Global variability in the detectability of power plant NO 2 plumes from space

We present the first global, data-driven analysis of power plant NO 2 plume detectability from space. Using TROPOspheric Monitoring Instrument (TROPOMI) observations (nadir pixel size 3.5–7 km) over 6000 of the world's highest-emitting power plants and hourly Continuous Emissions Monitoring Systems (CEMS) data for 500 US plants, we develop an automated algorithm that labels plumes and attributes them to their sources with 98 % accuracy. For the subsequent detectability analysis, we restrict to plants outside interference zones (at least 20 km from other major power plants and 45–90 km from cities (depending on city size)), which retains 45.0 % of US and 21.1 % of global NO x emissions in our datasets. We then train a machine learning model to predict plume detectability (the probability of detection given the observation conditions) from meteorological, environmental, sensor, and power-plant variables sampled at the single TROPOMI pixel over each plant (F1 score>0.66, AUC>0.8). Out of 25 variables, we find that NO x emission rate, surface altitude, surface albedo (NO 2 window), sensor zenith angle, primary fuel type, and wind speed jointly explain much of the variability in detectability. For US power plants, an hourly NO x emission rate of ≈ 400 kg h −1 corresponds to ∼ 50 % detectability, but detectability varies from < 20 % to > 60 % under different combinations of these conditions. These results provide the first empirical quantification of the physical and environmental factors that govern NO 2 plume visibility in TROPOMI data, establishing a foundation for models to use similar predictors as auxiliary variables when quantifying emission rates from plume appearance.