Formatted Title:

Development of a Consistent Model for Prediction of Equilibration in Polymeric Passive Samplers

Background/Objectives:

Polymeric passive sampling measures freely available concentrations of organic compounds in water, which better correlates with pollutant mobility and bioavailability. Using polymeric passive samplers to measure hydrophobic organic compounds (HOCs) in environmental conditions typically requires two key parameters: a compound-specific polymer-water equilibrium partition coefficient (K_pw) and the degree of equilibration achieved, represented by the fractional approach to steady state (f_ss). Calculating f_ss, which also depends on K_pw, involves preloading the passive sampling device (PSD) with performance reference compounds (PRCs) before deployment. However, using PRCs poses challenges since not all compounds have PRCs available, and highly hydrophobic compounds do not easily reach equilibrium with polymeric passive samplers.

The objective of this work is to address these challenges by developing a model capable of estimating f_ss for various families of organic contaminants (e.g., PCBs, PAHs, and dioxins/furans) using available PRC data. Additionally, QSAR models for widely used polymers, such as low-density polyethylene (LDPE) and polydimethylsiloxane (PDMS), were constructed to accurately estimate K_pw for various HOCs.

Approach/Activities:

PSDs consisting of LDPE sheets and PDMS on solid phase micro-extraction (SPME) fibers were loaded with ¹³C-labeled PAH and PCB PRCs for 5 months to achieve uniform concentration of PRCs in polymers. The PSDs were deployed for 30 days in sediments at different sites across the US. The observed loss of PRCs (analyzed with GC/MS/MS) was used to develop a model correlating the extent of equilibration of all compounds with their hydrophobicity. The proposed model is developed based on the transport of PRCs between the polymer and porewater considering retarded diffusion in sediments. Additionally, QSAR models to estimate K_pw for both LDPE and PDMS passive samplers were developed using a multilinear regression (MLR) approach to minimize root mean squared error (RMSE) compared to experimental data.

Results/Lessons Learned:

Analysis of PRC release from PSDs provides enough information to estimate the extent of equilibration of various contaminant molecules at a given site during a specific deployment period. The model accounts for retardation in sediments based on hydrophobicity in terms of polymer-water partitioning. This approach is useful for evaluating the equilibration of target molecules when PRCs are unavailable or impractical. Additionally, the QSAR models developed in this study offer highly accurate estimates for K_pw across different HOC families, with an RMSE of 0.226 log units for K_LDPE-wbased on 159 compounds and 0.184 log units for K_PDMS-wbased on 131 compounds.

Menu

Ashkan Alborzi

Biography