| Popis: |
Understanding how carbon dioxide behaves in an urban environment is one of the requirements of setting Carbon Credits and avoiding double counting. There are several different data sources, and techniques, available to capture the CO2 concentration, and in our study, we are looking at the combination of two completely different data streams: remotely sensed CO2, through the NASA OCO2/3 satellite mission (complemented with national flux-tower reading), and a local IoT network of CO2 gound sensors.Independently used, it is possible to indicate the CO2 concentration in a region with either data stream. E.g., it is possible to downscale the Remote Sensed (RS) data to a more granular level, but this will only be possible over the area where there are measurements because of the lack of ground-truthing points. The reverse is applicable for the IoT area, here it is possible to upscale the data, but the field of vision of the IoT is limited to the sites for which it is deployed.This study aims to develop a model that combines the two techniques by integrating the high-resolution land cover information, in this case, the Copernicus Urban Atlas, to fill in the OCO2/3 data gaps with the measurements of the ground sensors.To test this hypothesis that it is possible to both up- and downscale CO2 concentrations, a network of ground sensors will monitor the CO2 concentrations in a middle-sized town in France (Dijon), and we deployed the first set of 4 sensors on strategic land cover areas to establish the first baselines. By researching the relationships between CO2 concentrations and land cover, we hope to create an enhanced CO2 index that captures the local characteristics and gives better insights into the regional spatial-temporal dispersion patterns of CO2 concentrations and extrapolate the findings to regions where only either RS or IoT data is available. |
