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Remote Sensing of Air Pollutants and Carbon Emissions in Megacities
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Remote Sensing of Air Pollutants and Carbon Emissions in Megacities

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 May 2021) | Viewed by 25390

Special Issue Editors

Dr. Zhao-Cheng Zeng

E-Mail Website
Guest Editor
JIFRESSIE, UCLA & GPS, Caltech
Interests: atmospheric remote sensing; urban remote sensing
Prof. Dr. Gerrit de Leeuw

E-Mail Website
Guest Editor
Royal Netherlands Meteorological Institute (KNMI), R & D Satellite Observations, 3731 GA De Bilt, The Netherlands
Interests: aerosols; satellite remotes sensing; air quality; climate; aerosol-cloud interaction; sea spray aerosol
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Li

E-Mail Website
Guest Editor
School of Physics, Peking University, Beijing 100871, China
Interests: aerosol remote sensing and radiative effects
Special Issues, Collections and Topics in MDPI journals
Dr. Liping Lei

E-Mail Website
Guest Editor
Aerospace information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: remote sensing of atmosphere, satellite data mining and application in global change and greenhouses gases
Dr. Lei Zhu

E-Mail Website
Guest Editor
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: atmospheric chemistry; remote sensing of trace gases; data assimilation; air quality; atmosphere-land-ocean interactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Megacities, typically urban agglomerations with a population of more than 10 million, are responsible for most of the global anthropogenic carbon emissions, including CO2, CH4, and CO. Megacities are also producing an unprecedented amount of air pollution, including atmospheric aerosols (particulate matter) and trace gases (e.g., CO, O3, NOx, and SO2) that greatly harm public health and the environment. Comprehensive, accurate and consistent estimates of the emissions of air pollutants and anthropogenic carbon from megacities are essential for understanding human-induced climate change. In order to identify emission sources for emission control purposes, observation systems and networks must be capable of providing measurements with high spatial and temporal resolution in order to capture the fine-scale spatial/local emission gradients.

This Special Issue seeks contributions on the use of remote sensing techniques to measure and understand emissions of aerosols, trace gases and carbon from megacities across the world. The remote sensing techniques include, but are not limited to, ground-based networks, tower-based or mountaintop-based observatories, air-borne instruments and satellites.

Dr. Zhao-Cheng Zeng
Prof. Gerrit de Leeuw
Dr. Jing Li
Dr. Liping Lei
Dr. Lei Zhu
Guest Editor

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.

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Published Papers (5 papers)

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22 pages, 5456 KiB  
Article
Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation
by Yu Fu, Hao Gao, Hong Liao and Xiangjun Tian
Remote Sens. 2021, 13(19), 3880; https://doi.org/10.3390/rs13193880 - 28 Sep 2021
Cited by 14 | Viewed by 2942
Abstract
Large uncertainty exists in the estimations of greenhouse gases and aerosol emissions from crop residue burning, which could be a key source of uncertainty in quantifying the impact of agricultural fire on regional air quality. In this study, we investigated the crop residue [...] Read more.
Large uncertainty exists in the estimations of greenhouse gases and aerosol emissions from crop residue burning, which could be a key source of uncertainty in quantifying the impact of agricultural fire on regional air quality. In this study, we investigated the crop residue burning emissions and their uncertainty in North China Plain (NCP) using three widely used methods, including statistical-based, burned area-based, and fire radiative power-based methods. The impacts of biomass burning emissions on atmospheric carbon dioxide (CO2) were also examined by using a global chemical transport model (GEOS-Chem) simulation. The crop residue burning emissions were found to be high in June and followed by October, which is the harvest times for the main crops in NCP. The estimates of CO2 emission from crop residue burning exhibits large interannual variation from 2003 to 2019, with rapid growth from 2003 to 2012 and a remarkable decrease from 2013 to 2019, indicating the effects of air quality control plans in recent years. Through Monte Carlo simulation, the uncertainty of each estimation was quantified, ranging from 20% to 70% for CO2 emissions at the regional level. Concerning spatial uncertainty, it was found that the crop residue burning emissions were highly uncertain in small agricultural fire areas with the maximum changes of up to 140%. While in the areas with large agricultural fire, i.e., southern parts of NCP, the coefficient of variation mostly ranged from 30% to 100% at the gridded level. The changes in biomass burning emissions may lead to a change of surface CO2 concentration during the harvest times in NCP by more than 1.0 ppmv. The results of this study highlighted the significance of quantifying the uncertainty of biomass burning emissions in a modeling study, as the variations of crop residue burning emissions could affect the emission-driven increases in CO2 and air pollutants during summertime pollution events by a substantial fraction in this region. Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollutants and Carbon Emissions in Megacities)
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31 pages, 45540 KiB  
Article
Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology
by Tongshu Zheng, Michael Bergin, Guoyin Wang and David Carlson
Remote Sens. 2021, 13(7), 1356; https://doi.org/10.3390/rs13071356 - 1 Apr 2021
Cited by 10 | Viewed by 10125
Abstract
Satellite-based rapid sweeping screening of localized PM2.5 hotspots at fine-scale local neighborhood levels is highly desirable. This motivated us to develop a random forest–convolutional neural network–local contrast normalization (RF–CNN–LCN) pipeline that detects local PM2.5 hotspots at a 300 m resolution using [...] Read more.
Satellite-based rapid sweeping screening of localized PM2.5 hotspots at fine-scale local neighborhood levels is highly desirable. This motivated us to develop a random forest–convolutional neural network–local contrast normalization (RF–CNN–LCN) pipeline that detects local PM2.5 hotspots at a 300 m resolution using satellite imagery and meteorological information. The RF–CNN joint model in the pipeline uses three meteorological variables and daily 3 m/pixel resolution PlanetScope satellite imagery to generate daily 300 m ground-level PM2.5 estimates. The downstream LCN processes the estimated PM2.5 maps to reveal local PM2.5 hotspots. The RF–CNN joint model achieved a low normalized root mean square error for PM2.5 of within ~31% and normalized mean absolute error of within ~19% on the holdout samples in both Delhi and Beijing. The RF–CNN–LCN pipeline reasonably predicts urban PM2.5 local hotspots and coolspots by capturing both the main intra-urban spatial trends in PM2.5 and the local variations in PM2.5 with urban landscape, with local hotspots relating to compact urban spatial structures and coolspots being open areas and green spaces. Based on 20 sampled representative neighborhoods in Delhi, our pipeline revealed an annual average 9.2 ± 4.0 μg m−3 difference in PM2.5 between the local hotspots and coolspots within the same community. In some cases, the differences were much larger; for example, at the Indian Gandhi International Airport, the increase was 20.3 μg m−3 from the coolest spot (the residential area immediately outside the airport) to the hottest spot (airport runway). This work provides a possible means of automatically identifying local PM2.5 hotspots at 300 m in heavily polluted megacities and highlights the potential existence of substantial health inequalities in long-term outdoor PM2.5 exposures even within the same local neighborhoods between local hotspots and coolspots. Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollutants and Carbon Emissions in Megacities)
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19 pages, 4918 KiB  
Article
Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements
by Shupeng Wang, Ronald J. van der A, Piet Stammes, Weihe Wang, Peng Zhang, Naimeng Lu, Xingying Zhang, Yanmeng Bi, Ping Wang and Li Fang
Remote Sens. 2020, 12(14), 2204; https://doi.org/10.3390/rs12142204 - 10 Jul 2020
Cited by 20 | Viewed by 4385 | Correction
Abstract
In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the TanSat observations using the ACOS (Atmospheric CO2 Observations from Space) algorithm. The XCO2 product has been validated with [...] Read more.
In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the TanSat observations using the ACOS (Atmospheric CO2 Observations from Space) algorithm. The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for 2 years of TanSat data from 2017 to 2018. Based on the correlation of the XCO2 error over land with goodness of fit in three spectral bands at 0.76, 1.61 and 2.06 μm, we applied an a posteriori bias correction to TanSat retrievals. For overpass averaged results, XCO2 retrievals show a standard deviation (SD) of ~2.45 ppm and a positive bias of ~0.27 ppm compared to collocated TCCON sites. The validation also shows a relatively higher positive bias and variance against TCCON over high-latitude regions. Three cases to evaluate TanSat target mode retrievals are investigated, including one field campaign at Dunhuang with measurements by a greenhouse gas analyzer deployed on an unmanned aerial vehicle and two cases with measurements by a ground-based Fourier-transform spectrometer in Beijing. The results show the retrievals of all footprints, except footprint-6, have relatively low bias (within ~2 ppm). In addition, the orbital XCO2 distributions over Australia and Northeast China between TanSat and the second Orbiting Carbon Observatory (OCO-2) on 20 April 2017 are compared. It shows that the mean XCO2 from TanSat is slightly lower than that of OCO-2 with an average difference of ~0.85 ppm. A reasonable agreement in XCO2 distribution is found over Australia and Northeast China between TanSat and OCO-2. Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollutants and Carbon Emissions in Megacities)
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Other

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12 pages, 13806 KiB  
Technical Note
Decreased Anthropogenic CO2 Emissions during the COVID-19 Pandemic Estimated from FTS and MAX-DOAS Measurements at Urban Beijing
by Zhaonan Cai, Ke Che, Yi Liu, Dongxu Yang, Cheng Liu and Xu Yue
Remote Sens. 2021, 13(3), 517; https://doi.org/10.3390/rs13030517 - 1 Feb 2021
Cited by 15 | Viewed by 4136
Abstract
The COVID-19 pandemic has led to ongoing reductions in economic activity and anthropogenic emissions. Beijing was particular badly affected by lockdown measures during the early months of the COVID-19 pandemic. It has significantly reduced the CO2 emission and toxic air pollution (CO [...] Read more.
The COVID-19 pandemic has led to ongoing reductions in economic activity and anthropogenic emissions. Beijing was particular badly affected by lockdown measures during the early months of the COVID-19 pandemic. It has significantly reduced the CO2 emission and toxic air pollution (CO and NO2). We use column-averaged dry-air mole fractions of CO2 and CO (XCO2 and XCO) observed by a ground-based EM27/SUN Fourier transform spectrometer (FTS), the tropospheric NO2 column observed by MAX-DOAS and satellite remote sensing data (GOSAT and TROPOMI) to investigate the variations in anthropogenic CO2 emission related to COVID-19 lockdown in Beijing. The anomalies describe the spatio-temporal enhancement of gas concentration, which relates to the emission. Anomalies in XCO2 and XCO, and XNO2 (ΔXCO2, ΔXCO, and ΔXNO2) for ground-based measurements were calculated from the diurnal variability. Highly correlated daily XCO and XCO2 anomalies derived from FTS time series data provide the ΔXCO to ΔXCO2 ratio (the correlation slope). The ΔXCO to ΔXCO2 ratio in Beijing was lower in 2020 (8.2 ppb/ppm) than in 2019 (9.6 ppb/ppm). The ΔXCO to ΔXCO2 ratio originating from a polluted area was significantly lower in 2020. The reduction in anthropogenic CO2 emission was estimated to be 14.2% using FTS data. A comparable value reflecting the slowdown in growth of atmospheric CO2 over the same time period was estimated to be 15% in Beijing from the XCO2 anomaly from GOSAT, which was derived from the difference between the target area and the background area. The XCO anomaly from TROPOMI is reduced by 8.7% in 2020 compared with 2019, which is much smaller than the reduction in surface air pollution data (17%). Ground-based NO2 observation provides a 21.6% decline in NO2. The NO2 to CO2 correlation indicates a 38.2% decline in the CO2 traffic emission sector. Overall, the reduction in anthropogenic CO2 emission relating to COVID-19 lockdown in Beijing can be detected by the Bruker EM27/SUN Fourier transform spectrometer (FTS) and MAX-DOAS in urban Beijing. Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollutants and Carbon Emissions in Megacities)
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4 pages, 190 KiB  
Correction
Correction: Shupeng, W., et al. Carbon Dioxide Retrieval from TanSat Observations and Validation with TCCON Measurements. Remote Sensing 2020, 12(14), 2204
by Shupeng Wang, Ronald J. van der A, Piet Stammes, Weihe Wang, Peng Zhang, Naimeng Lu, Xingying Zhang, Yanmeng Bi, Ping Wang and Li Fang
Remote Sens. 2020, 12(21), 3626; https://doi.org/10.3390/rs12213626 - 4 Nov 2020
Cited by 1 | Viewed by 1900
Abstract
The authors wish to make the following corrections to this paper [...] Full article
(This article belongs to the Special Issue Remote Sensing of Air Pollutants and Carbon Emissions in Megacities)
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