Longitudinal estimates of sediment-water diffusive flux of PCB congeners in the Houston Ship Channel

Estuarine, Coastal and Shelf Science 164 (2015) 19e27

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Longitudinal estimates of sediment-water diffusive flux of PCB congeners in the Houston Ship Channel Nathan L. Howell a, *, Hanadi S. Rifai b a b

School of Engineering & Computer Science, West Texas A&M University, 2501 4th Avenue Canyon, TX 79016, USA Civil & Environmental Engineering, University of Houston, N107 Engineering Building 1, Houston, TX 77204-4003, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 July 2014 Received in revised form 15 June 2015 Accepted 21 June 2015 Available online 23 June 2015

Sediment-water diffusive exchange fluxes were estimated for PCB congeners between 2002 and 2012 for the Houston Ship Channel in Texas. These estimations were determined for four different sampling periods representing a unique effort to quantify fluxes, their magnitudes and variability, over a longer time period than similar studies. Total PCB fluxes, on the order of 0.10e250 mg/m2-yr, were most dependent on bulk sediment and total water concentrations and the partitioning models used. Diffusive flux directions were highly variable on a single-PCB analyte basis though they were generally sedimentto-water or indeterminate. Sediment concentrations from after Hurricane Ike (September 2008) were highly elevated (median increase 360%) from immediately before the event leading to much higher diffusive flux estimates. Lastly, sediment-water diffusive flux was 2e3 orders of magnitude less than net deposition and associated desorption fluxes leading to the possibility that burial would eventually lower the sediment impact to the estuary. This is, however, uncertain because depositing sediment still had elevated PCB concentrations. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Polychlorinated biphenyls (PCBs) Sediment-water diffusion Organic carbon Black carbon Chemical partitioning Hurricanes Regional index: USA Texas Houston ship channel

1. Introduction Pollutant exchange processes at the surface water-bed sediment interface has been an oft studied topic in chemical fate and transport because the flux quantity has relevant bearing on sediment bed risk assessment, understanding bioavailability, chemical cycling and mass balance, and sediment remediation (Thibodeaux, 1996). Diffusive flux measurements have often (though not always) been undertaken via experimental laboratory setup due to the difficulty of placing flux measurement devices at depth. Kupryianchyk et al. (2013), for example, placed Empore disks above sediment microcosms to examine the influence of bioturbation and sediment remediation strategies for reducing diffusive mass flux. Ortiz et al. (2004) simulated low flow conditions of a PCBcontaminated freshwater sediment from the Grasse River by running clean water over the top of a sediment column and

* Corresponding author. E-mail addresses: [email protected] (H.S. Rifai). http://dx.doi.org/10.1016/j.ecss.2015.06.024 0272-7714/© 2015 Elsevier Ltd. All rights reserved.

(N.L.

Howell),

[email protected]

periodically quantifying PCBs in the effluent. Eek et al. (2010) designed a benthic flux chamber using semi-permeable membrane devices (SPMDs) that was deployed in the field over contaminated sediment. More commonly, contaminant mass exchanges at the sedimentwater interface have been studied via models and theoretical conceptualizations. Achman et al. (1996) provided an important effort in sediment mass flux in the Hudson River Estuary through the examination of many exchange processes including diffusion of truly dissolved and colloidally associated-PCBs, sediment particle resuspension and desorption, and net sediment accumulation leading to burial. Their approach was a “box-model” that conceptualized the diffusive flux as being controlled entirely by a mass transport boundary layer, and they determined that all sediment fluxes were in the net serving as a major source to the estuary. In the upper Hudson River, Connolly et al. (2000) built a highly discretized finite-difference model (both sediment bed and water column) which was coupled to a sediment transport model specifically examining cohesive and non-cohesive sediment. They conceptualized sediment-water exchange in a differential equation

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N.L. Howell, H.S. Rifai / Estuarine, Coastal and Shelf Science 164 (2015) 19e27

with separate pore water and sediment particle dispersion coefficients, vertical pore water advection, and net sediment deposition. The diffusive exchange at the sediment-water interface in their work employed a mass transfer coefficient determined from concentration data gathered under low flow conditions. Lin et al. (2003) also modeled diffusive flux by a box-model for the purposes of gaging the effect of aeration and oxidative bed sediment changes in mass flux. Their flux model, originally postulated by Chen (1993), employed a double resistance approach (one for sediment bed diffusion and one for boundary layer diffusion) to generate mass fluxes of PCB-047 under various conditions having measured sitespecific dissolved organic matter (DOM) linear partition coefficients (Kdom). Few studies in the literature, however, have examined trends in sediment-water-diffusive fluxes for multiple PCB congeners over a longer period of time (>3e4 years). This is an important research question as it has significant implications for decision making regarding sediment remediation and restoration of affected water bodies. The present study fills this knowledge gap and models 65 PCB congeners that were quantified in a longitudinal dataset of sediment and water column PCB data spanning a 10-year period in the Houston Ship Channel (HSC) in Texas. The study models mass flux exchange via two mobile phases, the truly dissolved and colloidal dissolved organic carbon (DOC), and via two resistances: the diffusive mass transport boundary layer and sediment column sorption in an effort to assess bed sediment contribution to overall PCB cycling as compared to contaminated suspended sediment from urban dry and wet weather sources. The modeled longitudinal dataset quantified PCBs in water and sediment during four separate sampling events (Howell et al., 2008, 2011a; Lakshmanan et al., 2010). Fortuitously, one of the sampling events was undertaken post Hurricane Ike that landed in September 2008 along the Texas Gulf Coast; this paper uniquely examines the potential changes in sediment mass flux that may be attributed to storm surge from the hurricane. 2. Data and methods 2.1. Longitudinal PCB dataset Detailed descriptions of the data collection methods and the PCB dataset in the HSC can be found in elsewhere (Howell et al., 2008; Lakshmanan et al., 2010); a brief description is provided here. Water column PCB concentrations were determined as distinct operationally suspended and dissolved fractions separated by a one micron in-line glass fiber filter using a high volume sampler (truly dissolved concentrations were estimated from these measurements using Burkhard's Kdoc (Burkhard, 2000) for PCBs for naturally occurring DOC). Surficial sediment (upper 5 cm) was collected using a petite Ponar grab sampler to make a 3e5 grab composite sample. PCBs for both water column and sediment samples were conducted according to USEPA 1668A (2003) at commercial laboratories. All water samples were analyzed for dissolved organic carbon (DOC) and some were also analyzed for total organic carbon (TOC) which provided a value of particulate organic carbon (POC) by subtraction. All surficial sediments were analyzed for TOC and grain size; some were analyzed for black carbon (BC) using the method of Gustaffson et al. (1997). Procedures for quality assurance/quality control (QA/QC) were as follows. Field blanks were collected at a rate of 5% or higher and handled in the same manner as actual samples. For water, the field blank was an uninstalled XAD-2 resin column and a GFF. For the sediment, the field blank was clean playground sand (previously baked at 400
C to remove the organics, and then re-wetted). The criterion for field blanks was that any detected analyte had to

be  5% of a sample's detected concentration for that analyte in a given analytical batch. Field duplicates were collected during all sampling events at frequencies of 10% or higher to assess precision. The precision was evaluated using relative percent difference (% RPD). Samples had to meet the established QA/QC requirements for the study (an allowable %RPD < 50% and surrogate recovery range of 25e150% (USEPA Method 1668, 2003) for all labeled-PCB analytes). Any individual analyte that did not meet field duplicate precision, proper surrogate recovery range, or field blank result threshold was excluded from the analysis. The data used for flux determinations in this study was collected during four sampling periods 2002-04(T01), 2008(T02), 2009(T03), and 2011-12(T04). The samples were screened at an individual PCB analyte result level. Results were only included in this study if there was a quantification (sample result > lowest calibration standard) in both water and bed sediment from the same sampling period. PCB analytes were excluded if not quantified often enough; this resulted in a final set of 65 analytes found in most samples. P Summary statistics for PCB- 65 (all congener) concentrations in both water and bed sediment for T01 through T04 are given in Table 1 and Fig. 1. Mean total water concentrations ranged from 1.6 to 6.7 ng/L while mean sediment concentrations ranged from 25 to 504 mg/kg dry. The total water concentrations from T01 to T02 show P some increase in PCB- 65, and there is some observed increase from T03 to T04 though the increase is not statistically significant. P The PCB- 65 concentrations in sediment show some decline from T01 to T02 and a very large increase in overall magnitude and range in T03. However, by the time of the T04 sampling campaign, the sediment concentrations had fallen. It should be noted that some of the observed changes between T02 (before hurricane Ike) and T03 (after hurricane Ike) may be attributed to storm surge impacts as P will be seen later in the paper. Suffice it to say that the PCB- 65 concentrations in sediment show a very large increase in overall magnitude and range in T03 (one year after hurricane). 2.2. Sediment-water contaminant flux model formulation 2.2.1. Diffusive flux Water-to-sediment diffusive flux was modeled using formulations developed in Schwarzenbach et al. (2003) with minimal adjustment. Equations (1)e(6) below provide estimates of diffusive flux and the critical time between boundary layer flux control and sediment desorption flux control. Fig. 2 is a visual conceptualization of the diffusive flux as estimated from the equations.


Fwc/sc ¼ vbl

Cwc  Ceq wc 1 þ JðtÞ

(1)

Dsc eff ¼

  i h  aq aq sc sc sc 1 Dpcb þ Dsc bio f d þ Ddoc þ Dbio f doc t

Ksc=wc

Csc f wc t ¼ wc ¼ fsc dsc Ct fd

!

aq bl aq bl Dbl eff ¼ Dpcb f d þ Ddoc f doc

JðtÞ ¼

tcrit ¼

pt Dsc eff

!

Dbl 1 eff dmass Ksc=wc

Ksc=wc Dsc eff p vbl

(2)

(3)

(4)

(5)

(6)

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Table 1 Total (65) PCB and general parameter mean and median statistics for bulk water and dry weight surficial sediment for the four sampling campaigns. Sampling periods T01: 2002-04

water column

TSS (mg/L) DOC (mg/L) TOC (mg/L) Total PCBs (ng/L)

sediment column

fsolids ftoc Total PCBs (mg/kg dry)

T02: 2008

T03: 2009

T04: 2011-12

n

47

25

34

24

Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median

24 21 27 26 29 28 1.6 1.1 0.52 0.54 0.011 0.011 77 19

64 62 3.4 2.8 NS NS 1.6 1.4 0.49 0.47 0.011 0.011 18 9.9

33 30 6.2 6.2 6.4 6.3 10 1.8 0.46 0.42 0.0067 0.0061 500 36

21 13 6.8 6.4 7.7 7.3 6.7 2.4 0.54 0.52 0.0044 0.0042 25 14

NS ¼ Not sampled during that sampling period.

Fig. 1. Total PCBs concentrations for bulk water column and dry weight surficial sediment for different sampling campaigns. Open circles are standard outliers (>1.5*IQR) while asterisks are extreme outliers (>3*IQR).

Fig. 2. Diffusive water-to-sediment flux conceptual model (adapted from Schwarzenbach et al. (2003)) including a water-side mass transport boundary layer through which diffusive transport of freely dissolved and DOC-associated PCBs occurs and a sediment column through which freely dissolved and DOC-associated PCBs occurs only in the pore spaces and enhanced by bioturbation. In the deeper sediment (>5-cm) and shallower water (depth < dmom) the concentrations of contaminants and organic carbon sorbents are uniform and well-mixed. In surficial sediment, the organic carbon sorbents and other bulk sediment characteristics are homogenous while the contaminant concentration in freely dissolved, DOC-associated, and particle-sorbed phases varies in time and space. Two variables not given in Equations (1)e(6) are Csc/wc and Cwc/sc which are the sediment-side concentration in equilibrium with the water column at the interface and the water-sediment concentration in equilibrium with the sediment column at the interface, respectively.

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Fwc/sc (Equation (1)) is the water to sediment flux at the sediment-water interface (i.e., sediment to water flux is negative); sc f sc d and f doc are the truly dissolved and DOC-associated bulk volume sediment column fractions, respectively; t is sediment column tortuosity; Dsc eff (Equation (2))is the PCB sediment column effective diffusion coefficient for the total volume sediment concentration (mass PCB/total volume of sediment) in the sediment column sc modified by t, bulk volume sediment column fractions (f sc d , f doc ), aq and constituent diffusivities for molecular transport of PCBs (Dpcb ), surficial sediment bioturbation (Dsc bio ), and molecular transport of aq colloids (Ddoc ); fsc is sediment column porosity; Ksc/wc (Equation (3)) is the total volume partition coefficient between the bulk
sediment and bulk water; Cwc is the measured total water (dissolved þ suspended) concentration in the overlying water; Ceq wc is the total water concentration that would be in equilibrium with
the surficial sediment bed concentration (Csc (not shown in Equa
1 bl tions (1)e(6)), Ceq wc ¼ Csc ðKsc=wc Þ ); Deff (Equation (4))is the PCB analyte-specific effective diffusion coefficient through the mass transfer boundary layer dependent on freely dissolved and DOCbl associated mass fractions of a PCB congener (f bl d , f doc ) assuming that the only phases of importance in the boundary layer are freely dissolved and DOC (i.e., no significant particles, f bl p z0) and dependent on constituent diffusivities (Daq ,Daq ) according to a pcb doc similar logic as Dsc eff ; dmass is the mass transfer boundary layer thickness (Equations (9)e(10)); vbl is the boundary layer transfer velocity (Dbl eff =dmass ); t is the time since the sediment and water columns were brought in contact; and J(t) (Equation (5)) is a dimensionless time function that scales relative to a critical time tcrit (Equation (6)) for which J(tcrit) ¼ 1, the time at which overall water-sediment mass flux becomes less and less controlled by the sediment-water mass transport boundary layer (concentration gradient across boundary layer dCbl/dx / 0). The modeling formulation presented above assumes that the resistances to flux are primarily sorption-controlled sediment column diffusion and the mass transfer boundary layer at the sediment-water interface. The upper 5 cm of sediment (the depth range obtained from a grab sampler) are considered perfectly mixed with homogenous sediment properties (though contaminant concentrations do vary in time and space), and the same is true of the overlying water column (i.e., no transition layer of deeper benthic water).

depositional sediment fluxes at several locations. These locationspecific rates were spatially interpolated to PCB diffusive flux locations and combined with known measured suspended sediment concentrations (ng/g dry) to generate net sediment depositional contaminant mass fluxes and desorption/adsorption that would occur if re-equilibration upon deposition is instantaneous. 2.2.3. Constituent partitioning models Site-specific partitioning models for water and bed sediment were not determined for the HSC; rather, various models from the literature were used (see Table S-1 in Supplementary Information). In some cases, multiple models were used to examine the effect on the sediment flux estimation. Two linear free energy relationship (LFER) models were used for DOC and particulate organic carbon (POC) in both sediment and water columnsdthe DOC LFER by Burkhard (2000) and the POC LFER by Seth et al. (1999). Recent linear solvation energy relationship models for DOC and POC from Kipka and Di Toro (2011a, 2011b) were also employed. For some modeling scenarios, black carbon in bed sediment partitioning was included using the LFER (linear-sorption) of Werner et al. (2010), which they found to be valid for the lower PCB water concentrations (pg/L to mg/L) seen in the water column and sediment pore water. 2.2.4. Determination of fate-and-transport constants Diffusivities for several congeners were obtained from the EPA Sparc Performs Automated Reasoning and Chemistry (SPARC) model, and these congeners were subsequently fitted to log Kow data from Hawker and Connell (1988) to generate diffusivity as a function of log Kow for all congeners used ðDaq ðcm2 =sÞ ¼ pcb 7 6 2 4:6x10  logKow þ 8:9  10 ; r ¼ 0:92Þ. The mass transfer boundary layer dmass was determined from the momentum transfer boundary layer dmom (Equations (9)e(10), taken from Clark (2009))

dmom ¼ 11:6

n u

dmass ¼ 0:6Sc1=3 dmom

(9) (10)

Fdepo ¼ Fsedim Csc p

(7)

where n is the kinematic viscosity of water at 25
C (0.01 cm2/s), u* is the friction velocity at the sediment bed (u*¼(tb/rw)0.5; tb ¼ bed shear stress; rw ¼ water density at 25
C), and Sc is the Schmidt aq number. Colloid diffusivity (Ddoc ) in all model runs (except when its sensitivity was examined) was 7.0  107 cm2/s similar to the value used by Achman et al. (1996) for spherical 10 KDa colloids. Colloid transport is assumed to be through molecular diffusion and bioturbation with no influence from colloid-to-sediment particle partitioning. The bioturbation dispersion coefficient (Dsc bio ) was chosen as the average value for all estuarine sediments included in Thoms et al. (1995) (3.95  106 cm2/s). A complete summary of all model values is provided in Table S-2.

  sc Fdesorb ¼ Fsedim Cwc p  Cp

(8)

2.3. Sediment flux diffusion model sensitivity and controlling processes

2.2.2. Sedimentation and desorption/sorption flux Flux from depositing sediment was determined using sedimentation rates provided in Yeager et al. (2007) whereby the final contaminant mass flux was adjusted after sediment deposition to reflect the equilibration of deposited sediment and bed sediment concentration differences leading to loss or gain of PCBs in the sediment bed.

where Fdepo is sedimentation PCB flux (positive water-to-sediment direction), Fsedim is the net mass sedimentation rate determined from sediment cores (g sediment/m (Kupryianchyk et al., 2013)-yr), Fdesorb is the desorption or sorption flux (assumes complete equilibration) when the suspended sediment deposits onto the sediment bed (will ultimately be added to the diffusive flux of Equation sc (1)) and Cwc p and Cp are the particulate concentrations of PCB in the water and sediment column, respectively. Yeager et al.’s (2007) extensive historical sediment radionuclide core analysis of PCDD/Fs in the HSC provided estimated long-term

The diffusive bed-to-overlying water mass flux framework outlined here is potentially controlled by a number of variables; all were graphically and numerically examined at a range of log Kow (4e9), at a prediction time of ten years (a time period consistent with the sediment core dating of Yeager et al. (2007)), and with a base condition that is representative of general HSC observations. The purpose of this analysis was to determine model sensitivity to each parameter and the combined effect of model sensitivity and measurement uncertainty. The variables which were examined were those pertaining to

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the water column (POC, DOC, shear stress tb, and total water concentration Cwc t ) and those pertaining to the sediment column (DOC, POC, solid fraction, tortuosity, dry weight sediment concentration Csc p , DOC diffusivity Ddoc, bioturbation dispersion coefficient Dbio). Each parameter was varied at ±10% to ±50% of a base condition. A dimensionless sensitivity metric (msens) was evaluated as follows:

msens

. DFwc/sc F base . wc/sc ¼ Dpi pbase i

(11)

where pbase is the base condition parameter value, Dpi is the change i from pbase in the units of the parameter, and the fluxes (F) are i notated similarly. After msens is determined, a quantitative measure, influence, was determined as follows

. influence ¼ DFwc/sc F base ¼ msens x punc i wc/sc

(12)

where punc is the measurement uncertainty of the parameter based i on field duplicates. 3. Results 3.1. Sensitivity analysis Two of the most impactful parameters (highest influence) for wc diffusional flux determined here were found to be Csc p and Ct , the particulate concentration of PCB in the sediment column and the total water concentration, respectively. Both Cwc and Csc t p had high mean general sensitivity metric values (msens) of 1.90 and 0.90, respectively (e.g., for a 10% change in the bulk water concentration, a 10%*1.9 ¼ 19% change in mass flux occurred). Also, these variables had relatively high uncertainty of measurement (punc ¼ 15e33%). i wc This combination gives Csc a higher practical influence of p and Ct 28e30% on the mass flux estimate. In contrast to the aforementioned variables is the sediment column POC fraction (fpoc), which had a high sensitivity metric of 1.45, but its measurement uncertainty was relatively low (5%) leading to lower flux influence of 7.2%. Compared to the non-Kow specific parameters, the partition constants (Kpoc, Kdoc, and Kbc) had influence values (using the uncertainty ranges given in the models themselves) between 56 and 380% of the estimated mass flux. Thus, the model uncertainty of the partition constants contributes far more to overall diffusion flux uncertainty than any of the measured variables. The choice of time is particularly important in estimating sediment fluxes because early times in the model can change the mass flux greatly. The mass flux was examined for a base condition (10 years) for periods of 2e14 years which corresponds roughly with the time period to deposit 5 cm of sediment according to ranges provided by Yeager et al. (2007). The highest sensitivity was 120% from the base condition for a period of 2 years, but all other periods had sensitivities (msens) of 40% or less which corresponds fairly well with the small amount of sensitivity seen in sediment tortuosity (a relatively low sensitivity parameter). Therefore, a period of 10 years for the model was used for all calculations of model sensitivity and HSC fluxes.

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influence on diffusional flux predictions. We chose to examine the diffusional flux under two scenariosdthe use of Kpoc and Kdoc only (assuming fbc ¼ 0) and the use of Kpoc, Kdoc, and Kbc with spatially interpolated values of fbc. Black carbon was measured at several locations in 2011 though not during other sampling periods. Thus, for this scenario we chose to spatially interpolate black carbon values for all locations and assume to be the same for all of the time periods while all other parameters are sample-specific. Assuming a 0.05 kg BC/kg POC uncertainty for this interpolation at any given location, this gives an average flux influence of only 10%. The figure indicates that diffusive flux is positive to zero net flux in T01, negative to indeterminate in T02 (just before hurricane), often negative in T03 (one year after hurricane), and negative and zero flux at different points in T04. For all spatial plots, the flux is almost always indeterminate (<1 mg/m2-yr) downstream of the confluence with the San Jacinto River (SJR) (>40 km) and extending out into Galveston Bay (>55 km) no matter the time period. Areas upstream of the confluence (<40 km), the most industrialized area of the HSC, are known to have the highest concentrations of PCBs in many abiotic and biotic media (Howell et al., 2008, 2011b), and this upper region is generally negative (sediment-to-water direction) except for T01 when there was a lower level of water-to-sediment diffusive flux (0.5e6.0 mg/m2-yr). It is interesting that at T02, the inclusion of black carbon to the diffusive flux model for partitioning effectively changes the overall direction of the flux from negative (no BC) to either less negative or mostly indeterminate (including interpolated BC). Fig. 4 show main navigational channel sample location (20 of 56 sites) flux direction distributions (positive, negative, indeterminate) for all sample years for the more chlorinated PCBs (Cl5-10). These main channel locations should provide a good representation of the water body at large. If a diffusion flux prediction had uncertainties that, when applied, yielded both positive value fluxes, then the flux direction was deemed positive. If both negative, then negative flux. If one positive and one negative, then indeterminate. These individual congener flux direction determinations also show some distinctions in flux direction between all four sampling periods. The individual samples for T01 are over 90% predicted to be moving from water-to-sediment (positive). By T02, the majority (>60%) of the locations are indeterminate indicating that the main channel should be at a near equilibrium diffusive flux state for these more chlorinated congeners. After the hurricane (T03), however, the diffusive flux for most locations shows that each congener is 30e40% of the time diffusing contaminant from the sediment to the water column. At this time, there is not a single location that has sediment-sink (positive) flux direction. By T04, the flux directions are more mixed from congener to congener. Most locations have some samples that are now at a sediment-sink flux direction, but there are a great many indeterminate locations and large number of sediment-source locations. This variation between congeners suggests that the water column-sediment column exchange is attempting to come to a more consistent and equilibrium state, but it is likely that differences in congener sourcing (historical, contemporary, or mixed in from deep sediment) and transport (hydrophobicity) may make the move towards postehurricane equilibrium occur at different rates.

3.2. Temporal and spatial trends in PCB diffusive mass flux 3.2.1. Flux direction P Fig. 3 presents spatial trends in PCB- 65 for all time periods (solid blue line) with uncertainty ranges from partition models given as dashed and dash-dot lines. Only partition model uncertainty is indicated in the ranges shown due to its much stronger

3.2.2. Flux magnitude Flux magnitude can be examined in Fig. 3 by looking at the vertical scaling of the trend (each period re-scaled to show spatial dynamics). All fluxes (as predicted and not including uncertainty) are quite low (<7 mg/m2-yr) at T01, in the 0.10e10 mg/m2-yr range at T02, spanning as high as 250 mg/m2-yr at T03, and back down to

Fig. 3. Trends in total PCB (sum of 65 analytes) mass flux for sampling years 2002-04 (T01), 2008 (T02), 2009 (T03), and 2011-12 (T04) and according to Kdoc 12and Kpoc 17LFER partition models and with (left) and without (right) sediment black carbon (modeled linearly 20). Model predictions are in blue and upper and lower prediction uncertainty ranges (due to partition coefficient models) are given by gray dash-dot-dot (lower) and gray dashed (upper), lines. Major points along the main channel are the start of the ship channel proper (15 km), the confluence with the San Jacinto River (SJR, 40 km), and the connection to Galveston Bay (55 km). Note the different vertical scales used in different sampling periods. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

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Fig. 4. Main channel flux direction fractions per congener (only Cl5eCl10 shown) and for each sampling season according to the use of LFER partition models with sediment BC spatially interpolated. Flux directions are either positive (water-to-sediment), negative (sediment-to-water), or indeterminate (unable to distinguish from zero due to prediction uncertainty). All model scenarios and congeners are presented in more detail in the SI.

30e60 mg/m2-yr by T04. The two upper HSC peaks in positive flux at T01 correspond to regions near two tributaries that have several industrial facilities along their banks, and higher total PCB concentrations from these channels may contribute to larger sediment-enriching (i.e., elevated water concentrations) diffusive flux. The large negative flux at T02 at 15 km, the start of the channel proper, might possibly be explained by a higher sediment concentration. The location is a wide basin that receives suspended sediment deposition from further upstream in Houston. This is the same location where very large negative flux is estimated at T04. The largest magnitude flux (250 mg/m2-yr for LFERBC00%, 69 mg/m2-yr for LFER-BC-interpolated) out of all time periods is at about 25-km, which is at the confluence of the HSC with Vince Bayou (T03, one year after hurricane). This location has the fifth highest total PCB sediment concentration out of all T01T04 samples (870 mg/kg dry) but is only at approximately the median level of all total water concentrations from the dataset (1.9 ng/ L). The interaction of these differences in concentration per media provides a strong thermodynamic driver for a net sediment-source flux. P A wider area view of PCB- 65 flux magnitudes can be found in Fig. 5 with particular reference to Hurricane Ike. The majority of locations at T02 (pre-hurricane) and T04 (postehurricane) are mixed between sediment-source (blue), sediment-sink (red), and indeterminate (white) diffusive mass fluxes. At T03, the flux is moderately to highly sediment-to-water with particularly high values at two Superfund areas (shown in the figure in darkest blue, Patrick Bayou (western location) and the San Jacinto River Waste Pits (eastern location)). At both these high diffusive flux locations, there is no small group of PCB analytes making up the large values. Most of the 65 PCB analytes are high with several at a magnitude >10 mg/m2-yr. By T04, three-four years after the hurricane, the P overall PCB- 65 flux is more muted though it is still negative at the SJR Superfund site location, and at Patrick Bayou the diffusive flux is highly positive and sediment-source (dark red location, 130 mg/m2-

yr) due in part to a bed sediment concentration that had decreased 300-fold (6600e22 mg/kg dry) from its value at T03 while the total water concentration only decreased by 21% over the same period. 3.3. Diffusive flux in comparison with sedimentation flux P A comparison of the PCB- 65 distributions of sediment depositional flux, desorption flux, and diffusive water-sediment fluxes estimated from the LFER partitioning BC spatially interpolated scenario reveals that the two types of fluxes (deposition-related and diffusion) are at vastly different orders of magnitude. Most of the diffusive sediment-water total PCBs fluxes are on the order of 0.1e10 mg/m2-yr while the sediment deposition-related fluxes are on the order of 10e100,000 mg/m2-yr. The higher diffusive fluxes present at T03 (postehurricane) are closer in magnitude to the sediment depositional-based fluxes from that time, but even in this period of higher diffusive flux, the sediment depositional flux is higher. Total and individual process fluxes were examined along the main channel of the HSC, and the net mass flux from all processes (deposition, desorption, diffusive flux) in nearly every instance is almost congruent to the total sediment depositionbased flux (because the deposition-based fluxes are so much P larger). At the PCB- 65 level, the adsorption/desorption flux is nearly always adsorption meaning that diffusive flux coming out of the sediment bed would be absorbed onto depositing sediment particles that are lower in concentration than the particles already in the sediment bed. An examination of individual congener net mass fluxes reveals that most samples (60% or more), no matter the diffusive flux modeling scenario used, had 100% of their 65 congeners at net positive (water-to-sediment) mass flux. The overwhelmingly larger flux from net sediment deposition and adsorption would seem to counteract any variations in diffusive mass flux across the bed sediment as to essentially render it irrelevant for the purposes of overall mass transport from the water column to the sediment bed in favor of water to sediment transport.

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P Fig. 5. Diffusive flux (PCB- 65) to the sediment bed in T02(pre- Hurricane Ike), T03 (a year later), and T04 (a few years later). The flux model is that of the LFERs with black carbon spatially interpolated from known measurements (see Figure S-1 for spatial black carbon distribution). White symbols (1 to þ1 mg/m2-yr) correspond to a range where most flux estimate uncertainty ranges spanned a zero flux and are thus considered to have no net diffusive flux whereas blue and red shaded symbols correspond to net sediment-to-water () and water-to-sediment (þ) diffusive fluxes, respectively. Plots of all time periods and model scenarios can be found in the SI. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

Yet the comparison may not be as simple because the suspended sediment becomes bed sediment once it deposits onto the bed. This analysis neglects the contaminant mass flux related to resuspension. Resuspension desorption fluxes would be negative (sediment-to-water) and may be significant, especially if resuspension-based desorption is more efficient (closer to thermodynamic equilibrium) than deposition. The difficulty in using net sedimentation rates to estimate the effect of mass flux from sediment transport and accompanying adsorptive-desorptive exchange is that net sedimentation is based on very long time scales (decades) preserved in sediment cores and does not take into account the more dynamic contaminant transport events that occur from tides, high flows, dredging, navigation, and other sediment bed perturbations. Looking at other studies conducted in Mugu Lagoon, California (Lin et al., 2003) and Grasse River, New York (Ortiz et al., 2004), the HSC diffusive flux measurements are 1e2 orders of magnitude less for water and sediment concentrations that are not exceedingly different from the HSC. It may very well be that our application of this model underestimates diffusive fluxes such that they are more significant relative to deposition than the analysis shows. 4. Discussion There is much uncertainty in sediment-water exchange flux modeling presented in this research because several variables can control the flux magnitude and direction, and many of these variables are simply assumed to be constant within the sediment bed (e.g., pore water DOC, bed sediment PCB concentration) or are model-derived themselves (e.g., partition coefficients). Important methods to reduce uncertainty in flux estimates would be to quantify partitioning in a way that is more specific to both the water column and sediment column. Such relationships may not be the same even in the same water body since the DOC and POC in each environment may be sourced differently and exist at different states of mineralization or in different oxidative environments. The limited number of black carbon measurements enhances uncertainty to some degree, and even the nature of black carbon partitioning itself is generally not understood well enough over a broad range of sediments to be modeled with relatively high accuracy without site-specific experiments. The ability of the aforementioned measurements and models to approximate real sediment column-water column exchange fluxes could be confirmed through direct flux measurements such as sediment traps (resuspension

flux) and benthic flux chambers (diffusive sediment-water exchange), and the use of these methods is generally recommended if higher accuracy flux estimation is necessary. However, the use of these devices may still not serve all fate-and-transport needs because these devices can take more time to deploy and may not necessarily achieve as high a spatial resolution as grab samplebased concentrations. The inclusion or exclusion of black carbon in the modeling framework makes a large difference in the magnitude and sometimes the direction of PCB diffusive mass flux. The Werner et al. (2010) LFER models black carbon as a strong linear sorption isotherm. If, however, a non-linear isotherm (Koelmans et al., 2006; Barring et al., 2002) were used, the interaction would be even stronger. If non-linear sorption were to more appropriately describe HSC black carbon, then the sediment-to-water flux magnitude would be greatly reduced or reversed to a water-tosediment direction. It is interesting that black carbon, generally associated with anthropogenic impacts, would likely often be found with PCBs in a historically industrial area such as the HSC. In such cases, the freely dissolved concentrations of PCB in the pore water would be greatly reduced by this dual-pollution thereby limiting the diffusive flux from contaminated sediments possibly long enough for them to be buried. It was somewhat surprising that the sediment deposition-based PCB flux was so much greater than the sediment-water diffusive exchange flux. In one sense, this result seems positive for the estuary because PCBs are being added to the sediment bed faster than they are being released. Yet at the same time, this presents a concern in the long term for the HSC. Highly contaminated sediment gets buried via sedimentation, but in this case the sedimentation that is occurring contains PCBs that are still present at relatively high levels. The original source of these sediments is not entirely clear as much of it in the highly industrialized upper portion of the HSC may simply be the same sediment being flushed back and forth with the tide which merely redistributes the surficial sediment PCBs rather than burying them (some evidence supporting this finding is presented in Rifai et al. (2013) for PCDD/F contamination in the HSC). Another possibility is that PCBimpacted suspended sediment from further upstream causes the net sedimentation, and such a scenario might be restorative for the HSC as long as most of the suspended sediment was at lower chemical activity than the surficial sediment on which it deposits. More extensive investigations are necessary to test these hypotheses.

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The observed effects of Hurricane Ike on sediment flux and surficial sediment concentrations are not entirely clear. In a situation somewhat analogous to remedial dredging, the large resuspension sediment desorption load of PCBs that occurred from storm surge may have increased the water column PCB concentrations for a short time. Unlike remedial dredging, however, the surficial bed sediment concentrations actually increased possibly from being mixed with lower sediment layers that would not normally be part of the surface sediment mixed layer. Flux estimations showed that this greatly increased the mass flux of PCBs for at least a year after the hurricane's passage at a time when PCB sediment-water diffusive fluxes were coming closer to equilibrium primarily due to a greater sediment-water concentration differential. Water concentration increased, but sediment concentration increases were greater. The aforementioned results show that both the surficial sediment concentrations and the water-sediment diffusive fluxes decreased post-2009, but even three-four years after the hurricane, they remain elevated relative to 2008, just before the hurricane landed. It would appear that the hurricane may have hindered the natural recovery of the bed sediments in the case of PCBs, and it is not yet clear how much longer natural recovery might take as a result. In summary, this study has added vital incremental knowledge to the understanding and observation of sediment-water PCB exchange processes. The study has shown how these processes vary in time for a wide range of PCBs of differing hydrophobicities and how diffusive sediment flux can vary according to observed increases in sediment bed PCB concentration, presumably from storm surge and flooding associated with a large hurricane. Much of the difficulty in quantifying sediment-water exchange of PCBs rests in uncertainties related to field-relevant partitioning to carbonaceous sorbents. Future studies in the HSC and elsewhere could focus on better assessing sorbent quantity and quality while at the same time quantifying PCB diffusive and depositional mass fluxes directly. This information would improve mass flux estimates generally and enhance the ability to project the future of polluted estuarine regions with regard to many different types of persistent pollutants. Better mass flux estimates might also serve to inform remedial efforts so that active remedial solutions are placed in areas where diffusive pollutant release to water is greatest. Acknowledgments The authors would like to knowledge the NSF GK-12 Program, the Houston Endowment, the Texas Commission on Environmental Quality (TCEQ) and the US EPA for their financial support. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.ecss.2015.06.024. References Achman, D.R., Brownawell, B.J., Zhang, L.C., 1996. Exchange of polychlorinated biphenyls between sediment and water in the Hudson River Estuary. Estuaries 19 (4), 950e965. Barring, H., Bucheli, T.D., Broman, D., Gustasson, O., 2002. Soot-water distribution coefficients for polychlorinated dibenzo-p-dioxins, polychlorinated

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