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Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric column density of NO2 (TCDNO2) from the Geostationary Environment Monitoring Spectrometer (GEMS) during 2021–2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its accuracy with measurements from the TROPOspheric Monitoring Instrument (TROPOMI). GEMS displays acceptable accuracy in TCDNO2 measurements, with a correlation coefficient (R) of 0.68, an index of agreement (IOA) of 0.79, and a mean absolute bias (MAB) of 5.73321 × 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate to high accuracy of GEMS TCDNO2 across all Pandora stations, with R values spanning from 0.46 to 0.80. Comparing TCDNO2 from GEMS and TROPOMI at TROPOMI overpass time shows satisfactory performance of GEMS TCDNO2 measurements, achieving R, IOA, and MAB values of 0.71, 0.78, and 6.82182 × 1015 molecules/cm2, respectively. However, these figures are overshadowed by TROPOMI’s superior accuracy, which reports R, IOA, and MAB values of 0.81, 0.89, and 3.26769 × 1015 molecules/cm2, respectively. While GEMS overestimates TCDNO2 by 52 % at TROPOMI overpass time, TROPOMI underestimates it by 9 %. The deep learning bias corrected GEMS TCDNO2 (GEMS-DL) demonstrates a marked enhancement in the accuracy of original GEMS TCDNO2 measurements. The GEMS-DL product improves R from 0.68 to 0.88, IOA from 0.79 to 0.93, MAB from 5.73321 × 1015 to 2.67659 × 1015 molecules/cm2, and reduces MAB percentage (MABP) from 64 % to 30 %. This represents a significant reduction in bias, exceeding 50 %. Although the original GEMS product overestimates TCDNO2 by 28 %, the GEMS-DL product remarkably minimizes this error, underestimating TCDNO2 by a mere 1 %. Spatial cross-validation across Pandora stations shows a significant reduction in MABP, from a range of 45 %-105.6 % in original GEMS data to 24 %-59 % in GEMS-DL.

Uncertainty in ammonia (NH3) emissions causes the inaccuracy of fine particulate matter simulations, which is associated with human health. To address this uncertainty, in this work, we employ the iterative finite difference mass balance (iFDMB) technique to revise NH3 emissions over East Asia using the Cross-track Infrared Sounder (CRIS) satellite for July, August, and September 2019. Compared to the emissions, the revised NH3 emissions show an increase in China, particularly in the North China Plain (NCP) region, corresponding to agricultural land use in July, August, and September and a decrease in South Korea in September. The enhancement in NH3 emissions resulted in a remarkable increase in concentrations of NH3 by 5 ppb. in July and September, there is an increase in ammonium (NH4+) and nitrate (NO3-) concentrations by 5 μg/m3, particularly in the NCP region, while in August, both NH4+ and NO3- concentrations exhibit a decrease. For sulfate (SO42-), in August and September, the concentrations decreased over most regions of China and Taiwan, as a result of the production of ammonium sulfate; increased concentrations of SO42-, however, were simulated over South Korea, Japan, and the southern region of Chengdu, caused by higher relative humidity (RH). In contrast, during the month of July, our simulations showed an increase in SO42- concentrations over most regions of China. To gain a more comprehensive understanding, we defined a sulfur conversion ratio (SCR=(Spost-Sprior)/(SO42-post-SO42-prior)), which explains how changes in sulfur in the gas phase affect changes in sulfate concentrations. A subsequent sensitivity analysis performed in this study indicated the same relationship between changes in ammonia and its effect on inorganic fine particulate matter (PM2.5). This study highlights the challenge of controlling and managing inorganic PM2.5 and indicates that reducing the emissions of air pollutants do not necessarily lead to a reduction in their concentrations.