Global variability in the detectability of power plant NO₂ plumes from space

Abstract. We present the first global, data-driven analysis of power plant NO₂ plume visibility from space. Using TROPOMI observations over 6,000 of the world’s highest-emitting power plants and hourly CEMS data for 500 U.S. plants, we develop an automated algorithm that labels plumes and attributes them to their sources with 98 % accuracy. We then train a machine learning model to predict plume detectability from environmental, meteorological, and observational variables (F1 score > 0.65, AUC > 0.8). Out of 25 variables, we find that NO_x emission rate, surface albedo, wind speed, and sensor zenith angle jointly explain much of the detection variability. An hourly NO_x emission rate of ≈ 400 kg/h corresponds to a 50 % detection probability on average, but detection rates vary 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 NO₂ plume visibility in satellite data, establishing a foundation for models to use similar predictors as auxiliary variables when quantifying emission rates from plume appearance.