Identification of NO2 and SO2 pollution hotspots and sources in Jiangsu Province of China

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 (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 prom-inent 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), result-ing 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

Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China

Rapid industrialization and urbanization along with a growing population are contributing significantly to air pollution in China. Evaluation of long-term aerosol optical depth (AOD) data from models and reanalysis, can greatly promote understanding of spatiotemporal variations in air pollution in China. To do this, AOD (550 nm) values from 2000 to 2014 were obtained from the Coupled Model Inter-comparison Project (CIMP6), the second version of Modern-Era Retrospective analysis for Research, and Applications (MERRA-2), and the Moderate Resolution Imaging Spectroradiometer (MODIS; flying on the Terra satellite) combined Dark Target and Deep Blue (DTB) aerosol product. We used the Terra-MODIS DTB AOD (hereafter MODIS DTB AOD) as a standard to evaluate CMIP6 Ensemble AOD (hereafter CMIP6 AOD) and MERRA-2 reanalysis AOD (hereafter MERRA-2 AOD). Results show better correlations and smaller errors between MERRA-2 and MODIS DTB AOD, than between CMIP6 and MODIS DTB AOD, in most regions of China, at both annual and seasonal scales. However, significant under- and over-estimations in the MERRA-2 and CMIP6 AOD were also observed relative to MODIS DTB AOD. The long-term (2000–2014) MODIS DTB AOD distributions show the highest AOD over the North China Plain (0.71) followed by Central China (0.69), Yangtse River Delta (0.67), Sichuan Basin (0.64), and Pearl River Delta (0.54) regions. The lowest AOD values were recorded over the Tibetan Plateau (0.13 ± 0.01) followed by Qinghai (0.19 ± 0.03) and the Gobi Desert (0.21 ± 0.03). Large amounts of sand and dust particles emitted from natural sources (the Taklamakan and Gobi Deserts) may result in higher AOD in spring compared to summer, autumn, and winter. Trends were also calculated for 2000–2005, for 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan or FYP), and for 2011–2014 (during the 12th FYP). An increasing trend in MODIS DTB AOD was observed throughout the country during 2000–2014. The uncontrolled industrialization, urbanization, and rapid economic development that mostly occurred from 2000 to 2005 probably contributed to the overall increase in AOD. Finally, China's air pollution control policies helped to reduce AOD in most regions of the country; this was more evident during the 12th FYP period (2011–2014) than during the 11th FYP period (2006–2010). Therefore this study strongly advises the authority to retain or extend these policies in the future for improving air quality

AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA)

Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ångström exponent (AE). However, AOD represents the amount of aerosol suspended in the atmospheric column while the AE is a qualitative indicator of the size distribution of the aerosol estimated using AOD measurements at different wavelengths. Therefore, these two parameters do not provide sufficient information to unambiguously classify aerosols into these four types. Evaluation of the performance of GA classification applied to AErosol Robotic NETwork (AERONET) data, at sites for situations with known aerosol types, provides many examples where the GA method does not provide correct results. For example, a thin layer of haze was classified as BB and DD outside the crop burning and dusty seasons respectively, a thick layer of haze was classified as BB, and aerosols from known crop residue burning events were classified as DD, CC, and CM by the GA method. The results also show that the classification varies with the season, for example, the same range of AOD and AE were observed during a dust event in the spring (20th March 2012) and a smog event in the autumn (2nd November 2017). The results suggest that only AOD and AE cannot precisely classify the exact nature (i.e., DD, BB, CC, and CM) of aerosol types without incorporating more optical and physical properties. An alternative approach, AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA), is proposed to provide aerosol amount and size information using AOD and AE, respectively, from the Terra-MODIS (MODerate resolution Imaging Spectroradiometer) Collection 6.1 Level 2 combined Dark Target and Deep Blue (DTB) product and AERONET Version 3 Level 2.0 data. Although AEROSA is also based on AOD and AE, it does not claim the nature of aerosol types, instead providing information on aerosol amount and size. The purpose is to introduce AEROSA for those researchers who are interested in the generic classification of aerosols based on AOD and AE, without claiming the exact aerosol types such as DD, BB, CC, and CM. AEROSA not only provides 9 generic aerosol classes for all observations but can also accommodate variations in location and season, which GA aerosol types do not.</jats:p

Accuracy assessment of CAMS and MERRA-2 reanalysis PM2.5 and PM10 concentrations over China

Rapid industrialization and urbanization significantly contribute to air pollution in China. Essential constituents of air pollution are fine and coarse particulate matter which are the total mass of aerosol particles with aerodynamic diameters smaller than ≤2.5 μm (PM2.5) and ≤10 μm (PM10), respectively. These particles may cause severe health effects, and impact the atmospheric environment and climate. However, the limited number of ground-based measurements at sparsely distributed air quality monitoring stations hamper long-term air pollution impact studies over large areas. Although spatial data on PM2.5 and PM10 are available from reanalysis models, the accuracy of such data may be reduced in comparison with ground data and may vary regionally and seasonally. Therefore, a long-term evaluation of reanalysis-based PM2.5 and PM10 against ground-based measurements is needed for China. In this study, surface-level PM2.5 and PM10 concentrations from 2014 to 2020 obtained from the Copernicus Atmospheric Monitoring Service (CAMS), and from the second version of Modern-Era Retrospective analysis for Research and Applications (MERRA-2) were evaluated using ground-based measurements obtained from 1675 air quality monitoring stations distributed across China. High PM2.5 and PM10 (μg/m3) concentrations from ground-based measurements were observed in many parts of China (including the North China Plain: NCP, Yangtse River Delta:YRD, Pearl River Delta: PRD, Central China, Sichuan Basin: SB, and northwestern region: Tarim Basin). The patterns of the spatial distributions of PM2.5 and PM10 obtained from CAMS and MERRA-2 across China are similar to those of the ground-based monitoring data, but the concentrations from both models are substantially different. CAMS significantly overestimates PM2.5 and PM10 over most regions, in particular over urban and desert areas, whereas MERRA-2 seasonal and annual mean concentrations were more accurate over the highly polluted areas in central and eastern China. The lowest PM2.5 and PM10 concentrations were observed over the Tibetan Plateau and Qinghai, where CAMS and MERRA-2 datasets were substantially underestimated. Furthermore, both CAMS and MERRA-2 under-and over-estimate the PM concentrations in both low and high pollution conditions. Overall, this study contributes to understanding of the reliability of reanalysis data and provides a baseline document for improving the CAMS and MERRA-2 datasets for studying local and regional air quality in China

Spatio-temporal air quality assessment in Tehran, Iran, during the COVID-19 lockdown periods

Based on ground-based and satellite-based data, spatio-temporal analyses of air quality in Tehran were carried out during the lockdown periods (February-April) in 2020 and 2021. We evaluated the differences in temporal emissions of six air pollutants (NO2, CO, SO2, O3, PM2.5, and PM10) at various time scales, including diurnal, monthly, and relative changes. The results of ground-based measurements indicated that for all pollutants except O3, the magnitude decreased in 2020 (11-42%) compared to the baseline period (2015-2021). As a result of eased restrictions and unfavorable meteorological conditions, the reduction in air pollutants was lower in 2021 (5-32%), and PM2.5 and PM10 levels increased (3.75 and 11.22%). Satellite-based concentrations (NO2, CO, SO2, and AOD) varied from −8 to 54% in 2020 and from −41 to 60% in 2021 compared to 2019 as the pre-lockdown year. Concerning AOD, the trend is consistent with dust events during March and April in our region

Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions

This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007&ndash;2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36&ndash;57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health

Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions

This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007–2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36–57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health

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

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

Air pollution scenario over Pakistan: characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases

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