Measurements of submicron particles vertical profiles by means of topographic relief in a typical valley city, China

Accepted Manuscript Measurements of submicron particles vertical profiles by means of topographic relief in a typical valley city, China Suping Zhao, Ye Yu, Dahe Qin, Daiying Yin, Zhiheng Du, Jianglin Li, Longxiang Dong, Jianjun He, Ping Li PII:

S1352-2310(18)30813-6

DOI:

https://doi.org/10.1016/j.atmosenv.2018.11.035

Reference:

AEA 16400

To appear in:

Atmospheric Environment

Received Date: 23 April 2018 Revised Date:

20 October 2018

Accepted Date: 13 November 2018

Please cite this article as: Zhao, S., Yu, Y., Qin, D., Yin, D., Du, Z., Li, J., Dong, L., He, J., Li, P., Measurements of submicron particles vertical profiles by means of topographic relief in a typical valley city, China, Atmospheric Environment (2018), doi: https://doi.org/10.1016/j.atmosenv.2018.11.035. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Measurements of submicron particles vertical profiles by means of

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topographic relief in a typical valley city, China

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Suping Zhao a, b, c, Ye Yu a, Dahe Qin c, Daiying Yin d, Zhiheng Du c, Jianglin Li a,

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Longxiang Dong a, Jianjun He e, Ping Li a

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a

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Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou

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730000, China

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b

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200433, China

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Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions,

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Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Shanghai

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c

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Resources, Chinese Academy of Sciences, Lanzhou 730000, China

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d

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Resources, Chinese Academy of Sciences, Lanzhou 730000, China

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e

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Chinese Academy of Meteorological Sciences, Beijing 100081, China

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Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and

State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA,

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State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and

Corresponding author:

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Dr. Suping ZHAO (Surname), Key Laboratory of Land Surface Process & Climate

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Change in Cold & Arid Regions, Northwest Institute of Eco-Environment and

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Resources, Chinese Academy of Sciences, Lanzhou, 730000, Gansu, P.R. China

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Tel: +86 (0)931 4967090

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Fax: +86 (0)931 4967090

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Email: [email protected]

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ACCEPTED MANUSCRIPT Abstract

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To reveal PM1 vertical profiles and key affecting factors in a typical urban valley,

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daytime and nighttime PM1 (the particles with diameters less than 1 µm) samples

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were collected with medium volume air samplers during 26 December 2017 to 11

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January 2018 at five different altitudes by means of high topographic relief at urban

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areas of Lanzhou. The synchronous boundary layer temperature and humidity profiles

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were observed by a microwave radiometer. Daytime PM1 concentrations reduced by

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about 3.86 µg m-3 when the height above the surface increased by 100 m, which was

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much lower than that for nighttime (5.68 µg m-3 100 m-1) as particles were easily

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accumulated near the surface when the air was stable during the nighttime. The three

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typical PM1 vertical profiles were identified by K-means clustering technique. The

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most frequent cluster with elevated PM1 concentrations near the surface was closely

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related to temperature inversion around the ground, while the cluster with relatively

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uniform PM1 within the boundary layer was mainly induced by unstable atmospheric

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stratification and thus relatively good vertical dispersion. About 50%–60% of PM1

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variations could be attributed to atmospheric stratification near the surface in the

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valley city, which was much higher than that at the hilltop. The PM1 difference

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increased by 47.14 (36.91) µg m-3 when inversion layer thickness (intensity) increased

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by 100 m (1 oC 100 m-1). The newly calculated inversion index considering both

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inversion layer thickness and intensity explained about 87% of PM1 differences

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between near the surface and at the hilltop. The vertical dispersion had a more

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significant effect on PM1 than horizontal dispersion near the surface, while PM1 was

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more largely affected by horizontal dispersion at the hilltop, which was closely related

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to the valley terrain.

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Keywords: PM1; Vertical variations; Boundary layer; Valley city

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ACCEPTED MANUSCRIPT 1 Introduction

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Due to rapid urbanization processes, economic development and unreasonable energy

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structure in China, urban air quality increasingly deteriorated in the past two decades.

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Air pollution was recognized as the most severe environmental problem and attracted

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extensive attention from the Chinese government, the public and scientists (Zhang et

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al., 2015). The long-lasting and large-scale haze episodes in winter mainly occurred in

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economically developed city clusters such as the Beijing-Tianjin-Hebei (BTH) region,

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the Yangtze River Delta (YRD) region, the Pearl River Delta region (PRD), and the

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Sichuan Basin (Zhang et al., 2012; Zhang and Cao, 2015; Zhao et al., 2016, 2018).

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The elevated particulate matter concentration in the Chinese cities affected

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significantly human health (Liu et al., 2017; Song et al., 2017) and threatened

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sustainable development of the economy and society (Tie et al., 2016). A recent study

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suggested that ambient fine particulate matter (PM2.5) accounted for 15.5% (1.7

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million) of all cause deaths in China (Song et al., 2017). Furthermore, severe

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particulate matter pollution impacted largely regional and even global climate by

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direct and indirect effects (Ramanathan and Feng, 2009).

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The scarce information on the aerosol vertical distributions was one of the main

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underlying factors for uncertainties in the aerosol direct radiative forcing (Choi and

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Chung, 2014; Huneeus et al., 2011; Vuolo et al., 2014). Chung et al. (2005)

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demonstrated that the uncertainty in the aerosol vertical profile alone contributed as

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much as 0.5 W m-2 to global aerosol forcing uncertainty. Aerosol extinction

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coefficients vertical profiles was strongly dependent on vertical distribution of both

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aerosol concentration and composition (Li and Han, 2016). However, the vertical

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profiles of aerosol particles were mainly studied based on satellite (Lakshmi et al.,

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2017; Ma and Yu, 2014; Toth et al., 2016), ground-based remote sensing (Chew et al.,

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2013; Choi and Chung, 2014; Liu et al., 2017) and numerical model (Ma and Yu,

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2014; Noh et al., 2016; Vuolo et al., 2014). The relevant studies were less using in situ

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observations due to the lack of high vertical resolution data (Brines et al., 2016;

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Chilinski et al., 2016; Li et al., 2018; Querol et al., 2018; Sun et al., 2013; Zawadzka

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ACCEPTED MANUSCRIPT et al., 2017; Zhang et al., 2009; Zhang et al., 2017). The studies indicated that aerosols

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and high extinction coefficient often occurred within 2 km above ground, which was

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strongly influenced by the intensity of vertical transport (Li and Han, 2016; Liu et al.,

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2012; Ma and Yu, 2014; Sun et al., 2013; Q. Q. Wang et al., 2018). For example,

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Brines et al. (2016) studied vertical and horizontal variability of PM10 and

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apportioned its sources in the urban area of Barcelona and found that PM10 sources

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were different near the ground surface relative to the upper levels. In addition, Querol

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et al. (2018) studied vertical variability of ozone and ultrafine particles (UFPs) using

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tethered balloons and revealed atmospheric dynamics impacts. Their studies indicated

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that in contrast to O3 vertical top-down transfer, the newly formed UFPs within the

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planetary boundary layer (PBL) were transferred upwards progressively with the

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increase in PBL growth. The studies of Brines et al. (2016) and Querol et al. (2018)

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revealed that vertical profiles of particulate and gaseous pollutants significantly

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depended on PBL structure and vertical distributions of emission sources. However,

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the impact of PBL structure on the PM1 vertical profiles were less studied in valley

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cities (Z. L. Wang et al., 2018), which largely restricted studies on

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aerosol-meteorology feedbacks and aerosol climate effect.

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The previous studies mainly focused on economically developed cities of eastern

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China such as Beijing and Shanghai. The vertical patterns of aerosols and boundary

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layer structure and atmospheric stratification impacts were still unclear for the cities

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in complex terrain, western China. Lanzhou, surrounded by Qinghai-Tibet Plateau,

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Loess Plateau and Mongolian Plateau, was a typical valley city in western China. As

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an ideally natural laboratory to study aerosol-meteorology feedbacks in complex

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terrain, some relevant studies were conducted in urban areas of Lanzhou (Chambers et

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al., 2015; Chen et al., 1984; Wang et al., 2016; Zhang et al., 2001; Zhang, 2003;

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Zhang and Li, 2011; Zhao et al., 2015a, 2017). Furthermore, photo-chemical smog

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was observed in Lanzhou for the first time in China due to highly industrial emissions

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and unfavorable diffusion and dilution conditions induced by valley terrain (Chen et

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al., 1986; Zhang et al., 1998). However, the vertical variations of submicron aerosols

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ACCEPTED MANUSCRIPT and boundary layer impact were less studied in the valley city of Lanzhou using in

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situ observations. Therefore, the main objective of this study was to analyze PM1

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(particles with diameter lower than 1 µm) vertical patterns and reveal atmospheric

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stratification effects in a valley city based on in situ measured aerosol concentrations

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and boundary layer data.

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2 Data and methods

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2.1 Sampling sites

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Lanzhou, located in a long valley runs mainly from the east to the west with a length

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of about 30 km, maximum width of 8 km, and depth of 200–600 m, was a typical

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valley city in an arid area with a population of approximately 3.5 million. Annual

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rainfall was about 327.7 mm and mean temperature was about 9.3 oC. Due to effect of

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the valley terrain, mean surface horizontal wind speed was only ~0.3 m s-1 in winter,

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and the monthly calm frequency (the winds with no steady direction accounting for

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monthly total number of measurements) reached as high as 81% (Yang, 2018). The air

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pollutants emitted from lots of industries were difficult to disperse due to weak winds

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and stable stratification inhibiting turbulent diffusion aspect with the aspect ratio of

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the valley (depth versus width) of ~0.07 (Chu et al., 2008).

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With the help of high topographic relief, the sampling sites were located near the

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ground surface (Sites A, B and C) and in the middle (Site D) and top (Site E) of the

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valley. The instruments near the surface were mounted on high research building of

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Northwest Institute of Eco-Environment and Resources (NIEER, 36o 2' 59.46'' N, 103o

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51' 28.63'' E), Chinese Academy of Sciences. The sampling heights were about 2 m,

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17 m, 34 m, 320 m and 620 m above the ground for Sites A, B, C, D and E

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respectively. The sites were located in a residential and commercial area without

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nearby industrial emission sources. There were two major roads with traffic volume of

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more than 2000 cars per hour near the Sites A, B and C, one of which was 20 m from

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the research building, and the other was about 300 m west of the building (Fig. 1).

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December and January were found to be the most polluted months of the year in

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ACCEPTED MANUSCRIPT urban areas of Lanzhou (Guan et al., 2018; Zhou et al., 2018). Therefore, daytime

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(from 7:00 a.m. to 6:30 p.m.) and nighttime (from 7:00 p.m. to 6:30 a.m.) PM1

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samples were collected with medium volume air sampler (Ly-2034, Qingdao, China)

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at a flow rate of 100 L min-1 during 26 December 2017 to 11 January 2018 at the five

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sites. The large PM1 level during the period with low PBLH (planetary boundary layer

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height) ranging from 92.7 m to 474.3 m was more appropriately measured by filter

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method. A total of 165 PM1 samples were collected on quartz filter (91 mm in

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diameter, Whatman) during the study period. To prevent particle blockages in the

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sampler, the samplers were cleaned using an ultrasonic bath for 30 min before each

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sampling episode. In addition, the sampling flow rates were calibrated before

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collecting and monitoring each sample by a flow meter during sampling. All used

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tools during sampling and analysis were cleaned, and the operator wore plastic gloves.

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The quartz filters were pre-baked at 500 °C for 4 h to remove any absorbed organic

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and inorganic materials. After each sampling episode, the filter membranes were

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replaced, and the sampler was cleaned with 95% alcohol while the sampling volume

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was recorded. The sampled filters were wrapped with aluminum foil and stored in the

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portable refrigerator at -18 oC and brought back to lab and stored in the stationary

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refrigerator. After the campaign (about 20 days), all the filter membranes were

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weighed with a high-precision electronic balance (BT125D, Sartorius, Germany), and

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the mass concentrations were obtained by the weight differences between before and

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after sampling. After weighting the filters, all the filter membranes were stored in the

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stationary refrigerator in lab to do any chemical analysis in upcoming studies.

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2.2 Meteorological data

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The boundary-layer temperature and humidity profiles with high temporal and spatial

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resolutions were measured by ground-based multi-channel microwave radiometer

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(MWP967KV, China) on the roof of research building of NIEER. In addition, the

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ten-min meteorological data, including temperature, RH and horizontal wind speed

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and direction, were obtained with an automatic meteorological station co-located with

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the measured temperature and humidity profiles. The wind characteristics were 6

ACCEPTED MANUSCRIPT provided by a wind sensor (Model 034B Wind Sensor, Met One, USA) with a

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precision of 0.1 m s−1 for the intensity and 4° for the direction. The temperature and

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RH sensors (Vaisala, Campbell Scientific Model HMP45C) measured RH values with

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uncertainties of ±2% for RH values between 0% and 90% and ±3% for RH values

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between 90% and 100% and measured temperature with accuracy of ±0.4 °C for

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temperature between −40 °C and 60 °C. Furthermore, during the campaign we

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checked monthly to make sure the radiation shield was free from debris. The black

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screen at the end of the sensor was checked for contaminates. In addition, the

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sounding data in Yuzhong sounding station (35o 52' 12'' N, 104o 9' 0'' E) at 08:00 and

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20:00 each day was used in this study, which was obtained from University of

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Wyoming (http://weather.uwyo.edu/upperair/sounding.html). Beijing Time (BT)

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(=UTC+8) was used throughout this paper.

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To better understand vertical dispersion in the valley, the Lifted Index (LI) was used

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in this study, which was given by the following formula (Galway, 1956):

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LI=T500hPa -Tparcel

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where T500hPa was temperature in Celsius of the environment at 500 hPa. LI was

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defined as the temperature difference between an air parcel lifted adiabatically and the

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temperature of the environment at a given pressure height in the troposphere (lowest

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layer where most weather occurs) of the atmosphere, usually 500 hPa. The index was

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usually used to determine atmosphere stability which was ultimately an indicator of

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storm probability. The high LI values indicated that good diffusion conditions, and

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thus the pollutants near the ground surface were easily diluted and diffused to the

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upper air.

(1)

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The temperature inversion was found to be the most important factor controlling the

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pollutants near the surface over urban Lanzhou due to the valley terrain (Yang, 2018;

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Zhang et al., 2011). The specific atmospheric layer with increased temperature as the

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increase of altitude was identified as an inversion layer. Based on the measured 7

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radiometer profiles, the difference between the altitudes with the highest and lowest

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temperature within the layer was considered as inversion layer thickness (ILT). Within

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the inversion layer, the increased temperature per 100 m was defined as inversion

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layer intensity (ILI). ILT and ILI were calculated by the following formula:

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ILT = Alt HT − Alt LT

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ILI =

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where, TH (TL) was the highest (lowest) temperature within the inversion layer, and

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AltHT (AltLT) was the corresponding altitude.

TH − TL ILT ⋅100

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Only inversion layer thickness or intensity was considered in the previous studies.

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Therefore, to better evaluate the influence of temperature inversion on submicron

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particles concentrations, a new inversion index (II) was calculated using observed

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both inversion layer thickness and intensity. The newly calculated index considered

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more fully inversion layer impacts. The index was obtained using the following

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formula:

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II=

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where, µILT ( µILI ) and σILT (σILI) were mean value and standard deviation of inversion

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layer thickness (intensity), respectively. The first (second) term at the right of the

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formula represented normalized inversion layer thickness (intensity).

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ILT- µ ILT

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To better evaluate the effect of vertical mixing on PM1 vertical profiles in the typical

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urban valley, planetary boundary layer height (PBLH) and its variations were

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analyzed in this study. However, it was not straightforward to get PBLH, and several

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methods were used to derive PBLH from profiles of atmospheric parameters, such as

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potential temperature, specific humidity, and refractivity. One of the commonly used

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approach to derive PBLH from radiometer observations was from gradients/break

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points in these vertical profiles (Seidel et al., 2010). This approach was based on the

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assumption that break point/large gradient in the profile coincided with the PBL top. 8

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In our study, the altitude where these sharp gradients in potential temperature (θ)

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occur was defined the PBLH. The potential temperature (θ) was calculated by the

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following formula (Zhang and Li, 2011):

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θ =T 1 + 0.098 × ( h0 + z ) 

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Where T was air temperature, and h0 and z were altitude of the surface and height (m),

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respectively.

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Fig. 1 Sampling sites of PM1 (Sites A-E) in urban areas of Lanzhou.

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2.3 Clustering analysis

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To see temporal and spatial variations differences among the sites, cluster analysis

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was used to classify PM1 temporal variations and vertical patterns at the five sites into

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several groups with comparable values and similar variation trends within groups,

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respectively. The K means clustering algorithm available in MATLAB© was used in

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this study. The K means clustering technique divided the multidimensional data into

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predefined number of subgroups, which were as different as possible from each other,

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but as coincident as possible within themselves, by iteratively minimizing the sum of

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squared Euclidean distances from each member to its cluster centroid. The K means

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clustering technique used in the previous studies was justified as a preferred approach

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number of clusters was determined by statistical software SAS© in the present study

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based on the rule with maximum variances among the clusters and minimum

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variances within each cluster. The detailed explanations were referred to Zhao et al.

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(2016).

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2.4 Principal component analysis

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The principal component analysis (PCA) was used to determine contrasting main

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factors affecting PM1 concentrations between the bottom and top of the valley. The

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corresponding data-sets including horizontal wind speed, temperature, RH, snowfall,

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lifted index representing vertical dispersion and PM1 concentrations at the five sites

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were used by PCA. PCA was a classical technique for dimensionality reduction,

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which found plenty of environmental applications including atmospheric aerosol

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research (Chan and Mozurkewich, 2007; Huang et al., 1999). Briefly, the variable

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transformation was achieved by rotating the existing system of orthogonal coordinates

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N1. . .Np into a new system of orthogonal, uncorrelated variables (PCs) with their

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associated eigenvectors. The PCs were obtained by Varimax rotation, i.e. a fraction as

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large as possible of the total variance in the data set shall be attributed to a number of

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PCs as little as possible. The number of PCs was identified using the rule that the

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selected component had eigenvalues higher than 1 and total variance explained by all

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selected PCs was higher than 70%.

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3 Results and discussion

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3.1 Overview

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Before analyzing PM1 vertical profiles and atmospheric stratification effects, the

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daytime and nighttime PM1 variations at the five sites and the corresponding

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meteorological conditions during the measurement campaign were given in Fig. 2.

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The PM1 variations were similar among the sites, but the concentrations near the

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surface (Sites A, B and C) were generally higher than those at hillside and hilltop

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(Sites D and E) due to more anthropogenic emissions near the surface. The study of Q. 10

ACCEPTED MANUSCRIPT Q. Wang et al. (2018) found that contribution of local emissions to aerosols was about

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20-30% higher at lower altitudes than that at higher altitudes based on real-time

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continuous vertical measurements from ground level to 260 m during two severe

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winter haze episodes at an urban site in Beijing. The vertically resolved measurements

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were conducted using a container that could travel on the Beijing 325 m

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Meteorological Tower at a relatively constant speed of approximately 9 m min−1.

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Additionally, the PM1 was negatively correlated to the horizontal (wind speed) and

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vertical dispersions (lifted index, PBLH). The PBLH ranged from 92.7 m to 474.3 m

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with average value of 274.5 m during the measurement campaign, which was much

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lower than that in the plain cities due to complex terrain impacts (Wang et al., 2018).

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The PBLH was positively (negatively) related to lifted index (PM1 concentrations),

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indicating PBLH significantly contributed to PM1 variations during the study periods.

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More interesting, the days or nights with high PBLH generally had snowfall and

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strongly horizontal winds due to cold air impacts. Therefore, the low PM1 during the

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campaign was jointly affected by wet scavenging and strongly vertical and horizontal

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dispersion. Furthermore, it was interesting that the PM1 differences between near the

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surface and hilltop varied and the smallest differences corresponded to high lifted

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index and PBLH (good vertical dispersion). The good vertical dispersion was

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conductive to dispersing the pollutants from the surface to upper air, and thus the

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vertical distributions of the pollutants were more uniform and especially for the valley

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city, which will be analyzed more deeply in the following sections.

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As one of the four dust source regions affecting China, the dust storms originated

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from Hexi Corridor frequently affected its downstream Lanzhou City (Gong et al.,

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2003; Tao et al., 2007). PM10 concentrations increased by a factor of 3.4–25.6 during

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the dust event in urban Lanzhou (Zhao et al., 2015b). To identify if the spike of PM1

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on 28 December 2017 in urban Lanzhou was caused by a regional dust event, we

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analyzed the evolutions of hourly PM10 concentrations across the cities at the Hexi

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Corridor (Jiayuguan, Jiuquan, Zhangye, Jinchang, Wuwei) and its downstream cities

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(Baiyin, Lanzhou, Dingxi, Tianshui, Longnan). PM10 concentrations were higher than 11

ACCEPTED MANUSCRIPT 1000 ug m-3 during 28 to 29 December 2017 for the cities, which was the significantly

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highest among the days of the month. Furthermore, the maximum PM10

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concentrations at the downstream cities were clearly later than the upstream cities at

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dust source regions due to dust transport processes. The method was used to identify

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dust origins in the previous study (Zhao et al., 2015b). The above analyses indicated

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that high PM1 pollution on 28 December 2017 in urban Lanzhou was induced by a

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regional dust event originated from Hexi Corridor.

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Fig. 2 Daytime and nighttime PM1 variations at the five sites and the corresponding lifted index,

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wind conditions, temperature, PBLH (planetary boundary layer height), RH and snowfall during

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the study period. The samples corresponding to black bars (gray bars) of lifted index represented

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nighttime (daytime). The dust event was noted in the figure.

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3.2 PM1 vertical profiles

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Based on in situ observations, the reports on particulate matter vertical variations had 12

ACCEPTED MANUSCRIPT been not seen in urban areas of Lanzhou. Mean daytime and nighttime PM1 vertical

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profiles were shown in Fig. 3. Decreasing rates of PM1 were obtained using unary

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linear regression and were given in the figure. The mean PM1 concentrations were

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comparable among the sites near the surface (Sites A, B and C), but those were even

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higher than those at hillside (Site D) and hilltop (Site E) and especially for nighttime.

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In addition, daytime PM1 concentrations reduced by about 3.86 µg m-3 when the

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height increased by 100 m due to more emission sources near the surface, which was

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much lower than that for nighttime (5.68 µg m-3 100 m-1). The relatively uniform

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daytime PM1 within boundary layer was closely related to good vertical dispersion

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and thus the particles emitted from human activities near the surface were easily

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diffused to upper air during the daytime, while the atmospheric stratification was

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relatively stable and the pollutants were easily accumulated near the surface during

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the nighttime. Vertical convection as indicated by mixing layer height and temperature

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inversion was a major factor affecting the changes in vertical profiles of the pollutants

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(Q. Q. Wang et al., 2018), which could support our above results.

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The evolutions of daytime and nighttime PM1 vertical profiles and PBLH during the

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measurement campaign were illustrated to better reveal vertical variations of

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submicron particles in urban areas of Lanzhou (Fig. 4). The daytime or nighttime PM1

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vertical distributions varied largely among the profiles with higher PM1 near the

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surface, which was consistent with the above results. Generally, daytime PM1 at

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different altitudes were significantly higher than those in the nighttime and especially

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near the surface due to more human activities in the daytime. More interesting,

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besides impact of the regional dust event on nighttime of 28 December 2017 (Fig. 4 c),

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nighttime PM1 on 27 December 2017 and 1 and 4 January 2018 (Fig. 4 b, g, j) were

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much higher than that in the corresponding daytime and especially near the surface.

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The lower PM1 at daytime than nighttime mainly occurred before snowfall events

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accompanied by cold air outbreaks. The PBLH was generally high during cold air

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outbreaks (Li et al., 2017; Zhao et al., 2018). Therefore, the reduction of daytime PM1

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concentrations was mainly induced by increased PBLH at the daytime and the

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ACCEPTED MANUSCRIPT particles near the surface were easily diluted to the upper air. For example, the

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daytime PM1 decreased by 77.7 ug m-3 (33.9 ug m-3) and PBLH increased by 164.6 m

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(118.4 m) from 1 to 2 January 2018 (from 4 to 5 January 2018), indicating PBLH had

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an important impact on PM1 pollution in the typical urban valley (see Fig. 2).

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Fig. 3 Mean daytime and nighttime PM1 vertical profiles during the study period in Lanzhou. The

367

horizontal and vertical axes were given using logarithm to better see the variations. Decreasing

368

rates of daytime and nighttime PM1 were obtained using unary linear regression.

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Fig. 4 Evolutions of daytime and nighttime PM1 vertical profiles and PBLH during the study

371

period. The date of each profile was given in the figure. The dark yellow or green profile

372

represented observed dust or snowfall during the periods.

373

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3.3 Key factors affecting PM1 vertical profiles

375

The all PM1 data at the five sites during the campaign were used as variables to

376

perform K-means cluster analysis to see the differences of PM1 variations between the

377

surface (Sites A, B and C) and hillside and hilltop sites (Sites D and E). As expected,

378

the PM1 concentrations at the surface sites were separately clustered into Cluster 1,

379

while those at the hillside and hilltop sites were classified into Cluster 2. The averages 15

ACCEPTED MANUSCRIPT of PM1 variations within each cluster were shown in Fig. 5. The clustering results

381

indicated that PM1 variations during the periods had some differences between

382

surface and hilltop. During the highly polluted episodes, PM1 concentrations near the

383

surface were much higher than those at the hilltop mainly due to low PBLH and more

384

stable air during the polluted periods, which will be discussed more deeply in the

385

following sections.

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386

The vertical distributions of aerosols were influenced largely by planetary boundary

388

layer structure and especially atmospheric stratification (Q. Q. Wang et al., 2018). The

389

impact may be more significant for the valley city due to weak horizontal wind speed.

390

To better reveal the impact of atmospheric boundary layer structure on PM1 vertical

391

profiles, all observed PM1 vertical profiles (observed twice a day) during the

392

campaign were used as variables to perform K-means cluster analysis. Three quite

393

different clusters of PM1 vertical profiles were obtained by the statistical technique.

394

There was not enough data to perform cluster analysis and get statistically significant

395

results, but the PM1 vertical profiles had significant differences among the three

396

clusters obtained by cluster analysis in this study. The averages of PM1 vertical profile

397

within each cluster from clustering results were shown in Fig. 6a, and the

398

corresponding average temperature and water-vapor density vertical profiles within

399

each cluster were given in Fig. 6b and Fig 6c to see PM1 vertical profile differences

400

among the three clusters and to reveal boundary layer impacts. The frequencies of

401

each type were given in Fig. 6. The corresponding dates for each cluster were listed in

402

Table 1. Only one PM1 vertical profile on nighttime of 28 December 2017 was

403

separately categorized into Cluster 3 due to distinct profile with PM1 larger than 200

404

µg m-3 near the surface affected by dust plumes. The PM1 concentrations during dust

405

events were 2-3 times higher than those during normal periods near the surface. The

406

temperature and water vapor differences were larger and convection was stronger than

407

those of Clusters 1 and 2 (Fig. 6 b and c) due to invaded cold air along with dust

408

plumes, which was consistent with higher lifted index for Cluster 3 than the other two

409

clusters (Table 1).

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The four PM1 vertical profiles on daytime of 2 January and nighttime of 1, 2 and 7

412

January 2018 were classified into Cluster 1, and the others during the campaign were

413

divided into Cluster 2. Cluster 2 was most frequent among the clusters and 73.8% of

414

the PM1 vertical profiles were categorized into the cluster. The corresponding PM1

415

concentrations in each layer were significantly higher than those for Cluster 1, and

416

especially near the surface. The accumulated PM1 near the surface was closely related

417

to temperature inversion around the ground with inversion layer depth of 50 m and

418

intensity of 0.52 oC 100 m-1 (Fig. 5 c). Furthermore, the water-vapor density near the

419

surface was the highest and lifted index of (11.9±4.5) oC was the lowest among the

420

three clusters (reference to the Table 1), indicating air was relatively stable, which was

421

consistent with occurrence of temperature inversion for Cluster 2. For Cluster 1, the

422

vertical distribution of PM1 was more uniform than that for the other two clusters,

423

which was mainly induced by unstable atmospheric stratification and thus relatively

424

good vertical dispersion with mean lifted index of (16.1±3.4) oC, and the pollutants

425

near the surface were easily diffused to the upper air.

426

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Fig. 5 Averages of PM1 variations within each cluster from clustering results. All PM1 data at the

428

five sites during the campaign were used as variables to perform K-means cluster analysis. The

429

error bars represented PM1 standard deviations among the sites within each cluster.

17

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Fig. 6 Averages of (a) PM1, (b) temperature and (c) water-vapor density vertical profiles within

432

each cluster from clustering results. All PM1 vertical profiles during the campaign were used as

433

variables to perform K-means cluster analysis. The error bars represented standard deviation

434

among the profiles within each cluster.

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Table 1 Statistics of mean PM1 and the corresponding meteorological parameters for Cluster 1, 2

437

and 3 during the campaign. Clusters

Daytime

Nighttime

PM1

LI -3

o

C

WS

WD

T

ms

degree

o

%

Wm

-1

C

RH

Rn

Snowfall -2

Units

—

—

µg m

mm

1

2 Jan.

1, 2 and 7

38±4

16±3

2±1

148±51

4±3

43±13

-16±46

2

2018

Jan. 2018

2

The others

The others

92±15

12±5

2±0

103±28

4±2

47±13

9±79

4

3

—

28 Dec.

200±43

17

2

90

2

29

-36

0

2017

438 439

To better evaluate the impact of atmospheric stratification on PM1 at varying heights 18

ACCEPTED MANUSCRIPT above the surface, the temperature difference between 200 m above the surface and

441

the ground surface was used to represent atmospheric stratification. The relationships

442

between PM1 at the five sites and the temperature difference during the study period

443

were illustrated in Fig. 7 and the relationships were fitted using unary linear (Sites A,

444

B and C with heights 2 m, 17 m and 34 m) or polynomial regression (Sites D and E

445

with heights 320 m and 620 m). The changing rates of PM1 as increased temperature

446

difference were given in the subplots. PM1 concentrations increased significantly with

447

the increase of temperature differences for the surface sites with coefficients of

448

determination larger than 0.50, indicating that 50%–60% of PM1 variations could be

449

explained by the temperature differences (T200m-T0m) near the surface in the urban

450

valley. Our results were comparable to the results of He et al. (2017). PM1 increased

451

by about 15 µg m-3 when the temperature difference increased by 1 oC. However, PM1

452

variations as the temperature difference for the hillside (Site D) and hilltop (Site E)

453

were not the same as those near the surface (Fig. 7). The PM1 concentrations were

454

constant and even declined when the temperature difference exceeded a specific value,

455

especially for the hilltop, which was closely related to the fact that the submicron

456

particles were difficult to diffuse to upper air when the air was stable and thus PM1

457

concentrations were reduced at the hilltop. The above analyses indicated that

458

atmospheric stratification had more significant effect on PM1 near the surface than

459

that at the hillside and hilltop, which will be discussed more deeply in the following

460

sections and the importance of horizontal or vertical dispersion to PM1 at different

461

heights will be analyzed using an effective technique in the following sections.

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462 Fig. 7 Relationships between PM1 for Sites A-E and temperature difference between 200 m above

464

the surface and the ground surface. The relationships were fitted using unary linear or polynomial

465

regression. The changing rates of PM1 as increased temperature difference were given in the

466

subplots.

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The above results indicated that dependence of PM1 on the temperature differences

469

varied largely between the surface sites and the hillside and hilltop sites. The air layer

470

with increased temperatures as altitudes was identified as inversion layer. The

471

difference between the altitudes with the highest and lowest temperature within the

472

layer was considered as inversion layer thickness. To better understand the impact of

473

temperature inversion on PM1 differences between near the surface and at the hilltop,

474

the relationships between PM1 differences between Sites A and E and inversion layer

475

thickness or intensity or new calculated inversion index (II) was given in Fig. 8. The

476

relationships were fitted using unary linear regression and coefficients of 20

ACCEPTED MANUSCRIPT determination and the corresponding changing rates were shown in the subplot. PM1

478

differences between near the surface and at the hilltop increased significantly as

479

inversion layer thickness or intensity increased with coefficients of determination

480

higher than 0.50, but the relationship between PM1 differences and calculated

481

inversion index, a combination of inversion layer thickness and intensity, was better

482

with coefficient of determination of 0.87. That indicated that the newly calculated

483

inversion index could explain about 87% of variations of PM1 differences between

484

near the surface and at the hilltop. The inversion layer thickness and intensity should

485

be considered together to analyze the impact of temperature inversion on air pollution.

486

The PM1 difference increased by 47.14 (36.91) µg m-3 when inversion layer thickness

487

(intensity) increased by 100 m (1 oC 100 m-1), while that increased by 12.49 µg m-3

488

when the calculated inversion index increased by 1.

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Additionally, the relationship between PM1 differences between Sites A and E and

491

temperature differences between 200 m above the surface and near the surface was

492

illustrated in Fig. 8. The increase of the PM1 differences as the increased temperature

493

differences was obvious with coefficient of determination of 0.73. To evaluate the

494

fitting effects using linear regression equation, the significance of coefficient of

495

determination (R2) was tested using F test. The test results indicated that the fitting

496

was significant and passed the significance level of 0.01. The above analyses

497

indicated that atmospheric stratification and temperature inversion affected largely

498

vertical distributions of submicron particles. The particles were more easily

499

accumulated near the ground surface and were difficult to diffuse to the upper air

500

when temperature inversion was strong in the valley city.

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Fig. 8 Relationships between PM1 differences between Sites A and E and (a) inversion layer

503

thickness and intensity and new calculated inversion index based on thickness and intensity and (b)

504

temperature differences between 200 m above the surface and near the surface.

505

EP

502

The impact of vertical dispersion on PM1 vertical profiles was mainly analyzed in the

507

above sections. To determine the importance of vertical or horizontal dispersion on

508

reduced PM1 in the bottom and top of the valley, the T0m-200m (temperature differences

509

between the surface and 200 m above the surface) and horizontal wind speed were

510

used to represent vertical and horizontal dispersions, respectively. Theoretically, if the

511

ratio of PM1 to T0m-200m was much higher than that of PM1 to horizontal wind speed

512

for the same PM1, vertical dispersion was more effective to alleviate submicron

513

particle pollution. However, the two ratios varied largely and could not be compared.

514

Therefore, to better identify the importance of horizontal or vertical dispersion on

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ACCEPTED MANUSCRIPT 515

PM1 at different altitudes in an urban valley, the PM1 to T0m-200m and to horizontal

516

wind speed ratios were normalized using the following formula:

517

Normalized ratios =

518

where, the letter of A represented the ratios of PM1 to T0-200m or PM1 to horizontal

519

wind speed. µ and σ were the corresponding mean values and standard deviations,

520

respectively.

A-µ

(6)

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σ

521

The relationships between normalized ratios of PM1 to T0m-200m (vertical axes) and

523

those of PM1 to horizontal wind speed (horizontal axes) for Sites A and E were shown

524

in Fig. 9. To more intuitively see the importance of vertical or horizontal dispersion

525

on PM1 near the surface and at the hilltop, the scales of horizontal and vertical axes

526

were equal and between -3 and 3 for the subplots. The less differences between the

527

two normalized ratios maybe reflected different importance of horizontal or vertical

528

dispersion because the ratios were normalized to the relatively small range from -3 to

529

3. The normalized ratios of PM1 to T0m-200m were much higher than those of PM1 to

530

horizontal wind speed for the most cases near the surface (Fig. 9 a), while that was

531

opposite at the hilltop (Fig. 9 b). The vertical (horizontal) dispersion was more

532

important when the data was located above (below) the 1:1 line in Fig. 9. 73% (64%)

533

of data were located above (below) the 1:1 line near the surface (at the hilltop). The

534

above analyses indicated that vertical dispersion had a more significant effect on PM1

535

than horizontal dispersion near the surface, while PM1 was more largely affected by

536

horizontal dispersion at the hilltop, which was closely related to the valley terrain of

537

Lanzhou.

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To better understand the meteorological factors affecting submicron particle

540

concentrations at different altitudes, a principal component analysis (PCA) with

541

varimax rotation was performed for the surface and hilltop sites. PM1, lifted index,

542

horizontal wind speed, temperature, RH and snowfall were selected as variables for

543

the analysis (Fig. 10). LI (lifted index) could reflect vertical dispersion of the 23

ACCEPTED MANUSCRIPT pollutants. The factor loading higher than 0.5 or lower than -0.5 for the specific

545

variable indicated that the variable significantly interacted with the other variables

546

with high factor loadings. The analysis gave two components with eigenvalues higher

547

than 1, explaining 72% of the total variance. The first component (PC1) had

548

significantly high factor loadings for LI, RH and snowfall with negative correlation of

549

the variables to PM1 at the surface sites (Sites A, B and C, Fig. 10 a), suggesting that

550

PM1 was easily diluted and removed by strongly vertical dispersion and snow

551

particles near the surface. PC2 presented the highest factor loadings for PM1 at the

552

hilltop sites (Sites D and E), horizontal wind speed and temperature and had a

553

negative correlation between PM1 and meteorological parameters (horizontal wind

554

speed and temperature) (Fig. 10 b). The second component was selected to reflect the

555

impact of horizontal dispersion. The PCA results indicated that PM1 near the surface

556

of the urban valley could be reduced largely by improved vertical dispersion, while

557

that at the hilltop was influenced more significantly by horizontal dispersion, which

558

could support the preceding statements.

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559 560

Fig. 9 Normalized ratios of PM1 to T0m-200m (temperature differences between the surface and 200

561

m above the surface) vs. normalized ratios of PM1 to horizontal WS (horizontal wind speed) for (a)

562

near the surface and (b) hilltop.

24

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563

Fig. 10 Principal component analyses (PCA) of PM1, lifted index (LI), horizontal wind speed

565

(WS), temperature (T), RH and snowfall for the five sites.

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566

4 Conclusions

568

Based on PM1 in situ observations by means of high topographic relief in the urban

569

valley, the PM1 vertical profiles and meteorological conditions impacts were revealed

570

for the first time. The synchronous boundary layer temperature and humidity profiles

571

were obtained by a microwave radiometer. The temperature differences between 200

572

m above the surface and near the surface (T200m-T0m) and horizontal wind speed were

573

used to represent vertical and horizontal dispersions. The PM1 vertical profile types

574

were identified using K-means clustering technique. The influence of atmospheric

575

stratification on PM1 vertical distributions was quantified in the valley city. The most

576

important factor (vertical or horizontal dispersion) to reduced PM1 in the bottom or

577

top of the valley was identified using principal component analysis. The main

578

limitation of this study was that the measurement campaign was too short (16 days)

579

for obtaining conclusive results. However, intensive PM1 observations at both

580

daytime and nighttime at different altitudes and synchronized temperature profiles

581

were necessary to understand PM1 vertical distributions and boundary layer impacts.

582

Some main conclusions were obtained as follows.

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ACCEPTED MANUSCRIPT Daytime PM1 concentrations reduced by about 3.86 µg m-3 when the height increased

585

by 100 m, which was much lower than that for nighttime (5.68 µg m-3 100 m-1). The

586

daytime PM1 within boundary layer was more uniform than that for nighttime due to

587

relatively stable atmospheric stratification in the nighttime. In view of largely varied

588

PM1 vertical distributions among the profiles, K-means clustering technique was used

589

to classify the profiles and three typical PM1 profiles were obtained. The first cluster

590

corresponded to the dust episodes during the study period with PM1 larger than 200

591

µg m-3 near the surface. The most frequent cluster with elevated PM1 concentrations

592

near the surface was closely related to temperature inversion around the ground and

593

thus stable air. Additionally, uniform PM1 of the cluster was mainly induced by

594

unstable atmospheric stratification and thus relatively good vertical dispersion.

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595

The PM1 variations were strongly correlated among the sites near the surface. PM1

597

increased by about 15 µg m-3 when the temperature difference (T200m-T0m) increased

598

by 1 oC near the surface, while that were constant and even declined when the

599

temperature difference exceeded a specific value for the hillside and the hilltop. About

600

50%–60% of PM1 variations could be attributed to atmospheric stratification near the

601

surface in the valley city, which was much higher than that at the hilltop. The

602

inversion layer thickness and intensity should be considered together to analyze the

603

impact of temperature inversion on air pollution. The newly calculated inversion

604

index based on both inversion layer thickness and intensity could explain about 87%

605

of PM1 differences between near the surface and at the hilltop. The PM1 difference

606

increased by 47.14 (36.91) µg m-3 when inversion layer thickness (intensity) increased

607

by 100 m (1 oC 100 m-1), while that increased by 12.49 µg m-3 when the calculated

608

index increased by 1. The vertical dispersion had a more significant effect on PM1

609

than horizontal dispersion near the surface, while PM1 was more largely affected by

610

horizontal dispersion at the hilltop, which was closely related to the valley terrain of

611

Lanzhou.

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Acknowledgement 26

ACCEPTED MANUSCRIPT The study is supported by Natural Science Foundation of China (41605103), Youth

615

Innovation Promotion Association, CAS (2017462), CAS “Light of West China”

616

Program, Opening Project of Shanghai Key Laboratory of Atmospheric Particle

617

Pollution and Prevention (LAP3) (FDLAP16005) and the Excellent Post-Doctoral

618

Program (2016LH0020). The authors would like to thank Tong Zhang due to

619

providing boundary layer structure temperature and humidity data used in this study.

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620

References

622

Brines, M., Dall'Osto, M., Amato, F., Minguillón, M.C., Karanasiou, A., Alastuey, A.,

623

Querol, X., 2016. Vertical and horizontal variability of PM10 source contributions in

624

Barcelona during SAPUSS. Atmos. Chem. Phys., 16, 6785–6804.

625

Chambers, S.D., Wang, F.J., Williams, A.G., Deng, X.D., Zhang, H., Lonati, G.,

626

Crawford, J., Griffiths, A.D., Ianniello, A., Allegrini, I., 2015. Quantifying the

627

influences of atmospheric stability on air pollution in Lanzhou, China, using a

628

radon-based stability monitor. Atmos. Environ., 107, 233-243.

629

Chan, T.W., Mozurkewich, M., 2007. Application of absolute principal component

630

analysis to size distribution data: identification of particle origins, Atmos. Chem.

631

Phys., 7, 887-897.

632

Chen, C.H., Huang, J.G., Shi, J.K., Li, G.F., Peng, C.Y., 1984. Subsidence and

633

diffusion of elevated release sources over the valley city Lanzhou. Sci. Bull., 29(10),

634

1370-1374.

635

Chen, C.H., Huang, J.G., Ren, Z.H., Peng, X.A., 1986. Meteorological conditions of

636

photochemical smog pollution during summer in Xigu industrial area, Lanzhou. Acta

637

Sci. Circumstantiae, 6(3), 334-342.

638

Chew, B. N., Campbell, J. R., Salinas, S. V., Chang, C. W., Reid, J. S., Welton, E. J.,

639

Holben, B. N., Liew, S. C., 2013. Aerosol particle vertical distributions and optical

640

properties over Singapore. Atmos. Environ., 79, 599-613.

641

Chilinski, M. T., Markowicz, K. M., Markowicz, J., 2016. Observation of vertical

642

variability of black carbon concentration in lower troposphere on campaigns in

643

Poland. Atmos. Environ., 137, 155-170.

AC C

EP

TE D

M AN U

SC

621

27

ACCEPTED MANUSCRIPT Choi, J. O., Chung, C. E., 2014. Sensitivity of aerosol direct radiative forcing to

645

aerosol vertical profile. Tellus B: Chemical and Physical Meteorology, 66(1), 24376,

646

doi: 10.3402/tellusb.v66.24376.

647

Chu, P.C., Chen, Y.C., Lu, S.H., Li, Z.C., Lu, Y.Q., 2008. Particulate air pollution in

648

Lanzhou China. Environ. Int., 34, 698–713.

649

Chung, C. E., Ramanathan, V., Kim, D., Podgorny, I. A., 2005. Global anthropogenic

650

aerosol direct forcing derived from satellite and ground-based observations. J.

651

Geophys. Res., 110, D24207.

652

Galway, J.G., 1956. The lifted index as a predictor of latent instability. Bull. Am.

653

Meteorol. Soc. 37, 528–529.

654

Gong, S.L., Zhang, X.Y., Zhao, T.L., McKendry, I.G., Jaffe, D.A., Lu, N.M., 2003.

655

Characterization of soil dust aerosol in China and its transport and distribution during

656

2001 ACE-Asia: 2. Model simulation and validation. J. Geophys. Res., 108 (D9),

657

doi:10.1029/2002JD002633.

658

Guan Q., Li, F., Yang, L., Zhao, R., Yang, Y., Luo, H., 2018. Spatial-temporal

659

variations and mineral dust fractions in particulate matter mass concentrations in an

660

urban area of northwestern China. J. Environ. Manage., 222, 95-103.

661

He, J.J., Gong, S.L., Yu, Y., Yu, L.J., Wu, L., Mao, H.J., Song, C.B., Zhao, S.P., Liu,

662

H.L., Li, X.Y., Li, R.P., 2017. Air pollution characteristics and their relation to

663

meteorological conditions during 2014-2015 in major Chinese cities. Environ. Pollut.,

664

223, 484-496.

665

Huang, S., Rahn, K.A., Arimoto, R., 1999. Testing and optimizing two factor-analysis

666

techniques on aerosol at Narragansett, Rhode Island, Atmos. Environ., 33, 2169-2185.

667

Huneeus, N., Schulz, M., Balkanski, Y., Griesfeller, J., Prospero, J., Kinne, S., Bauer,

668

S., Boucher, O., Chin, M., Dentener, F., Diehl, T., Easter, R., Fillmore, D., Ghan, S.,

669

Ginoux, P., Grini, A., Horowitz, L., Koch, D., Krol, M. C., Landing, W., Liu, X.,

670

Mahowald, N., Miller, R., Morcrette, J.-J., Myhre, G., Penner, J., Perlwitz, J., Stier, P.,

671

Takemura, T., Zender, C. S., 2011. Global dust model intercomparison in AeroCom

672

phase I. Atmos. Chem. Phys., 11, 7781–7816, doi:10.5194/acp-11-7781-2011.

673

Lakshmi, N. B., Nair, V. S., Suresh Babu, S., 2017. Vertical structure of aerosols and

AC C

EP

TE D

M AN U

SC

RI PT

644

28

ACCEPTED MANUSCRIPT mineral dust over the Bay of Bengal from multisatellite observations. J. Geophys. Res.

675

Atmos., 122, 12845–12861, https://doi.org/10.1002/2017JD027643.

676

Li J. W., Han, Z. W., 2016. Aerosol vertical distribution over east China from

677

RIEMS-Chem simulation in comparison with CALIPSO measurements. Atmos.

678

Environ., 143, 177-189.

679

Li, X. B., Wang, D. S., Lu, Q. C., Peng, Z. R., Wang, Z. Y., 2018. Investigating

680

vertical distribution patterns of lower tropospheric PM2.5 using unmanned aerial

681

vehicle measurements. Atmos. Environ., 173, 62–71.

682

Li, Z., Lyu, S., Wen, L., Zhao, L., Ao, Y., Wang, S., 2017. Effect of a cold, dry air

683

incursion on atmospheric boundary layer processes over a high-altitude lake in the

684

Tibetan Plateau. Atmos. Res., 185, 32-43.

685

Liu J. J., Zheng, Y. F., Li, Z. Q., Flynn, C., Cribb, M., 2012. Seasonal variations of

686

aerosol optical properties, vertical distribution and associated radiative effects in the

687

Yangtze Delta region of China. J. Geophys. Res., 117, D00K38,

688

doi:10.1029/2011JD016490.

689

Liu, M. M., Huang, Y. N., Ma, Z. W., Jin, Z., Liu, X. Y., Wang, H. K., Liu, Y., Wang, J.

690

N., Jantunen, M., Bi, J., Kinney, P. L., 2017. Spatial and temporal trends in the

691

mortality burden of air pollution in China: 2004-2012. Environ. Int., 98, 75-81.

692

Liu, Q., He, Q. S., Fang, S. H., Guang, Y., Ma, C. Y., Chen, Y. H., Kang, Y. M., Pan,

693

H., Zhang, H., Yao, Y. F., 2017. Vertical distribution of ambient aerosol extinctive

694

properties during haze and haze-free periods based on the Micro-Pulse Lidar

695

observation in Shanghai. Sci. Total Environ., 574, 1502-1511.

696

Ma, X. Y., Yu, F. Q., 2014. Seasonal variability of aerosol vertical profiles over east

697

US and west Europe: GEOS-Chem/APM simulation and comparison with CALIPSO

698

observations. Atmos. Res., 140-141, 28-37.

699

Noh, Y. M., Lee, K., Kim, K., Shin, S. K., Müller, D., Shin, D. H., 2016. Influence of

700

the vertical absorption profile of mixed Asian dust plumes on aerosol direct radiative

701

forcing over East Asia. Atmos. Environ., 138, 191-204.

702

Querol, X., Alastuey, A., Gangoiti, G., Perez, N., Lee, H.K., Eun, H.R., Park, Y.,

703

Mantilla, E., Escudero, M., Titos, G., Alonso, L., Temime-Roussel, B., Marchand, N.,

AC C

EP

TE D

M AN U

SC

RI PT

674

29

ACCEPTED MANUSCRIPT Moreta, J.R., Revuelta, M.A., Salvador, P., Artíñano, B., Santos, S.G.D., Anguas, M.,

705

Notario, A., Saiz-Lopez, A., Harrison, R.M., Millán, M., Ahn, K.–H., 2018.

706

Phenomenology of summer ozone episodes over the Madrid Metropolitan Area,

707

central Spain. Atmos. Chem. Phys., 18, 6511–6533.

708

Ramanathan, V., Feng, Y., 2009. Air pollution, greenhouse gases and climate change:

709

Global and regional perspectives. Atmos. Environ., 43, 37–50,

710

https://doi.org/10.1016/j.atmosenv.2008.09.063.

711

Sabaliauskas, K., Jeong, C.-H., Yao, X., Jun, Y.-S., Evans, G., 2013. Cluster analysis

712

of roadside ultrafine particle size distributions. Atmos. Environ. 70, 64–74.

713

Seidel, D.J., Ao, C.O., Li, K., 2010. Estimating climatological planetary boundary

714

layer heights from radiosonde observations: comparison of methods and uncertainty

715

analysis. J. Geophys. Res. Atmos. 115, 1–15.

716

http://dx.doi.org/10.1029/2009JD013680.

717

Song, C., He, J., Wu, L., Jin, T., Chen, X., Li, R., Ren, P., Zhang, L., Mao, H., 2017.

718

Health burden attributable to ambient PM2.5 in China. Environ. Pollut., 223, 575-586.

719

Sun, X., Yin, Y., Sun, Y. W., Sun, Y., Liu, W., Han, Y., 2013. Seasonal and vertical

720

variations in aerosol distribution over Shijiazhuang, China. Atmos. Environ., 81,

721

245-252.

722

Tao, J., Huang, Y., Lu, D., 2007. Influence of sand-dust activities in Hexi Corridor on

723

PM10 concentration in Lanzhou and its assessment. J. Desert Res., 27(4), 672-676.

724

Tie, X. X., Huang, R. J., Dai, W. T., Cao, J. J., Long, X., Su, X. L., Zhao, S. Y., Wang,

725

Q. Y., Li, G. H., 2016. Effect of heavy haze and aerosol pollution on rice and wheat

726

productions in China. Sci. Rep., 6, 29612, doi: 10.1038/srep29612.

727

Toth, T. D., Zhang, J. L., Campbell, J. R., Reid, J. S., Vaughan, M. A., 2016. Temporal

728

variability of aerosol optical thickness vertical distribution observed from CALIOP. J.

729

Geophys. Res. Atmos., 121, 9117–9139, doi:10.1002/2015JD024668.

730

Tunved, P., Strom, J., Hansson, H.-C., 2004. An investigation of processes controlling

731

the evolution of the boundary layer aerosol size distribution properties at the Swedish

732

background station aspvreten. Atmos. Chem. Phys. 4, 2581–2592.

733

Vuolo, M. R., Schulz, M., Balkanski, Y., Takemura, T., 2014. A new method for

AC C

EP

TE D

M AN U

SC

RI PT

704

30

ACCEPTED MANUSCRIPT evaluating the impact of vertical distribution on aerosol radiative forcing in general

735

circulation models. Atmos. Chem. Phys., 14, 877–897.

736

Wang, F.J., Chambers, S.D., Zhang, Z.Y., Williams, A.G., Deng, X.D., Zhang, H.,

737

Lonati, G., Crawford, J., Griffiths, A.D., Ianniello, A., Allegrini, I., 2016. Quantifying

738

stability influences on air pollution in Lanzhou, China, using a radon-based “stability

739

monitor”: Seasonality and extreme events. Atmos. Environ., 145, 376-391.

740

Wang, Q. Q., Sun, Y. L., Xu, W. Q., Du, W., Zhou, L. B., Tang, G. Q., Chen, C.,

741

Cheng, X. L., Zhao, X. J., Ji, D. S., Han, T. T., Wang, Z., Li, J., Wang, Z. F., 2018.

742

Vertically resolved characteristics of air pollution during two severe winter haze

743

episodes in urban Beijing, China. Atmos. Chem. Phys., 18, 2495-2509,

744

https://doi.org/10.5194/acp-18-2495-2018.

745

Wang, X., Dickinson, R.E., Su, L., Zhou, C., Wang, K., 2018. PM2.5 pollution in

746

China and how it has been exacerbated by terrain and meteorological conditions. B.

747

Am. Meteorol. Soc., 99(1), 105-120.

748

Wang, Z. L., Huang, X., Ding, A. J., 2018. Dome effect of black carbon and its key

749

influencing factors: a one-dimensional modelling study. Atmos. Chem. Phys., 18,

750

2821–2834, https://doi.org/10.5194/acp-18-2821-2018.

751

Yang, X.L., 2018. The synoptic type and meteorological condition during extreme air

752

pollution events in Lanzhou City. Master Thesis, Lanzhou University, p. 18-19.

753

Zawadzka, O., Posyniak, M., Nelken, K., Markuszewski, P., Chilinski, M. T.,

754

Czyzewska, D., Lisok, J., Markowicz, K. M., 2017. Study of the vertical variability of

755

aerosol properties based on cable cars in-situ measurements. Atmos. Pollut. Res., 8,

756

968-978.

757

Zhang, Y.H., Shao, K.S., Tang, X.Y., Li, J.L., 1998. The study of urban photochemical

758

smog pollution in China. Acta Scientiarum Naturalium Universitatis Pekinensis,

759

34(2-3), 392-400.

760

Zhang, L., Chen, C.H., Murlis, J., 2001. Study on winter air pollution control in

761

Lanzhou, China. Water Air Soil Pollut., 127(1-4), 351-372.

762

Zhang, Q., Ma, X. C., Tie, X. X., Huang, M. Y., Zhao, C. S., 2009. Vertical

763

distributions of aerosols under different weather conditions: Analysis of in-situ

AC C

EP

TE D

M AN U

SC

RI PT

734

31

ACCEPTED MANUSCRIPT aircraft measurements in Beijing, China. Atmos. Environ., 43, 5526-5535.

765

Zhang, Q., 2003. A study of the interaction between concentration of pollutants and

766

the climatic and environmental factors of Lanzhou. J. Lanzhou Univ. Nat. Sci., 39(1),

767

99-106.

768

Zhang, Q., Li, H.Y., 2011. A study of the relationship between air pollutants and

769

inversion in the ABL over the city of Lanzhou. Adv. Atmos. Sci., 28(4), 879-886.

770

Zhang, R. Y., Wang, G. H., Guo, S., Zamora, M. L., Ying, Q., Lin, Y., Wang, W. G.,

771

Hu, M., Wang, Y., 2015. Formation of urban fine particulate matter. Chem. Rev., 115,

772

3803–3855, https://doi.org/10.1021/acs.chemrev.5b00067.

773

Zhang, X. Y., Wang, Y. Q., Niu, T., Zhang, X. C., Gong, S. L., Zhang, Y. M., and Sun,

774

J. Y., 2012. Atmospheric aerosol compositions in China: spatial/temporal variability,

775

chemical signature, regional haze distribution and comparisons with global aerosols.

776

Atmos. Chem. Phys., 12, 779–799, https://doi.org/10.5194/acp-12-779-2012.

777

Zhang, Y. L., Cao, F., 2015. Fine particulate matter (PM2.5) in China at a city level.

778

Sci. Rep., 5, 14884, https://doi.org/10.1038/srep14884.

779

Zhang, K., Wang, D. F., Bian, Q. G., Duan, Y. S., Zhao, M. F., Fei, D. N., Xiu, G. L.,

780

Fu, Q. Y., 2017. Tethered balloon-based particle number concentration, and size

781

distribution vertical profiles within the lower troposphere of Shanghai. Atmos.

782

Environ., 154, 141-150.

783

Zhao, S.P., Yu, Y., Yin, D.Y., He, J.J., 2015a. Meteorological dependence of particle

784

number concentrations in an urban area of complex terrain, Northwestern China.

785

Atmos. Res., 164-165, 304-317.

786

Zhao, S.P., Yu, Y., Xia, D., Yin, D., He, J., Liu, N., Li, F., 2015b. Urban particle size

787

distributions during two contrasting dust events originating from Taklimakan and

788

Gobi Deserts. Environ. Pollut., 207, 107-122.

789

Zhao, S. P., Yu, Y., Yin, D. Y., He, J. J., Liu, N., Qu, J. J., Xiao, J. H., 2016. Annual

790

and diurnal variations of gaseous and particulate pollutants in 31 provincial capital

791

cities based on in situ air quality monitoring data from China National Environmental

792

Monitoring Center. Environ. Int., 86, 92–106.

793

Zhao, S.P., Yu, Y., Qin, D.H., Yin, D.Y., He, J.J., 2017. Assessment of long-term and

AC C

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M AN U

SC

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764

32

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795

implication. Atmos. Environ., 170, 82-95.

796

Zhao, S.P., Yu, Y., Yin, D.Y., Qin, D. H., He, J. J., Dong, L. X., 2018a. Spatial patterns

797

and temporal variations of six criteria air pollutants during 2015 to 2017 in the city

798

clusters of Sichuan Basin, China. Sci. Total Environ., 624, 540–557.

799

Zhao, S.P., Yu, Y., Yin, D., Qin, D., He, J., Li, J., Dong, L., 2018b. Two winter PM2.5

800

pollution types and the causes in the city clusters of Sichuan Basin, Western China.

801

Sci. Total Environ., 636, 1228-1240.

802

Zhou, X., Zhang, T., Li, Z., Tao, Y., Wang, F., Zhang, X., Xu, C., Ma, S., Huang, J.,

803

2018. Particulate and gaseous pollutants in a petrochemical industrialized valley city,

804

Western China during 2013–2016. Environ. Sci. Pollut. Res., 25(15), 15174–15190.

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ACCEPTED MANUSCRIPT Highlights PM1 vertical profiles were revealed in urban areas of Lanzhou for the first time. Three typical PM1 profile types were identified using K-means clustering technique.

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PM1 concentrations reduced by about 3-5 µg m-3 when the height increased by 100 m.

50%–60% of PM1 variations were attributed to atmospheric stratification near the surface.

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PM1 was more largely affected by vertical dispersion near the surface.