Satellite imagery and ground-based micro-sensors for estimating carbon dioxide concentrations: Application to the Oran Region, Algeria
DOI:
https://doi.org/10.35208/ert.1705668Keywords:
Carbon dioxide, greenhouse gas, satellite image, OCO-2, micro-sensorAbstract
Climate change has been a significant international issue for several years. As humanity continues to experience its effects, this issue will only become more pressing. The trend suggests a long-term increase in global temperatures driven by record levels of heat-trapping greenhouse gases (GHG) in the atmosphere. The most significant of these gases is carbon dioxide (CO2), which has the greatest impact on the greenhouse effect. It is crucial to monitor CO2 concentrations in the atmosphere to evaluate the effectiveness of efforts to reduce GHG emissions and to identify any unusual increases. Images from the OCO-2 (Orbiting Carbon Observatory 2) satellite provide XCO2 data since 2014. Initially, this information is combined with measurements of CO2 concentrations on the ground recorded by the Assekrem station located in southern Algeria in order to build a model that can be applied to the Oran region. The performance of the model is characterized by R² = 0.948, RMSE = 1.017 ppm, and MAE = 0.83 ppm. Secondly, the XCO2 data are used to estimate CO2 concentrations in the urban environment of Oran city, in peri-urban areas, and industrial environments based on the created model. The results are compared to ground measurements recorded by a CO2 analyzer equipped with MH-Z19B micro-sensor, calibrated with a classic instrument. Calibration improvements were achieved through machine learning, with MLR emerging as the most effective method (R² = 70.7%). The developed methodology is fast, cost-effective, and adaptable for use in other regions to monitor the evolution of CO2 concentrations in ambient air. The novelty of this approach lies in the combined use of satellite observations, reference-grade monitoring, and low-cost sensors within a machine learning methodology. This multi-source integration offers significant potential for improving local CO₂ estimations and for expanding validation networks, particularly in under-instrumented regions.
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