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Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia

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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 August 2020) | Viewed by 31296

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Special Issue Editors

Dr. Jun Wang

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Guest Editor

Atmospheric and Environmental Research Lab, University of Iowa, 4133 Seamans Center, Iowa City, IA 52242-1503, USA
Interests: remote sensing; earth system modeling; internet of things; their integration to study air quality; wildfires; land–air interactions
Special Issues, Collections and Topics in MDPI journals

Dr. Richard Eckman

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Guest Editor

NASA Langley Space Flight Center, 1 Nasa Drive, Hampton, VA 23666, USA
Interests: atmospheric composition modeling, including air quality and oxidation efficiency in the troposphere; how pollution sourced aerosols impact cloud properties; stratospheric chemistry and ozone depletion; interactions between atmospheric chemistry and the climate; stratospheric aerosols and gas experiments quantifying ozone profile measurements

Prof. Dr. Rokjin Park

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Guest Editor

Seoul National University, San 56-1, Daehak-dong, Gwanak-gu, Seoul 151-747, Korea
Interests: atmsopheric chemistry; global chemistry and transport model; data assimilation

Dr. Liangfu Chen

E-Mail Website
Guest Editor

The Institute of Remote Sensing and Digital Earth, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Interests: satellite retrieval algorithms of atmospheric compositions; algorithm of cloud droplet size distribution from POLDER polarized measurements; assessment of variations in diurnal haze over the North China plain using measurements of Himawari-8/AHI; determination of satellite-estimated near-surface pollutant concentrations; regional discrepancies in spatiotemporal variations and driving forces of open crop residue burning emissions in China

Dr. Minghui Tao

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing of atmosphere; radiative transfer and particle scattering; air quality and climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last decade has seen advancements in both remote sensing techniques and chemistry transport models for understanding the variation of atmospheric composition in Asia, including the impact of such variation on regional and global climate and air quality. This session will explore the state of the science in the use of satellite, sub-orbital, aircraft, and ground-based remote sensing measurements to investigate the sources (including fires) and processes that regulate the spatial–temporal distribution of tropospheric aerosols and trace gases such as CO₂, O₃, CO, SO₂, NO_x, and NH₃. We invite contributions examining a wide range of topics, including new concepts of instrument and algorithm design for remote sensing of fires, aerosols, and trace gases; site measurements and field experiments for characterizing optical–physical–chemical properties of aerosols and their relationships with aerosol precursors (such as SO₂) and compounding pollutants (such as O₃); fluxes of particles and trace gases to the atmosphere, especially during fire events; long-range transport and local emission of pollutants; and integrated use of satellite data, surface measurements, and numerical models to derive surface PM_2.5 and surface pollutants.

Dr. Jun Wang
Dr. Richard Eckman
Dr. Rokjin Park
Dr. Liangfu Chen
Dr. Minghui Tao
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

Remote sensing
Troposphere
Tropospheric aerosols and trace gases
Chemistry transport modeling
Air quality
Global climate
Satellite
Fire modeling
Atmospheric composition
Carbon dioxide
Ozone
Carbon monoxide
Sulfur dioxide
Nitrogen oxides
Ammonia
Pollutants

Published Papers (9 papers)

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Research

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17 pages, 10126 KiB

Open AccessArticle

Classification of Active Fires and Weather Conditions in the Lower Amur River Basin

by Hiroshi Hayasaka, Galina V. Sokolova, Andrey Ostroukhov and Daisuke Naito

Remote Sens. 2020, 12(19), 3204; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193204 - 01 Oct 2020

Cited by 9 | Viewed by 2876

Abstract

Most wildland fires in boreal forests occur during summer, but major fires in the lower Amur River Basin of the southern Khabarovsk Krai (SKK) mainly occur in spring. To reduce active fires in the SKK, we carried out daily analysis of MODIS (Moderate Resolution Imaging Spectroradiometer) hotspot (HS) data and various weather charts. HS data of 17 years from 2003 were used to identify the average seasonal fire occurrence. Active fire-periods were extracted by considering the number of daily HSs and their continuity. Weather charts, temperature maps, and wind maps during the top 12 active fire-periods were examined to clarify each fire weather condition. Analysis results showed that there were four active fire-periods that occurred in April, May, July, and October. Weather charts during the top active fire-periods showed active fires in April and October occurred under strong wind conditions (these wind velocities were over 30 km h⁻¹) related to low-pressure systems. The very active summer fire at the end of June 2012 occurred related to warm air mass advection promoted by large westerly meandering. We showed clear fire weather conditions in the SKK from March to October. If a proper fire weather forecast is developed based on our results, more efficient and timely firefighting can be carried out. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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16 pages, 16829 KiB

Open AccessArticle

New Approach Evaluating Peatland Fires in Indonesian Factors

by Hiroshi Hayasaka, Aswin Usup and Daisuke Naito

Remote Sens. 2020, 12(12), 2055; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122055 - 26 Jun 2020

Cited by 17 | Viewed by 3760

Abstract

Until 2018, the El Niño–Southern Oscillation (ENSO) was used as an explanation for fires in Indonesia’s peatlands. However, when the 2019 fires occurred independently of El Niño, more suitable indicators and methods were required to (a) analyze, (b) evaluate and (c) forecast peatland fires. In this study, we introduced the OLR–MC index—one of the rain-related indices derived from OLR (outgoing longwave radiation) in MC (maritime continent) area in Indonesia. This index showed stronger correlation with active peatland fires than the conventional ENSO index, and is likely to be able to respond to heat and dry weather supposed to be under climate-change conditions. We then analyzed peatland fires in the top six fire years from 2002 to 2018 and showed that peatland fires occurred in three stages—surface fire, shallow peatland fire and deep peatland fire. To explain each stage, we proposed a one-dimensional groundwater level (GWL) prediction model (named as MODEL-0). MODEL-0 predicts GWL from daily rainfall. Analysis using MODEL-0 showed the GWL thresholds for the three fire stages were between -300 mm and -500 mm; peatland fire activities during the three fire stages were dependent on these GWL values. The validity of MODEL-0 was shown by comparison with the measured values of GWL in the top three fire years. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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17 pages, 6852 KiB

Open AccessEditor’s ChoiceArticle

by Disong Fu, Zijue Song, Xiaoling Zhang, Yunfei Wu, Minzheng Duan, Weiwei Pu, Zhiqiang Ma, Weijun Quan, Huaigang Zhou, Huizheng Che and Xiangao Xia

Remote Sens. 2020, 12(7), 1193; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071193 - 08 Apr 2020

Cited by 11 | Viewed by 2900

Abstract

Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM_2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM_2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM_2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM_2.5 in summer and minimum AOD, but maximum PM_2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM_2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM_2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM_2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM_2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM_2.5 from AOD. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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19 pages, 12777 KiB

Open AccessArticle

Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations

by Rong Li, Xin Mei, Liangfu Chen, Lili Wang, Zifeng Wang and Yingying Jing

Remote Sens. 2020, 12(6), 1041; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12061041 - 24 Mar 2020

Cited by 29 | Viewed by 3040

Abstract

The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO₂ of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO₂ grew from ~3.2 to 5.9 × 10¹⁵ molecules cm⁻² during 2005–2011 and deceased to ~3.9 × 10¹⁵ molecules cm⁻² in 2017. Unlike the steadily declining SO₂, variations of OMI NO₂ flattened out in 2016 and increased ~0.5 × 10¹⁵ molecules cm⁻² during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO₂ has a high correlation (R > 0.8), indicating that annual variations of SO₂ can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O₃ and NO_x, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O₃) and NO₂ since 2016 suggests that more control measures should be made to reduce O₃-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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18 pages, 5426 KiB

Open AccessArticle

Can MERRA-2 Reanalysis Data Reproduce the Three-Dimensional Evolution Characteristics of a Typical Dust Process in East Asia? A Case Study of the Dust Event in May 2017

by Wenrui Yao, Huizheng Che, Ke Gui, Yaqiang Wang and Xiaoye Zhang

Remote Sens. 2020, 12(6), 902; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12060902 - 11 Mar 2020

Cited by 36 | Viewed by 4086

Abstract

This study used the MERRA-2 reanalysis dataset and ground-based and satellite observational data to comprehensively analyze a typical dust storm event in east Asia on 2–7 May 2017 which engulfed most of China as well as ocean and Japan, and explore the accuracy and comprehensiveness of the MERRA-2 dataset in the analysis of dust processes. The results of comparison show that the description of the spatiotemporal distribution and evolution of the dust aerosols in the dust event using the MERRA-2 data is consistent with the data of AERONET, National Urban Air Quality Real-time Publishing Platform and Hamawari-8. Gobi Deserts was the most influential source area of this dust event with the highest emissions reaching 1.9 × 10⁶ μg/m³. The vertical motion of the atmosphere can lift dust from the source area above 500 hPa. There were low-pressure troughs at 500 and 850 hPa and the winds behind and in front of the trough led to the high-altitude, long-distance transport of dust. Dust gradually affected the northwest China, north China, northeast China, and even the ocean and Japan on 2–7 May. This study demonstrates that although there is some uncertainty about the source of dust emission in the MERRA-2 model, the data accurately simulated the evolution of the dust event and analyze it comprehensively, while the accuracy of simulating the long-term evolution of dust requires further evaluation. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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26 pages, 18945 KiB

Open AccessArticle

Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth

by Qiaolin Zeng, Jinhua Tao, Liangfu Chen, Hao Zhu, SongYan Zhu and Yang Wang

Remote Sens. 2020, 12(5), 881; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050881 - 09 Mar 2020

Cited by 10 | Viewed by 2997

Abstract

Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth “type” was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R² = 0.15), whereas the correlations for smoke and urban types were better (R² values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM_2.5 concentrations in Beijing–Tianjin–Hebei (BTH), Sichuan–Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R² values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM_2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM_2.5, and the relative error was low (most RE was within ±20%). The classified aerosol type method improved the accuracy of the PM_2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM_2.5 was analyzed and the PM_2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM_2.5 concentrations were predominantly distributed in areas of human activity and industrial areas. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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18 pages, 6225 KiB

Open AccessArticle

NO₂ Retrieval from the Environmental Trace Gases Monitoring Instrument (EMI): Preliminary Results and Intercomparison with OMI and TROPOMI

by Liangxiao Cheng, Jinhua Tao, Pieter Valks, Chao Yu, Song Liu, Yapeng Wang, Xiaozhen Xiong, Zifeng Wang and Liangfu Chen

Remote Sens. 2019, 11(24), 3017; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11243017 - 14 Dec 2019

Cited by 31 | Viewed by 4824

Abstract

Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405–465 nm), we performed retrievals of NO₂. Some first retrieval results of NO₂ from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO₂ show similar spatial distributions to OMI and TROPOMI (r > 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO₂ patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 × 10¹⁵ molec cm⁻²) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO₂ from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO₂ VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO₂ monitoring. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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14 pages, 50258 KiB

Open AccessLetter

Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM_2.5 in Northern China

by Rong Li, Xin Mei, Liangfu Chen, Zifeng Wang, Yingying Jing and Lifei Wei

Remote Sens. 2020, 12(4), 736; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040736 - 23 Feb 2020

Cited by 9 | Viewed by 2881

Abstract

Satellite aerosol optical depth (AOD) products have been widely used in estimating fine particulate matter (PM_2.5) concentrations near the surface at a regional scale, and perform well compared with ground measurements. However, the influence of limitations such as retrieval frequency and the spatial resolution of satellite AODs on the applicability of predicted PM_2.5 values has been rarely considered. With three widely used MODIS AOD products, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), Deep Blue (DB) and Dark Target (DT), here we evaluate the influence of their spatial resolution and sampling frequency by estimating daily PM_2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region of northern China during 2017 utilizing a mixed effects model. The daily concentrations of PM_2.5 derived from MAIAC, DB and DT AOD all have high correlations (R²: 0.78, 0.8, and 0.78) with the observed values, but the predicted annual PM_2.5 exhibits a distinct spatial distribution. DT estimation obviously underestimates annual PM_2.5 in polluted areas due to lower sampling of heavy pollution events. By contrast, the retrieval frequency (~40-60%) of MAIAC and DB AOD can represent well annual PM_2.5 in nearly all 83 sites tested. However, MAIAC and DB-derived PM_2.5 have a larger bias compared with observed values than DT, indicating that the large spatial variation of aerosol properties can exert an influence on the reliability of the statistical AOD-PM_2.5 relationship. Also, there is notable difference between MAIAC and DB PM_2.5 due to their different cloud screening methods. The MAIAC PM_2.5 with high spatial resolution at 1 km can capture much finer hotpots than DB and DT at 10 km. Our results suggest that it is crucial to consider the applicability of satellite-predicted PM_2.5 values derived from different aerosol products according to the specific requirements besides modeling the AOD-PM_2.5 relationship. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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16 pages, 13088 KiB

Open AccessLetter

Reversal of Aerosol Properties in Eastern China with Rapid Decline of Anthropogenic Emissions

by Minghui Tao, Lili Wang, Liangfu Chen, Zifeng Wang and Jinhua Tao

Remote Sens. 2020, 12(3), 523; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030523 - 06 Feb 2020

Cited by 13 | Viewed by 3138

Abstract

The clean air actions of the Chinese government since 2013 have led to rapid reduction in anthropogenic emissions during the last five years. In this study, we present a regional-scale insight into the transition of aerosol properties during this special period based on integrated Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), and ground-based AERONET (AErosol RObotic NETwork) observations. As a response, aerosols in eastern China have exhibited notable reversal in both the amount and optical properties. Regional haze pollution with Aerosol Optical Depth (AOD) > 1.0 in northern China declined from more than ~80 days per year to less than ~30 days. While fine-mode particles exhibited a continuous decrease by ~30-40% during the time period of 2013–2018, the levels of coarse aerosols had no regular variations. MISR fraction AOD of different size modes shows that there has been an obvious overall decline in coarse particles over eastern China, but natural sources such as long-range dust transport make a considerable contribution. The Single Scattering Albedo (SSA) increased steadily from 2001 to 2012 by more than ~0.05. In contrast, aerosol absorption has been getting stronger since 2013, with SSA increasing by ~0.03, due to a much larger reduction in sulfate and nitrate. The drastic transition of aerosol properties has greatly changed aerosol radiative forcing (ARF) in eastern China. The negative ARF at the top (TOA) and bottom (BOA) of the atmosphere decreased by ~30 and ~50 W/m², respectively, in Beijing during the 2001–2012 period. Although aerosol loading continued to decline after 2013, the magnitudes of TOA and BOA ARF have increased by ~10 and ~30 W/m², respectively, since 2013, due largely to the enhanced aerosol absorption. Our results suggest that more comprehensive observations are needed to improve understanding of the intense climate and environment effects of dramatic aerosol properties in eastern China. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Modeling of Fires and/or Tropospheric Composition in Asia)

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