Abstract: Objective　To reveal the spatiotemporal variation characteristics of urban near-ground carbon dioxide (CO2) mass concentration and its major influencing factors, this study focused on the Science and Technology New Area of Mianyang City. Based on the observation data collected throughout 2022, the monthly variation, seasonal differences, and spatial distribution pattern of near-ground CO2 mass concentration in the study area were analyzed, and the influence mechanisms of population activities and environmental factors were explored, so as to provide a scientific basis for urban CO2 emission assessment and related environmental management. Methods　Near-ground CO2 mass concentration data were collected at 26 monitoring sites in the study area from January to December 2022 using a vehicle-mounted CO2 detector. For spatial distribution simulation, three sites were selected as validation points and the remaining 23 sites were used as interpolation points. The seasonal data of CO2 mass concentration in spring, summer, autumn, and winter were interpolated, and the performance of three interpolation methods, namely Kriging interpolation, Inverse distance weighting (IDW), and back propagation (BP) neural network interpolation, were compared. Root mean square error (RMSE) was used to evaluate interpolation accuracy and determine the optimal interpolation method. And then the optimal method was used to generate the spatial distribution map of CO2 mass concentration in the study area. To analyze the influence of the factors on CO2 mass concentration, Pearson correlation analysis was conducted between CO2 mass concentration and factors such as population, normalized difference vegetation index (NDVI), air temperature, precipitation, and elevation (DEM). In addition, linear regression was used to further analyze the relationship between CO2 mass concentration and NDVI. Results and Discussion　The interpolation results showed that the RMSE values of the Kriging interpolation method in spring, summer, autumn, and winter were 5.494, 3.608, 0.241, and 2.335, respectively, all of which were lower than those of inverse distance weighting and BP neural network interpolation. This indicates that the Kriging method is more suitable for simulating the spatial pattern of near-ground CO2 mass concentration in the study area. In terms of temporal variation, the near-ground CO2 mass concentration showed clear monthly differences, with the maximum value occurring in April and the minimum in August. On a seasonal scale, CO2 mass concentration was highest in spring and lowest in summer, indicating that vegetation growth and carbon sink processes played an important role in reducing CO2 concentration. Spatially, the high CO2 mass concentration areas were mainly concentrated in the southeastern part of the study area, while the low-value areas were mainly distributed in the western part, and the central area showed relatively high concentrations. This spatial pattern of CO2 mass concentration was closely related to the higher population density, more intensive traffic and industrial activities, and lower vegetation coverage in the southeastern area. Correlation analysis showed that population was positively correlated with CO2 mass concentration, with a Pearson correlation coefficient r of 0.393, indicating that intensified human activities tended to increase near-ground CO2 levels. Among the environmental factors, NDVI had the strongest influence on CO2 mass concentration (r = −0.647), followed by precipitation (r = −0.618) and air temperature (r = 0.476), whereas DEM had a relatively weak effect (r = −0.295). In addition, the determination coefficient of linear regression between NDVI and CO2 mass concentration was 0.42, indicating that vegetation coverage played an important role in shaping the spatiotemporal differences of urban near-ground CO2, by affecting regional carbon sink capacity. Overall, the near-ground CO2 mass concentration in the study area was the result of the combined effects of human activities and natural ecological processes, and the differences in vegetation coverage and the intensity of population activities were important factors affecting spatial heterogeneity of CO2. Conclusions　Based on vehicle-mounted monitoring and spatial interpolation methods, this study systematically revealed the spatiotemporal variation patterns of near-ground CO2 mass concentration in the Science and Technology New Area of Mianyang City. The results showed that the Kriging interpolation method could accurately characterize the spatial distribution of CO2 in the study area. The near-ground CO2 mass concentration exhibited significant seasonal differences over time and a spatial pattern characterized by higher values in the southeastern part and lower values in the western part. Vegetation cover was the most important environmental factor affecting CO2 variation, while population activities were an important anthropogenic driver promoting the increase in CO2 levels. These findings can provide a reference for urban nearground CO2 monitoring, spatiotemporal variation assessment, and related environmental management.

Key words: CO2 mass concentration, spatiotemporal distribution, environmental factor analysis, interpolation comparison; city