Cities in South and Southeast Asia are developing rapidly without routine, up-to-date knowledge of air pollutant precursor emissions. This data deficit can potentially be addressed for nitrogen oxides (NOx) by deriving city NOx emissions from satellite observations of nitrogen dioxide (NO2) sampled under windy conditions. NO2 plumes of isolated cities are aligned along a consistent wind-rotated direction and a best-fit Gaussian is applied to estimate emissions. This approach currently relies on non-standardized selection of the area to sample around the city centre and Gaussian fits often fail or yield non-physical parameters. Here, we automate this approach by defining many (54) sampling areas that we test with TROPOspheric Monitoring Instrument (TROPOMI) NO2 observations for 2019 over 19 cities in South and Southeast Asia. Our approach is efficient, adaptable to many cities, standardizes and eliminates sensitivity of the Gaussian fit to sampling area choice, and increases success of deriving annual emissions from 40-60% with one sampling area to 100% (all 19 cities) with 54. The annual emissions we estimate range from 16±5 mol s-1 for Yangon (Myanmar) and Bangalore (India) to 125±41 mol s-1 for Dhaka (Bangladesh). With the enhanced success of our approach, we find evidence from comparison of our top-down emissions to past studies and to inventory estimates that the wind rotation and EMG fit approach may be biased, as it does not adequately account for spatial and seasonal variability in NOx photochemistry. Further methodological development is needed to enhance its accuracy and to exploit it to derive sub-annual emissions.

India implemented stay-at-home order (i.e. lockdown) on 24 March 2020 to decrease the spread of novel COVID-19, which reduced air pollutant emissions in different sectors. The Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to study the changes in air pollutants during the lockdown period in 2020 compared with similar period in 2019. We found that both meteorology and lockdown emissions contributed to daytime PM2.5 (-6% and -11%, respectively) and ozone (-6% and -8%, respectively) reduction averaged in April 2020 in the Indo-Gangetic Plain. However, the ozone concentration response to reductions in its precursors (i.e. NO2 and VOCs) due to the lockdown emissions was not constant over the domain. While ozone concentration decreased in most parts of the domain, it slightly increased in major cities like Delhi and in regions with many power plants. We utilized the reaction rates information in WRF-Chem to study the ozone chemistry. We found carbon monoxide, formaldehyde, isoprene, acetaldehyde, and ethylene as the major VOCs that contribute to the ozone formation in India. We used the ratio of chemical loss of radicals with radicals and NOx, and its corresponding formaldehyde to NO2 ratio (FNR) to find the ozone chemical regimes. Using the upper limit of FNR transition region (1.3), we found that most parts of India are within NOx-limited regime while urban regions and the regions with many power plants are in a VOC-limited regime. As a result, policy makers should study the characteristics of a region before implementing mitigation strategies.