The TROPOspheric Monitoring Instrument (TROPOMI) provides high-resolution, global measurements of carbon monoxide (CO) for environmental pollution monitoring. The Automated Plume Detection and Emission Estimation algorithm (APE), developed by SRON, identifies pollution plumes and estimates emissions based on the satellite data. This study implemented four machine learning algorithms to enhance APE and applied them to 180 steel plant locations for 6 years to estimate the average emissions from detected plumes using the divergence method. The models identified up to 136.1% more plumes than APE. Comparing the estimated emissions with the European Pollutant Release and Transfer Register (E-PRTR) dataset shows the ResNet-44 model achieves the lowest bias (1.20 kg/s) and standard deviation (2.36 kg/s) compared to APE, which had a bias of 3.41 kg/s and a standard deviation of 2.40 kg/s. This demonstrates the potential of machine learning to improve plume detection and emission estimation for remote sensing of pollution from space.

The TANGO mission represents a significant leap forward in the global monitoring of greenhouse gas emission plumes, particularly CO2 and NO2. Comprising two satellites, TANGO-Carbon and TANGO-Nitro, the mission is designed to operate from 2027 to 2031, offering unprecedented capabilities through stereo (temporally separated) and high spatial res- olution emission plume images. These capabilities create a unique framework that facilitates the development of novel estimation methods for emission rates of greenhouse gas emitters. Conventional methods for estimating emission rates of CO2 and NO2 rely on combining gas concentration measurements with wind velocity fields derived from meteorological data, often introducing substantial uncertainties due to the inherent inaccuracies in wind velocity estimation. To address this challenge, this research explores alternative methods enabled by TANGO’s innovative framework, which allows for the direct measurement of emission plume velocity from concentration data. By eliminating the dependency on uncertain me- teorological inputs, these methods promise to reduce the uncertainty in emission rate estimates.
Through detailed simulations and analyses, this research demonstrates that the TANGO mission can effectively establish a framework for directly measuring emission plume velocities. By simulating the data products of the TANGO satellites using Large-Eddy Simulations and applying advanced methods such as traditional Correlation Image Velocimetry (CIV) and Computer Vision Correlation Image Velocimetry (CVision-CIV), wind velocity fields were successfully estimated from plume imagery. These estimates were found to be of promising precision across a range of conditions, including varying wind velocities, emission rates of greenhouse gasses, and levels of measurement noise in the simulations. Results revealed that the CVision-CIV method outperforms the traditional CIV method, especially in scenarios with low signal-to-noise ratios. Wind velocity fields directly estimated from plume imagery were implemented in the Cross-sectional Flux Method to estimate CO2 emission rates. The emission rate estimates indicate that direct plume velocity measure- ments provide a more accurate estimate of emission source rates than conventional methods, which rely on indirect wind velocity estimates from meteorological data. The use of wind velocity fields obtained through the CVision-CIV method resulted in CO2 emission rate estimates with ±20% accuracy in most scenarios, particularly under optimal SNR conditions. Additionally, the study highlights the impact of mission parameters such as image resolu- tion and measurement noise on the accuracy of wind velocity estimations. It was found that higher image resolution and longer time intervals between measurements enhance the precision of wind velocity field estimates by reducing the relative effects of measurement noise.
In summary, this research demonstrates that direct estimation of wind velocities from emission plume imagery, as enabled by the TANGO mission’s advanced capabilities, can accurately be performed and significantly enhance the accuracy of emission rate estimates. The improved wind velocity estimation methods proposed in this thesis offer a promising advancement in remote sensing techniques for greenhouse gas monitoring.

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.