Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors

Abstract

Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China.

Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output

“…S10 in Supporting Information S1). The spatial distributions of LGB XCO 2 are generally consistent with those of previous studies based on the data retrieved by the GOSAT satellite (Bie et al, 2020;Lv et al, 2020). The relatively lower capacity of CO 2 assimilation associated with the cold and dry climate as well as the intensive anthropogenic activities could be the main causes of the relatively higher XCO 2 in North and East China (Xu et al, 2017).…”

“…The LGB XCO 2 was well correlated with the ground‐based observations from the LLN, SDZ, WLG, and Hefei stations, with R 2 of 0.61, 0.66, 0.67, and 0.76, respectively (Figure 2). As the WLG, SDZ, and LLN stations measured the near‐surface CO 2 concentrations, the CO 2 observations at these stations were systematically higher than the total column‐averaged densities, that is, XCO 2 (Lv et al., 2020). Due to the obviously diurnal variation in atmosphere CO 2 (lowest in the afternoon and highest in the early morning; Figure S4 in Supporting Information ), the bias could also be attributed to the temporal mismatch.…”

“…The relatively lower capacity of CO 2 assimilation associated with the cold and dry climate as well as the intensive anthropogenic activities could be the main causes of the relatively higher XCO 2 in North and East China (Xu et al., 2017). For Southern China, while there are relatively large biomass burning emissions, the abundant vegetation coverage and vigorous photosynthesis results in a moderate XCO 2 level due to the warm and humid climate (Lv et al., 2020). For Southwest and Northwest China, the relatively lower carbon emissions from fossil fuel combustion and biomass burning caused the lower XCO 2 .…”

“…For the two megacities in China, Beijing located in North China exhibited a rate of increase of 2.52 ± 0.18 ppm/year, which was lower than the 2.65 ± 0.12 ppm/year for Shanghai located in East China (Figure S15 in Supporting Information ). The carbon emission from fossil fuel combustion was considered to be the main reason for the increase of XCO 2 (Lv et al., 2020).…”

“…The XCO 2 exhibits wider temporal variations in As expected, the seasonal variation of XCO 2 was wider in Beijing than Guangzhou (Figure S15 in Supporting Information S1). The spatial heterogeneity of the seasonal variations of XCO 2 could result from the spatiotemporal patterns of plant photosynthesis and anthropogenic activities, including fossil fuel consumption and agricultural production (L. Liu et al, 2009;Lv et al, 2020;Xu et al, 2017). shows more detailed spatial variations and can be used to identify potentially important carbon sources and sinks for further research.…”