Sediment record of polycyclic aromatic hydrocarbons in Dianchi lake, southwest China: Influence of energy structure changes and economic development

Journal Pre-proof Sediment record of polycyclic aromatic hydrocarbons in Dianchi lake, southwest China: Influence of energy structure changes and economic development Xiaohua Ma, Hongbin Wan, Juan Zhou, Duan Luo, Tao Huang, Hao Yang, Changchun Huang PII:

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https://doi.org/10.1016/j.chemosphere.2020.126015

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CHEM 126015

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Received Date: 2 December 2019 Revised Date:

9 January 2020

Accepted Date: 22 January 2020

Please cite this article as: Ma, X., Wan, H., Zhou, J., Luo, D., Huang, T., Yang, H., Huang, C., Sediment record of polycyclic aromatic hydrocarbons in Dianchi lake, southwest China: Influence of energy structure changes and economic development, Chemosphere (2020), doi: https://doi.org/10.1016/ j.chemosphere.2020.126015. 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.

Credit Author Statement Dear Editor, I have made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of date for the work. I states that there is no plagiarism, data fraud, data tampering and other dishonesty.

Sincerely Changchun Huang, Xiaohua Ma

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Sediment record of polycyclic aromatic hydrocarbons in Dianchi Lake, southwest China:

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Influence of energy structure changes and economic development

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Xiaohua Maa,b, Hongbin Wana,b, Juan Zhoua,b, Duan Luoa,b, Tao Huang a,b,c,d, Hao Yang a,b,c,d,

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and Changchun Huanga,b,c,d*

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a

8

b

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Development and Application, Nanjing Normal University, Nanjing 210023, PR China

School of Geography Science, Nanjing Normal University, Nanjing 210023, PR China Jiangsu Center for Collaborative Innovation in Geographical Information Resource

10

c

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Education, Nanjing 210023, PR China

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d

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Province), Nanjing 210023, PR China

Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of

State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu

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*Corresponding author: Changchun Huang

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School of Geography Science, Nanjing Normal University, Nanjing 210023, PR China

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Tel: +86-10-62732006; Fax: +86-10-62731016.

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E-mail: [email protected]; [email protected].

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Sediment record of polycyclic aromatic hydrocarbons in Dianchi Lake, southwest China:

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Influence of energy structure changes and economic development

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Abstract: Sixteen polycyclic aromatic hydrocarbons (PAHs) in a sediment core from Dianchi

5

Lake, southwest China, were analysed. The influence of changes in China’s energy structure

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for 2–6 ringed PAHs was investigated to assess sources and the impact of socioeconomic

7

development on temporal changes in concentrations. The concentration of the ΣPAH16 ranged

8

from 746–2293 ng g-1. Prior to the 1960s relatively low concentrations of the ΣPAH16 and a

9

larger proportion of 2–3-ring PAHs indicated that biomass combustion was the main source of

10

PAHs. A rapid increase in the concentrations of 2–3 ring PAHs between 1975 and 2004 was

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attributed to population growth and coal consumption. A declining trend since 2004 was

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interpreted as being due to local changes in household energy usage. Increased concentrations

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of 4-ring PAH between 1975–2005 and 5–6-ring PAHs between the 1980s to 2004 showed

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correlations with increased coal consumption and the number of motor vehicles, respectively.

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These were caused by rapid urbanization and industrialization in the Dianchi watershed

16

following the implementation of the Reform and Open Policy in 1978. A subsequent decline

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in the concentrations of 4-ring and 5–6-ring PAHs may have been due to decreased coal

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consumption and improvements in emission standards, respectively. Source apportionment by

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a PMF model revealed that coal combustion (29.2%), vehicle emissions (24.2%), petrogenic

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sources (21.8%), and biomass combustion (24.9%) were the sources of PAHs in the lake

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sediment core, and that coal combustion was the most important regional source of PAHs

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pollution.

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Keywords: PAHs, lacustrine sediment, economic parameters, source apportionment, Dianchi

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Lake.

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1. Introduction 1

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Sixteen PAHs have been listed as priority control pollutants by the U.S. Environmental

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Protection Agency due to their carcinogenic, teratogenic, and mutagenic characteristics (Zhou

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et al., 2018; Dushyant et al. 2016; Wang et al., 2010; Pietzsch et al., 2010). PAHs are mainly

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sourced from the combustion of wood and coal as well as from coking and natural gas

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combustion (Donahue et al., 2006; Lin et al., 2015; Mai et al., 2003; Ma et al., 2017).

32

Therefore, PAHs are not only environmental contaminants, but also act as major indicators of

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anthropogenic influences since they are closely related to human activities (Viguri et al., 2002;

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Saldarriaga-Norena et al., 2015; Yuan et al., 2017; Xiao et al., 2014). Over the past

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several decades,

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Dianchi Lake basin have resulted in the discharge of many contaminants into the lake, and

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PAHs have become the main pollutant (Huang et al. 2007; Weng et al., 2016).

rapid

population

growth

and

economic

development

in

the

38

The Chinese government and experts take pollution by PAHs seriously. Previous studies

39

have reported that economic growth generally depends on energy consumption, and that

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contamination by PAHs can indicate human activity associated with economic growth

41

(Hafner et al., 2005; Itoh et al., 2010; Liu et al., 2013; Liu et al., 2009; Hartmann et al., 2005;

42

Zhang et al., 2016). In 2004, emissions of PAHs in China (up to 114 Gg) accounted for an

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estimated 22% of total global PAHs emissions (Lin et al., 2011; Zhang et al., 2009). Xu et al.

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(2006) reported that coal burning, biomass combustion, and the coking industry were

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responsible for 60%, 20%, and 16% of PAHs emissions in China, respectively. Several studies

46

have reported that from 2001 to 2005, the Liaohe River, northeast China, transported an

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average of 20 million tons of sediments to the Bohai Sea annually (Ma et al., 2017). Lin et al.

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(2011) found that petroleum residue was the main source of PAHs in coastal area of Bohai 2

49

Bay, and Liu et al. (2012) reported that the concentrations of the ΣPAH15 (excluding NAP) in

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sediment cores from the Yellow Sea were generally higher than of those in the South China

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Sea. Moreover, Guo et al. (2011a) reported that the incomplete combustion of wood and coal

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was the main source of PAHs to a sediment core from Lake Baiyangdian, northern China.

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Many studies have been undertaken to investigate the concentrations, temporal changes, and

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sources of PAHs, in addition to analyses of correlations between PAHs and

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socioeconomic development (Boonyatumanond et al., 2007; Hu et al., 2011; Li et al., 2001;

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Zhang et al., 2009; Ma et al., 2018; Jiang et al., 2016), but few studies have investigated the

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impact of life style and energy use structure on the concentrations of PAHs. Therefore, the

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evaluation of the implications of anthropogenic activities on the historical changes of PAHs

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could be useful for understanding the factors related to the historical changes of PAHs

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emissions.

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The present study aims to investigate whether the sedimentary record of PAHs is related

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to social and economic parameters (population size and energy consumption) in Dianchi Lake,

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southwest China, as a means of speculating on the possible factors affecting the temporal

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changes in PAHs in this region and to confirm chronological changes of PAHs sources in

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Dianchi Lake sediments.

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2. Material and methods

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2.1 Sampling and study region

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Dianchi Lake (24° 40′–25° 03 ′N, 102° 37′–102° 48 ′E) is located to the southwest of

3

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Kunming on the Yunnan-Guizhou Plateau in Yunnan Province, China (Fig. 1). The Dianchi

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Lake basin covers an area of 2920 km2. The lake has an average depth of 4.7 m, and is ~40

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km in length (from north to south) and 12.5 km wide (Du et al., 2011; Gu et al., 2017).

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A sediment core (102.67° E, 24.69° N) was collected in July 2014 from the eastern part

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of the lake using a gravity sampler with an internal diameter of 8.3 cm (Fig. 1). The sediment

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core (39 cm long) was cut into 1 cm segments, and each section was sealed in polythene bags

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at −4 °C and transported to the laboratory, where they were stored at −50 °C until further

76

analysis for PAHs. Samples from the sediment core were also used for the determination of

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excess 210Pb and 137Cs dating.

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Fig. 1 Location of a) Yunnan Province in China, b) Dianchi Lake in Yunnan Province, and c)

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the sampling site in Dianchi Lake.

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2.2 Microwave extraction and analysis by GC-MS

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Detailed descriptions of methods use for the extraction and cleaning of samples are 4

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provided in our previous studies (Ma et al. 2018; Zhang et al. 2017). Firstly, microwave

84

extraction with 25 ml hexane/acetone (1:1, v/v) solution. Secondly, each extract was then

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centrifuged three times with 20 mL hexane/acetone (1:1, v/v) solution. Thirdly, each

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concentrated extract was reduced to 1 mL. Fourthly, the extracts were purified with a 50 mL

87

solution of hexane/acetone (1:1, v/v). Finally, the extracts were reduced again to 1 mL and a

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Shimadzu QP2010 Plus GC-MS was used to analyse the concentrations of PAHs. (Ma et al.

89

2018).

90

The 16 types of PAHs that were analysed in the present study: NAP, ACE, ACY, FLO,

91

PHE, ANT, FLA, PYR, BaA, CHR, BbF, BKF, BaP, DahA, IcdP, and BghiP.

92

2.3 Quality assurance and quality control

93

All glassware was cleaned and then dried at 450 °C for 6 h. Strict quality control

94

procedures were used to analyse the data. Method blanks (solvent), spiked blanks (standards

95

spiked into the solvent), sample duplicates, and a sample from the National Institute of

96

Standards and Technology standard reference material (SRM 1941) were processed. An

97

internal calibration method based on a five-point calibration curve was used to quantify the

98

concentrations of the 16 PAHs. A mixture of five deuterated PAHs (naphthalene-d8,

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acenaphthened10, phenanthrene-d10, chrysene-d12, and perylene-d12) were used as recovery

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standards to monitor the matrix effects and procedural performance. The results were

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corrected for each solvent blank. The recovery of the surrogate standards added to the

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sediment samples varied from 76% to 107% (mean 83%). The variation of the PAH

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concentrations in the duplicates was < 15%. The detection limits were approximately three 5

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times larger than the signal-to-noise ratio for the blank samples, and ranged from 0.172 ng/ml

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to 1.23 ng/ml for the individual PAH compounds (Ma et al. 2018). All concentrations were

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reported on a dry weight basis and were not corrected for surrogate recoveries.

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2.4 Sediment core dating

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The dating of each sediment core was based on the activity of 210Pb. Briefly, the activity

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of 210Pb and 226Ra in the samples was measured using an Ortec HPGe GWL series, well-type,

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coaxial, low background, intrinsic germanium detector. The activities of 210Pb and 226Ra were

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determined from gamma emissions at 46.5 keV and 295 or 352 keV, respectively. These were

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emitted in gamma rays by the daughter isotope (214Pb), which was stored for three weeks prior

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to dating in sealed containers to enable radioactive equilibration. Unsupported 210Pb (210Pbex)

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was calculated as the difference between the measured total

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estimated supported

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[210Pbex = 210Pbtot-214Pb] (Ma et al. 2018; Huang et al. 2018).

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2.5 Positive matrix factorization (PMF) model for source apportionment

210

210

Pb at 46.5 keV and the

Pb activity, which was determined by the parent nuclide at 351 keV

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The PMF model is widely used for source apportionment and does not require source

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profile data (Zhang et al., 2012; Li et al., 2017). The model is an advanced multivariate factor

120

analysis method based on weighted least squares that was developed in 1994 (Paatero et al.,

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1994). The PMF model not only accounts for uncertainty of variables but also makes sure that

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all values are positive; thus, it is particularly suitable for environmental data (Comero et al.,

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2014). The United States Environmental Protection Agency PMF version 5.0 user guide 6

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introduces the model in detail (US EPA, 2014). In theory, the PMF model can be described by

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the Equation (1): p

xij = ∑ gik f kj + eij

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(1)

j =1

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where xij is the concentration of the ith species, which was determined by the jth sample; gik is

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the ith species concentration, which was detected in source k; fkj is the contribution of the kth

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source to the jth sample; and eij is the error for species j to sample i (Wang et al., 2015; Hopke,

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2003). The objective function Q(E) of the PMF model is defined by Equation (2): n

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m

p

2

Q ( E )= ∑∑ [( xij − ∑ g ik k kj ) / sij ] i =1 j =1

(2)

k =1

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where, Q(E) is the weighted sum of the squares for the difference in value between the

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original data set and the PMF output (Lin et al., 2013); and sij is the uncertainty in the jth PAH

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to sample i (Wang et al., 2016). Details of sij are provided by Yu et al. (2015).

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In the PMF model, all values of the uncertainly file are required to calculate the

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confidence level, and the concentration and uncertainly file should be positive for each value.

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Therefore, half of the detection limits took the place of the below detection limits data. In this

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study, the values of the uncertainly matrix were estimated by the respective equation in the

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user guide (US EPA, 2014).

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3. Results and discussion

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3.1 Historical changes of PAHs and their relationship with economic parameters

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Historical changes in the concentrations of PAHs in this study followed the general

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temporal trends of socioeconomic development data in Yunnan Province (Fig. 2) (Yunnan

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Statistical Yearbook, 2015). Due to the limitation of historical statistics, we only used GDP,

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total population, and rural population data from 1952–2014; total energy consumption, coal

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consumption, and petroleum consumption data from 1975–2014; natural gas consumption

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data from 1979–2014; the number of motor vehicles from 1990–2014.

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The concentration of the sum of the 16 PAHs (hereafter ΣPAH16) ranged from 746–2294

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ng g-1 (mean 1364 ng g-1). Fig. 2 shows a change in concentrations at the bottom of the

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sediment core (before the mid-1920s), which might reflect background PAHs concentrations.

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From 1926–1944, the ΣPAH16 decreased from 1143 ng g-1 to 842 ng g-1, which was probably

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due to the destruction of China's economy during World War II (1937–1945). (Guo et al.,

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2006; Ma et al., 2017). From 1945–1952, the ΣPAH16 increased rapidly from 842 ng g-1 to

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1428 ng g-1, which may have related to the reconstruction work and socioeconomic

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development following the establishment of the People’s Republic of China in 1949 (Liu et al,

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2012). During the period from 1952–1960, the ΣPAH16 decreased from 1428 ng g-1 to 746 ng

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g-1, which corresponded to a low level of GDP (12 × 102 to 25 × 102 million yuan in Yunnan

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from 1952–1960). During this period, the so-called Great Proletarian in China from

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1958–1960 might have affected socioeconomic development, and could have led to the

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decreased concentration of ΣPAH16 (Liu et al., 2005). The fluctuation in PAHs in the sediment 8

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core during the 1960s and 1970s corresponded with the Cultural Revolution in China from

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1966–1976, which influenced agricultural and industrial production in the region. A slight

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increase in GDP was also observed within this period (25 × 102 to 49 × 102 million yuan from

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1960–1976).

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Fig. 2 Historical changes in the concentration of the sum of 16 polycyclic aromatic

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hydrocarbons (ΣPAH16) in the sediment core from Dianchi Lake. a) ΣPAH16 and gross

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domestic product (GDP as 100 million yuan) in Yunnan Province, (ΣPAH16 = 0.43 ∗ GDP

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+1127, R2 = 0.7 (1978–2004)). b) ΣPAH16 and total energy consumption (104 Tons standard

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coal) (TEC) in Yunnan Province, (ΣPAH16 = 0.33 ∗ TEC +777, R2 = 0.72 (1978–2004)). GDP

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and total energy consumption data is from the Yunnan Statistical Yearbook (2015).

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The Cultural Revolution in China—especially the implementation of the Reform and

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Open Policy in 1978—led to the recovery of China's economy and a rapid increase in energy

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consumption as a result of urbanization and industrialization (Statistics Data on 65 Years of

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New China, 2014), which in turn caused a rapid increase in PAHs emissions (Liu et al, 2012;

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Xu et al, 2006). The Yunnan Province has also experienced rapid economic development in

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recent decades. This is especially the case for the Dianchi Lake region, which has the greatest

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level of industrialization and urbanization in the Dianchi basin. Fig. 2 shows a high

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correlation between the ΣPAH16 and GDP (correlation coefficient of 0.7, Fig. 2a) and the

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ΣPAH16 and total energy consumption (correlation coefficient of 0.72, Fig. 2b) between 1978

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and 2004. The concentration of the ΣPAH16 exhibited a sharp increase from 941 ng g-1 to 2294

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ng g-1 between 1978 and 2004, which was consistent with the dramatic increase in the level of

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GDP and total energy consumption during this period. Fig. 2 illustrates the increase in GDP

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from 69 × 102 to 3082 × 102 million yuan, and the increase in total energy consumption

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(Yunnan Statistical Yearbook 2015) from 1066 × 104 to 5210 × 104 Tons standard coal from

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1978–2004, which were associated with the fast economic growth that followed the initiation

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of the Reform and Open policy in 1978 (Hu et al., 2011; Liu et al., 2012; Ma et al., 2017).

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Despite the rapid and continuous economic development in the Yunnan Province since 2004,

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the concentration of the ΣPAH16 did not increase further (Fig. 2). Instead, the concentration of

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the ΣPAH16 in the sediment core showed a decreasing trend from the subsurface maximum

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until the time of sampling/2014 (Fig. 2). This phenomenon has also been reported in lake

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sedimentary records in other areas of our country, such as the sedimentary record in Chaohu

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Lake (Li et al., 2016), the sedimentary record in Qinghai Lake (Guo et al., 2010) and the 10

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sedimentary record in the Hongfeng Lake (Guo et al., 2011c), but it is different from the

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sedimentary records of Erhai Lake (Guo et al., 2011b), Bosten Lake and Sugan Lake (Guo et

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al., 2010). This may be attributed to government regulations and energy structure change

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(Guo et al., 2013; Li et al., 2013; Yunnan Statistical Yearbook 2015).

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The Chinese government has recognized the seriousness of environmental degradation in

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the past few decades and has taken active measures to mitigate the negative effects on the

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environment. For example, measures to establish wastewater treatment plants to prevent

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industrial sewage and urban sewage from being directly discharged into Dianchi Lake, and

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the construction of a pipeline around the lake to prevent diffuse pollution (Ouyang et al. 2015;

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Tuo et al. 2002; Xing et al. 2005). Although the total standard coal consumption increased

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from 834 × 104 to 3298 × 104 Tons standard coal between 1978 and 2004, the percentage

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proportion of total coal consumption to overall energy consumption decreased, whereas that

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of petroleum increased (Fig. 3; Yunnan Statistical Yearbook 2015). This is probably another

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reason for the decreased concentration of ΣPAH16 in recent years because coal burning and

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biomass burning are the major sources of PAHs emissions in Yunnan Province (Xu et al,

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2006).

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210 211

Fig. 3 Historical changes in the percentage contributions of coal, and petroleum and natural

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gas to the total energy consumption in Yunnan Province (Yunnan Statistical Yearbook, 2015).

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3.2 Vertical changes of different PAH rings and their relationship with economic

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parameters

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The vertical distributions of the concentrations of different PAH rings in the Dianchi

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Lake sediment core are presented in Fig. 4. The concentrations of 2–3-rings, 4-ring, and

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5–6-ring PAHs varied from 528–12108 ng g-1 (Fig. 4a, b), 102–533 ng g-1 (Fig. 4c), and

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107–554 ng g-1 (Fig. 4d), respectively. LMW PAHs of 2–3-rings dominated all ring numbers,

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and comprised between 47.8–77.5% of the total 16 PAHs. This was especially the case prior 12

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to the 1960s, when the vertical change was consistent with the ΣPAHs (Fig. 4). Before the

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2000s, the household energy usage structure in Yunnan Province was dominated by coal and

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biomass, in particular, biomass combustion was used for household cooking and basic heating

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purposes (Zhang et al., 2007). The correlation coefficients between the data shown in Fig. 4a

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and 4b for i) 2–3-ring PAHs and the total population was 0.79, and ii) 2–3-ring and coal

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consumption was 0.76 between 1975 and 2004. The results from this study indicate that the

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increases in LMW (i.e., 2–3-ring) PAHs before 2004 were consistent with the ever-increasing

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population and coal consumption (Fig. 4a, b), thus suggesting that the household energy usage

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structure was a major factor impacting the concentrations of PAHs, especially LMW PAHs

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during this period. However, despite an increase in the population since 2004, the

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concentrations of LMW PAHs declined (Fig. 4a). Simultaneously, there was a decrease in the

232

proportion of energy derived from coal consumption (63.3–43.1%) and an increase in the

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proportion of energy from petroleum (11.1–14.7%) (Fig. 3). This may be a reason for the

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more recent reduced concentration of LMW PAHs (Fig. 4a, b), because rapid socioeconomic

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development infers a dramatic change in lifestyles, especially with respect to the substitution

236

of coal combustion and biomass by cleaner energy (Liu et al., 2012).

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238 239

Fig. 4 Vertical distributions of the concentrations of 2–3-rings (2R), 4-rings (4R), 5–6-rings

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(5R) in the Dianchi Lake sediment core, compared with a) total population (P) (2R = 0.43 ∗ P 14

241

-719, R2 = 0.79 (1975–2004)), b) coal consumption (CC) (2R = 0.26 ∗ CC +457, R2 = 0.76

242

(1975–2004)), c) coal consumption (4R = 0.12 ∗ CC +150, R2 = 0.94 (1975–2005)), and d)

243

the number of civil motor vehicles (CMV) (5R = 5.5 ∗ CMV +243.9, R2 = 0.80 (1990–2004))

244

(Yunnan Statistical Yearbook, 2015).

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The concentrations of 4-ring PAHs were relatively low (< 300 ng g-1) before 1985,

246

between 1898 and 1960 the concentration decreased from 267 ng g-1 to 1027 ng g-1, but

247

subsequently increased considerably to a maximum of 5337 ng g-1 by 2005 before decreasing

248

to 271 ng g-1 in 2014. In general, coal combustion emits more levels of 4-ring PAHs than the

249

burning of petroleum products or natural gas (Lin et al., 2011, 2012; Ma et al., 2017;

250

Ravindra et al., 2008; Tang et al., 2015), and this was evident in our data. Fig. 4c shows that

251

the increasing trend of 4-ring PAHs in the sediment core from Dianchi Lake is in general

252

agreement with the increasing trend of coal consumption. In fact, the correlation coefficient

253

between the concentration of 4-ring PAHs and coal combustion reached 0.94 between 1975

254

and 2005.

255

Comparison of the concentrations of 5–6-ring PAHs with the number of motor vehicles

256

may reflect a relationship between PAHs pollution and vehicle exhaust emissions (Yunnan

257

Statistical Yearbook, 2015). As shown in Fig. 4d, the rapid increase in the concentration of

258

5–6-rings PAHs (from 195 ng g-1 to 554 ng g-1) generally agreed with the increase number of

259

automobiles (from 10 × 104 to 59 × 104) during the 1980s until 2004. Moreover, the

260

correlation coefficient between 5–6-ring PAHs and the number of motor vehicle was 0.80

261

between 1990 and 2004. However, as the number of motor vehicles continued to increase

262

(from 59 × 104 to 2159 × 104), the concentrations of 5–6-rings PAHs decreased (from 554 ng 15

263

g-1 to 296 ng g-1) after 2004. This may be because China adopted a series of emission

264

standards for automobiles (Tang et al., 2015). The production of PAHs from gasoline

265

automobile emissions is decidingd by a number of factors (e.g., driving style, fuel quality, and

266

engine type) (Ma et al., 2017; Ravindra et al., 2008; Westerholm et al., 1994). More

267

stringent standards for vehicle emissions contribute to the reduction of concentrations of

268

5–6-rings PAHs, but the net effect also depends on the number of vehicles in use (Tang et al.,

269

2015).

270

The change of concentrations of PAHs of different rings in the core from Dianchi Lake

271

suggests domestic coal combustion and biomass burning were the main sources of PAHs to

272

Dianchi Lake over time, and that in the last few years, vehicle exhaust emissions have also

273

contributed to PAH inputs. Concentrations of PAHs may have been mitigated by government

274

regulations and energy structure changes, which were also reported in previous studies in

275

other regions (Lima et al., 2003; Xu et al., 2006).

276

3.3 Source identification by PMF and the relationship between factor contributions and

277

economic parameters

278

Four main source components were classified, and the PMF source file is shown in Fig.

279

5. The contributions of four factors were likely associated with historical socioeconomic

280

development in Yunnan Province Fig. 6.

16

281

282 283

Fig. 5 Four main source components of sedimentary PAHs obtained using a positive matrix

284

factorization (PMF) model for Dianchi Lake. Where ACE: acenaphthene; ACY:

285

acenaphthylene; ANT: anthracene; BaA: benz(a)anthracene; BaP: benzo(a)pyrene; BbF:

286

benzo(b)fluoranthene; BghiP: benzo(g,h,i)perylene; BKF: benzo(k)fluoranthene; CHR:

287

chrysene; DahA: dibenz(a,h)anthracene; FLA: fluoranthene; FLO: fluorine; IcdP:

288

indeno(1,2,3-cd)pyrene; NAP: naphthalene; PHE: phenanthrene; PYR: pyrene.

289

Factor 1 accounted for 29.2% of the total variance and mainly comprised PHE, ANT,

290

PYR, and FLU, which are regarded as typical indicators of coal combustion (Fig. 5a) (Cao et

291

al., 2017; Dong and Lee, 2009; Ravindra et al., 2008; Yang et al., 2013). Moreover, factor 1

292

contributions were consistent with the total consumption of coal (Fig. 6a), and the correlation

293

coefficient between factor 1 contributions and the total consumption of coal reached 0.83 17

294

between 1975 and 2005; thus, factor 1 represents coal combustion.

295

Factor 2 contributed 24.2% of the total factor contributions, and was largely comprised

296

of CHR, BaP, BbF, BKF, IcdP, and BghiP (i.e., high molecular weight PAHs) (Fig. 5b). Many

297

previous studies have evidenced that BbF, BKF, BaP, and BghiP act as markers of gasoline

298

engine emissions (Yin et al., 2008; Motelay-Massei et al., 2007; Nemr et al., 2007; Wang et

299

al., 2009) and that IcdP provides an indicator of diesel emissions (Fang and Chang, 2004; Liu

300

et al., 2017; Li et al., 2003; Wang et al., 2016; Sofowote et al. 2008). Factor 2 contributions

301

were generally consistent with motor vehicle numbers (Fig. 6b), except after 2010 as a result

302

of improvements in emission standards (Tang et al. 2015). Furthermore, the correlation

303

coefficient of factor 2 contributions and the number of motor vehicles was 0.72 between 1990

304

and 2010; hence, factor 2 was attributed to traffic emissions.

305

Factor 3 explained 21.8% of the total variance and mainly comprised NAP and ACE (Fig.

306

5c) , which have been considered to be indicators of refined petroleum release or crude oil

307

leakage (Liu et al., 2017; Wang et al., 2016; Zakaria et al., 2002). Factor 3 contributions were

308

consistent with the total consumption of petroleum (Fig. 6c), and the correlation coefficient

309

between factor 3 contributions and the total consumption of petroleum was 0.68 between

310

1975 and 2010; therefore, factor 3 represents petrogenic sources.

311

Factor 4 contributed 24.9% of the total factor contribution and was comprised of ACY

312

and FLO (Fig. 5d), which have been regarded as an important indicators of wood combustion

313

(Qian et al., 2016; Khalili et al., 1995; Ramdahl, 1983; Ravindra et al., 2008). Factor 4 was

314

therefore identified as biomass burning sources. In rural areas, the household energy usage

315

structure in Yunnan Province is dominated by biomass burning, which is used mainly for 18

316

cooking and basic heating purposes (Zhang et al., 2007). Fig. 6d shows that the contribution

317

of factor 4 obtained using the PMF model changed in general agreement with changes in the

318

rural population. The correlation coefficient between factor 4 contributions and the rural

319

population was 0.58, and suggests that factor 4 represents biomass burning sources.

320

19

321 322

Fig. 6 Vertical changes of four factors obtained using the positive matrix factorization (PMF)

323

model for the Dianchi Lake sediment core, compared with a) coal consumption (CC) (factor 1

324

= 0.00043 ∗ CC + 1.275, R2 = 0.83 (1975–2005)), b) the number of civil motor vehicles

325

(CMV) (factor 2 = 0.0164 ∗ CMV + 1.41, R2 = 0.72 (1990–2010)), c) petroleum consumption

326

(PC) (factor 3 = 0.0016 ∗ PC + 0.756, R2 = 0.68 (1975–2010)), and d) rural population (RP)

327

(factor 4 = 0.000884 ∗ RP - 1.014, R2 = 0.58 (1952–2014)) (Yunnan Statistical Yearbook,

328

2015).

329

Based on the PMF results, four sources were successfully identified: 1) coal combustion

330

sources (29.2% of total factor contributions), 2) vehicle emissions (24.2% of total), 3)

331

petrogenic sources (21.8% of total), and 4) biomass combustion and contributed (24.9% of

332

total). Hence, coal combustion was identified as the dominant source of PAHs to Dianchi

333

Lake. 20

334

4. Conclusions

335

The present study determined that the concentration of the ΣPAH16 ranged from 747 ng

336

g-1 to 2294 ng g-1 (mean 1364 ng g-1) in a sediment core from Dianchi Lake, southwest China.

337

The ΣPAH16 was mainly composed of 2–3-rings PAHs The concentrations of 2–3-rings,

338

4-ring, and 5–6-ring PAHs varied from 528–1210 ng g-1 (Fig. 4a, b), 102–533 ng g-1 (Fig. 4c),

339

and 107–554 ng g-1 (Fig. 4d), respectively. LMW PAHs of 2–3-rings dominated all ring

340

numbers, and comprised between 47.8–77.5% of the total 16 PAHs. 4-rings and 5–6-rings

341

PAHs comprised between 12.4–29.9% and 9.8–27.2% of the total 16 PAHs, respectively. The

342

results of the PMF model demonstrated that coal combustion, vehicle emissions, petrogenic

343

sources, and biomass combustion were the main PAHs sources to the sediment core from

344

Dianchi Lake, but that coal combustion was the dominant source of PAHs since 1900.

345 346

Author contributions

347

C.C.H. and T.H. designed the experiments. X.H.M., H.B.W., J.Z., D.L. carried out the

348

experiments and performed the analyses. X.H.M., T.H., C.C.H., H.Y. substantially contributed

349

to interpreting the results and writing the paper.

350

Funding

351

This work was funded by the National Natural Science Foundation of China [Grant numbers

352

41673108 and 41773097], a project funded by the Priority Academic Program Development

353

of Jiangsu Higher Education Institutions.

354

Acknowledgements

21

355

We sincerely thank Yang Luo, Yang Gao, Zhili Chen and Linlin Zhang for their contributions

356

to the experiment assistance. We would like to thank Editage (www.editage.cn) for English

357

language editing.

358

References

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27

Highlights


Sixteen PAHs from Dianchi lake sediment, a plateau lake in China, were studied.




Sediment record of PAHs reflect trends in GDP and total energy consumption.




Sediment records of different PAH rings can reflect the changes of energy structure.




Coal and biomass combustion, vehicle emissions and petrogenic were the PAHs sources.




Time trend of factor contributions are consistent with economic parameters.

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: