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

Direct radiative forcing (DRF) of aerosols is driven by aerosol concentration, size, and mixing state, and solar radiation. This work introduces Core-Shell Mie model optimization (COSMO) to compute top of the atmosphere (TOA) forcing based on inversely constrained black carbon (BC) size and mixing state from AERONET, over two rapidly developing areas: Lumbini and Taihu. COSMO has both, a less negative TOA than AERONET and a wider range of variability, with the mean and standard deviation difference between COSMO and AERONET being 13 ± 8.1 W m⁻²at Lumbini and 16 ± 12 W m⁻²at Taihu. These differences are driven by particle aging and size-resolved BC emissions, with up to 17.9% of cases warmer than the maximum AERONET TOA, and 1.9% of the total possible cases show a net-warming at TOA (TOA > 0). A linearized correction is deduced which can be immediately implemented by climate models, and suggested ranges of BC size and mixing observations are made for future campaigns. Given that the COSMO TOA bias and uncertainty are larger than the forcing of locally emitted GHGs, active consideration of BC is necessary to reduce climate uncertainty in developing areas.

Rapid urbanization and broad use of biomass burning have led to important changes in NO_x [sum of nitrogen dioxide and nitrous oxide] emissions across South, Southeast, and East Asia, frequently occurring on day-to-day time scales and over areas not identified by existing emissions databases. Here we compute NO_x emissions using remotely sensed NO₂ [nitrogen dioxide] and a model-free mass-conserving inverse method, resulting respectively in 61 kt d⁻¹ and 40 kt d⁻¹ from biomass burning in Northern and Southern Continental Southeast Asia, and 14.3 kt d⁻¹ and 3.7 kt d⁻¹ from urbanization in China and Eastern South Asia, a net increase more than double existing inventories. Three observationally based physical constraints consistent with theory are found which current chemical transport models cannot match: more NO₂ per unit of NO_x emissions, longer and more variable in-situ lifetime, and longer-range transport. This result provides quantitative support for mitigation efforts targeting specific events, processes, or geographies.

Rapid economic and energy growth in Asia has led to change in the loadings of black carbon (BC) in the last two decades. This work uses remotely sensed column absorbing aerosol optical depth to quantify BC loading day-by-day and grid-to-grid. Variance maximization is used to uniquely compute an unbiased set of observations of remotely sensed NO₂ in space and time of urban and suburban sources, including regions with changing energy and industry. An inversely applied probabilistic MIE algorithm constrains the size, mixing state, and number of BC using four spectral observations. Significant differences in the particle size and mixing state are identified and attributed including: a drop around 2012 when China’s national air pollution policies were adopted, a drop ahead of the 2008 Olympics, and flattening from 2015 onward. A case of simultaneous reduction in BC mass and increase in BC number demonstrates PM_2.5 controls may not fully control BC.

Here we use multi-waveband single scattering albedo observations from ground-based instruments and satellite to constrain black carbon aerosol's physical properties and loading over Xuzhou, China, and Dhaka, Bangladesh. Our daily high-resolution findings reveal smaller black carbon cores and spatially variable morphology dominate both regions. Column loadings reveal higher black carbon mass in Dhaka, while higher total aerosol mass and number are observed in Xuzhou. These findings reflect differences in emission sources, atmospheric conditions, and regulatory policies. Spatial analysis reveals notable enhanced black carbon along Dhaka’s urban riverbanks (8–9 mg m⁻²), and over rapidly changing, small industrial sites in China, indicating overlooked sources. Complex daily interactions between wind, accumulation, and dispersion challenge traditional seasonal dynamics. These findings demonstrate high-resolution data can be tailored from available remote sensing platforms, providing nuanced insights into regional air quality, enhancing assessment capabilities and informing targeted mitigation strategies.