ECOTOXICOLOGYEditor-in-Chief

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Mark Hanson, University of Manitoba, Winnipeg, Manitoba, Canada

Coordinating Editor for Special Issues & Mini-Reviews

Richard D. Handy, University of Plymouth, Devon, UK

AIMS AND SCOPE

Ecotoxicology is an international journal devoted to the publication of fundamental research in ecotoxicology. Papers published in Ecotoxicology are aimed at under-standing the mechanisms and processes whereby chemicals exert their effects on ecosystems and the impact caused at the population or community level. It is not biasedwith respect to taxon or biome. Papers that indicate possible new approaches to regulation and control of toxic chemicals and those aiding in formulating ways ofconserving threatened species are particularly welcome. The journal includes not only original research papers but also technical notes and review articles, both invited andsubmitted. A strong, broadly based editorial board ensures as wide an international coverage as possible. ‘‘Authors and Publishers desiring a critical review of their books,monograph or reports should contact the Book Reviews Editor directly’’.

EDITORIAL COVERAGE

Ecotoxicology covers a wide range of potential topics since contributions may cover effects on any ecosystem, terrestrial, freshwater or marine. Cohesiveness of thejournal is maintained through adherence to the underlying theme of quantifying the effect of toxic chemicals on populations, communities and ecosystems. The aim ofpapers should be to elucidate mechanisms and processes by which chemicals exert their effects on populations, communities and ecosystems. Studies on individualsshould demonstrate linkage to population effects in clear and quantitative ways. Laboratory studies must show a clear linkage to specific field situations. The editorialpolicy is to exclude papers that deal only with the levels of pollutants in the environment and those dealing purely with toxicity testing.

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Associate Editors

Aquatic Ecotoxicology—Diane Nacci, USEPA Atlantic Ecology Division Research Laboratory,

Narragansett, RI, USA

Avian Ecotoxicology—Miguel Mora, Texas A&M University, USA

Ecological Risk Assessment—Sean Richards, University of Tennessee at Chattanooga, USA

Genetic and Molecular Ecotoxicology—Craig A. Downs, Haereticus Environmental Laboratory Clifford,

Virginia, USAChris Theodorakis, SIU at Edwardsville, IL, USA

Invertebrate Ecotoxicology—Pawel Migula, University of Silesia, Katowice, Poland

Marine Ecotoxicology—Ross Hyne, Center for Ecotoxicology, NSW, AustraliaE. Johnston, University of New South Wales, Australia

Microbial Ecotoxicology—Ji-Dong Gu, University of Hong Kong, PR of China

Plant Ecotoxicology—X.-Z. Yu, Hunan Agricultural University, Changsha, PR of China

Terrestrial Ecotoxicology—Robert S. Halbrook, Southern Illinois University, Carbondale, USA

Editorial Board

W. Ahlf, Technical University of Hamburg-Harburg, GermanyM. Bayley, University of Aarhus, DenmarkJ. Bickham, Purdue University, West Lafayette, IN, USAB.P. Bradley, University of Maryland, Baltimore, USAM. Depledge, Peninsula College of Medicine and Dentistry, UKN. Desneux, INRA, FranceA. Fairbrother, Parametrix, Inc., Corvallis, OR, USAJ. Freeman, Purdue University, West Lafayette, IN, USAJ. Fu, Wuhan Botanical Garden, Chinese Academy of Science,

PR of ChinaM.S. Greeley, Jr., Oak Ridge National Laboratory, TN, USAE. Heimbach, Leichlingen, GermanyG. Heinz, Patuxent Wildlife Research Center, Laurel, MD, USAS-Z. Huang, Chinese Academy of Science, Nanjing, PR of ChinaT.C. Hutchinson, Trent University, Peterborough, ON, CanadaA.N. Jha, University of Plymouth, UK

S. Kennedy, Canadian Wildlife Service, Ottawa, ON, CanadaH.G. Ochoa-Acuna, Purdue University, West Lafayette, IN, USAD. Osborn, CEH, Huntington, Cambridgeshire, UKS. Raisuddin, Hanyang University, Seoul, South KoreaG. Rand, Florida International University, Miami, FL, USAJ. Rinklebe, University of Wuppertal, GermanyA. Sarkar, National Institute of Oceanography, IndiaD. Savva, University of Reading, UKM.I. Schneider, Centro de Estudios Parasitologicos y de Vectores,Buenos Aires, Argentina

M.S. Sepulveda, Purdue University, West Lafayette, IN, USAA. Stewart, Oak Ridge Associated Universities, Oak Ridge, TN, USAH. Thompson, Central Science Laboratory, MAFF, UKA.J. Underwood, University of Sydney, NSW, AustraliaC.A.M. van Gestel, Vrije University, The NetherlandsC. Walker, Colyton, Devon, UKJ. Zhuang, University of Tennessee, Knoxville, TN, USA

INTRODUCTION

EHPC 2010: sharing knowledge on environmentalhealth for risk mitigationT.E. Ford · A.L. Bass · S. Cheng · G.N. Cherr · B. Cole ·E. Fairbairn · J.-D. Gu · R.S. Halbrook · F.E. Löffler · E.L. Madsen · N.A. McGinn 937

CONTAMINANTS

Polycyclic aromatic hydrocarbons in surface sedimentsof the Jialu RiverJ. Fu · S. Sheng · T. Wen · Z.-M. Zhang · Q. Wang · Q.-X. Hu · Q.-S. Li · S.-Q. An · H.-L. Zhu 940

Historical record and source apportionment of polycyclic aromatic hydrocarbons in the LianhuanLake sedimentsL. Sun · S. Zang · H. Xiao 951

Organic pollutants and ambient severity for the drinking water source of western Taihu LakeX. Gao · X. Shi · Y. Cui · M. Li · R. Zhang · X. Qian · Y. Jiang 959

Occurrence, abundance and elimination of class 1 integrons in one municipal sewage treatment plantL. Ma · X.-X. Zhang · S. Cheng · Z. Zhang · P. Shi · B. Liu · B. Wu · Y. Zhang 968

Assessment of estrogenic contamination and biologicaleffects in Lake TaihuG. Lu · Z. Yan · Y. Wang · W. Chen 974

TOXICITY (NON-MOUSE)

Biofilms as potential indicators of macrophyte-dominated lake healthM. Ma · J. Liu · X. Wang 982

Hydroxyl radical generation and oxidative stress in earthworms (Eisenia fetida) exposed to decabromodiphenyl ether (BDE-209)X. Xie · Y. Wu · M. Zhu · Y. Zhang · X. Wang 993

Time-dependent oxidative stress and histopathologicalchanges in Cyprinus carpio L. exposed to microcystin-LRJ. Jiang · X. Gu · R. Song · Q. Zhang · J. Geng · X. Wang ·L. Yang 1000

Genotoxicity of crude extracts of cyanobacteria fromTaihu Lake on carp (Cyprinus carpio)Q. Wu · M. Li · X. Gao · J.P. Giesy · Y. Cui · L. Yang · Z. Kong 1010

MOUSE MODELS

Toxicity of cyanobacterial bloom extracts from Taihu Lake on mouse, Mus musculusD. Li · Z. Liu · Y. Cui · W. Li · H. Fang · M. Li · Z. Kong 1018

Integration of gene chip and topological network techniques to screen a candidate biomarker gene(CBG) for predication of the source water carcinogenesis risks on mouse Mus musculusJ. Sun · S. Cheng · A. Li · R. Zhang · B. Wu · Y. Zhang ·X. Zhang 1026

Genome-wide screening of indicator genes for assessingthe potential carcinogenic risk of Nanjing city drinkingwaterR. Zhang · S. Cheng · A. Li · J. Sun · Y. Zhang · X. Zhang 1033

Comparative analysis of binding affinities betweenstyrene and mammalian CYP2E1 by bioinformaticsapproachesB. Wu · J. Sun · S.-P. Cheng · J.-D. Gu · A.-M. Li · X.-X. Zhang 1041

Assessing the toxicity of ingested Taihu Lake water on mice via hepatic histopathology and matrix metalloproteinase expressionZ. Zhang · W. Qin · S. Cheng · L. Xu · T. Wang · X.-X. Zhang · B. Wu · L. Yang 1047

Ecotoxicology

Volume 20 · Number 5 · July 2011

Special Issue: Sharing Knowledge on Environmental Health for Risk Mitigation

Guest Editors: Tim E. Ford · Shupei Cheng and Ji-Dong Gu

Reproductive toxicity in male mice exposed to Nanjing City tap waterD. Zhao · Y. Chen · K. Zhou · S. Cheng · T. Ma · C. Jiang · W. Yan · L. Zhu · X. Gu · X. Zhu · B. Wu · Y. Zhang · X. Zhang 1057

NMR-based metabolic profiling for serum of mouseexposed to source waterY. Zhang · W. Li · J. Sun · R. Zhang · B. Wu · X. Zhang ·S. Cheng 1065

Preliminary evaluation of gene expression profiles in liver of mice exposed to Taihu Lake drinking water for 90 daysY. Zhang · W. Li · R. Zhang · J. Sun · B. Wu · X. Zhang ·S. Cheng 1071

Serum biochemical analysis to indicate pathogenic risk on mouse Mus musculus exposure to source of drinking waterS.F. Yun · X.Y. Tian · S.P. Cheng · Y. Zhang · A.M. Li ·L.B. Zhang · X.X. Zhang · L. Chen · B. Wu · L.Q. Guo ·Y.Z. Shi 1078

RISK ASSESSMENT

Health risk of semi-volatile organic pollutants in Wujin river inflow into Taihu LakeR. Zhang · W. Wang · X. Shi · X. Yu · M. Li · L. Xiao · Y. Cui 1083

Evaluation of risk associated with organchlorine pesticide contaminated sediment of the Lake Lianhuan watershedF. Xuan · S. Zang 1090

Identification of trace organic pollutants in freshwatersources in Eastern China and estimation of their associated human health risksW. Shi · F. Zhang · X. Zhang · G. Su · S. Wei · H. Liu · S. Cheng · H. Yu 1099

Spatial distribution and ecological risk assessment ofmetals in sediments of Baiyangdian wetland ecosystemL. Su · J. Liu · P. Christensen 1107

Non-carcinogenic risk assessment of eight metals in thesource groundwater of Shaying River BasinT. Ni · W. Diao · J. Xu · N. Liu 1117

Risk assessment of polycyclic aromatic hydrocarbonsin aquatic ecosystemsB. Wu · R. Zhang · S.-P. Cheng · T. Ford · A.-M. Li · X.-X. Zhang 1124

Fuzzy synthetic model for risk assessment on HaiheRiver basinJ. Liu · Q. Chen · Y. Li · Z. Yang 1131

MONITORING AND CONTROL

Tetracycline adsorption on kaolinite: pH, metal cationsand humic acid effectsY. Zhao · J. Geng · X. Wang · X. Gu · S. Gao 1141

Developing a qPCR method to quantifyAhR–PCP–DNA complex for detection of environmental trace-level PCPX. Zhao · X. Pang · N. Chaisuwan 1148

Water pollution control technology and strategy for river–lake systems: a case study in Gehu Lake and Taige CanalY. Zhang · Y. Zhang · Y. Gao · H. Zhang · J. Cao · J. Cai · X. Kong 1154

Removal of dichloroacetic acid from drinking water by using adsorptive ozonationL. Gu · X. Yu · J. Xu · L. Lv · Q. Wang 1160

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1 23

Ecotoxicology ISSN 0963-9292Volume 20Number 5 Ecotoxicology (2011)20:940-950DOI 10.1007/s10646-011-0622-4

Polycyclic aromatic hydrocarbons insurface sediments of the Jialu River

Jie Fu, Sheng Sheng, Teng Wen, Zhi-MingZhang, Qing Wang, Qiu-Xiang Hu, Qing-Shan Li, Shu-Qing An & Hai-Liang Zhu

1 23

Your article is protected by copyright and

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available until 12 months after publication.

Polycyclic aromatic hydrocarbons in surface sedimentsof the Jialu River

Jie Fu • Sheng Sheng • Teng Wen • Zhi-Ming Zhang •

Qing Wang • Qiu-Xiang Hu • Qing-Shan Li •

Shu-Qing An • Hai-Liang Zhu

Accepted: 18 February 2011 / Published online: 31 March 2011

� Springer Science+Business Media, LLC 2011

Abstract The Jialu River, an important branch of the

Huaihe River in China, was seriously polluted because of

rapid economic growth and urbanization. In order to

evaluate the potential for serious environmental conse-

quences as a result of anthropogenic contamination, the

distribution of polycyclic aromatic hydrocarbons (PAHs)

has been investigated in surface sediment samples col-

lected in connection with field surveys of 19 sites along the

Jialu River. The total concentration of the 16 USEPA

priority PAHs ranged from 466.0 to 2605.6 ng/g dry

weight with a mean concentration of 1363.2 ng/g. Sedi-

ment samples with the highest PAH concentrations were

from the upper reaches of the river, where Zhengzhou City

is located; the PAH levels in the middle and lower reaches

were relatively low. According to the observed molecular

indices, PAHs originated largely from the high-temperature

pyrolytic process. According to the numerical effect-based

sediment quality guidelines (SQGs) of the United States,

the levels of PAHs in the Jialu River should not exert

adverse biological effects. The total benzo[a]pyrene

toxicity equivalent (TEQ) values calculated for samples

varied from 50.4 to 312.8 ng/g dry weight with an average

of 167.4 ng/g. The relationships between PAHs and

environmental factors, including chemical properties of

sediments, water quality, aquatic organisms, hydrological

conditions, and anthropogenic activities, are also discussed.

PAHs exerted a potential negative impact on the benthos.

Settlement percentage, population density and industrial

GDP per capita had a significant influence on the distri-

bution of PAHs.

Keywords Polycyclic aromatic hydrocarbons �Surface sediment � Environmental factors � Jialu River

Introduction

Polycyclic aromatic hydrocarbons (PAHs) with two or

more fused rings are an important class of persistent

organic pollutants (POPs) and prevalent in the environ-

ment. They originate from both natural and anthropogenic

sources. Natural sources include forest-fires, natural

petroleum seeps and post-depositional transformation of

biogenic precursors (Young and Cerniglia 1995). Apart

from the natural sources, many more anthropogenic PAHs

are derived from combustion of fossil fuels, vegetation and

other organic materials (Yunker et al. 2002). Accidental

spillages of fossil fuels, including crude oils and refined oil

products are also important anthropogenic sources (Qiao

et al. 2006). Because of their widespread distribution

throughout the environment and their often toxic and car-

cinogenic properties, PAHs are of special concern (Zhang

et al. 2010). The United States Environmental Protection

Agency (USEPA) has placed 16 of these PAHs in the

priority-pollutant list.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10646-011-0622-4) contains supplementarymaterial, which is available to authorized users.

J. Fu � Z.-M. Zhang � Q.-S. Li � H.-L. Zhu (&)

State Key Laboratory of Pharmaceutical Biotechnology,

School of Life Sciences, Nanjing University, Nanjing 210093,

People’s Republic of China

e-mail: zhuhl@nju.edu.cn

J. Fu � S. Sheng � T. Wen � Q. Wang � Q.-X. Hu �S.-Q. An (&) � H.-L. Zhu

State Key Laboratory of Pollution Control & Resource Reuse,

School of the Environment, Nanjing University

(XianLin Campus), Nanjing 210046, People’s Republic of China

e-mail: anshq@nju.edu.cn

123

Ecotoxicology (2011) 20:940–950

DOI 10.1007/s10646-011-0622-4

Author's personal copy

The solubility of PAHs in water is generally low. When

PAHs enter aquatic environments, they will rapidly

become associated with inorganic and organic suspended

particles and subsequently deposited in sediments (Chiou

et al. 1998). Thus, sediment is one of the most important

reservoirs of environmental hydrophobic POPs, such as

PAHs and polychlorinated biphenyls (PCBs) (Yang et al.

2005). Analysis of these sedimentary contaminants can be

used for assessment and interpretation their impact on

aquatic environments (Tolosa et al. 1996).

The Huaihe River, one of the seven largest rivers in

China, was seriously polluted because of excessive eco-

nomic growth, industrialization and urbanization in the

Huaihe River Basin (Wang et al. 2003). The deterioration

of water quality has severely affected the living standard of

the people in the basin. Some researchers have studied

PAH pollution in the surface sediments of the Huaihe River

(Wang et al. 2002; Huang et al. 2004). However, their

research focuses were not the spatial distribution of PAHs

in the sediments and they did not further discuss the

environmental factors.

The Jialu River is an important but polluted branch of

the Huaihe River. Originating from Xinmi County, Henan

Province, the Jialu River is 256 km long with its basin area

of 5,896 km2. It flows via Zhengzhou, Zhongmou, Weishi

and Xihua, and then down into the Shaying River near

Zhoukou City. The average discharge of the Jialu River

measured by a gauge station in Zhongmou was 15.11 m3/s

in 2007. The Jialu River basin has been undergoing rapid

economic growth and urbanization resulting in massive

discharges of wastewater and declining water quality

(Zhang et al. 2009). To date there have been no reports on

PAH contamination in the surface sediments of the Jialu

River. Therefore, the objectives of this article are (1) to

make clear the distribution of PAHs in the surface sedi-

ments of the Jialu River; (2) to identify possible PAH

sources; (3) to evaluate potential toxicological impacts and

(4) to discuss the relationships between PAHs and envi-

ronmental factors.

Materials and methods

Sampling and field surveys

The sampling and field surveys took place in September

2009 during a 20 day, 1,400 km journey. The sampling

locations are shown in Fig. 1a. Three sampling points (left,

middle, right) were set in the section (Fig. 1b). Triplicate

surface sediment samples (0–10 cm depth) were collected

from each point by stainless steel grab. The middle parts of

sediment samples from each point were mixed and sub-

sampled to ensure the representation. Approximately 1 kg

of the middle part of subsamples from the three sampling

points was put in clean glassware. Care was taken to avoid

any contamination during their transport to the laboratory

where they were stored at -20�C until further analysis.

Sampling for water quality and aquatic organisms were

conducted following standard method (Clesceri et al. 1998;

Angradi 2006).

Analysis of PAHs in sediments

Extraction and fractionation

Sediment samples were homogenized and freeze-dried

before extracting. 20 g of dried and homogenized sample

was extracted in a Soxhlet apparatus with 160 ml dichloro-

methane/acetone (1:1 v/v) at 70�C for 48 h. A mixture

of deuterated PAH compounds (1,4-dichlorobenzene-d4,

naphthalene-d8, acenaphthene-d10, phenanthrene-d10, chry-

sene-d12, and perylene-d12) (Aladdin Reagent Inc., Shang-

hai, China) as recovery surrogate standards was added to all

the samples prior to extraction. 1 g of activated copper was

added to the collection flask for desulphurization. After

extraction, the extract was concentrated to approximately

2 ml with a rotary evaporator and solvent-exchanged into

10 ml n-hexane, which was further reduced to approxi-

mately 1 ml. An alumina/silica gel column (6 cm/12 cm)

with 1 cm sodium sulfate on the top of the alumina was used

to clean-up and fractionate the extract. After eluting with

15 ml n-hexane, the second fraction containing PAHs was

collected by eluting 70 ml dichloromethane/n-hexane

(3:7 v/v). The PAH fraction was finally concentrated to 1 ml

under a gentle stream of nitrogen. A known quantity of

hexamethylbezene (Aladdin Reagent Inc., Shanghai, China)

was added as an internal standard prior to gas chromatog-

raphy–mass spectrometry (GC–MS) analysis.

GC–MS analysis

The concentrations of 16 USEPA priority PAHs [naph-

thalene (Nap), acenaphthylene (Acy), acenaphthene (Ace),

fluorine (Fl), phenanthrene (Phe), anthracene (Ant), fluo-

ranthene (Fla), pyrene (Pyr), benz[a]anthracene (BaA),

chrysene (Chr), benzo[b]fluoranthene (BbF), benzo[k]flu-

oranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,

3-c,d]pyrene (InP), dibenzo[a,h]anthracene (DahA), benzo

[g,h,i]perylene (BghiP)] were analyzed on a HP6890-

HP5975 GC–MS apparatus (Agilent Technologies Inc.,

Santa Clara, California). A HP-5 fused silica capillary

column (30 m, 0.25 mm, 0.25 lm) was used for separation

with helium as the carrier gas at a constant flow rate of

0.8 ml/min. Oven temperature was programmed as fol-

lows: 80�C hold for 1 min, raised at 6.0�C/min to 160�C,

PAHs in surface sediments of the Jialu River 941

123

Author's personal copy

then at 2.0�C/min to 300�C (held for 30 min). The injection

was set on a splitless mode at 290�C. A 1.0 ll sample was

injected with a 2.5 min solvent delay. Detection was con-

ducted by a mass selective detector (MSD) with electron

impact ionization (EI) in selected ion monitoring (SIM)

mode. The mass scanning ranged from m/z 20 to 650. Mass

spectra were compared to reference compounds in the

National Institute of Standards and Technology (NIST)

library. Identification of individual PAHs was based on the

comparison of retention time data between samples and the

standard solution containing 16 PAHs (Aladdin Reagent

Inc., Shanghai, China). Quantification was performed by

integration of the selected ion chromatograms extracted

from the total ion current. The internal calibration method

for quantification was applied based on five-point calibra-

tion curve for individual component.

Quality control and quality assurance

All analytical data were subject to strict quality control.

The instruments were calibrated daily with the calibration

standards and the relative percent difference between the

five point calibration and the daily calibrations were less

than 20% for all target analyses. Method blanks (solvent),

spiked blanks (standards spiked into solvent), matrix

spiked duplicates, sample duplicates were routinely ana-

lyzed together with the sediment samples.

Environmental data

Chemical properties of sediments

The chemical properties of freeze-dried sediments includ-

ing ammonia nitrogen (NH3–N), nitrate nitrogen (NO3–N),

total nitrogen (TN), total phosphorus (TP), and total

organic carbon (TOC) were determined according to the

standard method for soil analysis (Page 1982).

Water quality

The water transparency was assessed in the field. Other

monitoring indices, including NH3–N, NO3–N, TN,

orthophosphate, TP, and permanganate index (CODMn)

were determined in the laboratory following the standard

analytical method for water quality (Clesceri et al. 1998).

Aquatic organisms

Phytoplankton, zooplankton and benthos from each sample

were identified and counted. The total number of individ-

uals in every sample was converted to abundance. Biomass

was calculated according to the volumes of the individuals.

The Shannon–Wiener diversity index, H(S), was estimated

(Shannon and Weaver 1949). Benthic-Index of Biotic

Integrity (B-IBI) was established based on the benthic data

(Barbour et al. 1996). The coverage of aquatic plants was

evaluated in the field.

Hydrological conditions

The hydrological indexes including river width, depth,

sinuosity and velocity were determined in the field.

Anthropogenic activities

The shortest distance from the sampling location to the

main city (Zhengzhou, Kaifeng, Xuchang or Zhoukou) was

calculated according to the geographic coordinates. The

land-use of settlements was acquired from Landsat TM

images using ENVI 4.1 software. The settlement percent-

age was calculated from the land-use by neighborhood

Fig. 1 a Map of the Jialu River showing the sampling locations and b design scheme of sampling points in the Jialu River

942 J. Fu et al.

123

Author's personal copy

analysis using ArcGis 9.0 software. The population density

and industrial GDP per capita comes from Zhengzhou and

Zhoukou Statistical Yearbook (2008).

Statistical analysis

Cluster analysis, correlation analysis (CA) and principal

component analysis (PCA) were carried out using SPSS

13.0 for windows. Regression analysis was conducted

using Microsoft Office Excel 2003. Canonical correspon-

dence analysis (CCA) was executed using Canoco 4.5 for

Windows.

Results and discussion

Concentrations and distribution of PAHs

The retention time of 16 PAHs, their quantification ions,

detection limits and recoveries are summarized in Table

S1. Detection limits ranged from 0.4 to 1.9 ng/g dry weight

(d.w.) and recovery efficiencies varied between 69 and

92%. The surrogate recoveries were 46.2 ± 16.4% for 1,4-

dichlorobenzene-d4, 52.6 ± 12.7% for naphthalene-d8,

66.6 ± 11.6% for acenaphthene-d10, 74.5 ± 10.7% for

phenanthrene-d10, 93.6 ± 13.2% for chrysene-d12, and

88.6 ± 15.2% for perylene-d12 with the sediment samples.

The concentrations of the 16 PAHs in surface sedi-

ments are summarized in Table S2. The total concentra-

tion of the 16 USEPA priority PAHs ranged from 466.0

to 2605.6 ng/g d.w. with a mean concentration of 1363.2

ng/g. Sediment samples with the highest PAH concen-

trations were from the upper reaches of the river (S2, S3

and S4), where Zhengzhou City is located. The PAH

pollution levels are assigned by Baumard et al. (1998) as

low (L) (0–100 ng/g), moderate (M) (100–1,000 ng/g),

high (H) (1,000–5,000 ng/g) and very high (VH) ([5,000

ng/g) levels. Thus, samples from S2 to S13 and S18 were

classified as highly contaminated sediments and the rest

samples (S1, S14–S17 and S19) were moderate contam-

inated. To identify the homogenous groups of sampling

locations, a hierarchical cluster analysis was carried out

on the sampling locations (Culotta et al. 2006). Figure 2

shows the dendrogram using average linkage between

groups. The root of the tree consists of a single cluster

containing all sampling sites, and the leaves correspond to

individual sampling sites. Cutting the tree at a given

height will give a clustering at a selected precision. As

shown in Fig. 2, there are three great clusters at a rough

precision. The differentiation is confirmed not only by the

geographical position of the sampling locations but also

by PAH concentrations. The first cluster consists of three

sampling locations (S2, S3 and S4). This confirms our

experimental results as at those three locations we found

greater total PAHs concentrations than at the other loca-

tions ([2,000 ng/g). The second cluster consists of S5–S8

and S10. Except S6, the total concentrations of PAHs in

these samples were between 1,500 and 2,000 ng/g. The

third cluster can be divided into three subgroups: the first

consists of S13 and S18, the second is composed of S9,

S11 and S12, and the third includes the rest locations. The

total concentrations of PAHs in the first and second sub-

groups were 1,000–1,500 ng/g, and the concentrations in

the third subgroup were lower than 1,000 ng/g. In addition,

the cluster analysis would also suggest that we could

reduce the number of sampling locations and consequently

the number of analyses performed in future long term

monitoring of the Jialu River.

Wang et al. (2002) analyzed the normalized concentra-

tions of 13 PAHs (without Acy, Inp and BghiP) in the

sediment samples from Xinyang City and Huainan City in

the Huaihe River with semipermeable membrane devices

(SPMD) and disclosed the total concentrations of 13 PAHs

were 7.7 and 9.7 ng/mg organic carbon (o.c.), respectively,

with a mean concentration of 8.7 ng/mg. This is far below

the pollution level (424.3–4185.6 ng/o.c.) observed in our

study and the use of SPMD might affect the analysis

Fig. 2 Hierarchical dendogram for the sampling locations in the Jialu

River using average linkage between groups and Pearson correlation

as measure interval

PAHs in surface sediments of the Jialu River 943

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results. Huang et al. (2004) also investigated the PAHs in

surface sediments in Jiangsu Section of the Huaihe River

and found the total concentrations of 12 PAHs (without

Nap, Acy, BaA and BbF) were 690–6,630 ng/g d.w. with a

mean concentration of 3,740 ng/g, which are higher than

that in our observation.

A comparison of PAHs concentrations in surface sedi-

ments collected from different rivers is given in Table 1.

The PAH concentrations in surface sediments from the

Jialu River in this study are similar to those detected in the

River Gomti, India (Tripathi et al. 2009) and the Yellow

River, China (Xu et al. 2007), and higher than those found

at the Yalujiang River, China (Wu et al. 2003), the Sava

River, Serbia (Crnkovic et al. 2008), the Luan River, China

(Bai et al. 2008), the Daliao River, China (Guo et al. 2007)

and the Gao-Ping River, Taiwan (Doong and Lin 2004).

However, the levels were lower than those reported in the

Zhujiang River, China (Mai et al. 2002), the Passaic River,

USA (Huntley et al. 1995) and the Morava River, Czech

Republic (Vondracek et al. 2001).

Composition and source identification of PAHs

The concentration pattern of PAHs by ring number in the

surface sediments is shown in Fig. 3. As shown in Fig. 3,

3- and 4-ring PAHs are the most abundant. The average

proportions are 33.34 and 31.09%, respectively. 5-ring

PAHs are the third most abundant with an average pro-

portion of 22.94%. The average percentage of high-

molecular-weight (HMW) PAHs (4-, 5- and 6-ring PAHs)

is 62.22%, indicating the predominance of HMW PAHs. A

higher concentration of HMW PAHs than that of low-

molecular-weight (LMW) PAHs (2- and 3-ring PAHs) has

been commonly observed in sediments from river and

marine environments (Yan et al. 2009).

PAHs enter river environment systems mainly via

atmospheric fallout, urban runoff, municipal/industrial

effluents, and oil leakage (Guo et al. 2007). These

anthropogenic PAHs are formed mainly by two mecha-

nisms: fuel-combustion (pyrogenic) and discharge of

petroleum-related materials (petrogenic), and may be

Table 1 A comparison of

PAHs concentrations in surface

sediments collected from

different rivers (ng/g d.w.)

Location Range Mean Reference

Gao-Ping River, Taiwan 8–356 81 Doong and Lin (2004)

Daliao River, China 61.9–840.5 287.3 Guo et al. (2007)

Yalujiang River, China 68–1500 290 Wu et al. (2003)

Luan River, China 6.7–1585.7 342.9 Bai et al. (2008)

Sava River, Serbia 416.2–593.3 501.6 Crnkovic et al. (2008)

River Gomti, India 68–3153 1182 Tripathi et al. (2009)

Yellow River, China 464–2621 1414 Xu et al. (2007)

Zhujiang River, China 1434–10811 4892 Mai et al. (2002)

Morava River, Czech Republic 636–13205 4997 Vondracek et al. (2001)

Passaic River, USA 220–8000000 145000 Huntley et al. (1995)

Jialu River, China 466.0–2605.6 1363.2 This study

Fig. 3 Distribution of 2-, 3-, 4-,

5- and 6-ring PAHs in the

surface sediments from the Jialu

River

944 J. Fu et al.

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identified by ratios of individual PAH compounds. Some

ratios of selected PAH compounds can be used to assess

the possible origins of PAHs, including LMW/HMW, Nap/

Phe, Ant/(Ant ? Phe), Phe/Ant, Fla/Pyr, Chr/BaA, Fla/

(Fla ? Pyr), InP/(InP ? BghiP) and Pyr/BaP (Yunker

et al. 2002; Doong and Lin 2004; Qiao et al. 2006).

In order to identify the sources of PAHs in the Jialu River,

Phe/Ant was plotted against Fla/Pyr and Inp/(InP ? BghiP)

was plotted against Fla/(Fla ? Pyr) (Fig. 4). In general, a

Phe/Ant ratio \10 is seen as characteristic of combustion

processes, whereas a ratio[10 indicates petroleum input or

diagenetic (Baumard et al. 1998); Fla/Pyr \1 is attributed to

petrogenic source, while ratio[1 indicates pyrolytic origins

(Doong and Lin 2004). A Fla/(Fla ? Pyr) ratio \0.4 is

attributed to petrogenic source, ratio [0.5 is suggestive of

grass, wood and coal combustion, while a ratio between 0.4

and 0.5 is characteristic of petroleum combustion (Yunker

et al. 2002). An InP/(InP ? BghiP) ratio\0.2 is suggestive

of petroleum input and ratio[0.5 indicates wood and coal

combustion, while a ratio between 0.2 and 0.5 is character-

istic of petro-chemical fuel combustion (Luo et al. 2005).

As shown in Fig. 4a, except at S11, a Fla/Pyr ratio[1 and Phe/

Ant ratio\10 indicated a pyrolytic origin for all of the PAHs.

Figure 4b confirmed this kind of source and further indicated

this source was a biomass and coal combustion process.

Assessment of sediment quality and toxicity

A variety of approaches have been developed to use the

available ecotoxicology data on PAHs to set numerical

sediment quality guidelines (SQGs) (Chapman 1989).

Selection of the most appropriate SQGs for specific appli-

cations can be a difficult task for sediment assessors. Long

et al. (1995) developed two guideline values, an effects range

low (ERL) and an effects range median (ERM), to assess the

sediment quality with a ranking of low to high impact values.

The ERL and ERM values are intended to define chemical

concentration ranges that are rarely, occasionally, or fre-

quently associated with adverse biological effects.

The observed concentrations of PAHs in the Jialu River

were compared with the ERL and ERM values (Table 2).

Results in this study showed that the total PAH concen-

trations at all sites were below the ERL. In addition, for all

sites, individual PAHs did not exceed ERM, but at some

sites there was at least one PAH that might occasionally

cause biological impairment (with concentration higher

than ERL). These findings indicated that in some samples

there might be an individual PAH that occasionally cause

biological impairment, but no samples had constituents that

might frequently cause biological impairment (with con-

centration higher than ERM).

Fig. 4 Plot of isomeric ratios:

a Phe/Ant versus Fla/Pyr, and

b InP/(InP ? BghiP) versus Fla/

(Fla ? Pyr)

PAHs in surface sediments of the Jialu River 945

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Some PAHs and especially their metabolic products are

of great concern due to their documented carcinogenicity.

These potentially carcinogenic PAHs (CPAHs) include

BaA, BaP, BbF, BkF, DahA and InP (Savinov et al. 2003).

The total concentrations of CPAHs in sediments from the

Jialu River varied from 192.8 to 856.0 ng/g d.w. with the

average of 489.1 ng/g, and accounted for 20.07–51.58% of

the total PAHs concentrations.

Among the CPAHs, BaP is the only PAH for which

toxicological data are sufficient for derivation of a car-

cinogenic potency factor (Peters et al. 1999). The toxic

equivalency factors (TEFs) were used to quantify the

carcinogenicity of other PAHs relative to BaP and to

estimate BaP-equivalent doses (BaPeq dose) (Nadal et al.

2004). Calculated TEFs for BaA, BaP, BbF, BkF, InP,

DahA, and Chr are 0.1, 1, 0.1, 0.01, 0.1, 1, and 0.001,

respectively, according to the USEPA. In this study, we

converted the above mentioned seven PAH concentrations

into one toxic concentration for each site using the corre-

sponding TEFs. The total BaP toxicity equivalent (TEQ)

for all PAHs was calculated as:

Total TEQ ¼X

i

Ci � TEFi

where Ci was the concentration of individual PAHs (ng/g

d.w.) and TEFi was its corresponding toxic equivalency

factor.

Total TEQ values calculated for samples from the Jialu

River varied from 50.4 to 312.8 ng/g d.w., with an average

of 167.4 ng/g. The maximum total TEQ value was found at

sample location S5. Among different PAHs, contribution to

the total TEQ decreased in the order: DahA (45.65%)

[ BaP (35.53%) [ BbF (7.17%) [ BaA (6.86%) [ Inp

(4.49%) [ BkF (0.26%) [ Chr (0.04%) (Fig. 5). In com-

parison with other studies (Table 3), the total TEQ values

in sediments of the Jialu River were lower than that

detected in the Meiliang Bay, Taihu Lake, China (Qiao

et al. 2006) and close to that found at the Gulf of Gemlik,

Marmara Sea, Turkey (Unlu and Alpar 2009). However,

the TEQ levels were higher than those of other literature-

reported sites, such as the Guba Pechenga, Barents, Sea,

Russia (Savinov et al. 2003), the Sundarban Mangrove,

Wetland, India (Domınguez et al. 2010) and the coastal

lagoons in central Vietnam (Giuliani et al. 2008).

Relationship with environmental factors

The PAH concentration of sediments is an important pol-

lution index and has a possible impact on the environment

Table 2 Standard pollution criteria of PAH components for sedi-

ments (ng/g d.w.)

Compound ERL ERM This study

Average Maximum

Nap 160 2100 62.7 129.9

Acy 44 640 66.8 177.8

Ace 16 500 72.6 257.5

Fl 19 540 74.6 332.7

Phe 240 1500 195.6 486.3

Ant 853 1100 63.6 169.0

Fla 600 5100 154.1 486.5

Pyr 665 2600 85.9 291.4

BaA 261 1600 114.8 237.9

Chr 384 2800 66.5 194.9

BbF NA NA 120.1 213.0

BkF NA NA 43.2 86.6

BaP 430 1600 59.5 111.9

InP NA NA 75.2 168.2

DahA 63.4 260 76.4 173.7

BghiP NA NA 31.7 65.4P

PAHsa 4000 44792 1363.2 2605.6

NA not availablea PPAHs total concentrations of 16 PAHs

Fig. 5 Contributions of BaP,

DahA, BbF, InP, BaA, BkF and

Chr to the total TEQ

946 J. Fu et al.

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of the Jialu River though interaction with other environ-

mental factors. Therefore, it is meaningful to study the

relationship between PAH concentration and environmen-

tal factors. In fact, PAH concentration can be seen as a

chemical index in sediments and reflects the contamination

status of sediments together with other chemical properties

of sediment (Scheme 1). An interaction might exist

between PAH and water quality, because the sediments can

exchange chemicals with water phase (Jeffe 1991). The

contaminated sediments (with high PAH concentrations)

and declining water quality pose a direct impact on the

aquatic organisms (Zeng and Venkatesan 1999). On the

other hand, the PAH contamination is the result of

anthropogenic activities. Conversely, these released toxic

substances in aquatic environments may affect human

health via the food chain (Kannan et al. 2005). In addition,

the hydrological conditions might influence the pollution of

the Jianlu River, though the effect is usually weak.

Chemical properties of sediments. The determined

chemical properties of sediments indices include NH3–N

(C1), NO3–N (C2), TN (C3), TP (C4), and TOC (C5) (Fig.

S1). Through CA, we found a significant positive correla-

tion (r = 0.807, P \ 0.01) between total PAH concentra-

tions and TP (Table S3), indicating that PAH and

phosphorus pollution were influenced possibly by similar

mechanisms. The TOC contents of sediments did not

appear to correlate with the total PAH concentrations

(r = 0.353), suggesting that sediment contamination in the

study area may be dictated more predominantly by

anthropogenic inputs than by natural processes (Mai et al.

2002). PCA was performed here to identify characteristics

of chemical indices of sediments (C1–C5 and total PAH

concentrations) (Fig. 6a). It can be seen that two principal

components (eigenvalue [1) were extracted, which

explains 70% of the total variation. The first component

accounts for 47% of the total variance and captures the

total concentrations of nutrient elements and PAHs (C3–C5

andP

PAHs). The second component accounts for 23% of

the total variance and is significantly related to the ele-

mental speciation of nitrogen (C1 and C2).

Water quality. Water quality indices including trans-

parency (W1), NH3–N (W2), NO3–N (W3), TN (W4),

orthophosphate (W5), TP (W6), and CODMn (W7) were

determined (Fig. S2). There are positive correlations

between total PAHs concentrations and many pollution

indices (W2, W4, W5 and W7) (Table S3). This implies that

the PAHs and other contaminants were released together

into the aquatic environment and probably underwent

similar processes along the river. The best and worst water

qualities were found upstream and downstream of

Zhengzhou urban zone, respectively, suggesting that the

river received a large amount of wastewater containing

numerous contaminants (including PAHs) when it flowed

through Zhengzhou City (Zhang et al. 2009).

Scheme 1 Relationship

between PAHs and

environmental factors

Table 3 A comparison of total

TEQ in sediments (ng/g d.w.)Location Range Mean Reference

Coastal lagoons in central Vietnam 2–98 21 Giuliani et al. (2008)

Sundarban Mangrove, Wetland, India 6.95–119 59 Domınguez et al. (2010)

Guba Pechenga, Barents, Sea, Russia 11–300 \120 Savinov et al. (2003)

Gulf of Gemlik, Marmara Sea, Turkey 5.6–1838 134 Unlu and Alpar (2009)

Meiliang Bay, Taihu Lake, China 94–856 407 Qiao et al. (2006)

Jialu River, China 50.4–312.8 167.4 This study

PAHs in surface sediments of the Jialu River 947

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Aquatic organisms. We have determined many indices of

aquatic organisms, such as the abundance, biomass and

Shannon–Wiener diversity index H(S) of phytoplankton (O1,

O2 and O3), zooplankton (O4, O5 and A6) and benthos (O7, O8

and O9), B-IBI (O10), and the coverage of emergent plants

(O11), floating plants (O12) and submerged plants (O13) (Fig.

S3). Phytoplankton blooms are usually the result of eutro-

phication and some phytoplankton are important indicator

for water quality (Zhou et al. 2009). The abundance and

biomass of phytoplankton in the Jialu River showed positive

correlations with water quality and Chlamydomonas sp. is

the dominant species, suggesting moderate pollution. The

distribution of PAHs concentrations had the similar pattern

to the biomass of phytoplankton. Studies over the last several

decades have shown that the accumulation of PAHs in sed-

iments may have profound effects on benthic communities

(Griffiths et al. 1981). The PAH concentrations showed a

significant negative correlation with the abundance of ben-

thos (r = -0.589, P \ 0.01) and B-IBI (r = -0.655,

P \ 0.01) in our study (Table S3), indicating a possible

adverse effect of PAHs on benthos. In addition, the dominant

species of emergent plants and floating plants in the Jialu

River were Phragmites australis and Alternanthera philo-

xeroides, respectively, which are pollution tolerance species.

A positive correlation between PAH concentrations and

coverage of emergent plants or floating plants was also

observed in this study.

Anthropogenic activity. Four indices including the

shortest distance from the sampling location to the main

city (A1), settlement percentage (A2), population density

(A3) and industrial GDP per capita (A4) were evaluated

(Fig. S4). Settlement percentage, population density and

industrial GDP per capita showed significant positive cor-

relations to PAH concentrations (Table S3) and their

relationships were basically linear. This confirmed the

anthropogenic origins of PAHs and the amount of PAHs

detected was possibly related to urban runoff and munici-

pal/industrial effluents (Yan et al. 2009). CCA proposed by

Ter Braak (1986) was successfully used to investigate

community structure and its underlying environmental

basis. Here, we utilized CCA to investigate impact of

anthropogenic activities on the PAH distribution (Fig. 6b).

The first and second axis accounted for 78% of variation

and the analysis was significant at P \ 0.05 level. As

shown in Fig. 6b, many HMW PAHs such as BaA, Chr,

BbF, BkF, InP and DahA showed similar distributions.

A2–A4 exerted stronger impacts than A1 did, which is in

accordance with the results of CA. The positive influences

of A2–A4 were greater on LMW PAHs than on HMW

PAHs. A1 imposed its impact in the opposite way.

In addition, hydrological indices including river width

(H1), depth (H2), sinuosity (H3) and velocity (H4) showed

no correlations to PAH concentrations (Fig. S5, Table S3).

Based on the above discussion, we can see that the

distribution of PAH concentrations has the similar pattern

to many environmental factors. This is in accordance with

the pollution pattern in the Jialu River that is upper reaches

were highly polluted and the pollution levels in the middle

and lower reaches gradually decreased (Zhang et al. 2009).

The PAH pollution is strongly influenced by the anthro-

pogenic activities. In the upper reaches, urban zones are

widespread with large amounts of domestic waste and

wastewater outfall along the river banks, and there are also

many fishponds and farmlands in that area. Therefore, the

point and nonpoint sources pollution are both very

important. In the middle and lower reaches, the towns and

farmlands are reduced and large amounts of poplar trees

are planted in the river banks. A few bare lands are also

distributed in some sections and those sections are less

polluted.

Fig. 6 a PCA for chemical properties of sediments and b diagram of axis one and two for the CCA relating PAH concentrations and

anthropogenic activities in the Jialu River

948 J. Fu et al.

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Conclusions

Analysis of surface sediment samples from 19 sites along

the Jialu River showed that the levels of the 16 PAHs

ranged from 466.0 to 2605.6 ng/g d.w., with the mean

value of 1363.2 ng/g. The highest levels of PAHs were

found in the upper reaches of the river. 3- and 4-ring PAHs

were predominant in the sediments. Through fingerprinting

analysis, PAHs in the sediments were determined to be

from mostly pyrogenic origins. In some samples there were

individual PAHs that could occasionally cause biological

impairment, but no samples had constituents that may

frequently cause biological impairment. When the poten-

tially carcinogenic PAHs were normalized by multiplying

by respective TEFs, the maximum total TEQ was also

found at the upper reaches of the river. There were corre-

lations between PAHs distribution and many environmen-

tal factors. PAHs exerted a possible negative impact on the

benthos. Settlement percentage, population density and

industrial GDP per capita had a significant effect on the

distribution of PAHs.

Acknowledgments This work was supported by Major Projects on

Control and Rectification of Water Body Pollution (2008ZX07526-

002 & 2008ZX07101-001). The authors wish to thank Qing-Ye Sun,

Xiu-Lian Ma, Dong-Sheng An and Hong-Lin Wu from Anhui Uni-

versity for their kindly help in sample collection and identification of

aquatic organisms. The authors will also thank Lu-Ji Yu, An-Dong

Song and Hong-You Wan from Zhengzhou University for their

assistance in field survey. The authors are very grateful to the editor

and reviewers for their invaluable comments and suggestions and to

Prof. Shu-Pei Cheng from Nanjing University for his time and effort

on our paper.

Declaration The material is original and has not been submitted for

publication elsewhere. There is no conflict of interest in the manu-

script. The main work in sediment samples collection, treatment,

GC–MS analysis, data analysis and paper writing were finished by Jie

Fu. The collection of macro data was done by Sheng Sheng. The

determination of water quality and chemical properties of sediments

were carried out by Teng Wen. Zhi-Ming Zhang, Qing Wang, Qiu-

Xiang Hu and Qing-Shan Li contributed to sample collection, treat-

ment and analysis. Shu-Qing An and Hai-Liang Zhu are the corre-

sponding authors.

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Author's personal copy

1

Ecotoxicology

Supplementary materials

Polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the

Jialu River, Henan Province, China

Jie Fu • Sheng Sheng • Teng Wen

• Zhi-Ming Zhang • Qing Wang • Qiu-Xiang Hu

• Qing-Shan Li • Shu-Qing An* • Hai-Liang Zhu

Corresponding authors. Hai-Liang Zhu (Prof.), School of Life Sciences, Nanjing University,

Nanjing 210093, P. R. China, tel. & fax: +86 25 83592672, e-mail address: zhuhl@nju.edu.cn.

Suppl Mtrls (not cover letter - place cover letter in "comments")

2

Table S1 Retention time, quantification ions, detection limits and recoveries of 16

USEPA priority PAHs

No. Compounds Retention time (min) Quantification ions (m/z) Detection limits (ng/g d.w.) Recovery (%)

1 Nap 5.275 128, 129, 127 1.6 69

2 Acy 10.326 152, 151, 153 1.1 76

3 Ace 10.997 154, 153, 152 0.8 78

4 Fl 12.785 166, 165, 167 0.7 80

5 Phe 16.985 178, 179, 176 0.4 82

6 Ant 17.305 178, 176, 179 1.3 84

7 Fla 25.190 202, 101, 203 1.6 84

8 Pyr 26.869 202, 200, 203 1.7 86

9 BaA 38.754 228, 229, 226 0.7 92

10 Chr 39.062 228, 226, 229 1.9 92

11 BbF 49.581 252, 253, 125 1.6 92

12 BkF 49.829 252, 253, 125 1.1 88

13 BaP 52.282 252, 253, 125 0.8 86

14 InP 62.046 276, 138, 227 1.1 81

15 DahA 62.722 278, 139, 279 1.0 80

16 BghiP 63.991 276, 138, 227 1.0 80

Table S2 Concentrations of 16 USEPA PAHs (ng/g d.w.) in surface sediments from

the Jialu River, China

PAHs Sampling sites

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

Nap 6.6 129.9 112.6 139.2 71.2 99.5 73.5 64.3 73.0 51.0

Acy 15.7 177.8 136.4 130.8 174.8 106.4 157.0 89.8 62.1 73.8

Ace 23.5 257.5 176.7 132.8 111.7 110.1 171.2 102.3 52.7 97.6

Fl 25.9 332.7 220.3 134.2 22.9 56.0 41.0 40.2 92.7 103.3

Phe 77.4 315.4 359.3 486.3 286.0 195.0 187.3 239.3 141.2 212.8

Ant 21.1 132.4 70.3 133.8 83.7 39.2 34.0 142.1 64.3 75.2

Fla 60.5 249.5 486.5 279.8 155.8 181.1 82.3 217.5 100.0 186.6

Pyr 18.2 216.9 291.4 158.2 42.3 47.3 73.2 112.4 83.9 114.7

3

BaA 62.5 159.1 110.5 164.5 237.9 88.7 101.7 186.6 112.6 128.0

Chr 12.5 76.0 112.4 120.4 141.8 48.6 58.2 60.2 52.4 84.8

BbF 43.6 201.8 117.6 212.6 212.6 120.2 114.5 112.7 183.8 115.6

BkF 11.9 43.6 59.5 86.3 86.1 38.9 45.5 86.6 63.4 78.4

BaP 11.7 81.3 103.5 91.3 87.2 39.9 44.3 40.3 70.2 91.3

InP 20.4 85.3 53.7 137.6 58.5 55.1 168.2 105.7 161.4 87.4

DahA 42.8 102.2 54.3 72.2 173.7 80.8 94.8 82.9 123.9 110.5

BghiP 11.8 44.1 21.8 53.1 42.1 52.5 65.4 70.6 33.0 44.9

∑PAHs 466.0 2605.6 2486.8 2533.2 1988.3 1359.2 1512.1 1753.4 1470.7 1656.0

∑CPAHs 192.8 673.3 499.2 764.5 856.0 423.6 569.0 614.8 715.3 611.2

TEQ 67.2 228.6 186.7 216.0 312.8 147.5 178.1 164.7 240.6 235.8

∑PAHs/TOC

(ng/mg o.c.) 424.3 3377.8 4185.6 2729.4 1663.5 1324.6 1192.3 1711.9 945.1 1442.7

Table S2 Continued

PAHs Sampling sites

S11 S12 S13 S14 S15 S16 S17 S18 S19

Nap 104.1 68.2 23.9 59.1 35.0 1.8 46.7 2.8 29.7

Acy 2.8 25.8 16.2 18.9 16.2 36.7 5.6 20.4 2.3

Ace 10.1 14.3 38.8 16.6 10.4 11.6 7.2 30.7 3.5

Fl 58.2 60.1 77.4 48.6 19.0 14.0 16.8 28.1 26.2

Phe 199.1 70.0 78.5 180.2 169.6 64.0 192.2 129.6 132.3

Ant 17.0 35.1 60.0 25.1 20.7 49.9 20.4 169.0 14.2

Fla 69.4 130.1 147.5 63.1 61.4 73.5 86.6 145.2 151.3

Pyr 41.0 50.3 96.6 10.5 16.2 30.6 36.3 75.5 117.0

BaA 164.7 148.2 63.4 62.6 57.6 61.4 56.4 116.2 98.6

Chr 20.6 80.2 194.9 30.8 17.5 12.8 18.8 62.3 57.6

BbF 213.0 190.2 18.1 85.5 56.8 21.3 148.6 27.2 85.9

BkF 21.3 31.2 39.9 15.0 15.6 7.5 13.8 17.4 58.4

BaP 30.2 91.9 83.3 28.6 35.4 10.7 28.5 111.9 48.6

InP 53.0 83.0 99.7 55.2 57.3 18.0 55.3 16.4 57.0

DahA 98.3 83.1 5.2 1.3 56.9 74.2 4.6 124.9 65.5

BghiP 36.0 55.1 1.6 1.2 52.7 5.6 2.4 2.5 6.0

∑PAHs 1139.0 1216.8 1044.9 702.3 698.2 493.5 740.4 1080.1 954.2

∑CPAHs 580.6 627.6 309.6 248.3 279.4 192.9 307.3 413.9 414.1

TEQ 171.8 217.6 107.2 50.4 109.5 95.0 59.4 253.0 138.9

∑PAHs/TOC

(ng/g o.c.) 797.5 769.4 1223.8 733.2 493.2 493.4 601.2 1079.3 786.7

Table S3 Correlations between PAH concentrations and environmental factors†

4

C1 C2 C3 C4 C5 W1 W2 W3 W4 W5 W6 W7

2-ring PAH 0.390 0.072 0.328 0.584** 0.104 -0.210 0.887** -0.298 0.822** 0.705** 0.197 0.788**

3-ring PAH 0.331 -0.154 0.514* 0.807** 0.325 -0.262 0.916** -0.520* 0.747** 0.804** -0.070 0.870**

4-ring PAH 0.144 -0.119 0.608** 0.787** 0.533* -0.191 0.788** -0.491* 0.614** 0.649** -0.086 0.818**

5-ring PAH 0.539* -0.147 0.448 0.551* 0.158 0.105 0.585** -0.108 0.637** 0.691** 0.354 0.500*

6-ring PAH 0.335 -0.057 0.148 0.247 -0.170 0.014 0.473* 0.163 0.605** 0.447 0.346 0.277

∑PAHs 0.362 -0.141 0.566* 0.807** 0.353 -0.177 0.904** -0.432 0.784** 0.812** 0.051 0.852**

Table S3 Continued

O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 O11 O12

2-ring PAH 0.590** 0.768** -0.190 0.172 -0.168 0.383 -0.322 -0.309 -0.232 -0.489* 0.626** 0.586**

3-ring PAH 0.341 0.653** -0.243 0.121 -0.147 0.189 -0.566* -0.532* -0.287 -0.612** 0.686** 0.609**

4-ring PAH 0.397 0.636** -0.436 -0.006 -0.232 0.022 -0.545* -0.521* -0.360 -0.599** 0.620** 0.568*

5-ring PAH 0.462* 0.635** -0.005 -0.140 -0.291 0.150 -0.435 -0.451 -0.094 -0.499* 0.283 0.424

6-ring PAH 0.318 0.440 -0.077 -0.262 -0.314 0.149 -0.402 -0.459* -0.267 -0.456* 0.039 0.028

∑PAHs 0.448 0.730** -0.280 0.014 -0.242 0.162 -0.589** -0.572* -0.312 -0.655** 0.626** 0.596**

Table S3 Continued

A1 A2 A3 A4 H1 H2 H3 H4

2-ring PAH -0.301 0.655** 0.608** 0.739** 0.230 -0.348 -0.087 0.198

3-ring PAH -0.571* 0.891** 0.780** 0.903** 0.339 -0.431 0.017 0.358

4-ring PAH -0.564* 0.781** 0.671** 0.726** 0.386 -0.082 0.042 0.189

5-ring PAH -0.442 0.489* 0.420 0.519* 0.211 -0.079 -0.103 -0.035

6-ring PAH 0.007 0.207 0.188 0.250 0.153 -0.222 -0.119 -0.134

∑PAHs -0.560* 0.833** 0.726** 0.834** 0.357 -0.285 -0.014 -0.224

† Environmental factors include chemical properties of sediments: ammonia nitrogen (C1), nitrate

nitrogen (C2), total nitrogen (C3), total phosphorus (C4), and total organic carbon (C5); water

quality: transparency (W1), ammonia nitrogen (W2), nitrate nitrogen (W3), total nitrogen (W4),

orthophosphate (W5), total phosphorus (W6), and permanganate index (W7); aquatic organisms:

abundance, biomass and Shannon-Wiener diversity index of phytoplankton (O1, O2 and O3),

zooplankton (O4, O5 and O6) and benthos (O7, O8 and O9), Benthic-Index of Biotic Integrity (O10),

and coverage of emergent plants (O11) and floating plants (O12); anthropogenic activity: shortest

distance from the sampling location to the main city (A1), settlement percentage (A2), population

density (A3) and industrial GDP per capita (A4); and hydrological indices: river width (H1), depth

(H2), sinuosity (H3) and velocity (H4). * Correlation is significant at the 0.05 level (2-tailed)

** Correlation is significant at the 0.01 level (2-tailed)

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Fig. S1 Chemical properties of sediments: (a) ammonia nitrogen, (b) nitrate nitrogen,

(c) total nitrogen, (d) total phosphorus, and (e) total organic carbon

6

Fig. S2 Water quality: (a) transparency, (b) ammonia nitrogen, (c) nitrate nitrogen, (d)

total nitrogen, (e) orthophosphate, (f) total phosphorus and (g) permanganate index

7

Fig. S3 Aquatic organisms: (a) abundance of phytoplankton, (b) biomass of

phytoplankton, (c) H(S) of phytoplankton, (d) abundance of zooplankton, (e) biomass

of zooplankton, (f) H(S) of zooplankton, (g) abundance of benthos, (h) biomass of

benthos, (i) H(S) of benthos, (j) B-IBI, (k) coverage of emergent plants, (l) coverage

of floating plants. In addition, the coverage of submerged plants is zero in the Jialu

River.

8

Fig. S4 Anthropogenic activities: (a) the shortest distance from the sampling location

to the main city, (b) settlement percentage, (c) population density and (d) industrial

GDP per capita

Fig. S5 Hydrological conditions: (a) river width, (b) river depth, (c) sinuosity and (d)

velocity