Nitrogen dioxide (NO₂) is both an important primary trace gaseous pollutant and a precursor to ozone and fine particulate matter production. There exist three widely used and publically available tropospheric NO₂ Vertical Column Density (VCD) products based on OMI over East Asia, including QA4ECV from KNMI, OMNO2 from NASA and POMINO from Peking University. The spatiotemporal characteristics of tropospheric NO₂ VCDs in each product have been extensively studied. However, quantitative knowledge of the differences between the three products is still inadequate.This research firstly updates the POMINO product developed by our group to version 2.1, including bug fixes and algorithm improvement, and expanding the spatial domain to East Asia, much of Southeast Asia and most of South Asia. Compared with QA4ECV and OMNO2 v4, POMINO v2.1 takes into account the anisotropy of surface reflectance and complex radiative effects of aerosols in the process of tropospheric NO₂ AMF calculation. Then we quantitatively compare the NO₂ data of QA4ECV, OMNO2 v4 and POMINO v2.1 in 2015—2020 based on either POMINO v2.1 or each product’s valid pixels.Results show that updates of POMINO do not significantly affect the retrieved NO₂ VCDs (within 10% averaged over the spatial domain, dependent on seasons). When valid satellite pixels of three products are sampled consistently based on cloud radiation fraction of POMINO v2.1, the relative differences between the three products are about 10% averaged over Asia, although the maximum difference can reach 40% or more in severely polluted areas like Beijing-Tianjin-Hebei. A sensitivity test based on POMINO algorithm shows that tropospheric NO₂ VCDs with implicit aerosol correction (as QA4ECV and OMNO2 v4) in December 2017 are lower than those with explicit correction by about 26.4% over Beijing-Tianjin-Hebei, and more than 11% over the whole North China Plain. As far as the long-term trend is concerned, all the three products show a nearly 30% decrease of annual mean tropospheric NO2 VCDs in Beijing-Tianjin-Hebei in 2015—2020, in contrast to relatively small VCD changes over the Yangtze River Delta. When valid satellite pixels are sampled based on each product’s own cloud screening, POMINO v2.1 provides much more valid pixels in polluted situations by 11%—44% and reduces the sampling bias, as a result of its explicit representation of aerosol optical effects in the NO₂ and prerequisite cloud retrieval process.This research provides a basis for using and interpreting the three products, including their differences, effects of sampling and impacts of aerosol representation. Our results offer insight for better understanding of the pollution of nitrogen oxides and influences of current emission reductions.

satellite remote sensing;nitrogen oxides;OMI;tropospheric NO₂ VCDs;aerosol;data sampling;air pollution