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Effect of Biomass-Burning on Atmosphere Using Remote Sensing
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Effect of Biomass-Burning on Atmosphere Using Remote Sensing

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 July 2023) | Viewed by 8511

Special Issue Editors

Dr. Yu Wu

E-Mail Website
Guest Editor
Institute of Surface-Earth System Science, School of Earth System Science, Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin 300072, China
Interests: black carbon aerosol; radiative forcing; remote sensing; vector radiative transfer
Dr. Fangwen Bao

E-Mail Website
Guest Editor
Department of Ocean Sciences and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: aerosol remote sensing; satellite retrievals; air quality observations
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Wei

E-Mail Website
Guest Editor
Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Interests: remote sensing; artificial intelligence; big data; air pollution; aerosol; particulate matter; trace gas; cloud
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomass burning (e.g., wildfires, agricultural waste burning and biofuel burning) contribute significantly to atmospheric carbonaceous aerosols, including organic and elemental carbon aerosols, which account for 10–90% of total fine particulate matter (PM2.5) and have a very important impact on regional air quality, human health and Earth's energy budget.

Remote sensing is a powerful tool for global aerosol monitoring. The application prospects of biomass-burning monitoring using remote sensing is very extensive. An increasing number of studies have focused on addressing the limitations of traditional remote sensing methods, in an attempt to obtain more aerosol information from remote sensing signals. This provides strong support to further study the effects of biomass-burning aerosols and improve the capability of monitoring the atmospheric environment. We believe that the number of available high-precision aerosol-monitoring methods will increase in the near future. Thus, this special issue aims to invite scholars to publish articles on the latest progress in biomass burning remote sensing technologies, analyses and applications.

This Special Issue aims to bring together the latest studies covering anything from biomass-burning remote sensing to more comprehensive aims related to the integrated analysis of the impacts of biomass burning on climate and environment. We also welcome papers related to retrieval algorithms and machine learning applications for different sensors and platforms.

Authors are encouraged to submit articles on topics including, but not limited to, the following:

  • Biomass-burning monitoring based on different sensors and platforms;
  • Remote sensing algorithms for carbonaceous aerosols;
  • Regional and global environmental climate effects of biomass burning;
  • Physicochemical mechanisms of biomass-burning aerosols;
  • Analysis of global biomass burning cases;
  • Application of machine learning to biomass-burning remote sensing.

Dr. Yu Wu
Dr. Fangwen Bao
Dr. Jing Wei
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

  • biomass burning
  • remote sensing
  • carbon emission
  • carbonaceous aerosols
  • climate change
  • atmospheric pollutant

Published Papers (5 papers)

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Research

20 pages, 9432 KiB  
Article
Methane Emissions in Boreal Forest Fire Regions: Assessment of Five Biomass-Burning Emission Inventories Based on Carbon Sensing Satellites
by Siyan Zhao, Li Wang, Yusheng Shi, Zhaocheng Zeng, Biswajit Nath and Zheng Niu
Remote Sens. 2023, 15(18), 4547; https://doi.org/10.3390/rs15184547 - 15 Sep 2023
Viewed by 1078
Abstract
Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and [...] Read more.
Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies. Full article
(This article belongs to the Special Issue Effect of Biomass-Burning on Atmosphere Using Remote Sensing)
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18 pages, 8140 KiB  
Article
Seasonal Comparison of the Wildfire Emissions in Southern African Region during the Strong ENSO Events of 2010/11 and 2015/16 Using Trend Analysis and Anomaly Detection
by Lerato Shikwambana and Mahlatse Kganyago
Remote Sens. 2023, 15(4), 1073; https://doi.org/10.3390/rs15041073 - 15 Feb 2023
Viewed by 1712
Abstract
This study investigates the wildfire biomass-burning emission levels during strong El Niño–southern oscillation (ENSO) events of 2010–2011 (characterized by a strong La Niña event) and 2015–2016 (characterized by a strong El Niño event) over the southern African region. Specifically, the biomass-burning parameters of [...] Read more.
This study investigates the wildfire biomass-burning emission levels during strong El Niño–southern oscillation (ENSO) events of 2010–2011 (characterized by a strong La Niña event) and 2015–2016 (characterized by a strong El Niño event) over the southern African region. Specifically, the biomass-burning parameters of black carbon (BC), carbon monoxide (CO) and sulfur dioxide (SO2) were investigated. Of interest in the current study was the strong El Niño (2015–2016) and La Niña (2010–2011) events during the main fire seasons in southern Africa, i.e., June–July–August (JJA) and September–October–November (SON). Furthermore, the study looks at how meteorological parameters (temperature and precipitation) are influenced by the two strong ENSO events. The sequential Mann–Kendall (SQMK) test is used to study the long-term trends of the emission and meteorological parameters. Anomaly detection on the long-term emission trends and meteorological parameters are performed using the seasonal and trend decomposition loess (STL) and generalized extreme studentized deviate (GESD). Overall, the results show higher emission levels of SO2, CO, and BC during the JJA season compared to the SON season. The SQMK results show an increasing trend of SO2, CO, and BC over time, indicating an increase in the amount of biomass burning. The GESD showed significant anomalies for BC, SO2, and CO emanating from the two strong El Niño and La Niña events. On the other hand, no significant anomalies were detected for temperature and precipitation. The results in this study highlight the significant effect of strong ENSO events on wildfire emissions, thus retrospectively showing the potential effect of future events, especially in the context of climate change. Full article
(This article belongs to the Special Issue Effect of Biomass-Burning on Atmosphere Using Remote Sensing)
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19 pages, 11530 KiB  
Article
Estimation of Ground-Level PM2.5 Concentration at Night in Beijing-Tianjin-Hebei Region with NPP/VIIRS Day/Night Band
by Yu Ma, Wenhao Zhang, Lili Zhang, Xingfa Gu and Tao Yu
Remote Sens. 2023, 15(3), 825; https://doi.org/10.3390/rs15030825 - 01 Feb 2023
Cited by 6 | Viewed by 1498
Abstract
Reliable measures of nighttime atmospheric fine particulate matter (PM2.5) concentrations are essential for monitoring their continuous diurnal variation. Here, we proposed a night PM2.5 concentration estimation (NightPMES) model based on the random forest model. This model integrates the radiance of [...] Read more.
Reliable measures of nighttime atmospheric fine particulate matter (PM2.5) concentrations are essential for monitoring their continuous diurnal variation. Here, we proposed a night PM2.5 concentration estimation (NightPMES) model based on the random forest model. This model integrates the radiance of the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB), moon phase angle, and meteorological data. We collected 13486 samples from the Beijing Tianjin–Hebei (BTH) region. The determination coefficient (R2) of the NightPMES model was 0.82, the root mean square error (RMSE) was 16.67 µg/m3, and the mean absolute error (MAE) was 10.20 µg/m3. The applicability analysis of the moon phase angles indicated that the amount of data available increased by 60% while the accuracy remained relatively unchanged. In the seasonal model, the meteorological factors and DNB radiance were found to be the primary factors affecting the PM2.5 concentration in different seasons. In conclusion, this study provided a method for estimating nighttime PM2.5 concentration that will improve our understanding of air pollution and associated trends in PM2.5 variation. Full article
(This article belongs to the Special Issue Effect of Biomass-Burning on Atmosphere Using Remote Sensing)
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24 pages, 4718 KiB  
Article
An Integrated Quantitative Method Based on ArcGIS Evaluating the Contribution of Rural Straw Open Burning to Urban Fine Particulate Pollution
by Xin Wen, Weiwei Chen, Pingyu Zhang, Jie Chen and Guoqing Song
Remote Sens. 2022, 14(18), 4671; https://doi.org/10.3390/rs14184671 - 19 Sep 2022
Cited by 2 | Viewed by 1711
Abstract
This study presents a GIS-based method integrating hourly transport pathways and wind-field grid reconstruction, straw open burning (SOB) source identification, and a two-stage spatiotemporal multi-box modeling approach to quantify the contribution of external sources of SOB to elevated urban PM2.5 concentrations during [...] Read more.
This study presents a GIS-based method integrating hourly transport pathways and wind-field grid reconstruction, straw open burning (SOB) source identification, and a two-stage spatiotemporal multi-box modeling approach to quantify the contribution of external sources of SOB to elevated urban PM2.5 concentrations during a specific pollution episode (PE) at a high temporal resolution of 1 h. Taking Jilin Province as an empirical study, the contribution of SOB in province-wide farmlands to urban haze episodes in Changchun during the SOB season of 2020–2021 was evaluated quantitatively using a combination of multi-source datasets. The results showed that Changchun experienced three severe PEs and one heavy PE during the study period, and the total PM2.5 contributions from SOB sources were 352 μg m−3, 872 μg m−3, and 1224 μg m−3 during the three severe PEs, respectively; these accounted for 7%, 27%, and 23% of the urban cumulative PM2.5 levels, which were more obvious than the contribution during the PE. The total PM2.5 contribution from SOB sources (4.9 μg m−3) was only 0.31% of the urban cumulative PM2.5 level during the heavy PE. According to the analysis of the impact of individual factors, some policy suggestions are put forward for refined SOB management, including control spatial scope, burning time interval, as well as burning area limit under different urban and transport pathways’ meteorological conditions and different transport distances. Full article
(This article belongs to the Special Issue Effect of Biomass-Burning on Atmosphere Using Remote Sensing)
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17 pages, 6163 KiB  
Article
Real-Time Source Apportionment of PM2.5 Highlights the Importance of Joint Controls on Atmospheric Pollution in Cold Region of China
by Weiwei Chen, Mengduo Zhang, Wei Liu, Jing Fu and Li Guo
Remote Sens. 2022, 14(15), 3770; https://doi.org/10.3390/rs14153770 - 05 Aug 2022
Cited by 2 | Viewed by 1451
Abstract
Harbin is a northmost megacity in the cold regions of China and experiences severe PM2.5 pollution. However, comprehensive investigations for severe haze formation are few. In this study, we simultaneously measured aerosol composition in real time to assess the sources apportionment, regional [...] Read more.
Harbin is a northmost megacity in the cold regions of China and experiences severe PM2.5 pollution. However, comprehensive investigations for severe haze formation are few. In this study, we simultaneously measured aerosol composition in real time to assess the sources apportionment, regional transport and its interaction with meteorology from 1 October 2018 to 1 May 2019 by using the single particle aerosol mass spectrometer (SPAMS). The daily average PM2.5 concentration was 51.21 µg/m3 with the hourly maximum of 900.45 µg/m3. Winter coal combustion was the largest source of PM2.5 aerosols during this period. Open straw burning from surrounding and adjacent areas by short-distance transport could aggravate air quality deterioration in Harbin. Three extreme haze events (i.e., Ep1, Ep2 and Ep3) were observed in this study, showing the typical characteristics of local winter pollution. The pollutants of PM2.5 and SO2 emitted from coal combustion played an important role in haze episode during Ep1, whereas Ep2 was caused by the joint effect of coal combustion and straw burning. Ep3 was characterized by long-distance transport of windblown dust from southeast Inner Mongolia and northwest Harbin. Real-time source apportionment of fine particulate matter highlights the importance of joint control of coal and straw burning from the surrounding cities of Harbin. Full article
(This article belongs to the Special Issue Effect of Biomass-Burning on Atmosphere Using Remote Sensing)
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