An automated approach to estimate car- bon monoxide emissions from steel plants by utilizing TROPOMI satellite measure- ments

Abstract

Since the 13th of October 2017, the Tropospheric Monitoring Instrument (TROPOMI) aboard ESA’s Sentinel 5-Precursor (S5-P) satellite enables daily global measurements of carbon monoxide (CO) total column con- centrations at an unprecedented spatial resolution of 7×5.6 km. TROPOMI has the ability to detect distinct pollution plumes, arising from point source emissions, from which emission rates can be derived. We in- vestigate the potential of CO column concentrations observed by TROPOMI to estimate the CO emissions of point sources on an operational level. This study developed a Python framework that for pre-defined point sources automatically detects pollution plumes and from which it estimates CO emissions using a mass bal- ance approach directly from single overpass CO observations. The algorithm is based on concepts from the computer vision to identify the plume and extract the plume center line while respecting the plume orienta- tion. The emission rate is approximated from flux profiles through multiple plume cross-sections following the plume center line. The performance of the developed framework and its potential is demonstrated by the application on 132 identified steel plant facilities over a time period of more than 2.5 years. Currently the lack of accessible and quality-wise good data limits spatial or even temporal comparison of CO emissions from steel plants. Therefore the control and understanding of emission rates could greatly benefit from the proposed approach. In total we obtained 1,774 emission estimates for 97 facilities. Up to 119 measurements per facility are derived where for the majority of the facilities the average number of measurements is around 10. The obtained time series showed large variation in the distribution of measurements over time as well as the emission values itself. For a number of higher emission values, that exceeded up to 2 times the aver- age emission, measured for e.g. the Bhilai Steel Plant, India, the outliers corresponded with interference of another source. Although individual plumes could be identified for two sources (∼35 km apart) in the same Bhilai area, no non-merged plumes were detected for the Schwelgern and Huttenheim sites (∼18 km apart) in Duisburg, Germany. Moreover, we tested the agreement of our measurements with recorded or stated events: i) The emission estimate from the afternoon of the 24th of May 2019, Bhilai site, confirmed the manufacturers statement that the operations had continued that day despite a reported fire in the morning. ii) Our results did not match the significant global drop noted in steel production during the first period of 2020 as a result of the pandemic. The scattered distribution of measurements and their emission values over time seem to limit the representation of a small time frame needed for such analysis. iii) We found a positive correlation with a Pearson Coefficient of 0.76 between the European Pollutant Release Transfer Register (E-PRTR) and our data. For all examined facilities our obtained emissions were greater than reported by the facilities to E-PRTR. This might indicate an underestimation of the data registered. This first evaluation emphasizes the potential of TROPOMI observations to improve our understanding of point source emissions and to compliment existing data such as the E-PRTR. However, to be able to interpret the data from TROPOMI indeed structurally and to develop a reliable validation method extensive data-analysis on plant and area-level is required, especially to be able to rule out interfering factors.