Abstract. Carbon dioxide (CO2) is the most significant anthropogenic greenhouse gas. However, tracking CO2 levels can be challenging due to the uneven distribution of concentrations and the high cost of sensors. In this study, we explored several correction techniques to enable the large-scale use of affordable CO2 sensors, thereby enhancing the spatial resolution. We found that the low-cost CO2 sensor (HT-2000) closely aligned with the trends observed in data from a more accurate sensor (LI-840a). By applying multiple-point linear regression, we reduced the root mean square error (RMSE) to only 1 %–2 % of the measured value, which is accurate enough for urban monitoring at a local scale. Using a large network of low-cost sensors, we were able to map CO2 concentration in detail, capture fine spatial variations, and gain a clearer understanding of emission patterns at an urban road intersection and within a tunnel.
Park et al. (Mon,) studied this question.