Background Characteristics and Influence Analysis of Greenhouse Gases at Jinsha Atmospheric Background Station in China

Abstract

Central China has been acting as a major convergence zone for sources and sinks in China, such that the climate change studies of Central China have taken on critical significance. The Jinsha atmospheric background station refers to the sole background monitoring site in Central China. It is noteworthy that the greenhouse gas attributes of the Jinsha atmospheric background station represent the greenhouse gas conditions of Central China. The seasonal and daily variations in CO₂, CH₄, and CO in the scope of time between October 2019 to April 2021 at the station were examined in this study. The effect of meteorological conditions on greenhouse gas concentrations at the site was evaluated. Furthermore, the primary transmission origins affecting the station were identified using the backward trajectory through potential source contribution function analysis. As indicated by the results, the background concentrations at the Jinsha station in 2020 for CO₂, CH₄, and CO reached 424.1 ± 0.1 ppm, 2046.2 ± 0.6 ppb, and 324.1 ± 1.1 ppb, respectively. CO₂ varied on a daily basis with higher nighttime levels, which was affected by the boundary layer elevation, photosynthesis, and human activities. In autumn, CH₄ levels peaked under the effect of agricultural activities in Central China. However, CO₂ and CO concentrations reached the maximum in winter, majorly affected by the transmissions from the Beijing–Tianjin–Hebei region and Hubei. Under China’s comprehensive carbon neutrality, more attention should be paid to the emissions from winter heating and industrial activities in the Beijing–Tianjin–Hebei region, and effects exerted by transport in the monitoring process should be differentiated in depth.

1. Introduction

CO₂ and CH₄ have been reported as vital greenhouse gases in the atmosphere, and their radiative forcing accounts for 66% and 16% of long-lived greenhouse gases, respectively [1,2]. CO is capable of reacting with hydroxyl radical (OH) and then affecting the lifetime of CH₄, CO₂, and other greenhouse gases in the atmosphere [3]. Accordingly, the World Meteorological Organization/Global Atmosphere Watch suggests that the longtime observation elements comprise CO₂, CH₄, and CO [4,5]. Multiple greenhouse gas satellites have been launched into space over the past few years. Some articles [6,7,8] have conducted research on the observation of CO₂ and CH₄ using the GOSAT satellite, thus contributing to the global observations of atmospheric greenhouse gases. Some studies [9,10] have discussed the use of satellite remote sensing to monitor CO₂ and CH₄. The ground monitoring network continues to be critical in the greenhouse gas observation system [11] for its high precision and continuous observation capabilities [12]. In China, Mt. Waliguan global station (WLG, 36.29° N, 100.90° E, 3816 m AGL above ground level) has been confirmed as the first greenhouse gas background monitoring station in China to join the GAW (Global Atmosphere Watch) program [13]. The observational data from this station have been crucial to gaining insights into CO₂ [14], CH₄ [15], and CO [16] variations in the Asian continent over the past decade.

In recent years, the Chinese government has progressively stressed the issue of climate change and deepened the establishment of greenhouse gas ground monitoring stations in its territory. CO₂, CH₄, and CO online observations have been performed at the regional background stations in Shangdianzi, Lin’an, Longfengshan, Xianggelila, Akedala, and Jinsha (Hubei). The data provided by the above-mentioned observation stations have presented significantly more insights into regional-scale variations in greenhouse gas concentrations [17]. Moreover, the concentration observation data have been extensively employed for further regional flux inversion [18], attracting considerable attention in the scientific community [19]. Notably, some scientists have highlighted that the representativeness of ground stations can notably affect the inversion of CO₂ flux [20,21], stressing the importance of carefully analyzing the concentration observation data from ground stations before conducting flux inversion.

The Jinsha station, which has started to measure CO₂ and CH₄ concentrations since 2019, serves as the sole greenhouse background monitoring station in Central China. In this study, the first set of complete annual concentration data for CO₂, CH₄, and CO recorded at this site was presented, and then their diurnal and seasonal characteristics were analyzed. Based on the above-mentioned analysis, the potential contributing regions to this site were explored, such that valuable reference information for future data users were provided.

The rest of this study is organized as follows. In Section 2, the basic information about the Jinsha station is presented, including its geographical location and the measurement equipment employed at the site. In Section 3, the greenhouse gas concentration measurement data at the Jinsha station are presented, and an analysis is conducted to reveal the characteristics of greenhouse gas variations in the region. Lastly, in Section 4, the conclusion of this study is drawn.

2. Materials and Methods

2.1. Sampling Sites

The Jinsha background station (JS; 114.2° E, 29.63° N, 751.6 m a.s.l.) has been reported as the only background station built by the China Meteorological Administration in Central China. Located atop a hill in the Jinsha Administrative Area within Chongyang County, Hubei Province (Figure 1), the station is surrounded by bamboo forests in a 30 km radius. Moreover, the Jinsha station is 90 km north of Wuhan (the largest city in Central China with a population of 16 million). The medium and small industrial cities (Jingmen, Tianmen, Xiantao, and Ezhou) lie 100–300 km in the NW-N-NNE direction from the station. It is 170 km from Dongting Lake to the east. The southern direction is mainly mountainous, while the station is 80 km from Honghu Wetland and Jingzhou Plain to the west. The prevailing wind direction at the Jinsha station comprises northwest and east-southeast in winter, while it covers southwest and east-southeast in summer. The annual average precipitation reaches 1928 mm, with an average temperature of 15.0 °C.

2.2. Measurement System

Since October 2019, atmospheric CO₂, CH₄, and CO were continuously examined using a cavity ring-down spectrometer (G2401, Picarro, Inc., Santa Clara, CA, USA) at the Jinsha station. The air sample was acquired regularly from 15 m a.g.l and 30 m a.g.l, respectively. Subsequently, it was delivered to the instrument by a vacuum pump (N022, KNF Neuberger, Breisgau, Germany) via a dedicated 10 mm o.d. sampling line (Synflex 1300 tubing, Eaton, Cleveland, OH, USA). Next, the ambient air was filtered (7 μm) and pressurized at 1 atm. The ambient air was dried to a dew point of approximately −60 °C by passing it through a glass trap submerged in a −70 °C ethanol bath (MC480D1, SP Industries, Warminster, PA, USA). An automated sampling module equipped with a VICI 8-port multi-position valve was developed to sample from separate gas streams (standard gas cylinders and ambient air). The effective travel time of the air from the top of the inlet to the analyzer was less than 60 s. The ambient air was filtered, dried, and then pressurized by a sampling conditioning module to comply with the high-quality target of the WMO/GAW network. An automated sampling module was developed to sample the gas streams (ambient air and standard gas). The measurements conformed to the working standard. The precision and accuracy of the system were examined routinely using the target gas. The values were calibrated by a linear one-point fit from the nearest standard gas measurements (W). Over 95% of the valid 5 min mean data records were preserved except for data dearth due to maintenance of the sampling system or malfunction of the equipment. After the CO₂, CH₄, and CO mole fractions were computed, the valid data were reviewed manually, and any sample problems or inaccurate analyses were flagged. Lastly, the data were aggregated to hourly averages for in-depth analysis. The observing periods were from 1 October 2019 to 17 April 2021 at the Jinsha station. In the measurement, T was adopted to detect the stability of the system. The drift of CO₂ was 0.1 ppm, the drift of CH₄ was 0.1 ppb, and the drift of CO was within 0.5 ppb. Only when the latest calibration CO₂, CH₄, and CO values of T fell into the expected values of ±0.2 ppm, ±0.2 ppb, and ±5 ppb, respectively, could the measurement of air samples be retained. Moreover, as indicated by the result, over 95% of the observed data conformed to the standard.

2.3. Data Processing

This study primarily focused on the analysis of high-altitude (30 m) CO₂, CH₄, and CO data collected from the Jinsha station (G2401, Picarro, Inc., USA). Both standard gas and high-low-level sample gases were connected to the system under intervals of 5 min. Based on the data processing procedure, molar fractions from the last 2 min of the respective 5 min segment were computed. Next, the above-mentioned data were calibrated using the external standard method y = ax and then subjected to expert-level quality control. Furthermore, the quality-controlled data were obtained.

The robust extraction of background signal (REBS) method was employed for the post-quality control data. This method has been verified to be effective for screening background values in the long-term observation data at global and regional atmospheric background stations [22].

In the analysis of monthly variations, the hourly average background data from the Jinsha station were compared with the background data from other background stations. For daily variation analyses, the raw hourly data were categorized by season, i.e., spring (March to May), summer (June to August), fall (September to November), and winter (December to February). For the examination of the effect of wind direction and speed, the raw hourly data were combined with the wind direction, divided into 16 orientations, and analyzed for concentration effects by wind speed across the four seasons.

The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) dispersion model [23] was employed using the gridded meteorological data from NOAA’s Air Resources Laboratory (2020), with the aim of investigating the pollution transportation pathways of air masses at the Jinsha station. Filtered CO₂ and CH₄ pollution data were employed for data selection, corresponding to a 72 h backward trajectory, where January, April, July, and October represent winter, spring, summer, and fall, respectively. The potential source contribution function (PSCF) method was employed for determining the spatial potential source distribution. In this study, the geographical spatial area covered by the selected trajectories was gridded and divided into 0.25° × 0.25° horizontal grids (100 m above the ground, representing the boundary layer height). Subsequently, for all trajectories in the study period, the probability of a trajectory appearing in the respective grid (PSCF value) was calculated, representing the total retention time of all air masses in a certain grid divided by the total time.

3. Results and Discussion

3.1. Filtered Data

In terms of the post-quality-controlled data, the spatiotemporal representativeness of the observational data should be determined. It is imperative to distinguish baseline values unaffected by local or regional source-sink effects before these values are substituted into numerical models to examine their sources and sinks in depth. Figure 2 illustrates the baseline concentration value filtering results based on the local approximation regression method for the period between October 2019 and April 2021. In 2020, the proportions of background values for CO₂, CH₄, and CO reached 69.9%, 55.6%, and 74.3%, respectively. The background concentration values of 2020 reached 424.1 ± 0.1 ppm, 2046.2 ± 0.6 ppb, and 324.1 ± 1.1 ppb for CO₂, CH₄, and CO, respectively. CH₄ exhibited a lower proportion of background values in summer and autumn, with 35.0% in summer and 47.1% in autumn. Further, the background value proportion in other seasons remained above 60%.

3.2. Seasonal Cycle

The background values derived post-filtering from the Jinsha (JS) station were compared with values from the nearby latitude stations, including the Lin’an station (LAN; 119.44° E, 30.18° N; 138.6 m a.s.l.) and the Waliguan station (WLG; 36.29° N, 100.90° E; 3816 m a.s.l.), the only global background station in China.

In Figure 3a, the maximum CO₂ value at JS was recorded in December (439.5 ± 0.5 ppm), and the minimum value was reported in August (414.3 ± 0.3 ppm). The concentration at JS was lower compared with LAN, primarily due to the smaller industrial scale and anthropogenic emissions in Central China as opposed to Eastern China. Moreover, JS is farther from urban centers, whereas LAN is proximate to the Yangtze River Delta industrial zone. Thus, JS is less affected by CO₂ source emissions than LAN. The lowest CO₂ levels for JS, LAN, and WLG occurred between July and August, which were correlated with reduced summer anthropogenic emissions and heating demands. The peak concentrations for regional stations (e.g., JS and LAN) emerged in December, which are highly inconsistent with the peak at the global station WLG in April–May. This inconsistency can be attributed to the winter heating, human activities, combustion-derived CO₂ emissions, weaker atmospheric convection, and reduced carbon sequestration capabilities due to deciduous plant photosynthesis in winter.

As depicted in Figure 3b, the peak CH₄ concentration at JS occurred in September (2103.0 ± 3.0 ppb) and the trough CH₄ concentration was reported in August (1963.2 ± 3.5 ppb). The surrounding wetlands and farmlands (e.g., Honghu, Dongting Lake, and Poyang Lake wetlands) contributed to CH₄ emissions, leading to a significant rise post-August. CH₄ concentration fluctuated by 140 ppb between August and September, such that the highest methane concentration at JS was achieved in September. The seasonal fluctuations at JS and LAN were greatest in summer and fall, registering at 115.8 ppb and 97.6 ppb, respectively. In contrast, WLG displayed the least variation, ranging between 1.5–39 ppb. The emissions from Central China’s farmlands and wetlands in August exacerbated this surge from August to September.

Based on Figure 3c, as indicated by the analysis of the 2020 monthly CO variations for JS, LAN, and WLG, JS displayed a similar pattern to LAN, though it was slightly higher. The lowest (188.5 ± 5.4 ppb) and highest (499.3 ± 5.9 ppb) concentrations at JS were achieved in August and January, respectively. Both JS and LAN regional stations had significantly higher CO levels and seasonal variations compared with the global WLG station, where the lowest was in November (102.0 ± 0.9 ppb) and the highest in April (118.5 ± 1.2 ppb).

3.3. Average Diurnal Variations

As revealed by an examination of the diurnal variations using the hourly data (Figure 4a), in spring, summer, and autumn, the CO₂ concentrations at the Jinsha station reached their minimum around 15:00 Beijing time. This may be attributed to rapid vertical atmospheric mixing and the relatively higher boundary layer at this time [25], which minimizes CO₂ accumulation in the lower atmosphere. The most significant diurnal variation was observed in summer, primarily due to enhanced plant photosynthesis and convective lifting. The peak CO₂ levels appeared between 08:00 and 10:00, a consequence of CO₂ accumulation from nocturnal plant respiration and the most stable atmospheric conditions. The diurnal variations of CO₂ in winter were less distinct, affected primarily by continuous anthropogenic emissions and decreased carbon sequestration.

Figure 4b presents the 2020 diurnal average changes in CH₄ concentrations at the Jinsha station, suggesting that concentrations reached the maximum in autumn and the minimum in spring. A distinct diurnal variation was evident in spring, summer, and autumn. At night, the development of a stable boundary layer due to radiative inversion impeded the diffusion of atmospheric CH₄, causing accumulation from ground sources. Moreover, the diminished sunlight reduced ·OH concentrations, slowing down CH₄ consumption. The above-described factors can contribute to the elevated CH₄ concentrations between 08:00 and 10:00 (Beijing time). In contrast, post-sunrise, enhanced sunlight, and rising temperatures led to the increase of boundary layer convective diffusion and ·OH concentration. Accordingly, the CH₄ consumption increased, and atmospheric concentration decreased, with the lowest values observed between 12:00 and 14:00. Enhanced summer convection and longer daylight hours result in the maximum diurnal amplitude of CH₄, approximately 25 ppb. Both summer and autumn are affected by CH₄ emissions from surrounding paddy fields and wetlands. However, summer’s humidity and sunlight facilitated ·OH absorption of CH₄, resulting in relatively lower diurnal concentrations compared with autumn. The diurnal changes in winter were less consistent, which was probably due to diminished influencing factors.

Figure 4c illustrates the diurnal average changes of CO concentrations at the Jinsha station, which exhibits a pattern akin to CO₂: lower in summer and higher in winter. However, a notable difference lied in the appearance of two peak CO concentrations around 10:00 and 19:00, with troughs observed between 00:00 and 06:00. The above-mentioned result suggested that observed CO concentrations at the Jinsha station might be affected by vehicular emissions or anthropogenic activities from neighboring urban areas.

3.4. Impact of Local Surface Wind

Figure 5 illustrates the wind rose analysis based on the hourly data combined with wind direction and speed. A significant characteristic was observed in winter, i.e., CO, CO₂, and CH₄ concentrations were all significantly affected by the winds from the northwest to north. As revealed by the intense transportation by the predominant northwest winds, the elevated winter concentrations primarily arose from sources in the northwest region, specifically including areas (e.g., the urban belt of Wuhan in Hubei) and provinces (e.g., Shaanxi and Shanxi). The examination result of CH₄ across four seasons indicated that the primary origin of CH₄ was consistently the northwest, irrespective of wind speed, suggesting the northwest-to-north direction as a definitive source of CH₄ pollution. In general, the vast wetlands and agricultural areas in the northwest-to-northeast direction reached their peak CH₄ emission fluxes in summer and autumn. In the summer data for CH₄, there existed a discernible concentration gradient affected by wind direction, with higher values in the north compared with the south. This is further delineated by wind directional proportions: the summer season was dominated by winds from the southwest, whereas the autumn leaned towards a northern direction, resulting in CH₄ concentrations being relatively lower in summer than in autumn. The variation in CO concentrations with wind direction and across seasons largely paralleled that of CO₂, with minor differences observed in summer, primarily attributed to the stronger absorption of CO₂ by forest carbon sinks in this season. As revealed by the collective effect on all three gases, concentrations were notably lower when the season was dominated by southwest (SW) winds compared with those affected by north-north-west (NNW) winds.

3.5. Effect of Regional Transport

For each of the four seasons, trajectories corresponding to pollution episodes of CO₂ and CH₄ in January, April, July, and October were analyzed. In spring, the pollution source transport for CO₂ primarily originated from the northeast direction, as depicted in Figure 6a,c. In contrast, the springtime source transport for CH₄, unlike CO₂, showed an augmentation from the southwest direction, specifically encompassing the Hunan region (Figure 6e) (Cluster 2). In summer, as depicted in Figure 6b,f, CO₂ and CH₄ pollution source transport predominantly arose from the north and southwest air masses. In autumn and winter, the pollution source transport for CO₂ and CH₄ aligned, primarily stemming from the northern direction, traversing major cities (e.g., Taiyuan, Shijiazhuang, Zhengzhou, Wuhan, Hefei, and Nanjing). Disparities in the transmission pathways and average air mass values between CO₂ and CH₄ further highlighted that CO₂ was primarily affected by anthropogenic emissions from urban clusters, while CH₄ was largely affected by emissions from natural sources like wetlands [26].

3.6. Effect of Source Regions

The potential source areas identified through PSCF analysis (Figure 7) showed close correlations with the areas discerned by the cluster results. As depicted in the figure, the gradient of color represents the probability distribution of emission sources. To be specific, with a region’s color turning out to be darker, the region was more likely to have a strong emission source. As depicted in Figure 7, the potential pollution emissions for CO₂ and CH₄ were achieved similarly. With the exception of summer, high WPSCF values were located to the north of the station. In autumn and winter, the high WPSCF values stretched from the Beijing–Tianjin–Hebei region to the central-eastern region of Hubei. For spring, the above-mentioned values were noted from Anhui to the central-eastern region of Hubei. CO₂ in summer displayed a smaller high WPSCF region that was primarily situated around the Wuhan urban belt. However, the high WPSCF region of CH₄ in summer is more expansive than that of CO₂, where eastern Hubei, Henan, Hunan, and Jiangxi regions were predominantly covered.

3.7. Effect of COVID-19

According to relevant studies, there was a significant decrease in NO₂ levels in Wuhan from the end of January to April 2020, indicating a near-zero level of traffic-related carbon emissions during this period. Carbon emissions from factories also witnessed a certain degree of reduction. Therefore, this article also investigates the trend of CO₂ during the same period. Through detrend analysis (Figure 8) of CO₂ concentration from January to April 2020 and January to April 2021, by subtracting the trend values from the original values, it was found that the control measures taken in China due to the impact of COVID-19 had a certain effect on carbon dioxide concentration. This effect was evident in 2020, as the CO₂ concentration significantly decreased even after removing the seasonal trend. However, there was no significant impact observed in 2021.

4. Conclusions

In this study, the monthly and daily variations in CO₂, CH₄, and CO concentrations at the Jinsha background station were outlined, and the effects of wind and air mass transport were examined. As indicated by the findings of this study, at the Jinsha station, CO₂ and CO concentrations peaked in winter and reached the minimum in summer, whereas CH₄ concentrations reached the maximum in autumn and the minimum in spring. CO₂ was primarily affected by boundary layer lifting, the photosynthetic activity of plants, as well as industrial emissions. The primary pollution sources for CO₂ and CO were transport emissions from the Beijing–Tianjin–Hebei region and the northern region of Hubei. CH₄ was primarily affected by agricultural activities and wetland emissions. Its primary pollution sources are located north of Hubei and regions (e.g., Henan and Hebei). As indicated by the analysis of variations in all three gases, when wind direction or transmission routes are south-oriented, the concentration levels decline. In contrast, with a northward direction, levels increased. The main greenhouse gas pollution affecting Central China predominantly originated from the north. Under carbon neutrality, more attention should be paid to winter heating methods and industrial activities in regions (the Beijing–Tianjin–Hebei region) for CO₂ reduction. In terms of CH₄ reduction, a focus should be placed on agricultural production and wetland emission controls in summer and autumn.

In this study, the effects of meteorological factors and air mass transport were incorporated. It also briefly discussed the impact of COVID-19-related restrictions on the CO₂. However, the online observational data spanned merely over a year, limiting the insights into long-term trends. Furthermore, while the REBS method was employed at the Jinsha station as a preliminary background value screening method, utilizing multiple screening methods at regional background stations was likely to yield more effective results in terms of background value segregation.