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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. [ABSTRACT FROM AUTHOR]

Spatial and temporal distributions of tropospheric NO2 vertical column densities over Pakistan during the period 2002-2014 are discussed. Data products from three satellite instruments SCIAMACHY, OMI, and GOME-2 are used to prepare a database of tropospheric NO2 column densities over Pakistan and temporal evolution is also determined. Plausible NO2 sources in Pakistan are also discussed. The results show a large NO2 growth over all provinces and the major cities of Pakistan except the megacity of Karachi. Decline in industrial activities due to energy crises, worsening law and order situation, terrorist attacks, and political instability was explored as the main factor for lower NO2 VCDs over Karachi City. The overall increase can be attributed to the anthropogenic emissions over the areas with high population, traffic density, and industrial activities. Source identification revealed that use of fossil fuels by various sectors including power generation, vehicles, and residential sectors along with agriculture fires are among significant sources of NO2 emissions in Pakistan. Existing emission inventories such as EDGARv4.2 and MACCity largely underestimate the true anthropogenic NOx emissions in Pakistan. This study may provide vital information to policy makers and regulatory authorities in developing countries, including Pakistan, in order to devise effective air pollution abatement policies. [ABSTRACT FROM AUTHOR]

Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM 2.5) 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 PM 1 , PM 2.5 , and PM 10 data; (3) the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM 2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO 2), and VCD of sulfur dioxide (SO 2) 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 PM 2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with ground-based PM 2.5 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 PM 1 , PM 2.5 , and PM 10 Lahore, Gujranwala, and Okara were the top three; for tropospheric NO 2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO 2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan's entire population has been exposed to high PM 2.5 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 PM 2.5 < 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 PM 1 , PM 2.5 , PM 10 , tropospheric NO 2 VCD, and SO 2 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. • Lahore, Gujranwala, and Okara are the most polluted city based on PM 2.5. • Jhang, Multan, and Vehari are the most polluted cities based on AOD. • Aerosols, nighttime lights, population, cropland, and fire show same spatial patterns. • Pakistan's entire population is exposed to long-term PM x (x = 1, 2.5, & 10). • Pakistan's air quality is mainly affected by local anthropogenic sources. [ABSTRACT FROM AUTHOR]