CR 69:107-116 (2016)  -  DOI: https://doi.org/10.3354/cr01394

Spatial variability of daytime CO2 concentration with landscape structure across urbanization gradients, Shanghai, China

Chen Pan1,2, Xiyang Zhu1, Ning Wei1, Xudong Zhu3,4, Qiannan She1, Wenxiao Jia1, Min Liu1,*, Weining Xiang1

1Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China
2School of Geography Sciences, East China Normal University, Shanghai 200241, PR China
3Key Laboratory of the Coastal and Wetland Ecosystems, Ministry of Education, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, PR China
4State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, PR China
*Corresponding author:

ABSTRACT: Cities play an important role in the global carbon cycle. However, direct measurements of CO2 concentration in urban environments are still very limited. Using Shanghai as a case study, this paper investigated the spatial pattern of atmospheric CO2 concentration and its relationship with landscape structure across urbanization gradients. From March to April 2014, CO2 concentrations were measured at 2 m above ground level with a near-infrared gas analyzer along 6 transects with a total length of 335 km. The results showed that the mean near-surface CO2 concentration among the 6 transects was 445.8 ± 40.5 ppm. The average CO2 concentration in the inner city was higher (55.1 ppm) than that in the suburban area. Also, CO2 concentration showed a significant spatial heterogeneity, with the highest CO2 concentration in the northwest and the lowest in the southeast, in accordance with the urbanization gradients. Further analysis indicated that the spatial variability of CO2 concentration was mainly influenced by the urban landscape structure and depended largely on the percent of impervious surface cover (ISA) with a positive correlation and on the lower explanatory power for the percent of vegetation cover (Veg) with a negative correlation. This indicated that the trend in atmospheric CO2 in urban areas was likely to depend more on fossil fuel emissions than on vegetation change. The study also found that the Pearson’s correlation (R) between CO2 concentration and ISA or Veg achieved its highest value when the buffer distance was 5 km, which could be described by the stepwise regression equation CO2 = 0.99ISA - 0.18Veg + 378.18 (R2 = 0.44, p < 0.01).


KEY WORDS: Urban CO2 · Mobile measurements · Underlying landscape structure · Shanghai


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Cite this article as: Pan C, Zhu X, Wei N, Zhu X and others (2016) Spatial variability of daytime CO2 concentration with landscape structure across urbanization gradients, Shanghai, China. Clim Res 69:107-116. https://doi.org/10.3354/cr01394

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