Pengfei Chen1, Shichang Kang 1,2, Junhua Yang1, Tao Pu1, Chaoliu Li2,3, Junming Guo1, Lekhendra Tripathee1

 

State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences (CAS), Lanzhou 730000, China
CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100085, China
Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, CAS, Beijing 100101, China


 

Received: November 13, 2018
Revised: January 3, 2019
Accepted: February 18, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2018.10.0360  

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Cite this article:

Chen, P., Kang, S., Yang, J., Pu, T., Li, C., Guo, J. and Tripathee, L. (2019). Spatial and Temporal Variations of Gaseous and Particulate Pollutants in Six Sites in Tibet, China, during 2016–2017. Aerosol Air Qual. Res. 19: 516-527. https://doi.org/10.4209/aaqr.2018.10.0360


HIGHLIGHTS

  • Air pollution characteristics were analyzed in six cities of Tibetan Plateau.
  • Air pollutants are more prevalent in Lhasa and Nagchu than in other sites in Tibet.
  • All pollutants except O3 have higher concentrations in winter than those in summer.
  • Diurnal PM2.5, PM10, SO2, NO2, and CO values showed two peaks around noon and midnight.
 

ABSTRACT


Long-term air quality data with high temporal and spatial resolutions are necessary to understand some processes influencing air quality and corresponding environmental and health effects. In this study, spatiotemporal variations of PM2.5, PM10, SO2, NO2, CO, and O3 were investigated over a one-year period (June 2016–May 2017) at six sites of the Tibetan Plateau (TP). The annual mean concentrations of PM2.5 in all cities except Nagchu were below the Grade II standard (35 µg m–3), and the values in Nagri and Nyingchi were even less than the Grade I standard (15 µg m–3). PM10 concentrations showed similar distribution pattern with PM2.5. Evident seasonal variations of PM2.5, PM10, SO2, NO2, and CO concentrations were observed, with the highest seasonal average value being in winter followed by fall, spring, and summer, in descending order. By contrast, the 8-h O3 concentration showed an opposite seasonal variation because the O3 depended on lots of factors such as stratospheric incursions, weather conditions, and intensity of solar radiation. The diurnal trends of PM2.5, PM10, SO2, NO2, and CO concentrations in study region generally showed a flat “W” shape with two peaks occurring around noon (10:00–12:00) and midnight (21:00–23:00); these peaks were found to be affected by emission sources and weather conditions. However, the O3 concentration trends did not significantly differ among the six regions, with the maximum concentration being in the afternoon. In sum, cities on the TP showed slightly higher pollution levels in regions affected by anthropogenic activities such as Lhasa and Nagchu, whereas other cities showed good air quality. Beside long-range transport pollutants from surrounding regions, local emissions (e.g., biomass burning, religious activities) also contributed much to the atmospheric pollutants. This study provides a basis for the formulation of future urban air pollution control measures on the TP.


Keywords: PM2.5; Ozone; Air pollution; Distribution; Tibetan Plateau.


INTRODUCTION


As the world’s most populous developing country, China has experienced severe air pollution with considerable increases of air pollutants owing to rapid economic development and increasing urbanization in recent decades (Chan and Yao, 2008; Guo et al., 2014; Du and Li, 2016; Lin et al., 2018; Zhao et al., 2018). The Asian Development Bank reported that 70% of the most polluted cities in the world are situated in China (Bapna, 2012). On the basis of the Global Burden of Disease Study, 4.2 million deaths have been caused by air pollution in 2015, among which 1.6 million individuals were from China (Hu et al., 2014; Forouzanfar et al., 2016; Landrigan, 2016). One study reported that ambient PM2.5 accounts for 15.5% of all the causes of death in China (Song et al., 2017a). The elevated PM2.5 and ozone concentration significantly affects human health and threatens sustainable development of China (Liu et al., 2017b; Zhao et al., 2018). Therefore, these air quality data are requisite to understand pollution status and assess the human health risks of China (Wang et al., 2014).

The Chinese Ministry of Environmental Protection start to release hourly averaged concentrations of PM2.5, PM10, SO2, NO2, CO, and O3 recorded from the monitoring stations situated in over hundred cities in China from Jan 2013. The characteristics of these six pollutants and their influence factors have been widely studied in terms of released monitoring data (Hu et al., 2014; Wang et al., 2014; Huang et al., 2015; Ma and Jia, 2016; Zhang et al., 2016; Guan et al., 2017; He et al., 2017; Song et al., 2017b; Yin et al., 2017a; Zhou et al., 2017; Zhao et al., 2018). These researches have revealed that the pollutant concentrations in cold months are much higher than those in summer; this is in contrast to the case of ozone. Some studies suggested that the frequent heavy haze-fog episodes in China are mainly caused by large amount of air pollutant emissions during the cold season (Wang et al., 2013; Tan et al., 2016; Yin et al., 2017a). Other studies have demonstrated that weather conditions also play an important role in affecting the pollutants diffusion. In addition, high mountains like Qinghai-Tibet Plateau are considered to be barrier which can block pollutants transporting from South and East Asia (Cao et al., 2015; Jia et al., 2015; Zheng et al., 2015; Xu et al., 2016; Ding et al., 2017; Yin et al., 2017a). However, most of the previous studies simply analyzed the characteristics of pollutants in central and eastern China (Chai et al., 2014; Hu et al., 2014; Wang et al., 2014; Xie et al., 2015; Zhao et al., 2016; He et al., 2017; Wang et al., 2018), whereas little has been reported in western China especially the Tibetan Plateau (TP) which is located in large-scale complex terrain.

The TP is one of the world’s least scientifically studied regions regarding air pollutant concentration, variation, transportation, and health risk assessment. Pollutant emissions from the TP itself are relatively less compared with densely populated and industrialized parts of East of China and South Asia (Chen et al., 2018a). Local residents in most parts of the TP depend on traditional agriculture and animal husbandry for their livelihood, whereas the proportion of industry accounts for only 8% of the GDP of this huge area (http://www.tibet.stats.gov.cn/). Therefore, the TP is known as a remote background region worldwide. However, numerous recent researches have pointed out that air pollutants emitted from South Asia can reach this remote region by long-range transport (Cong et al., 2015; Lüthi et al., 2015; Kang et al., 2016; Li et al., 2016a; Chen et al., 2018b; Wu et al., 2018). In addition, local anthropogenic emissions such as yak dung burning and religious incense burning also contributes significantly to the atmospheric environment (Kang et al., 2009; Gong et al., 2011; Li et al., 2012; Chen et al., 2015; Xiao et al., 2015; Li et al., 2016a; Chen et al., 2018a).

This study presents the results of six criteria air pollutants in Lhasa, Ngari, Qamdo, Nyingchi, Nagchu, and Shigatse during 2016–2017. The main objectives are (1) to investigate the concentrations of the six criteria pollutants in the TP, and (2) to identify their seasonal variations and the relationships among the pollutants.


METHODS AND DATA



S
ite Information

Six sites, namely, Lhasa, Ngari, Qamdo, Nyingchi, Nagchu, and Shigatse, were chosen in this study (Fig. 1 and Table 1). Ngari, located on the border of China and Kashmir, India, is controlled by the seasonal shifting of atmospheric circulations, with Indian monsoon controlling in summer, whereas the westerlies predominated in other seasons (Gong et al., 2015). The region is covered by bare soil or grassland. The annual average temperature is approximately 2°C and annual precipitation was approximately 90 mm for the period 2010–2013. About 80% of rainfall occurs in the summer (June–September). Qamdo, located in the eastern of the TP, with an average elevation of 3500 m, has a semi-arid monsoon climate, which is dry and cold in winter and moist and moderately hot in summer. The annual average temperature is 7.6°C and precipitation is 400–600 mm. Nyingchi city, located downstream of the YarlungZangbo River, with an average elevation of 3100 m, has an annual average temperature of 8.7°C and precipitation of 650 mm. Lhasa is the capital of Tibet autonomous region. It is a famous historic tourist city and currently undergoing rapid urbanization and tourism development (Li and Wang, 2014; Li et al., 2016b; Li et al., 2018). The temperature ranges from –5.7 to 21.2°C (average: 9.0°C), and the relative humidity range 7%–75% with an average of 38.7% (Wan et al., 2016). The annual average precipitation is approximately 400 mm with most occurring during summer due to influence of Indian monsoon. Nagchu, located in the central of the TP, has an average temperature of –2°C and an annual mean precipitation of approximately 420 mm. Shigatse, known as the Tibet’s barn, is the second largest city in the TP. It has an elevation of approximately 3800 m and a population of about 720,000. Shigatse contributes approximately half of the total agricultural output of the Tibet Autonomous Region (Yang et al., 2016). The annual mean precipitation is approximately 433 mm, and the average temperature and wind speed are 2.5°C and 1.43 m s–1 in dry season and 14.3°C and 1.74 m s–1 in wet season, respectively (Yang et al., 2016).


Table 1. Detailed information of study regions on the TP.


Air Quality Data

In order to understand the characteristics of these six pollutants in cities of the TP, real-time hourly averaged monitoring data of PM2.5, PM10, SO2, NO2, CO, and O3 from June 2016 to May 2017 were obtained from the China National Environmental Monitoring Center (CNEMC) (http://113.108.142.147:20035/emcpublish/). Detailed information about the data obtaining were described by previous researches (Hu et al., 2014; Wang et al., 2014; Zhao et al., 2018). Briefly, automated monitoring systems were set at each site to monitor the ambient concentration of these six criteria pollutants according to the China Environmental Protection Standards HJ 193-2013 (http://www.es.org.cn/download/2013/7-12/2627-1.pdf) and HJ 655-2013 (http://www.es.org.cn/download/2013/7-12/2626-1.pdf). Two methods including micro-oscillating balance and β-absorption are used for measuring particulate matter (PM2.5 and PM10) concentrations; and chemiluminescence method, ultra-violet (UV) fluorescence method, and UV-spectrophotometry method are used for NO2, SO2, and Oconcentrations, respectively. For CO, both the nondispersive infrared absorption method and the gas filter correlation-infrared absorption method are used. Before publish these hourly averaged data, a sanity check was conducted at each station to remove problematic data points. The 8-h O3 concentrations were calculated when there were valid data for at least 6 h for every 8 h during that day, whereas the mean daily concentrations for other five pollutants were calculated only when there were valid data for more than 20 h (Hu et al., 2014; Wang et al., 2014; Zhao et al., 2018).


RESULTS AND DISCUSSION



Overview of Air Pollutants

The annual average concentrations of the six criteria pollutants in six cities of the TP were given in Table 2 and Fig. 1. The PM2.5 concentrations ranged 13.4–48.7 µg m–3, with Nagchu and Nyingchi having the highest and lowest concentrations, respectively. Lhasa, Shigatse, and Qamdo showed medium values of approximately 25 µg m–3. PM2.5 concentrations in all cities except Nagchu were below the Grade II standard (35 µg m–3), and the values in Nagri and Nyingchi were even less than the Grade I standard (15 µg m–3). PM10 concentrations exhibited a similar distribution pattern, with high values occurring in Nagchu and Lhasa, which exceeded the Grade II standard (70 µg m–3) and low values in Nagri and Nyingchi, which were below the Grade I standard (40 µg m–3). Nagchu had the highest SO2 concentrations (34.9 ± 10.8 ppb), which exceeded the Grade II standard (21 ppb), whereas other five cities had similar SO2 concentrations of approximately 10 ppb. Lhasa had the highest NO2 concentrations, followed by Nagchu, both exceeding the Grade II standard (20 ppb), whereas Shigatse, Qamdo, and Nagri had similar values of approximately 16 ppb and Nyingchi had the lowest concentration (8.31 ppb). The annual average CO concentrations ranged from 408 ppb (Nyingchi) to 1283 ppb (Nagchu). Nagchu, Lhasa, and Qamdo had higher values than those of the other three cities. The annual average 8-h O3 concentrations showed similar values in all cities except Nagchu. Spatial distributions of these six pollutants indicated that regional high concentrations were mainly concentrated on densely populated cities of the TP with higher GDP (Table 1).


Fig. 1. Geographical map and locations of the cities in the TP.Fig. 1. Geographical map and locations of the cities in the TP.


Table 2. Annual average concentrations of the six criteria pollutants in six cities of the TP.

The PM2.5/PM10 ratios were different among these six sites with the highest and the lowest values observed in Nagchu and Lhasa, respectively (Table 2). Shigatse was also found to have higher PM2.5/PM10 ratio than 52% which are comparable with those in some other regions such as North China Plain, west of Sichuan and Xi’an (Wang et al., 2014; Song et al., 2017b; Zhao et al., 2018). However, the values in other three cities were significantly lower than those obtained in urban cities such as Beijing, Shanghai, and Guangzhou (Wang et al., 2014). In general, the average PM2.5/PM10 ratio was approximately 46.1% in the six sites, which is significantly lower than the ratios obtained from the literature, namely, 65% in China before 2012 (Zhou et al., 2016; Xu et al., 2017), 58% in 31 Chinese provincial capital cities during 2014–2015 (He et al., 2017), and 56% in 190 Chinese cities during 2013–2014 (Zhang and Cao, 2015). The case may be in connection with the fact that Tibetan cities are easily affected by dust events such as floating dust, dust storms, and blowing dust, leading to a obvious increase in PM2.5–10 (particle size: 2.5–10 µm) mass concentrations. Previous studies has proved that mineral dust is one of the main aerosol compositions in some regions of the TP (Decesari et al., 2010; Zhang et al., 2001; Kang et al., 2016). And analysis of dust conducted at surrounding deserts (e.g., the Taklimakan, Gobi, and southwest Asian deserts) has also provided evidence for atmospheric particulates transporting on the TP (Huang et al., 2007; Liu et al., 2008; Xia et al., 2008).

As shown in Table 2, the concentrations of six pollutants on the TP are compared with those in other regions in China. The annual average PM2.5 concentrations were only close to those in the Sichuan Basin (Zhao et al., 2018) and considerably lower than those in the NCP, YRD, and in cities of other typically heavily polluted regions (Wang et al., 2014; Song et al., 2017a, b). However, the mean PM10 concentrations of Lhasa and Nagchu were comparable to those of cities such as Shanghai and Guangzhou. Moreover, the annual SO2 concentrations of all cities except Nagchu were lower than those in the NCP and were close to those of the YRD and Sichuan Basin. In addition, concentrations of NO2 and CO in Lhasa and Nagchu were comparable to those in typical polluted cities. The annual mean Oconcentrations in all six cities were slightly higher than those in other regions of China. These analyses indicate that the air in Lhasa and Nagchu is affected by anthropogenic activities to some extent, whereas other sites show background values. 


Temporal Variations

To better understand the air pollution status on the TP, the monthly averaged pollutant concentrations are shown in Fig. 2. PM2.5 concentrations in the study region exhibited considerable temporal variability, with the highest being in November and December and the lowest during June to August. The concentrations in other months were approximately the same. The elevated PM2.5 concentration in cold season might due to increased fuel burning for residential heating and cooking. In addition, unfavorable weather condition such as few precipitation and slow winds is unfit for air pollutants dilution and dispersion (Chai et al., 2014; Wang et al., 2014). Usually, industrial and vehicle emissions play an important part in some cities for high PM2.5 concentrations. For example, two previous studies reported that iron and steel manufacturing contributed 11% to PM2.5 concentration in Chengdu during 2011 (Tao et al., 2014) and industrial emissions account for 27% in Wuhan during 2011–2012 (Cheng et al., 2012). However, the proportion of industry accounts for only 8% of the GDP of the TP, thus biomass burning and religious activities as well as weather condition might be the main reason for PM2.5 variation.


Fig. 2. Mean annual variations of the monthly averages for six criteria air pollutant concentrations in cities of the TP.Fig. 2. Mean annual variations of the monthly averages for six criteria air pollutant concentrations in cities of the TP.

PM10 concentrations in Lhasa, Shigatse, and Qamdo showed similar seasonal variations as those of PM2.5 concentrations. However, PM10 concentrations were found to be the highest in fall in Nagchu and were did not differ significantly among the four seasons in Ngari and Nyingchi (Fig. 2). SO2, NO2, and CO also showed similar seasonal variations, with the highest and lowest values occurred in winter and summer, respectively. The seasonal pattern was found to be a typical feature in East Asia (Wang et al., 2014; Zhao et al., 2018). These seasonal variations were mainly affected by the meteorological conditions and emission sources. Usually, stagnant weather conditions occurred more frequently in winter which characterized by shallow mixing layers, few precipitation and slow winds. This can trap the pollutants emitted by local emissions or transported form other regions and elevating their concentrations near the surface (Tai et al., 2010; Wang et al., 2014). Emission source is another important factor influencing the seasonal distributions of pollutants. Previous studies have reported that pollutants emitted from IGP can be transported to the TP (Ji et al., 2015; Li et al., 2016a; Chen et al., 2017; Yang et al., 2018). For example, Yang et al. (2018) simulated the origin of anthropogenic black carbon transported to the TP, which showed that South Asia contributed 40–80% and 10–50% of surface black carbon in the non-monsoon and monsoon seasons, respectively. Whereas anthropogenic black carbon from eastern China accounted for less than 10% in the non-monsoon season but can be up to 50% in the monsoon season for the northeastern TP. Besides long-range transport pollutants, local emissions such as yak dung combustion also contributed part of pollutants throughout the TP (Chen et al., 2015; Li et al., 2016a). Along with fast development of China during last decades and the setup of Qinghai-Tibet Railway at 2006, the Tibetan Autonomous Region experienced intensively urbanization process, so that fossil combustion had intensively influenced the atmosphere of its urban area (Li et al., 2018). Research based on carbon isotope also provided direct evidence that local biomass burning are important to atmospheric pollution on the TP (Li et al., 2016a). This is further supported by previous researches based on regional climate-atmospheric chemistry model and positive matrix factorization receptor model. For example, Yang et al. (2019) reported the contribution proportions of various emission sources to PM2.5 in western China. The results showed that residential sector was the largest contributor to the PM2.5 concentrations with a contributions ratio of 56.2%, while the transportation and industrial sectors contributed 14.3% and 17.6%, respectively, in cities of the TP. In addition, the particle phase PAHs sources were quantified using the PMF model in Lhasa city and the results also revealed that biomass burning from local residents was the main source (48.4%) followed by vehicle emissions and coal combustion (Chen et al., 2018a).

Ozone concentration in urban regions is largely affected by local weather condition, large-scale circulation, and various emission sources (Shu et al., 2016; Li et al., 2017; Kim et al., 2018). Using the factor separation approach, Li et al. (2017) suggested that fossil fuel consumption contributed most in O3 formation and is the major causes of severe O3 pollution in eastern China. Furthermore, complex topography also significantly impacts O3 distributions (Kang et al., 2012). However, the effects of emissions and weather condition on Oconcentration have not been sufficiently studied in cities of the TP (Zheng et al., 1998; Cui et al., 2015). The seasonal variation of surface O3 is influenced by many factors including stratospheric intrusion, long-range transport air masses, local vertical mixing, and even deposition (Li et al., 2016c; Wang et al., 2017; Yin et al., 2017b; Zhao et al., 2018). In this study, the 8-h Oconcentration showed an opposite seasonal variation to other pollutants with the highest and the lowest values occurred in late spring-early summer and winter, respectively (Fig. 2). On the TP, the largest stratospheric incursions usually occurred in spring and the planetary boundary layer height is higher at same period which facilitated the impact of downward transport from the stratosphere to surface of these sites. In addition, the late spring to early summer period has more intense solar radiation because the monsoon leads to increased cloudiness in summer. The increased solar radiation promotes the photochemical production of surface O3 in such periods. In winter and fall, the shortwave radiation is weaker than that in other seasons, thus cause relatively low level of O3.

Fig. 3 showed the hourly average concentrations of six pollutants. The trends of PM2.5, PM10, SO2, NO2, and CO concentrations generally showed a flat “W” shape on the TP with two valleys occurred approximately in the early morning and the late afternoon (~16:00), respectively. In the early morning, reduced anthropogenic activities and pollutant deposition might be the main reason for low pollutant concentrations; whereas relatively high planetary boundary layer height is conducive to the dispersion and dilution of air pollutants in the late afternoon (Tie et al., 2007; Zhao et al., 2018). Correspondingly, two peaks occurred at noon (10:00–12:00) and night (21:00–23:00), respectively, when the planetary boundary layer height was low and human activity was high, which are conducive to the formation and concentration of pollutants. This pattern differs from those in eastern cities (e.g., Beijing), where have peak pollutant concentrations at 20:00 pm. The time difference between eastern cities and the study region might be the reason for two variation patterns. Notably, the daytime PM2.5 concentrations were somewhat lower than those during nighttime. It seems that substantially enhanced local emissions (e.g., religious activities, biomass burning for heating and cooking) after sunset cause this diurnal variation of PM2.5 concentrations on the TP (Li et al., 2018).


Fig. 3. Mean diurnal variations of the hourly averages of six criteria air pollutants in cities of the TP.Fig. 3. Mean diurnal variations of the hourly averages of six criteria air pollutants in cities of the TP.

In the present study, it was observed that the O3 concentrations did not show considerable differences among the six regions; the maximum concentration was in the afternoon (Fig. 3). Wind speed and planetary boundary layer height are generally regarded as the main factors influencing the diurnal cycle of surface O3. Previous studies have reported that high levels of surface O3 were associated with high wind speeds and high mixing heights on the TP. In addition, local photochemical production may also contribute to the higher concentration of O3 in the daytime (Tang et al., 2002; Yin et al., 2017b). Furthermore, Temperature can influence O3 production by accelerating the chemical reaction rate and increasing the VOCs (e.g., isoprene) emission (Coates et al., 2016), thus O3 increases with temperature (Abeleira and Farmer, 2017). On the TP, O3 concentration showed same diurnal variation with other studies conducted at Nam Co, Dangxiong, and Lhasa (Ran et al., 2014; Lin et al., 2015; Yin et al., 2017b). However, surface O3 at NCO-P and Waliguan showed different patterns because thermal circulation and mountain-valley breeze were the most influential factors in two regions respectively (Cristofanelli et al., 2010; Xue et al., 2011).


Correlations between Air Pollutants

The Pearson correlation coefficients (R) between pollutants were calculated for each of the six cities (Table 3). Over the 1-year period, PM2.5 was positively correlated with PM10, CO, SO2, and NO2 in all six cities, especially in Lhasa and Nyingchi (R > 0.5), thus suggesting their common sources from biomass (e.g., yak dung, woods, crop straw) burning and fossil fuel combustion such as vehicle emission. For Lhasa city, high mountains situate at its south and north sides making difficult for pollutants to diffuse when atmosphere is relatively stable, thus causing high correlations among PM2.5 and other pollutants (Li et al., 2018). Nyingchi covered mainly by alpine forest, therefore, biogenic emissions, local woods burning for cooking and heating, and vehicle consumption may be essential sources of PM and gas pollutants. Positive correlations with slightly weaker R values between PM2.5 and other pollutants were observed in other cities, which might imply influence of dust or weather condition. For example, Ngari is located in a place with relatively open geomorphological setting and was classified as barren region according to MODIS land cover classification (Liu et al., 2017a). Relatively weak correlations among these pollutants implied a disturbance of PM masses from dust impact (Liu et al., 2017a). Previous studies have reported frequent dust plumes in the lower atmosphere in the western TP which can possibly impact the distribution of aerosol masses of TP (Huang et al., 2007).

In this study, O3 was negatively correlated with the other pollutants with low (–0.25 < R < 0) to moderate (–0.5 ≤ R ≤ –0.25) correlation coefficients. Ocan be affected by lots of factors (Zhang et al., 2015; Derwent et al., 2016; Yin et al., 2017b; Gong et al., 2018), thus it is difficult to identify the exact impact factor here. However, previous studies reported that in the northern TP, horizontal and vertical wind transports were considered as major contributors to surface O3 (Shen et al., 2014). Whereas titration of O3 by NOX as well as the differences in altitude and meteorology such as photochemistry are main factors affecting surface Oin the central TP (Ran et al., 2014). In addition, frequent stratospheric intrusions were recorded in all seasons except spring along the southern ridge of the Himalaya, which might be the major factors (Cristofanelli et al., 2010). Therefore, surface O3 on TP may dominate by mixed processes involving photochemical reactions, vertical mixing and downward transport of stratospheric air mass which caused different correlations with other pollutants.


Table 3. Correlations of pollutants in six cities of the TP based on annual data during 2016–2017.


CONCLUSION


This study analyzed the spatial and temporal variations of PM2.5, PM10, SO2, NO2, CO, and O3 observed in six cities on the TP from June 2016 to May 2017. This was done to gain a deep understanding of the pollution levels of different pollutants, evaluate the attainment of air quality standards in various cities, and mark the seasonal variation of major pollutants. High concentrations of PM2.5, PM10, SO2, NO2, and CO were observed in Nagchu and Lhasa, especially during winter, indicating a strong contribution of anthropogenic activities. Beside long-range transport pollutants from surrounding regions, local emissions (e.g., biomass burning, religious activities) also contributed much to the atmospheric pollutants in the study region. However, pollutants in other cities, especially in Nagri and Nyingchi, met the newly revised ambient air quality standards (Grade I), indicating that even though some cities were affected by anthropogenic emissions, the air conditions in most regions were good and still can be looked as background sites. Seasonal variations of all pollutants except O3 were observed, with the highest concentrations in winter followed by fall, spring, and summer, in descending order. Weather conditions and emission sources are main factors influencing the seasonal variation of these pollutants. However, opposite Ovariation was observation with other pollutants due to the combined influence of stratospheric intrusion, solar radiation and weather conditions. The diurnal trends of PM2.5, PM10, SO2, NO2, and CO concentrations in the TP generally exhibited two peaks around noon and midnight, whereas O3 concentrations were similar among the six regions, with the maximum in the afternoon. Positive correlations were observed between PM2.5 and other pollutants (e.g., PM10, SO2, NO2, and CO), indicating that these pollutants might have common sources or experience similar chemical process. However, surface O3 exhibited negative correlations with other pollutants because of combined influences on it.


ACKNOWLEDGEMENTS


This study is supported by the National Natural Science Foundation of China (41705132) Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) (XDA20040501), CAS “Light of West China” Program and State Key Laboratory of Cryospheric Science (SKLCS-OP-2018-01). The manuscript has been edited by Wallace Academic Editing.



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