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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

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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
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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

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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

Published Papers (2 papers)

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Research

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Open AccessArticle

Quantifying the Long-Term MODIS Cloud Regime Dependent Relationship between Aerosol Optical Depth and Cloud Properties over China

and

Remote Sens. 2022, 14(16), 3844; https://doi.org/10.3390/rs14163844 - 09 Aug 2022

Cited by 2 | Viewed by 907

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 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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Open AccessTechnical Note

Clarifying Relationship between PM_2.5 Concentrations and Spatiotemporal Predictors Using Multi-Way Partial Dependence Plots

and

Remote Sens. 2023, 15(2), 358; https://doi.org/10.3390/rs15020358 - 06 Jan 2023

Cited by 1 | Viewed by 790

Abstract

Atmospheric fine particles (

{PM}_{2.5}

) 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

{PM}_{2.5}

concentrations. Partial dependence plots (PDP) were used to explore [...] Read more.

Atmospheric fine particles (

{PM}_{2.5}

) 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

{PM}_{2.5}

concentrations. Partial dependence plots (PDP) were used to explore the meteorology mechanisms between predictor variables and

{PM}_{2.5}

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

{PM}_{2.5}

concentration that can be given by the original PDP. Furthermore, the spatiotemporal heterogeneity of

{PM}_{2.5}

-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

{PM}_{2.5}

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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