Near surface CO₂ concentration and its quantitative relationship with character of underlying surface in Shanghai City, China

Land use change and fossil fuel combustion due to urbanization have a significant effect on global carbon cycle and climate change. It's important to have an explicit understanding of the spatial distribution of CO₂ to recognize and control GHG emission, which is helpful to reduce human-induced contribution to global climate change. The study area of this project was set in the city of Shanghai with intensive human activity and rapid urbanization. The monitoring of near surface CO₂ concentration along 3 transects was conducted across an urban-rural gradient by means of near infrared gas analyzer Li-840A in spring, 2014. Remote sensing data were also used to derive underlying surface information. Further quantitative analysis of the mechanism of CO₂ concentration's response to the characteristics of underlying surface was presented in this paper. The results showed that the average near surface CO₂ concentration was (443.4±22.0) μmol·mol^-1. CO₂ concentration in city center was in average 12.5% (52.5 μmol·mol^-1) higher than that in the suburban area. Also, CO₂ concentration showed a significant spatial differentiation, with the highest CO₂ concentration in the northwest, the second highest in the southwest, and the lowest in the southeast, which was in accordance with the urbanization level of the underlying surface. The results revealed that the vegetation coverage rate (C_Veg) was an important indicator to describe near surface CO₂ concentration with a negative correlation, and the impervious surface area coverage rate (C_ISA) had lower explanatory power with a positive correlation. The study also found that the determination coefficient (R²) between CO₂ concentration (C_CO2) and C_ISA or C_Veg achieved its highest value when the buffer distance was 5 km, and their quantitative relationships be described by a stepwise regression equation: C_CO2=0.32C_ISA-0.89C_Veg+445.13 (R²=0.66, P<0.01).