Luqman et al. (2021) Air pollution scenario over Pakistan: characterization and ranking of extrem... more Luqman et al. (2021) Air pollution scenario over Pakistan: characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases. Remote Sensing of Environment, 264. a112617 1-28.
Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysi... more Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic
This chapter depicts the state of the art in remote sensing for urban pollution monitoring, inclu... more This chapter depicts the state of the art in remote sensing for urban pollution monitoring, including urban heat islands, urban air quality, and water quality around urban coastlines. Recent developments in spatial and temporal resolutions of modern sensors, and in retrieval methodologies and gap-filling routines, have increased the applicability of remote sensing for urban areas. However, capturing the spatial heterogeneity of urban areas is still challenging, given the spatial resolution limitations of aerosol retrieval algorithms for air-quality monitoring, and of modern thermal sensors for urban heat island analysis. For urban coastal applications, water-quality parameters can now be retrieved with adequate spatial and temporal detail even for localized phenomena such as algal blooms, pollution plumes, and point pollution sources. The chapter reviews the main sensors used, and developments in retrieval algorithms. For urban air quality the MODIS Dark Target (DT), Deep Blue (DB),...
Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysi... more Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) data from 2000 to 2018. For evaluation purposes, (i) the SREM SR retrievals are validated against in situ SR measurements collected by Analytical Spectral Devices (ASD) from the South Dakota State University (SDSU) site, USA; (ii) cross-comparison betw...
In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Da... more In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Dark Target (DT) and Deep Blue (DB) algorithms provide aerosol optical depth (AOD) observations at 3 km (DT 3K) and 10 km (DT 10K), and at 10 km resolution (DB 10K), respectively. In this study, the DB 10K is resampled to 3 km grid (DB 3K) using the nearest neighbor interpolation technique and merged with DT 3K to generate a new DT and DB merged aerosol product (DTB 3K) on a 3 km grid using Simplified Merge Scheme (SMS). The goal is to supplement DB 10K with high-resolution information over dense vegetation regions where DT 3K is susceptible to error. SMS is defined as "an average of the DT 3K and DB 3K AOD retrievals or the available one with the highest quality flag". The DT 3K and DTB 3K AOD retrievals are validated from 2008 to 2012 against cloud-screened and quality-assured AOD from 19 AERONET sites located in Europe. Results show that the percentage of DTB 3K retrievals within the expected error (EE = ± (0.05 + 20%)) and data counts are increased by 40% and 11%, respectively, and the root mean square error and the mean bias are decreased by 26% and 54%, respectively, compared to the DT 3K retrievals. These results suggest that the DTB 3K product is a robust improvement over DT 3K alone, and can be used operationally for air quality and climate-related studies as a high-resolution supplement to the current MODIS product suite.
In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 me... more In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 merged Dark Target (DT) and Deep Blue (DB) aerosol products (DTB C6 and DTB C6.1) at 0.55 µm were validated from 2004-2014 against Aerosol Robotic Network (AERONET) Version 2 Level 2.0 AOD obtained from 68 global sites located over diverse vegetated surfaces. These surfaces were categorized by static values of monthly Normalized Difference Vegetation Index (NDVI) observations obtained for the same time period from the MODIS level-3 monthly NDVI product (MOD13A3), i.e., partially/non-vegetated (NDVI P ≤ 0.3), moderately-vegetated (0.3 < NDVI M ≤ 0.5) and densely-vegetated (NDVI D > 0.5) surfaces. The DTB C6 and DTB C6.1 AOD products are accomplished by the NDVI criteria: (i) use the DT AOD retrievals for NDVI > 0.3, (ii) use the DB AOD retrievals for NDVI < 0.2, and (iii) use an average of the DT and DB AOD retrievals or the available one with highest quality assurance flag (DT: QAF = 3; DB: QAF ≥ 2) for 0.2 ≤ NDVI ≤ 0.3. For comparison purpose, the DTB SMS AOD retrievals were included which were accomplished using the Simplified Merge Scheme, i.e., use an average of the DT C6.1 and DB C6.1 AOD retrievals or the available one for all the NDVI values. For NDVI P surfaces, results showed that the DTB C6 and DTB C6.1 AOD retrievals performed poorly over North and South America in terms of the agreement with AERONET AOD, and over Asian region in terms of retrievals quality as the small percentage of AOD retrievals were within the expected error (EE = ± (0.05 + 0.15 × AOD). For NDVI M surfaces, retrieval errors and poor quality in DTB C6 and DTB C6.1 were observed for Asian, North American and South American sites, whereas good performance, was observed for European and African sites. For NDVI D surfaces, DTB C6 does not perform well over the Asian and North American sites, although it contains retrievals only from the DT algorithm which was developed for dark surfaces. Overall, the performance of the DTB C6.1 AOD retrievals was significantly improved compared to the DTB C6 , but still more improvements are required over NDVI P , NDVI M and NDVI D surfaces of Asia, NDVI M and NDVI D surfaces of North America , and NDVI M surfaces of South America. The performance of the DTB SMS retrievals was better than the DTB C6 and DTB C6.1 retrievals with 11-13% (31%) greater number of coincident observations, 6-9% (14-22%) greater percentage of retrievals within the EE, and 30-100% (46-100%) smaller relative mean bias compared to the DTB C6.1 (DTB C6) at a global scale.
Journal of Geophysical Research: Atmospheres, 2015
This study evaluates the performance of different MODerate resolution Imaging Spectroradiometer (... more This study evaluates the performance of different MODerate resolution Imaging Spectroradiometer (MODIS) aerosol algorithms during fine particle pollution events over the Beijing-Tianjin-Hebei region using Aerosol Robotic Network aerosol optical depth (AOD). These algorithms include the Deep Blue (DB) Collection 5.1 (C5) and Collection 6 (C6) algorithms at 10 km resolution, the Dark Target (DT) C5 and C6 algorithms at 10 km, the DT C6 algorithm at 3 km, and the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m, 3 km, and 10 km resolutions. The DB C6 retrievals have 34-39% less uncertainties, 2-3 times smaller root-mean-square error (RMSE), and 3-4 times smaller mean absolute error (MAE) than DB C5 retrievals. The DT C6 has 4-8% lower bias, 4-12% less overestimation, and smaller RMSE and MAE errors than DT C5. Due to underestimation of surface reflectance and the use of inappropriate aerosol schemes, 87-89% of the collocations of the DT C6 at 3 km fall above the expected error (EE), with overestimation of 64-79% which is 15-27% higher than that for the DT C6 at 10 km. The results suggest that the DT C6 at 3 km resolution is less reliable than that at 10 km. The SARA AOD has small RMSE and MAE errors with 90-96% of the collocations falling within the EE. Overall, the SARA showed 15-16% less uncertainty than the DB C6 (10 km), 69-72% less than the DT C6 (10 km), and 79-83% less than the DT C6 (3 km) retrievals.
ABSTRACT An effective Simplified Aerosol Retrieval Algorithm (SARA) based on real viewing geometr... more ABSTRACT An effective Simplified Aerosol Retrieval Algorithm (SARA) based on real viewing geometry and encompassing a wide range of aerosol conditions and types (ωo = 0.30–1.0) was tested over Beijing, a city under the influence of both anthropogenic emissions and dust storms. The SARA AOD was retrieved at seven resolutions (500 m, 1 km, 2 km, 4 km, 6 km, 8 km, and 10 km) using MODIS level 1 and 2 data products (MOD02, MOD03, and MOD09) for the years 2012 and 2013. For comparison purposes, the MODIS dark-target (DT) AOD observations at 10 km resolution were obtained for the same time period. Both algorithms, SARA and MODIS DT were validated using Beijing_RADI and Beijing_CAMS AERONET AOD measurements and accuracy was evaluated using the Confidence Envelope of Expected Error (CEEE). The SARA AOD achieved very high correlation (0.972–0.994) with low Root Mean Square Error (RMSE ~ 0.067–0.133) and very high accuracy (87–95%). In comparison, the MODIS DT algorithm overestimated AOD for both low and high AOD observations with large RMSE (0.115–0.342) and very low accuracy (20–52%). The robustness and accuracy of SARA and the MODIS DT algorithms for low (AOD &lt; 0.40) and high (AOD &gt; 0.40) aerosol loadings was evaluated using the Fraction of Expected Error (FEE). The SARA algorithm achieved 46–60% higher average accuracy than the MODIS DT algorithm indicating SARA&#39;s greater accuracy and reliability in AOD retrieval over Beijing under low and high aerosol loadings, including severe dust storms. The SARA algorithm is unique among satellite based AOD retrievals, in its ability to depict extreme dust storms (AOD ~ 5.0) at fine spatial resolution (500 m) over urban and suburban areas such as Beijing, XiangHe and the Bohai Sea, as is demonstrated by the dust storm of 17th April 2006.
This article appeared in a journal published by Elsevier. The attached copy is furnished to the a... more This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are
This study evaluates the new Aqua MODIS Dark Target (DT) Collection 6 (C6) Aerosol Optical Depth ... more This study evaluates the new Aqua MODIS Dark Target (DT) Collection 6 (C6) Aerosol Optical Depth (AOD) (MYD04_3K) retrieval algorithm at 3 km resolution over Asian countries that have recently experienced severe and increasing air pollution. Retrievals showed generally low accuracy compared with the AErosol RObotic NETwork (AERONET), with only 55% of retrievals within the expected error (EE). The uncertainty appears mainly due to systematic overestimation at both low and high AOD levels. This is attributed to under-prediction of surface reflectance, similar to, but more severe than, the C6 DT product at 10-km resolution. This is because MYD04_3K observes more noise in the surface reflectance computations, due to retention of some bright pixels in the retrieval window which would be discarded at 10 km. Greatest uncertainty was observed at urban sites, especially those dominated by coarse aerosols. Results suggest that the DT at 3 km is less reliable than MODIS C6 AOD products at 10 km.
The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 sho... more The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 should have reduced pollution in smog-laden cities. However, moderate resolution imaging spectrometer (MODIS) satellite retrievals of aerosol optical depth (AOD) show a marked increase in aerosols over the Beijing–Tianjin–Hebei (BHT) region and most of Northeast and Central China, compared with the previous winter. Fine particulate (PM2.5) data from ground monitoring stations show an increase of 19.5% in Beijing during January and February 2020, and no reduction for Tianjin. In March and April 2020, a different spatial pattern emerges, with very high AOD levels observed over 50% of the Chinese mainland, and including peripheral regions in the northwest and southwest. At the same time, ozone monitoring instrument (OMI) satellite-derived NO2 concentrations fell drastically across China. The increase in PM2.5 while NO2 decreased in BTH and across China is likely due to enhanced production of s...
Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there h... more Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there has been a continuous effort to produce reliable ocean color products by taking the advantages of its medium spatial resolution (30 m) and higher Signal to Noise Ratio (SNR). A Provisional Aquatic Reflectance product for the L8 OLI (L8PAR) has been recently released to the public to explore its potential for ocean color applications. This study used a six-year data record of L8 for development of a regionally tuned algorithm (RTA20) for estimating Chlorophyll-a (Chl-a) concentrations around the complex coastal environment of Hong Kong, and is the first to report the usability of the L8PAR product for coastal areas. Furthermore, this study validated three previously developed algorithms, namely RTA16, RTA17 and RTA19, and two ocean color algorithms (OC2 and OC3) modified for L8 OLI by NASA’s Ocean Color group. Results indicate that the newly released L8PAR product has a high potential for ...
Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there h... more Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there has been a continuous effort to produce reliable ocean color products by taking the advantages of its medium spatial resolution (30 m) and higher Signal to Noise Ratio (SNR). A Provisional Aquatic Reflectance product for the L8 OLI (L8PAR) has been recently released to the public to explore its potential for ocean color applications. This study used a six-year data record of L8 for development of a regionally tuned algorithm (RTA20) for estimating Chlorophyll-a (Chl-a) concentrations around the complex coastal environment of Hong Kong, and is the first to report the usability of the L8PAR product for coastal areas. Furthermore, this study validated three previously developed algorithms, namely RTA16, RTA17 and RTA19, and two ocean color algorithms (OC2 and OC3) modified for L8 OLI by NASA’s Ocean Color group. Results indicate that the newly released L8PAR product has a high potential for ...
Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aeros... more Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern- Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with groundbased PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for 2 tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan's entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/m3, over all Pakistan from 2003 to 2020. This value exceeds Pakistan's National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/m3 annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 < 35 μg/m3). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan's air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~ 88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.
Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysi... more Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic
In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 me... more In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 merged Dark Target (DT) and Deep Blue (DB) aerosol products (DTB C6 and DTB C6.1) at 0.55 µm were validated from 2004–2014 against Aerosol Robotic Network (AERONET) Version 2 Level 2.0 AOD obtained from 68 global sites located over diverse vegetated surfaces. These surfaces were categorized by static values of monthly Normalized Difference Vegetation Index (NDVI) observations obtained for the same time period from the MODIS level-3 monthly NDVI product (MOD13A3), i.e., partially/non–vegetated (NDVI P ≤ 0.3), moderately–vegetated (0.3 < NDVI M ≤ 0.5) and densely–vegetated (NDVI D > 0.5) surfaces. The DTB C6 and DTB C6.1 AOD products are accomplished by the NDVI criteria: (i) use the DT AOD retrievals for NDVI > 0.3, (ii) use the DB AOD retrievals for NDVI < 0.2, and (iii) use an average of the DT and DB AOD retrievals or the available one with highest quality assurance flag (DT: QAF = 3; DB: QAF ≥ 2) for 0.2 ≤ NDVI ≤ 0.3. For comparison purpose, the DTB SMS AOD retrievals were included which were accomplished using the Simplified Merge Scheme, i.e., use an average of the DT C6.1 and DB C6.1 AOD retrievals or the available one for all the NDVI values. For NDVI P surfaces, results showed that the DTB C6 and DTB C6.1 AOD retrievals performed poorly over North and South America in terms of the agreement with AERONET AOD, and over Asian region in terms of retrievals quality as the small percentage of AOD retrievals were within the expected error (EE = ± (0.05 + 0.15 × AOD). For NDVI M surfaces, retrieval errors and poor quality in DTB C6 and DTB C6.1 were observed for Asian, North American and South American sites, whereas good performance, was observed for European and African sites. For NDVI D surfaces, DTB C6 does not perform well over the Asian and North American sites, although it contains retrievals only from the DT algorithm which was developed for dark surfaces. Overall, the performance of the DTB C6.1 AOD retrievals was significantly improved compared to the DTB C6 , but still more improvements are required over NDVI P , NDVI M and NDVI D surfaces of Asia, NDVI M and NDVI D surfaces of North America , and NDVI M surfaces of South America. The performance of the DTB SMS retrievals was better than the DTB C6 and DTB C6.1 retrievals with 11–13% (31%) greater number of coincident observations, 6–9% (14–22%) greater percentage of retrievals within the EE, and 30–100% (46–100%) smaller relative mean bias compared to the DTB C6.1 (DTB C6) at a global scale.
In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Da... more In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Dark Target (DT) and Deep Blue (DB) algorithms provide aerosol optical depth (AOD) observations at 3 km (DT 3K) and 10 km (DT 10K), and at 10 km resolution (DB 10K), respectively. In this study, the DB 10K is resampled to 3 km grid (DB 3K) using the nearest neighbor interpolation technique and merged with DT 3K to generate a new DT and DB merged aerosol product (DTB 3K) on a 3 km grid using Simplified Merge Scheme (SMS). The goal is to supplement DB 10K with high-resolution information over dense vegetation regions where DT 3K is susceptible to error. SMS is defined as " an average of the DT 3K and DB 3K AOD retrievals or the available one with the highest quality flag ". The DT 3K and DTB 3K AOD retrievals are validated from 2008 to 2012 against cloud-screened and quality-assured AOD from 19 AERONET sites located in Europe. Results show that the percentage of DTB 3K retrievals within the expected error (EE = ± (0.05 + 20%)) and data counts are increased by 40% and 11%, respectively, and the root mean square error and the mean bias are decreased by 26% and 54%, respectively, compared to the DT 3K retrievals. These results suggest that the DTB 3K product is a robust improvement over DT 3K alone, and can be used operationally for air quality and climate-related studies as a high-resolution supplement to the current MODIS product suite.
The Moderate resolution and Imaging Spectroradiometer (MODIS) level 2 operational aerosol product... more The Moderate resolution and Imaging Spectroradiometer (MODIS) level 2 operational aerosol products (MOD04/ MYD04), based on the Dark Target (DT) and the enhanced Deep Blue (DB) algorithms, have been providing daily global aerosol information. The MOD04/MYD04 has different data coverage for the DT and the DB algorithms due to differences in their retrieval methods. Recently in the collection 6 (C6), a DT and DB merged (DTB C6) AOD product has been introduced based on different thresholds of the Normalized Difference Vegetation Index (NDVI) to improve the data coverage. The main objective of this study is to increase the data coverage and reduce the error in the merged DTB C6 AOD product by introducing three new customized methods (DTB MX): (i) DTB M1 : " use an average of the DT and DB AOD retrievals or the available one for all the NDVI values " , as it is independent of the NDVI values, (ii) DTB M2 : " use an average of the DT and DB AOD retrievals or the available one for NDVI ≥ 0.2, and use the DB retrievals for NDVI b 0.2 " , and (iii) DTB M3 : " use AOD retrievals from the DB algorithm for NDVI N 0.3, and use an average of the DT and DB retrievals or the available one for NDVI ≤ 0.3 ". Validation of the DTB MX is conducted at a global scale from 2004 to 2014 using AERONET AOD measurements from 68 sites located in Asia (9), Europe (22), Southern Africa (8), and North (23) and South America (8), and for comparison purpose, the DTB C6 is evaluated for the same period. Results showed that the number of coincident observations of the DTB MX methods compared to the DTB C6 is increased by 31%, 41% 30%, and 108% for Asian, European, Southern African, and North and South American sites, respectively. At global scale, the number of coincident observations are increased for the DTB M1 , DTB M2 , and DTB M3 from 29,088 for DTB C6 , to 45,937, 45,028, 37,393 which are 58%, 57%, and 29%, respectively more than the for DTB C6 observations. For an equal number of coincident observations, the percentage of retrievals within the EE is increased by between 17% to 20% and the RMSE is decreased by up to 15% for the DTB MX methods, but R is the same as for the DTB C6. The percentage above the EE is also decreased by between 43% to 55% due to greater contribution of the DB retrievals. Overall, the performance of all the DTB MX methods is much better than the DTB C6 , but the DTB M1 is the most robust as it is independent of NDVI values, and significantly increases the data coverage. Therefore, it can be used operationally for global merged AOD retrievals.
Luqman et al. (2021) Air pollution scenario over Pakistan: characterization and ranking of extrem... more Luqman et al. (2021) Air pollution scenario over Pakistan: characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases. Remote Sensing of Environment, 264. a112617 1-28.
Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysi... more Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic
This chapter depicts the state of the art in remote sensing for urban pollution monitoring, inclu... more This chapter depicts the state of the art in remote sensing for urban pollution monitoring, including urban heat islands, urban air quality, and water quality around urban coastlines. Recent developments in spatial and temporal resolutions of modern sensors, and in retrieval methodologies and gap-filling routines, have increased the applicability of remote sensing for urban areas. However, capturing the spatial heterogeneity of urban areas is still challenging, given the spatial resolution limitations of aerosol retrieval algorithms for air-quality monitoring, and of modern thermal sensors for urban heat island analysis. For urban coastal applications, water-quality parameters can now be retrieved with adequate spatial and temporal detail even for localized phenomena such as algal blooms, pollution plumes, and point pollution sources. The chapter reviews the main sensors used, and developments in retrieval algorithms. For urban air quality the MODIS Dark Target (DT), Deep Blue (DB),...
Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysi... more Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) data from 2000 to 2018. For evaluation purposes, (i) the SREM SR retrievals are validated against in situ SR measurements collected by Analytical Spectral Devices (ASD) from the South Dakota State University (SDSU) site, USA; (ii) cross-comparison betw...
In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Da... more In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Dark Target (DT) and Deep Blue (DB) algorithms provide aerosol optical depth (AOD) observations at 3 km (DT 3K) and 10 km (DT 10K), and at 10 km resolution (DB 10K), respectively. In this study, the DB 10K is resampled to 3 km grid (DB 3K) using the nearest neighbor interpolation technique and merged with DT 3K to generate a new DT and DB merged aerosol product (DTB 3K) on a 3 km grid using Simplified Merge Scheme (SMS). The goal is to supplement DB 10K with high-resolution information over dense vegetation regions where DT 3K is susceptible to error. SMS is defined as "an average of the DT 3K and DB 3K AOD retrievals or the available one with the highest quality flag". The DT 3K and DTB 3K AOD retrievals are validated from 2008 to 2012 against cloud-screened and quality-assured AOD from 19 AERONET sites located in Europe. Results show that the percentage of DTB 3K retrievals within the expected error (EE = ± (0.05 + 20%)) and data counts are increased by 40% and 11%, respectively, and the root mean square error and the mean bias are decreased by 26% and 54%, respectively, compared to the DT 3K retrievals. These results suggest that the DTB 3K product is a robust improvement over DT 3K alone, and can be used operationally for air quality and climate-related studies as a high-resolution supplement to the current MODIS product suite.
In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 me... more In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 merged Dark Target (DT) and Deep Blue (DB) aerosol products (DTB C6 and DTB C6.1) at 0.55 µm were validated from 2004-2014 against Aerosol Robotic Network (AERONET) Version 2 Level 2.0 AOD obtained from 68 global sites located over diverse vegetated surfaces. These surfaces were categorized by static values of monthly Normalized Difference Vegetation Index (NDVI) observations obtained for the same time period from the MODIS level-3 monthly NDVI product (MOD13A3), i.e., partially/non-vegetated (NDVI P ≤ 0.3), moderately-vegetated (0.3 < NDVI M ≤ 0.5) and densely-vegetated (NDVI D > 0.5) surfaces. The DTB C6 and DTB C6.1 AOD products are accomplished by the NDVI criteria: (i) use the DT AOD retrievals for NDVI > 0.3, (ii) use the DB AOD retrievals for NDVI < 0.2, and (iii) use an average of the DT and DB AOD retrievals or the available one with highest quality assurance flag (DT: QAF = 3; DB: QAF ≥ 2) for 0.2 ≤ NDVI ≤ 0.3. For comparison purpose, the DTB SMS AOD retrievals were included which were accomplished using the Simplified Merge Scheme, i.e., use an average of the DT C6.1 and DB C6.1 AOD retrievals or the available one for all the NDVI values. For NDVI P surfaces, results showed that the DTB C6 and DTB C6.1 AOD retrievals performed poorly over North and South America in terms of the agreement with AERONET AOD, and over Asian region in terms of retrievals quality as the small percentage of AOD retrievals were within the expected error (EE = ± (0.05 + 0.15 × AOD). For NDVI M surfaces, retrieval errors and poor quality in DTB C6 and DTB C6.1 were observed for Asian, North American and South American sites, whereas good performance, was observed for European and African sites. For NDVI D surfaces, DTB C6 does not perform well over the Asian and North American sites, although it contains retrievals only from the DT algorithm which was developed for dark surfaces. Overall, the performance of the DTB C6.1 AOD retrievals was significantly improved compared to the DTB C6 , but still more improvements are required over NDVI P , NDVI M and NDVI D surfaces of Asia, NDVI M and NDVI D surfaces of North America , and NDVI M surfaces of South America. The performance of the DTB SMS retrievals was better than the DTB C6 and DTB C6.1 retrievals with 11-13% (31%) greater number of coincident observations, 6-9% (14-22%) greater percentage of retrievals within the EE, and 30-100% (46-100%) smaller relative mean bias compared to the DTB C6.1 (DTB C6) at a global scale.
Journal of Geophysical Research: Atmospheres, 2015
This study evaluates the performance of different MODerate resolution Imaging Spectroradiometer (... more This study evaluates the performance of different MODerate resolution Imaging Spectroradiometer (MODIS) aerosol algorithms during fine particle pollution events over the Beijing-Tianjin-Hebei region using Aerosol Robotic Network aerosol optical depth (AOD). These algorithms include the Deep Blue (DB) Collection 5.1 (C5) and Collection 6 (C6) algorithms at 10 km resolution, the Dark Target (DT) C5 and C6 algorithms at 10 km, the DT C6 algorithm at 3 km, and the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m, 3 km, and 10 km resolutions. The DB C6 retrievals have 34-39% less uncertainties, 2-3 times smaller root-mean-square error (RMSE), and 3-4 times smaller mean absolute error (MAE) than DB C5 retrievals. The DT C6 has 4-8% lower bias, 4-12% less overestimation, and smaller RMSE and MAE errors than DT C5. Due to underestimation of surface reflectance and the use of inappropriate aerosol schemes, 87-89% of the collocations of the DT C6 at 3 km fall above the expected error (EE), with overestimation of 64-79% which is 15-27% higher than that for the DT C6 at 10 km. The results suggest that the DT C6 at 3 km resolution is less reliable than that at 10 km. The SARA AOD has small RMSE and MAE errors with 90-96% of the collocations falling within the EE. Overall, the SARA showed 15-16% less uncertainty than the DB C6 (10 km), 69-72% less than the DT C6 (10 km), and 79-83% less than the DT C6 (3 km) retrievals.
ABSTRACT An effective Simplified Aerosol Retrieval Algorithm (SARA) based on real viewing geometr... more ABSTRACT An effective Simplified Aerosol Retrieval Algorithm (SARA) based on real viewing geometry and encompassing a wide range of aerosol conditions and types (ωo = 0.30–1.0) was tested over Beijing, a city under the influence of both anthropogenic emissions and dust storms. The SARA AOD was retrieved at seven resolutions (500 m, 1 km, 2 km, 4 km, 6 km, 8 km, and 10 km) using MODIS level 1 and 2 data products (MOD02, MOD03, and MOD09) for the years 2012 and 2013. For comparison purposes, the MODIS dark-target (DT) AOD observations at 10 km resolution were obtained for the same time period. Both algorithms, SARA and MODIS DT were validated using Beijing_RADI and Beijing_CAMS AERONET AOD measurements and accuracy was evaluated using the Confidence Envelope of Expected Error (CEEE). The SARA AOD achieved very high correlation (0.972–0.994) with low Root Mean Square Error (RMSE ~ 0.067–0.133) and very high accuracy (87–95%). In comparison, the MODIS DT algorithm overestimated AOD for both low and high AOD observations with large RMSE (0.115–0.342) and very low accuracy (20–52%). The robustness and accuracy of SARA and the MODIS DT algorithms for low (AOD &lt; 0.40) and high (AOD &gt; 0.40) aerosol loadings was evaluated using the Fraction of Expected Error (FEE). The SARA algorithm achieved 46–60% higher average accuracy than the MODIS DT algorithm indicating SARA&#39;s greater accuracy and reliability in AOD retrieval over Beijing under low and high aerosol loadings, including severe dust storms. The SARA algorithm is unique among satellite based AOD retrievals, in its ability to depict extreme dust storms (AOD ~ 5.0) at fine spatial resolution (500 m) over urban and suburban areas such as Beijing, XiangHe and the Bohai Sea, as is demonstrated by the dust storm of 17th April 2006.
This article appeared in a journal published by Elsevier. The attached copy is furnished to the a... more This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are
This study evaluates the new Aqua MODIS Dark Target (DT) Collection 6 (C6) Aerosol Optical Depth ... more This study evaluates the new Aqua MODIS Dark Target (DT) Collection 6 (C6) Aerosol Optical Depth (AOD) (MYD04_3K) retrieval algorithm at 3 km resolution over Asian countries that have recently experienced severe and increasing air pollution. Retrievals showed generally low accuracy compared with the AErosol RObotic NETwork (AERONET), with only 55% of retrievals within the expected error (EE). The uncertainty appears mainly due to systematic overestimation at both low and high AOD levels. This is attributed to under-prediction of surface reflectance, similar to, but more severe than, the C6 DT product at 10-km resolution. This is because MYD04_3K observes more noise in the surface reflectance computations, due to retention of some bright pixels in the retrieval window which would be discarded at 10 km. Greatest uncertainty was observed at urban sites, especially those dominated by coarse aerosols. Results suggest that the DT at 3 km is less reliable than MODIS C6 AOD products at 10 km.
The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 sho... more The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 should have reduced pollution in smog-laden cities. However, moderate resolution imaging spectrometer (MODIS) satellite retrievals of aerosol optical depth (AOD) show a marked increase in aerosols over the Beijing–Tianjin–Hebei (BHT) region and most of Northeast and Central China, compared with the previous winter. Fine particulate (PM2.5) data from ground monitoring stations show an increase of 19.5% in Beijing during January and February 2020, and no reduction for Tianjin. In March and April 2020, a different spatial pattern emerges, with very high AOD levels observed over 50% of the Chinese mainland, and including peripheral regions in the northwest and southwest. At the same time, ozone monitoring instrument (OMI) satellite-derived NO2 concentrations fell drastically across China. The increase in PM2.5 while NO2 decreased in BTH and across China is likely due to enhanced production of s...
Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there h... more Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there has been a continuous effort to produce reliable ocean color products by taking the advantages of its medium spatial resolution (30 m) and higher Signal to Noise Ratio (SNR). A Provisional Aquatic Reflectance product for the L8 OLI (L8PAR) has been recently released to the public to explore its potential for ocean color applications. This study used a six-year data record of L8 for development of a regionally tuned algorithm (RTA20) for estimating Chlorophyll-a (Chl-a) concentrations around the complex coastal environment of Hong Kong, and is the first to report the usability of the L8PAR product for coastal areas. Furthermore, this study validated three previously developed algorithms, namely RTA16, RTA17 and RTA19, and two ocean color algorithms (OC2 and OC3) modified for L8 OLI by NASA’s Ocean Color group. Results indicate that the newly released L8PAR product has a high potential for ...
Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there h... more Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on 11 February 2013, there has been a continuous effort to produce reliable ocean color products by taking the advantages of its medium spatial resolution (30 m) and higher Signal to Noise Ratio (SNR). A Provisional Aquatic Reflectance product for the L8 OLI (L8PAR) has been recently released to the public to explore its potential for ocean color applications. This study used a six-year data record of L8 for development of a regionally tuned algorithm (RTA20) for estimating Chlorophyll-a (Chl-a) concentrations around the complex coastal environment of Hong Kong, and is the first to report the usability of the L8PAR product for coastal areas. Furthermore, this study validated three previously developed algorithms, namely RTA16, RTA17 and RTA19, and two ocean color algorithms (OC2 and OC3) modified for L8 OLI by NASA’s Ocean Color group. Results indicate that the newly released L8PAR product has a high potential for ...
Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aeros... more Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern- Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with groundbased PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for 2 tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan's entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/m3, over all Pakistan from 2003 to 2020. This value exceeds Pakistan's National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/m3 annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 < 35 μg/m3). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan's air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~ 88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.
Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysi... more Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic
In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 me... more In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 merged Dark Target (DT) and Deep Blue (DB) aerosol products (DTB C6 and DTB C6.1) at 0.55 µm were validated from 2004–2014 against Aerosol Robotic Network (AERONET) Version 2 Level 2.0 AOD obtained from 68 global sites located over diverse vegetated surfaces. These surfaces were categorized by static values of monthly Normalized Difference Vegetation Index (NDVI) observations obtained for the same time period from the MODIS level-3 monthly NDVI product (MOD13A3), i.e., partially/non–vegetated (NDVI P ≤ 0.3), moderately–vegetated (0.3 < NDVI M ≤ 0.5) and densely–vegetated (NDVI D > 0.5) surfaces. The DTB C6 and DTB C6.1 AOD products are accomplished by the NDVI criteria: (i) use the DT AOD retrievals for NDVI > 0.3, (ii) use the DB AOD retrievals for NDVI < 0.2, and (iii) use an average of the DT and DB AOD retrievals or the available one with highest quality assurance flag (DT: QAF = 3; DB: QAF ≥ 2) for 0.2 ≤ NDVI ≤ 0.3. For comparison purpose, the DTB SMS AOD retrievals were included which were accomplished using the Simplified Merge Scheme, i.e., use an average of the DT C6.1 and DB C6.1 AOD retrievals or the available one for all the NDVI values. For NDVI P surfaces, results showed that the DTB C6 and DTB C6.1 AOD retrievals performed poorly over North and South America in terms of the agreement with AERONET AOD, and over Asian region in terms of retrievals quality as the small percentage of AOD retrievals were within the expected error (EE = ± (0.05 + 0.15 × AOD). For NDVI M surfaces, retrieval errors and poor quality in DTB C6 and DTB C6.1 were observed for Asian, North American and South American sites, whereas good performance, was observed for European and African sites. For NDVI D surfaces, DTB C6 does not perform well over the Asian and North American sites, although it contains retrievals only from the DT algorithm which was developed for dark surfaces. Overall, the performance of the DTB C6.1 AOD retrievals was significantly improved compared to the DTB C6 , but still more improvements are required over NDVI P , NDVI M and NDVI D surfaces of Asia, NDVI M and NDVI D surfaces of North America , and NDVI M surfaces of South America. The performance of the DTB SMS retrievals was better than the DTB C6 and DTB C6.1 retrievals with 11–13% (31%) greater number of coincident observations, 6–9% (14–22%) greater percentage of retrievals within the EE, and 30–100% (46–100%) smaller relative mean bias compared to the DTB C6.1 (DTB C6) at a global scale.
In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Da... more In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Dark Target (DT) and Deep Blue (DB) algorithms provide aerosol optical depth (AOD) observations at 3 km (DT 3K) and 10 km (DT 10K), and at 10 km resolution (DB 10K), respectively. In this study, the DB 10K is resampled to 3 km grid (DB 3K) using the nearest neighbor interpolation technique and merged with DT 3K to generate a new DT and DB merged aerosol product (DTB 3K) on a 3 km grid using Simplified Merge Scheme (SMS). The goal is to supplement DB 10K with high-resolution information over dense vegetation regions where DT 3K is susceptible to error. SMS is defined as " an average of the DT 3K and DB 3K AOD retrievals or the available one with the highest quality flag ". The DT 3K and DTB 3K AOD retrievals are validated from 2008 to 2012 against cloud-screened and quality-assured AOD from 19 AERONET sites located in Europe. Results show that the percentage of DTB 3K retrievals within the expected error (EE = ± (0.05 + 20%)) and data counts are increased by 40% and 11%, respectively, and the root mean square error and the mean bias are decreased by 26% and 54%, respectively, compared to the DT 3K retrievals. These results suggest that the DTB 3K product is a robust improvement over DT 3K alone, and can be used operationally for air quality and climate-related studies as a high-resolution supplement to the current MODIS product suite.
The Moderate resolution and Imaging Spectroradiometer (MODIS) level 2 operational aerosol product... more The Moderate resolution and Imaging Spectroradiometer (MODIS) level 2 operational aerosol products (MOD04/ MYD04), based on the Dark Target (DT) and the enhanced Deep Blue (DB) algorithms, have been providing daily global aerosol information. The MOD04/MYD04 has different data coverage for the DT and the DB algorithms due to differences in their retrieval methods. Recently in the collection 6 (C6), a DT and DB merged (DTB C6) AOD product has been introduced based on different thresholds of the Normalized Difference Vegetation Index (NDVI) to improve the data coverage. The main objective of this study is to increase the data coverage and reduce the error in the merged DTB C6 AOD product by introducing three new customized methods (DTB MX): (i) DTB M1 : " use an average of the DT and DB AOD retrievals or the available one for all the NDVI values " , as it is independent of the NDVI values, (ii) DTB M2 : " use an average of the DT and DB AOD retrievals or the available one for NDVI ≥ 0.2, and use the DB retrievals for NDVI b 0.2 " , and (iii) DTB M3 : " use AOD retrievals from the DB algorithm for NDVI N 0.3, and use an average of the DT and DB retrievals or the available one for NDVI ≤ 0.3 ". Validation of the DTB MX is conducted at a global scale from 2004 to 2014 using AERONET AOD measurements from 68 sites located in Asia (9), Europe (22), Southern Africa (8), and North (23) and South America (8), and for comparison purpose, the DTB C6 is evaluated for the same period. Results showed that the number of coincident observations of the DTB MX methods compared to the DTB C6 is increased by 31%, 41% 30%, and 108% for Asian, European, Southern African, and North and South American sites, respectively. At global scale, the number of coincident observations are increased for the DTB M1 , DTB M2 , and DTB M3 from 29,088 for DTB C6 , to 45,937, 45,028, 37,393 which are 58%, 57%, and 29%, respectively more than the for DTB C6 observations. For an equal number of coincident observations, the percentage of retrievals within the EE is increased by between 17% to 20% and the RMSE is decreased by up to 15% for the DTB MX methods, but R is the same as for the DTB C6. The percentage above the EE is also decreased by between 43% to 55% due to greater contribution of the DB retrievals. Overall, the performance of all the DTB MX methods is much better than the DTB C6 , but the DTB M1 is the most robust as it is independent of NDVI values, and significantly increases the data coverage. Therefore, it can be used operationally for global merged AOD retrievals.
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Papers by Muhammad Bilal
(PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG).
However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground
data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air
pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the
Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1
merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS
(Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-
Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone
Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of
sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer
Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML),
(7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic
NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring
stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with groundbased
PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT
(Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential
pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking
of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang,
Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra
DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for 2
tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala.
The results demonstrate that Pakistan's entire population has been exposed to high PM2.5 concentrations
for many years, with a mean annual value of 54.7 μg/m3, over all Pakistan from 2003 to 2020. This value exceeds
Pakistan's National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/m3 annual mean) for ambient air
defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e.,
mean annual PM2.5 < 35 μg/m3). The spatial analyses of the concentrations of aerosols and trace gases in terms
of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate
that Pakistan's air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions
from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric
NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~ 88% of the Pakistani cities
(80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in
spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the
Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere
Research Commission), policymakers, and the local research community to mitigate air pollution and its
effects on human health.
(PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG).
However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground
data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air
pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the
Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1
merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS
(Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-
Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone
Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of
sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer
Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML),
(7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic
NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring
stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with groundbased
PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT
(Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential
pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking
of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang,
Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra
DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for 2
tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala.
The results demonstrate that Pakistan's entire population has been exposed to high PM2.5 concentrations
for many years, with a mean annual value of 54.7 μg/m3, over all Pakistan from 2003 to 2020. This value exceeds
Pakistan's National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/m3 annual mean) for ambient air
defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e.,
mean annual PM2.5 < 35 μg/m3). The spatial analyses of the concentrations of aerosols and trace gases in terms
of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate
that Pakistan's air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions
from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric
NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~ 88% of the Pakistani cities
(80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in
spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the
Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere
Research Commission), policymakers, and the local research community to mitigate air pollution and its
effects on human health.