Aerosol layers in the free troposphere and their seasonal variations as observed in Wuhan, China

Journal Pre-proof Aerosol layers in the free troposphere and their seasonal variations as observed in Wuhan, China Junyi Shao, Fan Yi, Zhenping Yin PII:

S1352-2310(20)30064-9

DOI:

https://doi.org/10.1016/j.atmosenv.2020.117323

Reference:

AEA 117323

To appear in:

Atmospheric Environment

Received Date: 31 July 2019 Revised Date:

26 January 2020

Accepted Date: 1 February 2020

Please cite this article as: Shao, J., Yi, F., Yin, Z., Aerosol layers in the free troposphere and their seasonal variations as observed in Wuhan, China, Atmospheric Environment (2020), doi: https:// doi.org/10.1016/j.atmosenv.2020.117323. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2020 Published by Elsevier Ltd.

Author Contribution Statement Junyi shao: data curation, methodology, writing Fan Yi: review & editing Zhenping Yin: review & editing

1

Aerosol layers in the free troposphere and their

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seasonal variations as observed in Wuhan, China

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Junyi Shao1,2,3*, Fan Yi1,2,3, Zhenping Yin1,2,3 1 School of Electronic Information, Wuhan University, Wuhan, China, 2Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan, China, 3State Observatory for Atmospheric Remote Sensing, Wuhan, China

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* [email protected]

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Abstract: Free-tropospheric aerosol layers and their seasonal variation

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over Wuhan (30.5°N, 114.4°E), China, are presented based on a 532-nm

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polarization lidar measurements on 162 days from January through

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December 2013. Using the aerosol layer selection criterions, a total of

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402 free-tropospheric aerosol layer events were identified. The bottom

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height of the aerosol layers below 2 km accounts for 68% of the total,

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while approximately 76% of the layer’s top height ranges from 1 km to 4

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km. Out of the 402 events, 269 (67%) are optically-thin layers with

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aerosol optical depth (AOD) less than 0.1. The free tropospheric AOD2-7

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contribute ∼13-31% to the AOD0-7 and the free-tropospheric aerosol

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layers show considerable moderate variation. The aerosol layers have the

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maximum mean geometrical thickness of 1.2 km in spring, while the

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minimum mean thickness is 0.7 km in autumn, and the mean thickness is

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0.93 km and 1 km in summer and winter, respectively. The mean

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backscatter coefficient of aerosol layers during spring, summer, autumn

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and winter were 1.8 ± 1.4 Mm-1sr-1, 2.3 ± 2 Mm-1sr-1, 2.8 ± 2.7 Mm-1sr-1

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and 2.3 ± 2.2 Mm-1sr-1, respectively. Aerosol layers in different seasonal

4 5

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are classified by particle depolarization ratio, there are a large amount of

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non-spherical particles and mixed particles present in spring, autumn and

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winter, and the mean particle polarization ratio of aerosol layers during

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spring, summer, autumn and winter were 0.22, 0.06, 0.15 and 0.14,

29

respectively.

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Highlights:

Aerosol layer’s geometries and optical characteristics

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Aerosol layer’s seasonal variations

32

Monthly free tropospheric AOD

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Keywords: lidar; free troposphere; aerosol layer; planetary boundary

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layer

35 36 37 38 39 40 41

1. Introduction

42

Tropospheric aerosols influence Earth’s radiation budget, climate

43

and weather directly by scattering and absorbing radiation, indirectly by

44

acting as cloud condensation nuclei (Twomey et al., 1977; Twomey et al.,

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1984; Albrecht et al., 1989; Charlson et al., 1992; Hansen et al., 1997;

46

Kaufmanet al., 2002). The spatial and temporal distribution of

47

tropospheric aerosols and the respective aerosol types are poorly

48

understood and represent large uncertainty sources in our current climate

49

models for the prediction of radiative forcing and future climate change.

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Therefore, a detailed understanding of the regional geometries and optical

51

properties of aerosols is required (Hsu et al., 2000), contribute to a better

52

understanding of the phenomenon and thus provide local aerosol

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parameterizations for climate models (Sellegri et al., 2003; Osada 2003).

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The contribution of free tropospheric AOD and their direct effects

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are underestimated. Most aerosols are concentrated in the planetary

56

boundary layer (PBL), and the tropospheric column AOD is expected to

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be dominated by the PBL, especially in large cities with dense population

58

and industries. However, some research has shown that the contribution

59

of the free tropospheric AOD to the total column tropospheric AOD is

60

considerable. According to the two-year lidar and photometer

61

measurements in Taipe, China, the contribution of aerosols in the free

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atmosphere on columnar AOD are approximately 44–50% from

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February–April and approximately 26–37% in other months (Chen et al.,

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2009). Results from 9 years (2007–2015) datasets of Cloud-Aerosol Lidar

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with Orthogonal Polarization (CALIPSO) aerosol extinction product

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shows (Bourgeois et al., 2018), the contribution of aerosols in the free

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troposphere (FT) to atmospheric AOD may be highly underestimated and

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could reach a global value of greater than 31%.

69

As a potentially important climate forcing mechanism, it is difficult

70

to quantify the indirect effect of aerosols in the FT on climate. Three

71

aspects are listed here to describe the influences of aerosol layers

72

transport from other areas. First, the transport aerosol layers significantly

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contribute to the free tropospheric aerosol loading. This portion can be

74

even as large as 90% of the total free tropospheric aerosol content (Müller

75

et al., 2003). Second, the atmospheric lifetime of aerosols is much longer

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in the FT than in the PBL (Rosen et al., 1997) and could persist for

77

several weeks (Haywood et al., 2000). Longer resident time means longer

78

acting time and more physical and chemical reactions. Schumet et al.

79

(2018) showed that biomass burning of organic aerosols injected into the

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free troposphere is more persistent than organic aerosols in the boundary

81

layer. Finally, different aerosol chemical and physical properties have

82

different effects on cloud formation. The particle properties of the PBL

83

are closely linked to local sources, while the tropospheric aerosol layers

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are transported from other areas and even from continental areas

85

(Ansmann et al., 2005). With the great variability in sources and the

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process of coagulation, mixing, transport, and removal, the size

87

distribution of the particle diameter ranges from a few nanometres to

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several micrometres and often shows a complex multimodal shape

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(Damoah et al., 2004; Muller et al., 2003; Wangdinger et al., 2002).

90

The PBL and FT are not separated while some transport processes

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occur between the PBL and FT. The PBL is under the direct influence of

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the Earth’s surface, the height of the PBL changes over time and space

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from several hundred metres to several thousand metres. The atmosphere

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above PBL is called the FT. Entrainment effect is an important transport

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process between the PBL and FT. Under strong convective condition,

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aerosol-rich air masses mix with clean air masses (via updraft and

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downdraft) near the PBL top, which yields a transition zone between the

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PBL and the FT known as the “entrainment zone” (Stull 1988). Mattis et

99

al. (2008) indicate that the aerosol layers separated from the PBL by

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geometrical thickness of less than approximately 500 m were caused by

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the entrainment effect. Another important transport process between the

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PBL and FT is presented here. Aerosols fall from the atmosphere to the

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surface by gravity, which is known as dry deposition, and the removal

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efficiency of dry deposition is governed by the particle size and

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morphology (Zufall et al., 1998). Gobbi et al. (2007) quantified the

106

impact of Saharan dust on surface air quality in Italy. By monitoring the

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dust optical thickness in the PBL, Hamonou et al. (1999) identified an

108

isolated case of Saharan dust transport to the European PBL. A similar

109

procedure was performed by Rodríguez et al. (2002) and Gerasopoulos et

110

al. (2006), who showed the significance of the Saharan dust contribution

111

to the PM10 levels in the PBL. For other aerosol types, such as forest

112

fires and volcanic eruptions, these particles are often injected into the free

113

troposphere (Preißler et al., 2013). In addition, pyroconvection and

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orographic lifting are two regional processes that can transport aerosols

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from the surface to the FT (Fromm et al., 2006; Yumimoto et al., 2009;

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Bourgeois et al., 2015). In general, the aerosols in the FT are highly

117

variable in time and space.

118

Due to the large differences of sources, processes and weather

119

conditions, the aerosol layers can have distinctive regional characteristics.

120

For example, South African observations showed that higher and thicker

121

layers were observed during the second half of the year, which was partly

122

due to increased biomass burning activity (Giannakaki et al., 2015). The

123

layers characteristics of the dry season and wet season show strong

124

contrasts in Manaus, Brazil. An AOD of less than 0.05 at 532 nm was

125

observed in approximately 50% of all measurement cases during the wet

126

season in the Amazon (Baars et al., 2012). Thus, monitoring aerosols

127

from the ground is performed at many sites worldwide to study the

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aerosols characteristics under different conditions (relative humidity,

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temperature, wind, and source). The free-tropospheric aerosol layers were

130

investigated and classified over Évora, Portugal, and the layers were

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highest in summer with an overall mean layer height of (3.8 ± 1.9) km

132

and lowest in winter at (2.3 ± 0.9) km. The mean contributions of the

133

lofted layer were 17% and 22% at 355 and 532 nm, respectively (Preißler

134

et al., 2013). The geometrical properties and seasonal variations in

135

aerosol particle pollution in the FT at Leipzig, Germany, have been

136

acquired based on the framework of the German Lidar Network

137

(1997-2000) (Mattis et al., 2008). Winker et al. (2012) presented the

138

global 3-D distribution of aerosols as well as the seasonal and interannual

139

variations characterised by CALIPSO. In addition, the EUCAARI project

140

performed measurements in South Africa, China, India and Brazil (Hänel

141

et al., 2012; Komppula et al., 2012).

142

Lidar is a powerful tool for obtaining the geometries and optical

143

properties of free tropospheric aerosols, and lidar is conducive to

144

long-term observations. Lidar networks have been established to detect

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aerosols over wide areas, such as the Asian Dust Network (Sugimoto et

146

al., 2008), the European Aerosol Research Lidar Network (Bösenberg et

147

al., 2001, 2003) (EARLINET), the National Institute for Environmental

148

Studies (NIES) Lidar Network (Sugimoto et al., 2006), and the National

149

Aeronautics and Space Administration's (NASA's) Micro-pulse Lidar

150

Network (Welton et al., 2001).

151

In this study, we focus on the geometrical characteristics and optical

152

properties of aerosol layers and their seasonal variations. Polarization

153

lidar was implemented at our site (30.5°N, 114.4°E, 70 m above sea level)

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located in the central zone of Wuhan. Wuhan is an industrialized

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megacity in central China and has a resident population of ~10.2 million.

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Wuhan is crossed by the Yangtze River and hosts more than one hundred

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lakes, and Wuhan has a humid subtropical climate with abundant rainfall.

158

The local aerosol sources mainly come from traffic, various industrial

159

activities, and cooking emissions (van Donkelaar et al., 2010; Ma et al.,

160

2014).

161

In section 2, the technical aspects of the lidar and data analysis

162

method are described, including the method of determining the PBL

163

height, cloud height and aerosol layer boundaries. In section 3, the

164

statistical analysis of the geometries and optical properties of aerosol

165

layers as well as the monthly free tropospheric AOD and seasonal

166

variations are presented and discussed. In section 4, the discussion and

167

conclusions are presented.

168

2. Instrumentation and Methodology

169

2.1. Polarization lidar and its retrieving method

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The polarization lidar system located at Wuhan University has a

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two-channel configuration. The lidar transmitter uses a Nd:YAG laser to

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produce an emission of 120 mJ per pulse at 532 nm with a repetition rate

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of 20 Hz. The output laser beam passes through a Brewster polarizer to

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increase the polarization purity (up to 10000:1). The receiver consists of a

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Cassegrain telescope with a diameter of 300 mm and a field of view of 1

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mrad. After passing through an interference filter (0.3 nm bandwidth), the

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elastically backscattered light is incident on a polarization beam splitter

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prism (PBS), and two additional polarizers are placed on the two output

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sides of the PBS. The light emerging from the two polarizers is received

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by photomultiplier tubes (PMTs) and digitized by Licel.

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The raw lidar signal has a spatial resolution of 3.75 m and a

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temporal resolution of 1 min. The ± 45° calibration method is used to

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accurately calibrate the ratio of the system constants of the two channels

184

(Freudenthaler et al., 2009; Liu et al., 2013). Our polarization lidar has a

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complete receiver field-of-view overlap at 0.36 km. Details of the lidar

186

system description can be found in Wu C et al. (2016).

187

The photon count and analogue data are glued to form a reasonable

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photon count profile with a large dynamic range based on a method

189

developed by Newsom et al. (2009) and improved by Zhang et al. (2014).

190

The temporal resolution and spatial resolution are changed for different

191

uses. The classical Fernald method (Fernald, 1984) needs an assumption

192

of lidar ratio (50sr is used here), lead to relative error of 20% for AOD,

193

which is dependent on the deviations of the lidar ratio for the aerosol

194

layers (Hughes et al., 1985; Kafle and Coulter, 2013). Radiosonde data,

195

which were supplied by the Department of Atmospheric Science at

196

University

197

(http://weather.uwyo.edu/upperair/sounding.html), were used to account

198

for molecular backscattering. Our polarization lidar works at a single

199

wavelength (532 nm) and it not equipped with Raman channels. Thus, the

200

systematic error of our lidar is larger and do not provide information

of

Wyoming

201

about particle size compared with multi-wavelength Raman lidar.

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2.2 Particle depolarization ratio

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According to the Lorenz-Mie theory, spherical particles are

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homogeneous in the context that spherical particles conserve the

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polarization of the incident light, and the presence of non-spherical

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particles results in a non-zero polarization in the direction perpendicular

207

to the laser polarization (Sassen et al., 1991). The volume depolarization

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ratio （ ）is defined as the ratio of the backscatter raw signal on the

209

planes perpendicular and parallel to the laser beam (Freudenthaler et al.,

210

2009):

211

( )

( )=

II (

(1)

)

( ) and

II (z)

212

where

213

and parallel polarization modes of the z range, respectively. And K is the

214

gain ratio of perpendicular and parallel channel.

215

The

are the received intensities in the perpendicular

reflects the depolarization effect of atmospheric molecules

216

and aerosol particles on the incident laser, which cannot accurately reflect

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the depolarization effect of aerosol particles, especially when the aerosol

218

concentration is relatively low. The particle depolarization ratio （

219

can be calculated as follows:

220

=

(

) (

)

(

)

(

)

）

(2)

221

where

222

depolarization ratio of molecules, and R is the backscatter ratio. The

and

are the particle depolarization ratio and the

223

depolarization ratio of molecules is 0.004 in our system (Behrendt and

224

Nakamura, 2002). The

225

therefore, the

226

the atmosphere, such as ice crystals, sand (He Y and Yi F., 2014) and

227

volcanic ash (Zhuang J and Yi F., 2016).

228

reflects the degree of non-spherical particles;

can be used to identify the types of aerosol particles in

The systematic error of the

is less than 5% under our

229

configuration, the details of error analysis method can be found in

230

Freudenthaler et al. (2009).The uncertainty of the backscatter ratio is the

231

main error for the

232

calibrations, the ∆90 calibration method have been used in the last system

233

calibration (Freudenthaler, 2016). The error depends on the values of

234

backscatter ratio, aerosol layers with a small backscatter ratio cause

235

considerable errors of

236

cause nonsignificant errors. With an uncertainty of backscatter ratio is

237

10%, the error of

238

is 2 and 6, respectively. The importance of multiple scattering will

239

increase significantly when the AOD is greater than 1 (Eloranta et al,

240

1998). However, the maximum AOD of the aerosol layers in our study is

241

0.43; therefore, the multiple scattering error is negligible.

242

2.3 PBL height identification method

in our study. To improve the accuracy of the

while higher values of the backscatter ratio

are about 9-16% and 5% when the backscatter ratio

243

To define the aerosol layers in the FT, the top height of the PBL

244

must be determined. During the daytime, the aerosol concentration in the

245

entrainment zone is highly variable on small time scales, the height of

246

the variance maximum is also taken as the convective boundary layer

247

(CBL) height, which is called the variance method or standard deviation

248

method (Lammert and Bösenberg 2006; Pal et al., 2010; Menut et al.,

249

1999; Martucci et al., 2004). A strong decrease in aerosol concentration

250

occurs at the CBL top, and the height of the maximum gradient is taken

251

as the CBL height, which is called the gradient method (Emeis et al.,

252

2008). The vertical structure allows the CBL height to be inferred using

253

the wavelet covariance transform (WCT) method (Davis et al., 2000;

254

Brooks, 2003; Baars et al., 2008; Granados-Muñoz et al., 2012; Lewis et

255

al., 2013; Luo et al., 2014). The height of the night-time stable boundary

256

layer (SBL) height is difficult to determine via the standard deviation

257

method because of the lack of strong turbulent mixing. Standard

258

deviation method has lower temporal resolution while its works depend

259

on variance of time. For convenience, the gradient method is used to

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determine the PBL top height as well as the geometrical properties of

261

aerosol layers.

262

The range-corrected signal is used in the gradient method to

263

retrieve the PBL height. The top of the PBL is defined as the largest

264

local minimum of the first derivative of the range-corrected signal

265

(Bösenberg et al., 2003). Our full overlap height is used as the minimum

266

PBL height, while the SBL height ranges from tens to hundreds of

267

metres. The lidar system is able to detect the residual layer (RL) top at

268

night (Korhonen et al., 2014), and the RL top is used as the SBL height.

269

The gradient method assumes that the aerosol concentration is

270

significantly higher in the PBL than in the FT, which is usually the case.

271

However, in some serious pollution events, such as strong dust plumes,

272

the largest negative gradient may appear in the dust layer margin.

273

Therefore, prior studies of the seasonal dependence of the PBL height can

274

be used as a restriction condition. According to the 5-year study by

275

Kongwei et al. (2015), the mean maximum CBL height varies annually

276

and presents higher values in summer (1.56 ± 0.17 km in August) and

277

lower values in winter (0.88 ± 0.40 km in December). Thus, the threshold

278

value is 200 m larger than the maximum CBL height of 1.73 km.

279

2.4 Cloud determination method

280

Clouds lead to fast signal attenuation that block the observation of

281

aerosol layers. The cloud definition method is used to eliminate invalid

282

data. Clouds are easily identified via strong backscattered light. We

283

followed the method described by Wang et al. (2001) to obtain the cloud

284

layer height. The cloud base corresponds to the location where the signal

285

starts to increase in terms of the positive signal slope, the cloud top

286

corresponds to the location where the signal returns to either the

287

molecular backscattering or the noise level, and the maximum backscatter

288

ratio of the cloud is greater than 8. Data contains clouds lower than 7.5

289 290

km are considered invalid in our study. 2.5 Free tropospheric aerosol layers identification method

291

The bottom height and top height are the indispensable geometrical

292

properties of an aerosol layer. The backscatter ratio of the layers increases

293

from the bottom, reaches the maximum value at the peak and then

294

decreases to the background values at the top. Based on the structural

295

features, the gradient method is often used to define the aerosol layer in

296

FT (Flamant et al., 1997; Bösenberg et al., 2003; Mattis et al., 2008).

297

Hourly averaged profiles of the backscatter ratio are used to determine

298

the geometrical properties of the aerosol layers. The PBL height is

299

determined by the gradient method described in section 2.3; thus, the

300

bottom height of the aerosol layers must be above this height. Other

301

useful aerosol layers determined method are present here. Komppula et al.

302

(2010) defined the point at which the backscatter coefficient values are

303

below 0.1 Mm-1sr-1 as the aerosol layer top, and Baars et al. (2012)

304

defined the top height of the aerosol layer as the point at which the 1064

305

nm particle backscatter coefficient drops below the threshold value of

306

0.02 Mm-1sr-1. Mattis et al. (2008) defined the layer bottom as the local

307

maximum of the derivation and the layer top as the first local minimum

308

of derivation.

309

The first step is to calculate the signal to noise ratio (SNR) and

310

derivations of the signal. The SNR is calculated to ensure the quality of

311

signal. During the day, strong sunshine significantly increases the noise

312

level, especially on clear sky days in summer. The SNR is calculated as

313

follows: ( , )=

314

( , ) !"# ($,%) & &'($,%)

( , ) (( , )

(3)

315

where )( , ) is the backscattered lidar power, *( , ) is the sum of

316

the background noise power and dark current noise power, and

317

+

318

,- (

, ) is the signal standard deviation of )( , ). SNR less than 3

is considered too noisy for further analysis (Morille et al., 2007).

319

The hourly averaged backscatter ratio is smoothed with 150 m to

320

degrade the shake in the signal, and the gradient derivation is calculated

321

as follows:

322

./ 012 034( ) =

323

where z denotes the height and ( + 1) and ( − 1) the height bin

324

above and below. Here R is the hourly averaged backscatter ratio.

325

(

)

(

) (

(

) )



(4)

The second step is to identify the bottom and top of the aerosol layer.

326

The layer bottom corresponds to the location where the backscatter ratio

327

starts to increase in terms of the positive derivation, and the layer top

328

corresponds to the location where the derivation close to zero, and

329

meanwhile the backscatter ratio returns to the background value.

330

However, the structure of the aerosol layer is varied and irregular. There

331

were cases in which up to seven layers were observed simultaneously

332

with our layer detection method. After identifying the boundaries of

333

aerosol layers, it still needs to determine whether two layers are well

334

separated or possibly mixed if they are closely located. In this case, we

335

introduce the criteria about the distance between the boundaries of

336

different aerosol layers. If the two or more aerosol layers are not

337

separated for more than 180 m, the two or more layers are treated like the

338

same layer. And instead, the bottom height of the lowest layer and the top

339

height of the highest layer are taken as the boundaries of the mixed layer.

340

In order to prevent the influences from signal noise, some additional

341

criteria are applied: 1.the thickness of aerosol layers must be larger than

342

150 m; 2.the AOD of layer must be larger than 0.01; 3. the mean

343

backscatter coefficient should be larger than 0.15 Mm-1sr-1. The

344

identification method requires at least 5 bins with the spatial resolution of

345

30m, thus the thickness of aerosol layers must be larger than 150m. The

346

uncertainty of the backscatter coefficient by signal noise is usually less

347

than 0.15 Mm-1sr-1 and has no spatial correlation. Therefore, we use the

348

criteria that the mean backscatter coefficient less than 0.15 Mm-1sr-1 or the

349

layer AOD less than 0.01 which is in the same manner, to isolate the

350

influences from signal noise.

351

The particle backscatter ratio increases at the bottom of the aerosol

352

layer. Therefore, the derivation remains positive at a distance. The criteria

353

that three continuous bins with positive derivatives is used to indicate the

354

bottom of the aerosol layers. The first positive derivation height is

355

defined as the bottom of the aerosol layer. From the peak to the top height

356

of the layer, the backscatter ratio decreased from maximum to near

357

background values and the derivation increase from negative to closed to

358

zero. The backscatter ratio value at the bottom height is used as the

359

threshold value to define the top height. Thus, the top height of the layer

360

is defined as the point that backscatter ratio is less than the pre-defined

361

threshold and the derivation changes from large negative values to closed

362

to zero.

363 364 365 366 367 368 369

370 371

376

Fig. 1. Hourly averaged backscatter ratio profile (blue line) and its derivation (red line). The black horizontal line represents the PBL height, where the minimum of the first derivative of the range-corrected signal appears (the derivation of range-corrected signal is not shown). The green horizontal lines and red horizontal lines represent the layer bottom and top height, respectively. The black font “L1_b” represents the bottom of layer1, and the L1_t represents the top of layer1.

377

An example of our method in aerosol layer detection is illustrated in

378

Fig.1. The black transverse line at 0.96 km represents the PBL height,

379

which corresponds to the largest negative derivations. In this case, aerosol

380

layers are detected. The first layer ranges from 1.05 km to 2.4 km and

381

shows multi-modal peaks. The backscatter ratio gradually increases and

382

the derivations turn to be positive at 1.05 km; thus, 1.05 km is determined

383

to be the bottom of the layer, and the backscatter ratio at 1.05 km acts as

384

the threshold for this layer. The backscatter ratio reached the maximum

372 373 374 375

385

value of 4.02 at 1.5 km and then decreased sharply, although the largest

386

local negative derivation appeared above 60 m at the first peak. The

387

backscatter ratio decreased to 2.4 at the 1.55 km and then increased to 2.8

388

at 1.75 km, then decreased to be less than the threshold, and meanwhile

389

the derivation was closed to zero. Thus, 1.98 km is temporarily treated as

390

the top of this aerosol layer. The last step is to check whether the aerosol

391

layers above 1.98 km were separated for more than 180 m. The method

392

found there was an aerosol layer between 2.01 km and 2.43 km, which

393

was not separated for more than 180 m from the first layer. Thus, the two

394

layers are considered to be one layer. And the bottom, top height of the

395

combined layer is 1.05 km and 2.43 km, respectively.

396

The second layer with irregular shape is also accurately determined

397

by the method, the bottom height and top height are 2.6 km and 4.92 km,

398

respectively. Above 5 km, the backscatter ratio of background aerosol is

399

slightly larger than 1, which indicate the pretty low aerosol loadings. In

400

addition, the backscatter ratio at 5.34 km is slightly larger than

401

background values, as well. However, the derivation at 5.34 km is

402

positive and the derivations keep positive at a distance above 5.34 km,

403

thus 5.34 km is taken as the bottom of the third layer. The backscatter

404

ratio is lower than threshold values and the derivations approaches zero at

405

5.97 km, thus 5.97 km is taken the top height of fourth layer. Then the

406

AOD and thickness of layer is calculated to check if our two constraints

407

are satisfied. The third layer between 5.34 km and 5.97 km is optically

408

thin with low backscatter ratio, but was also successfully determined with

409

our method because our method does not rely on a constant threshold

410

value of the backscatter ratio. Instead, the method is based on variable

411

threshold and the derivatives of the backscatter ratio. Therefore, the

412

method can still work under the low aerosol loading in the FT. Above the

413

third layer, optically thin layers or signal shakes are ignored by the

414

constraints.

415

Although most of the results are consistent, certain problems are

416

observed under some conditions. A higher threshold value may result in

417

imprecise top height, under the condition that a certain height meet the

418

two criterions but the backscatter ratio of this height is much higher than

419

background value. Thus, the threshold values should be constrained. To

420

reduce the impact, a maximum value of 2 is included as an additional

421

constraint for the threshold value.

422

The final step is to delete the aerosol layers repeatedly occurred. In

423

order to increase the SNR, the lidar data was hourly averaged. Therefore,

424

if a layer occurs for more than one hour, the same layer could be recorded

425

more than once. To exclude the repeated layers, the layers with similar

426

geometries and optical properties in adjacent hours are considered to be

427

the same layer. The layer with the differences of geometries difference,

428

AOD and mean

less than 400m, 20% and 20% respectively, is

429

considered to be the same layer. The properties of the layer with the

430

maximum AOD are reserved as the properties of this layer. However,

431

aerosol layers feature a strong spatiotemporal variability, and the particle

432

can be easily transformed over time in the atmosphere by different

433

processes. Thus, the same layer is considered as two or more different

434

layers if the properties of the aerosol layer changes greatly. Therefore,

435

there could be more layers than practically observed.

436 437

2.6 HYSPLIT The

NOAA/ARL

(National

Oceanic

and

Atmospheric

438

Administration/Air Resources Laboratory) Hybrid Single Particle

439

Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler et al., 1997;

440

2003) was used to determine the origin of the free tropospheric aerosol

441

layers. The global reanalysis of meteorological data required by the

442

model from the archived model assimilation datasets of GDAS (NCEP

443

Global

444

ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1.

Data

Assimilation

System)

can

be

downloaded

from

445 446

3 Results and discussion

447

One year of lidar data (from 2013) is used for the statistical analysis

448

of the geometries and optical properties of the aerosol layers as well as

449

seasonal variations and contribution of free tropospheric AOD.

450

3.1 Distribution of lidar data and aerosol layers

451 452 453 454 455 456

Fig. 2. Monthly distribution of 2013 lidar observation. The X-axis represents the month, and the Y-axis represents the number of observation days. The histogram represents the number of observation days in that month. The seasonal distribution of observation days is relatively uniform. Noting that not all days contains 24 hours data

457

and no data are available in February because of the unfavourable weather conditions and personnel shortages in Chinese Spring Festival.

458

The lidar performed regular observations in 2013 when the weather

459

was not rainy or cloudy. Fig. 2 shows the observation number out of 162

460

days, but not all the days contain 24 hours of data because the lidar does

461

not work in rainy weather; therefore, approximately 2760 hours of lidar

462

data are available. No data are available in February because of the

463

unfavourable weather conditions and personnel shortages during the

464

Chinese Spring Festival. The data for autumn and winter are a little more

465

compare to that of spring and summer because there is more rain in

466

spring and summer.

467 468

472

Fig. 3. Vertical and temporal distributions of aerosol layers in the free troposphere in 2013. The vertical lines present the observed height ranges of free tropospheric aerosol layers. The aerosol layers are shown in different colours for identification purposes. Noting that we only counted the aerosol layers ranging from 0.36 km to 7.5 km.

473

In total, 402 free-tropospheric aerosol layers were observed during

474

2013 as shown in Fig. 3. We only counted the aerosol layers ranging from

475

0.36 km to 7.5 km, which is a suitable range for our lidar to research the

476

aerosol layers. The observed bottom height varies from 600 m to 6210 m,

477

while the observed top height varies from 880 m to 7440 m. The free

478

tropospheric aerosol layers show high variation in the geometrical

479

characteristics and are observed as a single layer in 72% of the cases,

480

while they are also characterised by two (22%), three (5%) or more

481

particle layers (1%).

469 470 471

482

The aerosol layers show little seasonal characteristics in geometrics,

483

except the seasonal mean thickness of aerosol layers show some

484

difference. The mean thickness of layers in spring, summer, autumn and

485

winter are 1.2 km, 0.93 km, 0.7 km and 1 km, respectively. And due to

486

the higher PBL height, the bottom height of the layers seems higher from

487

May to July.

488

3.2 Geometrical characteristics

489

The bottom height, top height and thickness of the aerosol layers

490

are shown in Fig. 4, respectively. The X-axis is corresponding to the

491

height range, the Y-axis on the left is corresponding to the frequency of

492

the height range, and the black trace presents the cumulative distribution

493

function and corresponding to the Y-axis on the right. In Table 1, the

494

frequency distributions of the geometries were shown here.

495

The bottom height of aerosol layers mainly below 2 km, which

496

accounts for 68% of the total. The bottom height of aerosol layers

497

between 2 km and 4 km account for 27%, and the frequency decreases as

498

the height increases between 2 km and 4 km. Less layers have a bottom

499

height above 4 km, which account only 5% of the total. The aerosol

500

layers often appear in the lower FT, which may be due to the

501

entrainment effect or other strong convective activities, such as biomass

502

burning. The higher altitude is likely less troubled by aerosol layers.

503 504

508

Fig. 4. Frequency distributions of the (a) bottom height, (b) top height and (c) thickness of free-tropospheric layers (histogram) and the cumulative distribution function (CDF) (black trace). The red, blue and green histograms are represent the bottom height, top height and thickness of aerosol layers, respectively. The Y-axis on the left is corresponding to the histogram and Y-axis on the right is for the black trace.

509

Tabel 1

510

Frequency distributions of bottom height, top height and thickness of the aerosol layers.

505 506 507

Bottom(km)

Frequency

Top(km)

Frequency

Thickness(km)

Frequency

0~2.0

68%

0~1.0

1%

0~1

61%

2~4.0

27%

1~4.0

76%

1~3

30%

4~6.5

5%

4~7.5

23%

3~5

9%

511 512

As seen in Fig. 4(b), the top height of the aerosol layers seldom

513

below 1 km, which accounts only 1% of the total. The aerosol layers with

514

lower top height often appear in the winter or night-time under a low PBL

515

height. Approximately 76% of the layer’s top height ranges from 1 km to

516

4 km, and the top height above 4 km and below 7.5 km account 23% of

517

the total.

518

The thickness of aerosol layers varies from a few hundred metres to

519

several thousand meters. The minimum thickness was 150 m and

520

observed in more than 6 layers, while the maximum thickness was 5040

521

m on 30 December 2013, the long transport dust plume mixed with the

522

smoke plume on that day led to such a thick layer in this day. The thick

523

layers are mainly caused by mixing of two or more layers. As seen from

524

the Fig. 4(c), the thickness of aerosol layers most frequently distributed

525

between 0 km and 1 km, account 61% of the total. The frequency

526

decreases rapidly above 1 km, the thickness ranges from 1 km to 3 km

527

account of 30% the total, and only 9% of layers has a thickness more than

528

3 km.

529

The geometrical characteristics of the aerosol layers in different area

530

have both similarities and differences. Mattis et al. (2008) found that

531

layers with bottom between 4 and 12 km seldom occurs. The bottom

532

height below 2 km and top height at or above 2 km accounted for 54%

533

and 96% in their study. Geometrical thickness less than 2 km accounted

534

for 48% of all the cases. In South Africa, approximately 72% of aerosol

535

layers occurred at heights greater than 1500 m and the PBL height can be

536

even larger than 3.5 km (Giannakaki et al., 2015). A study in the Amazon

537

Basin during both wet and dry seasons showed, top heights mostly

538

accumulated between 2 and 3 km during the wet season presented in 2008,

539

while a broad distribution was found for the dry season, with most top

540

height between 3 and 5 km (Baars et al., 2012).

541

3.3 Optical characteristics

542

Fig. 5 shows the AOD, mean backscatter coefficient and mean

543

frequency distribution of the aerosol layers in the FT, and the frequency

544

distributions of optical properties were shown in Table 2. Approximately

545

67% of the aerosol layers are optically thinner than 0.1, this finding is

546

consistent with the fact that most of the aerosol layers in the free

547

troposphere are optically thin at wavelength of 500 nm (Mattis et al.,

548

2008). The AOD of aerosol layers larger than 0.1 and less than 0.2

549

account for 23% of the total, and the AOD larger than 0.2 account for

550

10%. Larger AOD values were also accompanied by larger

551

which indicates the contribution of non-spherical particles.

values,

552 553 554 555

Fig. 5. Frequency distributions of the (a) AOD, (b) mean backscatter coefficient and (c) mean of free-tropospheric layers and the cumulative distribution function

556

(black trace). The red, blue and green histograms are represent the AOD, mean backscatter coefficient and mean of aerosol layers, respectively. The Y-axis on the

557

left is corresponding to the histogram and Y-axis on the right is for the black trace.

558

Tabel 2

559

Frequency distributions of AOD, backscatter coefficient and AOD

Frequency

of the aerosol layers.

Backscatter coefficient (Mm-1sr-1)

Frequency

Frequency 0.01~0.10

67%

0.16~4

87%

0~0.1

46%

0.10~0.20

23%

4~13.0

13%

0.1~0.2

29%

0.20~0.45

10%

0.2~0.35

25%

560 561

The minimum layer mean backscatter coefficient is 0.16 Mm-1sr-1,

562

and the maximum layer mean backscatter coefficient is 12.87 Mm-1sr-1. It

563

should be noted that the result of layer mean backscatter coefficient must

564

be greater than 0.15 Mm-1sr-1, because 0.15 Mm-1sr-1 is used as the

565

additional threshold values for backscatter coefficient. The backscatter

566

coefficient mostly ranges between 0.16 and 4 Mm-1sr-1, accounting for

567

approximately 87% of the total, and the backscatter coefficient larger than

568

4 Mm-1sr-1 accounts for 13%.

569

The cases with

less than 0 or larger than 0.35 are not taken into

570

account in the analysis. In Fig. 5(c), the frequency decreases as the

571

increases. The results show that 46% of the layer mean

572

29% are between 0.1 and 0.2, and 25% are greater than 0.2, which

573

implies that non-spherical particles make a considerable contribution to

574

the FT aerosol loading. In addition, 366 layers are counted in the

575

statistical analysis, which are less than the number of total layers.

576

Because the

577

noise in the perpendicular polarization channel.

578

3.4 Cluster analysis

are below 0.1,

of some layers is unavailable due to the large signal

579 580 582

Fig. 6. Cluster analysis of the backward trajectories based on daily 72-h backward trajectories at an arrival height of 2000 m. Four clusters were identified, and the frequency of each cluster (1–4) is given.

583

For most of the layers ranging from 1 km to 4 km, the arrival height

584

of 2 km is suitable for backward trajectories analysis. A cluster analysis

585

(offline version of HYSPLIT) based on HYSPLIT backward trajectories

586

for Wuhan with arrival times of 0000, 0600, 1200 and 1800 UTC for each

587

day was performed from January to December 2013. Four clusters were

588

identified as shown in Fig. 6. We found that 32% of the air masses come

589

from adjacent areas; 11% of the air masses come from the northwest of

590

China and are mostly dust aerosols; 30% of the air masses come from

581

591

north of Wuhan, which contains many big cities; and 27% of the air

592

masses come from the south of China. It seems that lower altitudes are

593

more affected by local sources. Lu et al. (2018) showed that

594

approximately 60% of the aerosols distributed over central China at 1500

595

m originated from local areas based on a cluster analysis.

596

3.5 Monthly variations of AOD

597

598 599

603

Fig. 7. Monthly variations of AOD0-7 and AOD2-7 observed by lidar. The AOD0-7 (in red) and AOD2-7 (in blue) are integrated aerosol extinction coefficients from ground to 7 km, and 2 km to 7 km, respectively. The error bar indicates the standard deviation. Noting that our lidar has a complete overlap from 0.36 km, the extinction coefficient at 0.36 km is used as the extinction coefficient below 0.36 km.

604

Tabel 3

605

Seasonal mean values of AOD0-7, AOD2-7 and ratio of AOD2-7 to AOD0-7

600 601 602

Season

AOD0-7

AOD2-7

AOD2-7/AOD0-7

Spring

0.42

0.11

26%

Summer

0.32

0.08

25%

Autumn

0.52

0.08

15%

Winter

0.63

0.09

14%

606 607

The monthly AOD2-7 and AOD0-7 are presented in Fig. 7. The aerosol

608

extinction coefficient is integrated from 2 km to 7 km, and from ground

609

to 7 km to get the AOD2-7 and AOD0-7, respectively. The hourly AOD are

610

averaged daily and then averaged monthly to obtain the monthly mean

611

AOD. The AOD2-7 can roughly represent the AODs in lower FT while 2

612

km is slightly higher than the maximum PBL height. In order to decrease

613

the effects of incomplete overlap, the extinction coefficient below 0.36

614

km is treated as constant and equals to the value at 0.36 km, which has

615

negligible influences from the incomplete overlap. The AOD0-7 is slightly

616

smaller than the real values while the extinction coefficient in ground is

617

often larger than that at 0.36 km. In Table 3, the seasonal mean values of

618

AOD0-7, AOD2-7 and the ratio of AOD2-7 to AOD0-7 are shown. The

619

seasonal mean AOD0-7 was approximately 0.42 (spring), 0.32 (summer),

620

0.52 (autumn), and 0.63 (winter), while the seasonal mean AOD2-7 was

621

0.11 (spring), 0.08 (summer), 0.08 (autumn), and 0.09 (winter), and the

622

contribution of AOD2-7 to AOD0-7 in each season are 26%, 25%, 16% and

623

14%, respectively. The seasons with mean PBL height from highest to

624

lowest are summer, spring, autumn and winter, respectively; thus, the real

625

values of free tropospheric AOD are slightly larger in the season with

626

lower PBL height. The large AOD values in winter and autumn are

627

reasonable, because of poor dissipation conditions. The good dissipation

628

conditions in spring and summer are among the reasons for the lower

629

AOD values, while the higher PBL height dilutes the aerosol

630

concentrations and good for diffusion. Another important reason is the

631

stronger scavenging processes because more rain occurs in spring and

632

summer. The monthly mean contribution of AOD2-7 to AOD0-7 was

633

approximately 13% to 31%, and the contribution is smallest in January

634

and largest in April. The contribution of AOD2-7 to AOD0-7 exceeds 20%

635

except in January and from October to December, because the AOD of

636

PBL is so high in those months.

637

3.6 Seasonal backscatter coefficient profile

638

639 640 641

Fig. 8. Seasonal averaged backscatter coefficient profile with a spatial resolution of 150 m. The green, red, black and blue lines represent spring, summer, autumn and winter, respectively.

642

Fig.8 shows the seasonal averaged profiles of the backscatter

643

coefficient during 2013. The hourly backscatter coefficient profiles are

644

averaged for different seasons, and smoothed with a vertical window

645

length of 600 m. The data were split into four seasons: spring (March–

646

May), summer (June–August), autumn (September–November) and

647

winter (December–February). The seasonal profiles of backscattering

648

coefficients have distinctly different shapes in PBL. The PBL height in

649

summer was obviously higher than that in other seasons and lowest in

650

winter. The PBL backscatter coefficient ranged from large to small in

651

winter, autumn, spring, and summer, and the mean PBL height opposite

652

to that. Because the higher PBL height dilute the aerosol concentration

653

and good for diffusion. Winter has the highest backscatter coefficient

654

values below 1 km, which was two or three times that in the other seasons,

655

because the haze often appeared in January 2013. The backscatter

656

coefficient even reached 40 Mm-1sr-1 at 0.36 km on 10 January, caused by

657

haze.

658

However, the mean backscatter coefficient profiles not show

659

significant seasonal characters in FT. The mean backscatter coefficient

660

decreased rapidly above PBL, and decrease to a small value at 4 km and

661

change slightly above 4 km in each season. The results indicate the

662

aerosols are concentrated below 4 km, consistent with our statistics of

663

aerosol layers geometrical distribution. The mean aerosol backscatter

664

coefficient was largest between 1 km and 1.6 km in autumn, while mean

665

aerosol backscatter coefficient was largest between 1.5 km and 3.8 km in

666

spring.

667

3.7 Classification of aerosol layers

668 669 670 671 672 673 674

Fig. 9. Seasonal classification of the aerosol layers based on particle depolarization ratio. The blue, red, and green histograms represent the frequency of spherical particles, mixed particles and non-spherical particles, respectively. The X-axis represent the season, and Y-axis represent the frequency. Noting that the number of layers in classification is 366, which is less than the total number of aerosol layers (402).

675

It must be mentioned again that the error of

is larger under the

676

condition with large

677

mentioned in section 2.2. A simple classification based on

is used to

678

obtain the seasonal characteristics as shown in Fig.9. A

value of

679

approximately 0.08-0.10 is usually attributed to a mixture of dust and

680

spherical particles, such as biomass burning (Murayama et al., 2003;

681

Sugimoto and Lee, 2006). Aged biomass burning and marine aerosols

682

also exhibit much smaller

683

2009). Groß et al. (2011) measured biomass burning aerosols with

684

different amount of dust layer with mean values of 0.12 <

685

pure dust layers had mean values of 0.25 to 0.33 at 532 nm. Thus, the

686

layers are divided into three types by the

687

0.12 are considered spherical particles, including industrial, urban

688

pollution, traffic emissions and biomass burning; values more than 0.12

689

and less than 0.25 are considered to be mixed particles; and values equal

690

to or more than 0.25 are considered to be non-spherical particles,

691

including dust and ash. The primary source of non-spherical particles in

692

Wuhan is the dust, so the mixed particles and the non-spherical particles

693

can be also called mixed dust and pure dust, respectively.

and small backscatter ratio, which have been

(Liu et al., 2008; Freudenthaler et al.,

< 0.2, while

: values equal to or below

694

The marine aerosol and pollen are not taken into account because

695

marine aerosol and pollen don’t play a significant role in regional aerosol

696

loadings. Since the lack of further instrumentation which can provide

697

lidar ratio and other information for detailed aerosol target classification.

698

Thus, we cannot separate the aerosol components in the detected layers.

699

However, it still provides some useful information about aerosol mixing

700

state.

701

7% of the layers have

values larger than 0.3, which indicate

702

certain observed dust layers are not mixed with other aerosols or undergo

703

hygroscopic swelling over long-range transport (Murayama et al., 1996;

704

Sassen, 2000). The backward trajectories show that these dust layers were

705

transported at high altitude or crossed area with less urban pollution.

706

Spherical particles, mixed particles and non-spherical particles account

707

for 45%, 39% and 16%, respectively. Most of aerosol layers with higher

708

mean backscatter coefficient are mixed particles or non-spherical

709

particles, and there are a lot of layers with high backscatter coefficient in

710

autumn and winter.

711

Therefore, non-spherical particles is one of the main sources in the

712

FT in Wuhan. As shown in Fig.9, mixed particles and non-spherical

713

particles often appear in spring, autumn and winter, which account for

714

13%, 29% and 24% of the 366 layers, respectively, while only 2% appear

715

in summer. The mean

716

autumn and winter are 0.22, 0.06, 0.15 and 0.14, respectively. This

717

finding is consistent with the results of Liu et al. (2008) and Lu et al.

718

(2018), who showed that southeast China is mainly affected by dust

of aerosol layers during spring, summer,

719

transported from northwest in spring, autumn and winter. The reason is

720

that, the monsoon changes from northwest to southeast in summer while

721

the dust plumes are mainly transported from northwest. The mean

722

backscatter coefficient of aerosol layers during spring, summer, autumn

723

and winter are 1.8 ± 1.4 Mm-1sr-1, 2.3 ± 2 Mm-1sr-1, 2.8 ± 2.7 Mm-1sr-1

724

and 2.3 ± 2.2 Mm-1sr-1 ， respectively. Although the non-spherical

725

particles or mixed particles appear rarely in summer, the mean

726

backscatter coefficient of layers is also very large in summer, indicates

727

the

728

contribution of background aerosols to free tropospheric AOD could not

729

be ignored even though the aerosol layers can contribute more than 90%

730

of the aerosols. It is the reason why the mean backscatter coefficient of

731

aerosol layers was largest in autumn but the seasonal mean AOD2-7 in

732

autumn was equal to that in summer.

733

4. Summary and conclusions

considerable

contribution

of

anthropogenic

pollution.

The

734

Approximately 2760 hours data were obtained during ground-based

735

lidar observations at Wuhan, China, in 2013. The geometrical

736

characteristics of the aerosol layer (bottom, top height and thickness) and

737

the optical characteristics (AOD,

738

are statistically analysed. Approximately 68% of the layer’s bottom

739

height are below 2 km, while 76% of the layer’s top height ranges from 1

740

km to 4 km. In addition, 61% of the layer’s thickness are less than 1 km.

, and mean backscatter coefficient)

741

Most layers are optically thin layers less than 0.1 and have small

742

values. The

743

than 0.2 account for 54%. The layer mean backscatter coefficient most

744

frequently ranges between 0.14 and 4 Mm-1sr-1, which accounts for

745

approximately 82% of the total.

of layers below 0.1 account for 46%, and

greater

746

The seasonal characteristics are presented and compared with that of

747

other locations, such as Brazil, South Africa and Europe. The aerosol

748

layers show little seasonal characteristics in geometrics, but show

749

moderate seasonal characteristics in optical properties. The seasonal mean

750

AOD0-7 are approximately 0.42 (spring), 0.32 (summer), 0.52 (autumn),

751

and 0.63 (winter). The mean

752

autumn and winter are 0.22, 0.06, 0.15 and 0.14, respectively. Mixed

753

particles with large

754

lot of layers has high mean backscatter coefficient in autumn and winter.

755

The vertical backscatter coefficient decreased rapidly above PBL, then

756

decrease to a small value at 4 km, indicate the aerosols are concentrated

757

mainly below 4 km.

758 759 760 761 762

Acknowledgements

of aerosol layers during spring, summer,

often appear in spring, autumn and winter, and a

763

The authors gratefully acknowledge the NOAA Air Resources

764

Laboratory for the HYSPLIT transport and dispersion model used in this

765

publication and the University of Wyoming for providing the radiosonde

766

data.

767 768 769 770 771 772 773 774 775 776 777 778 779 780 781

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782

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Declaration of interests 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. ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: