<span>The Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission, scheduled for launch in 2028, will observe CO₂, CH₄, and NO₂ emission plumes from more than 10,000 industrial facilities per year using two formation-flying CubeSats. Here, NO₂ plume structures exhibit substantially lower random noise than the corresponding CO₂ features, motivating a synergistic exploitation of both species for improved emission quantification and for enhanced characterization of atmospheric chemistry within plumes. Using large-eddy simulations in combination with the Integrated Mass Enhancement (IME) method, we assess NO₂-based masking of CO₂ plumes for emission rates in the range 2.0–12.5 Mt yr⁻¹. This yields CO₂ emission estimates with precisions between 18.5 % and 3.4 %, depending on the emission strength, and corresponding absolute biases that decrease from 15.3 % to 2.4 %. As an alternative approach, we analyze the observed CO₂/NO₂ ratio. By fitting an empirical model to measurement simulations of this ratio and subsequently reconstructing the CO₂ plume from NO₂ observations, we obtain a substantial reduction in the apparent noise of the reconstructed CO₂ plume. For the inferred emission rates, however, the precision remains largely unchanged. Consequently, despite reduced errors in individual pixel-level observations, plume reconstruction does not enhance the precision of CO₂ emission estimates, because it converts originally uncorrelated pixel noise into spatially correlated errors. Neglecting these spatial error correlations leads to a severe underestimation of the retrieval uncertainty. A key advantage of the empirical CO₂/NO₂ ratio model is its ability to characterize plume chemistry. Here CO₂ serves as non-decaying reference tracer. We demonstrate that an effective timescale for the NO → NO₂ conversion in emission plumes can be inferred for sources with CO₂ emissions > 5.0 Mt yr⁻¹. Application of the method to Environmental Mapping and Analysis Program (EnMAP) observations demonstrates its practical utility, confirming its applicability to real satellite data.</span>