工业革命以来，大气温室气体浓度急剧上升，由此导致的全球气候变暖引发了一系列生态环境问题，对地球生态系统构成极大威胁。CO₂作为温室气体中含量最高、增长最快的气体，成为当前全球变化研究领域最受关注的温室气体。作为CO₂主要排放国，中国于2020年提出了实现“碳达峰、碳中和”的“双碳”目标。科学认识中国XCO₂（column-averaged CO₂ dry air mole fraction，XCO₂）时空变化特征及影响因素可为国家制定碳减排政策和实现“双碳”目标提供科学参考。

本文以2009-2020年Mapping XCO₂和2021-2023年OCO-2卫星的XCO₂数据为基础，采用趋势分析、R/S分析、标准差椭圆、皮尔逊相关分析和HYSPLIT模型等方法，分析了2009-2023年中国XCO₂时空变化特征及影响因素，并明晰了中国人为CO₂排放特征和大气传输对区域CO₂浓度的影响。主要研究结论如下：

（1）Mapping-XCO₂和OCO-2 XCO₂数据与同期的鹿林站（LLN）、瓦里关站（WLG）和上甸子站（SDZ）地面监测数据具有较高一致性，其中Mapping-XCO₂与LLN、WLG和SDZ站地面监测数据线性拟合的R²值分别为0.96、0.93、0.50；OCO-2 XCO₂与LLN和WLG站的R²值分别为0.79、0.73（SDZ地面监测数据在2015年以后数据缺失）。

（2）年际上，2009-2023年中国年均XCO₂由385.98 ppm增长到419.74 ppm，增加速率为2.41 ppm/a，但在2020年“双碳”目标提出后，XCO₂年增长量逐年递减；季节上，XCO₂表现为春季>冬季>秋季>夏季；月尺度上，XCO₂每年4月最高，8月最低。空间分布上，2009-2023年XCO₂高值区域主要集中在京津冀、长三角等城市群地区，低值区分布在中国西南部和东北部地区。空间变化上，2015-2023年中国XCO₂平均中心整体向东偏北方向移动，其中2015-2018年，XCO₂平均中心向东部转移，2018-2021年向西部转移，2021-2023年再次向东部转移；一元线性回归分析和R/S分析结果表明，青藏高原、陕西北部、辽宁省、东南沿海部分地区的XCO₂增长速率相对较高且将会持续。

（3）2009-2023年中国XCO₂与NDVI呈显著负相关（R=-0.84，P<0.01），反映出植被覆盖度越高，其对CO₂的吸收作用越强；XCO₂与地表温度、气温和降水也均呈显著负相关（R=-0.51、-0.62、-0.66，P<0.05），地表温度、气温和降水主要通过影响区域植被生长状况进而影响大气XCO₂水平；XCO₂与化石燃料燃烧排放呈显著正相关（R=0.63，P<0.05），排放量越高的区域，二者的相关性越强。地理加权回归模型模拟结果显示，NDVI在全国大部分地区夏季模型中的回归系数表现为负值，特别是在东北和西北地区；化石燃料燃烧排放对应的回归系数峰值均出现在人口密集的中部地区、经济发达的东部城市群地区。

（4）非生长季XCO₂异常在表现人为CO₂排放方面具有重要作用。2009-2021年非生长季XCO₂异常值高值位于京津冀和长三角等城市群地区；受国家碳减排政策影响，2015-2021年京津冀和长三角等城市群地区人为CO₂排放呈下降趋势，华中和东北地区人为CO₂增长速度较高。以省级行政区为单位的非生长季XCO₂异常可表现各省的人为CO₂排放情况，东北地区人为CO₂排放呈上升趋势；华北、华东地区呈先上升后下降的变化趋势；华南地区人为CO₂排放在2009-2015年保持较低水平，但在2015-2021年人为CO₂排放增长较快；华中地区人为CO₂排放在2015-2021年增长较快；西北和西南地区变化平缓。对分别位于青藏高原地区、京津冀城市群和长三角城市群的瓦里关站、上甸子站和临安站CO₂传输路径进行分析，结果显示，影响瓦里关站CO₂浓度的气团主要来自于嘉峪关、酒泉一带和兰州-西宁城市群地区；影响上甸子站CO₂浓度的气团主要来自于北京西南和东南两个方向；影响临安站CO₂浓度的气团主要来自于其北部高人为排放地区。

Since the Industrial Revolution, the concentration of greenhouse gases in the atmosphere has risen sharply, resulting in global warming, which has led to a series of ecological and environmental problems and posed a great threat to the Earth's ecosystems; CO₂, as the most abundant and fastest-growing of the greenhouse gases, has become the greenhouse gas of greatest concern in the current field of global change research. As a major CO₂ emitter, China has set a "double carbon" goal of "carbon peak and carbon neutral" by 2020. A scientific understanding of the spatial and temporal variability of XCO₂ (column-averaged CO₂ dry air mole fraction (XCO₂)) in China and the factors affecting it can provide scientific references for the formulation of national carbon emission reduction policies and the realisation of the "dual-carbon" goal.

In this paper, based on the Mapping XCO₂ from 2009-2020 and the XCO₂ data from the OCO-2 satellite from 2021-2023, we analyse the characteristics of spatial and temporal changes of XCO₂ in China from 2009 to 2023 and the factors affecting them by using the methods of trend analysis, R/S analysis, standard deviation ellipse, Pearson's correlation analysis and the HYSPLIT model. and clarify the influence of anthropogenic CO₂ emission characteristics and atmospheric transport on the regional CO₂ concentration in China. The main conclusions of the study are as follows:

(1) The Mapping-XCO₂ and OCO-2 XCO₂ data showed high consistency with the ground monitoring data from Lulin (LLN), Waliguan (WLG) and Shangdianzi (SDZ) stations during the same period of time, with the R² values of the linear fit of Mapping-XCO₂ to the ground monitoring data from LLN, WLG and SDZ stations being 0.96, 0.93 and 0.50, respectively; The R² values of OCO-2 XCO₂ with LLN and WLG stations are 0.79, 0.73, respectively (SDZ ground monitoring data are missing after 2015).

(2) On the interannual scale, the average annual XCO₂ in China increased from 385.98 ppm to 419.74 ppm from 2009 to 2023, with an increase rate of 2.41 ppm/a, but after the "double carbon" target was proposed in 2020, the annual increase of XCO₂ decreased year by year; On the seasonal scale, XCO₂ shows that spring>winter>autumn>summer; on the monthly scale, XCO₂ is highest in April and lowest in August. In terms of spatial distribution, from 2009 to 2023, high XCO₂ values are mainly concentrated in urban agglomerations such as Beijing-Tianjin-Hebei and the Yangtze River Delta, while low XCO₂ values are distributed in the southwestern and northeastern regions of China. In terms of spatial changes, the average centre of XCO₂ in China shifted to the north-east direction as a whole from 2015 to 2023, with the average centre of XCO₂ shifting to the east in 2015-2018, to the west in 2018-2021, and to the east again in 2021-2023; the results of the one-way linear regression and R/S analyses showed that the Tibetan Plateau, northern Shaanxi, Liaoning Province and parts of the southeastern coast have relatively high XCO₂ growth rates and will continue.

(3) From 2009 to 2023, China's XCO₂ and NDVI show a significant negative correlation (R=-0.84, P<0.01), reflecting that the higher the vegetation cover, the stronger the absorption of CO₂; XCO₂ also shows a significant negative correlation (R=-0.51, -0.62, -0.66, P<0.05) with surface temperature, air temperature and precipitation, and surface temperature, air temperature and precipitation mainly affect regional vegetation growth conditions and then influence atmospheric XCO₂ levels. XCO₂ was significantly and positively correlated with fossil fuel combustion emissions (R=0.63, p<0.05), and the correlation was stronger in regions with higher emissions. The simulation results of the geographically weighted regression model show that the regression coefficients of NDVI in the summer model are negative in most regions of the country, especially in the Northeast and Northwest; the peaks of the regression coefficients corresponding to the emissions from the combustion of fossil fuels are found in the densely populated central region, and in the economically developed eastern metropolitan agglomeration region.

(4) Non-growing season XCO₂ anomalies play an important role in the expression of anthropogenic CO₂ emissions. 2009-2021 non-growing season XCO₂ anomalies are located in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations; due to the influence of the national carbon emission reduction policy, the anthropogenic CO₂ emissions of the urban agglomerations of Beijing-Tianjin-Hebei and Yangtze River Delta show a decreasing trend in 2015-2021, and the anthropogenic CO₂ in central and northeastern China grow at a higher rate. growth rate is higher. The XCO₂ anomaly of the non-growing season based on provincial-level administrative regions can show the anthropogenic CO₂ emissions of each province, with an upward trend of anthropogenic CO₂ emissions in Northeast China; an upward and then a downward trend in North China and East China; anthropogenic CO₂ emissions in South China stayed at a relatively low level in 2009-2015, but the anthropogenic CO₂ emissions grow faster in 2015-2021; Anthropogenic CO₂ emissions in Central China grow faster in 2015-2021; in Northwest and Southwest China, the change will be gentle. The CO₂ transport paths of Waliguan, Shangdianzi and Lin'an stations, which are located in the Tibetan Plateau region, Beijing-Tianjin-Hebei urban agglomeration and Yangtze River Delta urban agglomeration, respectively, are analysed, and the results show that the air masses affecting the CO₂ concentration at Waliguan station mainly come from Jiayuguan, Jiuquan and Lanzhou-Xining urban agglomeration areas; those affecting Shangdianzi station come from the south-west and south-east of Beijing; and those affecting the CO₂ concentration at Lin'an station mainly come from high anthropogenic emission areas in the northern part of the station.

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