Evaluating the Relationship between Equilibrium Passive Sampler

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Critical Review

Evaluating the Relationship between Equilibrium Passive Sampler Uptake and Aquatic Organism Bioaccumulation Abigail Sajor Joyce, Lisa M. Portis, Ashley Nicole Parks, and Robert M. Burgess Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b03273 • Publication Date (Web): 28 Sep 2016 Downloaded from http://pubs.acs.org on October 4, 2016

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Environmental Science & Technology

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Evaluating the Relationship between Equilibrium Passive Sampler Uptake and Aquatic

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Organism Bioaccumulation

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Abigail S Joyce*

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National Research Council

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U.S. Environmental Protection Agency

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ORD/NHEERL Atlantic Ecology Division

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Narragansett, RI USA 02882

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Lisa M Portis

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Physical Therapy Department

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University of Rhode Island

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Kington, RI USA 02881

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Ashley N Parks

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National Research Council

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Robert M Burgess

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*Phone: (401)782-3096; Fax: (401)782-9670; email: [email protected]

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ABSTRACT

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This review evaluates passive sampler uptake of hydrophobic organic contaminants

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(HOCs) in water column and interstitial water exposures as a surrogate for organism

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bioaccumulation. Fifty-five studies were found where both passive sampler uptake and organism

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bioaccumulation were measured and nineteen of these investigations provided direct

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comparisons relating passive sampler uptake and organism bioaccumulation. Polymers

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compared included low density polyethylene (LDPE), polyoxymethylene (POM), and

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polydimethylsiloxane (PDMS), and organisms ranged from polychaetes and oligochaetes to

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bivalves, aquatic insects, and gastropods. Regression equations correlating bioaccumulation (CL)

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and passive sampler uptake (CPS) were used to assess the strength of observed relationships.

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Passive sampling based concentrations resulted in log-log predictive relationships, most of which

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were within one to two orders of magnitude of measured bioaccumulation. Mean coefficients of

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determination (r2) for LDPE, PDMS, and POM were 0.68, 0.76, and 0.58, respectively. For the

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available raw, untransformed data, the mean ratio of CL and CPS was 10.8 ± 18.4 (n = 609).

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Using passive sampling as a surrogate for organism bioaccumulation is viable when

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biomonitoring organisms are not available. Passive sampling based estimates of

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bioaccumulation provide useful information for making informed decisions about the

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bioavailability of HOCs.

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Key Words: Passive sampling; Bioaccumulation; Bioavailability; Low density polyethylene (LDPE); Polyoxymethylene (POM); Polydimethylsiloxane (PDMS)

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INTRODUCTION

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Anthropogenic hydrophobic organic compounds (HOCs), like polychlorinated biphenyls

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(PCBs), some polycyclic aromatic hydrocarbons (PAHs), chlorinated pesticides such as

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dichlorodiphenyltrichloroethane (DDT) and its metabolites, polybrominated diphenyl ethers

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(PBDEs), and polychlorinated dioxins and furans are found throughout the environment. These

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types of compounds often bioaccumulate in aquatic organisms, undergo trophic transfer, and, in

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some cases, biomagnify up the aquatic food chain.1 In addition, some HOCs, at sufficiently

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elevated concentrations, will cause toxicity to these organisms.2 Because of their low water

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solubility and high affinity for organic matter, most HOCs are concentrated in the sediment

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organic and black carbon reservoirs and in organismal lipid reserves.2 Over time, sediments can

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release these legacy HOCs back into the water column via sorption/desorption mechanisms and

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diffusive and advective processes as well as bioaccumulation-based transfers. Therefore,

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sediments can serve as a major source of ecosystem and human exposure to HOCs.3 Continued

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environmental monitoring of these types of chemicals is essential for identifying historical as

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well as current HOC sources and evaluating the efficacy of remediation and emission reduction

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programs.

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Measuring the bioaccumulation of HOCs in aquatic systems (i.e., biomonitoring)

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provides direct evidence of a chemical’s potential for ecological and human health risks by

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demonstrating the contaminant’s bioavailability. That is, HOCs are present in many different

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environmental phases (e.g. particulate, dissolved, colloidal), bioavailable HOCs are freely

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dissolved in the aqueous phase and are able to pass through biological membranes for uptake by

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biomonitoring organisms.4 However, for these chemicals to be bioaccumulated by biomonitoring

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organisms, they must be in a bioavailable form (as defined above). Bioavailable contaminants

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accumulated in an organism’s lipids demonstrates those HOCs’ large affinity for the lipid phase.5

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The bioaccumulated concentration is often expressed on a lipid basis and is used to calculate

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partition coefficients such as the bioaccumulation factor (BAF) (L/g lipid):

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BAF =

CL C free

(1)



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and, the biota-sediment accumulation factor (BSAF) (g organic carbon (OC)/g lipid):

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BSAF =

CL COC

(2)

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where, CL is the lipid-normalized concentration of a given HOC (ng/g lipid) accumulated by an

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organism, Cfree is the freely dissolved water concentration of the HOC (ng/L), and COC is the

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organic carbon normalized concentration of the HOC in the exposure sediment (ng/g OC).

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Conventionally, bioavailability has been measured through the monitoring of aquatic organism

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bioaccumulation and several long term biomonitoring programs have been established using

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bivalves to track water column HOCs6-9 and assess the effectiveness of contaminated site

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remediation.10, 11

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Evaluating the bioavailability of HOCs can also be performed by studying the

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physicochemical characteristics of the environment since the magnitude of a particular HOC’s

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bioavailability is related to its chemical activity in the system.12, 13 Similar to tissue concentration

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normalization by lipid content, normalizing the total sediment particle concentration (CP) (ng/kg

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sediment dry) of a HOC by the fraction of organic carbon (fOC) (kg OC/kg sediment dry) has

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been shown to be effective for predicting the Cfree. Specifically, the Cfree of a given sediment is

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calculated by incorporating the HOC’s organic carbon normalized-water partitioning coefficent

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(KOC) (L/kg OC) as illustrated in Equation 3.2, 14 This equilibrium partitioning approach is based

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on the concept that HOCs strongly bound to sediments (e.g. particulate organic carbon, black

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carbon) are often not as readily bioavailable and do not directly contribute to the environmental

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exposure of the contaminant when compared to Cfree:

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C free =

CP f OC ⋅ K OC

(3)

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Another environmental phase affecting the bioavailability of HOCs is sedimentary black

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carbon. The calculation of Cfree in Equation 3 can be modified to incorporate the role of black

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carbon:

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C free =

f OC ⋅ K OC

CP −1 + f BC ⋅ K BC ⋅ C nfree

(4)

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where, fBC is the fraction of black carbon in the sediment (kg black carbon (BC)/kg sediment

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dry), KBC is the black carbon normalized partition coefficient (L/kg BC), and n is the unitless

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Freundlich exponent which captures the non-linear adsorption of HOCs to black carbon.15 Both

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approaches for calculating Cfree are useful for estimating bioavailability, but for several reasons,

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including uncertainty associated with the KOC and KBC values and confidence in fBC

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measurements, Equation 3 tends to over-estimate Cfree while Equation 4 will often underestimate

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Cfree.16

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Recently, passive sampling has been shown to be a successful tool for a more direct

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measurement of Cfree in comparison to using physicochemical characteristics of the

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environment.17, 18 Passive sampling provides an integrated measure of HOC Cfree by considering

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contaminant activity across all of the relevant environmental phases (e.g., water, sediments,

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dissolved organic carbon, colloids, and organisms), often providing a more accurate estimate of

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bioavailability than sediment-based or lipid-based measurements alone as shown in Equations 3

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and 4 for sediments and Equations 1 and 2 for lipids.19, 20 Passive samplers function by

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concentrating freely dissolved HOCs in the water column or sediment interstitial water into a

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polymer phase (i.e., the passive sampler) as a result of differences in the HOC’s chemical

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activity amongst the polymer and different environmental phases. At equilibrium, the HOC

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concentration ratio between the polymer and the water, expressed by the passive sampler-water

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partition coefficient (KPS; Lwater/kgpolymer or Lwater/Lpolymer) is constant. The relationship is:

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K PS =

CPS C free

(5)

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where, CPS (ng/kg polymer or ng/L polymer) is the concentration of the HOC in the polymer

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phase at equilibrium. The Cfree can be estimated by rearranging Equation 5 using KPS (based on

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literature or empirical values) and measuring CPS directly.

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Passive sampling became popular in the 1990s with the introduction of semi-permeable

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membrane devices (SPMDs)21 and solid phase microextraction (SPME).22 Much research has

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been dedicated to optimizing and simplifying their use in the water column and sediment

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interstitial water.23-27 In North America, three polymers are most popular for sampling HOCs in

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the water column and sediment interstitial waters: polydimethylsiloxane (PDMS) (used in

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SPME) and other silicones, low-density polyethylene (LDPE), and polyoxymethylene (POM).28

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Other polymers including ethylene vinyl acetate (EVA)29, 30 and polyacrylate31, 32 are also

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available, but applied to a lesser extent than PDMS, LDPE, and POM.

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There are a number of publications describing the development and utility of PDMS,25

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LDPE,24 and POM33, 34 as passive samplers and comprehensive reviews of their application in

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water column and sediment deployments have been published.26, 28, 35-39 In addition to

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demonstrating passive sampler dependability and effectiveness in measuring Cfree, these reviews

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also discuss passive sampling’s potential value as a surrogate for biomonitoring organisms. The

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concentration of HOCs accumulated by the polymer (CPS) allows for the calculation of Cfree but

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CPS can also be compared to the concentration of HOCs bioaccumulated by organisms (CL).

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Recent research has directly compared passive sampler uptake to bioaccumulation by organisms

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both in situ (i.e., in the field) and ex situ (i.e., in the laboratory). This type of comparison

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investigates a mechanistic assumption that the passive sampler polymer and the organism lipid

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have a similar, proportional affinity for a given HOC (i.e., CPS ~ CL). This paradigm stems from

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the concept that HOCs partition into PDMS, LDPE, and POM in a similar proportionality as they

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partition into lipids. These similarities are illustrated by the following linear free energy

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relationships (Equations 6 – 9) for lipid and each of the polymers discussed using the readily

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available octanol-water partition coefficient (KOW) as the independent variable:

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݈‫ ݃݋‬൬஼

஼ಽ

೑ೝ೐೐

൰ = 1.01݈‫ܭ݃݋‬ைௐ − 0.07, r2 = 0.89

(6)40


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஼

176

݈‫ ݃݋‬൬ ஼ುವಾೄ ൰ = 0.947݈‫ܭ݃݋‬ைௐ − 0.17, r2 = 0.89

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݈‫ ݃݋‬൬ ஼ಽವುಶ ൰ = 1.18݈‫ܭ݃݋‬ைௐ − 1.26, r2 = 0.95

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݈‫ ݃݋‬൬

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where the ratios of CPDMS, CLDPE, and CPOM over Cfree are the polymer-water partition coefficients

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for PDMS, LDPE, and POM, respectively, and r2 is the coefficient of determination. Because

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there can be uncertainties associated with KOW values, whenever possible, it is prudent to use the

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most measurable data and plot CL versus CPS directly. Due to the variations in the reported slopes

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and intercepts in the equations above, CL and CPS are unlikely to be equivalent but because the

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slopes and intercepts are of a similar scale, it is reasonable to expect that CL and CPS will agree

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within an order of magnitude.

(7)28

೑ೝ೐೐

஼

(8)28

೑ೝ೐೐

஼ುೀಾ ஼೑ೝ೐೐

൰ = 0.791݈‫ܭ݃݋‬ைௐ + 1.02, r2 = 0.95

(9)28

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There are several reasons for considering the use of passive samplers as surrogates for

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biomonitoring organisms. Perhaps most important, passive samplers can be deployed under

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many environmental conditions unsuitable for living biomonitoring organisms; for example, low

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dissolved oxygen, high or low water temperatures, and elevated toxicity (especially in sediment

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studies). Further, biomonitoring organisms are not always readily available from commercial

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suppliers or clean reference sites (e.g., episodic blue mussel die-offs caused by toxic algal

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blooms and pathogens limit numbers for biomonitoring). Booij et al. propose that passive

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samplers are superior monitoring tools because they can be deployed in essentially any aquatic

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environment and then readily compared to one another.41 Biomonitoring organisms are limited to

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specific habitats that do not allow for wider regional or global comparisons.

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This review focuses on the use of passive samplers in the water column and sediment

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interstitial waters as monitoring tools for assessing the bioavailability and bioaccumulation of

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HOCs. The objectives of this review are to (1) provide a brief overview of the state of the

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science for passive sampling – focusing on PDMS, LDPE, and POM, and bioaccumulation; (2)

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critically review the relationship between aquatic organism bioaccumulation and passive sampler

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uptake of HOCs by conventional biomonitoring organisms (e.g., polychaetes, oligochaetes,

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bivalves) when co-deployed or exposed to the same environmental matrices (e.g., water, 7 ACS Paragon Plus Environment


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sediments); and (3) discuss research data gaps that need to be addressed to successfully

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implement passive sampling as a surrogate for biomonitoring using organisms.

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BACKGROUND AND METHODS

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Data Collection and Analysis

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A literature search was performed using the ProQuest and American Chemical Society

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SciFinder systems based on the search terms “bioaccumulation”, “biouptake”,

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“biomagnification” and “passive sampling” on peer-reviewed reports published from 1990 to the

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present. The search provided 55 individual reports where HOCs were quantified in PDMS,

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LDPE, or POM as well as within living organisms in the marine or freshwater environment (in

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situ) or in the laboratory (ex situ). As mentioned SPMDs are another popular passive sampling

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medium, because SPMDs have a biphasic sampling system (e.g. polyethylene and triolein) CPS is

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more difficult to define as compared to single phase samplers like PDMS, LDPE, and POM.

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Further, SPMDs can be difficult to deploy in sediments because of the risk of losing triolein if

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the polyethylene tubing tears. For these reasons, SPMD will not be discussed in this review.

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Contaminants included PCBs, PAHs, DDTs, and a selection of contaminants of emerging

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concern (e.g., pyrethroid pesticides, flame retardants). A summary of these publications and

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relevant experimental details are given in the supporting information (Supporting Information

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(SI) Tables S1a, b). Of these reports, 19 provided a direct comparison of tissue and passive

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sampler concentrations including regression equations which we used as the primary comparison

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metric. Unpublished data from a study performed at our laboratory (U.S. EPA, Atlantic Ecology

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Division, Narragansett, Rhode Island, USA) investigating the uptake of PCBs by LDPE and

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POM, and the marine polychaete Nereis virens, from dilutions of a contaminated sediment, are

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also included in this data set (SI Table S2a, b). This study used the approach and methods

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described by Parks et al.42 In order to compare the different studies, all regression equations were

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adjusted to express CL in ng/g lipid and CPS in ng/g polymer and were correlated in a logarithmic

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form. A PDMS density of 0.97 g/mL was used to convert volume to mass. A summary of these

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19 publications and the equations relating tissue concentrations to passive sampler polymer

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concentrations is provided in Table 1.

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Passive Sampling and Bioaccumulation

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Passive Sampling Two sampling configurations predominate when using PDMS, LDPE,

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and POM: thin film sheeting and thin fiber coatings (SPME). These configurations allow for

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optimization tailored to a particular application’s needs. For instance, if shorter deployment

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times are necessary, a thin PDMS fiber coating or a relatively thin sheet would perform better, or

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if very low environmental concentrations are expected, a thicker or a larger sheet along with a

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longer deployment can result in lower detection limits. Sheets of EVA have been used as a

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passive sampling polymer but there have been few studies published including the deployment of

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both EVA and organisms.29, 30, 43, 44

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Thin sheets or films are commercially available for LDPE, POM, and PDMS (or silicone

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rubber). LDPE sheets can often be purchased from local hardware stores. They are commercially

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available in several different thicknesses: the 25 µm (1 mil), 51 µm (2 mil), and 72 µm (3 mil)

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thicknesses are commonly used in the current passive sampling literature. Custom LDPE is

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available from Brentwood Plastics, Inc. (St. Louis, MO, USA). POM is also commonly deployed

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as a thin sheet and is commercially available from the CS Hyde and Company (Lake Villa, IL,

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USA) and is often deployed in 25 µm and 72 µm thicknesses. Blocks of POM can also be

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purchased and then sliced to the desired thickness using the appropriate equipment.

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Manufacturers of silicone or PDMS film sheets are Specialty Silicone Products, Inc. (Ballston

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Spa, NY, USA), Altec Products Limited (Bude, UK), and Silex Ltd (Linford, Hampshire, UK).

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The films that are available commercially range from 20 to 1016 µm in thickness.

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SPME, a thin polymer coating usually on a silica fiber, is another common configuration

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for passive sampling, frequently used with PDMS. The small configuration allows for a

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relatively simple post-recovery extraction or even direct injection into a gas chromatograph (GC)

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injection port. Disposable and re-useable SPME fibers are commercially available (e.g., Supelco,

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St Louis, MO, USA; Fiberguide Industries, Stirling, NJ, USA) in several different polymer

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thicknesses (~10 to 100 µm) and types of polymers.

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Thin sheet and SPME passive samplers have been deployed in several different

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environmental settings. Within the context of this review, they are well suited to measure Cfree in

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fresh and marine water environments and can be used in sediment interstitial water and water

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column deployments.26, 28, 37 They can also be used to determine the flux of HOCs from the

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interstitial waters into the water column45 and from the water column into the atmosphere.46, 47

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The effective use of passive samplers requires that the target contaminant achieves

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equilibrium between the sampler and other environmental phases or the sampler concentration

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(CPS) is adjusted for non-equilibrium conditions. However, establishing that the target

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contaminants have achieved equilibrium between all of the environmental phases (including the

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passive sampler) is not trivial. There are at least four approaches to addressing the equilibrium

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status of the target contaminants.48 First, assume that equilibrium has been established based on

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previous data. Second, perform a temporal sampling, in which passive samplers are collected

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over time until a significant change in the concentration of the target contaminant in the passive

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sampler is not detected between time points. Third, deploy passive samplers of different

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thicknesses, if the samplers with different thicknesses have the same concentrations for a given

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target contaminant on a mass or volume polymer basis, equilibrium is assumed to have been

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achieved for that target contaminant. Each of these approaches has liabilities; for example,

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different deployments and sites require more or less deployment time, so assuming equilibrium

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based on past experience can be problematic. Both the second and third approaches require

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multiple analyses and/or field trips resulting in extra expense and time. A fourth approach, using

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performance reference compounds (PRCs) avoids these liabilities.49, 50 In this approach,

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chemicals that behave like the target contaminants are absorbed into the samplers before the

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deployment. During the deployment, the passive sampler desorbs the PRCs from the polymer

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into the environment at a rate comparable to the absorbing target contaminants. The equilibrium

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status of the target contaminants can be calculated and used to adjust for non-equilibrium

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conditions by measuring the loss of PRCs following the deployment. There are currently at least

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three approaches recommended for making the non-equilibrium calculations using the raw PRC

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concentrations including a first order equation,51-53 a diffusion-based model,49, 54, 55 and a

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sampling rate-based correction.56 When using PRCs, it is worth noting that for highly

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hydrophobic contaminants, the passive sampler may accumulate relatively low concentrations 10 ACS Paragon Plus Environment

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while under non-equilibrium conditions making quantification analytically challenging. This

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sensitivity loss is potentially problematic and makes considering analytical sensitivity an

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objective when determining deployment duration. For the analysis of passive sampler data

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reported here, we note in Table 1, if the equilibrium status of the passive sampler has been

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‘Assumed,’ ‘Established’ (i.e., equilibrium between the environmental phases has been

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demonstrated analytically), or ‘Adjusted’ (i.e., using one of the PRC-based approaches described

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above).

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Bioaccumulation In general, the studies cited in this report used some combination of established

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bioaccumulation methods. Studies with whole sediment exposures using polychaetes,

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oligochaetes and benthic bivalves were often based on American Society for Testing and

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Materials (ASTM), the Organization of Economic Cooperation and Development (OECD), and

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U.S. EPA bioaccumulation guidance.57-59 Similarly, many of the whole sediment studies with

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freshwater organisms were based on U.S. EPA freshwater bioaccumulation guidance.60 Finally,

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mussel bioaccumulation methods have been conducted for decades and continue to be a powerful

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biomonitoring tool in programs such as “Mussel Watch”6, 61-63 and generally followed methods

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described in Bergen et al.,64 Smedes,65 and ASTM.66 Studies which did not follow the

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approaches described above applied methods discussed in each publication.19, 67, 68

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Bioaccumulation studies with whole sediments were most frequently performed in the laboratory

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under static-renewal or flow-through conditions whereas mussel deployments were most often

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conducted in the water column in the field. Bioaccumulation studies were usually approximately

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30 days in duration although, in some cases, the exposures were longer and target contaminants

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were assumed to have achieved equilibrium with organismal lipids. It is recognized that this

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assumption may not always be true for higher molecular weight target contaminants.

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RESULTS AND DISCUSSION

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Passive Sampler – Bioaccumulation Relationships

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To reduce the complexity of the data analysis, sampler-organism comparisons were organized by contaminant class: PCBs, DDT and its metabolites, PAHs, and contaminants of 11 ACS Paragon Plus Environment


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emerging concern (CEC) (Figure 1). The regression lines were considered to be the preferable

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expression of the relationship between organism bioaccumulation and sampler uptake for

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comparison purposes. In the vast majority of cases, the regression equations were taken from the

331

published papers as reported. Each regression depicted in the figures represents the relationship

332

spanning only the range of data in the source publication. Because some of these studies

333

measured several different HOCs and were performed at or with sediment samples from different

334

sites, the lipid and polymer concentrations measured ranged several orders of magnitude. In

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order to visualize all of this data on one plot, the figures are shown using a logarithmic scale.

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Along with the sampler-organism regression lines, a gray shaded band representing the

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1:1 correlation plus or minus a factor of ten was superimposed onto the figures as a visual aid to

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indicate where the data would have occurred if the organism bioaccumulation and passive

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sampler uptake agreed within an order of magnitude, throughout the discussion this band will be

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referred to as the “10x band”. A regression line falling within the 10x band supports the

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mechanistic assumption that a passive sampler’s affinity for a given HOC is similar to that of an

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organism’s lipids.

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PCBs These contaminants were the most commonly studied class for comparing polymer

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accumulation with organism lipid bioaccumulation. In total, there were 12 studies with five

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species of polychaetes and bivalves. The comparisons show that, in general, the lipid

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concentrations were higher than the polymer concentrations and many of the resulting

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regressions reported slopes of approximately one (Figure 1a). Several studies illustrate a

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correlation between CL and CPS which roughly agree within an order of magnitude (Smedes,65

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Trimble et al.,69 Gschwend et al.,70 Friedman et al.,71 Burgess et al.,72 Janssen et al.67, and the

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current study (for LDPE)). In other studies, the regressions suggest that the relationship between

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CL and CPS differed by more than one order of magnitude (Beckingham and Ghosh,3 the current

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study (POM), and Gschwend et al. (PDMS)70). The regressions from Beckingham and Ghosh

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and the current study fell outside of the 10x band especially as CPS decreased. In the case of

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Beckingham and Ghosh,3 the organism lipid percentages were very small compared to the other

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studies which may contribute substantially to the behavior of the regression line. These low lipid

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percentages may have resulted from the presence of activated carbon used during the study 12 ACS Paragon Plus Environment

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which does not provide the same amount of organism nutrition as natural sediments not

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containing the activated carbon. Overall, not one polymer or organism displayed data readily

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distinguishable or anomalous relative to the others.

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DDTs Four studies were found to correlate DDT and its metabolite’s lipid bioaccumulation in

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mussels, polychaetes and bivalves with polymer concentrations, resulting in five regression lines

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(Figure 1b). Of these regression relationships, three fell within the 10x band over the majority of

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the measured data (Bao et al.73 and Joyce et al.74). These regression equations were comparable

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to those reported for PCBs in terms of the magnitude for both the slopes and intercepts (SI Table

368

S3). The other two studies report regression equations with regression lines above the 10x band.

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In the study by Pirogovsky, several DDT metabolites (e.g., DDE, DDD, DDMU, and DDNU)

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were measured in PDMS-SPME and mussel tissues, the reported slope (1.02) is in agreement

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with other studies; however, the intercept (1.2) is slightly larger than reported in the other

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studies.75 This discrepancy may be due to the low water column concentrations and relatively

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high detection limits of the SPMEs used in that study. In the study conducted by Maruya et al.,76

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although the reported slope (0.61) for the regression is similar to other relationships for the

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DDTs, the intercept was 2.7. In this study, the measured CL for the dataset ranged three orders of

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magnitude while the CPS ranged over five, resulting in the lower than average slope, the high

377

intercept may be exacerbated due to an outlier with a very low CPS value and a high CL value (SI

378

Figure S9c).

379 380

PAHs The literature yielded five studies in which PAH bioaccumulation was compared with

381

polymer uptake. Six different regressions were reported including studies with a gastropod, two

382

using polychaetes, an oligochaete, and two using mussels (Figure 1c). Generally, the slopes

383

demonstrated similar behavior to that observed for the PCBs and DDTs (SI Table S3). Four of

384

these studies fell within the 10x band (Cornelissen et al.,18 Smedes,65 and Fernandez and

385

Gschwend68). The study by Muijs and Jonker77 found the regression between CL and CPS above

386

the 10x band for the entire data span while the study by Vinturella et al.78 observed a CL and CPS

387

relationship greater than ten only at lower concentrations. These elevated relationships are likely

388

due to the elevated sediment contaminant concentrations used in each study and are discussed

389

further in the Highly Contaminated Sediments section. 13 ACS Paragon Plus Environment


390

CECs Reports were also published comparing passive sampler uptake with other classes of

391

compounds most notably PBDE flame retardants and pyrethroid pesticides (Figure 1d). Two

392

such reports were found in the literature search, one comparing oligochaetes with PDMS-SPME

393

and one comparing mussel bioaccumulation with LDPE. Figure 1d also includes a regression

394

relating bivalve bioaccumulation of PCBs with an EVA passive sampler. This regression is

395

included in Figure 1d because studies with EVA are less prevalent in the literature. The majority

396

of the reported data falls within the 10x band. However, unlike the regression lines for the PCBs,

397

DDTs, and PAHs, the CECs resulted in regression lines that reflected higher polymer

398

concentrations than the corresponding lipid concentrations (Meloche et al.,44 Harwood et al.79).

399

Speculatively, the regression from Harwood et al. may indicate higher concentrations in the

400

polymer due to biotransformations (and thus lower concentrations) in the organisms during the

401

exposures.79 Because no other regression relationships between lipid bioaccumulation and EVA

402

accumulation were found in the literature, it is difficult to critically compare the data from

403

Meloche et al.44 to the other PCB regressions (Figure 1a) given that the polymer EVA is more

404

polar than PDMS, LDPE, and POM. However, the expansive linear range of these values and the

405

high r2 observed (0.98) shows promise for using EVA passive sampling uptake to estimate HOC

406

bioaccumulation (including CECs) and these relationships should be further explored. The

407

regression relationship reported for PBDEs by Joyce et al.74 fell within the 10x band. Li et al.80

408

also reported a correlation for PBDE 209, this regression line lies above the 10x band suggesting

409

CL values were often more than an order of magnitude higher than measured CPS values. This

410

relatively large discrepancy may, in part, be due to the very lengthy passive sampler equilibrium

411

times required with PBDE 209.

412 413

For all classes of HOCs, slopes for the regressions ranged from 0.33 to 1.2 and the

414

intercepts ranged from -0.88 to 2.70 (SI Table S3). General agreement was observed between all

415

compound classes and polymers studied. Relative to general trends, in many cases studies with a

416

higher intercept (>1) were associated with lower slopes (600

465

ng/g dry). This observation suggests that highly contaminated sediments may lead to regressions

466

in which CL to CPS relationships differ by more than a factor of ten. Four of these studies resulted

467

in unexpectedly high intercepts: the current study (with POM), Muijs and Jonker,77 Maruya et

468

al.,76 and Beckingham and Ghosh,3 where reported intercepts ranging from 2.1 to 2.7 (Table 1).

469

Muijs and Jonker77 worked with field sediments from an abandoned manufactured gas plant with

470

total PAH concentrations approaching 50,000 mg/kg dry.84 This point can be further illustrated

471

using data from the current study. PCB contaminated sediments from New Bedford Harbor with

472

total PCBs approaching 400 mg/kg dry41 were diluted with a clean reference sediment resulting

473

in the following exposures: 25%, 50%, 75% and 100% contaminated sediments. As the amount

474

of contaminated sediment is diluted to lower PCB concentrations, the regression intercepts for

475

POM decrease from 2.9 to 1.2 and the slopes increase from 0.56 to 0.75 for the 100% and 25%

476

treatments, respectively (SI Figure S6a). Similar trends were observed in this study when LDPE

477

was used as well (although the intercepts are smaller) (SI Figure S6b). In both studies the

478

regressions moved toward the center of the 10x band with decreasing sediment contaminant

479

concentrations. This behavior suggests several options for a possible explanation: (1) the

480

elevated contaminants in the sediments create an artifact which masks the ‘actual’ relationship

481

between the organisms and passive samplers at low levels of accumulation or (2) analytical


Page 16 of 41

Page 17 of 41


482

detection limits in lipid and polymer samples with low levels of accumulation result in

483

uncertainties in the concentration data.

484 485

Effects of Equilibrium Status

486 487

The results of this review were also analyzed to evaluate how the determination of

488

equilibrium status of the passive samplers affected the estimated CPS values. Each regression

489

relationship was categorized by how equilibrium CPS was determined: established via temporal

490

sampling; corrected or adjusted using PRCs; or equilibrium was assumed (Table 1, Figure 2).

491

Two of the three studies in which equilibrium was established had regression lines within the

492

10x band over the entire measured data set. Studies in which CPS was adjusted to estimate

493

equilibrium concentrations (n = 12) also generally fell within the 10x band. The eleven

494

regressions in which equilibrium was assumed for CPS resulted in more data trending above the

495

10x band. This particular method for determining CPS represents studies using all three of the

496

reviewed polymers as well as each type of organism present within this review and encompasses

497

contaminants from each class of HOCs reviewed (e.g., PCBs, DDTs, PAHs, CECs). As such, it

498

is not surprising that studies in which equilibrium was assumed resulted in correlations with the

499

most variable results as well as the highest and the lowest concentrations trends. Regressions in

500

which equilibrium was assumed resulted in lines with the highest CL concentrations when

501

compared to CPS for PDMS, LDPE, and POM. This finding suggests some target contaminants

502

may not have actually achieved equilibrium with the passive sampling polymer in some of these

503

studies.

504 505

The success demonstrated by temporal sampling to confirm equilibrium is noteworthy,

506

but it can also be the most challenging approach for assessing equilibrium status. Considerable

507

variation was also observed for regressions in which CPS was determined using PRCs. This

508

variation may be due to increased uncertainty that is associated with using PRCs to correct for

509

non-equilibrium conditions as well as the differences in the models currently used to adjust

510

passive sampling data based on the PRCs. It should be noted that the study using EVA is not

511

included in Figure 2 as it is unclear if the position of the observed regression line is due to how



512

CPS was determined (e.g. assumed equilibrium, established equilibrium, or adjusted equilibrium

513

concentrations) or because EVA regressions have lower CL to CPS ratios.44

514 515

Predictive Power of Organism Lipid – Polymer Relationships

516 517

In most cases, strong logarithmic relationships existed between bioaccumulation and

518

polymer uptake which is indicative of a predictive relationship. These relationships were not

519

precisely 1:1 but were very often within a factor of approximately ten (Figure 2); which does not

520

disqualify the relationships from being predictive but means that regressions will need to be

521

considered on a passive sampler, target contaminant, and organism basis, suggesting that a

522

“universal” equation cannot be applied across a range of passive samplers, target contaminants,

523

and organisms to predict bioaccumulation from passive sampler uptake.

524 525

The strength of the various relationships in Table 1 and Figures 1 and 2 can be assessed

526

by examining the coefficients of determination (r2), which express the proportion of variability in

527

the data captured by the modeled log-log regressions. For LDPE, the nine studies had r2 values

528

ranging from 0.31 to 0.92 with a mean of 0.68 ± 0.20 while the 11 PDMS r2 values ranged from

529

0.59 to 0.91 and had a mean of 0.76 ± 0.10 (SI Table S4). The six r2 values for POM ranged

530

from 0.39 to 0.76 and had a mean of 0.58 ± 0.15. Finally, EVA had one reported r2 of 0.98.

531

These r2s suggest that polymer uptake effectively describes much of the bioaccumulation-uptake

532

variability and should provide confidence that using the modelled relationships has merit for all

533

polymers reviewed in this study. Another useful summary statistic describing these relationships

534

is the “p” value. This value indicates whether the relationship between organism

535

bioaccumulation and passive sampler uptake is statistically significant with values equal to or

536

less than 0.05 generally accepted as indicating a significant relationship. When the raw data was

537

available, p values were calculated, and in each case indicated a significant relationship between

538

CL and CPS (Table 1) and selected datasets are plotted in SI Figure S9a-g.

539 540

Organism Lipid - Polymer Relationships versus Target Contaminant Hydrophobicity

541


Page 18 of 41

Page 19 of 41

542


The log (CL/CPS) ratio was calculated for the raw data that was available and plotted

543

versus target contaminant hydrophobicity (expressed as the log KOW). This ratio provided

544

another way of expressing the slope of the relationship between polymer uptake and

545

bioaccumulation and, in principle, should be consistent for all data regardless of the target

546

contaminant or physiochemical properties. A log (CL/CPS) equal to 0 would indicate that the

547

uptake into the two phases was identical. Overall, the ratio averaged 0.50 ± 0.71 (n = 609). To

548

investigate how this ratio varied with the target contaminants physiochemical properties, ratios

549

were separated by compound class: DDTs and PCBs (combined) and PAHs. The average and

550

standard deviation were calculated for all ratios with a common log KOW and the data was plotted

551

(Figure 3a, b; SI Figures S7, S8). For the PCBs and DDTs, the average log (CL/CPS) for all

552

compounds was 0.52 ± 0.49 (n = 317) (Figure 3a). The individual ratios as well as their standard

553

deviations appear to increase slightly with increasing log KOW. This increase in standard

554

deviation could be due to the contaminants not being at equilibrium with the polymers or

555

increased variation associated with PRC corrections. For PAHs, the average of all log (CL/CPS)

556

was 0.50 ± 0.71 (n = 292) (Figure 3b). There is a relatively large ratio and large standard

557

deviations for the lower KOW PAHs like anthracene and phenanthrene. Values for both log

558

(CL/CPS) and standard deviations then decreased for moderate log KOW (5.5 - 6) molecules then

559

increased for the higher log KOW PAHs (≥6). The variability observed for the low KOW PAHs is

560

likely due to a combination of organism metabolism of the PAHs and low analytical recoveries

561

of these volatile compounds.85-87 Causes of the variation observed for the higher KOW PAHs is

562

likely error associated with passive sampler non-equilibrium and PRC corrections.

563 564

Basic and Conceptual Data Gaps

565 566

Analysis of the relationship between passive sampler uptake and aquatic organism

567

bioaccumulation suggests that passive samplers are promising surrogates for estimating

568

bioaccumulation, though there are basic and conceptual data gaps that require further research

569

and reporting. Addressing these data gaps will improve the future application of passive

570

sampling in the scientific and regulatory arenas.

571



572

Basic Data Gaps An array of target contaminants, organisms, field or laboratory conditions, and

573

equilibrium status approaches were used in the studies discussed in this review and are

574

summarized in SI Table S5. PDMS, LDPE, and POM have been widely used in comparisons

575

with organism bioaccumulation for a wide range of target contaminants including PCBs, PAHs,

576

and DDTs as well as pyrethroid pesticides and brominated flame retardants. There is one notable

577

data gap, POM has not been evaluated with DDTs or bivalves. Regarding the organisms used in

578

these comparisons, LDPE and POM have been compared primarily to marine organisms. While

579

there is little reason to conclude that freshwater and marine organisms will differ substantially in

580

CL under equilibrium conditions, having a balance of representative studies would be beneficial

581

to support the use of passive samplers for predicting bioaccumulation. The types of organisms

582

used in these comparisons range from oligochaetes and polychaetes to bivalves and gastropods to

583

a small number of insects with PDMS demonstrating the greatest diversity of organisms.

584

Relative to field versus laboratory studies, all of the passive samplers had more laboratory

585

studies than field studies. This imbalance is likely due to the logistical, time, and cost challenges

586

associated with conducting in situ field deployments compared to laboratory-based studies.

587

Finally, all of the POM comparisons assumed equilibrium conditions for CPS while LDPE and

588

PDMS also applied estimates based on temporal sampling and PRC corrections. To address these

589

basic data gaps more studies with LDPE and POM using freshwater organisms including insects

590

and amphipods are needed. In addition, more field studies using PDMS and POM applying

591

temporal sampling should be performed. Another basic data gap is reporting of the types of

592

summary statistics discussed above that assist in assessing the predictive strength of the

593

bioaccumulation-uptake relationships. In the future, researchers should include, at the least, the

594

r2 and p values for their regressions. Species-specific traits and a more quantitative analysis of

595

the lipid make up should be considered in bioaccumulation studies and may improve observed

596

relationships with passive sampler uptake.81-83

597 598

Conceptual Data Gaps It remains unclear how effectively passive sampling captures

599

bioaccumulation resulting from dietary ingestion of contaminated sediments. This particular

600

data gap is part of the on-going debate of what environmental phases represent the principle

601

sources of exposure to aquatic organisms. In 1991, Di Toro et al. described the equilibrium

602

partitioning (EqP) model for explaining the distribution of HOCs in the aquatic environment.14 20 ACS Paragon Plus Environment

Page 20 of 41

Page 21 of 41


603

The model describes a system in which at equilibrium the chemical activity of a given HOC is

604

equal across all phases, for example:

605 606

ܽ௙௥௘௘ = ܽ௉ௌ = ܽ௅ = ܽ௉

(10)

607 608

where, afree, aPS, aL, and aP are the respective chemical activities of a given HOC for the Cfree, CPS,

609

CL, and the total sediment particle concentration CP. In this model, if one knows the HOC

610

concentration in any phase and the partition coefficients quantifying the HOC’s distribution

611

between phases (e.g., KPS, KOC, KL), the concentration in any other phase can be determined.

612

Consequently, if one knows the value of Cfree based on passive sampling measurements then the

613

concentrations in the other phases can be determined including the CL. This paradigm has served

614

as the basis for the U.S. EPA’s Equilibrium Partitioning Sediment Benchmarks (ESBs)2, 88, 89 and

615

accurately predicts toxicity and bioaccumulation for several classes of HOCs.90-92 However,

616

there have been studies suggesting that Equation 10 does not accurately describe the chemical

617

activity of some HOCs. For example, Friedman and Lohmann concluded that for higher log

618

KOW HOCs, CL could not be completely explained by Cfree and that is was likely there were other

619

contributing factors to the measured CL.93 Other studies have reported similar findings that

620

question the universality of the EqP model.82, 94, 95 However, if Cfree is found to not be an

621

effective predictor of CL for higher KOW HOCs, the utility of passive sampling for predicting the

622

CL for these same HOCs may be thrown into doubt. This data gap is a fertile area for further

623

research.

624 625

Within the passive sampling literature, there is also a lack of consensus on whether field

626

or laboratory studies are more appropriate for comparing passive sampler uptake and organism

627

bioaccumulation. Janssen et al. concluded that laboratory studies are the best way to evaluate

628

equilibrium interstitial water concentrations which, most accurately represent target contaminant

629

bioavailability. However, field studies may be necessary to account for the most relevant “real

630

world” exposure conditions.67 Ghosh et al. reached a similar conclusion stating that equilibrium

631

between the passive sampler and various environmental phases can be achieved more readily

632

under laboratory conditions, but there is a loss of environmental reality attainable only under

633

field conditions.28 In addition, comparisons utilizing field conditions can be difficult because 21 ACS Paragon Plus Environment


634

target contaminants may take months or longer to reach equilibrium with the passive samplers.

635

As discussed above, lengthy field deployments have been circumvented by using PRCs with

636

passive samplers including LDPE,49, 56, 96 POM,97 and PDMS.73, 98 However, interpolating PRC

637

data is not always straightforward. In addition, PRCs can be expensive and may introduce

638

additional variability to the estimates of target contaminant concentrations that can adversely

639

affect the passive sampler uptake to organism bioaccumulation relationship.48

640 641

Perhaps the most environmentally significant application, actual passive sampling studies

642

bridging trophic levels are very limited. All of the studies discussed in this review involved

643

organisms that primarily accumulate target contaminants directly from contaminated water or

644

sediment and not via feeding on prey. In this way, the transfer of target contaminants from the

645

relevant environmental phases to the organism’s lipids is relatively simple (e.g., bivalves

646

filtering contaminated water). To apply the full potential of passive samplers it would be

647

desirable to use CPS data to estimate CL for higher trophic level organisms (e.g., pelagic fish,

648

wildlife, humans). The most likely route to accomplish this goal would involve using passive

649

sampler CPS data as an input term to bioaccumulation models like those developed by Gobas et

650

al.5, 99, 100 The CPS would serve the same role as the lower trophic level organism

651

bioaccumulation CL data ordinarily collected for inclusion in such models. However, very little

652

of this type of passive sampler CPS data utilization has yet been performed.101, 102 In a related

653

series of studies, investigations have compared measurements of contaminant uptake into

654

silicone (PDMS) coated inside jars. In these experiments, contaminated sediments were tumbled

655

in the PDMS-coated jars and the equilibrium PDMS accumulation compared to the actual

656

bioaccumulation by fish sampled from the areas where the contaminated sediments were

657

collected.103-107 Results demonstrated strongly predictive linear correlations between the silicone

658

estimates of bioaccumulation and actual fish bioaccumulation. However, the fish

659

bioaccumulation was often less than the silicone-based estimates of bioaccumulation suggesting,

660

that the organisms were not at equilibrium with the sediments in the field. These results may

661

indicate that the fish in the environment are being exposed to contaminants from several other

662

sources along with the contaminated sediments (including contaminated sediments from other

663

locations) and not exposed to contaminants in uncontaminated areas. Alternatively, these

664

findings may simply highlight differences in the sorptive capacities of the lipid versus PDMS. 22 ACS Paragon Plus Environment

Page 22 of 41

Page 23 of 41


665

This innovative work highlights one of the challenges associated with using passive samplers

666

with bioaccumulation modeling to predict higher trophic level organism bioaccumulation –

667

higher trophic level organisms are seldom sessile – and they do move around and can be exposed

668

to multiple sources of contamination while passive samplers are generally stationary. This

669

challenge and others will need to be addressed if passive sampling is to be used successfully to

670

predict higher trophic level bioaccumulation.

671 672

Implications

673 674

This review and analysis of the passive sampler uptake and bioaccumulation data

675

indicates that based on the regression relationships in the majority of the 27 regressions in Table

676

1, passive samplers accumulated target contaminants within one or two orders of magnitude of

677

the organisms, and, when raw data was available for testing, each of these regressions yielded a

678

statistically significant relationship between CL and CPS based on the p values. This finding

679

suggests that in many applications, passive samplers can be applied as surrogates for

680

biomonitoring organisms if they are not available or their deployment is problematic (e.g., low

681

dissolved oxygen), with the following caveats. First, in most cases, the CPS, while within an

682

order of magnitude of the CL, were often lower than the CL values. In studies where equilibrium

683

was assumed, this bias becomes more prevalent and may be a reflection of the potential for the

684

target contaminants to not be fully at equilibrium with the polymer. For cases where equilibrium

685

with the polymer was established or adjusted for using PRCs, the results of the regression

686

analyses indicate that the relationship between CL and CPS were not identical but were predictive.

687

Second, the increase in variability observed between the CL and CPS ratio with increasing log

688

KOW for PCBs and DDTs (Figure 3a), shows that extra care should be taken when interpreting

689

passive sampler data for the larger, higher KOW target contaminants. This is because the

690

uncertainty associated with the resulting estimated CLs will be larger than for lower KOW target

691

contaminants. Unfortunately, these larger KOW HOCs, while less bioavailable than smaller

692

contaminants, are often the HOCs of most interest in terms of trophic transfer and toxicity to

693

higher trophic organisms including pelagic fish, wildlife and humans.108, 109 While Figure 3a

694

indicates the variability in the CL and CPS relationship increases with increasing KOW, it does not

695

necessarily mean that the use of passive samplers as surrogates is flawed, but highlights the 23 ACS Paragon Plus Environment


696

challenges with accurately and precisely measuring CPS and CL for these larger KOW

697

contaminants. By comparison, the level of variability observed in the ratio for PAHs, across

698

molecules (Figure 3b), is likely due to a combination of metabolism and the same issues

699

affecting other high KOW contaminants. This variability occurred despite the relatively good

700

correlations between PAH uptake by passive samplers and bioaccumulation by organisms.

701 702

For scientific and regulatory applications, specifically for the classes of HOCs discussed

703

in this review, the use of passive samplers as surrogates for biomonitoring organisms is

704

recommended when necessary (e.g., when biomonitoring organisms are not available). As the

705

relatively high r2 values reported in this review show, using passive samplers will yield similar

706

spatial trends as well as detect temporal changes in water column and interstitial water

707

concentrations similar to those acquired in a conventional biomonitoring program. The accuracy

708

of this approach will improve if passive sampler equilibrium status is confirmed and the practice

709

is limited to the low and medium log KOW (i.e., 4 to 7) target HOCs. Passive sampling based

710

concentrations resulted predictive relationships, most of which were within one to two orders of

711

magnitude of the directly measured bioaccumulation (see Figure 2). Finally, the conclusions

712

reached here will only be strengthened with the performance of more comparisons of passive

713

sampler uptake and organism bioaccumulation.

714 715

ASSOCIATED CONTENT

716

Supporting Information

717 718

Additional information, tables, and figures as mentioned in text. The Supporting information is available free of charge at http://pubs.acs.org/journal/esthag.

719 720 721

ACKNOWLEDGEMENTS

722 723

The authors thank the AED reviewers for their comments: M Cantwell, J LiVolsi, R

724

McKinney, W Munns, J Serbst and G Thursby. The following colleagues are also thanked for

725

providing their raw data for analysis in the this review: L Fernandez (Northeastern University,

726

Boston, MA, USA); AMP Oen (Norwegian Geotechnical Institute (NGI), Norway, Oslo), E 24 ACS Paragon Plus Environment

Page 24 of 41

Page 25 of 41


727

Janssen (ETH, Zurich, Switzerland), G Cornelissen (NGI) and A Ruus (Norwegian Institute for

728

Water Research, Oslo, Norway); K Maruya (Southern California Coastal Water Research

729

Project, Costa Mesa, CA, USA); F Smedes (Deltares, Utrecht, The Netherlands); and C

730

Friedman Corning School of Ocean Studies, Maine Maritime Academy, Castine, ME, USA.

731

This is U.S. EPA ORD-015127. Mention of trade names or commercial products does not

732

constitute endorsement or recommendation for use. This report has been reviewed by the U.S.

733

EPA’s Office of Research and Development National Health and Environmental Effects

734

Research Laboratory, Atlantic Ecology Division, Narragansett, RI, and approved for publication.

735

Approval does not signify that the contents necessarily reflect the views and policies of the

736

Agency.

737 738 739 740

REFERENCES

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Page 35 of 41


CL (ng/g lipid)

1

CPS (ng/g polymer) 2 3

Figure 1. Uptake relationships between lipid-normalized tissue HOC concentrations (CL) and passive

4

sampler polymer HOC concentrations (CPS). The gray band represents a 1:1 correlation plus or minus a

5

factor of 10. Solid lines, dotted lines, and dashed lines reflect experiments performed using PDMS, LDPE

6

or POM, respectively. Contaminants are plotted by (a) PCBs: two LDPE studies were conducted by

7

Gschwend et al.,70 one in which CP was established (est.) by tumbling the LDPE in sediment until the

8

polymers had reached equilibrium (CL = 10.2CPS) and one study in which CP was adjusted (adj.) using PRCs

9

upon static exposure to sediments (CL = 5.28CPS), (b) DDTs metabolites, (c) PAHs, and (d) contaminants

10

of emerging concern: the dotted-dashed line represents a study using ethylene vinyl acetate (EVA) as a

11

passive sampler. Due to the limited number of publications and correlations using EVA, Meloche et al.44

12

was grouped with the contaminants of emerging concern. See supporting information Table S1 for more

13

experimental details.



Page 36 of 41

14 15

16 17

Figure 2. Uptake relationships between lipid-normalized tissue HOC concentrations (CL) and passive

18

sampler polymer HOC concentrations (CPS) sorted by sampler equilibrium correction method: black lines -

19

equilibrium was established (est.) using temporal sampling; orange lines - equilibrium was assumed based

20

on previous experiments; and blue lines were adjusted (adj.) to estimate CPS using modelling (e.g., based

21

on PRCs or other methods). Solid lines, dotted lines, and dashed lines reflect experiments performed using

22

PDMS, LDPE or POM, respectively. Two LDPE studies were conducted by Gschwend et al.,70 one in which

23

CPS was established by tumbling the LDPE in sediment until the polymers had reached equilibrium (CL =

24

10.2CPS) and one study in which CPS was adjusted using PRCs based upon static exposure to sediments (CL

25

= 5.28CPS). See Supporting Information Table S1 for more experimental details.

26


Page 37 of 41


27 28

Figure 3. Lipid normalized tissue HOC concentration - passive sampler polymer HOC concentration ratios

29

plotted against log KOW for (a) PCBs and DDTs and (b) PAHs. Ratios plotted are the mean and standard

30

deviation. Log KOWs for PCBs were taken from Hawker and Connell,110 DDTs were generated from

31

SPARC (http://www.archemcalc.com/sparc.html),111 and PAHs from Smedes.65

32

Information for more experimental details.


See Supporting


Page 38 of 41

1

Table 1. Data used to compare tissue bioaccumulation HOC concentrations with passive sampler HOC concentrations. Equations are

2

reported as presented in the figures. In cases where raw data were available, p values were calculated.

Reference

Vinturella et al. (2004)78 Gschwend et al. (2011)70 Gschwend et al. (2011)70 Friedman et al. (2009)71 Burgess et al. (2015)72

Target

Passive

Equilibrium

Contaminant

Sampler

Status

PAHs

LDPE

Assumed

PCBs

LDPE

Adjusted

PCBs

LDPE

Established

PCBs

LDPE

Adjusted

PCBs

LDPE

Adjusted

PAHs

LDPE

Adjusted

Fernandez and Gschwend (2015)68

Field (F) or Organism

Laboratory

Equation

(L) Study Marine polychaete N. virens Marine polychaete N. arenaceodentata Marine polychaete N. arenaceodentata Marine polychaete N. virens Marine blue mussel M. edulis Marine soft shell clam M. arenaria

L

L

L

L

F

F

log (CL) = 0.6log(CPS) + 1.8 r2 = 0.65 log (CL) = log(CPS) + 0.722 r2 = 0.59 log (CL) = log(CPS) + 1.00 r2 = 0.64 log (CL) = 0.82log(CPS) + 0.79 r2 = 0.94; p

Evaluating the Relationship between Equilibrium Passive Sampler

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