Data for Global variability in the detectability of power plant NO$_2$ plumes from space

This repository contains the data for the paper "Global variability in the detectability of power plant NO2 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 6,000 of the world's highest-emitting power plants and hourly Continuous Emissions Monitoring Systems (CEMS) data for 500 U.S. 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 U.S. 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 ($\text{F1 score} > 0.66$, $\text{AUC} > 0.8$). 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 U.S. power plants, an hourly NO$_x$ emission rate of $\approx$400 kg,h$^{-1}$ corresponds to $\sim$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. All of the data used in the paper is available. processed U.S. power plants: us-power-plant-list.csv processed global power plants: global-power-plant-list.csv world cities: world-cities.csv processed U.S. TROPOMI observations with corresponding variables: global-tropomi-observation-with-variables.csv processed global TROPOMI observations with corresponding variables: us-tropomi-observation-with-variables.csv ERA5 data with the following variables used in the paper could be downloaded at (U.S. 2019-2024, Global 2018): ERA5 hourly data on single levels from 1940 to present. Temperature TOA incident solar radiation Total column water vapour eastward wind northward wind TROPOMI data with the following variables used in the paper could be downloaded at (U.S. 2019-2024, Global 2018): US, Global. A sample file is: S5P_OFFL_L2__NO2____20220726T064118_20220726T082247_24784_03_020400_20220727T224455.nc. surface altitude surface altitude precision surface classification surface pressure surface albedo surface albedo nitrogendioxide window cloud pressure crb cloud fraction crb cloud albedo crb scene albedo apparent scene pressure snow ice flag aerosol index 354 388 scaled small pixel variance sensor altitude sensor azimuth angle solar azimuth angle sensor zenith angle solar zenith angle