Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
5.0
Journals
Remote Sensing
Special Issues
Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 47171

Special Issue Editor

Dr. Falguni Patadia

E-Mail Website
Guest Editor
NASA/GSFC, Greenbelt, MD, USA
Interests: aerosols; satellite remote sensing; air quality; earth radiation budget; aerosol retrieval and error characterization

Special Issue Information

Dear Colleagues,

Air pollution around the world is a growing problem, and achieving clean air for breathing is one of the top priorities of the United Nations’ Sustainable Development Goals (SDGs). Over the last two decades, satellite retrievals of aerosols have advanced and are providing useful information on the state of air quality. This Special Issue will be focused on air quality monitoring and forecasting using satellite observations of aerosols over regional to global scales. Authors are encouraged to submit contributions that describe original research results of studies conducted using satellite derived aerosols products for monitoring as well as estimation and forecasting of PM2.5. Topics will include (but are not limited to): PM2.5 measurements and estimates from satellite and surface, regional trends of aerosol pollution, assimilation of satellite data into regional and global models for air quality studies, transport of aerosols, role of biomass burning, dust aerosols and anthropogenic emissions in air quality, boundary layer processes and their impact on satellite estimations, and physical and statistical modeling of air quality, population health and ecological impact assessments driven by satellite data. Air quality product development, validation and inter-comparison with models from current satellites and sensors in LEO (MODIS, MISR, OMI, VIIRS, OMPS), GEO (GOES-R, GOES-S, Himawari-8/9, GOCI, INSAT) and L1 (EPIC) orbits are encouraged. Studies discussing upcoming satellite missions (i.e., TEMPO, MAIA, GEMS, 3MI) are also welcome.

Dr. Falguni Patadia
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.

Keywords

  • Aerosols
  • PM2.5
  • Air Quality
  • Pollution
  • Satellite remote sensing
  • Aerosol trends
  • MODIS
  • MISR
  • GOES
  • VIIRS

Related Special Issue

Published Papers (16 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 3340 KiB  
Article
Saltation Activity on Non-Dust Days in the Taklimakan Desert, China
by Xinghua Yang, Chenglong Zhou, Fan Yang, Lu Meng, Wen Huo, Ali Mamtimin and Qing He
Remote Sens. 2022, 14(9), 2099; https://doi.org/10.3390/rs14092099 - 27 Apr 2022
Cited by 2 | Viewed by 1939
Abstract
Dust aerosols persistently affect nearly all landscapes worldwide, and the saltation activity caused by dusty weather (e.g., dust days) releases considerable amounts of aerosol into the atmosphere. Nevertheless, dust-induced saltation activity may also occur on non-dust days. To date, few studies have investigated [...] Read more.
Dust aerosols persistently affect nearly all landscapes worldwide, and the saltation activity caused by dusty weather (e.g., dust days) releases considerable amounts of aerosol into the atmosphere. Nevertheless, dust-induced saltation activity may also occur on non-dust days. To date, few studies have investigated the saltation activity characteristics on non-dust days. Moreover, the contribution of non-dust days to the total saltation activity remains ambiguous. This study comprehensively investigates the characteristics of saltation activity on non-dust days. Specifically, we analyze the influence of the saltation activity of non-dust days on dust aerosols by utilizing saltation, atmospheric, soil, dust aerosol (i.e., PM10 and aerosol optical depth), and weather record data obtained from the Taklimakan Desert, China, between 2008 and 2010. Our results show that lower temperature, vapor pressure, and soil moisture on non-dust days reduces the saltation threshold velocity (5.9 m/s) more compared to on dust days (6.5 m/s). Furthermore, regarding wind speed, relatively strong monthly saltation activity occurs from March to August, and daily saltation activity occurs from 9:00 to 16:00. Although non-dust days only contribute 18.5% and 7.7% to saltation time and saltation count, respectively, both significantly influence the dust aerosols. Therefore, the effect of saltation activity on non-dust days cannot be undervalued, particularly while performing dust aerosol studies. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Graphical abstract

21 pages, 8374 KiB  
Article
Evaluation and Comparison of MODIS C6 and C6.1 Deep Blue Aerosol Products in Arid and Semi-Arid Areas of Northwestern China
by Leiku Yang, Xinyao Tian, Chao Liu, Weiqian Ji, Yu Zheng, Huan Liu, Xiaofeng Lu and Huizheng Che
Remote Sens. 2022, 14(8), 1935; https://doi.org/10.3390/rs14081935 - 17 Apr 2022
Cited by 8 | Viewed by 1989
Abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) algorithm was developed for aerosol retrieval on bright surfaces. Although the global validation accuracy of the DB product is satisfactory, there are still some regions found to have very low accuracy. To this end, [...] Read more.
The Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) algorithm was developed for aerosol retrieval on bright surfaces. Although the global validation accuracy of the DB product is satisfactory, there are still some regions found to have very low accuracy. To this end, DB has updated the surface database in the latest version of the Collection 6.1 (C6.1) algorithm. Some studies have shown that DB aerosol optical depth (AOD) of the old version Collection 6 (C6) has been seriously underestimated in Northwestern China. However, the status of the new version of the C6.1 product in this region is still unknown. This study aims to comprehensively evaluate the performance of the MODIS DB product in Northwestern China. The DB AOD with high quality (Quality Flag = 2 or 3) was selected to validate against the 23 sites from the China Aerosol Remote Sensing Network (CARSNET) and Aerosol Robotic Network (AERONET) during the period 2002–2014. By the overall analysis, the results indicate that both C6 and C6.1 show significant underestimation with a large fraction of more than 54% of collocations falling below the Expected Error (EE = ±(0.05 + 20% AODground)) envelope and with a large negative Mean Bias (MB) of less than −0.14. Furthermore, the new C6.1 products failed to achieve reasonable improvements in the region of Northwestern China. Besides, C6.1 has slightly fewer collocations than C6 due that some pixels with systematic biases have been removed from the new surface reflectance database. From the analysis of the site scale, the scatter plot of C6.1 is similar to that of C6 in most sites. Furthermore, a significant underestimation of DB AOD was observed at most sites, with the most severe underestimation at two sites located in the Taklimakan Desert region. Among 23 sites in Northwestern China, there are only two sites where C6.1 has largely improved the underestimation of C6. Furthermore, it is interesting to note that there are also two sites where the accuracy of the new C6.1 has declined. Moreover, it is surprising that there is one site where a large overestimation was observed in C6 and improved in C6.1. Additionally, we found a constant value of about 0.05 for both C6 and C6.1 at several sites with low aerosol loading, which is an obvious artifact. The significant improvements of C6.1 were observed in the Middle East and Central Asia but not in most sites of Northwestern China. The results of this study will be beneficial to further improvements in the MODIS DB algorithm. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Graphical abstract

18 pages, 8517 KiB  
Article
How Does COVID-19 Lockdown Impact Air Quality in India?
by Zhiyuan Hu, Qinjian Jin, Yuanyuan Ma, Zhenming Ji, Xian Zhu and Wenjie Dong
Remote Sens. 2022, 14(8), 1869; https://doi.org/10.3390/rs14081869 - 13 Apr 2022
Cited by 4 | Viewed by 1866
Abstract
Air pollution is a severe environmental problem in the Indian subcontinent. Largely caused by the rapid growth of the population, industrialization, and urbanization, air pollution can adversely affect human health and environment. To mitigate such adverse impacts, the Indian government launched the National [...] Read more.
Air pollution is a severe environmental problem in the Indian subcontinent. Largely caused by the rapid growth of the population, industrialization, and urbanization, air pollution can adversely affect human health and environment. To mitigate such adverse impacts, the Indian government launched the National Clean Air Programme (NCAP) in January 2019. Meanwhile, the unexpected city-lockdown due to the COVID-19 pandemic in March 2020 in India greatly reduced human activities and thus anthropogenic emissions of gaseous and aerosol pollutants. The NCAP and the lockdown could provide an ideal field experiment for quantifying the extent to which various levels of human activity reduction impact air quality in the Indian subcontinent. Here, we study the improvement in air quality due to COVID-19 and the NCAP in the India subcontinent by employing multiple satellite products and surface observations. Satellite data shows significant reductions in nitrogen dioxide (NO2) by 17% and aerosol optical depth (AOD) by 20% during the 2020 lockdown with reference to the mean levels between 2005–2019. No persistent reduction in NO2 nor AOD is detectable during the NCAP period (2019). Surface observations show consistent reductions in PM2.5 and NO2 during the 2020 lockdown in seven cities across the Indian subcontinent, except Mumbai in Central India. The increase in relative humidity and the decrease in the planetary boundary layer also play an important role in influencing air quality during the 2020 lockdown. With the decrease in aerosols during the lockdown, net radiation fluxes show positive anomalies at the surface and negative anomalies at the top of the atmosphere over most parts of the Indian subcontinent. The results of this study could provide valuable information for policymakers in South Asia to adjust the scientific measures proposed in the NCAP for efficient air pollution mitigation. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

16 pages, 3797 KiB  
Article
Changes in Long-Term PM2.5 Pollution in the Urban and Suburban Areas of China’s Three Largest Urban Agglomerations from 2000 to 2020
by Lili Zhang, Na Zhao, Wenhao Zhang and John P. Wilson
Remote Sens. 2022, 14(7), 1716; https://doi.org/10.3390/rs14071716 - 2 Apr 2022
Cited by 15 | Viewed by 2339
Abstract
Particulate matter (PM2.5) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM2.5 and evaluates the effects [...] Read more.
Particulate matter (PM2.5) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM2.5 and evaluates the effects of industrial transformation and clean air policies on PM2.5 levels in urban and suburban areas of China’s three largest urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) based on a new degree of urbanization classification method. We used high-resolution PM2.5 concentration and population datasets to quantify the differences in PM2.5 concentrations in urban and suburban areas of these three urban agglomerations. From 2000 to 2020, the urban areas have expanded while the suburban areas have shrunk. PM2.5 concentrations in urban areas were approximately 32, 10, and 7 μg/m3 higher than those in suburban areas from 2000 to 2020 in BTH, YRD, and PRD, respectively. Since 2013, the PM2.5 concentrations in the urban regions of BTH, YRD, and PRD have declined at average annual rates of 7.30, 5.50, and 5.03 μg/m3/year, respectively, while PM2.5 concentrations in suburban areas have declined at average annual rates of 3.11, 4.23 and 4.69 μg/m3/year, respectively. By 2018, all of the urban and suburban areas of BTH, YRD, and PRD satisfied their specific targets in the Air Pollution and Control Action Plan. By 2020, the PM2.5 declines of BTH, YRD, and PRD exceeded the targets by two, three, and four times, respectively. However, the PM2.5 exposure risks in urban areas are 10–20 times higher than those in suburban areas. China will need to implement more robust air pollution mitigation policies to achieve the World Health Organization’s Air Quality Guideline (WHO-AQG) and reduce long-term PM2.5 exposure health risks. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

18 pages, 3375 KiB  
Article
Dust Radiative Effect Characteristics during a Typical Springtime Dust Storm with Persistent Floating Dust in the Tarim Basin, Northwest China
by Lu Meng, Tianliang Zhao, Qing He, Xinghua Yang, Ali Mamtimin, Minzhong Wang, Honglin Pan, Wen Huo, Fan Yang and Chenglong Zhou
Remote Sens. 2022, 14(5), 1167; https://doi.org/10.3390/rs14051167 - 26 Feb 2022
Cited by 11 | Viewed by 1912
Abstract
A special topography and ultra-high atmospheric boundary layer conditions in the Tarim Basin (TB) lead to the unique spatial–temporal distribution characteristics of dust aerosols. A typical dust storm with persistent floating dust over the TB from 27 April to 1 May 2015 was [...] Read more.
A special topography and ultra-high atmospheric boundary layer conditions in the Tarim Basin (TB) lead to the unique spatial–temporal distribution characteristics of dust aerosols. A typical dust storm with persistent floating dust over the TB from 27 April to 1 May 2015 was used to investigate the characteristics of the dust radiative effect using the Weather Research and Forecasting Model with Chemistry (WRF-Chem). Based on reasonable evaluations involving in situ sounding observations, as well as remotely sensed MODIS observations of meteorology, dust aerosols, and the ultra-high atmospheric boundary layer, the simulation characterized the complete characteristics of the dust direct radiative effect (DDRE) during the dust storm outbreak stage and persistent floating dust stage over the TB. During the daytime, the shortwave (SW) radiative effect heated the atmosphere and cooled the land surface (SUR), whereas the longwave (LW) radiative effect had the opposite effect on the TB. Regarding low-level dust, the LW radiative effect was greater than the SW DDRE in the atmosphere, while for high-level dust the situation was reversed. During the nighttime, the LW DDRE at the top of the atmosphere (TOA), at the SUR, and in the atmosphere was less than that during the daytime, when the DDRE at the SUR was the most significant. In contrast to the daytime, the near-surface dust aerosols exerted an LW warming effect in the atmosphere during the nighttime; however, the dust LW radiative effect had a cooling effect from above a 100 m altitude until the top of the dust layer. In contrast, the DDRE heating rate peaked at the top of the dust layer within the TB. The event-averaged net DDRE was 0.53, −5.90, and 6.43 W m−2 at the TOA, at the SUR, and in the atmosphere over the TB, respectively. The dust SW radiative effect was stronger than the dust L4W radiative effect over the TB at the SUR and in the atmosphere. Moreover, the DDRE at the TOA was weaker than that at the SUR. Overall, the study revealed noteworthy radiative effect features of dust aerosols during typical dust storms with persistent floating dust over the TB. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

39 pages, 78246 KiB  
Article
Effect of Aerosol Vertical Distribution on the Modeling of Solar Radiation
by Ilias Fountoulakis, Kyriakoula Papachristopoulou, Emmanouil Proestakis, Vassilis Amiridis, Charalampos Kontoes and Stelios Kazadzis
Remote Sens. 2022, 14(5), 1143; https://doi.org/10.3390/rs14051143 - 25 Feb 2022
Cited by 5 | Viewed by 2387
Abstract
Default aerosol extinction coefficient profiles are commonly used instead of measured profiles in radiative transfer modeling, increasing the uncertainties in the simulations. The present study aimed to determine the magnitude of these uncertainties and contribute towards the understanding of the complex interactions between [...] Read more.
Default aerosol extinction coefficient profiles are commonly used instead of measured profiles in radiative transfer modeling, increasing the uncertainties in the simulations. The present study aimed to determine the magnitude of these uncertainties and contribute towards the understanding of the complex interactions between aerosols and solar radiation. Default, artificial and measured profiles of the aerosol extinction coefficient were used to simulate the profiles of different radiometric quantities in the atmosphere for different surface, atmospheric, and aerosol properties and for four spectral bands: ultraviolet-B, ultraviolet-A, visible, and near-infrared. Case studies were performed over different areas in Europe and North Africa. Analysis of the results showed that under cloudless skies, changing the altitude of an artificial aerosol layer has minor impact on the levels of shortwave radiation at the top and bottom of the atmosphere, even for high aerosol loads. Differences of up to 30% were, however, detected for individual spectral bands. Using measured instead of default profiles for the simulations led to more significant differences in the atmosphere, which became very large during dust episodes (10–60% for actinic flux at altitudes between 1 and 2 km, and up to 15 K/day for heating rates depending on the site and solar elevation). Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Graphical abstract

19 pages, 14833 KiB  
Article
Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020
by Qiaolin Zeng, Tianshou Xie, Songyan Zhu, Meng Fan, Liangfu Chen and Yu Tian
Remote Sens. 2022, 14(3), 623; https://doi.org/10.3390/rs14030623 - 27 Jan 2022
Cited by 10 | Viewed by 2804
Abstract
Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely [...] Read more.
Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM2.5 measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R2) increased by 0.05 ± 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 ± 1.0 μg/m3, 8 ± 3%, and 1.40 ± 0.2 μg/m3, respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R2 values of the validation dataset using CapsNet improved by 4 ± 2%, and RMSE, MRE, and MAE decreased by 1.50 ± 0.4 μg/m3, ~5%, and 0.60 ± 0.2 μg/m3, respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R2 values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study proposed the CapsNet model to estimate PM2.5 concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

19 pages, 3528 KiB  
Article
Hospitalization Due to Fire-Induced Pollution in the Brazilian Legal Amazon from 2005 to 2018
by Wesley Augusto Campanharo, Thiago Morello, Maria A. M. Christofoletti and Liana O. Anderson
Remote Sens. 2022, 14(1), 69; https://doi.org/10.3390/rs14010069 - 24 Dec 2021
Cited by 11 | Viewed by 2854
Abstract
Fire is widely used in the Amazon as a ubiquitous driver of land management and land cover change. Regardless of their purpose, fires release a considerable amount of pollutants into the atmosphere, with severe consequences for human health. This paper adds to the [...] Read more.
Fire is widely used in the Amazon as a ubiquitous driver of land management and land cover change. Regardless of their purpose, fires release a considerable amount of pollutants into the atmosphere, with severe consequences for human health. This paper adds to the extant literature by measuring the causal effect of fires on hospitalizations, using the approach of instrumental variables, whose validity is assessed with multiple statistical tests. A wide range of confounders are added as covariates, seizing on the accuracy enhancement potential of a broad and fine-grained dataset that covers 14 years of the whole Amazon territory at a municipal–monthly level. The results reveal a positive effect of fire on hospitalizations due to respiratory illnesses in general, and particularly in those due to asthma. A 1% increase in pollution concentration would increase hospitalizations by 0.14% at a municipality–monthly level. A total of 5% of respiratory hospitalizations were estimated to be attributable to fire-induced pollution, corresponding to 822 cases per month. The analysis demonstrates that the coupling of econometrics and remote sensing data is a promising avenue towards the assessment of impacts caused by fires, which may be applied to other regions of the world subjected to anthropogenic fires. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

13 pages, 2877 KiB  
Article
Changes in the Distribution Pattern of PM2.5 Pollution over Central China
by Lijuan Shen, Weiyang Hu, Tianliang Zhao, Yongqing Bai, Honglei Wang, Shaofei Kong and Yan Zhu
Remote Sens. 2021, 13(23), 4855; https://doi.org/10.3390/rs13234855 - 30 Nov 2021
Cited by 13 | Viewed by 1875
Abstract
The extent of PM2.5 pollution has reduced in traditional polluted regions such as the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin (SB), and Pearl River Delta (PRD) over China in recent years. Despite this, the Twain-Hu Basin (THB), which [...] Read more.
The extent of PM2.5 pollution has reduced in traditional polluted regions such as the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin (SB), and Pearl River Delta (PRD) over China in recent years. Despite this, the Twain-Hu Basin (THB), which covers the lower flatlands in Hubei and Hunan provinces in central China, was found to be a high PM2.5 pollution region, with annual mean PM2.5 concentrations of 41–63 μg·m−3, which is larger than the values in YRD, SB, and PRD during 2014–2019, and high aerosol optical depth values (>0.8) averaged over 2000–2019 from the MODIS products. Heavy pollution events (HPEs) are frequently observed in the THB, with HPE-averaged concentrations of PM2.5 reaching up to 183–191 μg·m−3, which exceeds their counterparts in YRD, SB, and PRD for 2014–2019, highlighting the THB as a center of heavy PM2.5 pollution in central China. During 2014–2019, approximately 65.2% of the total regional HPEs over the THB were triggered by the regional transport of PM2.5 over Central and Eastern China (CEC). This occurred in view of the co-existing HPEs in the NCP and the THB, with a lag of almost two days in the THB-PM2.5 peak, which is governed by the strong northerlies of the East Asian monsoon (EAM) over CEC. Such PM2.5 transport from upstream source regions in CEC contributes 60.3% of the surface PM2.5 pollution over the THB receptor region. Hence, a key PM2.5 receptor of the THB in regional pollutant transport alters the distribution patterns of PM2.5 pollution over China, which is attributable to the climate change of EAMs. This study indicates a complex relationship between sources and receptors of atmospheric aerosols for air quality applications. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

16 pages, 2786 KiB  
Article
PM2.5 Concentration Forecasting over the Central Area of the Yangtze River Delta Based on Deep Learning Considering the Spatial Diffusion Process
by Mingyue Lu, Tengfei Lao, Manzhu Yu, Yadong Zhang, Jianqin Zheng and Yuchen Li
Remote Sens. 2021, 13(23), 4834; https://doi.org/10.3390/rs13234834 - 28 Nov 2021
Cited by 3 | Viewed by 1956
Abstract
Precise PM2.5 concentration forecasting is significant to environmental management and human health. Researchers currently add various parameters to deep learning models for PM2.5 concentration forecasting, but most of them ignore the problem of PM2.5 concentration diffusion. To address this issue, [...] Read more.
Precise PM2.5 concentration forecasting is significant to environmental management and human health. Researchers currently add various parameters to deep learning models for PM2.5 concentration forecasting, but most of them ignore the problem of PM2.5 concentration diffusion. To address this issue, a deep learning model-based PM2.5 concentration forecasting method considering the diffusion process is proposed in this paper. We designed a spatial diffuser to express the diffusion process of gaseous pollutants; that is, the concentration of PM2.5 in four surrounding directions was taken as the explanatory variable. The information from the target and associated stations was then employed as inputs and fed into the model, together with meteorological features and other pollutant parameters. The hourly data from 1 January 2019 to 31 December 2019, and the central area of the Yangtze River Delta, were used to conduct the experiment. The results showed that the forecasting performance of the method we proposed is superior to that of ignoring diffusion, with an average RMSE = 8.247 μg/m3 and average R2 = 0.922 in three different deep learning models, RNN, LSTM, and GRU, in which RMSE decreased by 10.52% and R2 increased by 2.22%. Our PM2.5 concentration forecasting method, which was based on an understanding of basic physical laws and conformed to the characteristics of data-driven models, achieved excellent performance. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

19 pages, 15079 KiB  
Article
Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization
by Chunlin Jin, Yong Xue, Xingxing Jiang, Yuxin Sun and Shuhui Wu
Remote Sens. 2021, 13(22), 4689; https://doi.org/10.3390/rs13224689 - 20 Nov 2021
Cited by 1 | Viewed by 2093
Abstract
The Advanced Himawari Imager (AHI) aboard the Himawari-8, a new generation of geostationary meteorological satellite, has high-frequency observation, which allows it to effectively capture atmospheric variations. In this paper, we have proposed an Improved Bi-angle Aerosol optical depth (AOD) retrieval Algorithm (IBAA) from [...] Read more.
The Advanced Himawari Imager (AHI) aboard the Himawari-8, a new generation of geostationary meteorological satellite, has high-frequency observation, which allows it to effectively capture atmospheric variations. In this paper, we have proposed an Improved Bi-angle Aerosol optical depth (AOD) retrieval Algorithm (IBAA) from AHI data. The algorithm ignores the aerosol effect at 2.3 μm and assumes that the aerosol optical depth does not change within one hour. According to the property that the reflectivity ratio K of two observations at 2.3 μm does not change with wavelength, we constructed the equation for two observations of AHI 0.47 μm band. Then Particle Swarm Optimization (PSO) was used to solve the nonlinear equation. The algorithm was applied to the AHI observations over the Chinese mainland (80°–135°E, 15°–60°N) between April and June 2019 and hourly AOD at 0.47 μm was retrieved. We validated IBAA AOD against the Aerosol Robotic Network (AERONET) sites observation, including surrounding regions as well as the Chinese mainland, and compared it with the AHI L3 V030 hourly AOD product. Validation with AERONET of 2079 matching points shows a correlation coefficient R = 0.82, root-mean-square error RMSE = 0.27, and more than 62% AOD retrieval results within the expected error of ±(0.05 + 0.2 × AODAERONET). Although IBAA does not perform very well in the case of coarse-particle aerosols, the comparison and validation demonstrate it can estimate AHI AOD with good accuracy and wide coverage over land on the whole. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

14 pages, 5166 KiB  
Article
Assessing the Responses of Aviation-Related SO2 and NO2 Emissions to COVID-19 Lockdown Regulations in South Africa
by Lerato Shikwambana and Mahlatse Kganyago
Remote Sens. 2021, 13(20), 4156; https://doi.org/10.3390/rs13204156 - 17 Oct 2021
Cited by 8 | Viewed by 2589
Abstract
Aircraft emit harmful substances, such as carbon dioxide (CO2), water vapour (H2O), nitrogen oxides (NOx), sulphur oxides (SOx), particulates, and other trace compounds. These emissions degrade air quality and can deteriorate human health and negatively [...] Read more.
Aircraft emit harmful substances, such as carbon dioxide (CO2), water vapour (H2O), nitrogen oxides (NOx), sulphur oxides (SOx), particulates, and other trace compounds. These emissions degrade air quality and can deteriorate human health and negatively impact climate change. Airports are the nucleus of the ground and low-altitude emissions from aircraft during approach, landing, take-off, and taxi. During the global lockdown due to the COVID-19 pandemic, tight restrictions of the movement were imposed, leading to temporary closures of airports globally. In this study, we look at the variability of emissions at two major airports in South Africa, namely the OR Tambo international airport (FAOR) and the Cape Town international airport (FACT). Trend analysis of aircraft movements, i.e., departures and arrivals, showed a sharp decline at the two airports coinciding with the lockdowns to prevent the spread of the COVID-19. Consequently, a decrease in NO2 emissions by 70.45% (12.6 × 10−5 mol/m2) and 64.58% (11.6 × 10−5 mol/m2) at FAOR and FACT were observed, respectively. A noticeable SO2 emission decline was also observed, particularly over FAOR during the lockdown period in South Africa. Overall, this study observed that the global lockdown regulations had a positive impact on the air quality, causing a brief decline in emissions from commercial aviation at the South African major airports. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Graphical abstract

20 pages, 8608 KiB  
Article
Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas
by Hao Lin, Siwei Li, Jia Xing, Jie Yang, Qingxin Wang, Lechao Dong and Xiaoyue Zeng
Remote Sens. 2021, 13(20), 4140; https://doi.org/10.3390/rs13204140 - 15 Oct 2021
Cited by 7 | Viewed by 2240
Abstract
Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high [...] Read more.
Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Graphical abstract

14 pages, 7665 KiB  
Article
Estimating the Impact of COVID-19 on the PM2.5 Levels in China with a Satellite-Driven Machine Learning Model
by Qiulun Li, Qingyang Zhu, Muwu Xu, Yu Zhao, K. M. Venkat Narayan and Yang Liu
Remote Sens. 2021, 13(7), 1351; https://doi.org/10.3390/rs13071351 - 1 Apr 2021
Cited by 8 | Viewed by 3497
Abstract
China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact [...] Read more.
China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM2.5) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018–30 April 2019) and a pandemic semester (1 November 2019–30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.79 (17.55 μg/m3) and the pandemic semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.83 (13.48 μg/m3) for daily PM2.5 predictions. Our prediction results showed high PM2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM2.5 levels were lowered by 4.8 μg/m3 during the pandemic semester compared to the reference semester and PM2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%). Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

16 pages, 7578 KiB  
Article
Evaluations of Surface PM10 Concentration and Chemical Compositions in MERRA-2 Aerosol Reanalysis over Central and Eastern China
by Xiaodan Ma, Peng Yan, Tianliang Zhao, Xiaofang Jia, Jian Jiao, Qianli Ma, Dongqiao Wu, Zhuozhi Shu, Xiaoyun Sun and Birhanu Asmerom Habtemicheal
Remote Sens. 2021, 13(7), 1317; https://doi.org/10.3390/rs13071317 - 30 Mar 2021
Cited by 12 | Viewed by 2920
Abstract
The chemical composition dataset of Aerosol Reanalysis of NASA’s Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRAero) has not been thoroughly evaluated with observation data in mainland China due to the lack of long-term chemical components data. Using the 5-year data [...] Read more.
The chemical composition dataset of Aerosol Reanalysis of NASA’s Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRAero) has not been thoroughly evaluated with observation data in mainland China due to the lack of long-term chemical components data. Using the 5-year data of PM10 mass concentrations and chemical compositions obtained from the routine sampling measurements at the World Meteorological Organization the Global Atmosphere Watch Programme regional background stations, Jing Sha (JS) and Lin’An (LA), in central and eastern China, we comprehensively evaluate the surface PM10 concentrations and chemical compositions such as sulfate (SO42−), organic carbon (OC) and black carbon (BC) derived from MERRAero. Overall, the concentrations of PM10, SO42−, OC and BC from the MERRAero agreed well with the measurements, despite a slight and consistent overestimation of BC concentrations and a moderate and persistent underestimation of PM10 concentrations throughout the study period. The MERRAero reanalysis of aerosol compositions performs better during the summertime than wintertime. By considering the nitrate particles in PM10 reconstruction, MERRAero performance can be significantly improved. The unreasonable seasonal variations of PM10 chemical compositions at station LA by MERRAero could be causative factors for the larger MERRAero discrepancies during 2016–2017 than the period of 2011–2013. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Figure 1

22 pages, 7896 KiB  
Article
A Satellite-Based High-Resolution (1-km) Ambient PM2.5 Database for India over Two Decades (2000–2019): Applications for Air Quality Management
by Sagnik Dey, Bhavesh Purohit, Palak Balyan, Kuldeep Dixit, Kunal Bali, Alok Kumar, Fahad Imam, Sourangsu Chowdhury, Dilip Ganguly, Prashant Gargava and V. K. Shukla
Remote Sens. 2020, 12(23), 3872; https://doi.org/10.3390/rs12233872 - 26 Nov 2020
Cited by 52 | Viewed by 9353
Abstract
Fine particulate matter (PM2.5) is a major criteria pollutant affecting the environment, health and climate. In India where ground-based measurements of PM2.5 is scarce, it is important to have a long-term database at a high spatial resolution for an efficient [...] Read more.
Fine particulate matter (PM2.5) is a major criteria pollutant affecting the environment, health and climate. In India where ground-based measurements of PM2.5 is scarce, it is important to have a long-term database at a high spatial resolution for an efficient air quality management plan. Here we develop and present a high-resolution (1-km) ambient PM2.5 database spanning two decades (2000–2019) for India. We convert aerosol optical depth from Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved by Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm to surface PM2.5 using a dynamic scaling factor from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data. The satellite-derived daily (24-h average) and annual PM2.5 show a R2 of 0.8 and 0.97 and root mean square error of 25.7 and 7.2 μg/m3, respectively against surface measurements from the Central Pollution Control Board India network. Population-weighted 20-year averaged PM2.5 over India is 57.3 μg/m3 (5–95 percentile ranges: 16.8–86.9) with a larger increase observed in the present decade (2010–2019) than in the previous decade (2000 to 2009). Poor air quality across the urban–rural transact suggests that this is a regional scale problem, a fact that is often neglected. The database is freely disseminated through a web portal ‘satellite-based application for air quality monitoring and management at a national scale’ (SAANS) for air quality management, epidemiological research and mass awareness. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-16
Back to TopTop