Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point
Low-cost climate sensors suffer from poor accuracy, frequent calibration requirements, and limited adaptability. Method: This study proposes an end-to-end machine learning calibration framework tailored for agile hardware—featuring a rapidly reconfigurable embedded sensing system supporting modular multi-pollutant integration. A field co-calibration architecture was deployed at the Cape Point Global Atmospheric Watch station in South Africa, integrating reference sensor data and applying random forest regression for in-situ CO₂ calibration. Results: The proposed method significantly outperforms conventional calibration strategies, reducing error by over 40%, enabling low-cost sensors to achieve near-reference-grade performance, and extending manual calibration intervals by more than threefold. This work represents the first validation in the Southern Hemisphere of a hardware–algorithm co-driven calibration paradigm for low-cost environmental sensors, establishing a reusable technical pathway for large-scale, long-term, and highly robust environmental monitoring networks.