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24 pages, 6526 KiB

Open AccessArticle

Identification of NO₂ and SO₂ Pollution Hotspots and Sources in Jiangsu Province of China

by Yu Wang, Md. Arfan Ali, Muhammad Bilal, Zhongfeng Qiu, Alaa Mhawish, Mansour Almazroui, Shamsuddin Shahid, M. Nazrul Islam, Yuanzhi Zhang and Md. Nazmul Haque

Remote Sens. 2021, 13(18), 3742; https://doi.org/10.3390/rs13183742 - 18 Sep 2021

Cited by 25 | Viewed by 5457

Abstract

Nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide [...] Read more.

Nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO₂ and SO₂, the SO₂/NO₂ ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO₂ and SO₂ for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO₂ and SO₂ in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO₂ and SO₂ in winter. Notably, the occurrence frequency of NO₂ and SO₂ of >1.2 was highest in winter, which varied between 9.14~32.46% for NO₂ and 7.84~21.67% for SO₂, indicating a high level of pollution across Jiangsu Province. The high SO₂/NO₂ ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO₂ and SO₂ were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO₂ were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO₂ were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO₂ (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO₂ (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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18 pages, 3905 KiB

Open AccessArticle

A National-Scale 1-km Resolution PM_2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

by Chau-Ren Jung, Wei-Ting Chen and Shoji F. Nakayama

Remote Sens. 2021, 13(18), 3657; https://doi.org/10.3390/rs13183657 - 13 Sep 2021

Cited by 18 | Viewed by 3480

Abstract

Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM_2.5 is influenced by both the [...] Read more.

Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM_2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM_2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM_2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R²) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m³. For the 10-fold cross-validation, the cross-validation R² and RMSE of the model were 0.86 and 3.02 μg/m³, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R² (RMSE) of 0.94 (1.78 μg/m³). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM_2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM_2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM_2.5 on health outcomes in epidemiological studies. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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16 pages, 2893 KiB

Open AccessArticle

An Adjustment Approach for Aerosol Optical Depth Inferred from CALIPSO

by Zhaoliang Zeng, Zemin Wang and Baojun Zhang

Remote Sens. 2021, 13(16), 3085; https://doi.org/10.3390/rs13163085 - 5 Aug 2021

Cited by 5 | Viewed by 2673

Abstract

The verification and correction of CALIPSO aerosol products is key to understanding the atmospheric environment and climate change. However, CALIPSO often cannot detect the full profile of aerosol for the low instrument sensitivity near the surface. Thus, a correction scheme for the aerosol [...] Read more.

The verification and correction of CALIPSO aerosol products is key to understanding the atmospheric environment and climate change. However, CALIPSO often cannot detect the full profile of aerosol for the low instrument sensitivity near the surface. Thus, a correction scheme for the aerosol extinction coefficient (AECs) in the planetary boundary layer (PBL) is proposed to improve the quality of the CALIPSO-based aerosol optical depth (AOD) at 532 nm. This scheme assumed that the aerosol is vertically and uniformly distributed below the PBL, and that the AECs in the whole PBL are equal to those at the top of the PBL; then, the CALIPSO AOD was obtained by vertically integrating AECs throughout the whole atmosphere. Additionally, the CALIPSO AOD and corrected CALIPSO AOD were validated against seven ground-based sites across eastern China during 2007–2015. Our results show that the initial CALIPSO AOD obtained by cloud filtering was generally lower than that of the ground-based observations. After accounting for the AECs in the PBL, the adjustment method tended to improve the CALIPSO AOD data quality. The average R (slope) value from all sites was improved by 7% (46%). Further, the relative distance between the ground track of CALIPSO and the ground station exhibited an influence on the validation result of CALIPSO AOD. The retrieval precision of CALIPSO AOD worsened with the increase in water vapor in the atmosphere. Our findings indicate that our scheme significantly improves the accuracy of CALIPSO AOD, which will help to provide alternative AOD products in the presence of severe atmospheric pollution. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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16 pages, 5370 KiB

Open AccessArticle

Recommendations for HCHO and SO₂ Retrieval Settings from MAX-DOAS Observations under Different Meteorological Conditions

by Zeeshan Javed, Aimon Tanvir, Muhammad Bilal, Wenjing Su, Congzi Xia, Abdul Rehman, Yuanyuan Zhang, Osama Sandhu, Chengzhi Xing, Xiangguang Ji, Mingjie Xie, Cheng Liu and Yuhang Wang

Remote Sens. 2021, 13(12), 2244; https://doi.org/10.3390/rs13122244 - 8 Jun 2021

Cited by 6 | Viewed by 3925

Abstract

Recently, the occurrence of fog and haze over China has increased. The retrieval of trace gases from the multi-axis differential optical absorption spectroscopy (MAX-DOAS) is challenging under these conditions. In this study, various reported retrieval settings for formaldehyde (HCHO) and sulfur dioxide (SO [...] Read more.

Recently, the occurrence of fog and haze over China has increased. The retrieval of trace gases from the multi-axis differential optical absorption spectroscopy (MAX-DOAS) is challenging under these conditions. In this study, various reported retrieval settings for formaldehyde (HCHO) and sulfur dioxide (SO₂) are compared to evaluate the performance of these settings under different meteorological conditions (clear day, haze, and fog). The dataset from 1st December 2019 to 31st March 2020 over Nanjing, China, is used in this study. The results indicated that for HCHO, the optimal settings were in the 324.5–359 nm wavelength window with a polynomial order of five. At these settings, the fitting and root mean squared (RMS) errors for column density were considerably improved for haze and fog conditions, and the differential slant column densities (DSCDs) showed more accurate values compared to the DSCDs between 336.5 and 359 nm. For SO₂, the optimal settings for retrieval were found to be at 307–328 nm with a polynomial order of five. Here, root mean square (RMS) and fitting errors were significantly lower under all conditions. The observed HCHO and SO₂ vertical column densities were significantly lower on fog days compared to clear days, reflecting a decreased chemical production of HCHO and aqueous phase oxidation of SO₂ in fog droplets. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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17 pages, 6916 KiB

Open AccessArticle

WRF-Chem Simulation for Modeling Seasonal Variations and Distributions of Aerosol Pollutants over the Middle East

by Muhammad Zeeshaan Shahid, Farrukh Chishtie, Muhammad Bilal and Imran Shahid

Remote Sens. 2021, 13(11), 2112; https://doi.org/10.3390/rs13112112 - 27 May 2021

Cited by 10 | Viewed by 4477

Abstract

Atmospheric aerosols and dust have become a challenge for urban air quality. The presented study quantified seasonal spatio-temporal variations of aerosols, tropospheric ozone, and dust over the Middle East (ME) for the year 2012 by using the HTAP emission inventory in the WRF-Chem [...] Read more.

Atmospheric aerosols and dust have become a challenge for urban air quality. The presented study quantified seasonal spatio-temporal variations of aerosols, tropospheric ozone, and dust over the Middle East (ME) for the year 2012 by using the HTAP emission inventory in the WRF-Chem model. Simulated gaseous pollutants, aerosols and dust were evaluated against satellite measurements and reanalysis datasets. Meteorological parameters, temperature, and wind vector were evaluated against MERRA2. The model showed high spatio-temporal variability in meteorological parameters during summer and low variability in winter. The correlation coefficients for all the parameters are estimated to be 0.92, 0.93, 0.98, and 0.89 for January, April, July, and October respectively, indicating that the WRF-Chem model reproduced results very well. Simulated monthly mean AOD values were maximum in July (1.0–1.5) and minimum in January (0.1–0.4) while April and October were in the range of 0.6–1.0 and 0.3–0.7 respectively. Simulated dust concentrations were high in April and July. The monthly average aerosol concentration was highest over Bahrain, Kuwait, Qatar, and the United Arab Emirates and Jeddah, Makkah. The contributions to urban air pollution were highest over Makkah city with more than 25% from anthropogenic sources. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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16 pages, 8329 KiB

Open AccessArticle

Aerosol Trends during the Dusty Season over Iran

by Robabeh Yousefi, Fang Wang, Quansheng Ge, Jos Lelieveld and Abdallah Shaheen

Remote Sens. 2021, 13(6), 1045; https://doi.org/10.3390/rs13061045 - 10 Mar 2021

Cited by 16 | Viewed by 2685

Abstract

This study assessed the aerosol climatology over Iran, based on the monthly data of aerosol optical depth (AOD) derived from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2) and the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, sea level [...] Read more.

This study assessed the aerosol climatology over Iran, based on the monthly data of aerosol optical depth (AOD) derived from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2) and the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, sea level pressure, wind speed, temperature, relative humidity, precipitation, and soil moisture from the ERA5 reanalysis dataset were applied to investigate the climate-related effects on temporal AOD changes. Our analysis identified positive and negative AOD trends during 2000–2010 and 2010–2018, respectively, which are likely linked to aeolian dust changes. The dust-driven AOD trends were supported by changes in the Ångström exponent (AE) and fine mode fraction (FMF) of aerosols over Iran. During the early period (2000–2010), results of AOD-meteorology correlation analyses suggest reduced soil moisture, leading to increased dust emissions, whereas our results suggest that during the later period (2010–2018) an increase of soil moisture led to decreased AOD levels. Soil moisture appears to be a key factor in dust mobilization in the region, notably in southwestern Iran, being influenced by adjacent mineral dust sources. These phenomena were affected by large-scale sea level pressure transformations and the associated meteorology in the preceding winter seasons. Using a multiple linear regression model, AOD variability was linked to various meteorological factors in different regions. Our results suggest that climatic variations strongly affect the dust cycle, with a strong dependence on wintertime conditions in the region. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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15 pages, 3771 KiB

Open AccessArticle

Validation of GOSAT and OCO-2 against In Situ Aircraft Measurements and Comparison with CarbonTracker and GEOS-Chem over Qinhuangdao, China

by Farhan Mustafa, Huijuan Wang, Lingbing Bu, Qin Wang, Muhammad Shahzaman, Muhammad Bilal, Minqiang Zhou, Rashid Iqbal, Rana Waqar Aslam, Md. Arfan Ali and Zhongfeng Qiu

Remote Sens. 2021, 13(5), 899; https://doi.org/10.3390/rs13050899 - 27 Feb 2021

Cited by 31 | Viewed by 6156

Abstract

Carbon dioxide (CO₂) is the most important greenhouse gas and several satellites have been launched to monitor the atmospheric CO₂ at regional and global scales. Evaluation of the measurements obtained from these satellites against accurate and precise instruments is crucial. [...] Read more.

Carbon dioxide (CO₂) is the most important greenhouse gas and several satellites have been launched to monitor the atmospheric CO₂ at regional and global scales. Evaluation of the measurements obtained from these satellites against accurate and precise instruments is crucial. In this work, aircraft measurements of CO₂ were carried out over Qinhuangdao, China (39.9354°N, 119.6005°E), on 14, 16, and 19 March 2019 to validate the Greenhous gases Observing SATellite (GOSAT) and the Orbiting Carbon Observatory 2 (OCO-2) CO₂ retrievals. The airborne in situ instruments were mounted on a research aircraft and the measurements were carried out between the altitudes of ~0.5 and 8.0 km to obtain the vertical profiles of CO₂. The profiles captured a decrease in CO₂ concentration from the surface to maximum altitude. Moreover, the vertical profiles from GEOS-Chem and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker were also compared with in situ and satellite datasets. The satellite and the model datasets captured the vertical structure of CO₂ when compared with in situ measurements, which showed good agreement among the datasets. The dry-air column-averaged CO₂ mole fractions (XCO₂) retrieved from OCO-2 and GOSAT showed biases of 1.33 ppm (0.32%) and −1.70 ppm (−0.41%), respectively, relative to the XCO₂ derived from in situ measurements. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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27 pages, 14010 KiB

Open AccessArticle

Spatiotemporal Investigations of Multi-Sensor Air Pollution Data over Bangladesh during COVID-19 Lockdown

by Zhongfeng Qiu, Md. Arfan Ali, Janet E. Nichol, Muhammad Bilal, Pravash Tiwari, Birhanu Asmerom Habtemicheal, Mansour Almazroui, Sanjit Kumar Mondal, Usman Mazhar, Yu Wang, Sajib Sarker, Farhan Mustafa and Muhammad Ashfaqur Rahman

Remote Sens. 2021, 13(5), 877; https://doi.org/10.3390/rs13050877 - 26 Feb 2021

Cited by 36 | Viewed by 6909

Abstract

This study investigates spatiotemporal changes in air pollution (particulate as well as gases) during the COVID-19 lockdown period over major cities of Bangladesh. The study investigated the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites, [...] Read more.

This study investigates spatiotemporal changes in air pollution (particulate as well as gases) during the COVID-19 lockdown period over major cities of Bangladesh. The study investigated the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites, PM_2.5 and PM₁₀ from Copernicus Atmosphere Monitoring Service (CAMS), and NO₂ and O₃ from TROPOMI-5P, from March to June 2019–2020. Additionally, aerosol subtypes from the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) were used to explore the aerosol types. The strict lockdown (26 March–30 May 2020) led to a significant reduction in AOD (up to 47%) in all major cities, while the partial lockdown (June 2020) led to increased and decreased AOD over the study area. Significant reductions in PM_2.5 (37–77%) and PM₁₀ (33–70%) were also observed throughout the country during the strict lockdown and partial lockdown. The NO₂ levels decreased by 3–25% in March 2020 in the cities of Rajshahi, Chattogram, Sylhet, Khulna, Barisal, and Mymensingh, in April by 3–43% in Dhaka, Chattogram, Khulna, Barisal, Bhola, and Mymensingh, and May by 12–42% in Rajshahi, Sylhet, Mymensingh, and Rangpur. During the partial lockdown in June, NO₂ decreased (9–35%) in Dhaka, Chattogram, Sylhet, Khulna, Barisal, and Rangpur compared to 2019. On the other hand, increases were observed in ozone (O₃₎ levels, with an average increase of 3–12% throughout the country during the strict lockdown and only a slight reduction of 1–3% in O₃ during the partial lockdown. In terms of aerosol types, CALIPSO observed high levels of polluted dust followed by dust, smoke, polluted continental, and clean marine-type aerosols over the country in 2019, but all types were decreased during the lockdown. The study concludes that the strict lockdown measures were able to significantly improve air quality conditions over Bangladesh due to the shutdown of industries, vehicles, and movement of people. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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27 pages, 32026 KiB

Open AccessArticle

Interdecadal Changes in Aerosol Optical Depth over Pakistan Based on the MERRA-2 Reanalysis Data during 1980–2018

by Rehana Khan, Kanike Raghavendra Kumar, Tianliang Zhao, Waheed Ullah and Gerrit de Leeuw

Remote Sens. 2021, 13(4), 822; https://doi.org/10.3390/rs13040822 - 23 Feb 2021

Cited by 26 | Viewed by 3895

Abstract

The spatiotemporal evolution and trends in aerosol optical depth (AOD) over environmentally distinct regions in Pakistan are investigated for the period 1980–2018. The AOD data for this period was obtained from the Modern-era retrospective analysis for research and applications, version 2 (MERRA-2) reanalysis [...] Read more.

The spatiotemporal evolution and trends in aerosol optical depth (AOD) over environmentally distinct regions in Pakistan are investigated for the period 1980–2018. The AOD data for this period was obtained from the Modern-era retrospective analysis for research and applications, version 2 (MERRA-2) reanalysis atmospheric products, together with the Moderate-resolution imaging spectroradiometer (MODIS) retrievals. The climatology of AOD_MERRA-2 is analyzed in three different contexts: the entire study domain (Pakistan), six regions within the domain, and 12 cities chosen from the entire study domain. The time-series analysis of the MODIS and MERRA-2 AOD data shows similar patterns in individual cities. The AOD and its seasonality vary strongly across Pakistan, with the lowest (0.05 ± 0.04) and highest (0.40 ± 0.06) in the autumn and summer seasons over the desert and the coastal regions, respectively. During the study period, the annual AOD trend increased between 0.002 and 0.012 year⁻¹. The increase of AOD is attributed to an increase in population and emissions from natural and/or anthropogenic sources. A general increase in the annual AOD over the central to lower Indus Basin is ascribed to the large contribution of dust particles from the desert. During winter and spring, a significant decrease in the AOD was observed in the northern regions of Pakistan. The MERRA-2 and MODIS trends (2002–2018) were compared, and the results show visible differences between the AOD datasets due to theuseof different versions and collection methods. Overall, the present study provides insight into the regional differences of AOD and its trends with the pronounced seasonal behavior across Pakistan. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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17 pages, 4885 KiB

Open AccessArticle

Inferring Near-Surface PM_2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model

by Wenhao Xue, Jing Wei, Jing Zhang, Lin Sun, Yunfei Che, Mengfei Yuan and Xiaomin Hu

Remote Sens. 2021, 13(3), 505; https://doi.org/10.3390/rs13030505 - 31 Jan 2021

Cited by 15 | Viewed by 3256

Abstract

Much of the population is exposed to PM_2.5 (particulate matter) pollution in China, and establishing a high-precision PM_2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, [...] Read more.

Much of the population is exposed to PM_2.5 (particulate matter) pollution in China, and establishing a high-precision PM_2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM_2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM_2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM_2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R²), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m⁻³ and 8.25 (8.78) μg m⁻³, respectively. The monthly, seasonal, and annual mean PM_2.5 were also successfully captured (CV-R² = 0.91–0.92, RMSE = 4.35–6.72 μg m⁻³). Then, the spatial characteristics of PM_2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM_2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m⁻³). In addition, 19 provinces or administrative regions have annual PM_2.5 concentrations >35 μg m⁻³, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m⁻³), Tianjin (~49.65 μg m⁻³), and Henan Province (~48.60 μg m⁻³). Our estimated surface PM_2.5 concentrations are accurate, which could benefit further research on air pollution in China. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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18 pages, 5677 KiB

Open AccessArticle

Ground-Based MAX-DOAS Observations of Tropospheric NO₂ and HCHO During COVID-19 Lockdown and Spring Festival Over Shanghai, China

by Aimon Tanvir, Zeeshan Javed, Zhu Jian, Sanbao Zhang, Muhammad Bilal, Ruibin Xue, Shanshan Wang and Zhou Bin

Remote Sens. 2021, 13(3), 488; https://doi.org/10.3390/rs13030488 - 30 Jan 2021

Cited by 30 | Viewed by 5590

Abstract

Reduced mobility and less anthropogenic activity under special case circumstances over various parts of the world have pronounced effects on air quality. The objective of this study is to investigate the impact of reduced anthropogenic activity on air quality in the mega city [...] Read more.

Reduced mobility and less anthropogenic activity under special case circumstances over various parts of the world have pronounced effects on air quality. The objective of this study is to investigate the impact of reduced anthropogenic activity on air quality in the mega city of Shanghai, China. Observations from the highly sophisticated multi-axis differential optical absorption spectroscope (MAX-DOAS) instrument were used for nitrogen dioxide (NO₂) and formaldehyde (HCHO) column densities. In situ measurements for NO₂, ozone (O₃), particulate matter (PM_2.5) and the air quality index (AQI) were also used. The concentration of trace gases in the atmosphere reduces significantly during annual Spring Festival holidays, whereby mobility is reduced and anthropogenic activities come to a halt. The COVID-19 lockdown during 2020 resulted in a considerable drop in vertical column densities (VCDs) of HCHO and NO₂ during lockdown Level-1, which refers to strict lockdown, i.e., strict measures taken to reduce mobility (43% for NO₂; 24% for HCHO), and lockdown Level-2, which refers to relaxed lockdown, i.e., when the mobility restrictions were relaxed somehow (20% for NO₂; 22% for HCHO), compared with pre-lockdown days, as measured by the MAX-DOAS instrument. However, for 2019, a reduction in VCDs was found only during Level-1 (24% for NO₂; 6.62% for HCHO), when the Spring Festival happened. The weekly cycle for NO₂ and HCHO depicts no significant effect of weekends on the lockdown. After the start of the Spring Festival, the VCDs of NO₂ and HCHO showed a decline for 2019 as well as 2020. Backward trajectories calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicated more air masses coming from the sea after the Spring Festival for 2019 and 2020, implying that a low pollutant load was carried by them. No impact of anthropogenic activity was found on O₃ concentration. The results indicate that the ratio of HCHO to NO₂ (RFN) fell in the volatile organic compound (VOC)-limited regime. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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16 pages, 5287 KiB

Open AccessArticle

Solar Brightening/Dimming over China’s Mainland: Effects of Atmospheric Aerosols, Anthropogenic Emissions, and Meteorological Conditions

by Hejin Fang, Wenmin Qin, Lunche Wang, Ming Zhang and Xuefang Yang

Remote Sens. 2021, 13(1), 88; https://doi.org/10.3390/rs13010088 - 29 Dec 2020

Cited by 18 | Viewed by 3222

Abstract

Surface solar radiation (SSR) is the main factor affecting the earth’s climate and environment and its variations and the reason for these variations are an important part of climate change research. In this research, we investigated the long-term variations of SSR during 1984–2016 [...] Read more.

Surface solar radiation (SSR) is the main factor affecting the earth’s climate and environment and its variations and the reason for these variations are an important part of climate change research. In this research, we investigated the long-term variations of SSR during 1984–2016 and the quantitative influences of atmospheric aerosols, anthropogenic emissions, and meteorological conditions on SSR over China’s mainland. The results show the following: (1) The annual average SSR values had a decline trend at a rate of −0.371 Wm⁻² yr⁻¹ from 1984 to 2016 over China. (2) The aerosol optical depth (AOD) plays the main role in inducing variations in SSR over China, with r values of −0.75. Moreover, there are marked regional differences in the influence of anthropogenic emissions and meteorological conditions on SSR trends. (3) From a regional perspective, AOD is the main influencing factor on SSR in northeast China (NEC), Yunnan Plateau and surrounding regions (YPS), North China (NC), and Loess Plateau (LP), with r values of −0.65, −0.60, −0.89, and −0.50, respectively. However, the main driving factors for SSR in northwest China (NWC) are “in cloud optical thickness of all clouds” (TAUTOT) (−0.26) and black carbon (BC) anthropogenic emissions (−0.21). TAUTOT (−0.39) and total precipitable water vapor (TQV) (−0.29) are the main influencing factors of SSR in the middle-lower Yangtze Plain (MYP). The main factors that influence SSR in southern China (SC) are surface pressure (PS) (−0.66) and AOD (−0.43). This research provides insights in understanding the variations of SSR and its relationships with anthropogenic conditions and meteorological factors. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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16 pages, 2636 KiB

Open AccessArticle

Spatio-Temporal Characteristics of PM_2.5, PM_10, and AOD over Canal Head Taocha Station, Henan Province

by Miao Zhang, Dongyu Wu, Bo Su, Muhammad Bilal, Yuying Li and B. Larry Li

Remote Sens. 2020, 12(20), 3432; https://doi.org/10.3390/rs12203432 - 19 Oct 2020

Cited by 6 | Viewed by 2786

Abstract

In this study, spatio-temporal characteristics of particulate matter (PMx; x = 2.5 and 10) mass concentrations and aerosol optical properties were analyzed over the water source area of the South–North Water Diversion Central Line. For this purpose, PM_2.5 and PM₁₀ mass [...] Read more.

In this study, spatio-temporal characteristics of particulate matter (PMx; x = 2.5 and 10) mass concentrations and aerosol optical properties were analyzed over the water source area of the South–North Water Diversion Central Line. For this purpose, PM_2.5 and PM₁₀ mass concentrations were collected at the Taocha(TC)station from October 2018 to September 2019, and aerosol optical depth (AOD) was obtained from the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite from 2007 to 2019. The monthly, seasonal, and daily statistical analyses and related comparisons were conducted in the present study. The results showed that the PM₁₀ concentrations meet China’s ambient air secondary quality standard (100 μg/m³ annual mean), whereas PM_2.5 did not meet China’s ambient air secondary quality standard (35 μg/m³ annual mean) at the TC station, no obvious seasonal and diurnal variations are observed, and these particulates are caused by local emissions and outside sources. A significant positive correlation of PM_2.5 and PM₁₀ was observed with relative humidity and temperature, whereas no relationship was found with wind direction. The results also showed low (~0.1) AOD in spring, autumn, and winter, whereas slightly higher AOD (~0.3) was observed in summer. This may be caused by straw burning from long-distance transportation. This study may provide new data support for comprehensive ecological measures such as strengthening the ecological environment and water quality protection in the Middle Route Project of the South–North Water Diversion. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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14 pages, 1670 KiB

Open AccessTechnical Note

Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas

by Muhammad Bilal, Alaa Mhawish, Md. Arfan Ali, Janet E. Nichol, Gerrit de Leeuw, Khaled Mohamed Khedher, Usman Mazhar, Zhongfeng Qiu, Max P. Bleiweiss and Majid Nazeer

Remote Sens. 2022, 14(2), 373; https://doi.org/10.3390/rs14020373 - 13 Jan 2022

Cited by 16 | Viewed by 3325

Abstract

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m [...] Read more.

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and −0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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12 pages, 3928 KiB

Open AccessTechnical Note

Hourly Ground-Level PM_2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

by Changsuk Lee, Kyunghwa Lee, Sangmin Kim, Jinhyeok Yu, Seungtaek Jeong and Jongmin Yeom

Remote Sens. 2021, 13(11), 2121; https://doi.org/10.3390/rs13112121 - 28 May 2021

Cited by 18 | Viewed by 3472

Abstract

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM_2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data [...] Read more.

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM_2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM_2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m³, mean bias error (MBE) = −0.340 μg/m³, and coefficient of determination (R²) = 0.698) and the cross-validation (RMSE = 9.166 μg/m³, MBE = 0.293 μg/m³, and R² = 0.49). Although the R² was low due to underestimated high PM_2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m³ and 1 μg/m³, respectively) for the hold-out validation and cross-validation. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosols over Asia: Methods and Applications)

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