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Central Asia's arid climate and growing population make it susceptible to air pollution from mineral dust and anthropogenic sources. To assess the impact of these pollution sources on local air quality, the chemical composition of PM 10 aerosol particles was collected within a year at Dushanbe, Tajikistan. Primary sources of the pollutants, their impact on PM 10 composition, and their seasonal trends were assessed. The measurement approach employed analysis of trace metals, ions, organic compounds, laboratory investigations, diagnostic ratio calculations, all complemented by positive matrix factor analysis. The results reveal that for about 70% of the year, PM 10 levels (80 ± 55 μg m−3 as the annual average) in Dushanbe were higher than the WHO limits, with varying levels during the seasons. Summer concentrations peaked up to 434 μg m−3 due to a combined mineral and road dust influence. Interestingly, winter concentrations reached 152 μg m−3 due to strong contributions (about 64%) from residential heating and fossil fuel combustion, often triggered during sub-10 °C temperatures. Mineral dust events were associated with elevated concentrations of trace metals, including Zn and Pb, alongside organic carbon and high-chain n - alkanes. During such events, Ca/Fe, Ca/Al ratios exceeded those of Saharan dust, suggesting a high salt content in Asian dust, potentially influenced by dried-off lake emissions. It was striking to observe that winter periods exhibited concerning levels of carcinogenic PAHs, such as benzo(b)fluoranthene (<25 ng m−3) and benzo[a]pyrene (<12 ng m−3, BaP), with average values of 4.1 ± 5.9 ng m−3 (BaP) that significantly exceed WHO recommendations. The high PM 10 and PAH exposure could pose severe health risks, necessitating collective attention from the local authorities in developing new mitigation strategies concerning the reduction of emissions from residential heating of coal and wood in winter to curb combustion emissions and reduce exposure. [Display omitted] • Dushanbe's PM10 levels breach WHO guidelines for 70% of the year (mean 80 μg m−3). • Ca/Fe ratios hint at higher Asian dust salt content to Saharan dust due to dried-off lakes. • Residential heating and fossil fuel combustion contribute 64% of winter PM10 values. • Sub-10 °C temperatures heightened PM10 levels harboring carcinogenic PAHs in Winter. • Mineral and road dust controls 74% of summer air pollution (434 μg m−3). [ABSTRACT FROM AUTHOR]

Knowledge of the vertical distribution and layering of aerosols and identification of the corresponding aerosol sources are needed to improve our understanding of the spatial and temporal variability of aerosol pollution. To achieve this goal, we combined both passive and active remote-sensing techniques to provide a 3D view of local aerosol levels and regional to long-range pollution transport. We studied aerosol optical depth (AOD) data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm at 1-km spatial resolution along with active multiwavelength polarization lidar observations of vertical aerosol profiles in Haifa, Israel, Aerosol Robotic Network (AERONET) sun photometer observations at the lidar site, and local-network observations of aerosol concentrations (PM 2.5). This comprehensive dataset enabled analyzing the performance of the MAIAC AOD retrieval in cases of complex aerosol layering and mixing states which are typical of the Eastern Mediterranean. While satellite-derived and ground-based AOD measurements generally showed good agreement, 35 out of 100 measurements showed low correspondence. Analysis of those cases revealed that overestimation of AOD was mostly related to cloud-contaminated pixels and aerosol water-uptake effects in moist, cloud-free air at cloud level. Furthermore, AOD over- and underestimations were related to the presence of complex aerosol mixture and layering conditions, especially when dust was mixed with aged anthropogenic aerosol pollution and marine aerosols with lofted anthropogenic pollution. In these cases 50–70% of measurements were outside of the expected error limit. Perhaps these conditions are not considered in the MAIAC retrieval. Finally, we investigated the link between AOD spatial variability and the MAIAC AOD bias, and performed a cluster analysis corroborating the strong impact of cloud contamination on MAIAC AOD quality. Our observation-based results raise the importance of carefully analyzing the uncertainties in satellite AOD measurements that are used as an important input variable in numerous health-related exposure studies and climate models. Image 1 • We analyzed the performance of MAIAC aerosol optical depth (AOD) retrieval. • Dust mixed with aged anthropogenic aerosol pollution challenges AOD accuracy. • Complex aerosol layering leads to MAIAC AOD underestimation. • High cloud coverage results in MAIAC AOD overestimation. • Spatial variability of aerosol retrieval may indicate a potential AOD bias. [ABSTRACT FROM AUTHOR]