Environmental Pollution 191 (2014) 80e92

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AQUATOX coupled foodweb model for ecosystem risk assessment of Polybrominated diphenyl ethers (PBDEs) in lake ecosystems Lulu Zhang, Jingling Liu* State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, No.19, Xinjiekou Waidajie, Haidian District, 100875 Beijing, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 November 2013 Received in revised form 18 February 2014 Accepted 4 April 2014 Available online xxx

The AQUATOX model considers the direct toxic effects of chemicals and their indirect effects through foodwebs. For this study, the AQUATOX model was applied to evaluating the ecological risk of Polybrominated diphenyl ethers (PBDEs) in a highly anthropogenically disturbed lake-Baiyangdian Lake. Calibration and validation results indicated that the model can adequately describe the dynamics of 18 biological populations. Sensitivity analysis results suggested that the model is highly sensitive to temperature limitation. PBDEs risk estimate results demonstrate that estimated risk for natural ecosystems cannot be fully explained by single species toxicity data alone. The AQUATOX model could provide a good basis in ascertaining ecological protection levels of “chemicals of concern” for aquatic ecosystems. Therefore, AQUATOX can potentially be used to provide necessary information corresponding to early warning and rapid forecasting of pollutant transport and fate in the management of chemicals that put aquatic ecosystems at risk. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: Polybrominated diphenyl ethers (PBDEs) Ecosystem risk assessment AQUATOX model Pelagic-benthic coupled foodweb

1. Introduction Polybrominated diphenyl ethers (PBDEs) have been widely used as additive brominated flame retardants in plastics, textiles, electronic appliance, and other materials used for consumer products. Due to their bioaccumulation potential, environmental persistence, and potential toxicities for human, PBDEs are included amongst six environmentally hazardous substances in the European Union (Hu et al., 2010a). Although several regulatory measures have been initiated at local, regional and global levels to control the production and use of some of these chemicals (Harrad and Abdallah, 2011), PBDEs use is not regulated in Asia. Furthermore, the domestic demand of brominated flame retardants (BFRs) has increased at a rate of 8% annually in China (Mai et al., 2005), the total production of BFRs was 120,750 tons in 2009 (GZCCM, 2010). In addition, as China is currently the world’s largest importer and recycler of e-waste, the import of e-waste from overseas has aggravated PBDEs contamination. The ubiquitous presence and the lipophilic nature of PBDEs lead to their biomagnification in aquatic organisms along the foodweb, which may pose a threat to aquatic

* Corresponding author. E-mail addresses: [email protected] (L. Zhang), [email protected] (J. Liu). http://dx.doi.org/10.1016/j.envpol.2014.04.013 0269-7491/Ó 2014 Elsevier Ltd. All rights reserved.

organisms and the health of the ecosystem. Although the biomagnification potential of PBDEs has been well documented in recent studies (Wolkers et al., 2004; Wan et al., 2008; Wu et al., 2009), few studies have evaluated the ecological risk of PBDEs in a freshwater ecosystem (Cristale et al., 2013; Seguí et al., 2013). Conventional methods and technologies used to assess the ecological risk of chemicals have many disadvantages (Kennedy et al., 1995); they are expensive (Lei et al., 2008), time-consuming (Zhang et al., 2013), and contain a large measure of uncertainty (Fleeger et al., 2003). Ecological models are a cost-effective alternative tool to estimate the ecological risk of toxic pollutants (Ray et al., 2001; Kumblad et al., 2003; Park et al., 2008). Examples of such ecological risk models are the Integrated Fate and Effect Model (IFEM) (Bartell et al., 1988), the Comprehensive Aquatic Systems Model (CASM) (Bartell et al., 1999), CATS-5 (Traas et al., 2001), and AQUATOX (Park and Clough, 2004). The AQUATOX model has been widely applied by researchers in streams, ponds, lakes, estuaries, reservoirs, and experimental enclosures (Park et al., 2005; Rashleigh et al., 2009; McKnight et al., 2010; Wang et al., 2012). Thus, AQUATOX is the most comprehensive of the general ecological risk models presently available. The advantage of the AQUATOX model is not only considering the direct toxic effects of chemicals, but also contains the indirect effects of pollutants through foodwebs.

L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92

Indirect effects of xenobiotics are defined as a change in species interactions caused by a chemical, and the indirect effects might be more common than direct chemical effects (Brock et al., 2000; Relyea and Hoverman, 2006; Clements and Rohr, 2009). Indirect effects can be transmitted within or across trophic levels of speciesinteraction webs by chemicals that cause sublethal effects (trait changes), lethal effects (density changes), or both. The same is probably true for contaminants, because at concentrations commonly found in nature, contaminants almost certainly cause more trait changes than direct mortality does (Preisser et al., 2005; Rohr et al., 2008). Unfortunately, because of their subtlety (Lurling and Scheffer, 2007; Rohr et al., 2009), the indirect effects have remained understudied and not thoroughly incorporated into ecological risk assessment (Clements and Rohr, 2009). At present, community ecology theory has been proposed as a framework for predicting indirect effects of contaminants to facilitate their integration into ecological risk assessment (Rohr et al., 2006). The direct and indirect effects of PBDEs may pose an unacceptable risk to aquatic organisms, wildlife and humans (Eljarrat et al., 2004), so it was selected for investigation for this study. In addition, more and more studies have been pay attention to the emerging pollutants in China, especially for PBDEs (Bao et al., 2012; Lau et al., 2012). Previous studies showed persistent organic pollutants (POPs) such as PBDEs were detected in the sediments from 12 Chinese lakes (Wu et al., 2012). And the bioaccumulation of PBDEs in the lake foodweb has been addressed in previous studies (Hu et al., 2010b). However, the ecological risk of PBDEs in the highly impacted Baiyangdian Lake has not previously been considered. Therefore, developing an ecosystem risk estimation model for Baiyangdian Lake could subsequently be used as a generic chemical risk estimation model for highly impacted lake ecosystems in China. The aims of this study were to examine the utility of the AQUATOX-Baiyangdian model in assessing ecological risks relating to PBDEs contamination of natural aquatic ecosystems. As far as it can be determined, this study is the first published account of an AQUATOX application that not only includes the direct toxic effects of PBDEs, but also includes the indirect effects of PBDEs through foodwebs as well as the first published ecological risk assessment of PBDEs by pelagic-benthic coupled foodweb based model.

2. Materials and methods 2.1. The physical and biological characteristics of the lake 2.1.1. The physical characteristics Baiyangdian Lake (38
440 e38
590 N, 115
450 e116
060 E) covers an area of approximately 366 km2, situated within the jurisdiction of the city of Baoding, Hebei Province, China. The extensive shoreline of Baiyangdian Lake makes it highly sensitive to the changes of the surrounding landscapes and environment. Basic hydrological data and water quality parameters were collected from Baiyangdian Lake field observations and historical records. They were compiled (summarized in Table S1, “S” designated the tables and figures in the supplementary material) to establish initial model inputs from which to determine the environmental characteristics of the lake. According to the characteristics of land use, we select 8 study sites to analyze the ecological risk of the aquatic ecosystem which caused by PBDEs pollution (Fig. 1), the study sites can be subdivided into 3 anthropogenic disturbance levels: greatly influenced by wastewater discharge (Sites 1 and 2), impacted by aquaculture and densely populated villages (Sites 3, 6, and 8), and the least human disturbances (Sites 4, 5, and 7) (Table 1).

2.1.2. The biological characteristics In the study site, the conceptual model of lake ecosystem was represented in Fig. 2, which can be divided into three compartments: (1) primary producers (periphyton, macrophytes, and phytoplankton), (2) consumers (zoobenthos, zooplankton, and fish), and (3) detritus. Each box or circle represents one model population or nonliving ecosystem component, and arrows denote the flow of energy or biomass.

81

Fig. 1. The sampling sites on Baiyangdian Lake (S1-Fuhe Inlet, S2-Nanliuzhuang, S3Wangjiazhai, S4-Shaochedian, S5-Zaolinzhuang, S6-Quantou, S7-Caiputai, and S8Duancun).

2.2. Sample collection 2.2.1. Phytoplankton Quantitative sample collection of phytoplankton is as follows: organic glass hydrophore was used to collect water samples, the volume of water sample was 1000 mL. Lugol’s solution was used to fix samples, and 1% (vol) formalin solution was used to preserve samples. The sample collection, treatment, analysis method for the phytoplankton samples were carried out according to the standard methods from Lake Ecosystem Observation Method (Chen et al., 2005). 2.2.2. Periphyton To collect periphyton samples, an artificial substrate (carbon fiber) was exposed in the littoral zone. Three Perspex carriers with 10 activated carbon fiber coupons (each fiber was 2 cm wide and 10 cm long) were placed near the Phragmites bed of

Table 1 The anthropogenic disturbance levels of the sampling sites. Sampling site

Coordinates

Land-use characteristics

S1

N38.9044
E115.9238


S2

N38.9045
E115.9348


S3 S4 S5

N38.9177
E116.0114
N38.9407
E115.9997
N38.9021
E116.0804


S6 S7 S8

N38.8604
E116.0282
N38.8249
E116.0102
N38.8470
E115.9506


Greatly influenced by wastewater inflow from Baoding City Greatly influenced by wastewater inflow from the Fu River, minor aquaculture, small village Major aquaculture, dense village Minor aquaculture The outlet of the Baiyangdian, minor human disturbances Major aquaculture, near to village Minor aquaculture, small village Major aquaculture, dense village

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L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92

Fig. 2. The conceptual model of the whole lake foodweb (The value of number in the box is the initial biomass, mg/L for phytoplankton and zooplankton, and g/m2 for periphyton, macrophyte and zoobenthos, and the biomass which come from the field monitoring data in March, 2009).

each sampling site. The carriers were placed horizontally and positioned at a depth of 20 cm below the water surface. The fibers were placed vertically and exposed to lake water for 21 days. The fibers were scratched off using brush tweezers and washed repeatedly with sterile water to ensure that no periphyton residues remained. The detached periphyton samples were then divided into two portions. One portion of the wet periphyton sample was added to a 5% formalin solution in filtered lake water for composition studies, and the other portion of the wet periphyton sample was transferred into plastic bottles for other studies.

2.2.6. Fish Fish were sampled in both the pelagic and littoral zones with a variety of gear (electrofishing, gill nets, trap nets, seines, minnow traps) (Baker et al., 1997), the fish samples were fixed in 5%w10% buffered formaldehyde, all individuals captured were identified to species, and each species sample should be contained different size of fish.

2.3. AQUATOX model and parameterization 2.2.3. Macrophyte Macrophytes were collected in every sampling site. Triplicate samples of aboveground standing crop were harvested from 0.25 m2-quadrats with mesh towers (to collect floating macrophytes) at each depth. Plants were identified to species, dried at 80
C to constant weight, and then weighed. Samples were calibrated to the initial biomass of macrophyte populations.

2.2.4. Zooplankton Zooplankton was collected with a single vertical tow beginning 0.5 m from the bottom at the lake’s deepest area (Baker et al., 1997). Two nets (pore size: 64 mm) were towed (10 min) slowly to collect both macrozooplankton and microzooplankton; both sizes were identified to species.

2.2.5. Benthic macroinvertebrate Benthic macroinvertebrate samples were collected with a 1/16 m2 Peterson grab to collect sample, and three replicate samplings were taken at each of the eight sample sites. The samples were sieved over 0.595 mm mesh and fixed in 5% buffered formaldehyde. After sorting and determining the species abundance, the organisms were preserved in 75% alcohol. In the laboratory, samples were identified to the lowest possible taxonomic level (usually genus).

AQUATOX is a comprehensive ecosystem model that predicts the environmental fate and ecological risk of chemicals in aquatic ecosystems. AQUATOX provides probabilistic modeling approaches by allowing the user to specify types of distribution and key statistics for any or all input variables. The chemical fate component of AQUATOX predicts partitioning of a chemical into water, sediment, particulate, and biota. The effects component includes a direct toxic effect constituent used to extrapolate potential chemical effects on aquatic ecosystem biomass production from acute and chronic toxicity data (LC50 or EC50) of single species from various organisms modeled, indirect effects such as an increase in detritus as well as nutrient recycling from detritus, and dissolved oxygen sag due to increased decomposition.

2.3.1. Biomass and physiological parameters Species typically found in Baiyangdian Lake have been previously modeled (Zhang et al., 2013), the model comprise of 18 dominant populations. The original version of AQUATOX was adapted to the foodweb structure of Baiyangdian Lake. The initial biomass of modeled organisms was obtained from literature, observed data, or approximated via historical records. The main parameters of the model have been surveyed for primary producers and consumers in Table 2. The growth of each population is determined by environmental conditions, population biomass, and the specific physiological parameters of each population (Tables S2 and S3). Relevant

L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92 Table 2 List of the parameters that have been surveyed for primary producers and consumers in the scientific literature. Populations (Abbreviation including Table S2 and S3) (Units) Producers populations Biomass (B0) (mg/L or g/m2) Saturating light (Ls) (Ly/d) P-half saturation (KP) (mg/L) N-half saturation (KN) (mg/L) Inorganic C half saturation (mg/L) Temperature response slope optimal temperature (T0)(
C) Maximum temperature (
C) Minimum adaptation temperature (
C) Maximum photosynthetic rate (/d) (Pm) Photorespiration coefficient respiration rate at 20
C (Rresp) (g/g d) Mortality coefficient (Mc)(/d) Exponential mortality coefficient (/d) N to organics (% dry weight) P to organics (% dry weight) Light extinction (Le) (/m g/m3) Wet weight/dry weight ratio (W/D) (m/d) Sinking rate (Rsink)a Exponential sedimentation coefficienta Carrying capacity (g/m2)a Reduction in still watera Maximum current velocity (cm/s)a Critical force (N)a Percent in riffle Percent in pools

Consumers populations Biomass (B0) (mg/L or g/m2) Half saturation feeding (HS) (mg/L) Maximum consumption rate (Cm) (g/g d) Minimum prey for feeding (Pmin) (mg/L) Temperature response slope Optimal temperature (T0) (
C) Maximum temperature (
C) Minimum adaptation temperature (
C) Endogenous respiration (/d) Respiration rate (Rresp) Specific dynamic action excretion/respiration ratio N to organics (% dry weight) P to organics (% dry weight) Wet weight/dry weight ratio (W/D) Gametes weight/Biomass ratio (/d) Gamete mortality (/d) Mortality coefficient (MC) (/d) Carrying capacity (CC) (mg/L) Maximum current velocity (cm/ s) Mean lifespan (/d) Initial lipid content (Lf) (% wet weight) Mean weight (g) Percent in riffle Percent in pools

Note. a Only for some species.

physiological parameters were either taken from the original the AQUATOX model or acquired from biological and ecological literature (USEPA, 2004a,b,c). Zhang et al. (2013) briefly describes the basic equations used in AQUATOX to simulate daily biomass changes in each population. Detailed descriptions of the equations used can be found at the following web link: http://water.epa.gov/scitech/ datait/models/aquatox/data.cfm. 2.3.2. PBDEs model parameters PBDEs parameters included initial concentrations, physicochemical properties, and toxicity data. Maximum and minimum PBDEs measured field concentrations in Baiyangdian Lake were used as initial chemical concentrations for the model. These concentrations were obtained from both field observations and literature (Hu et al., 2010a). For the lowly brominated congeners (tri- to hepta-BDE), BDE47 and BDE99 were the most abundant, which contributed 52.1% and 44.1% to the sum of tri- to hepta-BDEs in the sediments, respectively. In addition, According to the result by Hu et al. (2010b), BDE-47 was the predominant PBDEs congener in most samples except

Table 3 Main physical and chemical properties of BDE-47 (According to the U.S. EPA). Chemical property CAS register no.

Value

Chemical property

5,436,431 Octanol-water partition coefficient at 25
C, log Kow Molecular weight (g/mol) 485.80 Organic carbon partition coefficient at 25
C, log Koa 0.0015 Vapor pressure Solubility in water (mm Hg at 25
C) (mg/L at 24
C) Henry’s law constant 0.301 Boiling point (
C) ((Pa m3)/mol at 25
C) 161.73 Relative density (at 25
C) Melting point (
C)

Value 6.77 10.686 2.41  107 405.51 2.28

83

Table 4 BDE-47 toxicity data application (some data calculated by the Interspecies Correlation Estimation (ICE) on http://www.epa.gov/ceampubl/fchain/webice/ iceBasicInfo.html). Accepted species Test time Toxicity Toxicity value References endpoint (mg/L) Diatoms Greens Bluegreens Cryptomonas Bacillariophyta Chlorophyta Cyanophyta Myriophyllum Duckweed Rotifer Copepoda Chironomidae Asian Snail Mussel Crab Shrimp Catfish Carp

48 96 48 48 48 48 48 48 48 96 96 96 96 96 96 96 360 360

EC50 EC50 EC50 EC50 EC50 EC50 EC50 EC50 EC50 LC50 LC50 LC50 LC50 LC50 LC50 LC50 LC50 LC50

1.99e2.25 0.79e1.52 0.353 1.52 2.75 5.55 0.601 319.46 319.46 3.44 72 359.60 258.00 425.31 451.97 217.45 1034.31 1618.15

Källqvist et al., 2006 According to ICE website According to ICE website According to ICE website According to ICE website According to ICE website According to ICE website According to ICE website According to ICE website According to ICE website Breitholtz et al., 2008 According to ICE website According to ICE website According to ICE website According to ICE website According to ICE website According to ICE website According to ICE website

Note: EC50: concentration resulting in 50% growth reduction in organisms tested; LC50: concentration resulting in 50% mortality in organisms tested.

for river snails and swan mussels. So in the study, we choose BDE-47 as the typical PBDEs. Tables 3 and 4 list the main physicochemical properties and single species toxicity data, respectively, of PBDEs summarized from literature or calculated by relevant databases (USEPA, 2006). When experiments applying identical test durations were made on a single species, the geometric means of EC50 and LC50 values were used (USEPA, 2004b). Due to the Animal and Plant toxicity require different parameters, so we choose the buttons Estimate animal LC50s and Estimate plant EC50s at the bottom of model screen.

2.4. Determining PBDEs concentration in sediment and organisms The total PBDEs concentration was modeled for this study. This measure is the most useful modeling endpoint from a management perspective. PBDE congeners in sediments were analyzed following previously established method with some modifications (Hu et al., 2010) in August 2009. The concentration of PBDEs in sediments ranged from 0.50 to 2.73 mg/g dry weight (DW) in 8 study sites, the concentrations were showed in Fig. 3. Among the PBDE congeners, BDE209 was the most predominant among all the PBDE congeners in the study area, with contributions to the total PBDEs ranging from 52.69 to 81.57% in sediment samples. For the tri- to hepta-BDE, BDE28 and BDE47 were the most abundant, which respectively contributed 0.00e14.45% and 0.00e15.77% to the sum of tri- to hepta-BDEs in the sediments from Baiyangdian Lake. AQUATOX assumes PBDEs found in sediment are associated with detritus, which is modeled in AQUATOX as organic matter. Initial levels of PBDEs in macrophyte, phytoplankton, periphyton, fish/invertebrates were set at values from which preliminary model runs were equilibrated. Elimination rates for macrophytes were calculated based on Gobas et al. (1999): k2ðMacrophyteÞ ¼

1 1:58 þ 0:000015kow  NondissocÞ

(1)

where kow is the octanolewater partition coefficient (unitless), and Nondissoc is the fraction of un-ionized toxicants (unitless). Elimination rates for algae (including periphyton and phytoplankton) were calculated based on Skoglund et al. (1996): k2ðAlgaeÞ ¼

2:4E þ 5 ðkow  LipidFrac  WetToDryÞ

(2)

where LipidFrac is the fraction lipid (wet weight) entered into the “chemical toxicity” screen, and WetToDry is the translation from the wet to dry weight (user input). Elimination rates for fish and invertebrates were calculated based on Barber (2003): k2ðFish=InvertebratesÞ ¼

C  WetWt0:197 LipidFrac  kow

(3)

84

L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92

Fig. 3. PBDEs concentration in the sediment of Baiyangdian Lake.

where C ¼ 445 for fish and C ¼ 890 for invertebrates; WetWt refers to the wet weight of an organism (g); LipidFrac is the lipid fraction in an organism (g lipid/ g organism wet); and kow is the octanolewater partition coefficient (unitless). Estimated lipid fraction and mean wet weight values were generally based on AQUATOX defaults (provided in Table 2).

2.5. Model calibration and validation Calibration of the model was performed with an experimental data set obtained under control conditions (i.e., without toxicant) during the 1-year survey period in phytoplankton biomass. Biomass values measured on March, 2009 were used as initial values for the biomass of the 18 populations. Time series of light intensity, wind speed, water temperature, and pH were also used as inputs. The model was run using these initial best estimates. Small changes in the input values of some parameters helped to increase the quality of the simulation following an iterative procedure. On each step of the calibration process, the accuracy of the simulations was estimated by the computation of three indices (Smith et al., 1997). First, the total difference between simulated and measured values was calculated as the root mean square error (RMSW) as follows: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ,ffi u n X 100 u 2 t RMSE ¼ ðPi  Oi Þ n O i¼1

EF ¼

i¼1

Oi  O

2



Pn

i¼1 ðPi



S2Ii ¼

s20;Ii s2Ii

(7)

where S2Ii is the sensitivity of output to changes in input; s20;Ii is the variance of output contributed by the uncertainty in the ith input parameter; and s2Ii is the variance in the lognormal distribution of the ith input parameter.

  Oi  O Pi  P rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi!qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   2 2 Pn  Pn i¼1 Oi  O i¼1 Pi  P

To examine a potential utility of the model in assessing ecological risk within an ecological context, risk estimate results obtained using AQUATOX-Baiyangdian were compared to no observed effect concentration (NOEC) values derived from multispecies field experiments. Probabilities of a 20% reduction in biomass for model populations were compared to experiment-derived NOEC (Exp-NOEC) values and other relevant criterion. This was because 20% is the minimum detectable difference in population characteristics in the field (Suter, 1993; Suter and Mabrey, 1994).

(5)

3.1. Calibration and validation

 is the mean of the observed values Oi, where n is the total number of observations, O and Pi represents the simulated values. Values for EF can be negative or positive with a maximum value of 1. A positive value indicates that the simulated values describe the trend in the measured data better than the mean of the observations. A negative value shows the opposite. Third, in order to assess whether simulated and measured values followed the same pattern, the correlation coefficient r was calculated as: Pn 

r ¼

To estimate uncertainty for each item of the input data and deduce probable errors in the output, the highest possible sensitivity input parameters should be identified. Efficient sampling from distributions is ascertained by using the Latin hypercube sampling (LHS) method (USEPA, 2004b). Sensitivity (S2Ii ) can be calculated as the ratio between output and input standard deviations:

3. Results

 Oi Þ2

2 Pn  i¼1 Oi  O

i¼1

2.6. Sensitivity analysis

2.7. Compared model-NOEC with experiment-NOEC (4)

 is the mean of the observed values Oi, where n is the total number of observation, O and Pi represents the simulated values. The lower the value of RMSE is, the better the model fits to the observed data. Second, the modeling efficiency (EF) provided a comparison between the efficiency of the chosen model and the efficiency of describing the data as the mean of the observations:  Pn 

were measured in 8 sample sites and the model parameterization that was defined during the calibration phase.

(6)

 is the mean of the observed values Oi, where n is the total number of observations, O and P is the mean of simulated values Pi. In order to validate the calibrated model, we simulated the functioning of 6 communities of the same sample sites during the same period of 1-year used for the calibration phase. The simulation was based upon the mean initial conditions which

Fig. 4 shows the comparison between observed and simulated values for the 6 biological communities included in the model for which observed values were available. In this respect, model performance showed it could portray the effects of ecological interactions. Overall, the AQUATOX reference simulation provided a reasonable representation of Baiyangdian Lake biological population behaviors. The corresponding values of the indices used to estimate the quality of the outputs of the calibrated model are summarized in Table 5. The value of RMSE is range from 6.75 to 20.45, the value of EF is range from 0.82 to 0.92, and the value of r is range from 0.995 to 0.998. A visual comparison of the observed and simulated values indicates that the simulation provided an acceptable representation of the behavior of the biomass of six biological communities.

L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92

85

Fig. 4. Results of the calibration phase. Comparison between measure data and model result for (a) phytoplankton community biomass, (b) periphyton community biomass, (c) macrophyte community biomass, (d) zooplankton community biomass, (e) benthos community biomass, and (f) fish community biomass.

3.2. Sensitivity analysis Table 6 lists the 3 parameters that were found to significantly influence the outcomes of the model, ranked in the decreasing

order of sensitivity indices. The larger the sensitivity index, the greater the contribution of the model parameters to the changes of the various state variable was. According to the result of sensitivity analysis, the first sensitive parameters refer to the maximum

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L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92

Table 5 Values of the goodness-of-fit criteria computed for the simulated biological state variables during the calibration phases. Community

RMSE

EF

r

Phytoplankton Periphyton Macrophyte Zooplankton Benthos Fish

17.48 7.85 6.75 20.45 10.90 10.67

0.90 0.92 0.87 0.82 0.91 0.82

0.996 0.996 0.996 0.998 0.998 0.995

photosynthetic rate (Pm) for Cryptomonas population; the respiration rate (R) for Diatom, Greens, Bacillariophyta, Chlorophyta, Cyanophyta, Myriophyllum, and Asian Mud snail populations; and the optimal temperature for Blue-greens, Duckweed, Rotifer, Copepoda, Chironomidae, Crab, Carp, and Catfish populations. The result suggests that the model in AQUATOX is highly sensitive to temperature limitation (Table 6).

competition pressure on Blue-greens, Cryptomonas, Chlorophyta, Rotifer, Copepoda, Chironomidae, and Catfish populations. It is interesting to note that at a 1.85 mg/g (S6) PBDEs exposure level, zero estimates of risk were detected for all producer populations even though PBDEs exposure concentrations increased. A likely explanation for this pattern of behavior is that the direct toxic effect of PBDEs can be reduced by a decrease in predatory pressure from consumers. At the same time, reduction risks in consumer populations rapidly increased, the probability was up to 2.87e 71.24%. Reasons for higher reduction risk in consumers are associated with the direct toxic effects of PBDEs as well as the biomagnification effect. At the higher exposure concentration in S1 and S2 (the range from 2.40 to 2.73 mg/g), except Myriophyllum population, the risk of a 10% reduction biomass were rapidly increased for the rest of producer populations, the probability was up to 1.27%e31.07%. While the reduction risk for consumer populations slightly increased.

3.4. Compared the model-NOEC with experiment-NOEC 3.3. PBDEs risk estimation Fig. 5 shows the risk of 10% biomass change on the different exposure concentrations for each population in Baiyangdian Lake. At the lower exposure concentration in S3, S4, S5, S7, and S8 (the range from 0.50 to 0.80 mg/g), the risk of a 10% reduction in Diatom, Green, Bacillariophyta, Cyanophyta, Myriophyllum, Duckweed, Mussel, Crab, Shrimp, Asian Mud Snail, and Carp populations were ranged from 0.007% to 0.531%. The probability of population biomass percentage increases were found for Blue-greens, Cryptomonas, Chlorophyta, Rotifer, Copepoda, Chironomidae, and Catfish. A likely explanation was that the probability of population biomass reduction was increased for Mussel, Crab, Shrimp, Asian Mud Snail, and Carp populations production decreases grazing and

Fig. 6 presents the results for the probability of a 20% reduction in model populations under different PBDEs exposure concentrations. For the producer populations, apart from Blue-greens population, the rest of populations showed low probability of a 20% reduction in biomass. It is interesting to note that the probability of a 20% reduction in most of producer populations rapidly increases as PBDEs concentrations exceed 2.40 mg/g. For the consumer populations, except for Rotifer, Carp, and Catfish populations, the probability of biomass reduction for the rest of populations were below 20%. For the Rotifer and Carp populations 50% probability of a 20% reduction were below 1.85 mg/g. It is interesting to note that the probability of a 20% reduction in consumer populations slightly increases as PBDEs concentrations exceed 1.85 mg/g.

Table 6 Sensitivity parameter order for per population annual production in AQUATOX in response to a 20% increase in input parameters. Population

Phytoplankton Diatoms Greens Blue-greens Cryptomonas Periphyton Bacillariophyta Chlorophyta Cyanophyta Macrophyte Myriophyllum Duckweed Zooplankton Rotifer Copepoda Benthic insect Chironomidae Benthic macroinvertebrate Mussel Crab Shrimp Asian Mud snail Fish Carp Catfish

Order of controlling physiological parameters (sensitivity index) First

Second

Third

R of Diatoms (20.12) R of Greens (19.37) T0 of Blue-greens (42.4) Pm of Cryptomonas (21.6)

T0 of Diatoms (14.21) Pm of Greens (15.14) Pm of Blue-greens (32.7) R of Cryptomonas (14.93)

Pm of Diatoms (13.14) Pm of Greens (14.07) R of Blue-greens (21.46) e

R of Bacillariophyta (61.7) R of Chlorophyta (53.2) R of Cyanophyta (70.9)

T0 of Bacillariophyta (3.24) Mc of Chlorophyta (14.1) Mc of Cyanophyta (15.3)

R of Bacillariophyta (3.75) T0 of Rotifer (3.12) e

R of Myriophyllum (41.2) T0 of Duckweed (48.7)

T0 of Myriophyllum (33.3) Pm of Duckweed (18.2)

e R of Duckweed (7.05)

T0 of Rotifer (21.46) T0 of Copepoda (22.78)

R of Rotifer (10.32) R of Shrimp (13.71)

R of Shrimp (3.15) T0 of Shrimp (2.18)

T0 of Chironomidae (52.6)

Mc of Chironomidae (24.3)

R of Shrimp (2.50)

T0 of Rotifer (24.12) T0 of Crab (28.14) T0 of Rotifer (17.52) R of Asian Mud snail (19.72)

T0 of Mussel (12.73) T0 of Rotifer (13.16) R of Shrimp (12.95) T0 of Asian Mud snail (14.07)

R of Bacillariophyta (8.11) R of Shrimp (11.93) T0 of Shrimp (12.74) T0 of Rotifer (10.95)

T0 of Carp (22.32) T0 of Catfish (16.43)

T0 of Duckweed (13.47) R of Chlorophyta (10.15)

R of Bacillariophyta (11.98) R of Bacillariophyta (7.68)

Note: Parameter definitions: Pm is the maximum photosynthetic rate; R is the respiration rate; T0 is the optimal temperature; Ls is the light saturation intensity; and Mc is the morality rate.

(c) S3

6

4.781

The risk of 10% biomass change (%)

5

4

3

1.792

2

1.031 1 0.442

0.214 0

-0.01

0.124

-0.055

-0.048

-0.326

0.342

-0.078

-0.112

-0.084

-0.109

-0.079 -0.241 -0.485

-1 Populations

(a) S4 6

4.812

The risk of 10% biomass change (%)

5

4

3

1.791

2

1.043 1 0.445

0.214 0

-0.009 -0.055

0.124 -0.048

-0.326

-0.113

0.344

-0.078

-0.085 -0.109

-0.079 -0.243 -0.487

-1 Populations

(b) S5 6

4.875

The risk of 10% biomass change (%)

5

4

3

1.79

2

1.061 1 0.463 0.215 0

-0.009 -0.055

0.125 -0.049

-0.329

-0.116

-0.079

0.347 -0.087 -0.111

-0.091 -0.251

-0.506 -1 Populations

(c) S3 Fig. 5. The risk of 10% biomass change on the different exposure concentrations: (a) 0.50 mg/g; (b) 0.57 mg/g; (c) 0.62 mg/g; (d) 0.73 mg/g; (e) 0.80 mg/g; (f) 1.85 mg/g; (g) 2.40 mg/g; and (h) 2.73 mg/g.

88

L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92 6

4.918

The risk of 10% biomass change (%)

5

4

3

1.788

2

1.089 1 0.474 0.215 0

-0.008 -0.055

0.126 -0.049

-0.331

-0.119

-0.08

0.349 -0.088 -0.114

-0.102 -0.281 -0.525

-1 Populations

(d) S7

Fig. 5. (continued).

L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92

89

Fig. 5. (continued).

Fig. 6. Probability of a 20% reduction in model populations versus PBDEs exposure concentrations. Diatom Exp-NOEC concentration range: 1.99e2.25 mg/L (Källqvist et al., 2006); Copepod Exp-NOEC concentration: 72 mg/L (Breitholtz et al., 2008).

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4. Discussions At its current level of accuracy, the reference simulation may be sufficient enough to provide additional information useful in improving the assessment and management of ecological risks of chemicals (calibrated to the observed or reported biomass for Baiyangdian Lake populations). According to the results from Zhang et al. (2013), changes in aquatic population biomass may signify impacts of multiple stressors existing within a system. Although the model can simulate both direct and indirect effects by comparing model biomass results with measured data, it can be concluded that model results were higher than measured data overall. An explanation of this phenomenon may result from various types of pollutants existing within the lake, leading to a decrease in aquatic biomass. 4.1. The extrapolation to other highly impacted lakes With rapid economic development and population increase, persistent organic pollutants derived from industrial activities have been discharged into the water, resulting in serious organic pollution in highly impacted lake ecosystems, thus PBDEs have been extensively investigated in China’s lake ecosystem. According to the study of Wu et al. (2012) and Hu et al. (2010a), the highest concentration of PBDEs in surface sediments was found in Baiyangdian Lake (from 5.5 to 300.7 ng/g) (Hu et al., 2010a), followed by lakes Dianchi (46.7 ng/g), Chaohu (9.20 ng/g), and Taihu (6.51 ng/g). These findings are not surprising because these lakes located in north, eastern or southwestern China, are adjacent to large cities and receiving significant local inputs of urban runoff and sewage discharge. In addition, in lakes Baiyangdian, Chaohu, Dianchi, and Taihu, BDE-209 was the dominant congener in the surface sediments, and the patterns were similar to that of the deca-BDE technical mixture (La Guardia et al., 2007). This suggests that urban runoff and sewage discharge is likely to be the main source of BDE-209 in these lakes (Wu et al., 2012). Therefore, the AQUATOX model can be extrapolated to the other highly impacted lake ecosystems in China. The AQUATOX model could additionally be used to design mesocosm or field toxicological tests. Lei et al. (2008) demonstrated the usefulness of AQUATOX in determining test range concentrations with respect to nitrobenzene field toxicity tests. Predictions from that modeling study indicate that if field experiments took into account both direct and indirect PBDEs effects for foodweb structures similar to Baiyangdian Lake, test concentrations for both mescosm and field tests should range from 0.80 to 2.40 mg/g. This is because at 0.50e0.80 mg/g PBDEs levels, minimal risk was observed for certain modeled populations, but most of the higher trophic level populations were at relatively higher risk for PBDEs levels greater than 0.80 mg/g. Furthermore, sensitivity analysis result ascertained that the model was highly sensitive to parameters related to temperature limitations and respiration rates, which is consistent with a study by Sourisseau et al. (2008) and Lei et al. (2008). This suggests that particular attention should be paid to the estimation of these parameters when AQUATOX is used for ERA of toxicants in aquatic ecosystems. 4.2. The indirect effects for different populations Sensitivity analysis was carried out to examine the relative contribution of direct and indirect effects of AQUATOX parameters on the annual production of each model population (Table 6). The results from sensitivity analysis can provide useful information for the improvement of the model for risk assessment. For example, Rotifer T0 (T0 ¼ 3.24%) and Shrimp R (R ¼ 3.75%) were the critical

sources of Mussel population biomass variability. These results signify that interspecific relationships are important in indirectly determining the behavior of Mussel populations. And the annual biomass of benthic macroinvertebrate population was influenced by Rotifer T0 and Bacillariophyta R values. The results suggest that indirect effects are the most influential parameters in determining benthic macroinvertebrate population annual production. AQUATOX-Baiyangdian estimated risks of direct toxic effects on each population and the indirect ecological effects that were distributed through the modeled coupled pelagic-benthic foodweb. Model predictions indicate that complex responses in risk estimation result from the differential sensitivity of organisms to toxicants and predatory pressure. PBDEs risk estimate results demonstrate that effects of toxic chemicals on natural ecosystems should differ from the linear extrapolations of laboratory responses of single species. This suggests that estimated risk for natural ecosystems cannot be fully explained by single species toxicity data alone (Naito et al., 2002; Zhang et al., 2013). The result demonstrated that effects of the indirect coupled pelagic-benthic foodweb greatly contributed to risk estimates. Model results show the likelihood of increases in Blue-greens, Cryptomonas, Chlorophyta, Rotifer, Copepod, Chironomidae, and Catfish populations at 0.50 mg/ge0.80 mg/g PBDEs exposure levels, resulting from a decrease in grazing and competition pressure from benthic macroinvertebrate and Carp populations. Foodweb effects indicate that pelagic and benthic populations were undergoing coupling. It is interesting to note that risk estimates were zero for all producer populations at 1.85 mg/g PBDEs exposure levels even though PBDEs exposure levels increased while at the same time the risk of reduction in consumer populations rapidly increased. For producers, a likely explanation of this pattern of behavior is that direct toxic PBDEs effects can be reduced by a decrease in predatory pressure from consumers. For consumers, the same reasons for a rapid increase in risk were associated with direct toxic PBDEs effects in addition to the biomagnification effect. 4.3. Ecological threshold for PBDEs The ecological thresholds are defined as significant changes in an ecological state variable as a consequence of continuous changes in an independent variable (Muradian, 2001). Threshold responses, which may be triggered by either natural or anthropogenic disturbances (e.g. contaminant gradients) (Carpenter et al., 1999; Qian et al., 2003), have been reported in lake communities (Scheffer et al., 2001; Groffman et al., 2006), it has important implications in ecosystem management. Although quantifying the specific location of a breakpoint is difficult (Nyström, 2006), many studies have focused on the study of the method to detect ecological thresholds. For example, Sonderegger et al. (2009) developed a method to detect ecological thresholds based on significant changes in the slope of a stressor-response relationship. For this study, the AQUATOX model can be used to detect ecological thresholds based significant change in the biomass. Such as, the risk of reduction in consumer populations rapidly increased at 1.85 mg/g PBDEs exposure levels; and the risk of reduction in producer populations rapidly increased at 2.40 mg/g PBDEs exposure levels. Thus, we can conclude that all biological populations were relatively stable below 0.80 mg/g PBDEs exposure levels. This implies that the model could be a good starting point in establishing an ecological threshold of chemical toxicants in aquatic ecosystem management. 5. Conclusions AQUATOX is an ecosystem effects model that predicts both direct and indirect ecological effects of coupled pelagic-benthic

L. Zhang, J. Liu / Environmental Pollution 191 (2014) 80e92

foodwebs for highly impacted lake ecosystems in China. Sensitivity analysis demonstrated result suggests that the model in AQUATOX is highly sensitive to temperature limitation. PBDEs risk estimation demonstrated that the model successfully estimated direct toxic effect risks on each population and the indirect ecological effects that were distributed throughout the coupled pelagic-benthic ecosystem foodweb. In addition, the model could be a good starting point in establishing an ecological threshold of chemical toxicants. This modeling study has demonstrated that AQUATOXBaiyangdian could be used as a generic ecosystem effect model by which to estimate ecological risks of PBDEs on highly impacted aquatic ecosystems. Further model analysis will determine the extent of its applicability and reliability for specific risk assessment tasks and predictions related to the aquatic ecological risk management of other chemicals. Acknowledgments This study was supported by the China Postdoctoral Science Foundation (2014M550647) and National Water Pollution Control Major Project of China (2012ZX07203-006). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.envpol.2014.04.013. References Bao, L.J., Maruya, K.A., Snyder, S.A., Zeng, E.Y., 2012. China’s water pollution by persistent organic pollutants. Environ. Pollut. 163, 100e108. Baker, J.R., Peck, D.V., Sutton, D.W., 1997. Field Operations Manual for Lakes, Environmental Monitoring and Assessment ProgrameSurface Waters’, EPA/620/R97/001. U.S. Environmental Protection Agency, Corvallis, OR, p. 276. Barber, M.C., 2003. A review and comparison of models for predicting dynamic chemical bioconcentration in fish. Environ. Toxicol. Chem. 22 (9), 1963e1992. Bartell, S.M., Gardner, R.H., O’Neill, R.V., 1988. An integrated fate and effect model for estimation of risk in aquatic systems. In: Adams, W.J., Chapman, G.A., Landis, W.G. (Eds.), Aquatic Toxicology and Hazard Assessment, American Society for Testing and Materials, Special Technical Publication 971, vol. 10. American Society for Testing and Materials, Philadelphia, PA, pp. 261e274. Bartell, S.M., Lefebvre, G., Kaminski, G., Kaminski, G., Carreau, M., Campbell, K.R., 1999. An ecosystem model for assessing ecological risks in Québec rivers, lakes, and reservoirs. Ecol. Model. 124, 43e67. Breitholtz, M., Nyholm, J.R., Karlsson, J., Andersson, P.L., 2008. Are individual NOEC levels safe for mixtures? A study on mixture toxicity of brominated flamee retardants in the copepod Nitocra spinipes. Chemosphere 72 (9), 1242e1249. Brock, T., Lahr, J., Van den Brink, P.J., 2000. Ecological Risks of Pesticides in Freshwater Ecosystems. Part 1: Herbicides. Alterra Green World Research, Alterra, Waginengen, Netherlands. Carpenter, S.R., Ludwig, D., Brock, W.A., 1999. Management of eutrophication for lakes subject to potentially irreversible change. Ecol. Appl. 9, 751e771. Chen, W.M., Huang, X.F., Zhou, W.P., 2005. Lake Ecosystem Observation Methods. China Environmental Science Press. Clements, W.H., Rohr, J.R., 2009. Community responses to contaminants: using basic ecological principles to predict ecotoxicological effects. Environ. Toxicol. Chem. 28 (9), 1789e1800. Cristale, J., Katsoyiannis, A., Sweetman, A.J., Jones, K.C., Lacorte, S., 2013. Occurrence and risk assessment of organophosphorus and brominated flame retardants in the River Aire (UK). Environ. Pollut. 179, 194e200. Eljarrat, E., De La, C.A., Raldua, D., Duran, C., Barcelo, D., 2004. Occurrence and bioavailability of polybrominated diphenyl ethers and hexabromocyclododecane in sediment and fish from the Cinca River, a tributary of the Ebro River (Spain). Environ. Sci. Technol. 38 (9), 2603e2608. Fleeger, J.W., Carman, K.R., Nisbet, R.M., 2003. Indirect effects of contaminants in aquatic ecosystems. Sci. Total Environ. 317 (1e3), 207e233. Gobas, F.A.P.C., Wilcockson, J.B., Russel, R.W., Haffner, G.D., 1999. Mechanism of biomagnification in fish under laboratory and field conditions. Environ. Sci. Technol. 33, 133e141. Groffman, P., Baron, J., Blett, T., Gold, A., Goodman, I., Gunderson, L., Levinson, B., Palmer, M., Paerl, H., Peterson, G., Poff, N., Rejeski, D., Reynolds, J., Turner, M., Weathers, K., Wiens, J., 2006. Ecological thresholds: the key to successful environmental management or an important concept with no practical application? Ecosystems 9, 1e13.

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