Relook on the Linear Free Energy Relationships Describing the Partitioning Behavior of Diverse Chemicals for Polyethylene Water Passive Samplers

Abstract

Over the past 3 decades, low-density polyethylene (PE) passive sampling devices have been widely used to scout organic chemicals in air, water, sediments, and biotic phases. Experimental partition coefficient data, required to calculate the concentrations in environmental compartments, are not widely available. In this study, we developed and rigorously evaluated linear free energy relationships (LFERs) to predict the partition coefficient between the PE and the water phase (log K pe−w ). Polyparameter (pp) LFERs based on Abraham solute parameters performed better (root-mean-square error, rmse = 0.333−0.350 log unit) in predicting log K pe−w compared to the two oneparameter (op) LFERs built on n-hexadecane−water and octanol− water partition coefficients (rmse = 0.41−0.42 log unit), indicating that one parameter is not able to account for all types of interactions experienced by a chemical during PE−water exchange. Dimensionality analyses show that the calibration dataset used to train pp-LFERs fulfills all the requirements to obtain a robust model for log K pe−w . Van der Waals interactions of the molecule tend to favor the PE phase, and polar interactions of the molecule favor the water phase. The PE phase is the most sensitive to polarizable chemicals compared to other commonly used passive sampling polymeric phases such as polydimethylsiloxane, polyoxymethylene, and polyacrylate. For op-LFERs, the PE phase is better represented by the hexadecane phase than by the octanol phase. A computational method based on the conductor-like screening model for real solvents theory did good job in estimating log K pe−w for chemicals that were neither very hydrophobic nor very hydrophilic in nature. Our models can be used to reliably predict the log K pe−w values of simple neutral organic chemicals. This study provides insights into the partitioning behavior of PE samplers compared to other commonly used passive samplers.

Exploring the role of octanol-water partition coefficient and Henry’s law constant in predicting the lipid-water partition coefficients of organic chemicals

“…2 in SM). This was expected as there is a considerable overlap in information among ASDs 39 , which warrants a careful selection of calibration dataset to avoid inflation in the fitted coefficients of ASM equations 18 .…”

“…Though this database is gradually expanding, the experimental methods for the determination of ASM descriptors are challenging and require careful curation and considerations 17 . Additionally, there is redundancy in the information encoded in the ASDs, which can lead to inflated pp-LFERs if the calibration datasets are not carefully chosen 18 . Lastly, the methods based on quantum-chemical calculations are relatively sophisticated and require commercial software, which is not widely accessible to the users.…”

Exploring the role of Henry’s law constant in formulating two-parameter linear free energy relationships for facile prediction of lipid-water partition coefficients of neutral organic chemicals

“…The PCA tests performed on a set of ASDs indicate that the rst two dimensions represent 75.7% of the information for the logK lw LFER dataset and 79.1% for the logK pw LFER dataset (Section 2 in SM). This was expected as there is a considerable overlap in information among ASDs [35], which warrants a careful selection of calibration dataset to avoid in ation in the tted coe cients of ASM equations [15].…”

“…For instance, the op-LFERs are unable to account for all types of intermolecular interactions that diverse chemical families can experience during the partitioning process [1,14]. On the other hand, the available experimental database of ASDs for calibrated pp-LFERs is limited to less than a few thousand chemicals [15]. Though this database is gradually expanding, the experimental methods for the determination of ASM descriptors are challenging and require careful curation and considerations [16].…”

“…Though this database is gradually expanding, the experimental methods for the determination of ASM descriptors are challenging and require careful curation and considerations [16]. Additionally, there is redundancy in the information encoded in the ASDs, which can lead to in ated pp-LFERs if the calibration datasets are not carefully chosen [15]. Lastly, the methods based on quantum-chemical calculations are relatively sophisticated and require commercial software, which are not widely accessible to the users.…”