • trace gases (O3, NO2, SO2, CO)
  • aerosols
  • machine learning-based modeling
  • multi-source data
  • spatiotemporal characteristics
  • effect of urbanization, landscape patterns or human activity

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Published Papers (4 papers)

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21 pages, 12569 KiB  
Article
Spatiotemporal Weighted for Improving the Satellite-Based High-Resolution Ground PM2.5 Estimation Using the Light Gradient Boosting Machine
by Xinyu Yu, Mengzhu Xi, Liyang Wu and Hui Zheng
Remote Sens. 2023, 15(16), 4104; https://doi.org/10.3390/rs15164104 - 21 Aug 2023
Cited by 8 | Viewed by 1944
Abstract
Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult [...] Read more.
Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China’s surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was >0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m3, and the maximum decrease reached 90.51 μg/m3. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a ‘northeast–southwest’ spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m3 in 2015 to 46.75 μg/m3 in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale. Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollution)
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18 pages, 11445 KiB  
Article
Regional Representativeness Analysis of Ground-Monitoring PM2.5 Concentration Based on Satellite Remote Sensing Imagery and Machine Learning Techniques
by Rui Luo, Meng Zhang and Guodong Ma
Remote Sens. 2023, 15(12), 3040; https://doi.org/10.3390/rs15123040 - 10 Jun 2023
Cited by 4 | Viewed by 1741
Abstract
The issue of urban air quality in China has become increasingly significant due to industrialization and rapid urbanization. Although PM2.5 is the major air pollutant in most cities of northern China and has a direct negative impact on human health, there is [...] Read more.
The issue of urban air quality in China has become increasingly significant due to industrialization and rapid urbanization. Although PM2.5 is the major air pollutant in most cities of northern China and has a direct negative impact on human health, there is a problem of under-representativeness at Chinese monitoring stations. In some cities, due to the relatively fewer national control stations and the fact that the stations are located closer to pollution sources, under the current assessment system, the monitoring data are not sufficient for the fairness of air quality assessment in different cities. In this article, the multispectral data of Landsat 8 data, air quality data, and meteorological data from ground monitoring stations have been integrated together and imported to different PM2.5-estimation models established based on the multi-layer back propagation neural network (MLBPN), support vector regression (SVR), and random forest (RF), respectively. According to the evaluation indices of R2, RMSE, and ME, the estimation model based on the MLBPN revealed the best PM2.5 estimation results and was therefore employed for the regional representativeness analysis in the study area of Xi’an, Shaanxi, China. The annual average PM2.5 concentration in the study area is depicted after error correction using Kriging interpolation, which can be further used to evaluate and analyze the representativeness of monitoring stations in Xi’an. By calculating the difference between the actual station annual average and the annual average of estimated PM2.5 concentration in the whole region, it can be found that the regional annual average value of PM2.5 in Xi’an is overestimated. To sum up, this article proposes a feasible method for the spatial positioning of the air quality monitoring stations to be established. Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollution)
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20 pages, 8680 KiB  
Article
Quantifying the Long-Term MODIS Cloud Regime Dependent Relationship between Aerosol Optical Depth and Cloud Properties over China
by Yanglian Li, Tianyi Fan, Chuanfeng Zhao, Xin Yang, Ping Zhou and Keying Li
Remote Sens. 2022, 14(16), 3844; https://doi.org/10.3390/rs14163844 - 9 Aug 2022
Cited by 4 | Viewed by 2433
Abstract
Aerosols modify cloud properties and influence the regional climate. The impacts of aerosols on clouds differ for various cloud types, but their long-term relationships have not been fully characterized on a cloud regime basis. In this study, we quantified the cloud regime-dependent relationship [...] Read more.
Aerosols modify cloud properties and influence the regional climate. The impacts of aerosols on clouds differ for various cloud types, but their long-term relationships have not been fully characterized on a cloud regime basis. In this study, we quantified the cloud regime-dependent relationship between aerosol optical depth (AOD) and cloud properties over China using Moderate-Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2019. Daily clouds in each 1° by 1° grid were categorized into seven cloud regimes based on the “k-means” clustering algorithm. Overall, the cloud height increased, the cloud thickness and liquid water path increased, and the total cloud cover decreased for all cloud regimes during the study period. Linear correlations between AOD and cloud properties were found within stratocumulus, deep convective, and high cloud regimes, showing consistency with the classic aerosol–cloud interaction paradigms. Using stepwise multivariable linear regression, we found that the meteorological factors dominated the variation of cloud top pressure, while AOD dominated the variation of total cloud cover for most cloud regimes. There are regional differences in the main meteorological factors affecting the cloud properties. Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollution)
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18 pages, 15654 KiB  
Technical Note
Clarifying Relationship between PM2.5 Concentrations and Spatiotemporal Predictors Using Multi-Way Partial Dependence Plots
by Haoze Shi, Naisen Yang, Xin Yang and Hong Tang
Remote Sens. 2023, 15(2), 358; https://doi.org/10.3390/rs15020358 - 6 Jan 2023
Cited by 26 | Viewed by 3616
Abstract
Atmospheric fine particles (PM2.5) have been found to be harmful to the environment and human health. Recently, remote sensing technology and machine learning models have been used to monitor PM2.5 concentrations. Partial dependence plots (PDP) were used to explore [...] Read more.
Atmospheric fine particles (PM2.5) have been found to be harmful to the environment and human health. Recently, remote sensing technology and machine learning models have been used to monitor PM2.5 concentrations. Partial dependence plots (PDP) were used to explore the meteorology mechanisms between predictor variables and PM2.5 concentration in the “black box” models. However, there are two key shortcomings in the original PDP. (1) it calculates the marginal effect of feature(s) on the predicted outcome of a machine learning model, therefore some local effects might be hidden. (2) it requires that the feature(s) for which the partial dependence is computed are not correlated with other features, otherwise the estimated feature effect has a great bias. In this study, the original PDP’s shortcomings were analyzed. Results show the contradictory correlation between the temperature and the PM2.5 concentration that can be given by the original PDP. Furthermore, the spatiotemporal heterogeneity of PM2.5-AOD relationship cannot be displayed well by the original PDP. The drawbacks of the original PDP make it unsuitable for exploring large-area feature effects. To resolve the above issue, multi-way PDP is recommended, which can characterize how the PM2.5 concentrations changed with the temporal and spatial variations of major meteorological factors in China. Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollution)
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