Modeling the Toxicokinetics of Multiple Metals in the Oyster

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Modeling the toxicokinetics of multiple metals in oyster Crassostrea hongkongensis in dynamic estuarine environment Qiao-Guo Tan, Weitao Zhou, and Wen-Xiong Wang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04906 • Publication Date (Web): 13 Dec 2017 Downloaded from http://pubs.acs.org on December 19, 2017

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Environmental Science & Technology

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Modeling the toxicokinetics of multiple metals in the oyster Crassostrea hongkongensis in a dynamic estuarine environment Qiao-Guo Tan1,2, Weitao Zhou1, Wen-Xiong Wang1,2,3*

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1. Key Laboratory of the Coastal and Wetland Ecosystems of Ministry of Education, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China 2. Center for Marine Environmental Chemistry and Toxicology, Xiamen University, Xiamen, Fujian 361102, China 3. Division of Life Science, The Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong

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*Corresponding author: [email protected]

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TOC Art

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ABSTRACT

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Metal contamination is a major problem in many estuaries. Toxicokinetic models are useful

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tools for predicting metal accumulation in estuarine organisms and managing the associated

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ecological risks. However, obtaining toxicokinetic parameter values with sufficient predictive

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power is challenging for dynamic estuarine waters. In this study, we determined the

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toxicokinetics of multiple metals in the oyster Crassostrea hongkongensis in a dynamic

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estuary polluted by metals using a 48-day transplant experiment. During the experiment,

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metal concentrations in oysters, water and suspended particles were intensively monitored at

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3-d interval. The toxicokinetic parameters were then estimated using the Markov chain

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Monte Carlo (MCMC) method. The calibrated model was capable of successfully simulating

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the time-course of metal bioaccumulation in oysters and was further validated by predicting

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the bioaccumulation at another site in the estuary. Furthermore, the model was used to assess

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the relative importance of different pathways in metal bioaccumulation. With the MCMC

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method, distributions instead of single values were assigned to model parameters. This

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method makes the model predictions probabilistic with clearly defined uncertainties, and are

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thus particularly useful for the risk assessments of metals in aquatic systems.



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INTRODUCTION

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In aquatic environments, the relationship between metal bioaccumulation by aquatic

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organisms and the environmental exposure (dissolved and particulate metals) is complex and

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dependent on a myriad of biotic and abiotic factors.1 Quantifying the relationship is important

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for interpreting biomonitoring data in relation to the levels of contamination.2-4

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Hypothetically, when equilibrium partitioning of metals is attained between organisms and

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environmental compartments (e.g., water, suspended particles, and sediments), a simple

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bioconcentration factor can adequately measure the aforementioned relationship.5 However,

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reaching such equilibrium requires long-term exposure under stable conditions, including

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metal concentrations and physicochemical and biological factors affecting bioavailability.

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Therefore, such equilibrium in real aquatic environments is rare, if not impossible. To better

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understand bioaccumulation in variable or dynamic aquatic environments, such as riverine

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and estuarine waters, kinetic measures are often required.5-7

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A good toxicokinetic model should be sufficiently sophisticated for simulating the major

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bioaccumulation processes, but simple enough for convenient application. The

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one-compartment first-order toxicokinetic model meets both criteria. The model is simple

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with only four to five parameters and yet considers processes including aqueous uptake,

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dietary uptake, elimination, and growth dilution.5, 8, 9 This model is also known as biokinetic

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or biodynamic model in the area of metal ecotoxicological studies; the parameter values are

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usually derived in laboratory under stable conditions.8, 9 Such laboratory-derived parameters

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have been tested in a number of previous studies for predicting metal bioaccumulation in the

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field and have shown good predictive powers.8, 10-12 Nonetheless, less precise predictions are

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also reported in some studies.11, 13

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Applying parameters calibrated under constant laboratory conditions to dynamic

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estuarine waters is more challenging. In case of unsatisfactory prediction, the model

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parameters need to be updated with the field data in a scientifically sound way, otherwise the

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newly obtained biomonitoring data would not contribute to the enhancement of the model's

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predictive power and be missed. Moreover, although the toxicokinetic model is kinetic in

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nature, previous studies seldom used the model in its kinetic form for predicting the

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time-course metal bioaccumulation.8, 10, 11 Instead, the steady-state concentration in organisms

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was calculated by assuming a constant exposure for predicting metal bioaccumulation in the

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field. This practice does not fully explore the predictive power of the toxicokinetic model and


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may not be appropriate in dynamic environments (e.g., estuaries) where steady-state or

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constant exposure is not possible.

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In this study, we transplanted the oyster (Crassostrea hongkongensis) to a region of

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Jiulong River estuary significantly contaminated by multiple metals. Our objective was to

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calibrate and validate the toxicokinetics of multiple metals simultaneously in a real estuarine

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environment. The parameter values reported in the literature were used as prior information.

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During the 48-d oyster transplant, the diffusive gradients in thin films (DGT) technique was

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used for measuring labile metal concentrations in water. Metal concentrations in suspended

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particles were also measured periodically to reflect the dietborne exposure. The

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bioaccumulation of metals in oysters were subsequently simulated with the one-compartment

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first-order toxicokinetic model using metal concentrations in water and suspended particles as

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driving variables. The Markov chain Monte Carlo (MCMC) method was used for parameter

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fittings to provide distributions of parameters and better uncertainty analysis.

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MATERIAL AND METHODS

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Study area. The two sites for oyster transplant were in the north arm of the Jiulong River

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estuary (Fig. S1), which is close to Xiamen City in the southeast of China and has a

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subtropical climate. Site 1 was directly affected by the effluent discharged from Huyu

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floodgate; site 2 was approximately 2 km downstream (see Fig. S1 for the map). Upstream of

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Huyu floodgate is a drainage canal, which receives both domestic and industrial wastewater

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from Jiaomei Town. Levels of multiple metals are high in the canal. The wastewater was

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discharged into Jiulong River estuary during low tides irregularly but on average every 2 to 3

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days. The wastewater discharge substantially elevated the metal concentrations in the

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estuarine water nearby. The average concentrations of dissolved Cr, Ni, Cu, Zn measured

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recently by Wang and Wang during their two sampling events were around 10 µg L-1, 30 µg

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L-1, 10 µg L-1, and 10 µg L-1, respectively,7 which were similar to those reported by Weng

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and Wang earlier.14

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Oyster transplant and sampling. The 48-d oyster transplant experiment was

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conducted between November 16th, 2013 and January 3rd, 2014. The oysters were juveniles

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(2–3 months) of C. hongkongensis collected from an oyster farm in Jiuzhen Harbour

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(24°2′15″N, 117°42′44″E), where no obvious metal contamination was detected.14 At each

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site, around 300 oysters were placed in a mesh bag which was then fastened to a bamboo pole. 5 ACS Paragon Plus Environment


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The bamboo pole was inserted firmly into the sediment to keep the oysters suspended under

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water during the whole tidal cycles. Losses of oysters due to mortality or other causes were

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not counted but were estimated to be minimal. Of the ~300 oysters transplanted, 170 were

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sampled for metal analyses, 85 were sampled for determining metal subcellular distribution

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(results not presented in this study), and the others were discarded.

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Samples of 10 oysters were collected at 3 d intervals from each site. Oyster shells were

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washed with on-site water to remove sediments. The oysters were then placed in clean

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ziplock bags and transported to the laboratory in an icebox immediately. In the laboratory, the

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oysters were opened with a stainless steel oyster knife. The soft parts were thoroughly rinsed

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with deionized water to remove any visible sediment particles, dried at 80 °C for 48 h, and

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weighed. The oysters were not depurated, following the methods commonly used by

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environmental authorities in biomonitoring programs, e.g., marine monitoring programs of

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China and the mussel watch program of the United States.15, 16 The measured bioaccumulated

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concentrations of some metals may thus include contributions from gut content due to the

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higher concentrations of these metals in the particulate phases than in the oysters 17 , but this

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was considered negligible by theoretical calculation 18. The oyster tissues were digested with

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concentrated HNO3 in 15-mL polyethylene tubes. Two mL of 65% HNO3 was added to each

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tube and tubes were left at room temperature for 8-12 h until most solids were dissolved. The

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tubes were further heated at 80 °C for 8 h using a dry block heater. After heating, the liquids

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became clear with some lipid precipitates in the bottom of the tubes. The standard reference

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material SRM1566b (oyster tissue) was digested together with each batch of the samples.

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Samples of water and suspended particles. Water samples were collected each time

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with the oyster sampling. Three replicates of 500 mL water were taken using polyethylene

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bottles by immersing them in the water at a depth of around 10 cm with nitrile-gloved hands.

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The bottles were previously acid washed (soaked in 5% HNO3 for at least 24 h) and rinsed 6

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times with deionized water, and were placed in double ziplock bags before use. The bottles

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were rinsed with site water 3 times before collecting water samples.

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In the laboratory, salinity of water samples was measured with a salinity refractometer

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(Suwei LS10T); pH and temperature were measured with a benchtop pH meter (pH 510,

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EUTECH). Each water sample was filtered through a pre-weighed 0.45-µm polycarbonate

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filter for analyzing the particulate metals. The filters were dried at 80 °C for 48 h and

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weighed. Each filter together with the particles were digested in a mixture of 4.5 mL of 65% 6 ACS Paragon Plus Environment

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HNO3 and 1.5 mL of 35% HCl using a microwave digestor (Preekem Coolpex). The

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temperature program was 3 min at 150 oC, 180 oC and 200 oC, respectively, with another 30

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min at 220 oC. The digested samples were centrifuged at 3000 g for 5 min, and the

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supernatants were decanted into new polyethylene tubes for metal concentration analysis.

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Three blank filters and the standard reference material (coastal sediment, GBW07314) were

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also digested with each batch of samples and served as procedure blanks and quality control

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samples, respectively.

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DGT measurement. The DGT technique was used for measuring labile metal

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concentrations in water. The DGT probes were purchased from DGT Research Ltd

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(Lancaster, UK). A DGT probe was deployed at each site every 3 d during the oyster

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transplant experiment following the method described by Dunn et al. and was retrieved ~3 d

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later.19 The exact time of DGT deployment and the average temperature during the period of

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deployment were recorded. In the laboratory, the retrieved DGT probes were first cleaned

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with deionized water and then dissembled. The chelex gel layer was placed in a 2-mL

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centrifuge tube using acid-washed plastic forceps and was eluted with 1 mL of 1 mol L-1

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HNO3 for 24 h. Metal concentrations in the eluent were determined after appropriate dilution.

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Labile metal concentrations in water were calculated using the equation and diffusion

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coefficients provided by the DGT manufacturer (see Supporting Information for more details).

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Time-integrated average metal concentrations during each 3 d were expected to be provided

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by this method.

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Metal concentration analysis. Metal concentrations in samples of oyster, suspended

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particles and DGT eluents were determined using inductively coupled plasma mass

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spectrometry (ICP-MS, Agilent 7700x). The internal standards, including 45Sc, 72Ge, 115In,

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and 209Bi, were used for correcting instrument drift and matrix effects. A quality control

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sample was measured every 10 samples. For the standard reference material GBW07314, the

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recovery of Co, Ni, Cu, Zn and Cd was in the range of 90% to 110%; whereas the average

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recovery of Cr and Pb were 80% and 84%, respectively. The lower recoveries of the two

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metals were probably due to the pseudo-total (instead of the total) digestion method that we

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used. The recoveries of all metals from the standard reference material SRM1566b were

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within the range of 90 to 108%. The reported metal concentrations were thus not corrected

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for recoveries. 7 ACS Paragon Plus Environment


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Toxicokinetic modeling. Both water and food are potential sources of metals for the

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oysters. By assuming first-order kinetics for the uptake and elimination processes, the

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time-course of metal concentration in oysters can be described by a one-compartment

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toxicokinetic model:8, 9 ௗ஼౟౤౪ (௧)

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ௗ௧

= ݇୳ ∙ ‫ )ݐ( ୵ܥ‬+ ݇୤ ∙ ‫ܥ‬୤ (‫ )ݐ‬− (݇ୣ + ݃) ∙ ‫ܥ‬୧୬୲ (‫)ݐ‬

(1)

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where Cint (t) is the metal concentration in oysters (µg g-1 dry weight); Cw (t) is the

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concentration of dissolved metals (µg L-1), which was measured using the DGT technique in

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this study; Cf (t) is the metal concentration in food particles ingested by oysters (µg g-1); ku is

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the uptake rate constant of dissolved metals (L g-1 d-1); kf is the uptake rate constant of metal

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from food (g g-1 d-1); ke is the elimination rate constant (d-1); g is the growth rate constant

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(d-1); and t is the time of exposure (d). The parameter kf can be considered as the product of

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ingestion rate (IR, g g-1 d-1) and assimilation efficiency (AE, dimensionless) described in

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previous studies.8, 9 In the present study, it is difficult to estimate IR and AE separately; kf is

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thus used for simplicity and practicality.

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Parameter estimation and model prediction. The growth rate constant (g) was

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estimated based on the increases of individual dry weight of oysters at each site (Fig. S2).

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The values of g were subsequently kept fixed during the estimation of the other three

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parameters, i.e., ku, kf, and ke, for each metal. The parameters were estimated using

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bioaccumulation data collected from site 1, while data of site 2 were used to validate the

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model.

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The parameters were estimated following the procedures proposed by Ashauer et al..20

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Firstly, least-square fittings with the Marquardt algorithm were used to find the parameter

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values that minimize the sum of the squared residuals. The starting parameter values were set

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based on the literature on metal toxicokinetics (or wherein called biokinetics) in oysters and

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are listed in Table S1.21-24 Subsequently, MCMC fittings with the Metropolis-Hastings

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algorithm were applied to the bioaccumulation data to generate parameter samples of ku, kf,

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and ke. The best-fit values from the least-square fittings were used as priors for the MCMC

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fittings. Uniform distributions, with the lower and higher bound at 0.1 fold and 10 fold of the

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best-fit values, were used as the prior distributions (see Table S2). A parameter sample is a

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large number of accepted parameter combinations, from which correlation among parameters

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can also be inferred (see Fig. S7-S13 for examples).25 The parameter sample thus can be 8 ACS Paragon Plus Environment

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considered as a joint posterior distribution of the model parameters. The parameter samples

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were further used to generate predictions of metal bioaccumulation in the oysters and the 95%

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confidence intervals. All the model fittings and predictions were conducted with the software

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openmodel (version 2.4.2) developed by Neil Crout of the University of Nottingham.

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RESULTS AND DISCUSSION

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Physicochemical parameters. The salinity of water samples varied markedly from 3 to

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15 and 5 to 22 at the site 1 and 2, respectively (Fig. S3). Higher salinity was observed at site

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2, which was closer to the sea end. Similarly, pH values also varied substantially. Most of the

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measured pH were relatively low and ranged from 6.5 to 7.5. The low and even acidic pH

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were probably caused by the wastewater discharge from the Huyu floodgate. The salinity and

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pH were measured in grab samples taken at different tidal phases (but usually during low

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waters) on different days; the observed variations were thus a combined effect of spring-neap

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and low-high tide cycles.

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Tide is semi-diurnal in the Jiulong River estuary. Salinity and pH were usually higher at

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high tides. For example, adjacent to the sites of this study, it was recorded that salinity

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increased from ~5 at low tides to ~25 at high tides; in the meantime, pH increased from

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7.0-7.4 to 7.6-7.8.7 The concentrations of dissolved organic carbon (DOC) also varied with

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tide and increased from around 0.3 mM C at high tides to more than 2 mM C at low tides.7

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Bioavailability of metals is determined by multiple variables, including salinity, pH, and

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DOC; therefore, metal bioaccumulation rates in waters with dynamic water chemistry, such

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as Jiulong River estuary, are expected to vary with time in a very complex way even if metal

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concentrations are stable.

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Metal concentrations in water and suspended particles. Metal concentrations in

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water (measured by DGT probes) and suspended particles also varied considerably during the

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48 d of oyster transplant (Fig. 1; Fig. 2). The patterns of variation were similar for the two

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study sites, driven by both tidal mixing and wastewater drainage. For the particulate metals,

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similar patterns were observed among different metals (i.e., Cr, Ni, Cu, and Zn), suggesting

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the same source of contamination (Fig. 2; Fig. S5). The elevated levels of Cr, Ni, Cu, and Zn

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in Jiulong River estuary and the concurrence of these metals were also previously reported.26

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In contrast, the correlations among metals in water were relatively weak (Fig. S4).



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Dissolved metal. The concentrations of dissolved metals measured by DGT probes are

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summarized in Fig. 1. The DGT technique provides time-integrated concentrations of labile

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metals during the consecutive deployment periods of around 3 d each. Metal concentrations

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were generally higher at site 1, which was closer to the floodgate, than at site 2. Compared to

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other clean or contaminated estuaries (Table S4), the study sites were subjected to significant

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contamination of Ni (site 1: 10–61 µg L-1; site 2: 6–35 µg L-1), Cu (site 1: 3–53 µg L-1; site 2:

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2–19 µg L-1) and Zn (site 1: 7–35 µg L-1; site 2: 4–34 µg L-1). The concentrations of Co, Cd,

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and Pb were slightly elevated, but were still at relatively low levels (Fig. 1; Table S4). The

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measured Cr concentrations were quite low (site 1: 0.54 ± 0.43 µg L-1; site 2: 0.30 ± 0.28 µg

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L-1); however, it should not be interpreted as low Cr contamination in this area but rather due

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to the DGT technique (see discussion below). Actually, noticeably high Cr concentrations (2–

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18 µg L-1) were detected in the 0.2-µm filtered water samples by Wang and Wang near the

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study sites during two independent sampling events.7

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The DGT probes used in this study measured only the fraction of dissolved metal species

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which are potentially more bioavailable. In theory, they are cationic species that can bind to

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chelex resin, including free metal ions, simple inorganic complexes, and small organically

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complexed species.19 Therefore, the DGT-reactive proportion is metal-specific and dependent

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on the conditions of water. In a study of estuarine waters, the DGT-reactive metal

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concentrations as a fraction of 0.45 µm-filterable concentrations was 21 ± 2% for Cu, 29 ± 11%

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for Pb, 28 ± 5% for Zn, and 27 ± 12% for Ni.19 In another study of near-pristine coastal

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waters, the ratio of DGT-reactive to 0.45 mm-filterable concentrations was 51% for Cu, 108%

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for Cd, 104% for Co, and 122% for Pb.27 The Cr species measured by the DGT probes were

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cationic species (e.g., Cr3+, Cr(OH)2+); whereas the dominant species of Cr in estuarine

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waters was Cr(VI) anionic species. For example, in the Columbia River and estuary, CrO42-

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was found to account for more than 90% of the dissolved Cr; Cr(III) species had strong

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tendency of particle adsorption, which limited their availability as dissolved species.28 This

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explained why in this study Cr concentrations measured in DGT probes were 1 to 2 orders of

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magnitude lower than the concentrations of filterable Cr.

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Particulate metal. The concentrations of metals in suspended particles are summarized

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in Fig. 2. Metal concentrations were similar between the two study sites. The particles also

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showed elevated concentrations of Cr (90–1870 µg g-1; average: 245 µg g-1), Ni (24–834 µg

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g-1; average: 94 µg g-1), Cu (52–658 µg g-1; average: 132 µg g-1), and Zn (99–519 µg g-1,

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average: 251 µg g-1) (Table S5), consistent with the measurements in waters. The 10 ACS Paragon Plus Environment

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concentrations of Co and Pb were comparable or slightly higher than those observed in other

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rivers and estuaries.14, 29, 30 Interestingly, Cd concentration in the Jiulong River estuary (~0.1

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µg g-1) was much lower than those observed elsewhere (Table S5). It is similar to the

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concentrations (0.04–0.24 µg g-1) found in intertidal sediments in areas adjacent to Jiulong

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River estuary,26 both suggesting low natural background and anthropogenic contamination of

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Cd in this area.

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Jiulong River estuary is a shallow water estuary and usually turbid. Therefore, a large

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proportion of the sampled suspended particles may be resuspended surface sediments, in

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addition to biotic and organic detritus. Filter-feeding bivalves, including oysters, can select

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particles with variable efficiency before ingestion, preferring particles of higher nutritional

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value.31 Therefore, the sampled particles should not be considered equal in composition to

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those ingested by the oysters. Nevertheless, metal bioavailability in sediments was found

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comparable to that in phytoplankton cells. For example, the assimilation efficiencies of Cd

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(30%–40%), Se (26%–35%), and Zn (34–44%) from sediment particles were only slightly

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lower or similar to those observed in phytoplankton cells; sediment particles thus contributed

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an important fraction to metal bioaccumulation in oysters.22

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The samples of suspended particles were grab samples taken every 3 d. Although taking

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samples at 3 d intervals during the 48-d experiment was labor intensive, it was still uncertain

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whether the samples were representative enough. Considering the dynamic nature of

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estuarine waters and the complex composition of food ingested by oysters, it is challenging to

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find a time-efficient and cost-efficient method to measure concentrations of metals in the

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"real" food of oysters. Taking grab samples like what we did in this study is a matter of

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expediency.

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Metal accumulation in oysters and toxicokinetics. The bioaccumulation of most of

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the studied metals in the oysters was well described by a simple toxicokinetic model, using

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metal concentrations in water and suspended particles as driving variables (Fig. 3). Large

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inter-individual variations were observed; therefore, median concentrations were used in the

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parameter fittings to reduce the possible bias caused by extreme values. The estimated

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parameter values and confidence intervals are listed in Table 1. The information on parameter

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samples, including the posterior distribution of parameters and the correlation among

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parameters, are presented in Fig. S7 to S13. Model parameters were calibrated with the

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bioaccumulation data from site 1, and were further validated with the results observed at site 11 ACS Paragon Plus Environment


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2. The time-course of oyster metal concentrations at site 2 was well predicted by the

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calibrated model (Fig. S6), with most of the predictions within a factor of 0.5 to 2 of observed

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concentrations (Fig. 5).

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Generally, metals with more obvious bioaccumulation were better simulated with the

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toxicokinetic model. Increases in the concentrations of Cr, Cu, and Zn were observed in the

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oysters. Median Cr, Cu and Zn concentrations increased from 7 µg g-1 to ~20 µg g-1, 1200 µg

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g-1 to ~9000 µg g-1, and 3400 µg g-1 to ~5000 µg g-1, respectively (Fig. 3). The increases of

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Co and Ni concentrations were less conspicuous; in contrast, slight decreases were observed

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for Cd and Pb, of which the modeling were relatively poor (Fig. 3).

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Toxicokinetics. The dissolved uptake rate constants (ku) were estimated with DGT-labile

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metal concentrations in this study (Table 1). In principle, they should be higher than ku values

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reported in the literature, where kus were estimated with total dissolved (i.e., filterable) metal

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concentrations. For example, ku of Zn estimated in this study was 5.6 ± 2.2 L g-1 d-1. If we

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assumed a percentage of 28% for DGT-labile Zn to dissolved Zn,19 the ku based on dissolved

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Zn should be 1.6 ± 0.6 L g-1 d-1, which was more comparable to those previously measured

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for the same species of oyster, i.e., 1.15 to 2.40 L g-1 d-1 measured by Pan and Wang,23 and

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2.05 L g-1 d-1 by Ke and Wang.22 Therefore, to use the ku values estimated in this study, metal

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concentrations in water should either be measured using DGT probes or judiciously

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converted from the measurements of other methods.

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The dietary assimilation rate constant (kf) was the product of assimilation efficiency (AE)

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and the specific ingestion rate (IR), i.e., kf = AE × IR. The values of AE are metal-, organism-,

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and food-specific, and can be anywhere between 0 and 1, but is often observed between 0.1

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to 0.9.32 The IR for oysters was also highly variable, usually assumed between 0.02 g g-1 d-1

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and 0.6 g g-1 d-1 based on the laboratory measurements.22, 23 The IR should be rather uncertain

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especially in dynamic estuarine waters. Therefore, using a simple parameter kf to relate

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dietary assimilation proportionately to the metal concentration in food particles is necessarily

327

a simplification. The estimated kf values were very different between metals (Table 1), and

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can be as high as 0.65 ± 0.25 g g-1 d-1 for Cu, suggesting that IR should be no less than 0.65 g

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g-1 d-1 on average, since 0 ≤ AE ≤1. Consequently, it can be inferred that AEs of Cr, Co Ni,

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and Pb were extremely low, i.e., lower than 1%, which was possibly due to that these metals

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were mainly refractory species contained in inorganic particles. The low assimilation

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efficiency of Cr (0–5%) is already well-known.32

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The elimination rate constant (ke) of metals in the oyster was in the order: Ni (0.32 ± 0.18 12 ACS Paragon Plus Environment

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d-1) > Co (0.025 d-1), Zn (0.012 d-1) > Cd, Cr, Pb, Cu (0.0017–0.0074 d-1) (Table 1). The kes

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of Ni, Zn, and Cd were previously determined for C. hongkongensis (see Table S3).21-23 The

336

kes in the literature overlap the relatively wide ke ranges estimated in this study. The kes of the

337

other metals were not measured in C. hongkongensis but in a number of other oysters, using

338

various methods, generating kes of variable reliability. Not surprisingly, the literature ke

339

values were scattered and also overlaps the values estimated in this study (Table S3).

340

For Ni, Co, and Zn, the kes are higher than or similar to the growth rate constant (g,

341

0.015–0.018 d-1) of oysters, indicating that the efflux processes played an important role in

342

counteracting metal uptake and controlling tissue concentration of these metals. In contrast,

343

the kes of Cd, Cr, Pb, and Cu were much lower than g, indicating that growth dilution was the

344

major mechanism in balancing metal bioaccumulation. These four metals were difficult to be

345

eliminated once incorporated into oyster tissues.

346

MCMC simulation. With MCMC fittings, model parameters were assigned probability

347

distributions instead of the deterministic best-fit values. A parameter sample is a large

348

number (~10000 in this study) of combinations of parameter values that can lead to

349

acceptable agreement between model predictions and observations. The parameter samples

350

can thus be considered as posterior distributions of the parameters and are plotted in Fig. S7

351

to S13. Posterior distributions are also summarized by statistics calculated from the parameter

352

samples, including average, standard deviation, and 95% confidence intervals defined by the

353

2.5th to 97.5th percentiles (Table 1).

354

Due to the continuously varying water conditions (e.g., salinity, pH, DOC, temperature)

355

in estuarine waters, the biological variability, and other sources of uncertainty, the model

356

parameters are not expected to be single specific values but rather value distributions. In

357

addition, the parameters may be correlated, either truly correlated or correlated due to the

358

model design. The correlation between parameters cause difficulties in parameter estimation

359

and uncertainty analysis. These are where the MCMC simulations are particularly useful.

360

MCMC fittings yield a joint probability distribution of the model parameters, which describes

361

the simultaneous variation of all parameters and intrinsically considers the correlation

362

between parameters.25, 33 The joint distributions were then used to predict the confidence

363

intervals (shown in Fig. 3 and Fig. S6) and the relative importance of different pathways in

364

metal bioaccumulation (see below).

365 366

Metal accumulation pathways. Using parameter samples derived from the MCMC

367

fittings, it is convenient to assess the relative importance (and the associated uncertainties) of 13 ACS Paragon Plus Environment


Page 14 of 26

368

water and food as the source of metals for oysters (Fig. 5). The bioaccumulation of Cr, Co, Ni,

369

and Cd were clearly dominated by the dissolved phase; whereas Pb might be mainly

370

assimilated from food. For Cu and Zn, both sources were similarly important. The low

371

bioavailability of Cr(III) in food particles was well documented.32 In this study, it is possible

372

that the particulate Cr was mainly Cr(III) species contained in mineral particles and thus

373

contributed negligibly to the Cr bioaccumulation. The low contribution of water to the

374

bioaccumulation of Pb is also expected, because Pb concentrations were low in water and

375

high in particles due to the high particle reactivity of Pb.

376

The quantitative information on the pathways (i.e., water or food) of metal

377

bioaccumulation is crucial for understanding metal biogeochemistry, ecological risks,

378

biomonitoring data.34-37 In previous studies, one well accepted method for delineating the

379

accumulation pathway is modeling. The model parameters (i.e., AE, ku, and ke) were firstly

380

quantified using well-controlled laboratory experiments, and were then used in the following

381

calculation in a deterministic way. In this study, we used posterior distributions (from MCMC

382

fittings) instead of fixed values of the model parameter values and provide better uncertainty

383

analysis with more clearly defined confidence intervals (Fig. 5).

384 385

Model limitations and implications. The model parameters were calibrated by

386

exposing oysters to multiple metals in a real estuarine environment. Theoretically, the

387

parameter values obtained in this study are conditional on that very specific scenario of

388

physicochemical conditions, metal concentrations, particle loads, and so on. Although the

389

model parameters were validated using data collected from another site with good

390

performance (Fig. S6), the two sites were rather similar in metal exposure conditions, which

391

weakened the confidence in the predictive power of the calibrated parameters. Nevertheless,

392

the parameter values (with uncertainties) should be more realistic and robust than those

393

calibrated under stable laboratory conditions, since they had been averaged over a wide range

394

of exposure conditions in the real estuarine environment. In addition, the MCMC method was

395

intrinsically convenient to incorporate new data input. The parameters can be updated to

396

simulate the new scenario of concern, where the parameters values from this study would

397

serve as valuable prior information.

398

The suspended particles sampled in this study were a mixture of algae, organic detritus,

399

suspended sediment particles, among others, which were not distinguished during metal

400

analysis and modeling. The composition of food particles may have important effects on 14 ACS Paragon Plus Environment

Page 15 of 26


401

metal bioavailability to oysters.38, 39 Using one single toxicokinetic parameter (kf) for

402

modeling metal accumulation from a spectrum of different particles can be considered as a

403

simplification of the complex actual environment and may limit the accuracy of model

404

predictions.

405

The model can predict the ecological risks associated with metal bioaccumulation (if the

406

bioaccumulation results in adverse effects) and also can be used to back-calculate for setting

407

water quality criteria. For example, the water quality standard (or guideline) of China and

408

Australia on Cd (0.5 and 5 µg L-1) were tested with the model developed in this study, and

409

were found to be not stringent enough for producing oysters safe for human consumption, i.e.,

410

with Cd concentration less than 2 µg g-1 fresh weight. In order to produce ‘safe oysters’, the

411

criterion value of Cd needs to be lowered to 0.11 µg L-1 (Fig. S14).

412

When interpreting biomonitoring data of dynamic aquatic environments, the

413

toxicokinetic model provides a back-calculation of metal exposure integrated or averaged

414

over a certain timespan before the sampling. One important implication of the toxicokinetic

415

modeling is that for a metal with a longer biological half-life (t1/2 = 0.693/ke), the

416

biomonitoring data may reflect its exposure over a longer past period. The average biological

417

half-life estimated for the oyster is in the following order: Ni (2.2 d), Co (28 d), Zn (58 d), Cd

418

(94 d), Cr (105 d), Pb (114 d), and Cu (408 d). The very short half-life of Ni indicates that Ni

419

concentrations measured in the oysters can only reflect Ni exposure during the past several

420

days; whereas for other metals, the timespan reflected can be months or even longer.

421

Being kinetic (instead of assuming steady state) is both the strength and weakness of the

422

model. The toxicokinetic model can capture the whole time-course of bioaccumulation rather

423

than simply a steady-state concentration regardless whether a steady-state can be reached. In

424

addition, the toxicokinetic model can deal with fluctuating exposure, which is what happens

425

in the real environment, instead of assuming an unrealistic stable exposure. However,

426

calibrating or making full use of the toxicokinetic model demands much more variable input

427

(e.g., metal concentrations in water and food) and thus requires more intensive sampling.

428

Although, with the development of automatic in situ or on-site sampling and measurement

429

techniques,40 collecting data for the toxicokinetic model may become much easier in the

430

future.

431

In summary, we demonstrated the feasibility of using a simple toxicokinetic model for

432

simulating the bioaccumulation of metals in a complex and dynamic environment. We also

433

introduced the methodology of determining model parameters in the real estuarine 15 ACS Paragon Plus Environment


Page 16 of 26

434

environment. Using distributions instead of single values for model parameters make the

435

predictions of the model probabilistic and with clearly defined uncertainties, and thus

436

particularly useful for risk assessments. Although this study was conducted with one specific

437

oyster species in one specific estuary, the methods we developed can be useful for other

438

biological species and other water bodies.

439 440

ACKNOWLEDGEMENT

441

We dedicated this paper to the memory of Dr. Guo Feng, who passed away right at the

442

beginning of this project. This study was supported by the National Natural Science

443

Foundation of China (21477099, 21237004), the General Research Fund (16140616) from

444

the Hong Kong Research Grants Council, and the Natural Science Foundation of Fujian

445

Province (2014J01165).

446 447

Supporting Information. Settings of initial and prior parameter values (Table S1, S2);

448

metal toxicokinetics of oysters reported in the literature (Table S3); metal concentrations in

449

estuarine waters and suspended particles reported in the literature (Table S4, S5 ); map of

450

study sites (Fig. S1); oyster growth (Fig. S2) and salinity and pH data (Fig. S3); pairwise

451

correlation between metal concentrations in water (Fig. S4) and suspended particles (Fig. S5);

452

metal bioaccumulation at site 2 (Fig. S6); posterior distribution of parameters and correlation

453

among parameters (Fig. S7-S13); Cd bioaccumulation in oysters under hypothetical

454

exposures (Fig. 14); information on DGT measurements and model implementation in

455

openmodel.


Page 17 of 26


456

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10. Pan, K.; Wang, W.-X. Validation of biokinetic model of metals in the scallop Chlamys nobilis in complex field environments. Environ. Sci. Technol. 2008, 42 (16), 6285-6290.

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13. Kraemer, L. D.; Campbell, P. G. C.; Hare, L. Modeling cadmium accumulation in indigenous yellow perch (Perca flavescens). Can. J. Fish. Aquat. Sci. 2008, 65 (8), 1623-1634. 17 ACS Paragon Plus Environment


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14. Weng, N.; Wang, W.-X. Variations of trace metals in two estuarine environments with contrasting pollution histories. Sci. Total Environ. 2014, 485, 604-614.

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15. Apeti, D. A.; Johnson, W. E.; Kimbrough, K. L.; Lauenstein, G. G. National Status and Trends Mussel Watch Proram: Sampling Methods 2012 Update; NOAA National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Asssessment: Silver Spring, MD, 2012; p 39.

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16. General Administration of Quality Supervision, Inspection and Quarantine of P. R. China; Standardization Administration of P. R. China, The Specification for Marine Monitoring—Part 6: Organism Analysis (GB 17378.6—2007). In China Standards Press: Beijing, 2007; p 74.

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17. Robinson, W. E.; Ryan, D. K.; Wallace, G. T. Gut contents: A significant contaminant of Mytilus edulis whole body metal concentrations. Arch. Environ. Contam. Toxicol. 1993, 25 (4), 415-421.

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18. Wang, W.-X.; Griscom, S.B.; Fisher, N.S. Bioavailability of Cr(III) and Cr(VI) to marine mussels from solute and particulate pathways. Environ. Sci. Technol. 1997, 31, 603-611.

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19. Dunn, R. J. K.; Teasdale, P. R.; Warnken, J.; Schleich, R. R. Evaluation of the diffusive gradient in a thin film technique for monitoring trace metal concentrations in estuarine waters. Environ. Sci. Technol. 2003, 37 (12), 2794-2800.

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20. Ashauer, R.; Hintermeister, A.; Caravatti, I.; Kretschmann, A.; Escher, B. I. Toxicokinetic and toxicodynamic modeling explains carry-over toxicity from exposure to diazinon by slow organism recovery. Environ. Sci. Technol. 2010, 44 (10), 3963-3971.

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21. Yin, Q.-J. Calibration of metals against salinity and high turnover of nickel in marine bivalves. Master Thesis, The Hong Kong University of Science and Technology, Hong Kong, China, 2017.

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22. Ke, C.; Wang, W.-X. Bioaccumulation of Cd, Se, and Zn in an estuarine oyster (Crassostrea rivularis) and a coastal oyster (Saccostrea glomerata). Aquat. Toxicol. 2001, 56 (1), 33-51.

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23. Pan, K.; Wang, W.-X. Reconstructing the biokinetic processes of oysters to counteract the metal challenges: Physiological acclimation. Environ. Sci. Technol. 2012, 46 (19), 10765-10771.

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24. Pan, K.; Wang, W.-X. Biodynamics to explain the difference of copper body concentrations in five marine bivalve species. Environ. Sci. Technol. 2009, 43 (6), 2137-2143.

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25. Hack, C. E. Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models. Toxicology 2006, 221 (2), 241-248.

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26. Tan, Q.-G.; Ke, C.; Wang, W.-X. Rapid assessments of metal bioavailability in marine 18 ACS Paragon Plus Environment

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sediments using coelomic fluid of sipunculan worms. Environ. Sci. Technol. 2013, 47 (13), 7499-7505.

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27. Munksgaard, N. C.; Parry, D. L. Monitoring of labile metals in turbid coastal seawater using diffusive gradients in thin-films. J. Environ. Monit. 2003, 5 (1), 145-149.

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28. Ernstberger, H.; Zhang, H.; Davison, W. Determination of chromium speciation in natural systems using DGT. Anal. Bioanal. Chem. 2002, 373 (8), 873-879.

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29. Benoit, G.; Oktay-Marshall, S. D.; Cantu, A.; Hood, E. M.; Coleman, C. H.; Corapcioglu, M. O.; Santschi, P. H. Partitioning of Cu, Pb, Ag, Zn, Fe, Al, and Mn between filter-retained particles, colloids, and solution in six Texas estuaries. Mar. Chem. 1994, 45 (4), 307-336.

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30. Elbaz-Poulichet, F.; Garnier, J.-M.; Guan, D. M.; Martin, J.-M.; Thomas, A. J. The conservative behaviour of trace metals (Cd, Cu, Ni and Pb) and As in the surface plume of stratified estuaries: example of the Rhône River (France). Estuar. Coast. Shelf Sci. 1996, 42 (3), 289-310.

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31. Evan Ward, J.; Shumway, S. E. Separating the grain from the chaff: particle selection in suspension- and deposit-feeding bivalves. J. Exp. Mar.Biol. Ecol. 2004, 300 (1), 83-130.

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32. Wang, W.-X.; Fisher, N. S. Assimilation efficiencies of chemical contaminants in aquatic invertebrates: A synthesis. Environ. Toxicol. Chem. 1999, 18 (9), 2034-2045.

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33. Weijs, L.; Yang, R. S. H.; Das, K.; Covaci, A.; Blust, R. Application of Bayesian population physiologically based pharmacokinetic (PBPK) modeling and Markov chain Monte Carlo simulations to pesticide kinetics studies in protected marine mammals: DDT, DDE, and DDD in harbor porpoises. Environ. Sci. Technol. 2013, 47 (9), 4365-4374.

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34. Croteau, M.-N.; Luoma, S. N. Delineating copper accumulation pathways for the freshwater bivalve Corbicula using stable copper isotopes. Environ. Toxicol. Chem. 2005, 24 (11), 2871-2878.

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35. Hédouin, L.; Metian, M.; Teyssié, J.-L.; Fichez, R.; Warnau, M. Delineation of heavy metal contamination pathways (seawater, food and sediment) in tropical oysters from New Caledonia using radiotracer techniques. Mar. Pollut. Bull. 2010, 61 (7–12), 542-553.

552 553 554

36. Munger, C.; Hare, L. Relative importance of water and food as cadmium sources to an aquatic insect (Chaoborus punctipennis): Implications for predicting Cd bioaccumulation in nature. Environ. Sci. Technol. 1997, 31 (3), 891-895.

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37. Wang, W.-X.; Fisher, N. S. Delineating metal accumulation pathways for marine invertebrates. Sci. Total Environ. 1999, 237, 459-472.

557 558 559

38. Lee, J. H.; Birch, G. F.; Simpson, S. L. Metal-contaminated resuspended sediment particles are a minor metal-uptake route for the Sydney rock oyster (Saccostrea glomerata) — A mesocosm study, Sydney Harbour estuary, Australia. Mar. Pollut. Bull. 2016, 104 (1), 19 ACS Paragon Plus Environment


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561 562 563 564

39. Lee, J. H.; Birch, G. F.; Cresswell, T.; Johansen, M. P.; Adams, M. S.; Simpson, S. L. Dietary ingestion of fine sediments and microalgae represent the dominant route of exposure and metal accumulation for Sydney rock oyster (Saccostrea glomerata): A biokinetic model for zinc. Aquat. Toxicol. 2015, 167 (Supplement C), 46-54.

565 566 567

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Page 21 of 26


568

Tables and Figures

569

Table 1 The estimated values of the toxicokinetic parameters (ku, kf, and ke). The values are

570

mean ± standard deviation; values in parenthesis are 95% confidence intervals. The statistics

571

summarize the parameter samples generated by the MCMC fittings of the site 1 data (see Fig.

572

S7–S13 for more information of the parameter samples).

573

Metal Cr

Co

Ni

Cu

Zn

Cd

Pb

ku (L g-1 d-1)

kf (g g-1 d-1)

ke (d-1)

1.3 ± 0.2

0.00035 ± 0.00021

0.0066 ± 0.0012

(0.9, 1.6)

(0.00004,0.00078)

(0.0044, 0.0087)

0.071 ± 0.035

0.0032 ± 0.0018

0.025 ± 0.017

(0.011, 0.137)

(0.0003, 0.0074)

(0.002, 0.071)

0.39 ± 0.23

0.0039 ± 0.0026

0.32 ± 0.18

(0.07, 0.89)

(0.0006, 0.0100)

(0.05, 0.72)

6.3 ± 1.9

0.65 ± 0.25

0.0017 ± 0.0014

(3.8, 10.5)

(0.06, 0.95)

(0.0001, 0.0044)

5.6 ± 2.2

0.39 ± 0.15

0.012 ± 0.004

(0.4, 9.2)

(0.15, 0.74)

(0.003, 0.020)

1.5 ± 0.6

0.16 ± 0.09

0.0074 ± 0.0036

(0.4, 2.6)

(0.02, 0.31)

(0.0009, 0.0136)

0.11 ± 0.07

0.0011 ± 0.0006

0.0061 ± 0.0020

(0.01, 0.25)

(0.0003, 0.0024)

(0.0033, 0.0099)

574



Page 22 of 26

575

Fig. 1 Labile metal concentrations in water measured at ~3 d intervals using DGT probes at

576

the two study sites during the 48-d oyster transplantation. 2.0

Cr 1.5 1.0 Site 1 Site 2

-1

DGT-measured metal concentration in water (µg L )

0.5 0.0 1.5

40

Co

1.2

Zn 30

0.9

20

0.6 0.3

10

0.0 80

0 0.4

Ni

Cd

60

0.3

40

0.2

20

0.1

0

0.0

60

0.6

Cu

50

Pb 0.5

40

0.4

30

0.3

20

0.2

10

0.1

0

0.0 0

577

10

20

30

40

Days of exposure

50

0

10

20

30

40

50

Days of exposure


Page 23 of 26


578

Fig. 2 Metal concentrations in suspended particles collected at the two study sites during the

579

48-d oyster transplantation. Values are mean ± standard deviation (n = 3). 104

Cr 103

-1

Metal concentration in suspended particles (µg g )

102 Site 1 Site 2

10

1

102

103

Co

Zn

101

102

100 104

101 101

Ni

Cd

3

10

100

102

10-1

101

10-2

103

102

Cu

Pb

102

101

101 0

580

10

20

30

40

Days of exposure

50

0

10

20

30

40

50

Days of exposure



Page 24 of 26

581

Fig. 3 Metal bioaccumulation in soft tissues of oysters at site 1 during the 48-d

582

transplantation. The × symbols represent measured concentrations; the red curves and the

583

shaded areas are average concentrations and 95% confidence intervals predicted by the model

584

based on the parameter samples from the MCMC fittings. See Fig. S7–S13 for the parameter

585

samples. 60

Cr 45

Metal concentration in oyster tissue (µg g-1 dry weight)

30 15

measured Modeled

0 4

10000

Co

Zn

8000

3

6000 2 4000 1

2000

0

0

160

40

Ni

Cd

120

30

80

20

40

10

0

0

15000

20

Cu

12000

Pb 15

9000 10 6000 5

3000 0

0 0

586

10

20

30

40

Days of exposure

50

0

10

20

30

40

50

Days of exposure


Page 25 of 26


Fig. 4 The comparison between observed and model predicted metal concentrations in

588

oysters at site 2. The observed values are the measured median concentrations of ~10

589

replicated oysters. The predicted values are the average concentrations predicted by the

590

model based on the parameter samples from the MCMC fittings of data from site 1. The solid

591

line is the curve y = x; the two dashed lines are the curves y = 2·x and y=x/2, respectively.

-1

Observed concentration (µg g )

587

592

104

Cu

Zn

103 102 101 100

Cr

Ni

Pb Cd Co

10-1 10-1 100

101

102

103

104

Predicted concentration (µg g-1)

593



Page 26 of 26

594

Fig. 5 The comparison between water and food (suspended particles) as the source of metals

595

accumulated by the oysters (C. hongkongensis) at study site 1 during the 48-d transplant. The

596

curves and the shaded areas are average concentrations and 95% confidence intervals

597

predicted by the model based on the parameter samples from the MCMC fittings of data from

598

site 1.

599


Modeling the Toxicokinetics of Multiple Metals in the Oyster

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