Criteria for assessing the ecological risk of nonylphenol for aquatic life in Chinese surface fresh water

Accepted Manuscript Criteria for assessing the ecological risk of nonylphenol for aquatic life in Chinese surface fresh water Liangmao Zhang, Caidi Wei, Hui Zhang, Mingwei Song PII:

S0045-6535(17)30937-2

DOI:

10.1016/j.chemosphere.2017.06.035

Reference:

CHEM 19429

To appear in:

ECSN

Received Date: 13 March 2017 Revised Date:

24 May 2017

Accepted Date: 9 June 2017

Please cite this article as: Zhang, L., Wei, C., Zhang, H., Song, M., Criteria for assessing the ecological risk of nonylphenol for aquatic life in Chinese surface fresh water, Chemosphere (2017), doi: 10.1016/ j.chemosphere.2017.06.035. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Criteria for assessing the ecological risk of nonylphenol for

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aquatic life in Chinese surface fresh water Liangmao Zhang a, Caidi Wei a, Hui Zhang b*, Mingwei Song a*

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Affiliation

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a

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University, Key Laboratory of Arable Land Conservation (Middle and Lower Reaches

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of Yangtze River), Ministry of Agriculture, Wuhan 430070, China;

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b

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Laboratory of Environmental Planning and Management of Huazhong Agricultural

Smart City Research Institute, College of Civil Engineering, Shenzhen University,

Shenzhen 518060, China;

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Tel.: +86-15871384900;

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E-mail: [email protected] (Mingwei Song)

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E-mail: [email protected] (Hui Zhang)

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Address: No.1, Shizishan Street Hongshan District Wuhan Hubei Province 430070

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P.R.China.

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ACCEPTED MANUSCRIPT Highlights

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The minimum sample size was 12 for acute and 13 for chronic NP toxicity

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Both maximum and continuous concentration criteria were derived for NP

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NP pollution presented a low to moderate risk in Chinese surface fresh waters

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ACCEPTED MANUSCRIPT Abstract

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The typical environmental endocrine disruptor nonylphenol is becoming an

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increasingly common pollutant in both fresh and salt water; it compromises the growth

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and development of many aquatic organisms. As yet, water quality criteria with respect

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to nonylphenol pollution have not been established in China. Here, the predicted “no

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effect concentration” of nonylphenol was derived from an analysis of species

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sensitivity distribution covering a range of species mainly native to China, as a means

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of quantifying the ecological risk of nonylphenol in surface fresh water. The resulting

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model, based on the log-logistic distribution, proved to be robust; the minimum sample

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sizes required for generating a stable estimate of HC5 were 12 for acute toxicity and 13

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for chronic toxicity. The criteria maximum concentration and criteria continuous

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concentration were, respectively 18.49 µg L-1 and 1.85 µg L-1. Among the 24 sites

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surveyed, two were associated with a high ecological risk (risk quotient >1) and 12 with

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a moderate ecological risk (risk quotient >0.1). The potentially affected fraction ranged

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from 0.008% to 24.600%. The analysis provides a theoretical basis for both short- and

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long-term risk assessments with respect to nonylphenol, and also a means to quantify

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the risk to aquatic ecosystems.

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Keywords: Nonylphenol; Species sensitivity distribution; Criteria maximum

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concentration; Criteria Continuous concentration;

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1 Introduction

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As the main degradation product of nonylphenol ethoxylates (NPEOs),

ACCEPTED MANUSCRIPT nonylphenol (NP) has been shown to mimic the effects of estrogen (Zha et al., 2007),

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inhibit the activity of antioxidant enzymes (Wu et al., 2011), tissue-specific effects

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(Watanabe et al., 2004) and cause baseline narcosis (Tollefsen et al., 2008). It has also

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been suggested to have mutagenic, carcinogenic and teratogenic properties (Gao and

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Tam, 2011); its presence in surface water compromises the growth and development of

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many aquatic organisms (Lee et al., 2008; Teil et al., 2016). The leakage of

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industry-generated NP has a substantial negative impact on ecosystem structure and

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function (Soares et al., 2008). As yet, the extent of the risk of NP pollution to aquatic

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ecosystems in China has not quantified, and in particular, local water quality criteria

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(WQC) (Stephan et al., 1985; Wu et al., 2010) are yet to be established. A “predicted no-effect concentration” (PNEC) has been suggested as a measure

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for WQC: its derivation is based on the two parameters “criterion maximum

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concentration” (CMC) and “criterion continuous concentration” (CCC) (Stephan et al.,

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1985). The sensitivity of a range of organisms to a given stress factor can be modeled

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by their species sensitivity distribution (SSD) (van Straalen, 2002; Dyer et al., 2008).

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Because the actual distribution of toxicity endpoints is not generally known, toxicity

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data, usually in the form of the half maximal effective concentration (EC50) and/or the

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“no observed effect concentration” (NOEC), are typically used to produce an SSD and

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to construct a cumulative distribution function (González-Doncel et al., 2006). Once

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the probability distribution function has been characterized, the “potentially affected

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fraction” (PAF) for each of the species represented within the community can be

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determined by applying a fixed level of stress (Klepper et al., 1998). Following this step,

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ACCEPTED MANUSCRIPT the concentration of the pollutant corresponding to the cumulative probability of a set

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threshold percentage is calculated, this being referred to as the “maximum

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environmental hazardous concentration” (HCp, where p is typically set to 5). The HCp

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provides a theoretical basis for developing an environmental quality standard, and can

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be used to determine the value of PNEC.

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Here, a set of ecological toxicity endpoint data relating to the NP sensitivity of a

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set of mainly Chinese native aquatic species was used to estimate WQC, CMC and

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CCC. Two ecological risk assessment methods, namely the quotient method and the

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probability method, were combined to assess the risk of NP to China’s surface fresh

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water, with the intention of providing a theoretical basis for an objective ecological risk

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assessment.

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2 Materials and methods

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2.1 NP toxicity data

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NP toxicity levels were adopted from the existing literature and the US EPA

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toxicity database (cfpub.epa.gov/ecotox/); data were selected based on their suitability,

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reliability and accuracy (Wheeler et al., 2002; Lei et al., 2009): the detailed selection

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criteria employed are presented in the Supplementary Material S3, S6 and S7. To best

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reflect the local situation, the greatest emphasis was placed on species native to China

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and on the three widely distributed introduced species Oncorhynchus mykiss, Lepomis

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macrochirus and Oreochromis aureus. Acute toxicity data were acquired by exposing

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ACCEPTED MANUSCRIPT the invertebrate species for 48 h and the vertebrate ones and the algae for 96 h; the

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relevant toxicological endpoints were the LC50 (median lethal concentration) or the

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EC50 (half maximal effective concentration), which reflect the compounds' effect on

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survival and growth. Chronic toxicity was assessed in tests, lasting at least four days,

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with respect to biomass, growth, reproduction, survival, morphology, histology and

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hematology; the relevant toxicological endpoints were NOEC (no observed effect

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concentration) where available, but otherwise EC10 (10% effective concentration) (Jin

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et al., 2015), half of the LOEC (lowest observed effect concentration) or the MATC

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(maximum acceptable toxicant concentration). In the situation where multiple endpoint

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data were available for a given species, the geometric mean was taken as representative.

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The decision procedures employed are detailed in Supplementary Material S2.

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2.2 Establishment of the SSD model and statistical analysis

The construction of SSD model was based on implementing both the parameter

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fitting and the non-parametric estimation method (Newman et al., 2000; Wang et al.,

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2015). The former assumes that the toxicity value reflects a certain distribution, here

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taken to be log-logistic (Aldenberg and Slob, 1993). The log-logistic cumulative

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distribution function is given by the expression:

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Y =

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1+ e

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, where Y represents the cumulative probability, X the value of the logarithm of the

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toxicity value; α and β are the calculated constants of the function. The development of

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which creates a level of uncertainty with respect to the estimates of the model

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parameters. Regression bootstrapping has been recommended for datasets of modest

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size (Grist et al., 2002), and can be used to quantify the uncertainty associated with the

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model (Efron and Tibshirani, 2010). Hence, here both α and β were determined using

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this method. The goodness-of-fit of the distributions was evaluated by implementing

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either the Kolmogorov-Smirnov or the Anderson-Darling test (Stephens, 1974). The

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establishment of the model and its verification were performed using software within

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the R v3.2.4 package (www.r-project.org). The SSD curve can be used to obtain the

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HCp: an HC5 implies that 95% of the species are unaffected (Straalen and Rijn, 1998).

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It can also be used to estimate the cumulative probability at a given dose. PAF is used to

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evaluate the ecological risk of a pollutant present at a given concentration. The HCp is

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used to obtain PNEC (=HCp/AF), where AF is an assessment factor, set here to two for

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the acute toxicity data and to one for the chronic toxicity data, consistent with the

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relevant literature (Stephan et al., 1985). HC5 and PAF were estimated using the

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bootstrap method (10,001 repetitions), allowing a confidence interval to be estimated

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following the implementation of the percentile method (Efron, 1982).

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2.3 The minimum sample size

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Reliable estimates of HC5 require there to be a certain minimum amount of data. A

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combination of bootstrap analysis and the log-logistic method was used to determine

ACCEPTED MANUSCRIPT the minimum sample size. The detailed process of this method is: First, 10,001 samples

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were derived from the original data by random sampling with replacement. Next, a set

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of 10,001 SSD curves was constructed, from which the HC5 was estimated (see

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Section 2.2). During this process, samples of size <5 were subjected to the

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Kolmogorov-Smirnov test, while those ≥5 were subjected to the Anderson-Darling test.

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Only those curves which passed the test (P values > 0.05) were used to calculate HC5.

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For the Kolmogorov-Smirnov test and Anderson-Darling test, the log-logistic

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cumulative distribution was chosen as the reference; the relevant parameters were

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estimated using a maximum likelihood method. The minimum sample size of the NP

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was then determined using change point analysis (Killick and Eckley, 2014; Zhao and

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Chen, 2015). For the change point analysis process, the HC5 data which to find have

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distinct characteristics of a change in mean and the tipping point is detected based on

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the mean changing with using the PELT (pruned exact linear time) method.

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The three established methods for ecological risk assessment are the risk quotient

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(RQ), the probabilistic ecological risk assessment and the multi-level risk assessment

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method. The choice here was for RQ, which represents the ratio between the

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environmental exposure concentration (EEC or PEC) - measured either by

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environmental monitoring or via model prediction – and PNEC. On the basis of the RQ,

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risk was divided into three strata: high (RQ≥1), moderate (1>RQ≥0.1) and low

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only provides a rough estimate of the risk level (Lei et al., 2009), it was combined with

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a probabilistic ecological risk assessment (Solomon and Sibley, 2002).

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3 Results

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3.1 Toxicity data and SSD models

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The acute toxicity of NP to fresh water organisms covered 27 species belonging to

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18 families and five phyla (Supplementary Material S4). The LC50 or EC50 values

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ranged from 35.00 µg L-1 (EC50 for the arthropod Hyalella azteca) to 9,740 µg L-1 (LC50

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for the chordate Clarias gariepinus). Chronic toxicity levels were determined for 22

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species belonging to 17 families and nine phyla (Supplementary Material S5). Here, the

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NOEC values ranged from 2.2 µg L-1 (Cyprinus carpio) to 901.0 µg L-1 (Lemna minor).

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The resulting SSD curves are shown as Fig. 1. Importantly, different species can show a

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variable level of both acute and chronic toxicity. The molluscs (mainly clams) and algae

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clustered in the upper part of both SSDs, since they tended to be the least sensitive

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species with respect to NP; based on LC50 or EC50 values, most arthropods were

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deemed to be somewhat sensitive, so were grouped within the lower part of the acute

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SSD. However, when faced with chronic toxicity, they clustered in the central-upper

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portion of the distribution, confirming that when assessed on the basis of either

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reproduction or survival, some arthropods appeared to be less sensitive than certain

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vertebrates based on reproduction, histology or hematology endpoints in long term tests.

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The vertebrate species (mainly fish) were widely distributed in both the acute and the

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chronic SSDs, although most of the species lay in the lower-middle portion of the

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distribution.

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The HC5 and the α and β parameters of the acute and chronic SSDs estimated by

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the maximum likelihood method were within the 95% confidence interval produced by

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the bootstrap method, and the 95% confidence intervals obtained from both original

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two datasets were similar to those derived from the simulated datasets (Supplementary

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Material S8). On this basis, the SSD model was held to be robust. The outcome of the

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goodness-of-fit tests are shown in Table 1: since the P values were above 0.05, the SSD

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models were deemed to reflect the distribution of NP sensitivity among the set of

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aquatic organisms. Increasing the number of species considered resulted initially in a

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sharp decline in HC5 up to the point when no further change to HC5 was induced by

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adding to the number of species represented (Fig. 2). The change point analysis

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indicated that the minimum sample size for the acute data was 12 and for the chronic

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data was 13. The bootstrap method-derived estimates for the HC5's, along with their

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associated 95% confidence interval matched well (acute: 36.98 µg L-1, chronic: 1.85 µg

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L-1) with those estimated using the maximum likelihood method (acute: 34.99 µg L-1,

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chronic: 1.67 µg L-1) (Table 2), implying their inherent reliability. Based on the HC5 and

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AF values, the resulting CMC and CCC values was calculated as 18.49 µg L-1 and 1.85

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ACCEPTED MANUSCRIPT µg L-1 for acute and chronic toxicity, respectively.

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4 Discussion

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4.1 The SSD and WQC for NP

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The SSD, which allows for an assessment of the ecotoxicological hazard of a

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compound based on single species toxicity tests (Maltby et al., 2005), has been widely

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used to establish environmental quality criteria and to assess ecological risk (Posthuma

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et al., 2001). Here, toxicity data related to mostly native species, but including three

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non-native species widely cultured in China, along with Danio rerio, the international

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standard toxicity test species (Yang et al., 2012), was used to develop a multi-species

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SSD model for NP. While the recommendation is to establish a national WQC using

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toxicity data relevant for native species (Stephan et al., 1985), not every country has

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collected sufficient toxicity data to allow this. However, increasingly, it appears that

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there is little significant difference between SSDs derived from native and non-native

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species (Jin et al., 2011; Wang et al., 2013). A key outcome of the SSD is the estimate of

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HC5, which is strongly influenced by the sample size. Combining a bootstrap analysis

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with the log-logistic method allowed an estimate to be made here of the minimal sample

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size (12 for acute toxicity data and 13 for chronic toxicity data) sufficient to deliver a

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robust prediction for HC5. This result is largely consistent with the conclusion of both

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Wheeler et al. (2002) and Zhao and Chen (2015) that a sample of at least ten is needed.

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CMC and CCC have been widely adopted to quantify WQC (March et al., 2007;

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ACCEPTED MANUSCRIPT Rand et al., 2010). For the CMC associated with NP pollution, the US EPA has has

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suggested a level of 28 µg L-1 for fresh water ecologies (US EPA, 2005), which is rather

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higher than the present estimate of 18 µg L-1. The NP CCC, according to Hahn et al.

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(2014) and Jin et al. (2014b), should be around 1 µg L-1 based on a full dataset, and

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somewhat above this level when based on biochemical and molecular biology; both of

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these estimates are consistent with the present one. According to the USA-based EPA,

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the CCC in fresh water is 6.6 µg L-1, a level which is three to four fold greater; this

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difference reflects a policy of not using histological or hematological observations for

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setting criteria, concentrating instead on tracking survival and growth. Consequently,

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this CCC ignores the two lowest NOECs used here: the hematological effects observed

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by Schwaiger et al. (2000) with Cyprinus carpio and the histological effects observed

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by Zha et al. (2007) with Gobiocypris rarus, but could also be methodologically-based

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(SSD vs Acute-Chronic Ratios method). The PNEC tends to be heavily influenced by a

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small number of low chronic values, while the difference between the methods used for

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extrapolation may also result in variation in the final result.

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Based on histological and hematological effects, the most sensitive species were

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Gobiocypris rarus and Cyprinus carpio, although in terms of growth, neither of these

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species of fish appeared to be especially sensitive to NP. When could obtained

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sufficient basic data based on the native species, the PNEC derived based on the same

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kind of effect endpoints, particularly in the more sensitive effects can better reflect the

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harm condition of the aquatic life from exposure to NP. Apart from the influence of NP

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on species abundance, its presence also has a significant influence over an ecosystem's

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diversity and sustainability, factors which are not reflected in the SSD. Therefore, the

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establishment of SSD model based on ecological data may provide a more accurate

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assessment of the ecological risk associated with NP.

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4.2 Ecological risk assessment in the surface waters of China

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The NP data for the 24 surface waters were obtained by Wang et al. (2012a). As is

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clear from Supplementary Material S9, the concentration of NP in the sampled surface

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waters ranged from 0.005 µg L-1 to 9.770 µg L-1, averaging 0.805 µg L-1. These levels

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are rather higher than have been recorded in more developed economies such as

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Germany (up to 0.13 µg L-1) (Kuch and Ballschmiter, 2001), Switzerland (up to 0.48 µg

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L-1) (Fenner et al., 2002) and Austria (up to 0.89 µg L-1) (Hohenblum et al., 2004). The

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range in NP concentration in the sampled surface waters, along with their associated

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PAF values, are illustrated in Fig. 3, and the calculated RQs are presented in Fig. 4. The

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range in RQ and PAF for the 24 sites was, respectively, 0.003 to 5.281 (mean 0.435) and

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0.008% to 24.600% (mean 2.200%). The high risk group (RQ>1) included the Jialing

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River in Chongqing and the Qiantang River in Zhejiang province, the moderate risk

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group (1>RQ≥0.1) included over half of the sites, most of which (e.g. the Mai Po

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Marshes Nature Reserve in Hongkong, the Yundang Lagoon in Fujian province and the

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Ningbo Inner River in Zhejiang province) are sited in an estuary within an industrially

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developed region. The highest RQ was recorded in water from the Qiantang River,

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where the PAF was >0.2. This river's water has become heavily polluted by emissions

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ACCEPTED MANUSCRIPT from many factories and chemical plants (Su et al., 2011). The lowest RQ (0.003) and

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PAF (0.008%) were recorded in water from Dian Lake (Yu province), which suffers

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heavily from eutrophication (Wang et al., 2012b). The region is not heavily

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industrialized, which probably accounts for the low RQ and PAF values in the lake.

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5 Conclusions

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NP can cause serious environmental problems, yet the WQC needed to perform an

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objective risk assessment for Chinese surface waters has not yet been elaborated. The

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aim of this study was to exploit the SSD concept to derive a credible WQC for NP. The

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level of NP pollution in most of the sampled waters presented a low to moderate risk,

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but a number of heavily polluted bodies of water face a high risk. A comprehensive risk

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assessment needs to involve as full a set ecological factors as possible, but the relevant

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data for NP remain rather limited. There is a priority both to develop more sophisticated

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risk evaluation methods and to actively monitor WQC at as many sites as possible.

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Acknowledgement

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Thanks to financial support from the National Nature Science Foundation of

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China (41101494) and the Fundamental Research Funds for the Central Universities,

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and are appreciated Jinsong Zhao kindly provided the R code.

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appropriate protection for native species? Environ. Toxicol. Chem.34, 1793. Killick, R., Eckley, I.A., 2014. changepoint: An R Package for Changepoint Analysis. J. Stat. Softw. 58, 1-19. Klepper, O., Bakker, J., Traas, T.P., van de Meent, D., 1998. Mapping the potentially affected fraction (PAF) of species as a basis for comparison of ecotoxicological risks between substances and regions. J. Hazard. Mater. 61, 337-344. Kuch, H.M., Ballschmiter, K., 2001. Determination of endocrine-disrupting phenolic compounds and estrogens in surface and drinking water by HRGC-(NCI)-MS in the picogram per liter range. Environ. Sci. Technol. 35, 3201-3206. Kwok, K.W., Leung, K.M., Lui, G.S., Chu, V.K., Lam, P.K., Morritt, D., Maltby, L., Brock, T., Van den Brink, P.J., Warne, M.S.J., 2007. Comparison of tropical and temperate freshwater animal species' acute sensitivities to chemicals: Implications for deriving safe extrapolation factors. Integr. Environ. Assess. Manag. 3, 49-67. Lee, J., Lee, B.C., Ra, J.S., Cho, J., Kim, I.S., Chang, N.I., Kim, H.K., Kim, S.D., 2008. Comparison of the removal efficiency of endocrine disrupting compounds in pilot scale sewage treatment processes. Chemosphere 71, 1582-1592. Lei, B., Huang, S., Wang, Z., 2009. Theories and methods of ecological risk assessment. Prog. Chem. 21, 350-358. Maltby, L., Blake, N., Brock, T., Van den Brink, P.J., 2005. Insecticide species sensitivity distributions: importance of test species selection and relevance to aquatic ecosystems. Environ. Toxicol. Chem. 24, 379-388. March, F.A., Dwyer, F.J., Augspurger, T., Ingersoll, C.G., Wang, N., Mebane, C.A., 2007. An evaluation of freshwater mussel toxicity data in the derivation of water quality guidance and standards for copper. Environ. Toxicol. Chem. 26, 2066-2074. Newman, M.C., Ownby, D.R., Mézin, L.C.A., Powell, D.C., Christensen, T.R.L., Lerberg, S.B., Britt‐Anne, A., 2000. Applying species-sensitivity distributions in ecological risk assessment: assumptions of distribution type and sufficient numbers of species. Environ. Toxicol. Chem. 19, 508-515. Posthuma, L., Suter II, G.W., Traas, T.P., 2001. Species sensitivity distributions in ecotoxicology. CRC press. Rand, G.M., Carriger, J.F., Gardinali, P.R., Castro, J., 2010. Endosulfan and its metabolite, endosulfan sulfate, in freshwater ecosystems of South Florida: a probabilistic aquatic ecological risk assessment. Ecotoxicology 19, 879-900. Salgueiro-Gonzalez, N., Turnes-Carou, I., Besada, V., Muniategui-Lorenzo, S., Lopez-Mahia, P., Prada-Rodriguez, D., 2015. Occurrence, distribution and bioaccumulation of endocrine disrupting compounds in water, sediment and biota samples from a European river basin. Sci. Total. Environ. 529, 121-130. Soares, A., Guieysse, B., Jefferson, B., Cartmell, E., Lester, J., 2008. Nonylphenol in the environment: a critical review on occurrence, fate, toxicity and treatment in wastewaters. Environ. Int. 34, 1033-1049. Solomon, K.R., Sibley, P., 2002. New concepts in ecological risk assessment: where do we go from here? Mar. Pollut. Bull. 44, 279-285. Stephan, C.E., Mount, D.I., Hansen, D.J., Gentile, J., Chapman, G.A., Brungs, W.A.,

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1985. Guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms and their uses. National Technical Information Service, Springfield, VA, USA, No. PB85-227049. Stephens, M.A., 1974. EDF Statistics for Goodness of Fit and Some Comparisons. J. Am. Stat. Assoc. 69, 730-737. Straalen, N.M.V., Rijn, J.P.V., 1998. Ecotoxicological Risk Assessment of Soil Fauna Recovery from Pesticide Application, New York. Su, S., Zhi, J., Lou, L., Huang, F., Chen, X., Wu, J., 2011. Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques. Phys. Chem. Earth. 36, 379-386. Teil, M.-J., Moreau-Guigon, E., Blanchard, M., Alliot, F., Gasperi, J., Cladière, M., Mandin, C., Moukhtar, S., Chevreuil, M., 2016. Endocrine disrupting compounds in gaseous and particulate outdoor air phases according to environmental factors. Chemosphere 146, 94-104. Tollefsen, K.E., Blikstad, C., Eikvar, S., Finne, E.F., Gregersen, I.K., 2008. Cytotoxicity of alkylphenols and alkylated non-phenolics in a primary culture of rainbow trout (Onchorhynchus mykiss) hepatocytes. Ecotox. Environ. Safe. 69, 64-73. US EPA, 2005. Aquatic life ambient water quality criteria—nonylphenol. US Environmental Protection Agency (EPA), Office of Water, Office of Science and Technology, Washington DC, EPA-822-R-05-005. van Straalen, N.M., 2002. Threshold models for species sensitivity distributions applied to aquatic risk assessment for zinc. Environ. Toxicol. Pharmacol. 11, 167-172. Wang, L., Ying, G.G., Chen, F., Zhang, L.J., Zhao, J.L., Lai, H.J., Chen, Z.F., Tao, R., 2012a. Monitoring of selected estrogenic compounds and estrogenic activity in surface water and sediment of the Yellow River in China using combined chemical and biological tools. Environ. Pollut. 165, 241-249. Wang, X.N., Liu, Z.T., Yan, Z.G., Zhang, C., Wang, W.L., Zhou, J.L., Pei, S.W., 2013. Development of aquatic life criteria for triclosan and comparison of the sensitivity between native and non-native species. J. Hazard. Mater. 260, 1017-1022. Wang, Y., Wu, F., Giesy, J.P., Feng, C., Liu, Y., Qin, N., Zhao, Y., 2015. Non-parametric kernel density estimation of species sensitivity distributions in developing water quality criteria of metals. Environ. Sci. Pollut. Res. 22, 13980-13989. Wang, Z., Zhang, Z., Zhang, J., Zhang, Y., Liu, H., Yan, S., 2012b. Large-scale utilization of water hyacinth for nutrient removal in Lake Dianchi in China: the effects on the water quality, macrozoobenthos and zooplankton. Chemosphere 89, 1255-1261. Watanabe, H., Suzuki, A., Goto, M., Lubahn, D.B., Handa, H., Iguchi, T., 2004. Tissue-specific estrogenic and non-estrogenic effects of a xenoestrogen, nonylphenol. J. Mol. Endocrinol. 33, 243-252. Wheeler, J.R., Grist, E.P., Leung, K.M., Morritt, D., Crane, M., 2002. Species sensitivity distributions: data and model choice. Mar. Pollut. Bull. 45, 192–202. Wu, F., Meng, W., Zhao, X., Li, H., Zhang, R., Cao, Y., Liao, H., 2010. China

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Figure captions

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Fig. 1. The SSD for NP based on acute and chronic toxicity data. The red solid line

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represents the SSD and the blue dotted lines indicate the 95% confidence interval. The

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shadowed area is the estimated 95% confidence interval based on a bootstrap analysis

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of simulated data.

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Fig. 2. The sensitivity of HC5 to sample size.

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Fig. 3. The PAF related to the level of NP pollution in a selection of surface fresh water

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in China.

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Fig. 4. The RQ for NP in a selection of surface fresh water in China.

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Table. 1. Goodness-of-fit of the SSD models.

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Table. 2. Estimation of HC5 values based on the SSD model.

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ACCEPTED MANUSCRIPT Table 1 Goodness-of-fit of the SSD models Testing method

Statistic

P value

K-S test A-D test K-S test A-D test

0.12405 0.49204 0.11343 0.35069

0.7548 0.7534 0.9097 0.8948

Acute toxicity (FA) Fresh water Chronic toxicity (FC)

Table 2 Estimation of HC5 values based on the SSD model

Estimated values of HC5 by maximum likelihood method

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Type

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Type

Acute toxicity (FA)

36.98(14.72,83.18)

34.99(15.03,81.28)

Chronic toxicity (FC)

1.85(0.50,5.51)

1.67(0.53,5.33)

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