The vertical profiles of carbonaceous aerosols and key influencing factors during wintertime over western Sichuan Basin, China

Atmospheric Environment 223 (2020) 117269

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The vertical profiles of carbonaceous aerosols and key influencing factors during wintertime over western Sichuan Basin, China Daiying Yin a, b, Suping Zhao c, d, *, 1, Jianjun Qu a, Ye Yu c, Shichang Kang d, e, Xiaolin Ren f, Jing Zhang f, Yong Zou g, Longxiang Dong b, Jianglin Li b, Jianjun He h, Ping Li b, c, Dahe Qin d a

Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China University of Chinese Academy of Sciences, Beijing, 100049, China Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China d State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China e CAS Centre for Excellence in Tibetan Plateau Earth Sciences, Beijing, 100101, China f Maerkang Meteorological Bureau, Maerkang, 624000, China g Lixian Meteorological Bureau, Lixian, 624000, China h State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China b c

H I G H L I G H T S

� Vertical profiles of OC & BC were first observed at West Sichuan Basin (WSB). � Carbonaceous aerosol pollution was severe at southeast Tibetan Plateau. � OC and BC vertical profiles exhibited “high-low-high” pattern. � Pollutants inside SB can be transported to about 3 km above SB by vertical dispersion. � Potential sources largely varied as the increased altitudes at WSB. A R T I C L E I N F O

A B S T R A C T

Keywords: Carbonaceous aerosols Vertical profiles Sichuan basin Tibetan plateau HYSPLIT

With Tibetan Plateau higher than 4 km to the west, the location of Sichuan Basin is unique all around the world and provides a good platform to study air pollution in the urban agglomerations over the complex terrain. To fill in the blanks on vertical distributions of PM1 (the particles smaller than 1 μm) and carbonaceous aerosols within the basin, by means of high topographic relief, PM1 were off-line sampled during 20 January to 2 February 2018 at eight sites with increasing altitudes from the basin to southeastern margins of the Tibetan Plateau. The regional potential sources for each site were revealed by HYbrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and concentration-weighted trajectory (CWT) method. The lowest carbonaceous aerosol levels occurred at Lixian, while the highest OC (organic carbon) (EC, elemental carbon) was at Hongyuan (the altitude of 3500 m) (Ande, a rural site) due to more primary emissions. The pollutants inside the basin can be transported the altitudes from 2 km to 3 km by vertical dispersion, but they cannot be dispersed to higher altitudes. The vertical stratification of the pollutants was obvious and easily formed “high-low-high” pattern from Sichuan Basin to southeastern Tibetan Plateau, especially during highly polluted episodes. The regional potential sources significantly varied as the increased altitudes. Regional pollution was significant inside the basin. The sources at the altitudes from 2 km to 3 km originated from southeastern margins of the Plateau and surrounding cities, while those at higher altitudes were transported from southeastern margins of the Plateau. The impact of basic meteorological variables (temperature, wind speed and vapor pressure) on carbonaceous aerosols was opposite between the basin and Plateau sites. This study was essential to understanding formation mechanisms of severe pollution episodes and thus to making control measures for the urban agglomerations inside the mountainous terrain.

* Corresponding author. Key Laboratory of Land Surface Process & Climate Change in Cold & Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, Gansu, PR China. E-mail address: [email protected] (S. Zhao). 1 Co-first author. https://doi.org/10.1016/j.atmosenv.2020.117269 Received 16 July 2019; Received in revised form 17 November 2019; Accepted 5 January 2020 Available online 7 January 2020 1352-2310/© 2020 Elsevier Ltd. All rights reserved.

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1. Introduction

Table 1 Daytime and nighttime mean PM1 and carbonaceous aerosols (OC, SOC, EC) and SOC accounting for OC at the eight cities from Sichuan Basin to southeast Ti­ betan Plateau.

In recent years, the long-lasting severe haze episodes occurred frequently in some large Chinese urban agglomerations, such as North China Plain (Cai et al., 2017; Zhao et al., 2013), Yangtze River Delta (Ding et al., 2013; Fu et al., 2008), Sichuan Basin (Zhao et al., 2018a, 2018b; 2019a) and Guanzhong Basin (Huang et al., 2014). Besides more primary emissions, high secondary formation and unfavorable weather conditions under climate warming and the two-way feedbacks between aerosol pollution and meteorological conditions were considered to be main factors inducing regional haze pollution over China (Cai et al., 2017; Huang et al., 2014; Zhong et al., 2019). The high particulate matter concentrations had a large threat to human and ecological health and economic development (Maji et al., 2018; Tie et al., 2016). The high concentrations of aerosols can warm the upper air and cool the surface by direct radiative forcing. The temperature increased by ~0.7 � C averagely in upper air and mean temperature decreased by ~2.2 � C near surface under polluted condition in North China (Huang et al., 2018), which made the air was increasingly stable and stagnant, and further aggravated air pollution within boundary layer, and formed aerosol-meteorology feedback (Li et al., 2017). During the cumulative stages of heavy aerosol pollution episodes, PM2.5 growth was governed by the feedback, which explained over 70% of the increase (Liu et al., 2019). Furthermore, the recent study found that impact of the feedback on severe haze pollution depended significantly on aerosol chemical composition and the vertical distributions (Ding et al., 2016; Wang et al., 2018a,b,c,d). As the most important light-absorbing component, the impact of black carbon (BC) on the boundary-layer meteorology was very sensitive to the altitude of BC layer. The BC near the capping inversion could more effectively suppress the boundary layer develop­ ment and weaken the turbulent mixing (Wang et al., 2018a,b,c,d). Few studies revealed the vertical distributions of carbonaceous aerosols by in-situ observations with the help of unmanned aerial vehicle (Bates et al., 2013; Zhu et al., 2019), cable cars (Zawadzka et al., 2017), tethered balloon (Li et al., 2015; Lu et al., 2019), meteorological tower (Xie et al., 2019; Zhou et al., 2018) and large topography (Zhao et al., 2019a, 2019b). The studies found that aerosol vertical profiles varied largely and depended on time and place. BC concentrations were sometimes higher in upper air than near surface due to local sources and vertical mixing effects (Wang et al., 2018a,b,c,d; Zhao et al., 2019c). However, most of the previous studies mainly focused on the plain cities in eastern China. The information on the vertical distributions of carbonaceous aerosols is scarce at the mountainous cities in western China, which largely limits formulation of haze prevention measures over the complex terrain. Sichuan Basin, located at southwest China, is one of the most populous regions in China with two megacities of Chengdu and Chongqing in the basin. With Tibetan Plateau higher than 4 km to the west, Qin Mountains to the north and the Yunnan-Guizhou Plateau to

Cities Units

Day or night

PM1/ug m 3

OC/ug m 3

EC/ug m 3

SOC/ ug m

Chengdu

Daytime

119.9 � 28.0 118.8 � 24.8 103.3 � 27.7 103.6 � 23.9 119.8 � 28.7 132.8 ± 33.0 107.8 � 13.1 112.0 � 37.0 85.3 � 25.8 77.1 � 34.1 61.4 � 38.4 67.2 � 28.9 69.3 � 25.3 73.1 � 17.6 79.6 � 37.1 118.0 � 39.8

13.1 � 5.7 13.4 � 5.3 11.9 � 5.9 12.4 � 5.0 14.2 � 6.5 20.1 ± 10.4 13.3 � 2.9 12.7 � 6.2 8.3 � 2.4 7.2 � 3.1 6.4 � 3.2 5.2 � 2.0 9.2 � 2.4 7.6 � 3.4 17.7 ± 5.0 23.6 ± 13.8

4.5 � 1.7 5.4 � 2.1 4.9 � 2.1 4.9 � 1.9 6.5 � 2.7 6.7 ± 2.7 6.0 � 1.9 5.4 � 1.8 3.3 � 1.3 3.0 � 1.5 2.5 � 1.8 2.0 � 1.2 2.9 � 1.0 2.4 � 1.9 5.5 ± 1.8 6.4 ± 3.3

4.0 � 3.6 2.6 � 1.8 2.6 � 3.7 3.3 � 2.1 3.3 � 3.4 8.9 ± 7.8 3.1 � 2.4 3.4 � 3.7 1.3 � 0.8 0.8 � 0.7 1.6 � 0.5 1.3 � 0.6 2.4 � 2.0 2.1 � 1.4 2.8 ± 1.8 6.1 ± 5.2

Nighttime Deyang

Daytime Nighttime

Ande

Daytime Nighttime

Dujiangyan

Daytime Nighttime

Wenchuan

Daytime Nighttime

Lixian

Daytime Nighttime

Maerkang

Daytime Nighttime

Hongyuan

Daytime Nighttime

3

SOC/ OC/% 27.4 � 14.5 19.5 � 11.9 19.7 � 13.0 26.1 � 11.2 21.6 � 12.5 38.5 ± 21.1 22.7 � 15.3 22.6 � 14.6 19.4 � 17.9 13.8 � 16.1 36.1 � 27.3 32.2 � 22.9 25.5 � 16.7 38.8 � 29.6 16.5 ± 10.2 24.2 ± 7.4

the south, location of the basin is unique all around the world, which provides a good platform to study air pollution in the urban agglomer­ ations over the complex terrain. Due to high primary emissions and unfavorable diffusion conditions (weak winds and long-lasting multilayer temperature inversion) induced by the special terrain, the basin suffers from severe fine particle pollution and has become one of the most polluted regions in China (Chen and Xie, 2012). Many studies were conducted in Sichuan Basin to reveal heavy air pollution causes under the unique terrain (Hu et al., 2019; Qiao et al., 2019; Tian et al., 2019; Wang et al., 2018a,b,c,d; L. Zhang et al., 2019). The high relative hu­ midity helped to aggravate air pollution due to hygroscopic growth (Tie et al., 2017), and annual mean aerosol optical depth (AOD) in the basin was the highest across China (Luo et al., 2014). Biomass burning was identified as an important contributor to airborne particles due to widespread burning activity after harvest in the Sichuan Basin (Chen

Fig. 1. Locations of PM1 sampling sites during the campaign. 2

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Fig. 2. Variations of PM1, OC and EC during the study period at Chengdu, Deyang, Ande, Dujiangyan, Wenchuan, Hongyuan (Cluster 1), Lixian and Maerkang (Cluster 2). The averages for each Cluster and the corresponding differences were also given in the subplots. The blue shaded regions represent a cold air process invading Sichuan Basin. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

and Xie, 2014; Wang et al., 2013). However, the previous studies about aerosol chemical composition mainly concentrated on the two mega­ cities within the basin, while the vertical distributions of aerosols were scarce and its roles to severe pollution formation were not well under­ stood under the special terrain. Organic and black carbon levels were reported in the inner part of Tibetan Plateau (Chen et al., 2019; Kang et al., 2019), while those have been not studied at West Sichuan Plateau. The daytime and nighttime PM1 samples were collected from the western the basin to southeastern Tibetan Plateau with high topographic relief during the campaign. The main aim of this study is to understand vertical profiles of carbonaceous aerosols at West Sichuan Basin (WSB) and to reveal key influencing factors from meteorology to local and regional emissions. This study showed vertical profiles of aerosol chemical composition by means of high topographic relief at WSB for the first time, which provided references for the upcoming study of vertical distributions of air pollutants over complex terrain.

2. Data and methods 2.1. Field observations Besides the severest air pollution with dense population and industry distribution, WSB had the highest topographic relief across the basin (Zhao et al., 2018a). The sharply increased latitude acted as a barrier to the air mass travelling from the east, which was supposed to be a factor of the pollution accumulation in the mountain front area (Liao et al., 2017). The weak winds and multi-layer temperature inversion occurred more frequently at western than eastern Sichuan Basin. The formation mechanisms of haze pollution at WSB may be more complex than the other regions within the basin. Therefore, eight sites at WSB were selected to collect PM1 samples and reveal the vertical profiles of carbonaceous aerosols and key influencing factors. The PM1 samples were collected at Chengdu (CD), Deyang (DY), Ande (AD), Dujiangyan (DJY), Wenchuan (WC), Lixian (LX), Maerkang (MEK) and Hongyuan (HY) during 20 January to 2 February 2018 (Fig. 1). The altitudes of the sampling sites varied from 500 m (CD) to 3500 m (HY) with the linear distance between CD and HY of about 250 km, and the special terrain provided a good platform to study aerosols 3

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Fig. 3. Variations of (a) PM1, (c) OC and (e) EC vertical profiles and the corresponding mean vertical profiles at daytime and nighttime (b, d and f) during the campaign. The lifted index variation during the period was also showed in the subplots.

vertical distributions at Western Sichuan Basin. The observation sites were set on the rooftop with different sampling heights among the sites. The PM1 samples at Maerkang site were collected on the rooftop of 10m-high building due to no obvious anthropogenic sources nearby the site, while the sampling heights were higher than 20 m for the other sites with the maximum height of ~40 m above the surface at Chengdu City, and the samples were less impacted by local sources. There have not been obvious pollution sources nearby the sites, and the observations can represent background air at the corresponding altitudes. Therefore, although PM1 levels were certainly affected by local anthropogenic sources for the sites, as a new method of observing aerosol vertical profiles with high topographic relief (Zhao et al., 2019b), the vertical pattern can truly reveal the vertical distribution of the real atmosphere in the Sichuan Basin. To better study vertical profiles of carbonaceous

aerosols and reveal if the influencing factors were different between daytime and nighttime, two samples (daytime and nighttime) were obtained for every day. Daytime (from 7:00 a.m. to 6:30 p.m.) and nighttime (from 7:00 p.m. to 6:30 a.m.) PM1 samples were collected with medium volume air sampler (LY-2034, Qingdao, China) at a flow rate of 100 L min 1 at the eight sites. The spare 30 min from the collected to the next sample were used to replace the filter membranes, record the sampling volume, and clean the sampler with 95% alcohol. The morning (evening) rush hours were from 7:30 a.m. to 8:30 a.m. (from 5:30 p.m. to 6:30 p.m.), and the cooking time was similar to the rush hours. Therefore, the time segmentation between daytime and nighttime samples considered the traffic jam and cooking time, and the particulate matter emitted from motor vehicles or cooking were collected at daytime. For the almost 12-h sampling, aerosol aging 4

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Fig. 4. Clustering results of (a) all OC and (b) EC vertical profiles during the period. The error bars were standard deviations. The yellow shaded regions represented Lixian and Maerkang with relatively low OC/EC concentrations. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

progress maybe significantly enhanced BC light absorption by mixing with the other pollutants during transport (Y. Zhang et al., 2019). However, this study mainly focused on the vertically gradient distribu­ tions of OC/EC levels within Sichuan Basin, while the optical properties

were beyond the scope of the study. So, the BC aging progress may have little impact on our analyses and conclusions. A total of 186 PM1 samples were collected on quartz filter (91 mm in diameter, Whatman) during the campaign. The samplers were main­ tained regularly during the period, and preparation and weights of quartz filters can refer to Zhao et al. (2019c). The hourly meteorological parameters (wind speed and direction, temperature, relative humidity, visibility, etc.) during the campaign were downloaded from National Meteorological Information Center (NMIC, http://data.cma.cn/). Lifted index (LI), representing the intensity of vertical dispersion, was used and calculated with the below equation:

Table 2 Frequencies of the three clusters of carbonaceous aerosols profiles and the cor­ responding lifted index and meteorological conditions at the sites from West Sichuan Basin (Chengdu and Deyang) to southeast Tibetan Plateau (Wenchuan, Lixian, Maerkang and Hongyuan). Items

Cluster 1

Cluster 2

Cluster 3

Frequencies/% Lifted index/oC Wind speed/m s

55.0 21.2 � 2.8 2.0 � 0.7 1.8 � 0.6 2.9 � 0.7 2.0 � 0.7 1.2 � 0.3 2.3 � 1.0 3.9 � 2.5 4.0 � 2.3 2.6 � 2.5 0.8 � 2.6 2.8 � 3.2 3.9 � 3.5 74.1 � 12.3 69.0 � 13.5 59.3 � 6.3 61.5 � 6.6 39.6 � 8.8 57.8 � 18.3 0.7 0.9 0.5 1.7 0.1 0.9 7.2 � 3.9 11.4 � 5.3 19.5 � 7.3 22.0 � 8.4 32.8 � 2.1 26.3 � 5.1

15.0 12.5 � 3.4 1.4 � 0.3 1.4 � 0.2 1.7 � 0.6 1.2 � 0.2 0.9 � 0.2 2.8 � 0.5 8.1 � 0.9 8.1 � 0.1 6.1 � 1.9 2.0 � 2.3 0.7 � 2.9 6.9 � 2.0 85.0 � 1.5 85.3 � 3.4 62.7 � 6.5 62.4 � 6.5 36.9 � 11.3 37.4 � 6.4 0.0 0.0 0.0 0.0 0.0 0.0 1.9 � 0.4 3.4 � 0.7 9.4 � 1.8 17.0 � 6.3 32.4 � 2.1 29.5 � 1.7

30.0 21.1 � 2.1 1.6 � 0.5 1.3 � 0.3 2.9 � 0.8 1.9 � 0.6 1.4 � 0.4 2.6 � 1.3 2.5 � 0.7 2.7 � 0.6 0.6 � 0.5 1.9 � 1.2 2.0 � 2.1 7.3 � 3.3 76.3 � 10.5 69.1 � 9.9 56.7 � 5.5 64.5 � 7.6 43.6 � 15.7 56.3 � 16.0 0.2 0.0 0.0 1.1 0.0 0.9 3.6 � 1.4 7.1 � 3.5 14.1 � 3.9 11.9 � 2.8 32.2 � 4.6 23.2 � 9.9

1

Temperature/oC

Relative Humidity/%

Precipitation/mm

Visibility/km

Chengdu Deyang Wenchuan Lixian Maerkang Hongyuan Chengdu Deyang Wenchuan Lixian Maerkang Hongyuan Chengdu Deyang Wenchuan Lixian Maerkang Hongyuan Chengdu Deyang Wenchuan Lixian Maerkang Hongyuan Chengdu Deyang Wenchuan Lixian Maerkang Hongyuan

LI ¼ T500hPa

Tparcel

(1)

where T500hPa and Tparcel were temperature in Celsius of the Environment at 500 hPa and an air parcel lifted adiabatically, respectively. The high LI values indicated that atmospheric dispersion was good, and the pol­ lutants within the boundary layer were easily dispersed to the upper air. The detailed introduction can refer to Zhao et al. (2019c). 2.2. OC/EC analysis Carbon is one of the most abundant constituents of ambient partic­ ulate matter and is either present as organic carbon (OC), which is mainly volatile and reactive in a heated air stream, and comes from primary emissions and secondary formation, or as elemental carbon (EC), which is non-volatile and non-reactive, and emits from primary sources, or as carbonate (Weingartner et al., 2003). Black carbon (BC) is defined as the fraction of carbonaceous aerosol absorbing light over a broad region of the visible spectrum, and is measured by determining the attenuation of light transmitted through the sample. In this study, OC and EC for the samples were analyzed using the thermal/optical carbon analyzer (DRI Model, 2001A, Desert Research Institute, USA). The instrument analyzed OC/EC with the thermal/optical reflectance (TOR) method, following the Interagency Monitoring of PROtected Vi­ sual Environments (IMPROVE) protocol (Chow et al., 2007). Briefly, the OC and EC were measured by progressively heating a punch area of 0.296 cm2 from the quartz fiber filters. The detailed working principle of 5

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Fig. 5. Relationships between OC and EC for (a) Chengdu plain (Chengdu, Deyang, Ande, Dujiangyan) and (b) Southeast Tibetan Plateau during the campaign. The relationships were given separately for daytime (red dots) and nighttime (blue dots). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6. Variations of SOC accounting for OC as increased PM1 during daytime (red dots) and nighttime (blue dots) at the eight sites during the campaign. The coefficients of determination with an asterisk passed significance level of 0.01. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

the instrument can refer to Zhao et al. (2019c). The EC-tracer method has been used widely to calculate secondary organic carbon (SOC) by the following equation (Turpin and Lim, 2001): SOC ¼ OC

EC � ðOC=ECÞmin

2.3. Hysplit model The HYbrid-Single Particle Lagrangian Integrated Trajectory (HYS­ PLIT) model (Draxler et al., 2009) developed by the National Oceanic and Atmospheric Administration’s (NOAA) was used in this paper to identify potential source regions of carbonaceous aerosols at the cities of Sichuan Basin (SB). To understand air masses transport processes, 72-h backward trajectories arriving at 1000 m above ground level (AGL) and initializing at the hour of 16:00 (Beijing Time) were calculated using 0.5� � 0.5� Global Data Assimilation System (GDAS) data from National

(2)

where SOC and OC represent the estimated secondary OC and measured total OC, respectively. The units of SOC, OC and EC are μg m 3. (OC/ EC)min is the minimum of the ratios of OC to EC during the study period. The estimated SOC is only an approximation with uncertainties.

6

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Fig. 7. 72-h backward trajectories calculated by HYSPLIT model at (a) Chengdu, (b) Ande, (c) Deyang and (d) Dujiangyan. The color of the trajectories represents EC levels at the cities with the time span between two dots of 6 h for each trajectory.

Centers for Environmental Prediction (NCEP). A concentration-weighted trajectory (CWT) method developed by Hsu et al. (2003) was used to calculate the trajectory weighted con­ centration. In that method, each grid cell is assigned a weighted con­ centration by averaging pollutant concentrations that have associated trajectories crossing the grid cell as follows: 1 Cij ¼ PM

XM

τ

l¼1 ijl

Cl τijl l¼1

multidimensional data into predefined number of subgroups, which are as different as possible from each other, but as coincident as possible within themselves, by iteratively minimizing the sum of squared Euclidean distances from each member to its cluster centroid. The detailed introduction of that method can refer to Zhao et al. (2019a). 3. Results and discussion

(3)

3.1. Overviews

where Cij is the mean weighted concentration in the ijth cell, l is the index of the trajectory, Cl is the pollutant concentration measured on the arrival of trajectory l, M is the total number of trajectories, and τijl is the time spent in the ijth cell by trajectory l. A high Cij value indicated that air parcels travelling over the ijth cell would be associated with high pollutant concentration at the receptor site.

Before evaluating vertical distributions of carbonaceous aerosols from the basin to Tibetan Plateau, the mean values of daytime and nighttime PM1 and carbonaceous aerosols at the eight cities during the campaign were calculated in Table 1. Generally, besides Hongyuan site, mean PM1 and OC and EC concentrations during the campaign inside the basin were much higher than southeast edge of Tibetan Plateau (Table 1). Ning et al. (2018) also found that concentrations of particulate matter decreased dramatically with increasing altitude at West Sichuan Basin. Furthermore, daytime PM1 and the corresponding carbonaceous aerosols at Ande (a rural site) were comparable to those at Chengdu with the distance of about 32 km between the two sites. The previous study also indicated that particulate matter exhibited stronger horizontal ho­ mogeneities in the bottom of the basin as compared with those in North China Plain and Yangtze River Delta (Ning et al., 2018). However, nighttime mean PM1, OC and EC concentrations at Ande were (132.8 � 33.0) μg m 3, (20.1 � 10.4) μg m 3 and (6.7 � 2.7) μg m 3, which were 11.8%, 50.0% and 24.1% higher than those at Chengdu. More inter­ estingly, nighttime averagely secondary organic carbon (SOC) of (8.9 �

2.4. K-means clustering analysis The K-means clustering analysis was used in the previous studies, and it has been justified as a preferred technique for environmental data analysis (Zhao et al., 2019a, 2019c). In this study, to better evaluate vertical distributions of carbonaceous aerosols inside WSB, K-means clustering method was used to classify OC or BC vertical profiles from Sichuan Basin to southeastern Tibetan Plateau into several groups with comparable vertical profiles. The K-means clustering algorithm avail­ able in MATLAB© was used in this study. Daytime and nighttime OC or BC concentrations in the cities were used as variables of the K-means clustering analyses, respectively. The technique divides the 7

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Fig. 8. It is similar to Fig. 7, but for (a) Wenchuan, (b) Lixian, (c) Maerkang and (d) Hongyuan.

7.8) μg m 3 at Ande was 2.4 times higher than that at Chengdu, and SOC accounting for OC at Ande was 38.5%, which was significantly higher than the other basin sites. The severest carbonaceous aerosol pollution during nighttime in rural areas among the basin sites may be related to frequent refuse burning at rural areas and regional transportation from the surrounding cities, which will be analyzed deeply in the following sections. PM1 and carbonaceous aerosols at Hongyuan with altitude of ~3500 m were much higher than the other Plateau sites. For example, daytime (nighttime) OC, EC and SOC at Hongyuan were 1.22 (2.54) times, 0.90 (1.60) times and 0.59 (3.36) times higher than the corresponding mean concentrations for the other Plateau sites (Wenchuan, Lixian and Maerkang). Daytime SOC accounting for OC at Hongyuan was the lowest among the all sites from the basin to Tibetan Plateau, indicating the primary emissions were more important than secondary formation at Hongyuan during the campaign. The percentages of carbonaceous aerosols (the sum of OC and EC) accounting for PM1 were 29.1% (day­ time) and 25.4% (nighttime) at Hongyuan, which was significantly higher than those at the other sites ranging from 10.7% to 20.2%. The above analysis indicated that air pollution was severe at southeastern Tibetan Plateau, especially for carbonaceous aerosols, which was significantly higher than other regions of the Plateau (Niu et al., 2018; Wan et al., 2016; Wang et al., 2018a,b,c,d). As the most important light-absorbing component, the more carbonaceous particles at south­ eastern margin of Tibetan Plateau maybe largely affected atmospheric boundary layer structure and then air quality over lower-altitude Sichuan Basin (Wang et al., 2018a,b,c,d). The above two paragraphs and Table 1 only described mean con­ centrations of PM1 and the corresponding carbonaceous components

during the campaign, the PM1, OC and EC variations during the period were showed in Fig. 2. To better reveal the changing trends of the pol­ lutants, the variations during the campaign at the eight sites were grouped by K-means technique with the smallest difference within one group. Using the method, the pollutants in Chengdu, Deyang, Ande, Dujiangyan, Wenchuan and Hongyuan sites were clustered into Cluster 1, while those in Lixian and Maerkang were clustered into Cluster 2. Averaged variations of PM1, OC and EC within each cluster and the corresponding differences between the two clusters were also given in Fig. 2. For Cluster 1, PM1 and carbonaceous aerosols first decreased to the lowest values during the period (nighttime of 24 January 2018) due to cold air outbreaks (the blue shaded regions in Fig. 2), and then increased and decreased slowly during the campaign. However, more interestingly, the pollutants generally increased during the campaign at Lixian and Maerkang sites, which was significantly different from Cluster 1. The PM1, OC or EC difference between the two clusters was mainly controlled by Cluster 1, and large (small) difference occurred during severe (light) pollution episodes. That phenomenon may be closely related to good vertical mixing and more uniform vertical dis­ tributions of the pollutants during light air pollution, which can be supported by the previous studies at urban Lanzhou, a typical valley city (Zhao et al., 2019b; 2019c; 2019d). Besides local emissions, vertical convection and temperature inversion were also found to be two major factors controlling the changes in EC vertical profiles at an urban site in Beijing, China (Wang et al., 2018a,b,c,d). 3.2. Vertical profiles of PM1 and carbonaceous aerosols Daytime and nighttime PM1 samples were collected at eight sites 8

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Fig. 9. EC Concentration-weighted trajectory (CWT) calculated by HYSPLIT model at (a) Chengdu, (b) Ande, (c) Deyang and (d) Dujiangyan. The black dots in the subplots represent the corresponding sites. EC rose plot at each site was also given at top left corner at the corresponding subplots. EC rose at Ande was missed due to the lack of wind speed and direction data during the period.

from the Sichuan Basin to southeastern Tibetan Plateau with increasing altitude to well understand vertical distributions of submicron particles and carbonaceous aerosols (Fig. 3). From the PM1, OC and EC vertical profile variations perspective (Fig. 3a, b and c), the pollutants within the basin and those at Lixian and Maerkang with altitudes ranging from 2000 m to 3000 m appeared opposite variation tendency, while that at Hongyuan had similar variation tendency with the sites within the basin. The above information indicated that vertical stratification of the pol­ lutants was obvious from Sichuan Basin to southeastern Tibetan Plateau, especially during highly polluted episodes. For example, during the first two days of the campaign, PM1 and the corresponding carbonaceous aerosols were mainly accumulated near the surface and presented lowvalue regions at Lixian and Maerkang, while had relatively high con­ centrations at Hongyuan, and thus the pollutants formed “high-lowhigh” (HLH) pattern from the basin to upper air inside Sichuan Basin. The higher EC levels at higher altitudes was also observed at some urban sites (Sarangi et al., 2016; Tripathi et al., 2005; Wang et al., 2018a,b,c,d) and the remote site (Reddy et al., 2013; Satheesh et al., 2008). The specific vertical pattern was closely related to poorly vertical dispersion, which can be verified by the lowest lifted index during the period. The lifted index represents ability of atmospheric vertical dispersion, and its definition was given in the previous studies (Zhao et al., 2019b). The pollutants inside the basin can be transported to Lixian and Maerkang by vertical mixing, but they cannot be transported to Hongyuan with higher altitude. Therefore, the vertical distributions of the pollutants were more uniform during daytime than nighttime due to stronger vertical mixing during daytime, which can support the above results. To better identify typical OC and EC vertical profiles and to reveal the key influencing factors, the all observed OC and EC vertical profiles during the campaign were grouped by K-means clustering technique,

and mean OC/EC profiles for each cluster were showed in Fig. 4. The frequencies of each cluster, and the corresponding lifted index and meteorological parameters at the eight sites were also calculated in Table 2. For Cluster 2, OC and EC concentrations inside the basin (Chengdu, Deyang, Ande and Dujiangyan) and Wenchuan and Hon­ gyuan at the Plateau were the highest among the three clusters, while carbonaceous aerosols at Lixian and Maerkang were much lower than the other two clusters. The OC and EC HLH patterns from the basin to the Plateau were the most obvious among the three clusters, especially for more primarily emitted EC. As compared with Clusters 1 and 3, the weaker winds, higher temperature and relative humidity and less pre­ cipitation for Cluster 2 (Table 2) were helpful to accumulating the pol­ lutants near the surface, and the pollutants were difficult to disperse to upper air. The mean lifted index of (12.5 � 3.4) oC for Cluster 2 was significantly lower than the other two clusters, and thus vertical dispersion was relatively poor for the cluster. Compared with Cluster 2, the mean OC and EC vertical distributions were more uniform for Clusters 1 and 3 with larger lifted index. More than half of observed OC or EC vertical profiles during the campaign were grouped into Cluster 1, and the corresponding OC and EC levels at the sites with latitude lower than 3000 m were lower than those at Hongyuan, which was different from the other clusters. Furthermore, rain or snow events occurred at each site for Cluster 1 (Table 2), which can effectively remove the pol­ lutants from the air (Zhao et al., 2015a), and thus the concentrations of carbonaceous aerosols were the lowest among the three clusters. 3.3. Key factors influencing OC/EC vertical profiles 3.3.1. Regional potential sources Before revealing potential sources of carbonaceous aerosols at the 9

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Fig. 10. EC Concentration-weighted trajectory (CWT) calculated by HYSPLIT model at (a) Wenchuan, (b) Lixian, (c) Maerkang and (d) Hongyuan. The black dots in the subplots represent the corresponding sites. EC rose plot at each site was also given in top left corner at the corresponding subplots.

Fig. 11. EC lagged correlations among the sites during the campaign.

sites, we first saw the relationships between OC and EC for Chengdu plain and southeastern Tibetan Plateau (Fig. 5), and the relationships can give the information if OC and EC sources are similar. Generally, the relationships between OC and EC was good inside the basin and at the Plateau, indicating that sources of OC and EC were consistent at the regions. However, the relationship at daytime for Chengdu plain was weaker than the other relationships with coefficient of determination of 0.61, which may be due to easily formed SOC by a series of chemical

reactions during daytime inside the basin with more precursors from motor vehicles and industries (Wang et al., 2019). SOC accounting for OC is widely used to evaluate the contribution of secondary formation to aerosol pollution. To demonstrate the contri­ bution of SOC to OC for varying levels of PM1 at different altitudes, the variations of the fraction of SOC in OC as PM1 at the eight sites were showed in Fig. 6. The variations of SOC accounting for OC was signifi­ cant as varying PM1 levels. Generally, the contributions of SOC to OC 10

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Fig. 12. Relationships between carbonaceous aerosols and (a, e) temperature, (b, f) wind speed, (c, g) vapor pressure, and (d, h) visibility at (a, b, c and d) Chengdu and (e, f, g and h) Lixian during the period. The relationships were fitted using unary linear regression and the corresponding coefficients of determination were also given in the subplots.

Fig. 13. Variations of equivalent potential temperature profiles at Chengdu during the campaign.

increased from less than 20% to about 60% as PM1 increased at the sites inside the basin (Chengdu, Deyang, Ande and Dujiangyan), which was comparable with the results of Zhao et al. (2019c). However, the SOC accounting for OC increased from more than 40% to more than 90% when PM1 increased from ~100 μg m 3 to 200 μg m 3 during nighttime at Hongyuan. Furthermore, for the urban sites inside the basin (Chengdu and Deyang), the contributions of SOC to OC increased more signifi­ cantly at daytime than nighttime, which was consistent with the pre­ vious explanations in the above paragraph. The SOC accounting for OC

less varied as PM1 at Wenchuan with mean percentage of ~10%. More interestingly, unlike the other sites, the contributions of SOC to OC significantly decreased with increases of PM1 at Lixian and Maerkang, indicating that carbonaceous aerosols were mainly from primary emis­ sions during heavy pollution episodes at two Plateau sites. In view of the results of Wang et al. (2018a,b,c,d), the carbonaceous aerosols from primary emissions at Plateau sites may have a large impact on the for­ mation of heavy air pollution episodes inside the basin by aerosol-meteorology feedbacks (Ding et al., 2016). 11

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Fig. 14. Weather charts at 00UTC (08:00 Beijing Time) 24 January 2018 and at 06UTC (14:00 Beijing Time) 21 January 2018. The charts were provided by Korea Meteorological Administration (KMA).

Fig. 15. Relationships between OC concentrations and lifted index during the campaign at the eight sites. The relationships were fitted using unary linear regression and the corresponding coefficients of determination were also given in the subplots.

The above analysis indicated that sources of carbonaceous aerosols may be similar for the sites inside the basin or over Tibetan Plateau. The backward trajectories are much straight-forward to see the transporting routes for the typical cases to study the transportation, and also helps to distinguish the local and/or transported sources. The 72-h backward trajectories arriving at the eight sites were firstly showed, and the cor­ responding EC levels for each trajectory also were showed by varying color in Figs. 7 and 8. For the sites within the basin, EC concentrations were inversely proportional to trajectory length, indicating that the pollutants mainly originated from local sources or surrounding cities within the basin. The most polluted air masses came from east part of Sichuan Basin, which can be supported by the results of Liao et al. (2017). Due to huge terrain impacts, the air masses originating from Hexi Corridor or the Yunnan-Guizhou Plateau moved southeastwards or northeastwards and arrived at Chengdu, Deyang and Ande passing

through high EC emission regions. The air masses moved very slowly for the high EC pollution and circulated around pollution areas closer to urban areas. Therefore, the emitted pollutants along the routes may be entrained by the air masses and then impacted EC levels at the down­ stream sites within the basin. As compared with the basin sites (BS), the trajectories were much longer for the Plateau sites (PS), indicating that air mass traveled faster, which may be related to more intense winter westerlies over the Tibetan Plateau. The weakly inverse relationships between EC levels and the corresponding trajectory length implied that air pollution may be affected by long-range transport for the Plateau sites. The air masses with high EC levels originating from Nepal and India can be transported to the Plateau sites, and then affected air quality over the Tibetan Plateau. Based on three-years observations and model-based flux estimation, Gong et al. (2019) found that persistent organic pollutants (POPs) emitted in the lowlands of the Himalayas can 12

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Fig. 16. (a) Variations of OC and lifted index at Chengdu and Lixian during the period. The green shaded areas represent OC at Chengdu decreased largely and OC slightly increased at Lixian, and the yellow areas represent OC at Chengdu increased sharply and OC slightly decreased at Lixian. (b) OC relationship between Chengdu (hor­ izontal axis) and Lixian (vertical axis) during the campaign. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

basin can be transported to Tibetan Plateau by vertical dispersion, which can support the previous results.

be transported to high altitudes and further to the inner part of the Ti­ betan Plateau, which can support our results. Concentration-weighted trajectory (CWT) calculated by HYSPLIT model was often used to identify the regional potential sources of the specific pollutant (Liu et al., 2013; Zhao et al., 2019a). EC CWT plots obtained by HYSPLIT model for the eight sites were showed in Figs. 9 and 10. To better determine the direction of sources, EC rose plot during the campaign at each site was drew by statistical software of R and were given at top left corner at the corresponding subplots. EC rose plot at each site can be obtained by combination of EC concentrations with wind direction data. The wind speed and direction data at Ande site was missed during the campaign, so the EC rose cannot be given in the study. High EC CWT values for the sites at Chengdu, Ande, Deyang and Dujiangyan were mainly concentrated on the grids inside the basin (Fig. 9), indicating that intra transport of the pollutants was obvious across Sichuan Basin due to the specific bowl-like terrain limiting transports of the pollutants to the other regions. For the Plateau sites, the potential sources of EC at Wenchuan were located at the surrounding cities, such as Chengdu, which was similar with the sites inside the basin. However, for the higher altitude sites (Lixian, Maerkang and Hongyuan), the sources at the grids of Tibetan Plateau had increasingly large contribution to the sites as altitude increased from Lixian to Hongyuan. Furthermore, high CWT values mainly focused on south­ eastern margins of the Plateau, and the pollutants at the regions affected the sites by long-range transportation. The sources originated from the cities of Chengdu and Deyang may affect EC concentrations at Lixian and Maerkang, while EC at Hongyuan was less affected by the sources inside the basin. EC rose plot gave similar information with CWT method at the sites. Briefly, the regional pollution characteristics were obvious inside Sichuan Basin, and the sources at Lixian and Maerkang were from southeastern margins of the Plateau, Chengdu and Deyang, while those at Hongyuan were transported from southeastern margins of the Plateau. The regional potential sources significantly varied as the increased altitudes, which may be major causes of largely varied EC/OC vertical profiles during the campaign (Fig. 4). To better understand regional air pollution inside the study areas, and transport of the pollutants from a site to another site, EC lagged correlations among the sites were also calculated during the campaign (Fig. 11). As it can be seen from Fig. 11, the EC correlation coefficients among the basin sites were higher than 0.4 when EC lagged time was smaller than one and a half days, indicating EC regional pollution and transport was obvious inside Sichuan Basin, which was consistent with the results obtained by HYSPLIT model. However, EC concentrations at the basin sites strongly related to those at the Plateau sites when lagged time was between 3 and 4 days, indicating that the pollutants inside the

3.3.2. Meteorology impacts As two typical sites, Chengdu and Lixian were selected as the representative sites inside the basin and over Tibetan Plateau, respec­ tively. The relationships between OC or EC and meteorological variables (temperature, wind speed, vapor pressure and visibility) during the campaign were first analyzed in Fig. 12. In general, the impact of carbonaceous aerosols on reduced visibility were more significant at Chengdu than Lixian, which was partly due to more water vapor in winter at Chengdu, and visibility can be reduced largely due to water vapor impacts. However, it was very impressive that the impact of temperature, wind speed and vapor pressure on carbonaceous aerosols was opposite between the basin and Plateau sites. OC and EC levels increased as increased vapor pressure at Chengdu due to easier occur­ rence of hygroscopic growth under frequently stable air conditions, while decreased carbonaceous aerosols with increased vapor pressure at Lixian may be due to wet scavenging of raindrop or snow particles. Tie et al. (2017) and Wu et al. (2019) also found that aerosol liquid water caused by hygroscopic growth could play an important role in the PM2.5 formation and accumulation, which can support our results. In addition, the pollutant level was generally increased (decreased) as increased temperature (wind speed) (Zhao et al., 2015b). The effect of tempera­ ture and wind speed on OC and EC concentrations at Chengdu obeyed the rule. However, more interestingly, at the Plateau site of Lixian, OC and EC decreased (increased) with increases of temperature (wind speed). The high levels of carbonaceous aerosols with strong winds and low temperature may be related to invasion of cold air, and the pollut­ ants at the source regions can be transported the Plateau sites along with cold air, which can be verified by backward trajectory model. The clustering results of OC/EC vertical profiles also indicated that Cluster 3 with high OC and EC concentrations occurred in the conditions of high wind speed and low air temperature. The pollutants from South Aisa (e. g., India, Nepal and Bangladesh) can be transported to the inner part of the Tibetan Plateau by passing over the natural barrier of the Himalayas (Gong et al., 2019; Wang et al., 2015). The above paragraph only analyzed the impact of surface local meteorology on carbonaceous aerosols. The vertical dispersion or mix­ ing and atmospheric stratification were also considered to be main factors influencing the vertical distributions of the pollutants (Zhao et al., 2019b, 2019c). The equivalent potential temperature below 3 km was significantly lower after than before 24 January 2018, while that above 3 km was comparable during the campaign (Fig. 13), which was mainly due to invaded cold air during 24 January 2018 (see left subplot 13

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of Fig. 14). Furthermore, the air at ~3 km above the surface during 21–22 January 2018 was much warmer than that at other days induced by warm advection (the right subplot of Fig. 14), and thus the air was more stable with low lifted index (also see Fig. 3). Therefore, the pol­ lutants were easily accumulated near the surface and were difficult to be dispersed to upper air, and stratification of the pollutants were obvious during the period. The lifted index is defined as the temperature difference between an air parcel lifted adiabatically and the temperature of the environment at a given pressure height in the troposphere of the atmosphere, usually 500 hPa (Zhao et al., 2019c). The index is usually used to determine the stability of the atmosphere. To better evaluate the impact of atmospheric stability on vertical distributions of carbonaceous aerosols, we analyzed the relationships between OC concentrations and lifted index at the eight sites from Sichuan Basin to southeastern margins of Tibetan Plateau (Fig. 15). OC levels largely decreased as lifted index increased at the basin sites, Wenchuan and Hongyuan. Reduced OC varied from 0.29 μg m 3 at Wenchuan to 1.01 μg m 3 at Ande when lifted index increased by 1 � C. The largest decreasing rates occurred at the most polluted sites of Ande (1.01 μg m 3) and Hongyuan (0.68 μg m 3) with high OC concentrations. However, more interesting phenomenon was that OC concentrations increased by ~0.3 μg m 3 when the lifted index increased by 1 � C at Lixian and Maerkang. The results indicated that the pollutants inside Sichuan Basin can be transported to Lixian and Maer­ kang with altitude of about 3 km by vertical dispersion, while Hongyuan was less affected by the sources within the basin, which can be sup­ ported by CWT method. By means of high topographic relief at urban Lanzhou, Zhao et al. (2019b) also found that the air pollution at the hilltop (about 620 m above the valley) was largely affected by the sources from urban valley by vertical mixing. To better evaluate vertical mixing of carbonaceous aerosols and the impacts on vertical distributions, we selected two sites inside the basin (Chengdu) and over the Tibetan Plateau (Lixian), and saw variations of OC and lifted index during the campaign (Fig. 16a) and OC relationships between the two sites (Fig. 16b). The OC levels significantly increased at Lixian (vertical axis of Fig. 16) as OC increased at Chengdu (horizontal axis of Fig. 16) when OC was lower than 15 μg m 3 at Chengdu, while the changing trend was opposite when OC was higher than 15 μg m 3 at Chengdu (Fig. 16b), indicating that the pollutants had obvious stratifi­ cation and were easily accumulated inside the basin during the days with low lifted index (Fig. 16a). Furthermore, the pollutants within the basin were the main sources for southeastern margins of Tibetan Plateau during the good vertical dispersion. As it can be seen from the OC var­ iations during the period at Chengdu and Lixian (Fig. 16a), significantly decreased OC at Chengdu and slightly increased OC at Lixian with the relatively high lifted index (the green graded areas of Fig. 16a) reflected that good vertical dispersion induced by atmospheric circulation trans­ ported the carbonaceous aerosols from the basin to the Tibetan Plateau. At the other episodes (the yellow graded areas of Fig. 16a), OC at Chengdu largely increased while that slightly decreased at Lixian as accumulated pollutants inside the basin were difficult to be transported to Tibetan Plateau due to weak vertical mixing.

Generally, the regional pollution characteristics were obvious inside Sichuan Basin. The carbonaceous aerosol pollution was severer inside the basin than southeastern margins of the Tibetan Plateau. The lowest carbonaceous aerosol pollution was always occurred at Lixian, while the highest OC and EC concentrations were at Hongyuan (the altitude of 3500 m) and Ande (a rural site). The severe rural OC and EC pollution during nighttime was closely related to frequent refuse burning, and the primary emissions were more important than secondary formation over the Tibetan Plateau. The sources at Lixian and Maerkang were from southeastern margins of the Plateau, Chengdu and Deyang, while those at Hongyuan were transported from southeastern margins of the Plateau. The regional potential sources significantly varied as the increased altitudes, which may be main causes of largely varied EC/OC vertical profiles during the campaign. The pollutants inside the basin can be transported to the sites with altitudes from 2 km to 3 km by vertical dispersion, but they cannot be transported to the higher altitude. The vertical stratification of the pollutants was obvious and easily formed HLH pattern from Sichuan Basin to southeastern Tibetan Plateau, especially during highly polluted episodes. The impact of basic meteorological variables (temperature, wind speed and vapor pressure) on carbonaceous aerosols was opposite between the basin and Plateau sites. By means of high topographic relief, this work revealed typically vertical profiles of PM1 and carbonaceous aerosol and key influencing factors including meteorology and local and regional sources for the first time, which was essential to understanding formation mechanisms of severe pollution episodes and making control measures for the urban agglomerations inside the complex terrain. However, the temperature and humidity profiles with high temporal resolutions within atmo­ spheric boundary layer (ABL) were not observed during the campaign, and thus the interactions between the profiles of aerosols and meteo­ rology within ABL cannot be studied in this study. The feedbacks be­ tween meteorology and pollution may be important to promote new air pollution control measures at the city clusters inside the complex basin terrain, which will be revealed in the next work.

4. Conclusions

Acknowledgement

To fill the gap about vertical profiles of aerosol chemical composition within Sichuan Basin, by means of high topographic relief, the vertical distributions of carbonaceous aerosols (OC & EC) from west Sichuan Basin (Chengdu, Deyang, Ande and Dujiangyan) to southeastern mar­ gins of Tibetan Plateau (Wenchuan, Lixian, Maerkang and Hongyuan) were studied with PM1 in-situ observations from 20 January to 1 February 2018 for the first time. The key factors influencing OC/EC vertical profiles were revealed from meteorology to local and regional sources. The regional potential sources were identified using HYSPLIT backward trajectory model and CWT method. Some main conclusions were obtained as follows.

The study is supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) (XDA20040501), National Natural Science Foundation of China (41605103; 41901010), Youth Innovation Pro­ motion Association, CAS (2017462), CAS “Light of West China” Pro­ gram, and the Excellent Post-Doctoral Program (2016LH0020).

Author contributions section Suping Zhao: Conceptualization, Methodology, Software. Daiying Yin: Data curation, Writing-Original draft preparation. Jianjun Qu: Writing- Reviewing and Editing. Ye Yu: Supervision. Shichang Kang: Funding acquisition. Xiaolin Ren: Resources, Software. Jing Zhang: Resources, Software. Yong Zou: Resources, Software. Longxiang Dong: Visualization, Investigation. Jianglin Li: Project administration. Jianjun He: Data Curation. Ping Li: Software, Validation. Dahe Qin: aSupervision. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.atmosenv.2020.117269. 14

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