Long-Term Trends and Spatiotemporal Variations in Atmospheric XCH 4 over China Utilizing Satellite Observations

: As the second most abundant greenhouse gas after carbon dioxide (CO 2 ), methane not only plays an important role in global and regional photochemical reactions, but also has an important impact on energy balance and climate change. To explore the long-term trends and spatiotemporal variation of methane concentration over China, we veriﬁed the accuracy of the column-averaged, dry air-mixing ratio of CH 4 (abbreviated as XCH 4 hereafter) merged by SCIAMACHY and GOSAT products, utilizing the data of six surface observation stations in China and the surrounding areas. The root mean square error (RMSE) was mostly less than 2.5%, and the correlation coefﬁcients (r) were 0.77, 0.84, 0.66, 0.42, 0.62 and 0.75. Furthermore, we analyzed the temporal and spatial variation patterns of the XCH 4 concentration over China from 2003 to 2020. The results showed that the XCH 4 concentration had an increasing trend over China from 2003 to 2020; the average growth rate was 6.64 ppb · a − 1 , and the value range of the increase rate was from 4.66 ppb · a − 1 to 8.46 ppb · a − 1 . The lowest XCH 4 concentration was located over Tibet (1764.03 ppb), and the high values were located in the Sichuan Basin, Central China (Hunan, Hubei, and Henan) and East China (Anhui and Jiangxi) (1825–1845 ppb). The XCH 4 concentration was higher in autumn and summer, low in winter and spring, and had obvious seasonal variations. Human factors such as population density, GDP and energy consumption have a signiﬁcant impact on the XCH 4 concentration over China.


Introduction
Methane is the second most abundant greenhouse gas after CO 2 . Its life cycle in the atmosphere is long, and its greenhouse effect per unit concentration is 25 times that of CO 2 . The continuous growth of atmospheric methane impacts the radiation balance of the Earth and directly affects climate change [1]. Studies have shown that human industrialization has caused the concentration of methane in the atmosphere to more than double in the past 100 years. If methane emissions are not controlled, the contribution of methane to the greenhouse effect may reach 25% of CO 2 in the next 50 years, which will further aggravate the trend of global warming [2]. Since the industrial revolution, the content of methane in the atmosphere has increased rapidly. At present, the concentration of methane in the atmosphere is more than twice that before industrialization [3,4]. The increase in methane concentration in the atmosphere in the industrial era is mainly due to human activities [5]. The main sources of atmospheric methane originate from the biological processes of the anaerobic environment; the atmospheric methane produced by abiotic processes accounts for only 20%, approximately [6,7], mainly including natural sources (wetlands, termites, etc.) and anthropogenic sources (coal mining leakage, paddy field emission, ruminant emission, etc.). In view of the important role of methane in the atmosphere, since the 1980s, the World Meteorological Organization (WMO) has successively established a network of atmospheric background monitoring stations in different longitudinal and latitudinal areas warming. The key to controlling methane emission is to master the distribution of methane concentration in China, to understand the temporal and spatial characteristics of methane concentration changes in China in the past two decades, and to provide a reliable data basis helpful to the formulation of emission reduction measures in China.
This study evaluates the applicability of satellite-observed XCH 4 products using surface observed data over China, and studies the spatiotemporal variation of XCH 4 concentration on a regional and grid-scale in addition to the effect of human activities on methane emission in China. This study provides a scientific basis for China to formulate a national methane action plan. Respective data collection processes and data characteristics, as well as data processing and analysis methods, are described in Section 2. The accuracy of the satellite-observed XCH 4 is verified, and XCH 4 spatiotemporal variation and the influence of anthropogenic factors are analyzed and discussed in Sections 3.1-3.4. This is followed by a discussion of existing problems and notes for our next work in Section 4, followed by a short conclusion.

Satellite-Observed Methane Concentration Data
SCIAMACHY is onboard the ENVISAT satellite of ESA. The spectral range is 214−2380 nm, covering the ultraviolet, visible and near-infrared bands. The spectral resolution is 0.2-1.4 nm. It contains the absorption bands of a variety of trace gases, including CO 2 , CH 4 , O 3 , and SO 2 , as well as other information. SCIAMACHY obtains the total amount of atmospheric column information through sky-bottom observations, adopts the multi-wave spectral segment division method, designs different integration times according to the requirements of different data rates and signal-to-noise ratios, and improves the spatial resolution to 60 km × 30 km. At present, there are mainly two types of CH 4 products based on the retrieval of SCIAMACHY detection information: (1) CH 4  TANSO is onboard the Japanese greenhouse gas observation satellite GOSAT, which is the world's first spacecraft dedicated to measuring the concentrations of CO 2 and CH 4 . TANSO includes two sensors: a Fourier Transform Spectrometer (FTS) and a Cloud and Aerosol Imager (CAI). TANSO-FTS is mainly used to observe the absorption spectra of greenhouse gases such as CO 2 and CH 4 reflected by the Earth. The TANSO-FTS spectrum covers 750-1430 nm and has four bands. The 760 nm band (758-775 nm) is used to obtain the oxygen concentration, convert the CO 2 content into the mixing ratio, and determine the photon path length. The 1600 nm and 2000 nm bands are used to observe the CO 2 concentration, and 1600 nm can also be used to observe the CH 4 concentration. TANSOspecific medium-and long-wave infrared channels (5.5-14.3 µm) can be used to obtain CO 2 , CH 4 , water vapour and atmospheric temperature profiles.
The XCH 4 concentration data used in this study are from the Copernicus climate data center (https://cds.climate.copernicus.eu/) (accessed on 5 January 2022), with the version XCH 4 _ EMMA (L2 4.3) [25] concentration data of XCH 4 from January 2003 to June 2020. The data are an XCH 4 product synthesized by integrating the inversion products of SCIAMACHY and GOSAT and using the EMMA algorithm of the University of Bremen. This product is a daily value product, and the file format is the Network Common Data Format (NetCDF). In this research, MATLAB software was used to read the data, and the Inverse Distance Weight (IDW) spatial interpolation method was used to generate the mean XCH 4 spatiotemporal distribution grid datasets from January 2003 to June 2020, with a grid scale of 1 • × 1 • .

Surface Observation Data
The surface station observation data, used to verify the accuracy of satellite observation XCH 4 concentration data over China in this study, came from the World Data Centre for Greenhouse Gases (WDCGG). We obtained data from 6 observation stations in and near China (see Figure 1). Waliguan (WLG), Shangdianzi (SDZ) and Lulin (LLN) are located in China [26,27], while Ulaan UUL (UUM), Anmyeon do (Amy) and Pha Din (PDI) are distributed in other countries. In this study, outliers were eliminated according to twice the standard deviation. See Table 1 for detailed site information.   [30,31] and IMAP-DOAS [32] algorithms, which are physics algorithms and improvements to the DOAS algorithm. In addition to calibration, pre-filtering and post-filtering differences, the WFM-DOAS algorithm uses the weight function of the total amount of absorption gas column to replace the absorption interface for least square fitting, and the IMAP-DOAS algorithm directly iterates the concentration of the target component column to fit the simulated differential optical thickness with the observed value.
GOSAT satellite-observed XCH 4 uses the UoL-FP [33] (University of Leicester Full Physics) algorithm developed at the University of Leicester and SRON's RemoTeC (Developed by the Ministry of the Environment (MOE), the National Institute for Environmental Studies (NIES), and the Japan Aerospace Exploration Agency (JAXA) in Japan) [34] (remote sensing of greenhouse gases for carbon cycle modelling) algorithm developed at the Netherlands Institute for Space Research. The two retrieved algorithms synchronously correct the atmospheric and surface parameters during the retrieved process, so as to improve the retrieved accuracy. However, there are differences in the correction methods which they used, the most important of which are the correction methods for aerosol optical properties and distribution. UoL-FP algorithm assumes that there are only a few types of typical aerosols in the atmosphere, and synchronously corrects the column total amount and vertical distribution of various aerosols, while the RemoTeC algorithm uses an abstract aerosol model to synchronously retrieve the scale spectrum distribution, column total amount and vertical distribution of aerosols.

Inverse Distance Weighted Interpolation Method
IDW method is the most representative method in the distance weight coefficient method series. This method performs linear weighting on the observation points around the estimated point to obtain the value of the estimated point. The weight coefficient is inversely proportional to the distance. If v e is used to represent the value of the point to be estimated, then: where v j (j = 1, 2 . . . , n) is the variable value at the point (x i , y j ) around the point to be evaluated. w j is its corresponding weight coefficient, which can be calculated using Equation (2): where n is the number of adjacent points participating in interpolation, and f d ej is the weight function of the distance between the point to be interpolated (x e , y e ) and the neighboring point (x j , y j ). The most commonly used is the distance reciprocal function or the distance reciprocal square function [35,36].

Linear Sinusoidal Trend-Fitting Mode
The linear sinusoidal trend-fitting model has great advantages in analyzing the longterm change law of air pollution affected by seasonal changes [37,38]. We used the linear sinusoidal-fitting model to analyze the variation characteristics of the monthly mean XCH 4 concentration over China from January 2003 to June 2020. The model formula is shown in Equation (3): where Y i is the XCH 4 concentration in the ith month fitted by the model, and X i is the XCH 4 concentration in the ith month observed by satellite; a, b, c, d and e are constant parameters of the model; a reflects the overall level of XCH 4 concentration; b represents the monthly mean variation trend of XCH 4 concentration; c × sin 2π d × X i + e represents the period of XCH 4 concentration change in the long time series; c represents the amplitude of periodic variation; and d represents the period of change. Moreover, d indicates the frequency of change in one year, therefore, the fitting parameter in this study is fixed as 12. M i represents the residual between the fitted XCH 4 concentration and the actual value; Weatherhead pointed out that the residual M i has autocorrelation [39], which is expressed as: where ε t refers to autocorrelation noise, and ϕ refers to the autocorrelation of residuals. The autocorrelation relationship between the residuals will affect the accuracy of the trend of the monthly mean XCH 4 concentration. Weatherhead et al. considered that the accuracy of the trend σ B is a function of the autocorrelation ϕ, the time span T, and the standard deviation of residuals σ N , as shown in Equation (5): If B and σ B meet |B/σ B | > 2, the monthly mean variation trend B is considered significant (95% confidence interval) [40].

Mann-Kendall (M-K) Nonparametric Test
The M-K trend test is often used to analyze the trend changes in a time series of factors such as precipitation, runoff, atmospheric temperature and atmospheric pollutants. Its advantage is that the samples do not follow a specific distribution and are rarely affected by outliers; therefore, they are easy to calculate. For time series variables (x 1 , x 2 , . . . x n ), n is the length of the time series, and the M-K method defines the statistic variable S: S is a normal distribution, its mean value is 0, variance Var(S) = n(n − 1)(2n + 5)/18, when n > 0, and the standard normal statistic is calculated as follows: If Z > 0, it means that the XCH 4 concentration shows an increasing trend in this time series; otherwise, it is decreasing the trend, and the larger the absolute value, the more obvious the trend is [41].

Accuracy Verification of Satellite Observation Data
We used observation data from the six surface stations to verify the accuracy of the satellite observation data. The location-and time-matched methane concentrations of each surface observation station were extracted by ArcGIS spatial analysis tools based on the coordinates of the six stations. The linear regression analysis results (see Figure 2 and Table 2) showed that the correlation coefficients of the six observation stations were 0.77, 0.84, 0.66, 0.42, 0.62 and 0.75, respectively, p < 0.001, and the correlation was significant. The correlation analysis between satellite-observed XCH 4 concentration and surface-observed CH 4 concentration showed that the other five points were strongly correlated except for the PDI site. Therefore, satellite-observed XCH 4 concentration could reflect the overall distribution and temporal variation trends of XCH 4 concentration over China. Possible reasons for a low determination coefficient R for the PDI, SDZ and WLG stations (especially for the PDI station) are as follows. (1) Due to the influence of satellite observation orbit, weather, and other factors, observed data cannot be obtained every day over China. Therefore, instead of using the observed daily mean methane concentration data, we synthesized the monthly mean methane concentration data from satellite observation and ground obser-vation, respectively, to analyze their correlation. (2) The satellite-observed data comprised the methane concentration over the 1 • × 1 • grid area, while the surface-observed data comprised the methane concentration over the observation station. (3) There were few instances of surface-observed data at the three stations (PDI, SDZ and WLG).   Figure 2 shows that the methane concentration observed by each surface station was higher than the XCH 4 concentration observed by satellite. The reason for this may be that the CH 4 concentration observed by the surface station was the near-surface methane concentration, and the satellite-observed XCH 4 concentration used in the study was the column-averaged dry air-mixing ratio of methane.

Temporal Variation Characteristics of Atmospheric XCH 4 over China
By fitting the mean XCH 4 concentration from 2003 to 2020 using the linear sinusoidal trend-fitting model, we can quantitatively describe the inter-annual change rule of the XCH 4 concentration over China (Figure 3). The results show that the fitting coefficient R 2 was 0.94 (p < 0.001), indicating that the simulation can reflect the seasonal and long-term trends of XCH 4 concentration well. In January 2003, the mean XCH 4 concentration over China was about 1750.00 ppb and increased by 0.55 ppb·month −1 until June 2020. The annual mean XCH 4 concentration growth rate was 6.64 ppb·a −1 . Moreover, the monthly difference of XCH 4 concentration in the 12-month cycle was 15.85 ppb·a −1 , indicating that there were relatively obvious seasonal changes in the 12-month cycle.    Figure 5 shows that under a grid scale of 1 • × 1 • , the XCH 4 concentration of all grid units over China showed a significant growth trend, with the growth rate ranging from 4.66 ppbv·a −1 to 8.49 ppbv·a −1 . Those regions with high growth rates were mainly distributed over regions that have strong population activity and oil and natural gas exploitation. With the rapid economic development of North China, including Shanxi, Shaanxi and other provinces with large coal resource development, and Beijing, Tianjin and Hebei with large proportions of heavy industry, anthropogenic pollution has led to an increase in methane emissions. The increase in XCH 4 concentration in Northwest China is mainly due to: (1) geological methane being the second largest natural methane source after wetland methane-Northwest China has become an important oil/gas field accumulation area [42]; (2) methane emissions from animal husbandry, aquaculture and other industries increasing with the increase in human material demand [43]. Northeast China is one of the major grain bases in China. With the expansion of paddy areas and methane emission from natural swamps in the Sanjiang Plain, the concentration of XCH 4 concentration is increasing. At the same time, the region is China's heavy industry and energy base, especially for the development and processing of petrochemical resources. Although the XCH 4 concentration over the Qinghai-Tibet Plateau is low in China, it is increasing in some regions. The main reasons are as follows: (1) the increase in temperature leads to the gradual melting of the permafrost accumulated for many years, the activity of anaerobic bacteria in the permafrost is enhanced, and a large amount of methane is released at the same time [44]; (2) the increase in temperature, precipitation, glacial snowfall and the melting of permafrost have expanded the lake area of the Qinghai-Tibet Plateau and increased the emissions of lake methane [45]. The growth in XCH 4 concentrations in South China is relatively stable, which may reflect their relatively stable natural and anthropogenic sources. The reduction in paddy areas and the change in land use in South China have reduced the methane emissions from paddy fields. However, methane emissions from development and energy utilization have made up for the reduction in methane emission from paddy fields, and the concentration of XCH 4 still shows an increasing trend. Figure 6 shows that the seasonal mean XCH 4 concentration is autumn > summer > winter > spring over China, which is consistent with the results of Wang et al. [23]. Table A1 (Appendix A) shows that the maximum value of the XCH 4 concentration over China year-round occurs in August and September, in summer and autumn, and the minimum value mainly occurs in February in spring. The seasonal variation in methane concentration is mainly controlled by two influencing factors: source (natural and human emissions) and sink. Methane mainly comes from natural ground sources and industrial changes related to human activities [46]. Among them, agricultural activities have seasonal changes; rice paddies are planted in summer, straw is burned in autumn. However, there are basically no significant seasonal changes in industry. In addition, the seasonal variation in methane concentration affected by anthropogenic emissions is small, mainly due to the strong seasonality of natural emissions, high temperature and humidity in summer, and the emission increases from biomass-related methanogens. In winter, the temperature is low, and the emissions of methane from natural sources such as wetlands, frozen soil and plants are greatly reduced. Various factors lead to the overall characteristics of high methane concentrations in summer and autumn and low methane concentrations in winter and spring. Seasonal variation characteristics represent seasonality in most parts of the country. However, due to economic development and different source and sink distributions in different regions, there is strong heterogeneity in the spatial distribution of the seasonal variation in methane concentrations. We compared our results with Wu et al. [24], and found that they are consistent in terms of temporal variation trends. The results of Wu et al. showed that the mean monthly increased rate of CH 4 column concentration was 0.42 ppb·month −1 over China from 2002 to 2016, and the monthly difference was 20.02 ppb·a −1 in the 12-month cycle. Our results showed that the mean monthly increase rate of XCH 4 was 0.55 ppb·month −1 concentration over China from 2003 to 2019, and the monthly difference was 15.85 ppb·a −1 in the 12-month cycle. There were slight differences in seasonal variation. Wu et al. results showed that the mean seasonal CH 4 column concentration followed the order of summer > autumn > winter > spring, while our results showed that the mean seasonal XCH 4 concentration was autumn > summer > winter > spring. Figure 7 shows the spatial distribution of the XCH 4 concentration over China from 2003 to 2019. The XCH 4 concentration ranged from 1764.03 ppb to 1845.28 ppb, with an average of 1799.12 ppb and a standard deviation of 17.36 ppb. In general, the spatial distribution of methane varied greatly in different regions of China, and there were high-value aggregation circles in different regions due to different regional sources and sinks. On the whole, the XCH 4 concentration was high in the southeast and low in the northwest. Methane is a long-lived gas that remains in the atmosphere for approximately 12.5 years. Therefore, the spatial distribution of methane concentration is not only controlled by regional emission sources but also influenced by atmospheric transport. The high XCH 4 concentration areas were distributed over the Sichuan Basin, Central China (Hunan, Hubei and Henan) and East China (Anhui and Jiangxi). The concentration value was approximately 1825-1845 ppb, and the secondary high values were located on the Yangtze River Delta (Jiangsu, Shanghai and Northern Zhejiang), Pearl River Delta (Guangdong and Guangxi), Shandong and Hebei. The low value area was distributed over the Qinghai-Tibet Plateau, with a concentration of approximately 1764.03 ppb. In terms of spatial distribution, high-value areas were mostly distributed in China's main rice-growing areas; densely populated areas with large energy consumption and developed industrial areas. This shows that methane concentration was jointly affected by natural emissions and anthropogenic emissions. The Intergovernmental Panel on Climate Change (IPCC) pointed out that the increase in atmospheric methane concentration is mainly affected by anthropogenic emission sources, and the rapid growth of methane is mainly caused by the imbalance between XCH 4 sources and sinks [47,48].

Spatial Distribution Characteristics of Atmospheric XCH 4 Concentrations over China
Methane concentration emissions can be divided into natural sources and anthropogenic sources. Among them, natural emissions such as swamp wetlands, forest vegetation and frozen soil release account for approximately 30~40%; paddy fields, biomass combustion, energy consumption, and landfill and ruminant emissions account for approximately 60-70%. Due to the imbalance of sources and sinks in different regions, the methane concentrations in different regions are distributed differently. From the perspective of natural sources, the high-value areas of methane emissions from natural wetlands in China should be mainly distributed in Qinghai, Tibet, Inner Mongolia, Heilongjiang, Xinjiang and Jiangsu, accounting for 17.2%, 14.0%, 12.6%, 10.6%, 7.9% and 4.2%, respectively [49]. There are many marsh wetlands on the Qinghai-Tibetan Plateau, but their concentration is low in China. The inconsistency between the spatial distribution and concentration distribution of wetlands indicates that wetland emissions are not the leading factor of high methane concentrations. With global warming, a large amount of methane hydrate released by the melting of glaciers and permafrost has entered the atmosphere, which is also an important potential source of the rapid growth of methane concentrations in Northeast China and the Qinghai-Tibet Plateau. In addition, vegetation is also a potential natural source of methane emission, mainly distributed in Yunnan, Guizhou and Northeast China [50,51].
Anthropogenic methane emissions are mainly related to the regional economy, industrialization and urbanization. Rice-planting areas are mainly distributed in South China. The high value areas shown in Figure 7 correspond to the distribution of rice-concentrated planting areas in China. Sufficient water and a rich organic matrix provide good environmental conditions for the breeding of methanogens. At the same time, straw combustion after rice harvest also produces a large amount of methane gas. Hubei, Hunan, Anhui, Shandong, Jiangsu, Shanghai, Sichuan and other places have a high population density, and the demand for urban wastewater, domestic waste treatment and energy consumption also increases the concentration of methane. There are also sub-high-value areas in Hubei, Henan and Northern Anhui, where emission intensity is slightly lower than that in Sichuan. This area is the main energy-producing area in China, and the exploitation of oil and natural gas causes large methane emissions. On the one hand, the high methane concentration on the North China Plain comes from the cultivation of rice; on the other hand, energy consumption also increases the atmospheric methane concentration. The high XCH 4 concentration areas of Xinjiang are distributed in the petroleum and natural gas production areas of the Tarim Basin and the Junggar Basin [52,53]. The Tibetan Plateau has a high altitude and few human activities; therefore, XCH 4 concentration is always low. However, Lhasa is located in southeastern Tibet. As a major tourism capital city in China, Lhasa has strong human activities and high energy consumption, making this region an area with a high XCH 4 concentration.
Wu et al. [24] research results showed that the multi-year mean CH 4 column concentration ranged from 1784 ppb to 1877 ppb, with a mean of 1832 ppb and a standard deviation of 14 ppb over China from 2002 to 2016. The highest values of CH 4 concentration were mainly distributed in the north of Northeast China, the north of Xinjiang and the Sichuan Basin, and the lowest values were mainly distributed in Tibet and Qinghai. Han's [2] research results showed that the 3-year mean CH 4 column concentration ranged from 1645 ppb to 1826 ppb, with a mean of 1749 ppb over China from 2003 to 2005. The highest value of CH 4 concentrations were mainly distributed in the Sichuan Basin and Central China, and the lowest values were mainly distributed in Tibet and Qinghai. In terms of the multi-year mean methane concentration spatial distribution, our results are consistent with Han's [2] and partially different from Wu et al. [24].

Analysis of the Influence of Anthropic Factors on Atmospheric XCH 4 Concentration over China
The source of atmospheric methane mainly comes from ecosystems and human activities; of these, methane emissions caused by human activities play a major role. Among human methane emissions in China, the methane emissions caused by energy consumption account for 45.3%. In the figure, it can be found that east of China's population line (Hu Line) is the high value area of China's population density, which is consistent with the spatial distribution of XCH 4 concentration. It can be seen from the Figure 8 that the changing trends in XCH 4 concentration in China from 2003 to 2019 are consistent with the changing trends in China's GDP and energy consumption. By analyzing the correlation between the annual mean value of XCH 4 concentration and GDP and energy consumption in China from 2003 to 2019, it was found that the correlation coefficient R 2 values of XCH 4 concentration and GDP and energy consumption were 0.98 and 0.92, respectively (p < 0.001). This shows that population density, economic development and energy consumption have a strong impact on the increase in atmospheric methane concentration. The red curve in Figure 8 shows the annual mean XCH 4 concentration variation over the Beijing-Tianjin-Hebei region from 2003 to 2019. The XCH 4 concentration decreased by 6.65 ppb and 9.26 ppb in 2008 and 2009, respectively, compared with 2007. It began to rise gradually after 2010. The reason for this was that in order to ensure air quality during the 2008 Beijing Olympic and Paralympic Games, the Beijing-Tianjin-Hebei region strengthened the management of motor vehicles and strictly controlled the emission of key polluting enterprises.

Conclusions
In this study, the XCH 4 concentration products synthesized by SCIAMACHY and GOSAT were processed into monthly mean products, and the accuracy was verified by using observation data from six stations, including WLG, SDZ and LLN. Finally, the spatiotemporal evolutionary trends in XCH 4 concentration over China and the influencing factors of human activities were analyzed and summarized using the data. The following conclusions were obtained.
Through the correlation analysis between the XCH 4 concentration observed by satellite and the observation data of LLN, UUM, WLG, PDI, SDZ and Amy, the correlation coefficients were 0.77, 0.84, 0.66, 0.42, 0.62 and 0.75, respectively. The RMSEs were 35.28, 41.72, 33.06, 40.85, 45.51 and 22.09, respectively (average XCH 4 concentration was greater than 1810 ppb). The RMSE was within 2.5%, which can be used to analyze the change in XCH 4 concentration over China.
Through the analysis of the time changes in synthesized XCH 4 concentration products from January 2003 to June 2020, it was found that the increase rate of XCH 4 concentration was 6.64 ppb a −1 for all of China. From the 1 • × 1 • grid scale, the concentration of XCH 4 in all of China showed an increasing trend, with the lowest growth rate being 4.66 ppb a −1 , and the highest growth rate being 8.49 ppb·a −1 . Seasonal variation was obvious, showing that the concentration was higher in summer and autumn, and lower in winter and spring.
Through the analysis of the spatial pattern of synthetic XCH 4 concentration products from January 2003 to June 2020, it was found that XCH 4 in China was high in the southeast and low in the northwest. The high value areas were mainly distributed in the Sichuan Basin, Central China (Hunan, Hubei, and Henan), East China (Anhui and Jiangxi) and the southwest, with concentrations of approximately 1825~1845 ppb. The secondary high values were located on the Yangtze River Delta (Jiangsu, Shanghai and northern Zhejiang), Pearl River Delta (Guangdong and western Guangdong), Shandong, Hebei and other regions. The low value area was distributed on the Qinghai-Tibet Plateau, with a concentration of approximately 1764.03 ppb.
Through the analysis of human factors affecting XCH 4 concentrations over China, it was found that population density, GDP and energy consumption had significant effects on regional CH 4 concentrations.
Since the mean dry air-mixing ratio of the atmospheric methane column observed by satellite was used in this study, and methane is a long-life gas in the atmosphere, XCH 4 concentration cannot be directly used to reflect near-surface methane emission. In our follow-up study, we will try to obtain methane observed data by surface-observed stations over China, and estimate near-surface methane concentrations using machine learning or deep learning approaches [54,55], so as to provide support for greenhouse gas emission control in China.

Conflicts of Interest:
The authors declare no conflict of interest.