Linjun Cheng1, Dongsheng Ji 2,3, Jun He4, Liang Li1, Li Du1, Yang Cui2,5, Hongliang Zhang6, Luxi Zhou7, Zhiqing Li8, Yingxin Zhou9, Shengyuan Miao9, Zhengyu Gong1, Yuesi Wang2,3

China National Environmental Monitoring Center, Beijing 100012, China
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100191, China
Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
International Doctoral Innovation Centre, Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
University of Chinese Academy of Sciences, Beijing 100049, China
Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
National Academies of Sciences, Engineering, and Medicine, Washington, DC 20001, USA
Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
Yunnan Wuyi Expressway Construction Headquarters, Yunnan, Kunming 650300, China


Received: November 3, 2018
Revised: December 13, 2018
Accepted: January 25, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2018.11.0397  

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Cite this article:

Cheng, L., Ji, D., He, J., Li, L., Du, L., Cui, Y., Zhang, H., Zhou, L., Li, Z., Zhou, Y., Miao, S., Gong, Z. and Wang, Y. (2019). Characteristics of Air Pollutants and Greenhouse Gases at a Regional Background Station in Southwestern China. Aerosol Air Qual. Res. 19: 1007-1023. https://doi.org/10.4209/aaqr.2018.11.0397


HIGHLIGHTS

  • Characteristics of air pollutants and greenhouse gases were shown at the GGS site.
  • There were 82 days beyond the WHO standard of O3.
  • Air pollutants and greenhouse gases showed marked seasonal and diurnal variations.
  • Regional transport obviously affected the variation of these species of interest.
 

ABSTRACT


The characteristics of air pollutants and greenhouse gases at regional background sites are critical to assessing the impact of anthropogenic emissions on the atmospheric environment, ecosystems and climate change. However, observational studies are still scarce at such background sites. In this study, continuous hourly observations of air pollutants (O3, CO, SO2, NOx, PM2.5 and PM10) and greenhouse gases (CO2, CH4 and N2O) were performed for one year (from January 1 to December 31, 2017) at the Gongga Mountain background station (GGS; 101°97′E, 29°55′N; elevation: 3541 m) in southwestern China. The concentrations and variations of these air pollutants and greenhouse gases were determined, and the effect of transboundary atmospheric transport on the air pollution at the study site was investigated. The results showed that the average annual concentrations (mixing ratios) of the O3, CO, SO2, NO2, CO2, CH4, N2O, PM2.5 and PM10 were 74.7 ± 22.0 µg m–3, 0.3 ± 0.2 mg m–3, 0.5 ± 0.6 µg m–3, 1.7 ± 1.3 µg m–3, 406.1 ± 9.5 ppm, 1.941 ± 0.071 ppm, 324.5 ± 14.8 ppb, 6.5 ± 6.2 µg m–3 and 10.6 ± 11.2 µg m–3, respectively. The concentrations (mixing ratios) of the abovementioned substances at the GGS are comparable to those at other background sites in China and around the world. The slight differences among concentrations at different sites may be mainly attributable to the impacts of anthropogenic emissions near the background sites and meteorological conditions. High values of O3 were observed in spring and summer, while SO2 and PM2.5 showed higher concentrations in summer than in autumn. Relatively high CO, NO2 and PM10 values were mostly observed in spring and winter. Relatively low CO2 concentrations were observed in summer due to the vigorous summertime photosynthesis of vegetation. The lowest concentrations for CH4 were recorded in summer, whereas the levels in the other three seasons were similar to each other; by contrast, the highest N2O concentrations were observed in summer due to enhanced microbial activity resulting from high ambient summer temperatures. A diurnal variation in O3 was observed, with early morning minima and afternoon maxima. CO and NO2 displayed higher concentrations in the daytime than in the nighttime. A slight increase in both PM2.5 and PM10 concentrations was also recorded in the daytime. These patterns were closely related to scattered anthropogenic emissions and regional atmospheric transport. Nevertheless, CO2 exhibited lower concentrations in the daytime than in the nighttime, although CH4 showed no obvious diurnal variation. The N2O concentration peaked between 10:00 and 12:00 (local time), which can be ascribed to the enhancement of microbial activity due to the increased soil temperature. The results based on the relationship between the wind and the concentrations of air pollutants and greenhouse gases were almost consistent with those based on the potential contribution source function. It appears that O3 and its precursors in parts of Inner Mongolia and Gansu, Ningxia, Sichuan, Chongqing and Hubei Provinces as well as adjacent areas of Hunan, Guizhou and Guangxi Provinces contributed to the increase in O3 at the study site. The potential source areas for CO and SO2 were similar and mainly distributed in India and Pakistan and areas of Inner Mongolia and Gansu and Guizhou Provinces in China. Potential source areas for NO2, PM2.5 and PM10 were found in neighboring countries of South Asia in addition to domestic regions, including Inner Mongolia, Gansu Province and the Cheng-Yu economic region. Furthermore, parts of Yunnan Province (China) as well as India and Pakistan were potential source areas for CO2, CH4 and N2O.


Keywords: Air pollutants; Greenhouse gases; Background station; Southwestern China.


INTRODUCTION


Increased occurrence levels of air pollutants (ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter with a diameter of 2.5 micrometers or less (PM2.5) and particulate matter with a diameter of 10 micrometers or less (PM10)) and greenhouse gases (carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O)) caused by intensified anthropogenic emissions have adversely affected the atmospheric environment, ecosystems, climate change and human health (Orru et al., 2017; Wuebbles et al., 2017). Hence, there have been increasing concerns about air pollutants and greenhouse gases at local, regional and even global scales (Meng et al., 2009; Lin et al., 2011; Thunis et al., 2016). Ground-level O3 is a powerful oxidant that can harm lung function and irritate the respiratory system; O3 is also linked to premature deaths, heart attacks and other cardiopulmonary problems (www.epa.gov/ozone-pollution-and-your-patients-health; Weinhold, 2008). In addition, O3 acts as a greenhouse gas (IPCC, 2001). As a precursor of O3, CO influences the oxidization of the atmosphere via interactions with hydroxyl radical (OH) (Gligorovski et al., 2015). Frequent occurrences of acid rain and smog are regional-scale environmental problems in China, and SO2and NOx play important roles in the formation of both problems (Ji et al., 2014; Seinfeld and Pandis, 2016). Additionally, NOx is a photochemical precursor resulting in the substantial enhancement of global background O3 concentrations (Lin et al., 2014; Sun et al., 2016). CO2, CH4 and N2O are the most important greenhouse gases (Watson et al., 1992) that can absorb infrared radiation emitted from the earth and partially reradiate this radiation back to the earth’s surface (Seinfeld and Pandis, 2016). Therefore, to assess the impact of anthropogenic activities on the atmospheric environment, ecosystems, climate change and human health, it is necessary to conduct long-term continuous measurements of air pollutants (O3, CO, SO2, NOx, PM2.5 and PM10) and greenhouse gases (CO2, CH4 and N2O). Nonetheless, colocated and simultaneous measurements of air pollutants and greenhouse gases at regional background sites are scarce, although a series of studies have been performed in regions with high anthropogenic emissions.

In contrast to studies in urban areas, studies on air pollutants and greenhouse gases at regional background sites not only provide valuable information on the influence of human activities on the atmospheric environment and global change but also are helpful for understanding the transboundary transport of air pollution at a regional scale. Regional background sites are affected by very limited local anthropogenic emissions; consequently, the medium- or long-range transport of air pollutants could be the main contributor to local air pollution. Therefore, given their critical importance, a number of studies on air pollutants and greenhouse gases have been carried out at several regional background sites of the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions in China (Chao et al., 2014; Pu et al., 2015; Wang et al., 2016). The results of these studies reflected the distinctive air pollution characteristics in the abovementioned regions in China and showed significant impacts of human activities on regional air quality. However, such studies at regional background sites in southwestern China are still scarce. The Gongga Mountain background station (GGS) is representative of the regional background in southwestern China (Fu et al., 2008; Zhang et al., 2012; Zhang et al., 2014; Li et al., 2017). Several field observations have been conducted at this station on volatile organic compounds, polar organic tracers in PM2.5 and total particulate, reactive gaseous mercury and major chemical species of PM10 in specific periods or months (Fu et al., 2008; Zhang et al., 2012; Zhang et al., 2014; Li et al., 2017). However, to the best of our knowledge, no long-term continuous measurement of air pollutants and greenhouse gases has been performed at any regional background sites in southwestern China.

In this study, we present observations of major air pollutants, including O3, CO, SO2, NOx, PM2.5 and PM10, as well as greenhouse gases, such as CO2, CH4 and N2O, at the GGS (101°97′E, 29°55′N; elevation: 3541 m) in southwestern China for the first time. The occurrence levels and temporal variations in these pollutants are discussed in detail, and potential contribution areas of the above substances are identified using the potential source contribution function (PSCF) method.


METHODS AND INSTRUMENTS


 
Sampling Site

As shown in Fig. 1, the GGS (101.97°E, 29.55°N; elevation: 3541 m) was established in the Gongga Mountain Observation and Experimental Station of Alpine Ecosystem, which is located in the Hailuogou scenic area of the southeastern edge of the Qinghai-Tibetan Plateau. The Hailuogou scenic area is famous for its large unique glacier and forest park areas. Neither private vehicular transport nor industrial activities operate near the sampling site. This station is approximately 250 km from Chengdu, the capital of Sichuan Province. There are two major roads in the north and east, 500 m and 400 m away from the station, respectively. The study area is dominated by the Southeast Asian Monsoon, with an annual average temperature, relative humidity, wind speed, atmospheric pressure and visibility range of 2.1 ± 6.6°C, 82.5 ± 20.9%, 1.7 ± 0.9 m s–1, 656.5 ± 25.0 hPa and 21.7 ± 24.8 km, respectively. In addition, higher precipitation mainly occurs in both summer and autumn.


Fig. 1. Location of the GGS atmospheric background station.Fig. 1. 
Location of the GGS atmospheric background station.

 
Instruments and Measurement Data

The sampling campaign was conducted from January 1, 2017, to December 31, 2017. All instruments were deployed, operated and maintained by following the regulations and standard operating procedures defined by the Ministry of Ecology and Environment of the People’s Republic of China (http://bz.mep.gov.cn/bzwb/dqhjbh/jcgfffbz/index_2.shtml). The precision, detection limits, and calibration methods of all analyzers/monitors for major air pollutants of interest, including O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10, have been described elsewhere in detail (Ji et al., 2014; Christiansen et al., 2015; Cortus et al., 2015). Briefly, O3, CO, SO2, NO-NO2-NOx, CO2, CH4 and N2O were observed using an ultraviolet photometric analyzer (Model 49i; Thermo Fisher Scientific (Thermo), USA), a gas filter correlation nondispersive infrared method analyzer (Model 48i TLE; Thermo, USA), a pulsed-fluorescence analyzer (Model 43i-TLE; Thermo, USA), a chemiluminescence analyzer (Model 42i-TL; Thermo, USA), a cavity ring-down spectroscopy analyzer for CO2 and CH4 (Model G2301; Picarro, Inc., USA) and a gas filter correlation N2O analyzer (Model 320EU; Teledyne Technologies, USA), respectively. PM2.5 and PM10 were simultaneously monitored using a Tapered Element Oscillating Microbalance with a Filter Dynamics Measurement System (TEOM-FDMS; 1405-DF TEOM; Thermo, USA). The gases were calibrated daily by injecting a mixture of calibration gases (Scott-Marrin, Inc., CA, USA) and scrubbed ambient air. In addition, an internal catalytic converter was used to calibrate the CO blank. The TEOM-FDMS was calibrated with free-particle and standard filters (Thermo, USA). Meteorological parameters such as relative humidity (RH), wind direction (WD), wind speed (WS) and atmospheric temperature (T) were recorded via a colocated automatic meteorological station (Model AWS310; Vaisala, Finland). All data were processed using an Igor-based software (Wu et al., 2018).


Source Area Identification

The PSCF method has been extensively used in identifying source locations of atmospheric species (Lupu and Maenhaut, 2002). PSCF is defined as the probability that an air parcel with a concentration more than a specified threshold reaches the study site after having resided in a certain grid cell of the spatial domain of interest (Lupu and Maenhaut, 2002). In this study, the potential source areas are identified based on the PSCF method using a GIS-based tool, named Trajstat, which can perform a comprehensive investigation of the geographical distribution of atmospheric species origins (Wang et al., 2009).

 
RESULTS AND DISCUSSION



Occurrence Levels

The time series of O3, CO, SO2, NO, NO2, PM2.5 and PM10 concentrations as well as CO2, CH4 and N2O mixing ratios are presented from January 1 to December 31, 2017, in Fig. 2. In addition, the means and standard deviations (STDEVs) of air pollutants and greenhouse gases are listed in Table 1. For clarification, due to regular maintenance activities (e.g., periodic zero-span checks, data dump/ collection, calibrations, etc.) and disruptions by severe weather conditions, a number of datasets were not recorded during the above sampling period. The annual average concentrations (mixing ratios) of O3, CO, SO2, NO, NO2, PM2.5, and PM10 as well as CO2, CH4 and N2O were 74.7 ± 22.0 µg m–3, 0.3 ± 0.2 mg m–3, 0.5 ± 0.6 µg m–3, 0.8 ± 2.3 µg m–3, 1.7 ± 1.3 µg m–3, 6.5 ± 6.2 µg m–3, 10.6 ± 11.2 µg m–3, 406.1 ± 9.5 ppm, 1.941 ± 0.071 ppm and 324.5 ± 14.8 ppb, respectively. According to the Chinese Ambient Air Quality Tier II Standards (CAAQS), there were no sampling days when the thresholds of O3 (maximum 8 h mean: 160 µg m–3), NO2 (24 h mean: 80 µg m–3), SO2 (24 h mean: 150 µg m–3), CO (24 h mean: 4 mg m–3), PM2.5 (24 h mean: 75 µg m–3) and PM10 (24 h mean: 150 µg m–3) were exceeded. However, there were 82, 1 and 1 days violating the limits of O3 (maximum 8 h mean: 100 µg m–3), PM2.5 (24 h mean: 25 µg m–3) and PM10 (24 h mean: 50 µg m–3), respectively, according to the World Health Organization (WHO) air quality standards. Notably, 54.9% of all days exceeded the WHO ozone standard in the spring, which is consistent with the spring O3 maximum observed in the Northern Hemisphere (Monks, 2000; Lin et al., 2015). In addition, the sampling days exceeding the WHO PM standards were closely related to the long-range transport of PM from regions of intense anthropogenic emissions and/or local emissions.


Fig. 2. Time series of O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 concentrations and mixing ratios in 2017.Fig. 2. Time series of O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 concentrations and mixing ratios in 2017.

Table 1. Average concentrations and standard deviations (STDEVs) of O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 in 2017.

Frequency distributions are a commonly used visualization tool used to display the number of observations within a given interval. In this study, histograms showing the normalized, lognormal and cumulative frequency distributions of O3, CO, SO2, NO, NO2, PM2.5 and PM10 concentrations and CO2, CH4 and N2O mixing ratios were plotted and are presented in Fig. 3. The results show that all of the air pollutants of interest in this study possessed a unimodal bell-shaped lognormal distribution pattern during the entire sampling period. For example, O3 concentrations ranging from 30 to 130 µg m–3 were dominant, and the distribution shape for O3 was generally symmetrical, with the highest frequency at 90 µg m–3, occupying more than 95% of the total sample data. It was reported that the frequency distribution of O3 concentrations can be a good indicator for the studied type of sampling site because this distribution captures the impact of NOx emission sources mainly associated with combustion activities (Escuderoet al., 2014). Based on the diurnal variations in both O3 and NO (Section Seasonal and Diurnal Variations), in such a remote site, NO titration most likely seldom affects the O3 frequency distribution, although other processes, such as deposition or scavenging, might play more important roles in removing O3 from the troposphere (Monks et al., 2015). The frequency distributions of CO, SO2, NO2, PM2.5 and PM10 were more skewed to the right compared with the other distributions and featured a pronounced peak at approximately 0.25 mg m–3, 0.22 µg m–3, 1.0 µg m–3, 4 µg m–3 and 6 µg m–3, respectively, indicating the impact of relatively clean background continental air masses, while the long tails on the right side of the graph toward high concentration levels with a low cumulative distribution could indicate the minor influence of local rural pollution and/or urban pollution plumes. NO concentrations ranging from 0.1 to 1.0 µg m–3 were dominant, accounting for approximately 98% of the total sample data. Although the NO dataset was only slightly more than half of that for the remaining pollutants, the right-skewed frequency distribution of NO was similar to the above trend, with a long tail on the right side. In addition, Fig. 3 clearly shows that the frequency distributions of the CO2, CH4 and N2O mixing ratios were very narrow and were in the ranges of 380.1–435.0 ppm, 1.802–2.232 ppm and 300.0–379.9 ppb, respectively; such patterns might indicate that high emissions of these gases had almost no impacts during the study period.


Fig. 3. Frequency distributions of O3, CO, SO2, NO, NO2, CO2, CH4, N2O, PM2.5 and PM10 concentrations or mixing ratios.Fig. 3.
 Frequency distributions of O3, CO, SO2, NO, NO2, CO2, CH4, N2O, PM2.5 and PM10 concentrations or mixing ratios.

As shown in Table 2, a comparative analysis was conducted between the observations in the present study and those reported earlier at background stations in China and worldwide. The annual mean O3 concentration (74.7 ± 22.0 µg m–3) at the GGS was higher than the values observed at the Dinghushan (DHS) background site in southern China (24.6 ppb; Chao et al., 2014), the Jinsha (JS) background station in central China (24.6 ppb; Lin et al., 2011) and the Lin’an (LA) background station in the YRD of China (34.7 ppb; Xu et al., 2008); and it was similar to the values observed at the Shangdianzi (SDZ) background station in the BTH region of China (36.9 ppb; Lin et al., 2008) and the remote highland site of Dangxiong (38.5 ppb; Lin et al., 2015); while it was lower than those observed at Nam Co, a remote highland site in the inland Tibetan Plateau of China (47.6 ± 11.6 ppb, 4730 m a.s.l.), the Xinglong background station in northern China (49.2 ppb, 960 m a.s.l.), and the Nepal Climate Observatory-Pyramid (49 ± 12 ppbv, 4745 m a.s.l.; Cristofanelli et al., 2010). Given that the GGS site is at a much higher latitude than the other sites, the contribution of stratospheric ozone intrusion at the GGS may be more significant than the stratosphere-to-troposphere transport at most of the other stations discussed here (Mauzerall et al., 1996). The concentrations of NO2 at the GGS were lower than those at the DHS, SDZ, and LA sites, which was consistent with the fact that the DHS, SDZ, and LA sites are in rapidly developing areas with much more intensive industrial emissions and a high increase in the number of motor vehicles. In contrast, the concentrations of SO2 and CO observed at the GGS were lower than those observed at the DHS, SDZ, JS and LA stations, which also indicates the limited influence of anthropogenic activities at the GGS.


Table 2. Comparison of the present study with previous measurements from background stations in China and around the world.

The annual mean CO2 concentration (406.1 ± 9.5 ppm) at the GGS was similar to that at the background sites of Mt. Dodaira, Japan (> 400 ppm in 2013); King’s Park, Hong Kong, China (407.6 ppm in 2013); Korea (404.9 ppm in 2013); Mauna Loa (global background site; 406.53 ppm in 2017) and LA (404.7 ± 8.2 ppm, 405.6 ± 5.3 ppm and 407.0 ± 5.3 ppm for 2009, 2010 and 2011, respectively). However, the annual mean CO2concentration at the GGS was higher than that observed from September 2006 to August 2007 at the Waliguan (WLG; 383.5 ppm), SDZ (385.9 ppm), LA (387.8 ppm) and Longfengshan (LFS; 384.3 ppm) background sites. This trend suggests that CO2 concentrations at the abovementioned background sites in China have increased significantly over the past decade, possibly due to the rapid economic development and extensive enhancement of energy consumption (http://www.stats.gov.cn/tjsj/ndsj/2017/indexch.htm). The average CH4 concentrations at the GGS were higher than those observed at the Antarctica site (1.740–1.766 ppm), the WLG background site (1.864 ppm) and the Shangri-La background site (1.861 ppm) as well as the global background concentration(1.798–1.824 ppm) (Bian et al., 2016) but were similar to those at the SDZ (1.914 ppm), LA (1.965 ppm) and LFS (1.939 ppm) background sites. In addition, the monthly average N2O concentrations, which ranged from 321.3 to 329.8 ppb in the GGS, were similar to those at Zhongshan Station, East Antarctica, which varied from 320.5 to 324.8 ppb (Ye et al., 2016), but higher than those at the Xinglong (316.7 × 10–9, from 1995 to 2000) and WLG (314.9 × 10–9, from 1995 to 2000) background sites.

Compared to the observations at other background sites in the world, the PM2.5 concentrations measured at the GGS site (6.5 ± 6.2 µg m–3) were slightly lower than or similar to those at the Portugal (9.4 µg m–3), Germany (10 µg m–3) and Scandinavia (6.6 µg m–3) background sites and were considerably lower than those recorded at the Switzerland (14.5 µg m–3) and Austria (19.7 µg m–3) background sites. The annual PM10concentration was almost the same as a previous result (10.8 µg m–3) from the Stockholm regional background site in Europe (Jonsson et al., 2013). Although most background monitoring sites are distant from urban areas, they might be affected by anthropogenic emissions to different extents. In addition, synoptic conditions play an important role in PM levels (Ji et al., 2012); for instance, differences in the precipitation intensity and atmospheric stagnation of various sites could affect the removal, transport and accumulation of particulate matter. Furthermore, the transboundary transport of air masses from regions of intense anthropogenic activities can lead to spatial variation in the levels of air pollutants at various background sites.

Overall, the air quality at the GGS site was better than that at the background sites located in the most populated and developed city clusters, such as the BTH, YRD and PRD regions of China. In addition to meteorological conditions and various sinks, the spatial variation in the levels of air pollutants in different regions could be highly affected by regional emission intensities of major air pollutants of interest (National Bureau of Statistics of the People’s Republic of China, 2017).

 
Seasonal and Diurnal Variations

The average seasonal variations in O3, CO, SO2, N2O, PM2.5, PM10, NO2, CO2 and CH4 and the RH, T and WS values at the GGS are shown in Fig. 4. The highest values of O3 were observed in spring, while the lowest values occurred in autumn. The maximum O3 value in spring is consistent with the spring O3 maximum frequently observed in the Northern Hemisphere (Monks, 2000; Vingarzan, 2004; Ran et al., 2014; Lin et al., 2015). Stronger ultraviolet (UV) radiation has been observed in spring at the GGS (Liu et al., 2017), which was favorable for O3 production and further contributed to the highest O3 concentrations in spring. The lower levels of CO from April to July were almost opposite to the seasonal variation in O3, which could be caused by the yield of O3 from CO oxidation under stronger UV radiation (Seinfeld and Pandis, 2016). The higher CO2 concentrations observed in April and May might originate from CO oxidation, which supports the above deduction to a degree. However, vigorous summertime photosynthesis resulted in a decline in CO2 concentrations in the summer. The O3 levels were relatively lower in late summer and autumn compared with those in spring. The high occurrence frequency of precipitation could scavenge more O3 in late summer and autumn than the other two seasons. SO2 and PM2.5 showed the highest concentrations in summer and the lowest concentrations in autumn. As shown in Fig. 2, high concentration spikes of both SO2 and PM2.5 could be observed. Given that the lifetime of SO2 is normally short under conditions of high RH and O3 concentrations (Seinfeld and Pandis, 2016), it might be reasonable to infer that local emissions outweighed the regional transport of both pollutants, resulting in the increased concentrations of RH and O3 at this background site. Note that pulse spikes of SO2 and PM2.5accompanied the increase in CO and NO2 concentrations. In addition, PM10 did not increase as significantly as PM2.5 during the sampling period. Hence, all these observations could lead to the hypothesis that the local air quality at such a remote site might be affected more by traffic emissions during summer than during other seasons. Because the Hailuogou scenic area is most attractive to tourists during summer, this difference could be ascribed to vehicular emissions from tourists. The seasonal pattern of CO was similar to that of NO2 and PM10. The highest CO, NO2 and PM10 values were mostly observed in winter. The lowest CO2 concentrations were observed in summer, which could be because vigorous summertime photosynthesis resulted in a decline in CO2 mixing ratios. The lowest CH4 concentrations were recorded in summer, while the CH4 levels in the other three seasons were very similar to each other. Such a seasonality of the CH4 mixing ratio in a clean-background environment is possibly related to OH radicals that are dependent on the seasonally varying intensity of ultraviolet radiation (Necki et al., 2003). In contrast, higher N2O concentrations were observed in summer than during the other seasons due to increases in the emission of N2O caused by enhanced microbial activity at higher ambient temperatures (Wang et al., 2018).


Fig. 4. The average seasonal variations in O3, CO, SO2, NO2, CO2, CH4, N2O, PM2.5 and PM10 (a) as well as RH, T and WS; (b) at the GGS.Fig. 4.
 The average seasonal variations in O3, CO, SO2, NO2, CO2, CH4, N2O, PM2.5 and PM10 (a) as well as RH, T and WS; (b) at the GGS.

The average diurnal variations in O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 as well as meteorological conditions, namely, RH, T and WS, at the GGS are shown in Fig. 5. The diurnal cycles of O3 showed minimum and maximum values in the early morning and afternoon, respectively. During the nighttime, the ozone concentration decreased slowly, mainly due to the titration of NO and deposition processes (Seinfeld and Pandis, 2016); during the daytime, with the increase in solar radiation after sunrise, O3 started increasing as more ozone was generated by photochemical reactions (Seinfeld and Pandis, 2016). Note that the daily amplitude of O3 at the background site was lower than that at urban sites (Reddy et al., 2011). The diurnal variations in CO and NO2 resulted in higher concentrations in the daytime and lower concentrations in the nighttime. The diurnal variations in NO and N2O resulted in obvious peaks at approximately 09:00 and 10:00, respectively. No obvious diurnal variation in SO2 was observed. The diurnal variations in CO and NO2 might be ascribed to two reasons. One reason could be that increasing anthropogenic activities in the daytime around the study site enhanced the primary emissions of these gases. The other reason may possibly be related to the evolution of the planetary boundary layer (PBL) and the transboundary transport of air pollutants. It is known that CO and NO2 concentrations increase with the decay of the nocturnal boundary layer, consequently making the high concentrations of these pollutants over source areas migrate to downwind receptor sites and leading to enhancements in CO and NO2 concentrations at the receptor sites during the daytime. The PM2.5 and PM10 concentrations did not show the obvious bimodal distribution that they show at urban sites (Wang et al., 2015). Similar to CO and NO2, a slight increase in the PM2.5 and PM10 concentrations was recorded in the daytime. Although the GGS is a regional background site, scattered anthropogenic emissions in the daytime could partly lead to the increase in the PM2.5 and PM10 concentrations; in addition, secondary aerosol formation via the atmospheric transformation of precursors from local sources or regional transport might contribute to higher concentrations of PM2.5 and PM10 during the daytime than during the nighttime.


Fig. 5. Average diurnal variations in O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 concentrations or mixing ratios (a) as well as RH, T and WS; (b) at the GGS station.Fig. 5. 
Average diurnal variations in O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 concentrations or mixing ratios (a) as well as RH, T and WS; (b) at the GGS station.

The average diurnal variation in CO2 is shown in Fig. 5. The diurnal cycle of CO2 reflects a diurnal periodicity of atmospheric vertical mixing, the release and uptake of CO2 by respiration, photosynthesis in the biosphere and human activities. In the daytime, photosynthesis is dominant in the net exchange between the biosphere and atmosphere, which could lead to the decline in CO2 concentrations. During the nighttime, photosynthesis stops and vertical mixing is weak, while plant respiration continues. The CO2 emitted by plant respiration accumulates near the ground. In addition, anthropogenic emissions also contributed slightly to the observed CO2, although the GGS site was distant from densely populated areas. The CH4 mixing ratios showed a very weak diurnal variation, with minima during midday, which might be caused by the elevated PBL, as anthropogenic emissions of CH4 are scarce in such a remote mountain area. N2O peaked at 10:00–12:00 and remained almost stable over the other time period. The N2O peak can probably be attributed to N2O emissions caused by enhanced microbial activity with the increase in soil temperatures. With the increase in solar radiation in the daytime, the soil temperature did not immediately increase with the atmospheric temperature due to the different heat capacities of the soil and atmosphere. With the increase in the soil temperature (generally 2 h later than that in the atmospheric temperature), the enhancement of microbial activity led to N2O peaks. In addition, N2O peaks did not occur with NO peaks, suggesting that they did not come from the same sources, i.e., vehicular emissions (Vojtíšek-Lom et al., 2018).

 
Impacts of Transboundary Air Pollution and Local Emissions

The impact of the regional transport and local emissions of air pollutants and greenhouse gases can be visualized based on both polar plots of wind and occurrence levels of the abovementioned pollutants as well as PSCF results of the hourly resolved input data. Fig. 6 shows the dependent distributions between WS/WD and the concentrations of all pollutants of interest. The concentrations of air pollutants and greenhouse gases showed evident wind sector gradients. For example, higher O3, SO2 and PM2.5 concentrations were correlated with winds from the northeast (NE), and lower concentrations were correlated with northwest (NW) winds. This wind sector dependence of O3, SO2 and PM2.5 concentrations is highly consistent with the spatial distribution of their concentrations in the surrounding areas. It was reported that relatively high and low O3, SO2 and PM2.5 concentrations were observed in the Cheng-Yu region and Tibetan Plateau, located to the northeast and northwest of the study site, respectively (China Environmental Statement, 2016). Clearly, zones with high concentrations of NO2, CO, CO2 and CH4 were in the center of these dependent distribution areas where low WS was recorded, indicating that local emissions might play an important role in the accumulation of these air pollutants. In contrast to other air pollutants, higher PM10 concentrations were more correlated with high WS, although PM2.5 concentrations did not vary as much as the PM10. This result suggests that wind-blown sand or dust contributed significantly to PM10. In addition, winds from the southeast resulted in relatively high PM10 concentrations, possibly implying that mid- and/or long-range transport of aerosols from both the south and southeast could contribute to PM10.


Fig. 6. The dependent distributions between wind speed and direction and the concentrations of O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 at the GGS station.Fig. 6. The dependent distributions between wind speed and direction and the concentrations of O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 at the GGS station.

Fig. 7 shows potential source areas for O3, CO, SO2, NO2, PM2.5 and PM10 concentrations as well as the CO2, CH4 and N2O mixing ratios during the study period. As shown in Fig. 7, the potential source areas of air pollutants and greenhouse gases showed obvious spatial distributions.It appeared that parts of Inner Mongolia and Gansu, Ningxia, Sichuan, Chongqing and Hubei Provinces as well as adjacent areas of Hunan, Guizhou and Guangxi Provinces were potential source areas for O3, which is consistent with the link between WD and relatively high O3 concentrations. With the rapid economic development in inland areas of China, more intensive emissions of O3 precursors could lead to O3 formation during transport processes (China Environmental Statement, 2016). The potential source areas for CO were mainly focused on neighboring countries in South Asia, such as India and Pakistan, which were associated with high pollution emissions (https://nar.ucar.edu/sites/default/files/labs/nesl/ACD-EmissionsParticulates02.jpg), in addition to areas of Inner Mongolia and Gansu and Guizhou Provinces. Considering that SO2 has similar emission sources as CO, the potential source areas for SO2 overlapped with those of CO, but an intense SO2 band caused by the emissions of power plants appeared in parts of Inner Mongolia and Gansu Province (Liu et al., 2015). The potential source areas for NO2, PM2.5 and PM10 were also found in neighboring countries in South Asia in addition to Inner Mongolia, Gansu Province and the Cheng-Yu economic region. High emissions of air pollutants in the abovementioned areas may result in the increase in NO2, PM2.5 and PM10 concentrations at the GGS site via transboundary transport. This result is almost consistent with that obtained by Qu et al. (2008), who found that anthropogenic sources in the Cheng-Yu economic region (Sichuan Basin), southeastern Yunnan Province and South Asian countries evidently influence Zhuzhang (in a mountainous rural area of southwestern China, 3583 m a.s.l.). For CO2, the potential source areas were focused on South Asian countries neighboring China and the adjacent areas of the Tibet Autonomous Region, Yunnan Province and Myanmar, which could be caused by high fuel consumption resulting in high CO2 emissions (http://edgar.jrc.ec.europa.eu/overview.php?v=CO2andGHG1970-2016). The potential source areas of CH4 were mainly distributed in Myanmar, India and Pakistan and parts of Yunnan Province, China. The potential source areas of N2O were recorded in the Cheng-Yu economic region, Yunnan Province, borders of the Tibet Autonomous Region of China and countries of South Asia; it is understandable that these areas with intense agricultural activities are the main sources of N2O and CH4 (Davidson and Kanter, 2014; Saunois et al., 2016).


Fig. 7. Potential source areas for O3, CO, SO2, NO2, PM2.5 and PM10 concentrations as well as CO2, CH4 and N2O mixing ratios during the study period. The color code denotes the PSCF probability. The location of the site is indicated by .
Fig. 7. Potential source areas for O3, CO, SO2, NO2, PM2.5 and PM10 concentrations as well as CO2, CH4 and N2O mixing ratios during the study period. The color code denotes the PSCF probability. The location of the site is indicated by .
 


CONCLUSION


This report presents the first year-long (from January 1 to December 31, 2017) and real-time measurement study of O3, NOx, SO2, CO, CO2, CH4, N2O, PM2.5 and PM10 performed at the GGS background site in southwestern China. The frequency of occurrence and temporal variations of this pollution were discussed in detail, and potential contribution areas of the above substances were identified using PSCF. The conclusions of this study are as follows.

High O3 and PM2.5 concentrations that exceeded WHO thresholds were observed and were closely associated with the yield from CO oxidation and long-range transport and/or local emissions of PM. The O3, CO, SO2, NOx, CO2, CH4, N2O, PM2.5 and PM10 concentrations presented typical lognormal patterns during the entire study period. The concentrations of these air pollutants at the GGS site were comparable to those at most background sites around the world. The slight differences in the levels of major air pollutants and greenhouse gases between background sites may be affected, to different extents, by anthropogenic emissions.

Obvious seasonal and diurnal variations were observed in the concentrations of O3, CO, NOx, SO2, CO2, CH4, N2O, PM2.5 and PM10. High values were observed for O3 in spring and summer, and the diurnal variations were characterized by higher daytime than nighttime values. SO2 and PM2.5 reached their highest concentrations in summer and their lowest concentrations in autumn. Relatively high CO, NO2 and PM10 concentrations were mostly observed in spring and winter. CO and NO2 exhibited higher concentrations in the daytime than in the nighttime. The vigorous summertime photosynthesis of vegetation resulted in lower CO2 concentrations during summer than during the other seasons. The lowest concentrations for CH4 were recorded in summer, whereas its levels in the other three seasons were similar to each other. High temperatures enhanced microbial activity, resulting in higher N2O concentrations during summer (compared to the other seasons) and in the daytime (compared to the nighttime).

Meteorological conditions significantly affect the background concentrations of the studied air pollutants. These pollutants can accumulate because of stagnant meteorological conditions and/or the contribution of mid- and long-range transport. The potential source areas for the air pollutants and greenhouse gases showed an obvious spatial distribution. The high potential source areas were distributed in parts of Inner Mongolia and Gansu, Ningxia, Sichuan, Chongqing and Hubei Provinces as well as adjacent areas of Hunan, Guizhou and Guangxi Provinces. High emissions in India and Pakistan also played an important role in increasing the CO, SO2, NO2, PM2.5 and PM10 concentrations. In addition, anthropogenic emissions from Inner Mongolia and Gansu Province contributed greatly to the enhancement of CO, SO2, PM2.5 and PM10. Increases in the CO2 concentration due to high fuel consumption were traceable primarily to South Asian countries neighboring China, and the adjacent areas of the Tibet Autonomous Region, Yunnan Province and Myanmar, whereas the enhancement of CH4 and N2O concentrations in the Cheng-Yu economic region, Yunnan Province, the Tibet Autonomous Region of China, Myanmar, India and Pakistan were attributable to intense agricultural activities.

The results of this study contribute to a better understanding of how anthropogenic activities impact the occurrence of air pollution against regional backgrounds, which may improve the modeling of regional air quality in the future.

 
ACKNOWLEDGEMENTS


This work was supported by the Beijing Municipal Science and Technology Project (D17110900150000 and Z171100000617002), CAS Key Technology Talent Program and National Research Program for Key Issues in Air Pollution Control (DQGG0101). The authors would like to thank all members participating in the air quality campaign at the GGS background site.



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