Asia accounts for approximately half of global anthropogenic emissions of major pollutants, yet emission inventories remain uncertain and ground-based monitoring is sparse across much of the region. To address these challenges, this study applies a multi-species satellite data assimilation framework to estimate emissions and concentrations of key chemical species during the NASA's ASIA-AQ campaign. The assimilation improves agreement with airborne observations for O₃, NO_x, CO, and CH₂O, with the largest gains for CO (correlation increasing from 0.63–0.64 to 0.77) and CH₂O (biases reduced by 41–70 %). Domain-wide, the optimized emissions show increases of 15 % for NO_x and 9 % for CO, and a 52 % reduction in isoprene. Comparisons with multiple emission inventories reveal large discrepancies, with normalized standard deviation ranging from 11 % for NO_x in mainland China to 68 % for CO in Taiwan. Over Thailand, the assimilation increases fire emissions from 3.6 Tg (GFASv1.2) to 8.3 Tg, while FINNv2.7 produces estimates roughly twice as high, highlighting persistent divergence among fire emission estimates. Source-receptor analysis reveals strong meteorological control on transboundary pollutant: long-range transport contributes up to 62 % of surface O₃ in Manila during strong monsoon conditions, whereas Seoul exhibits local NO_x-saturated chemistry. During a strong transport episode, Indian emissions account for 72–78 % of the free tropospheric O₃ response over Taipei and Chiang Mai, and 24 % over Seoul, highlighting an overlooked transport pathway affecting Asian air quality. These results highlight the value of satellite data assimilation and the need for improved inventories and coordinated action on local and transboundary pollution.