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Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications II

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 9001

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Special Issue Editors

Dr. Muhammad Bilal

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Guest Editor

School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Interests: atmospheric remote sensing; air quality; aerosols; air quality and human health; aerosol classification; aerosol retrievals; remote sensing of land and atmospheric parameters; atmospheric correction of remote sensing data
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Janet E. Nichol

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Guest Editor

Department of Geography, School of Global Studies, University of Sussex, Brighton BN19RH, UK
Interests: geo-informatics; environmental change; landscape change; urban climate; air pollution; climate change; tropical forest ecology; aerosols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Asia is the most populated region in the world, with vast and still growing urban and industrial complexes and vehicle usage, as well as distinct climatic conditions. Due to all these factors, Asia produces a large number of toxic pollutants that affect human health, climate change, the Earth’s radiation budget, air quality, and atmospheric visibility. Published research demonstrates that Asia contributes the most to world air pollution, due to the significant increase in aerosol pollutants from both anthropogenic and natural sources. Ground-based and satellite-based remote sensing technologies play an important role in the understanding of aerosol sources and types, aerosol radiative forcing, aerosol retrievals, the formation of secondary aerosols, and estimation of particulate matter.

This SI welcomes all manuscripts presenting advances in remote sensing techniques, new methodologies, and applications with new scientific contributions for estimation of particulate matter, aerosol type classification, aerosol optical depth retrievals, aerosol radiative forcing, and related topics.

This Special Issue is the second edition of Special Issue: “Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications”.

Prof. Dr. Muhammad Bilal
Prof. Dr. Janet E. Nichol
Guest Editors

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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

aerosol remote sensing
air pollution/air quality
Health effects of aerosols
AOD retrievals
aerosol classification
source apportionment
aerosol radiative forcing
PMx estimation/prediction
dust storm climatology
smoke/haze/smog pollution
biomass burning aerosols
trace gases

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Published Papers (6 papers)

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Research

22 pages, 6023 KiB

Open AccessArticle

Long-Term Dynamics of Atmospheric Sulfur Dioxide in Urban and Rural Regions of China: Urbanization and Policy Impacts

by Fang Wang, Abdallah Shaheen, Robabeh Yousefi, Quansheng Ge, Renguang Wu, Jos Lelieveld, Dimitris G. Kaskaoutis, Zifeng Lu, Yu Zhan and Yuyu Zhou

Remote Sens. 2024, 16(2), 391; https://doi.org/10.3390/rs16020391 - 18 Jan 2024

Viewed by 1015

Abstract

High levels of sulfur dioxide (SO₂) due to human activities pose a serious air pollution issue in China, especially in urban agglomerations. However, limited research has investigated the impact of anthropogenic emissions on higher SO₂ concentrations in urban regions compared to rural areas in China. Here, we analyzed the trends in SO₂ concentrations from 1980 to 2021 in China using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) dataset. SO₂ column concentrations from the Copernicus Atmosphere Monitoring Service (CAMS) and the Ozone Monitoring Instrument (OMI) during the years 2007–2021 were also examined for validation and comparison purposes. Eight representative areas, including four urban regions (Pearl River Delta [PRD], Beijing-Tianjin-Hebei [BTH], Yangtze River Delta [YRD], and Sichuan Basin [SCB]) and four rural regions (Northeast Region [NER], Mongolian Region [MR], West Region [WR], and Tibetan Plateau Region [TR]) were selected for the analysis. Overall, a significant but fluctuating increase in SO₂ concentrations over China was observed during 1980–2021. During 1980–1997 and 2000–2010, there was an increase in SO₂ concentration, while during 1997–2000 and 2010–2021, a decreasing trend was observed. The average increase in SO₂ concentration was approximately 16 times higher in urban regions than in the rural background. We also found that SO₂ dynamics were highly associated with expansion of urban areas, population density, and gross domestic product. Nonetheless, since 2007, SO₂ concentrations have exhibited a downward trend, which is mainly attributed to the air pollution policies implemented by the Chinese government. Our findings highlight the need for further studies on the impact of SO₂ on regional climate change in China. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications II)

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17 pages, 5500 KiB

Open AccessArticle

High Spatial Resolution Nighttime PM_2.5 Datasets in the Beijing–Tianjin–Hebei Region from 2015 to 2021 Using VIIRS/DNB and Deep Learning Model

by Yu Ma, Wenhao Zhang, Xiaoyang Chen, Lili Zhang and Qiyue Liu

Remote Sens. 2023, 15(17), 4271; https://doi.org/10.3390/rs15174271 - 30 Aug 2023

Cited by 2 | Viewed by 1083

Abstract

The concentration of particulate matter (PM_2.5) can be estimated using satellite data collected during the daytime. However, there are currently no long-term evening PM_2.5 datasets, and the application of low-light satellite data to analyze nighttime PM_2.5 concentrations is limited. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), meteorology, Digital Elevation Model, moon phase angle, and Normalized Digital Vegetation Index were used in this study to develop a Deep Neural Network model (DNN) for estimating the nighttime concentrations of PM_2.5 in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2021. To evaluate the model’s performance from 2015 to 2021, a ten-fold cross-validation coefficient of determination was utilized (CV − R² = 0.51 − 0.68). Using a high spatial resolution of 500 m, we successfully generated a PM_2.5 concentration map for the BTH region. This finer resolution enabled a detailed representation of the PM_2.5 distribution over the area. Interannual and seasonal trends in nighttime PM_2.5 concentrations were analyzed. Winter had the highest seasonal spatial PM_2.5, followed by spring and autumn, whereas summer had the lowest. The annual concentration of PM_2.5 at night steadily decreased. Finally, the estimation of nighttime PM_2.5 was applied in scenarios such as continuous day–night changes, rapid short-term changes, and single-point monitoring. A deeper understanding of PM_2.5, enabled by nightly PM_2.5, will serve as an invaluable resource for future research. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications II)

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18 pages, 8372 KiB

Open AccessArticle

Spatial–Temporal Fusion of 10-Min Aerosol Optical Depth Products with the GEO–LEO Satellite Joint Observations

by Xinghui Xia, Tianhao Zhang, Lunche Wang, Wei Gong, Zhongmin Zhu, Wei Wang, Yu Gu, Yun Lin, Xiangyang Zhou, Jiadan Dong, Shumin Fan and Wenfa Xu

Remote Sens. 2023, 15(8), 2038; https://doi.org/10.3390/rs15082038 - 12 Apr 2023

Cited by 2 | Viewed by 1339

Abstract

Geosynchronous equatorial orbit (GEO) satellite-derived AOD possesses huge advantages for monitoring atmospheric aerosol with high frequency; however, the data missing existing in the satellite-derived AOD products dramatically limits this expected advantage due to cloud obscuration and aerosol retrieval algorithm. In recent years, numerous AOD fusion algorithms have been proposed, while these algorithms are mostly developed to blend daily AOD products derived from low Earth orbit (LEO) satellites and generally neglect discrepancies from different categories of products. Therefore, a spatiotemporal fusion framework based on the Bayesian maximum entropy theorem, blending GEO with LEO satellite observations and incorporating data discrepancies (GL-BME), is developed to complementarily recover the Advanced Himawari-8 Imager (AHI) AOD products over East Asia. The results show that GL-BME significantly improves the average spatial completeness of AOD from 20.3% to 67.6% with ensured reliability, and the accuracy of merged AODs nearly maintains that of original AHI AODs. Moreover, a comparison of the monthly aerosol spatial distribution between the merged and original AHI AODs is conducted to evaluate the performance and significance of GL-BME, which indicates that GL-BME could further restore the real atmospheric aerosol situation to a certain extent on the basis of dramatic spatial coverage improvement. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications II)

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19 pages, 26752 KiB

Open AccessArticle

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

by Yuan Fu, Zixiang Zhou, Jing Li and Shunwei Zhang

Remote Sens. 2023, 15(7), 1908; https://doi.org/10.3390/rs15071908 - 2 Apr 2023

Cited by 1 | Viewed by 1263

Abstract

Aerosols impact vegetation productivity by increasing diffuse radiation and changing temperature and humidity conditions. In this study, climate simulations of the Jinghe River Basin in 2020 based on aerosol and aerosol−free scenarios were carried out using the control variable method and the aerosol optical depth parameter as the external input data of Weather Report Forecast (WRF)−solar. These two output results were used as input data for the Carnegie Ames Stanford Approach (CASA) model to calculate the impact of aerosols on vegetation productivity. The results showed that WRF−solar accurately simulated changes in meteorological factors such as temperature, rainfall, solar radiation, and relative humidity in the Jinghe River Basin, with a correlation coefficient above 0.85. Aerosols significantly change the ratio of diffuse to direct radiation, act as a cooling function to reduce temperature, and affect rainfall by interacting with clouds. The scenario simulation results showed that under the influence of aerosols, the total solar radiation was reduced by 224.98 MJ/m², accounting for 3.44% of the total annual radiation. Correspondingly, the average net primary productivity of vegetation in the Jinghe River Basin in 2020 decreased by 26.64 gC/m², which was not conducive to vegetation photosynthesis and carbon fixation in the basin. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications II)

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16 pages, 5935 KiB

Open AccessArticle

Spatiotemporal and Vertical Distribution of Asian Tropopause Aerosol Layer Using Long-Term Multi-Source Data

by Hongchao Liu, Ren Li and Junjie Ma

Remote Sens. 2023, 15(5), 1315; https://doi.org/10.3390/rs15051315 - 27 Feb 2023

Viewed by 1404

Abstract

The Asian tropopause aerosol layer (ATAL) is an enhanced aerosol concentration layer in the upper troposphere and lower stratosphere over Asia, and it has important effects on radiation balance, atmospheric circulation, regional climate, and atmospheric chemical processes. However, despite its importance, the specific structure and long-term variation trend of the ATAL have been rarely analyzed, which is critical for assessing the impact of ATAL on climate change and evaluating the performance of climate models. This study compared and analyzed the three-dimensional spatial distribution characteristics and temporal variability using CALIPSO, SAGEII, and MERRA-2 data and discussed the possible causes of the variation. The results showed that the ATAL began to appear in the mid-to-late 1990s and then strengthened rapidly until 2010, after which this trend was no longer observed. Moreover, significant heterogeneity existed in the distribution of aerosol concentration in the ATAL, showing north–south differences (NSDs) in both time and space. In addition, it was found that besides surface emissions, atmospheric circulation, the strength of convective transport, and stratosphere–troposphere exchange processes also contribute to this pattern. This study has important implications for quantifying the climate consequences of the ATAL. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications II)

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18 pages, 5013 KiB

Open AccessArticle

Enhancement of Photovoltaic Power Potential in China from 2010 to 2020: The Contribution of Air Pollution Control Policies

by Yujie Zhang, Wenmin Qin, Lunche Wang, Chao Yang, Xin Su and Jinyang Wu

Remote Sens. 2023, 15(1), 228; https://doi.org/10.3390/rs15010228 - 31 Dec 2022

Cited by 2 | Viewed by 1974

Abstract

China is expected to have a total installed photovoltaic capacity of 1300 GW in 2050, accounting for 39% of the national electricity consumption. However, air pollutants consisting of gases and particulates have attenuation effects on the solar radiation reaching the photovoltaic panels. This work purports to assess the influence of air pollutants on the photovoltaic power potential. We calculated the hourly point-of-array irradiance (POAI) in China for 2010–2020 with a spatial resolution of 0.1° × 0.1° using the PV_LIB model and assessed the effect of air pollutants on POAI. The results indicated that the annual average POAI in China for 2010–2020 ranged from 118 to 286 Wm⁻². The Air Pollution Control Action Plan (APPCAP) has played a certain role in photovoltaic power potential, and POAI has increased in areas where surface concentrations of air pollutants have declined. Especially in North China, the surface concentrations of CO, NO₂, PM₁₀, PM_2.5, and SO₂ decreased throughout the APPCAP period with −0.446, −4.985, −35.610, −30.700, and −26.251 μgm⁻³, respectively, corresponding to an increase in POAI of up to 4.917 Wm⁻². The surface concentrations of CO, NO₂, PM₁₀, PM_2.5, and SO₂ were negatively correlated with POAI, with correlation coefficients of −0.764, −0.854, −0.204, −0.110, and −0.664, respectively. Surface concentrations of air pollutants (CO, NO₂, PM₁₀, PM_2.5, and SO₂) and clear-sky POAI in 2018 showed a High-Low clustering in Northeast China and North China. This study demonstrates the role of China’s air pollution control policy in enhancing photovoltaic power potential. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications II)

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