Quantifying NOx from Indian Large Point Sources: Satellite Plume Inversion and Inventory Comparison (2019–2022)

Megacities, large point sources including industries and power generation units are among the dominant contributors to anthropogenic nitrogen oxides (NOx) emissions in India. Accurately quantifying their emissions remains challenging because of uncertainties in activity data, pollution-control deployment, and assumptions used in bottom-up inventories. In this study, we present a multi-year satellite-based assessment of NOx emissions from Indian large point sources using nitrogen dioxide (NO₂) observations from the TROPOspheric Monitoring Instrument (TROPOMI) for 2019–2022, a period that spans pre- and post-COVID-19 years in which India’s power generation facilities continued to operate at reduced but sustained capacity. TROPOMI tropospheric NO₂ vertical column densities at a native spatial resolution of about 3.5 km × 5.5 km at nadir are combined with co-located wind fields from the ERA5 reanalysis to retrieve stack-scale emissions.We apply an Exponentially Modified Gaussian (EMG) plume inversion method, following established point-source retrieval frameworks, to ERA5-rotated TROPOMI NO₂ plumes for 20 major power generation plants across India. For each plant, we extract downwind NO₂ enhancements above a locally defined background and derive annualized NO₂ and NOx emissions by fitting the EMG function to the observed cross-plume vertical column density distribution under suitable wind and cloud conditions. These top-down emission estimates are compared with NOx emissions reported by bottom-up inventories and uncertainties due to activity factors, flue-gas desulfurization and de-NOx control implementation, and temporal variability in plant load were identified. This work provides one of the most up-to-date and spatially resolved satellite-based evaluations of NOx emissions from India’s power generation sector and demonstrates the capability of EMG plume inversion, in combination with high-resolution TROPOMI and ERA5 data, to benchmark and refine bottom-up global emission inventories where local emissions inventories are not available.