Special Issue "Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Dr. Muhammad Bilal
Website
Guest Editor
School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 21004, 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; satellite-based PM estimation
Prof. Dr. Janet E. Nichol
Website
Guest Editor
Department of Geography, School of Global Studies, University of Sussex, Brighton BN1 9RH, UK
Interests: remote sensing of environmental change; aerosol remote sensing; urban climate; air pollution; climate change; tropical forest ecology

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 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 those 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.


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

Manuscript Submission Information

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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 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

  • aerosol remote sensing
  • air pollution
  • AOD retrievals
  • aerosol classification
  • source apportionment
  • radiative forcing
  • PM estimation
  • dust storm
  • haze pollution
  • smog
  • NO2
  • CO2
  • SO2
  • O3

Published Papers (7 papers)

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Research

Open AccessArticle
Validation of GOSAT and OCO-2 against In Situ Aircraft Measurements and Comparison with CarbonTracker and GEOS-Chem over Qinhuangdao, China
Remote Sens. 2021, 13(5), 899; https://doi.org/10.3390/rs13050899 - 27 Feb 2021
Viewed by 540
Abstract
Carbon dioxide (CO2) is the most important greenhouse gas and several satellites have been launched to monitor the atmospheric CO2 at regional and global scales. Evaluation of the measurements obtained from these satellites against accurate and precise instruments is crucial. [...] Read more.
Carbon dioxide (CO2) is the most important greenhouse gas and several satellites have been launched to monitor the atmospheric CO2 at regional and global scales. Evaluation of the measurements obtained from these satellites against accurate and precise instruments is crucial. In this work, aircraft measurements of CO2 were carried out over Qinhuangdao, China (39.9354°N, 119.6005°E), on 14, 16, and 19 March 2019 to validate the Greenhous gases Observing SATellite (GOSAT) and the Orbiting Carbon Observatory 2 (OCO-2) CO2 retrievals. The airborne in situ instruments were mounted on a research aircraft and the measurements were carried out between the altitudes of ~0.5 and 8.0 km to obtain the vertical profiles of CO2. The profiles captured a decrease in CO2 concentration from the surface to maximum altitude. Moreover, the vertical profiles from GEOS-Chem and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker were also compared with in situ and satellite datasets. The satellite and the model datasets captured the vertical structure of CO2 when compared with in situ measurements, which showed good agreement among the datasets. The dry-air column-averaged CO2 mole fractions (XCO2) retrieved from OCO-2 and GOSAT showed biases of 1.33 ppm (0.32%) and −1.70 ppm (−0.41%), respectively, relative to the XCO2 derived from in situ measurements. Full article
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Open AccessArticle
Spatiotemporal Investigations of Multi-Sensor Air Pollution Data over Bangladesh during COVID-19 Lockdown
Remote Sens. 2021, 13(5), 877; https://doi.org/10.3390/rs13050877 - 26 Feb 2021
Viewed by 830
Abstract
This study investigates spatiotemporal changes in air pollution (particulate as well as gases) during the COVID-19 lockdown period over major cities of Bangladesh. The study investigated the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites, [...] Read more.
This study investigates spatiotemporal changes in air pollution (particulate as well as gases) during the COVID-19 lockdown period over major cities of Bangladesh. The study investigated the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites, PM2.5 and PM10 from Copernicus Atmosphere Monitoring Service (CAMS), and NO2 and O3 from TROPOMI-5P, from March to June 2019–2020. Additionally, aerosol subtypes from the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) were used to explore the aerosol types. The strict lockdown (26 March–30 May 2020) led to a significant reduction in AOD (up to 47%) in all major cities, while the partial lockdown (June 2020) led to increased and decreased AOD over the study area. Significant reductions in PM2.5 (37–77%) and PM10 (33–70%) were also observed throughout the country during the strict lockdown and partial lockdown. The NO2 levels decreased by 3–25% in March 2020 in the cities of Rajshahi, Chattogram, Sylhet, Khulna, Barisal, and Mymensingh, in April by 3–43% in Dhaka, Chattogram, Khulna, Barisal, Bhola, and Mymensingh, and May by 12–42% in Rajshahi, Sylhet, Mymensingh, and Rangpur. During the partial lockdown in June, NO2 decreased (9–35%) in Dhaka, Chattogram, Sylhet, Khulna, Barisal, and Rangpur compared to 2019. On the other hand, increases were observed in ozone (O3) levels, with an average increase of 3–12% throughout the country during the strict lockdown and only a slight reduction of 1–3% in O3 during the partial lockdown. In terms of aerosol types, CALIPSO observed high levels of polluted dust followed by dust, smoke, polluted continental, and clean marine-type aerosols over the country in 2019, but all types were decreased during the lockdown. The study concludes that the strict lockdown measures were able to significantly improve air quality conditions over Bangladesh due to the shutdown of industries, vehicles, and movement of people. Full article
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Open AccessArticle
Interdecadal Changes in Aerosol Optical Depth over Pakistan Based on the MERRA-2 Reanalysis Data during 1980–2018
Remote Sens. 2021, 13(4), 822; https://doi.org/10.3390/rs13040822 - 23 Feb 2021
Viewed by 282
Abstract
The spatiotemporal evolution and trends in aerosol optical depth (AOD) over environmentally distinct regions in Pakistan are investigated for the period 1980–2018. The AOD data for this period was obtained from the Modern-era retrospective analysis for research and applications, version 2 (MERRA-2) reanalysis [...] Read more.
The spatiotemporal evolution and trends in aerosol optical depth (AOD) over environmentally distinct regions in Pakistan are investigated for the period 1980–2018. The AOD data for this period was obtained from the Modern-era retrospective analysis for research and applications, version 2 (MERRA-2) reanalysis atmospheric products, together with the Moderate-resolution imaging spectroradiometer (MODIS) retrievals. The climatology of AODMERRA-2 is analyzed in three different contexts: the entire study domain (Pakistan), six regions within the domain, and 12 cities chosen from the entire study domain. The time-series analysis of the MODIS and MERRA-2 AOD data shows similar patterns in individual cities. The AOD and its seasonality vary strongly across Pakistan, with the lowest (0.05 ± 0.04) and highest (0.40 ± 0.06) in the autumn and summer seasons over the desert and the coastal regions, respectively. During the study period, the annual AOD trend increased between 0.002 and 0.012 year−1. The increase of AOD is attributed to an increase in population and emissions from natural and/or anthropogenic sources. A general increase in the annual AOD over the central to lower Indus Basin is ascribed to the large contribution of dust particles from the desert. During winter and spring, a significant decrease in the AOD was observed in the northern regions of Pakistan. The MERRA-2 and MODIS trends (2002–2018) were compared, and the results show visible differences between the AOD datasets due to theuseof different versions and collection methods. Overall, the present study provides insight into the regional differences of AOD and its trends with the pronounced seasonal behavior across Pakistan. Full article
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Open AccessArticle
Inferring Near-Surface PM2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model
Remote Sens. 2021, 13(3), 505; https://doi.org/10.3390/rs13030505 - 31 Jan 2021
Viewed by 597
Abstract
Much of the population is exposed to PM2.5 (particulate matter) pollution in China, and establishing a high-precision PM2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, [...] Read more.
Much of the population is exposed to PM2.5 (particulate matter) pollution in China, and establishing a high-precision PM2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R2), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m−3 and 8.25 (8.78) μg m−3, respectively. The monthly, seasonal, and annual mean PM2.5 were also successfully captured (CV-R2 = 0.91–0.92, RMSE = 4.35–6.72 μg m−3). Then, the spatial characteristics of PM2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m−3). In addition, 19 provinces or administrative regions have annual PM2.5 concentrations >35 μg m−3, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m−3), Tianjin (~49.65 μg m−3), and Henan Province (~48.60 μg m−3). Our estimated surface PM2.5 concentrations are accurate, which could benefit further research on air pollution in China. Full article
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Open AccessArticle
Ground-Based MAX-DOAS Observations of Tropospheric NO2 and HCHO During COVID-19 Lockdown and Spring Festival Over Shanghai, China
Remote Sens. 2021, 13(3), 488; https://doi.org/10.3390/rs13030488 - 30 Jan 2021
Viewed by 695
Abstract
Reduced mobility and less anthropogenic activity under special case circumstances over various parts of the world have pronounced effects on air quality. The objective of this study is to investigate the impact of reduced anthropogenic activity on air quality in the mega city [...] Read more.
Reduced mobility and less anthropogenic activity under special case circumstances over various parts of the world have pronounced effects on air quality. The objective of this study is to investigate the impact of reduced anthropogenic activity on air quality in the mega city of Shanghai, China. Observations from the highly sophisticated multi-axis differential optical absorption spectroscope (MAX-DOAS) instrument were used for nitrogen dioxide (NO2) and formaldehyde (HCHO) column densities. In situ measurements for NO2, ozone (O3), particulate matter (PM2.5) and the air quality index (AQI) were also used. The concentration of trace gases in the atmosphere reduces significantly during annual Spring Festival holidays, whereby mobility is reduced and anthropogenic activities come to a halt. The COVID-19 lockdown during 2020 resulted in a considerable drop in vertical column densities (VCDs) of HCHO and NO2 during lockdown Level-1, which refers to strict lockdown, i.e., strict measures taken to reduce mobility (43% for NO2; 24% for HCHO), and lockdown Level-2, which refers to relaxed lockdown, i.e., when the mobility restrictions were relaxed somehow (20% for NO2; 22% for HCHO), compared with pre-lockdown days, as measured by the MAX-DOAS instrument. However, for 2019, a reduction in VCDs was found only during Level-1 (24% for NO2; 6.62% for HCHO), when the Spring Festival happened. The weekly cycle for NO2 and HCHO depicts no significant effect of weekends on the lockdown. After the start of the Spring Festival, the VCDs of NO2 and HCHO showed a decline for 2019 as well as 2020. Backward trajectories calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicated more air masses coming from the sea after the Spring Festival for 2019 and 2020, implying that a low pollutant load was carried by them. No impact of anthropogenic activity was found on O3 concentration. The results indicate that the ratio of HCHO to NO2 (RFN) fell in the volatile organic compound (VOC)-limited regime. Full article
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Open AccessArticle
Solar Brightening/Dimming over China’s Mainland: Effects of Atmospheric Aerosols, Anthropogenic Emissions, and Meteorological Conditions
Remote Sens. 2021, 13(1), 88; https://doi.org/10.3390/rs13010088 - 29 Dec 2020
Viewed by 431
Abstract
Surface solar radiation (SSR) is the main factor affecting the earth’s climate and environment and its variations and the reason for these variations are an important part of climate change research. In this research, we investigated the long-term variations of SSR during 1984–2016 [...] Read more.
Surface solar radiation (SSR) is the main factor affecting the earth’s climate and environment and its variations and the reason for these variations are an important part of climate change research. In this research, we investigated the long-term variations of SSR during 1984–2016 and the quantitative influences of atmospheric aerosols, anthropogenic emissions, and meteorological conditions on SSR over China’s mainland. The results show the following: (1) The annual average SSR values had a decline trend at a rate of −0.371 Wm−2 yr−1 from 1984 to 2016 over China. (2) The aerosol optical depth (AOD) plays the main role in inducing variations in SSR over China, with r values of −0.75. Moreover, there are marked regional differences in the influence of anthropogenic emissions and meteorological conditions on SSR trends. (3) From a regional perspective, AOD is the main influencing factor on SSR in northeast China (NEC), Yunnan Plateau and surrounding regions (YPS), North China (NC), and Loess Plateau (LP), with r values of −0.65, −0.60, −0.89, and −0.50, respectively. However, the main driving factors for SSR in northwest China (NWC) are “in cloud optical thickness of all clouds” (TAUTOT) (−0.26) and black carbon (BC) anthropogenic emissions (−0.21). TAUTOT (−0.39) and total precipitable water vapor (TQV) (−0.29) are the main influencing factors of SSR in the middle-lower Yangtze Plain (MYP). The main factors that influence SSR in southern China (SC) are surface pressure (PS) (−0.66) and AOD (−0.43). This research provides insights in understanding the variations of SSR and its relationships with anthropogenic conditions and meteorological factors. Full article
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Open AccessArticle
Spatio-Temporal Characteristics of PM2.5, PM10, and AOD over Canal Head Taocha Station, Henan Province
Remote Sens. 2020, 12(20), 3432; https://doi.org/10.3390/rs12203432 - 19 Oct 2020
Viewed by 589
Abstract
In this study, spatio-temporal characteristics of particulate matter (PMx; x = 2.5 and 10) mass concentrations and aerosol optical properties were analyzed over the water source area of the South–North Water Diversion Central Line. For this purpose, PM2.5 and PM10 mass [...] Read more.
In this study, spatio-temporal characteristics of particulate matter (PMx; x = 2.5 and 10) mass concentrations and aerosol optical properties were analyzed over the water source area of the South–North Water Diversion Central Line. For this purpose, PM2.5 and PM10 mass concentrations were collected at the Taocha(TC)station from October 2018 to September 2019, and aerosol optical depth (AOD) was obtained from the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite from 2007 to 2019. The monthly, seasonal, and daily statistical analyses and related comparisons were conducted in the present study. The results showed that the PM10 concentrations meet China’s ambient air secondary quality standard (100 μg/m3 annual mean), whereas PM2.5 did not meet China’s ambient air secondary quality standard (35 μg/m3 annual mean) at the TC station, no obvious seasonal and diurnal variations are observed, and these particulates are caused by local emissions and outside sources. A significant positive correlation of PM2.5 and PM10 was observed with relative humidity and temperature, whereas no relationship was found with wind direction. The results also showed low (~0.1) AOD in spring, autumn, and winter, whereas slightly higher AOD (~0.3) was observed in summer. This may be caused by straw burning from long-distance transportation. This study may provide new data support for comprehensive ecological measures such as strengthening the ecological environment and water quality protection in the Middle Route Project of the South–North Water Diversion. Full article
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