Passive sampling methods for contaminated sediments: scientific rationale supporting use of freely dissolved concentrations

Abstract

Passive sampling methods (PSMs) allow the quantification of the freely dissolved concentration (Cfree ) of an organic contaminant even in complex matrices such as sediments. Cfree is directly related to a contaminant's chemical activity, which drives spontaneous processes including diffusive uptake into benthic organisms and exchange with the overlying water column. Consequently, Cfree provides a more relevant dose metric than total sediment concentration. Recent developments in PSMs have significantly improved our ability to reliably measure even very low levels of Cfree . Application of PSMs in sediments is preferably conducted in the equilibrium regime, where freely dissolved concentrations in the sediment are well-linked to the measured concentration in the sampler via analyte-specific partition ratios. The equilibrium condition can then be assured by measuring a time series or a single time point using passive samplers with different surface to volume ratios. Sampling in the kinetic regime is also possible and generally involves the application of performance reference compounds for the calibration. Based on previous research on hydrophobic organic contaminants, it is concluded that Cfree allows a direct assessment of 1) contaminant exchange and equilibrium status between sediment and overlying water, 2) benthic bioaccumulation, and 3) potential toxicity to benthic organisms. Thus, the use of PSMs to measure Cfree provides an improved basis for the mechanistic understanding of fate and transport processes in sediments and has the potential to significantly improve risk assessment and management of contaminated sediments.

Keywords: Bioavailability; Chemical activity; Passive sampling; Risk assessment; Sediments.

Figure 1

Conceptual view of contaminant cycling in sediment highlighting the central role of freely dissolved concentration, C_free.

Figure 2

A. Polymer-water partition coefficients (log K_{polymer-water}) for common polymers used as passive samplers as a function of K_OW for PAHs (K_PDMSw = 0.93 log K_OW + 0.17, average value for different PDMS types (Smedes et al. 2009), K_LDPEw = 1.22 log K_OW – 1.22 (Lohmann 2012), K_POMw = 0.99 log K_OW + 0.12 (Hawthorne et al. 2011), K_Paw = 1.11 log K_OW - 0.37 (Lohmann 2012). B. Estimated lipid-polymer partition coefficient (calculated using the K_OW − K_lipw QSAR of (Endo et al. 2011) and the QSARs in Figure 2A (log K_lip-polymer = log K_lipw − log K_{polymer-water}).

Figure 3

28-d Hyalella survival as a function of PDMS fiber concentrations of hydrophobic organic contaminants determined in ex-situ sediment analyses using GC/FID analysis. The solid red line represents the predicted PDMS effect concentration obtained using equation (7). The dashed red lines illustrate a factor of two range of uncertainty from the theoretical estimate derived using the target lipid model.

Figure 4

A. Comparison of chemical activities in field sediments at selected depth intervals at 3 sampling sites. Chemical activities were obtained by summing 9 individual PAHs. The grey zone indicates the region where lethality caused by baseline toxicity is expected. B. Contribution of the 9 PAHs to C_free at Dumping site. C. Contribution to the chemical activity at the Dumping site. Figure adapted with permission from Witt G et al. (2009). © (2009) Elsevier.

Figure 5

Setting Environmental Quality Benchmarks with PSMs. K_lipw refers to the target-lipid to water partition coefficient, and K_PSDw refers to the polymer-water partition coefficient.

Figure 6

Polymer-specific sediment quality benchmarks (PQBs) for benthic toxicity assessments of PAHs. Values are based on narcosis Secondary Chronic Values (SCVs) in Burgess et al. (2013). SCVs (in mg/L) were converted to polymer-based values using equation (10) and empirical K_{polymer-water} relations for each polymer: log K_PDMSw - log K_OW relations listed in (Smedes et al. 2009), log K_LDPEw - log K_OW equation (1) and best fit log K_LDPEw values in (Lohmann 2012), and the K_POMw - log K_OW relation from Hawthorne et al. (2011).