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 CO₂concentration in detail, capture fine spatial variations, and gain a clearer understanding of emission patterns at an urban road intersection and within a tunnel.