Towards Carbon Dioxide emission estimation with a stationary hyperspectral camera

<p>Carbon Dioxide (CO₂) is the most important anthropogenic greenhouse gas driving global climate change. Strong point sources like coal-fired power plants contribute roughly 30% to global CO₂ emissions. Precise knowledge about the distribution and strength of these sources is the target of many ongoing and planned research missions, e.g., the Orbiting Carbon Observatory-2 (OCO2, Wu et al., 2018), the Copernicus CO2 Monitoring (CO2M, Sierk et al., 2019), and high-resolution missions like CO2Image (Strandgren et al., 2020). Spatially resolving CO₂ exhaust plumes with imaging spectroscopy allows an estimate of the source&#8217;s emission. CO₂ imaging efforts in the shortwave-infrared spectral range have been exclusively in top-down viewing geometry from satellites (e.g., Cusworth et al., 2021) or airplanes (e.g., Foote et al., 2021).<br>We present first results of CO₂ emission estimation from hyperspectral imaging in ground-based viewing geometry. We deploy a NEO HySpex SWIR-384 camera stationary in the vicinity of a strong emission source. Thus, the camera can repeatedly take images of shortwave-infrared spectra (1&#8722;2.5 &#956;m) from sky-scattered sunlight. This allows us to retrieve atmospheric CO₂ enhancements with an adapted matched filter algorithm (Foote et al., 2020, 2021) in the 2 &#956;m absorption band. Imaging in a horizontal viewing geometry enables observing the time-averaged vertical profile of the exhaust plume. First case studies at a local power plant (7 MtCO2/yr) in Mannheim demonstrate our ability to reliably detect CO₂ exhaust plumes above chimneys. Our ongoing efforts focus on modeling the temporal evolution of the plume rise (Janicke and Janicke, 2001) and use it with the integrated mass enhancement of the observed plume to estimate the instantaneous emissions of the source. Such estimates can complement bottom-up inventories and state-of-the-art top-down measurements in the future. Furthermore, this technique may readily apply to greenhouse gases like methane, which we plan to examine in an upcoming field campaign in the Upper Silesian Coal Basin.</p><p><br><strong>References</strong><br>Cusworth et al., 2021: Quantifying Global Power Plant Carbon Dioxide Emissions With Imaging Spectroscopy, https://doi.org/10.1029/2020AV000350<br>Foote et al., 2020: Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior, http://arxiv.org/abs/2003.02978<br>Foote et al., 2021: Impact of Scene-Specific Enhancement Spectra on Matched Filter Greenhouse Gas Retrievals from Imaging Spectroscopy, https://doi.org/10.1016/j.rse.2021.112574<br>Janicke, U. and Janicke, L., 2001: A Three-Dimensional Plume Rise Model for Dry and Wet Plumes, https://doi.org/10.1016/S1352-2310(00)00372-1<br>Sierk et al., 2019: The European CO2 Monitoring Mission: Observing Anthropogenic Greenhouse Gas Emissions from Space, https://doi.org/10.1117/12.2535941<br>Strandgren et al., 2020, Towards Spaceborne Monitoring of Localized CO2 Emissions: An Instrument Concept and First Performance Assessment, https://doi.org/10.5194/amt-13-2887-2020<br>Wu et al., 2018: Carbon Dioxide Retrieval from OCO-2 Satellite Observations Using the RemoTeC Algorithm and Validation with TCCON Measurements, https://doi.org/10.5194/amt-11-3111-2018</p>