Analysis on the variations of atmospheric CO₂ concentrations along the urban–rural gradients of Chinese cities based on the OCO-2 XCO₂ data

ABSTRACT

Anthropogenic CO₂ emissions contribute the most to the growth of atmospheric CO₂ concentrations. These emissions are largely concentrated in urban areas where human activities are intense. Studies have been conducted to explore the urban and rural difference in CO₂ concentrations based on ground-based measurements. The launch of NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite provides a new opportunity to monitor CO₂ concentrations and their spatial and temporal variations at city scale. The objective of this study is to analyse the spatial and temporal distributions of CO₂ concentrations along the urban–rural gradients of Chinese cities, using the column averaged CO₂ dry air mole fraction (XCO₂) data derived from OCO-2. We used both conceptual and physical urban–rural gradients to analyse variations in CO₂ concentrations over space. The results show that the urban and rural difference in CO₂ concentrations of these cities can be monitored. And, the seasonal variations of CO₂ concentrations in these cities can also be detected using the XCO₂ data. Moreover, the variations in CO₂ concentrations along the urban–rural gradients have four main types with significant enhancements of CO₂ concentrations were observed in urban areas, urban–rural transitional areas, rural areas, and without regular patterns, respectively. The results are generally different from the common assumption that CO₂ concentrations peak in central urban areas and decline in rural areas. In conclusion, the XCO₂ data can be used to analyse the spatial-temporal variations of CO₂ concentrations along the urban–rural gradients of Chinese cities, and the results have important policy implications for mitigating CO₂ emissions.

Acknowledgements

This work was financially supported by the National Science Foundation of China (41401215), Shenzhen Oversea Talent Program of Technical Innovations (KQCX2014052114595626), and National Key Research Plan of China (2017YFC0505800).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41401215];Shenzhen Oversea Talent Program of Technical Innovations [KQCX2014052114595626]; and National Key Research Plan of China [2017YFC0505800].