Long-Term Trends of Atmospheric CH4 Concentration across China from 2002 to 2016

Abstract

Spatiotemporal variations of atmospheric CH₄ from 2002 to 2016 across China were detected, based on the Atmospheric Infrared Sounder (AIRS) sixth-layer CH₄ concentration. The CH₄ concentration showed good consistency with the ground measurements of surface CH₄ concentration from the World Data Centre for Greenhouse Gases (WDCGG) (R² = 0.83, p < 0.01), indicating that the remotely-sensed CH₄ reflected the spatial and temporal variations of surface CH₄ concentration. Across China, three hotspots of CH₄ concentration were found in northern Xinjiang, the northeast of Inner Mongolia/Heilongjiang, and the Norgay plateau in northwest Sichuan. The CH₄ concentration showed obviously seasonal variations, with the maximum CH₄ concentration occurring in summer, followed by the autumn, winter, and spring. Furthermore, the CH₄ concentration showed significantly increasing trends across China, with the rate of increase ranging from ~0.29 to 0.62 ppb·month⁻¹, which would bring a 0.0019~0.014 mK potential rise in surface temperature response over China. In particular, the most rapidly increasing rates occurred in the Qinghai-Tibet plateau, while relatively low rates occurred in southeast China.

1. Introduction

Methane (CH₄) is one of the main greenhouse gases [1], with a contribution of 4–9% to the earth’s greenhouse effects [2,3]. Moreover, CH₄ is an active chemical gas with a long life in atmosphere, which has a significant impact on the earth–atmosphere radiation budget and climate change [4]. The atmospheric CH₄ concentration has increased from 720 ppbv in 1750 to 1803 ppbv in 2011, an increase of almost 150 percent over the pre-industrial level (IPCC5), reached 1834 ppb in 2015, and is still accelerating [5,6,7]. The rapid increase of CH₄ was mainly caused by the imbalance of CH₄ sources and sinks, since a large amount of CH₄ was emitted from human activities in the industrial era [8]. Understanding the spatial and temporal variations of CH₄ in the atmosphere is essential for studying climate change [9].

Natural sources account for 35% to 50% of the total sources for CH₄, which mainly include wetlands, termites, and oceans [10]. The remaining sources are anthropogenic, including energy utilization, landfill, ruminants, rice paddies, and biomass burning [5,11,12]. The CH₄ emissions from wetlands increased by 7% from 2003 to 2007, due to the warming in the mid-latitudes and the Arctic [13], and they have remained constant at 186 ± 24 Tg CH₄ per year from 2010 to 2012 [7]. China has the largest wetlands area among Asia’s countries, which emit 1.7–10.5 Tg CH₄ per year [14,15,16]. Meanwhile, the contribution of the CH₄ emissions from rice cultivation to the total anthropogenic emissions decreased by 30% between 1980 and 2010. Also, the increase of CH₄ emissions between 1980 and 2010 was mainly attributed to coal exploitation (70% of the total increase) which occurred mostly after 2000, followed by livestock (26%), mostly before 2000 [6,7,17,18]. CH₄ emissions from livestock were reported to be ∼11.1 Tg CH₄ per year [10], and the contribution of the additional +7 Tg CH₄ per year came from fossil-fuel-related emissions from 2000–2006 to 2008–2012 [6,18]. In the last 20 years, there has been a rapid change in the sources of related CH₄, since China has undergone one of the largest and most rapid developments of industry and agriculture [19]. The potential change in sources of CH₄ emissions will greatly influence the CH₄ concentration in the atmosphere. However, few studies have been conducted to evaluate the long-term spatiotemporal variations of atmospheric CH₄ concentrations across China [20,21,22,23,24,25].

There are several ways to measure CH₄ concentration in the atmosphere, such as by taking ground-based measurements, aircraft sampling, and space-borne remote sensing. Compared with other methods, space-borne remote sensing provides an opportunity for evaluating the spatial and temporal changes of CH₄ concentration in the atmosphere on a global scale, which can effectively make up for the deficiency of near-surface monitoring measurements [26,27,28]. Several sensors provide CH₄ concentrations in the atmosphere, including the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), the Greenhouse Gas Observation Satellite (GOSAT), and the Atmospheric Infrared Sounder (AIRS). Compared with those from SCIAMACHY and GOSAT, AIRS provides the longest sequence of CH₄ observations since 2002. In addition, AIRS possesses a cloud-clearing capability [29,30,31] that enables partly cloudy scenes to be retrieved, thus achieving 70% effective daily coverage. The errors of CH₄ products from AIRS were basically controlled to within −1.4%~0.1%, and the root mean square ranged from 0.5% to 1.6% [32]. The AIRS retrievals have been used to analyze the distribution, evolution, and transmission of CH₄ concentration in the atmosphere in China [20,21,22,23,24,25]. However, as satellite observations of atmospheric composition research in China began later, an in-depth, large-scale, long time-series analysis of the characteristics of the change in CH₄ concentration in China has not been conducted.

This study aimed to analyze the spatiotemporal variations of CH₄ concentration in the atmosphere over China, based on the monthly CH₄ concentration retrieved from the AIRS. First, the monthly and spatial variations of CH₄ products from AIRS were compared with the ground-based observations, to test whether the remotely sensed CH₄ concentration were in good agreement with the surface CH₄ concentration; second, the spatial distributions of CH₄ concentration in China were analyzed and three hotspots are further discussed; and finally, the long-term trends of CH₄ from 2002 to 2016 across China were detected, and the surface temperature responses for a 20-year timeframe were calculated, to indicate climate change.

2. Materials and Methods 2.1. Atmospheric CH₄ Concentration from AIRS

AIRS was launched on 4 May, 2002 aboard the National Aeronautics and Space Administration (NASA) EOS/Aqua platform. This sensor consists of 2378 channels, covering 649–1136, 1217–1613, and 2169–2674 cm⁻¹ at high spectral resolution (λ/ Δ λ = 1200, 0.5 cm⁻¹) [33]. The satellite operates in a near polar, sun-synchronous orbit, with an equator crossing time of 13:30 (ascending node). The footprint of AIRS is 13 km at nadir, over a 24-h period, AIRS nominally orbits the complete globe twice per day [32]. The errors of CH₄ products were basically controlled within −1.4%~0.1%, and the root mean square error (RMSE) ranged from 0.5% to 1.6% [32]. Also, as supported by using the aircraft measurements from the National Oceanic and Atmospheric Administration (NOAA), ESRL/GMD (the Global Monitoring Division at the NOAA Earth System Research Laboratory) and the campaigns INTEX-A and -B, the correlation of AIRS mid-upper tropospheric CH₄ with aircraft measurements was ~0.6–0.7, and the bias and the RMSE differences were less than ±1% and 1.2%, respectively. By studying the seasonal cycles of CH₄ concentration, the variation of CH₄ concentration in the mid-upper troposphere of AIRS was in accordance with the results of NOAA aircraft measurements [32].

In this study, AIRS Version 6 (V6) Level 3 (L3) Standard products from September 2002 to September 2016, a total of 169 months, were adopted (http://disc.sci.gsfc.nasa.gov/). These monthly data present the CH₄ volume mixing ratio, reported at 24 levels from 1000 hPa to 1.0 hPa, which are binned into 1° × 1° grid cells. AIRS data are stratified according to the characteristics of atmospheric pressure, and the strongest sensitivity of the AIRS instrument appeared at a height of 200 hPa~500 hPa in different latitudes in the Northern Hemisphere [32,34]. This is because the actual land surface is unevenly distributed with different altitudes, and no data are available in the areas with high altitudes, such as the Qinghai-Tibet plateau and the Yungui plateau at lower layers. Considering the continuous coverage of CH₄ data across China, and its indication of on the ground emissions, the data of 500 hPa of AIRS are used in this study. The original AIRS CH₄ data are stored in HDF format, which are then converted to raster data format. The inverse distance-weighting (IDW) method was used to fill in gaps in the original data.

2.2. Ground Measurements of CH₄ Concentration

To test the representations of the spatial and temporal variations of CH₄ concentration from AIRS, the ground observations of CH₄ concentration were used for comparison with the remotely sensed CH₄. The near-surface CH₄ concentrations are from the WDCGG (http://ds.data.jma.go.jp/gmd/wdcgg/) which comes under the Global Atmosphere Watch (GAW) programme of the World Meteorological Organization (WMO). GAW is mainly responsible for long-term observations of greenhouse gases, the ozone layer, solar radiation, precipitation chemistry, aerosols, reactive gases, persistent organic pollutants, heavy metals, isotopes, meteorological elements, etc. [35,36,37]. More data observations and quality control methods are constrained. Detailed information can be found in the literature [38,39].

In this study, nine observation sites (Figure S1) located within and close to China were selected. Among them, three sites—Mt. Waliguan (WLG), Shangdianzi (SDZ), and Lulin (LLN)— are located within China, and the other six—Ulaan Uul (UUM), Tae-ahn Peninsula (TAP), Anmyeon-do (AMY), Ryori (RYO), Minamitorishima (MNM), and Pha Din (PDI)—are located around China. The monthly data available for the corresponding period of CH₄ concentration were used in this study. We deleted the CH₄ concentrations that seriously deviated from the main trend of the data at each site, before using in further studies. More specific information and locations are shown in Table S1.

2.3. CH₄ Emissions in China from EDGAR

The gridded CH₄ emissions across China were from the Emissions Database for Global Atmospheric Research (EDGAR) (http://edgar.jrc.ec.europa.eu/archived_datasets.php#ghg). The current development of EDGAR is a joint project of the European Commission Joint Research Centre and the Netherlands Environmental Assessment Agency. The new database structure can calculate emissions by country and by sector, and this includes specific technologies for fuel/treatments and emission reduction measures. In addition, to facilitate the use of EDGAR data in air pollution and climate simulations on different scales at the same time, a newly developed map was used to allocate multiple emission sources in each country by 1° × 1° grids [40,41]. The emission data from 2002 to 2012 were obtained from EDGAR v. 4.3.2. Twelve years of annual CH₄ emissions data were calculated, to compare with the corresponding satellite retrievals. Furthermore, the emissions by different sectors across China in 2007 were also calculated.

2.4. Surface Temperature Data

The surface temperature data in China from 2002 to 2016 were accessed from the NOAA National Center for the Environmental Prediction of the Department of Energy (NCEP/DOE) Reanalysis 2 [42]. The dataset was an improved version of the NCEP, with temporal resolutions of 4-times per day, daily, and monthly coverage. The spatial resolution was 1° × 1° by grids, which denoted a 2 m altitude above the ground. In this paper, the monthly averaged temperatures from September 2002 to September 2016 were selected.

2.5. A Linear Trend Model with a Seasonal Component to Simulate the Trends of CH₄ Concentration

To detect the long-term trends and seasonal variations in CH₄ concentration from 2002 to 2016, a linear model with a seasonal component was proposed. This model has frequently been found to be useful in detecting the trends of UV radiation, total ozone, and NO₂ concentration [43,44,45]:

_Yi=A+B_Xi+Csin(D_Xi+E)+_Mi

where, _Yi represents the monthly CH₄ concentration (ppb·month⁻¹) at month i; X_i is the number of months after September 2002; _Mi is the residual unexplained by the fit function; and A, B, C, D, and E are the fit parameters.

“A + BX_i” is a linear function representing the linear trend of CH₄ concentrations from 2002 to 2016, where A represents the value in September 2002 and B is the change in the slope of the monthly values. “Csin (DX_i + E)” represents the seasonal component of annual CH₄ concentration. The seasonal component contains an amplitude C, a frequency D and a phase shift E. The fit of the frequency D leads to an expected period of one year; therefore, this fit parameter is fixed to π/6 for the final analyses. The residual M_i is the difference between the model and the measured values.

2.6. Trajectory Analysis

The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) was used to identify the influence of air masses on the accumulation of CH₄ in the atmosphere (http://www.arl.noaa.gov/ready/hysplit4.html). The HYSPLIT model can calculate the paths of the air masses from one region to another. It documents the vector for air pollutant transport, which determines the origins of air masses and establishes source–receptor relationships [46,47]. The model calculation method is a hybrid between the Lagrangian approach, using a moving frame of reference for the advection and diffusion calculations as the trajectories or air parcels move from their initial locations, and Eulerian methodology which uses a fixed three-dimensional grid as a frame of reference to compute pollutant air concentrations. Using GDAS (global data assimilation system) meteorological data as the input, a 24 h backward trajectory was modeled at 3 km above the starting point, to analyze the mid-upper tropospheric atmospheric trajectory. We simulated the monthly back-trajectories based on four weeks in a month. In each week, we selected six back-trajectories to represent the main directions of the air-mass. Overall, we had 24 back-trajectories every month. In order to show the seasonal changes of the airflow trajectories more clearly, typical directions were chosen to denote the main back-trajectories in April, July, October, and January in 2007, which represent the air-mass in spring (March to May), summer (June to August), autumn (September to November), and winter (December to February of the next year), respectively.

2.7. Surface Temperature Response

The absolute global surface temperature change potential (AGTP) can directly reflect the impact of a certain greenhouse gas emission from the perspective of surface temperature rise [48,49]. The gas emissions (kg) over the time period multiplied by the corresponding warming potential AGTP (K·kg⁻¹) can estimate the surface temperature response variations (ΔT) [49,50,51]. In this paper, ΔT indicates the warming potential caused by the accumulation of CH₄ emissions during a 15-year time period.

AGT_P(CH4)t=_ACH4×∑j=12[(∝×_cj∝−_dj)×(^e−t∝−^e−t_dj)]

Δ_Tt=E×AGT_P(CH4)t

where, AGT_P(CH4)t is absolute global temperature potential of CH₄, K·kg⁻¹ and t is the 20-year time horizon; _ACH4 represents radiative forcing of CH₄, 2.1 × 10⁻¹³ W (kg·m²)⁻¹; α is the life cycle of CH₄, 12.4 years; _cj is climate sensitive parameters and _dj is response times (IPCC, 2014). _c1 and _c2 are 0.631 and 0.429, respectively; _d1 and _d2 are 8.4 and 409.5, respectively; ^e−t∝ is known as an impulse radiative flux (IRF); E stands for the CH₄ emission, kg·yr⁻¹. In Equation (4): Δ_Tt is the temperature change response, K. An annual emission (kg) is multiplied by the AGTP values to arrive at the potential of temperature change (ΔT) in a given year.

3. Results and Discussion 3.1. Comparison of the Seasonal and Spatial Variations Indicated by AIRS CH₄ Concentrations Using Ground Measurements

3.1.1. Comparison of the Seasonal and Spatial Variations Indicated by AIRS CH₄ Concentrations Using Ground Measurements

To test the sensitivity of monthly variations in AIRS CH₄ concentration, the in situ CH₄ concentrations collected at the nine sites from WDCGG were used [52,53]. The scatter plots of the AIRS CH₄ concentration, and the ground measurements of CH₄ concentration showed good consistency for each site over the long time-series, with the correlation coefficients ranging from 0.55 to 0.81 (Figure 1). However, the ground data were obviously higher than those of the satellite retrievals, since AIRS detected the CH₄ concentration 3 km above the ground. The CH₄ decreased with the atmospheric altitude, which meant that the CH₄ concentration was higher at lower altitude. The difference in the atmospheric altitude of the two data sources resulted in higher values for AIRS concentrations. Similar results were obtained in previous studies [22,23,25].

The highest correlation coefficient was 0.81 at WLG, with the lowest monthly average deviation and RMSE. The lowest correlation coefficient was 0.55 in TAP. Although the RMSE and the bias in PDI were the highest, their correlation coefficient reached 0.74. This phenomenon might be affected by the frequent atmospheric transmission in the tropics [23]. The satellite data in SDZ were less correlated with the ground-based measurements, and the corresponding RMSE and bias were also relatively high. This is mainly because that SDZ was located in Beijing, one of the largest cities in China. The CH₄ concentration at the surface level was greatly affected by human activities and land-use changes [24]. The altitudes in WLG and LLN were much higher than at other stations, as shown by the minimum monthly average deviation and the RMSE, which to some extent validated the available AIRS data. Instead of the inland sites, the CH₄ concentration in MNM and RYO (near-marine) was also in better agreement with the surface measurements. Compared with the near-infrared waveband, AIRS mainly uses the thermal infrared waveband to invert the concentration of CH₄ in the atmosphere, which is also less influenced by clouds and aerosols [32,34,54,55].

We should note that the RMSE in SDZ, PDI, TAP, and AMY was higher than in the other five monitoring sites. These sites with high RMSE values were located in populated areas, which had high levels of anthropogenic CH₄ emissions. These emissions resulted in a high CH₄ concentration at the surface level in local areas, but did not induce high concentrations in the atmosphere, since the ground CH₄ was transferred into the neighboring areas [56,57].

3.1.2. Comparison of the Spatial Variations of AIRS CH₄ Concentrations with Ground Measurements

To study whether AIRS data can indicate the spatial variations of ground CH₄ concentration, the averages of the annual and monthly CH₄ concentrations for each year from AIRS and ground measurements at the nine sites were compared (Figure 2 and Figure S2). The annual ground measurements and the remotely sensed data also showed good consistency, with a correlation coefficient of 0.83 (p < 0.01). The consistency between the ground measurements and the AIRS CH₄ data in each year showed similar results to those of the data as a whole (Figure S2), with the correlation coefficients ranging from 0.39 to 0.70. The average slope was around 1.20, indicating that the spatial variation trend of all of the stations on an annual scale was consistent, which further demonstrated that AIRS could be used to replace the near-surface data in order to study variations of trends on a large regional scale, during a long time-series.

The high correlation coefficients of the monthly and annual CH₄ concentrations from AIRS and ground measurements showed that the AIRS CH₄ concentration reflected the monthly and spatial variations of the near-surface CH₄ concentrations and could be used in further studies. However, we should note that these ground measurements were mainly conducted at locations that had few human activities, and this good consistency between the ground CH₄ concentration and the AIRS CH₄ mainly indicated that AIRS CH₄ concentration could reflect the monthly and spatial variations of CH₄ concentrations near the ground in remote areas. Whether the AIRS CH₄ concentration can reflect the spatial and temporal variations of the near-surface in well-developed areas needs to be further studied.

3.2. Spatial Variations of CH₄ from AIRS over China

The multi-year-averaged AIRS CH₄ concentration across China during 2002–2016 is described in Figure 3a. The CH₄ concentration ranged from ∼1784 to 1877 ppb, with a mean of 1832 ppb and a standard deviation of 14 ppb. On the whole, although CH₄ concentration did not show great spatial variation across China, it still presented some spatial clusters. The CH₄ concentration presented apparently latitudinal zonal distribution characteristics, showing high CH₄ concentrations in the north, and low CH₄ concentrations in the south (Figure 3b). The spatial pattern of atmospheric CH₄ was influenced by emission sources and the accumulation of atmospheric transport. Zhang [58] pointed out that CH₄ emission sources demonstrated latitude and seasonal changes, and that the photochemical reaction of CH₄ with OH radicals is affected by solar radiations [59]. Methane at low latitudes is more frequently consumed by photochemical reactions, and it has a life cycle of about 12.5 years. Therefore, the accumulation of CH₄ in the atmosphere is explained by latitudinal differences, which are characterized by lower CH₄ concentrations in south China than in the north, while in central China, the concentrations are relatively stable because of the effect of the low Qinghai-Tibet values.

Moreover, the rice paddies and wetlands, and their related natural characteristics and human activities, resulted in higher CH₄ concentrations in the eastern regions of China than those in western China [22,60]. Three hotspots of CH₄ concentration over China were found. In this regard, taking the air-mass backward trajectories of different seasons in 2007 (Figure 4) as being representative, we further analyzed the cause of the high-value accumulations.

Region I features oil/gas production, with the two well-known oil/gas production bases of Tarim Basin and Junggar Basin [61]. Geological CH₄ is the second largest natural source after wetlands [62]; leakage of oil/gas basin geology, fossil fuels, raw coal, and leak explorations for natural gas are all likely to lead to high values of CH₄ concentrations in northern Xinjiang. Table 1 shows the incidence of higher CH₄ emissions from oil/gas production and fossil fuel fires in Region I, compared with the national average. Also, the formation of high CH₄ concentration centers is related to the local terrain, and to air-mass transmission (Figure 4a). Table 2 suggests that the back-trajectories might carry a large amount of CH₄ emissions to Region I in summer and winter. In summer, the air masses were mainly from the north, and the CH₄ concentrations were still low to the south of the Tianshan Mountains, due to the barrier of high altitude, which would cause a locally high concentration of CH₄. In winter, the air-mass was mainly from the west, but a few air clusters from the east were still circling over a small range, leading to the accumulation of CH₄ concentration in autumn and winter in this region, compared with the surrounding atmosphere. Moreover, based on the re-analysis of the monitoring data of meteorological stations in northern Xinjiang, the annual average wind speed in northern Xinjiang was 2.89 m/s from 1960 to 2013, and the strongest wind speeds occurred in spring and winter, accounting for 41.8% and 25.5% of the total timeframe, respectively [22]. Also, Liu [63] pointed out that according to surface monitoring, the dominant wind direction on the surface in northern Xinjiang from 1999 to 2013 was from the west, west-northwest, and northwest, with occurrence frequencies of 11.9%, 10.2% and 12.9%, respectively. The CH₄ concentration in this region remained at high values throughout the year, especially in summer and winter, followed by autumn and spring, which was also consistent with the seasonal characteristics in Region I.

Region II had some potential CH₄ sources. First, this area had the largest area of permafrost and island-like permafrost, which was a potential source of CH₄ emissions in the case of drastic climate change or geological movement [64]. Second, this area had a large area of wetland forest, which was one of the main CH₄ sources [65]. Ding and Wang estimated that 67% of methane emissions are from freshwater swamps in northeast China [66]. Third, burning coal/oil/gas for heating in winter also leads to large CH₄ emissions in Region II (Table 1). Moreover, the high CH₄ concentrations in northern Inner Mongolia may also be related to the breeding of large numbers of ruminants [67,68]. According to Table 1, a part of CH₄ emissions in Region II came from animal husbandry, including enteric fermentation and manure management. Paddy fields and municipal waste landfills in Region II also produce a large number of CH₄ emissions. Furthermore, considering the elevation and the local terrain, Region II possesses a plateau in the northwest and a plain in the southeast, which means that this area is relatively open in the south. In summer, the air-mass mainly comes from the northwest, and in spring, autumn, and winter, from the southeast (Figure 4b). Therefore, the high concentrations of CH₄ components brought by the air-mass hardly diffuse, especially in autumn and winter, which would cause a high rate of CH₄ accumulation.

The high CH₄ concentration in Region III mainly resulted from the transport of high methane emissions, as well as the transmitted CH₄ from the lower troposphere to the upper troposphere during the monsoon period [66,69,70]. CH₄ emissions from rice paddies in the Sichuan basin during summer are one of the main reasons, and they cause the CH₄ to increase significantly over a larger range around the natural sources, in summer [71]. Table 1 demonstrates that the higher CH₄ emissions in Region III came from agricultural soil and municipal landfill, compared with other regions. The CH₄ emissions of landfill gas are positively correlated with the amount of landfill gas. The higher the population density, and the more developed the economy is, the higher the landfill gas output will be [72,73]. The air-mass from the southeast brings large amounts of CH₄ gases, and these accumulate here, due to the high plateau pressure (Figure 4c). Atmospheric transport has a far greater impact on Region III than on the other two regions, with possible CH₄ emissions being more than twice as large as those in other regions (Table 2). The evidence is that a strong enhancement of the CH₄ plume was found by AIRS, from the surface to about 10 km altitude, along the latitude of 35° over 70°–140°E in summer [9], which is consistent with our conclusions. In addition, there is a stable high-value center along the Himalayas, which may be associated with geological emissions of the fault zone, but the specific reasons need to be further confirmed [21].

We found that the average atmospheric CH₄ concentrations of the three regions were higher than the average CH₄ concentration, and that the maximum values appeared in Region III. CH₄ in Region II was higher than that of the emission inventories in China (Table 1), which indicates that high levels of CH₄ emissions do exist on the surface. Meanwhile, the CH₄ emissions in Region I and Region III were relatively lower than the national average, confirming a strong transmission from the troposphere by the air-mass. Methane has a long life cycle as a greenhouse gas, and it is remarkable that the CH₄ distribution in the upper troposphere, as detected by satellites, was not only correlated with emission sources on the ground, but was also related to the transmission of atmospheric motion. This was also an important reason for explaining the uncertainty of the sources of CH₄ spatial distribution, which we should not neglect.

3.3. Seasonal Variations of CH₄ Concentration over China

The averaged CH₄ concentrations in China over the four seasons during 2002–2016 were 1816.99, 1850.39, 1840.85, and 1827.32 ppb, respectively. The peak of CH₄ concentration occurred in summer, the lowest was in spring, and the autumn and winter concentrations were within this range. The averaged CH₄ concentration in spring was about 1816 ppb. Contrasted with spring, the CH₄ concentration over the entire area was almost 1850 ppb in summer, an increase of nearly 33 ppb. Then, the CH₄ concentration decreased slightly in autumn and winter. Compared with the average, the AIRS CH₄ concentration in China’s sites (WLG, SDZ, LLN) showed quite similar variations around the averaged CH₄ concentration (Figure S3). The CH₄ concentrations at all sites showed the highest values in summer, and the lowest values in winter, followed by autumn and spring. In contrast, the highest CH₄ concentrations for other regions were found to be in autumn, followed by spring, summer, and winter. The different characteristics between China and other regions may be caused by the location and different patterns of atmospheric CH₄ convection transport in different areas [9,56]. On the whole, the seasonal CH₄ variations of AIRS in China remained consistent with the surface sites. As the temperature in summer and autumn is significantly higher than that in spring and winter, the increase of the temperature can undoubtedly create favorable conditions for methane biological source emissions. Studies have shown that there is a significant correlation between CH₄ emissions and temperature [25].

CH₄ mainly reacts with OH radicals in the troposphere via photochemical reactions. Due to the uniform solar radiation in low latitudes, the concentration of OH radicals are basically stable, with little seasonal variation [59,74]. The areas to the west of the Hu line appeared with obvious seasonal variations, and the averaged CH₄ concentration difference between summer and winter was nearly 60 ppb. The combined effects of enhanced emission sources and pollution gases transmitted from the bottom troposphere to the mid-upper troposphere from the southeast contribute to the obvious high CH₄ concentration in summer. The oxidation-cleaning effect of OH radicals was clearly weaker than that of the transportation of pollutants [75]; the reacted OH radicals by CH₄ accounted for 20% [76,77,78]. The spatiotemporal distributions of the troposphere atmospheric OH radicals were simulated from Mozart-4 in China. Based on the results, the concentrations of OH radicals were the weakest in winter, and were relatively higher in the eastern regions, with heavier pollution in summer. In north China and south China, there are apparent high CH₄ centers, and the lowest OH values were found in the western plateau regions, due to less human activity, which were caused by the differences in the emission intensities of the precondition sources of the OH radicals [79].

Figure 5 shows the spatial distribution of the averaged CH₄ concentration in different seasons. It can be clearly seen that most of the maximum CH₄ concentration months in China were distributed in summer, among which the maximal grid numbers of CH₄ concentration in August and September accounted for 66.8% and 29.0%, respectively.

The maximum monthly distribution shows that the seasonal variation of CH₄ concentration in China was characterized by high values in summer and autumn, and low values in spring and winter [22]. The highest CH₄ concentration in southern China occurred in summer, and the lowest in spring, which was closely related to natural emission sources controlled by biological sources [22,24,60]. Also, high summer temperatures, and increased microbial activities and quantities may lead to rising methane emissions in rice paddies, wetlands, and swamps. Numerous studies have shown that the anaerobic processes of microorganisms in biological sources were closely related to temperature changes [80]. It is worth noting that the maximum methane concentrations in some parts of northwest China are in winter, and the minimums are in spring, which is consistent with previous results [22,81]. A time delay of one or two months represents the cumulative effect of atmospheric concentration and atmospheric transport [81]. The minimum methane concentration in the Qinghai-Tibet plateau appeared in winter, and CH₄ biological sources emissions and permafrost releases of methane basically disappeared in winter, because of the low temperature [21], and lower impact from human activities.

3.4. Long-Term Trends of CH₄ Concentrations across China

The sinusoidal curve (Figure 6a) across China from 2002 to 2016 gained a high R² (0.89), indicating that the simulated model can reflect the seasonal and long-term trends of CH₄ in China. The simulated CH₄ concentration was 1800 ppb across China in January 2002, and then increased by 0.42 ppb·month⁻¹ until December 2016. Moreover, the monthly differences in CH₄ concentration were −20.04 in each yearly cycle, indicating relatively obvious seasonal variations over a 12-month cycle. Comparing the fitting coefficients among different surface sites (Table 3) the monthly average at all of the stations showed increasing trends, in line with the CH₄ trend across China. Thus, the amplitude of seasonal change was quite different in each site. Notably, the differences in the amplitude of the seasonal cycle may contribute to the increasing proportion of the anthropogenic sources. The seasonal amplitude coefficient C was smaller in SDZ, LLN, TAP, and PDI, which may be related to frequent human activities. As China is developing rapidly, the proportion of methane emissions that are caused by human factors such as urban waste discharge, the development of animal husbandry, the increase of methane emissions from rice fields, and energy combustion, all contribute to lowering the seasonal CH₄ change. Nevertheless, natural sources, such as wetlands methane, have increased slightly, and have even remained constant over recent years [6,7,10,18].

We also calculated the correlations between the CH₄ concentration and emissions (Figure 6b). On a national scale, CH₄ emissions had a strong influence on the CH₄ concentration (R = 0.89, p < 0.01). The national CH₄ emissions increased by 32.2% from 2002 to 2012, while the CH₄ concentration increased by 3.14%. Furthermore, the simulated slope of the CH₄ concentration to emissions was 1.58 × 10⁻⁶, indicating quite a strong relationship between atmospheric methane and emissions, and that part of the atmosphere methane gases sank, transferred, or chemically reacted in different ways. Moreover, the emissions from the east coast of China could also be advected in the atmosphere over the Pacific Ocean, and consequently not detected in the AIRS signal over China. Obviously, there was no direct equivalence between the atmospheric methane concentration and its emissions.

The monthly increased CH₄ concentrations at each pixel are shown in Figure 7. The monthly CH₄ change rates ranged from ∼0.29 to 0.62 ppb·month⁻¹, with a mean of 0.42 ppb·month⁻¹, and a standard deviation of 0.07 ppb·month⁻¹. The fitting degree ranged from 0.41 to 0.95 (p < 0.0001), as most grids showed high fitness (Figure S4).

The CH₄ concentration across China showed positive growth, with dramatic growth mainly distributed across the Qinghai-Tibet plateau and in northwestern China, and the lowest growth in the south. This shows the need to pay attention to the rapidly increasing rate of CH₄ concentration in the mid-upper troposphere, in western China.

The Qinghai-Tibet plateau is gradually becoming warmer and wetter [82]. The increased temperature has led to a deepening of the frozen active layer, releasing a large amount of methane [83]. Moreover, the net primary productivity (NPP) in the Qinghai-Tibet plateau is gradually increasing year-by-year, due to the rising temperature and precipitation [84,85], and also due to the annual average effective photosynthetic radiation amount reaching the national maximum of 2800 MJ·cm⁻²·yr⁻¹ [86]. The increased temperature, the precipitation amount, the melting of glacier snow, and the permafrost have enlarged the lake area in the Qinghai-Tibet plateau, which increased the CH₄ emissions [87,88,89,90]. All of these factors rapidly increased the changing rate of CH₄ concentrations in the Qinghai-Tibet plateau.

The changes in CH₄ concentration in northeast China are related to the natural swamp emissions in the Sanjiang Plain. The relatively rapidly increasing rates of CH₄ concentration, extending from the Qinghai–Tibet plateau to the northeast, showed a faster growth rate along the ecotone between agriculture and animal husbandry. Farm soil is an important atmospheric CH₄ source, in addition to the atmospheric photochemical reaction and changes in land use patterns. With yearly increases, excessive farming and grazing, and the degradation of natural grasslands to farmland, the ability of the soil to absorb CH₄ is reduced [67,91]. This area is reported to have increasing amounts of precipitation, and the wetter environment will increase CH₄ emissions [84,85].

The increase in CH₄ concentration in northwestern China was mainly influenced by policies, including the migration of industrial centers from east to west, and the adjustment of industrial structures, which may lead to more energy burning and fossil fuel emissions [92,93,94]. Geological methane is the second largest natural methane source after wetlands methane. The micro-seepages of sedimentary basins, mud volcanism, sub-marine seeps, and geothermal fluxes (volcanoes and hot springs), are important atmospheric CH₄ sources that are seriously underestimated, due to the complexity of their geological genesis [95]. Northwest China has become an important gathering area of oil/gas fields [62]. Some scholars have also tested the varying degrees of CH₄ emission fluxes of micro-leakage methane in oil-bearing basins [95,96,97]. Secondly, with the increasing demand, methane emission from animal husbandry and the breeding industry is also increasing [98,99].

In contrast with the Qinghai-Tibet plateau, the changing trends of CH₄ concentration in south China are much more stable, which might reflect relatively stable natural sources and anthropogenic sources. The natural sources in this area are mainly paddy fields. In China, the share of rice planting area in the world decreased from 22.8% in 1990, to 19.7% in 2000, and the estimated CH₄ emissions from rice fields reduced from 30 Tg·a⁻¹ to 10 Tg·a⁻¹ over the last 20 years. At the same time, measurements and research that validated CH₄ emissions from Chinese paddy fields gradually decreased, and were mainly related to the amount of fertilizer application and water management measures. Methane emissions from the exploitation and utilization of energy compensated for the reduction from rice fields. However, it still showed a trend of slow increase [100,101].

Furthermore, the increased CH₄ concentration from 2002 to 2016 was calculated. The variation range of CH₄ growth in China during the 15-year period was 14~103 ppb, showing great differences among the different regions. On the whole, the variation in northern China was stronger than that in the southern coastal areas. Here in this study, the annual AGTP of CH₄ was calculated to be 4.6 × 10⁻¹⁴ K·kg⁻¹ for a 20-year time frame, according to Equations (3) and (4) [50]. From 2002 to 2016, the CH₄ concentration increased from 1813.64 ppb to 1870.56 ppb, the increased CH₄ concentration could cause the average surface temperature to rise by 0.0075 mK for a 20-year time frame, and the contribution of the increase in atmospheric methane to ΔT for a 20-year time frame ranged from 0.0019 mK to 0.014 mK for the whole of China (Figure 8b). The highest global surface temperature response was observed in the Qinghai-Tibet plateau, with the lowest response resulting from emissions in Hainan Province. The above discussion clearly shows that although the surface temperature response is global, the contribution of CH₄ in the atmosphere varies across different regions. The surface temperature response provides important information. Meanwhile, we found that the average of the annual temperature change in the Qinghai-Tibet plateau significantly increased over 2002 to 2016 (Figure 8a), and was much higher than that of other areas, followed by the northwest region. The rest of the regions remained basically stable. The rise in temperature is also a factor that indicates dramatic changes in this region.

As one of the long life-cycle greenhouse gases, the accumulation of CH₄ could lead to climate change, even over a small range. The increases of atmospheric CH₄ concentration in the Qinghai–Tibet plateau will lead to a rise in temperature, and at the same time the temperature can also promote CH₄ emissions. The relationship between CH₄ emissions and temperature is one of positive feedback; therefore, the methane changes in the Tibetan plateau deserve further study. The regional differences are very important, and they merit further discussion. Also, comparisons with other types of measurements or other satellite observations will help us to better explain the mechanism in greater depth.

4. Conclusions

In this study, the long time-series data of CH₄ concentration derived from the AIRS sensor were used to discuss the temporal and spatial variation characteristics of CH₄ across China, from 2002 to 2016. The AIRS CH₄ concentration reflected the spatial and temporal variations of the surface CH₄ concentration, as derived from a comparison between the AIRS CH₄ products, and the results from nine observation sites in eastern Asia.

The spatial distribution of CH₄ in the mid-upper troposphere in China was characterized by north/east highs, and south/west lows. The lowest values of CH₄ concentration were generally distributed in the Qinghai-Tibet Plateau, and there were three significantly high-value zones, namely, northwest Xinjiang, northeast Heilongjiang/Inner Mongolia, and the Norgay Plateau in northwestern Sichuan. Combined with air-mass back-trajectories analyses, the results showed that the special spatial distribution pattern was affected by both emissions and atmospheric transmission. Additionally, the high value zones aggregations in the mid-upper troposphere on the Qinghai-Tibet Plateau were closely related to summer monsoon transmission in Asia.

The CH₄ concentration in China showed obvious seasonal changes. The lowest values of CH₄ concentration were generally distributed during March and April, which showed a high summer/autumn concentration, and low spring/winter concentration characteristics.

The CH₄ concentration in China showed an increasing trend. The CH₄ concentration from the Qinghai-Tibet plateau to northeast China, on a month-scale, showed the most dramatic changes, which may be closely related to rising temperatures and precipitation. Finally, the estimated surface temperature response caused by the increase in CH₄ concentration from 2002 to 2016, over a 20-year time frame, ranges from 0.0019 mK to 0.014 mK across China as a whole.

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Sources	REGION I	REGION II	REGION III	China
Energy manufacturing	0.35	1.60	2.31	4.04
Coal/oil/gas production	711,265.40	113,917.30	93,848.85	133,246.97
Animal husbandry	41.50	68.71	54.80	108.24
Agricultural soil	15.81	151.97	309.57	352.35
Fossil fuel fires	10.21	7.72	no data	8.20
Waste solid/wastewater	131.64	260.09	866.48	593.81
Averaged CH4 Emissions	137,240	283,430	135,100	260,400
CH4 concentration (ppb)	1844.67	1844.75	1846.22	1833.89

* The IPCC 1996 code is used for the specification of the sectors.

Seasons	REGION I	REGION II	REGION III
Spring	2471.99	107,814.43	463,804.09
Summer	52,801.26	20,600.35	218,368.48
Autumn	1461.59	149,896.14	222,366.25
Winter	56,916.27	207,415.99	469,519.92
Year	28,412.78	121,431.7	343,514.7

* The emission data obtained from EDGAR v. 4.3.2 (2002–2012).

Sites	A	B	C	R2
China	1800.08	0.42	−20.04	0.89
WLG	1791.40	0.47	−28.48	0.91
SDZ	1796.34	0.45	−23.71	0.85
LLN	1798.60	0.31	−15.94	0.78
UUM	1842.02	0.43	−16.85	0.85
TAP	1804.25	0.40	−14.74	0.83
AMY	1844.88	0.66	−20.82	0.79
PDI	1799.55	0.29	−16.61	0.76
RYO	1814.30	0.36	−13.44	0.83
MNM	1785.27	0.35	−12.70	0.84

Supplementary Materials

The following are available online at https://www.mdpi.com/2072-4292/11/5/538/s1, Figure S1: Distribution of nine ground-based stations in eastern Asia, Figure S2: Scatter plots of the monthly averaged surface CH4 concentrations from WDCGG and AIRS retrievals in 9 stations around China on annual scale from 2002 to 2016, respectively, Figure S3: Comparison of the seasonal CH4 concentration over China between AIRS and ground-based observations, Figure S4: R2 of the linear trend model in CH4 concentration by grids over China, Table S1: Specific information of the nine ground-based observation sites.

Author Contributions

X.W. and X.Z conceived the idea; X.W. and X.Z. conducted the analyses; Z.W., X.H. and X.C. contributed to the writing and revisions.

Funding

This study was supported by the National Natural Science Foundation of China (No. 41471343), and the major projects of the National Social Science Fund (17ZDA061).

Acknowledgments

This study was supported by the National Natural Science Foundation of China (No.41471343), and the major projects of the National Social Science Fund (17ZDA061).The monthly AIRS data used here are provided by NASA/ESRL (http://disc.sci.gsfc.nasa.gov/) and the data from ground-based sites was obtained from the World Data Centre for Greenhouse Gases (WDCGG) (http://ds.data.jma.go.jp/gmd/wdcgg/). We also greatly appreciate the free use of CH4 emissions data from Emissions Database for Global Atmospheric Research (EDGAR) (http://edgar.jrc.ec.europa.eu/archived_datasets.php#ghg) and the monthly surface temperature data from NOAA NCEP/DOE Reanalysis 2 (https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html).We gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (https://ready.arl.noaa.gov/HYSPLIT_traj.php) used in this paper.

Conflicts of Interest

The authors declare no competing financial interests.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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