Environmental assessment of heavy metal transport and transformation in the Hangzhou Bay, China

Accepted Manuscript Title: Environmental Assessment of Heavy Metal Transport and Transformation in the Hangzhou Bay, China Author: Hongwei Fang Lei Huang Jingyu Wang Guojian He Danny Reible PII: DOI: Reference:

S0304-3894(15)30115-1 http://dx.doi.org/doi:10.1016/j.jhazmat.2015.09.060 HAZMAT 17133

To appear in:

Journal of Hazardous Materials

Received date: Revised date: Accepted date:

22-4-2015 16-9-2015 27-9-2015

Please cite this article as: Hongwei Fang, Lei Huang, Jingyu Wang, Guojian He, Danny Reible, Environmental Assessment of Heavy Metal Transport and Transformation in the Hangzhou Bay, China, Journal of Hazardous Materials http://dx.doi.org/10.1016/j.jhazmat.2015.09.060 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Environmental Assessment of Heavy Metal Transport and Transformation in the Hangzhou Bay, China Hongwei Fang1, Lei Huang1, Jingyu Wang1 , Guojian He1, Danny Reible2* [email protected] 1

The State Key Laboratory of Hydro Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, P. R. China 2 Department of Civil & Environmental Engineering, Texas Tech University, Lubbock, TX 79409-1023 *

Corresponding author. Tel.: 806/834-8050.

1

Highlights  An integrated model of hydrodynamics, sediment and heavy metal transport  Simulated heavy metal transport and transformation in the Hangzhou Bay.  Evaluated accidental discharge of 137Cs from the QFNPP was assumed.  The sediment effects on the mobility of heavy metals were analyzed.

2

Abstract The environmental impact of heavy metal (Cu, Cd, Zn, Pb, Ni, 90Sr and

137

Cs) transport and

transformation in the Hangzhou Bay (China) was assessed through a comprehensive model that integrates hydrodynamics, sediment and heavy metal transport. A mechanistic surface complexation model was used to estimate the adsorption and desorption of heavy metal by suspended sediment under different aqueous chemistry conditions. The dynamics of metal exchange to and from the seabed was also assessed. The primary processes regulating heavy metal

distribution,

i.e.,

convection-diffusion,

adsorption-desorption,

sedimentation-resuspension, as well as other physical and chemical processes related to mass exchange between adjacent sediment layers, were considered in detail. The accidental discharge of

137

Cs was simulated as an example and results showed that

137

Cs transported

along the coast driven by tidal flow. Most 137Cs distributed near the outfall and accumulated in the seabed sediment. The proposed model can be a useful tool for predicting heavy metal transport and fate and provide a theoretical basis to guide field sampling, assessment of risks and the design of remediation strategies. Keywords: Environmental impact; Heavy Metal Transport; Sediment; Numerical Modeling; Hangzhou Bay

3

1. Introduction Heavy metals are easily accumulated in the food chain, exerting a significant challenge and risk to human health and environment [1]. Hangzhou Bay, a typical funnel-shaped estuary, is located in the East China Sea (Fig. 1), immediately south of the Yangtze Estuary. There is serious heavy metal pollution due to the rapid development of surrounding economy. Large quantities of heavy metals, such as Cu, Cd, Zn, Pb and Ni, are directly and continuously discharged into Hangzhou Bay from manufacturing and processing plants (e.g., Jinshan petrochemical), causing deteriorating impacts on the quality of the marine environment [2]. Radionuclides (e.g.,

90

Sr and

137

Cs) are discharged into Hangzhou Bay

through the cooling water system of the Qinshan First Nuclear Power Plant (QFNPP) which commenced operations in December, 1991. The goal of the present work is to develop a modeling tool capable of predicting heavy metal transport and fate and apply it to Hangzhou Bay to guide assessment of risks and potential remedial planning and design. Heavy metals have a strong affinity for sediments which greatly impacts their mobility. Heavy metals are easily adsorbed on suspended sediment, and then deposited on the seabed, leading to heavy metal enrichment in seabed sediment. When environmental conditions change, heavy metals associated with sediment could be released into the overlying water, threatening the aquatic biota [3]. Marine sediment serves as the principal sink as well as potentially a significant source of heavy metals [2]. High suspended sediment concentration (SSC) exists in the Hangzhou Bay due to strong tidal effects. It’s necessary to consider metal exchange with suspended and bed sediment to evaluate the mobility and risks of heavy metals in this and other similar systems. In-situ sampling is widely used to evaluate the pollution status of sediment in estuaries [4, 5]. However, it’s time-consuming and costly and lacks the ability of prediction into future conditions. Numerical modeling is an effective tool for predicting the mobility of heavy metals, and aiding environmental risk assessment and the design of effective remediation strategies. In recent decades, a lot of models have been proposed to simulate the mobility of heavy metals [6-8]. Most of these models consider the sediment effects simply using empirical parameters (e.g., the distribution coefficient Kd), without a comprehensive understanding of the intrinsic mechanisms. It is difficult to choose reasonable values for these parameters, and generally they are site-specific and not easily extended, weakening the model performance. In this study, an integrated hydrodynamic, sediment and heavy metal transport model,

4

involving convection-diffusion, adsorption-desorption, sedimentation-resuspension etc., was proposed to more accurately predict heavy metal transport and transformation in the Hangzhou Bay. Both heavy metals (Cu, Cd, Zn, Pb, Ni) and radionuclides (90Sr and

137

Cs)

were considered to estimate the environmental risk, and the simulated results were compared to observed data. Then an assumed accidental discharge of

137

Cs from the QFNPP was

simulated, through which the sediment effects on the mobility of heavy metals were further analyzed.

2. Materials and Methods 2.1 Hydrodynamics and Sediment Transport Suspended sediment and bed load exchange frequently in the Hangzhou Bay, due to the shallow water, strong tidal current and a large tidal range [9]. The suspended sediment concentration (SSC) and heavy metal concentration are typically uniformly distributed in the vertical direction. In this study, a two-dimensional, depth-averaged, hydrodynamic and suspended sediment transport model was applied, incorporating the dynamics of the topography of the seafloor [10, 11]. A summary of the equations and related parameters may be seen in Supplementary Material. The topography of Hangzhou Bay was obtained from the State Oceanic Administration of China, with a resolution of 1 min in both longitude and latitude (see Figure S1). The grid consists of 194×168 cells, the size of which is 600×600 m, extending from 120.5° to 121.9° in longitude and from 29.9° to 30.9° in latitude. In the hydrodynamic model, tidal surface elevations and phases were specified along the eastern boundary of the computational domain using estimates generated from globe astronomical forecasting software. The discharge from Xin’anjiang Reservoir, which is located upstream of Qiantang River and put into operation in 1960, was used to derive the xand y-direction velocities at the western boundary [11]. A full spring/neap tidal cycle with a period of 15 days were simulated and repeated for longer simulations. The median diameter of suspended sediment particles in the Hangzhou Bay ranges from 0.006 mm to 0.016 mm [12], which was characterized by a mean size of 0.01 mm in this calculation. Flocculation was considered by modifying the particle diameter with a flocculation factor to represent the flocculation effects on settling velocity (see Supplementary Material). Representative mean annual sediment concentration of 0.5 kg/m3 was specified along the western boundary (i.e., Qiantang River). Meanwhile, the sediment concentration decreasing from 2.0 kg/m3 (north) to 0.5 kg/m3 (south) was defined as the eastern boundary condition, representing the

5

significant sediment discharge from the Yangtze River [12]. All the equations were solved using an alternating direction implicit (ADI) method (i.e., a two-step scheme) with the upwind scheme applied for the discretization, which resulted in a tridiagonal system of one-dimensional linear algebraic equations [11]. Detailed information on the numerical method may be seen in the Supplementary Material. The hydrodynamic and sediment transport sub-models were solved by the uncoupled method, both starting from rest, until stable oscillations were obtained. The resulting model predictions were compared to the water column velocity and suspended sediment concentration collected from the literatures [13-15]. Model results were stored in files that would be later read by the heavy metal transport sub-model. 2.2 Heavy Metal Transport A conceptual model of the heavy metal transport including both the physical and chemical dynamics is shown in Fig. 2. The physical domain was divided vertically into a water column and an active sediment layer. The active sediment layer was assumed to consist of a surficial aerobic layer and a deeper anaerobic layer to distinguish the chemical reactions and metal behavior in the different redox conditions [16]. The redox conditions in these layers was assumed to develop rapidly compared to the rate of sediment erosion or deposition, thus the layers were effectively static but moved up and down with erosion and deposition over time. The discharged heavy metals are transported in both the dissolved and particulate phase due to its high affinity to sediment particles. Mass exchange occurred between adjacent layers through diffusion, particle mixing, sedimentation and resuspension etc. The heavy metal transport model included four governing equations describing the concentration variations of heavy metals dissolved in water (Cw), bound to suspended sediment (Cs), and that present in the aerobic layer (CTb,1) and anaerobic layer (CTb,2), i.e.,   HC w  t



  HSC s  t H1

H2

CTb,1 t CTb,2 t

  HUC w 



x



  HSUC s  x

  HVC w  y 



  HSVC s  y

 

C w    C w  w  Ex H    Ey H    HC  S w x  x  y  y  

 

 Ex H x 

  SC s   x

 

   Ey H  y 

  SC s   y

s    SHC  Ss 

(1)

(2)

  H1CTb,1  S b,1

(3)

  H 2 CTb,2  S b,2

(4)

6

where λ is the decay constant for radionuclide (i.e., λ=0 for other heavy metals), H is the water depth; H1 and H2 denote the thicknesses of aerobic and anaerobic layers, respectively; U and V represent the depth-averaged velocities in the x- and y- directions, and S is the depth-averaged SSC; Ex and Ey are the diffusion coefficients. Moreover, Sw, Ss, Sb,1 and Sb,2 are the source and sink terms associated with different physical and chemical processes, i.e., functions of R1~R13 (Fig. 2). Model implementation including numerical discretization, boundary conditions, parameters and models for the source terms are summarized below. 2.2.1 Heavy Metal Sources The products of human activities, such as industrial wastewater, domestic sewage and agricultural production, are often the main sources of heavy metal pollutants in aqueous environment [17]. Here the heavy metal source was simply represented by a source term R1. Large quantities of heavy metal are continuously discharged into Hangzhou Bay from the local point sources (e.g., Jinshan petrochemical or QFNPP). Qiantang River is the main river system discharging sediment and heavy metal directly into the Hangzhou Bay. Moreover, some of the sediment and heavy metals from the Yangtze Estuary enters the Hangzhou Bay and imposes a profound influence on the heavy metal distribution. All these sources were considered in this study. 2.2.2 Adsorption of Heavy Metals Various hydroxyl groups exist on the surface of sediment particles [18]. The chemical reactions with these hydroxyl groups largely describe adsorption and desorption of heavy metals under oxic conditions. The diffuse layer model (DLM) has been extensively applied to describe surface adsorption. The surface hydroxyl group was denoted as ≡SOH. The surface protonation and deprotonation of the sediment were expressed as follows [19]:

 SOH 2  SOH+H + , K aint1

(5)

 SOH SO  +H + ,

(6)

K aint2

where Ka1int and Ka2int are the intrinsic acidity constants. For the sediment from Yangtze River, which flows into the East China Sea on the eastern end of Hangzhou Bay, values of 2.35 and 4.85 were recommended for log Ka1int and log Ka2int, respectively [20]. The adsorption of the heavy metal ion M2+ on the sediment surface was expressed as follows [21]:

 SOH+M 2+  SO  -M 2+ +H + ,

K1int

 SOH+M 2+ +H 2O  SO  -MOH + +2H + , K 2int 7

(7) (8)

where K1int and K2int are the intrinsic surface complexation constants for heavy metal adsorption. Table 1 lists the intrinsic surface complexation constants for some common heavy metals, including Cu2+, Cd2+, Zn2+, Pb2+, Ni2+, 90Sr2+, and 137Cs+ [21, 22]. Then the surface adsorption was reduced to the solution of a series of equations representing different chemical reactions. Figure 3 shows the simulated adsorption edges (i.e., adsorption amount versus pH) of Zn and Cd using DLM, indicating a good performance of the surface complexation model. Thus the distribution coefficient Kd(Φ) could be obtained using DLM to estimate the adsorption of heavy metal under different aqueous chemistry conditions, and the variable Φ represented SSC, pH, ionic strength, the type of heavy metal, specific surface area of sediment, etc. [23, 24]. Based on the kinetic adsorption theory, the temporal evolution of dissolved heavy metal concentration due to adsorption and desorption could be expressed as follows [29]:   HC w  t

  Hk1C w  Hk2 SC s

(9)

where k1 and k2 represent the rates of adsorption and desorption, respectively. Assuming an equilibrium state was achieved, then Eq. (9) became   HC w  t

  Hk1Ceqw  Hk2 SCeqs  0

(10)

where Ceqw and Ceqs denote the equilibrium concentrations of heavy metal in the dissolved and particulate phase, respectively. Generally, the distribution coefficient Kd(Φ) was defined as Ceqs/ Ceqw, so k1  k2 S

Ceqs Ceqw

 k2 SK d   

(11)

If the adsorption rate k1 was assumed to be the same for the equilibrium and non-equilibrium states, then substituting Eq. (11) into Eq. (9) yielded   HC w  t

  HSk2  K d    C w  C s 

(12)

Therefore, the source term R2 for the adsorption of heavy metals was expressed as follows: R 2  HSk2  K d    C w  C s 

(13)

2.2.3 Mass Exchange

Seawater is composed of water, various chemical elements (e.g., Na+, K+, Ca2+, Mg2+, Cl-, 8

SO42-, Br-, HCO3-) which influence metal speciation and toxicity. Heavy metals are immobilized primarily to antigenic sulfide minerals through sulfate reduction under anaerobic conditions (Eq. (14)). They are released into the surrounding environment due to the oxidation of sulfide minerals under aerobic conditions (Eq. (15)) [30]. Therefore, the mobility and bioavailability of heavy metals are directly correlated with the dissolved oxygen concentration and redox potential, which vary continuously in the active sediment layer along the vertical direction. For simplicity, the active sediment layer was divided into aerobic and anaerobic layers to distinguish the different redox reactions of heavy metal [16]. And fdi and fpi were defined to represent the percentages of dissolved and particulate phases respectively, where i=1, 2 corresponded to aerobic and anaerobic layers. Anaerobic conditions:

M 2  aq  +CH 2O+SO 24  aq   MS s  +CO 2 +2H 2O

(14)

Aerobic conditions:

H2O MSs  +O 2  M 2  aq  +SO 24  aq 

(15)

(1) Mass exchange between layers There is a concentration gradient of heavy metal between adjacent layers, and heavy metal exchanges through turbulent exchange at the sediment-water interface and due to molecular diffusion and any bed advective processes within the sediment. The exchange rate was assumed to be proportional to the concentration difference between layers. The source term for the transfer of heavy metal from the overlying water to the aerobic layer (R3), and that from the aerobic layer to the anaerobic layer (R4) could be expressed:

R3  K L01  C w  f d1CTb,1 

(16)

R 4  K L12  f d1CTb,1  f d2CTb,2 

(17)

where KL01 and KL12 denote the mass transfer coefficients. (2) Bioturbation Moreover, heavy metal exchanges between aerobic and anaerobic layers through the mixing activities of benthic organisms (i.e., bioturbation). And the source term (R5) could be expressed: R5  12  f p1CTb,1  f p2CTb,2 

(18)

where ω12 is the rate of bioturbation mixing, affected by the benthic biomass, biological activity, water temperature and dissolved oxygen concentration etc. (3) Seafloor topography evolution Estuaries in China typically contain a significant amount of suspended sediment, which

9

leads to evolution of the seafloor topography and affects the mobility of heavy metal greatly. In this study, the thicknesses of aerobic and anaerobic layers were set as 3 mm and 10 cm respectively [16]. R6, R7, R10 and R12 were the deposition source terms (Fig. 2), where R6 and R7 represented the increments of the dissolved and particulate heavy metal in the aerobic layer; R10 and R12 represented the buried heavy metal in the aerobic and anaerobic layers, respectively. Expressions of R6, R7, R10 and R12 were shown in Eqs. (19) ~ (22) depending on the deposition depth (Figure S2):





Z *  0

(19)





Z *  0

(20)

R 6  Z * / t  C w R 7  Z * / t 1     s C s

 Z * / t  CTb,1

  Z  H 1 *

R10  

b,1 * w s  H 1 / t  CT   Z  H 1  / t   C  1     s C 

 Z / t  C  R12   H / t  C  H / t  C  *

T

b,2

2

T

*

(21)

  Z  H 1 

b,2 T

b,2

2

Z  H 1 *





 Z  H 2 / tCT *

H 2  Z  H 1  H 2

b,1



*





  H 1 / t  CT  Z  H 1  H 2 / t  C  1     s C b,1

*

w

s



(22)

Z  H 1  H 2 *

where ΔZ* represents the variation of bed elevation due to deposition or erosion obtained by the topography evolution equation (see Eq. S6); ε is the porosity; ρs is the sediment density with the value of 2650 kg/m3. Similarly, R8, R9, R11 and R13 denoted the source terms related to erosion (Fig. 2), and the expressions were shown in Eqs. (23) ~ (26):  Z * / t  f d1CTb,1 R8   b,1 * b,2  H 1 / t  f d1CT    Z  H 1  / t  f d2CT

 H 1  Z   *

Z   H 1 *

 Z * / t  f p1CTb,1 R9   b,1 * b,2  H 1 / t  f p1CT    Z  H 1  / t  f p2CT

 H 1  Z  

(23)

*

Z   H 1 *

(24)





Z *  0

(25)





Z *  0

(26)

R11  Z * / t CTb,2 R13  Z * / t CTb,2

Finally, the sources terms Sw, Ss, Sb,1 and Sb,2 in Eqs. (1) ~ (4) were expressed as follows: S w  R1  R 2  R3  R6  R8

(27)

Ss  R 2  R 7  R9

(28)

S b,1  R3  R 4  R5   R6  R7    R8  R9   R10  R11

(29)

10

S b,2  R 4  R5  R10  R11  R12  R13

(30)

2.2.4 Numerical Solution and Parameters

The transport and transformation of Cu, Cd, Zn, Pb, Ni, 90Sr, and 137Cs in the Hangzhou Bay were simulated using the heavy metal transport sub-model. Similarly, Eqs. (1) ~ (4) were also solved using the ADI method (see Supplementary Material). Heavy metals discharged from the local point sources (i.e., Cu, Cd, Zn, Pb, Ni from Jinshan petrochemical and 137

90

Sr,

Cs from QFNPP) were considered. The effluent load of the Jinshan petrochemical is 4×105

t/d [31]. As the heavy metal concentrations in the effluent were not available, they were obtained from a calibration procedure. The cooling water effluent discharge of QFNPP is 21.6 m3/s, and an average concentration of 4.1 mBq/L and 1.0 mBq/L were observed for 90Sr and 137

Cs respectively [32]. The heavy metal transport sub-model was started from background concentrations. The

background concentrations of heavy metals in the suspended phase and the active sediment layer were referred to Wang [31], as shown in Table 2. The corresponding background in the dissolved phase was obtained from the metal Kd recommended by the IAEA [33]. Meanwhile, the initial concentrations of 90Sr and 137Cs in the simulated domain were assumed to be zero. In order to minimize the influence of initial conditions, the model ran 1 year to convergence before the actual model calculation. A summary of parameters involved in the models is given in Table 3 along with their source. In particular, the desorption kinetic coefficient k2 is very similar even for elements with a rather different geochemical behavior, and a value of 1.16×10-5 s-1 was used as suggested by Nyffeler [35]. Mass transfer coefficient KL01 and KL12 were derived from the molecular diffusion coefficient, which is of the order of magnitude of 10-5 cm2/s [16]. The arithmetic mean for the biodiffusion coefficient is 3.95×10-6 cm2/s for estuarine systems [36], with which the bioturbation mixing rate ω12 was estimated. The calculation was carried out from December, 1991 to 2001, with a time step of 36 s. There are 5 stations for the measurement of tide level (H_ZJD, H_ZP, H_SJD, H_JS, H_LHPZ), 13 stations for velocity (U01~U13), 7 stations for SSC (S01~S07) and 7 stations for heavy metal (R01~R04 for radionuclide and R05~R07 for other heavy metals). Simulation results were compared with observed data in the following section.

3. Results A brief description of the hydrodynamic and sediment transport results is given. Next, 11

model results for heavy metal transport are discussed. 3.1 Hydrodynamics and Sediment Transport

The tide in the Hangzhou Bay is an anomalistic semidiurnal tide. Data measured by Zhao [13] in September 2000 are used for the calibration. Simulated tide level (H_ZP) and velocity (U06) are compared with observed values during the neap tide, as shown in Fig. 4. And model-data comparisons of other tide level and velocity gauge stations are present in Figure S3 and Figure S4 respectively. It is obvious that both the simulated tide level and velocity agree well with the measured data. Statistics showed that the mean absolute error (MAE) of the simulated tide level, velocity and direction are 0.25 m, 0.31 m/s and 17.46° respectively, indicating a good performance of the hydrodynamic sub-model. The current field during the flood and ebb tides are shown in Figure S5, indicating a significant coastal current. Hangzhou Bay is close to the Yangtze Estuary, and both the water and sediment exchange frequently in the adjacent regions. Previous studies [14] have shown that significant amount of sediment was transferred from the Yangtze Estuary to Hangzhou Bay, resulting in a high SSC in Nanhui Flat. Moreover, high SSC was also observed in Andong Flat due to the shallow water depth and complex topography, and in the Bayhead of Hangzhou Bay due to the considerable sediment input from the Qiantang River. The average value of simulated SSC during a whole 15 day tidal period is shown in Fig. 5. The regions of high SSC coincided with the locations reported by Xie [14]. Simultaneously, the simulated sediment concentration processes during the neap tide are compared with the observed data at station S05 and S07 [15], as shown in Fig. 6. The sediment transport sub-model yielded quite satisfactory results of SSC, with the MAE of 0.28 kg/m3. As SSC plays an important role in heavy metal transport, these satisfactory results provide the foundation for effective heavy metal transport modeling. 3.2 Heavy Metal Transport

The proposed model was used to simulate the transport of heavy metals (Cu, Cd, Zn, Pb, Ni,

90

Sr and

137

Cs) in the Hangzhou Bay. These assumed steady emissions from the two

source points identified in Fig. 1. The average simulated values (R01~R07) were compared to the observed data collected from the literatures [2, 9, 32]. Details of the observed data are summarized in Supplementary Material. The observed and simulated concentrations of heavy metals are compared in Fig. 7, with logarithmic coordinates applied. Solid line indicated that the simulated values equaled to the observed values, i.e., y=x. The dashed lines corresponded to the boundaries of 200% and 50% deviations (+/- a factor of two). Overall, the simulated

12

results were in reasonable agreement with the observations, within a bound of 50% and 200% agreement, due to the reasonable analysis of heavy metal sources and the characterization of heavy metal transport processes. As shown in Fig. 7, the concentration of heavy metal on suspended sediment had the same order of magnitude to that on seabed sediment, which was due to the frequent exchange between suspended and seabed sediment [9]. For heavy metal on seabed sediment, the concentrations satisfied that Zn > Ni > Cu > Pb > Cd, i.e., Zn had the maximum concentration. However, the contribution of Zn to the total potential ecological risk was limited due to the small toxicity coefficient in the analysis of potential ecological risk indexes [38, 39]. In contrast, Cd posed significant contribution to the total potential ecological risk due to its high toxicity to biota, which should be of great concern. According to sediment quality guidelines [40], only the Ni concentration on seabed sediment was larger than the Effects Range Low (ERL), indicating a moderate or light pollution in the Hangzhou Bay. These guidelines do not indicate the mobility and availability of metals, however, and so modeling suggests that Cd may be of more concern due to the rapid exchange between sediments and water in the Bay. The heavy metals in water are more directly related to effects since the suspended and dissolved phases are more available to biota. The concentration of 90Sr in water is larger than 137

Cs, which was mostly due to the higher affinity of

The recommended Kds for

137

137

Cs to sediment particles than

90

Sr.

Cs and 90Sr in coastal environment were 4.0 m3/kg and 8×10-3

m3/kg respectively [33], i.e., sediment transport had a greater impact on the mobility of 137Cs. So the mobility of 137Cs in the Hangzhou Bay is discussed in the following sections. The installed capacity of QFNPP is 300 MW and the cooling water effluent discharge is 21.6 m3/s [32]. An average concentration of 1.0 mBq/L for 137Cs was observed in the cooling water system which is approximately double the average concentration of R01~R04 (about 0.6 mBq/L). The simulated distribution of

137

Cs in the Hangzhou Bay, i.e., the average

concentration during the whole tidal period after 1 year’s run, is shown in Fig. 8, including 137

Cs dissolved in water, bound to suspended sediment, as well as that present in the aerobic

and anaerobic layers. Overall,

137

Cs transported along the coast driven by tidal flow, with a

net flux to the bay mouth. Most 137Cs distributed near the QFNPP outfall due to the sediment effects, and accumulated in the seabed sediment. The concentration in the aerobic layer (Fig. 8c) was greater than anaerobic layer (Fig. 8d), implying a gradual transfer from the aerobic layer to anaerobic layer. Simulation results showed that about 18% of 137Cs was adsorbed by suspended sediment, 13

79% was immobilized in the seabed sediment (i.e., 8% in aerobic layer and 71% in anaerobic layer), and only 3% remained in the dissolved phase. The seabed sediment, especially near the outfall, was gradually accumulating

137

Cs until it was in equilibrium with the continuous

release from QFNPP. In addition, a fraction of dissolved

137

Cs transported to the central part

of Hangzhou Bay, resulting in a high concentration region. Nonetheless, the concentration of dissolved 137Cs in most regions of the Hangzhou Bay was close to the background value (i.e., 0.4~1.1 mBq/L), indicating minimal impact on the marine environment under normal emission.

4. Discussion The Fukushima Daiichi power plant accident on March 11, 2011 has attracted wide attentions to the safe operation of nuclear power plants. The accurate prediction of the transport of discharged radionuclide as a result of a serious accident is needed to identify the potential risks of such an accident. The estimated total amount of

137

Cs discharged directly

into the ocean through a crack in a concrete wall at Fukushima was 0.94 PBq. An equivalent spill was simulated at QFNPP, assuming that the incident occurred over five days from April 1 [41]. Three specific cases were simulated to further analyze the effects of the Cesium release on sediment and water concentrations in the Hangzhou Bay. Case 1: the sediment effects were neglected, i.e., all discharged

137

Cs remained in water

and was transported through convection-diffusion. This led to very high estimated concentrations of 137Cs in the Hangzhou Bay, which was an overestimate of the risk. Figure 9 shows the average concentration of

137

Cs during the whole tidal period just after the

accidental discharge. It could be found that

137

Cs transported from the outfall to offshore

quickly, resulting in a relatively large influenced region, and the simulated

137

Cs

concentration was greater than 4.0 Bq/L in most regions of Hangzhou Bay. Case 2: the sediment effects were considered, but a constant Kd (i.e., 4.0 m3/kg) was used during the simulation, which was a common approach employed in previous models. Similarly, the average concentration of

137

Cs during the whole tidal period just after the

accidental discharge is shown in Figure S6. It could be found that most 137Cs distributed near the QFNPP outfall, and accumulated in the seabed sediment. The

137

Cs associated with

sediment would be constantly released into the overlying water after the accidental discharge, especially under the strong tidal effects in the Hangzhou Bay, which would maintain a high concentration of 137Cs in the Hangzhou Bay for a long period. Figure 10 shows the average concentration of 137Cs during the whole tidal period that is 1 14

year after the accidental discharge. High concentrations of fraction of

137

137

Cs were still observed, and a

Cs transported to the central part of Hangzhou Bay driven by tidal flow. As

shown in Fig. 10, the dissolved and particulate phase exhibited similar distribution due to the constant Kd (i.e., Cs=Kd·Cw). In reality, geochemical conditions (e.g., SSC) vary significantly in the Hangzhou Bay and constant Kd is a significant oversimplification. Case 3: the sediment effects were also considered, and Kd(Φ) was obtained using DLM, i.e., the proposed model in this study. Similar to case 2, sediment near the outfall served as a sink of

137

Cs during the accidental discharge, i.e., most

137

Cs was adsorbed, temporarily

reducing the concentration in water and lowing its ecological risk. Then the seabed sediment became an important internal source of 137Cs after the accident. The 137Cs would be released into the overlying water as a result of specific disturbances (e.g., during storm surges), posing a long-term risk to aqueous environment. Figure S7 shows the average concentration of

137

Cs during the whole tidal period just

after the accidental discharge. Most 137Cs has not yet been transported away, i.e., distributing near the outfall, where the effect of spatial heterogeneity of Kd is minimal. Similar distributions between the dissolved and particulate phase was observed as in case 2 (Figure S6), but relatively more 137Cs was adsorbed by suspended sediment. The influenced region expanded with time, and more 137Cs transported from the outfall to offshore. Figure 11 shows the average concentration of

137

Cs during the whole tidal period

that is 1 year after the accident discharge. It was observed that the dissolved and particulate phase exhibited different distributions. Most of the particulate 137Cs remained near the outfall, while the dissolved

137

Cs accumulated in the central part of Hangzhou Bay, indicating a

stronger mobility for the dissolved phase. These characteristics were not well reproduced using a constant Kd (Fig. 10), where similar distribution still existed between the dissolved and particulate phase (i.e., Cs=Kd·Cw). Actually, here the calculated Kd ranged from 0.13 to 34.6 m3/kg, which agreed well with the IAEA technique report [33], i.e., a range of 0.3~20 m3/kg with the recommended value of 4.0 m3/kg. Thus the more sophisticated DLM model yielded substantially different results as to the locations and significant of risk from a constant partition coefficient model.

5. Conclusions In this study, a comprehensive model was proposed to assess the environmental impact of heavy metals (Cu, Cd, Zn, Pb, Ni,

90

Sr and

137

Cs) transport and transformation in the

Hangzhou Bay. The model was found to reasonably reproduce flow, sediment transport and 15

heavy metal transport in the bay within the constraints of available data. The mobility of 137

Cs discharged from QFNPP was of special concern. Results show that

137

Cs transported

along the coast driven by tidal flow. Most of the discharged 137Cs distributed near the outfall, and accumulated in the seabed sediment, which would gradually become an important internal source of

137

Cs to the bay. The dissolved phase had a stronger mobility than

particulate phase, and a fraction of dissolved

137

Cs transported to the central part of

Hangzhou Bay. Nonetheless, the concentration of dissolved

137

Cs was close to the

background value (i.e., 0.4~1.1 mBq/L), indicating minimal impact on the marine environment under normal emission. Results of the assumed accidental discharge showed that sediment effects, as well as its heterogeneity, are essential to describing the transport of 137Cs. The simulation of a large

137

Cs spill at QFNPP leads to near shore sediment contamination

but relatively low levels in the larger bay. The proposed model was a useful tool to predict the potential impacts of such a spill and to both define potential risks and locations likely requiring remedial action.

Acknowledgements This research was financially supported by the National Natural Science Foundation of China (No. 51139003 and No. 11372161). The authors would like to thank Prof. Q. He at the East China Normal University and Environmental Radiation Monitoring Center of Zhejiang Province for their valuable input.

16

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[13] X. Zhao, Three-dimensional mathematical model for water quality and its application in Eastern Zhejiang coastal waters, Zhejiang University, Hangzhou, 2009. [14] D.F. Xie, Z.B. Wang, S. Gao, H.J. De Vriend, Modeling the tidal channel morphodynamics in a macro-tidal embayment, Hangzhou Bay, China, Cont. Shelf Res. 29 (2009) 1757-1767. [15] W. Xiong, 3D numerical simulation of tidal currents and sediment using unstructured mesh model, Dalian University of Technology, Dalian, 2012. [16] D.M. DiToro, Sediment flux modeling, Wiley-Interscience, New York, 2001. [17] S.K. Sundaray, B.B. Nayak, T.K. Kanungo, D. Bhatta, Dynamics and quantification of dissolved heavy metals in the Mahanadi river estuarine system, India, Environ. Monit. Assess. 184 (2012) 1157-1179. [18] J.A. Davis, D.B. Kent, Surface complexation modeling in aqueous geochemistry, Rev. Mineral. Geochem. 23 (1990) 177-260. [19] X.H. Wen, Q. Du, H.X. Tang, Surface complexation model for heavy metal adsorption on natural sediment, Environ. Sci. Technol. 32 (1998) 870-875. [20] H.X. Tang, Y. Qian, X.H. Wen, The characteristics of particles and refractory organics in water and the principle of control technology, China Environmental Science Press, Beijing, 2000. [21] J. Lützenkirchen, Surface complexation modelling, Academic Press, The Netherlands, 2006. [22] N. Mamier, A. Delisee, F. Fromage, Surface complexation modeling of Yb (III) and Cs (I) sorption on silica, J. Colloid Interf. Sci. 212 (1999) 228-233. [23] L. Huang, H.W. Fang, M.H. Chen, Experiment on surface charge distribution of fine sediment, Sci. China Technol. Sc. 55 (2012) 1146-1152. [24] Z.H. Chen, H.W. Fang, Analysis of the complex morphology of sediment particle surface based on electron microscope images, Sci. China Technol. Sc. 56 (2013) 280-285. [25] S.H. Peng, W.X. Wang, J.S. Chen, Partitioning of trace metals in suspended sediments from Huanghe and Changjiang Rivers in Eastern China, Water Air Soil Poll. 148 (2003) 243-258. [26] C.K. Jain, D.C. Singhai, M.K. Sharma, Adsorption of zinc on bed sediment of River Hindon: adsorption models and kinetics, J. Hazard. Mater. 114 (2004) 231-239. [27] J. Hamilton-Taylor, L. Giusti, W. Davison, W. Tych, C.N. Hewitt, Sorption of trace metals (Cu, Pb, Zn) by suspended lake particles in artificial (0.005 M NaNO3) and natural (Esthwaite Water) freshwaters, Colloid. Surface. A 120 (1997) 205-219. 18

[28] A. Franchi, A.P. Davis, Desorption of cadmium (II) from artificially contaminated sediments, Water Air Soil Poll. 100 (1997) 181-196. [29] R. Periáñez, J.M. Abril, Modelling the dispersion of non-conservative radionuclides in tidal waters-Part 1: conceptual and mathematical model, J. Environ. Radioactiv. 31 (1996) 127-141. [30] H. Yongseok, Experimental and mathematical investigation of dynamic availability of metals in sediment, The University of Texas at Austin, Austin, 2009. [31] B. Wang, Study on environmental geochemistry of heavy metals in sediments of Changjiang Estuary and adjacent area, Ocean University of China, Qingdao, 2008. [32] J.D. Ye, G.J. Zeng, Z.G. Cao, H.F. Wang, B. Chen. Radioactivity monitoring in environmental water around QNPP Base, Radiat. Prot. Bull. 26 (2006) 18-24. [33] I. IAEA, Sediment distribution coefficients and concentration factors for biota in the marine environment. Technical Reports Series No. 422, 2004. [34] S. Dick, W. Schonfeld, Water transport and mixing in the North Frisian Wadden Sea-results of numerical investigations, Ger. J. Hydrog. 48 (1996) 27-48. [35] U.P. Nyffeler, Y.H. Li, P.H. Santschi, A kinetic approach to describe trace element distribution between particles and solution in natural aquatic systems, Geochim. Cosmochim. Acta. 48 (1984) 1513-1522. [36] D.D. Reible, Processes, assessment and remediation of contaminated sediments, Springer Science and Business Media, New York, 2013. [37] I. AQCS, Catalogue for reference materials and intercomparison exercises 1998/1999, International Atomic Energy Agency, Analytical Control Services, Vienna, Austria, 1998. [38] L. Håkanson, An ecological risk index for aquatic pollution control: a sedimentological approach, Water Res. 14 (1980) 975-1001. [39] K.R.E. Saraee, M.R. Abdi, K. Naghavi, E. Saion, M.A. Shafaei, N. Soltani, Distribution of heavy metals in surface sediments from the South China Sea ecosystem, Malaysia, Environ. Monit. Assess. 183 (2011) 545-554. [40] E.R. Long, D.D. Macdonald, S.L. Smith, F.D. Calder, Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments, Environ. Manage. 19 (1995) 81-97. [41] Y. Masumoto, Y. Miyazawa, D. Tsumune, T. Tsubono, T. Kobayashi, H. Kawamura, C. Estournel, P. Marsaleix, L. Lanerolle, A. Mehra, Z.D. Garraffo, Oceanic dispersion simulations of 137Cs released from the Fukushima Daiichi Nuclear Power Plant, Elements 8 (2012) 207-212. 19

Figure Captions

Fig. 1. The regional map for Hangzhou Bay.

20

Fig. 2. Conceptual model of heavy metal transport and transformation.

21

Fig. 3. Adsorption edges of Zn (top, 103.1 mg/kg) and Cd (bottom, 2.76 mg/kg). Data sources: ● Yangtze Estuary in China [25], ■ River Hindon in India [26], ◄ Esthwaite Water in Britain [27], ★ Potomac River in USA [28], and ► Back River in USA [28]. The dash dot line, soiled line and dash line represent three types of surface complexation model respectively: Constant Capacitance Model (CCM, C=0.6 F/m2), Diffuse Layer Model (DLM) and Triple Layer Model (TLM, C1=1.0 F/m2, C2=0.6 F/m2).

22

Fig. 4. Variations of tidal level (top) and velocity (bottom) during the neap tide (September 5~6, 2000).

23

Fig. 5. Distribution of suspended sediment concentration in the Hangzhou Bay, i.e., the average simulated value during a whole 15 day tidal period.

24

Fig. 6. Variations of sediment concentration at station S05 and S07 during the neap tide (September 5~6, 2000).

25

Fig. 7. Comparison between the simulated and observed concentration of heavy metal.

26

Fig. 8. The simulated distribution of 137Cs in the Hangzhou Bay, i.e., the average concentration during the whole tidal period after 1 year’s run: (a) dissolved phase, (b) suspended sediment phase, (c) aerobic layer phase and (d) anaerobic layer phase.

27

Fig. 9. The simulated distribution of 137Cs in the Hangzhou Bay when the sediment effects were neglected, i.e., the average concentration during the whole tidal period just after the accidental discharge.

28

Fig. 10. The simulated distribution of 137Cs in the Hangzhou Bay when a constant Kd was used, i.e., the average concentration during the whole tidal period that is 1 year after the accidental discharge: (a) dissolved phase, (b) suspended sediment phase, (c) aerobic layer phase and (d) anaerobic layer phase.

29

Fig. 11. The simulated distribution of 137Cs in the Hangzhou Bay using the proposed model, i.e., the average concentration during the whole tidal period that is 1 year after the accidental discharge: (a) dissolved phase, (b) suspended sediment phase, (c) aerobic layer phase and (d) anaerobic layer phase.

30

Tables Table 1 Intrinsic surface complexation constant for heavy metal

a

Heavy Metal

Cu2+

Cd2+

Zn2+

Pb2+

Ni2+

logK1int

1.39

-1.96

-0.96

0.44

-1.96

-5.44a

-5.50b

logK2int

8.92

6.28

7.50

8.25

6.38

3.59a

2.05b

Estimated from KMOH [21]; b From [22].

31

90

Sr2+

137

Cs+

Table 2 Background concentrations of heavy metals for calculation [31]

Phase

*

Cu

Cd

Zn

Pb

Ni

Cs (mg/kg) *

54.5

0.4

107.8

47.6

73.2

Cb (mg/kg)

24.5

0.11

78.5

24.5

42.3

†

Suspended phase; †Active sediment layer phase.

32

Table 3 Model parameters for calculation Paremater

Symbol

Value

Unit

Reference

Water kinematic viscosity

υ

1.06×10-6

m2/s

Standard value

Sediment density

ρs

2650

kg/m3

Standard value

Diffusion coefficients

Ex , Ey

2.0

m2/s

[34]

Sediment porosity

ε

0.5

—

[8]

Desorption kinetic coefficient

k2

1.16×10-5

1/s

[35]

Mass transfer coefficient

KL01

8.0×10-7

m/s

[16]

Mass transfer coefficient

KL12

1.2×10-7

m/s

[16]

Bioturbation mixing rate

ω12

1.2×10-8

m/s

[36]

Proportion of pore water

fd1

0.05

—

[16]

Proportion of pore water

fd2

0.05

—

[16]

Proportion of sediment

fp1

0.95

—

[16]

Proportion of sediment

fp2

0.95

—

[16]

Half-life of radionuclide

T90Sr

28.78

year

[37]

Half-life of radionuclide

T137Cs

30.14

year

[37]

33