Uncertainty in Octanol−Water Partition Coefficient:  Implications for Risk Assessment and Remedial Costs

Abstract

The assessment of human health and ecological risks at chemically contaminated sites often includes the use of models to assess chemical transport, fate, and exposure/toxicity. These models require input data on a variety of physical and chemical properties for each compound of concern. Small changes in some of these parameters may result in significant differences in estimated human health or ecological risks and in the extent of required remediation efforts. The octanol-water partition coefficient (K ow ) for hydrophobic organic compounds is one such parameter, particularly because it is often used to estimate additional partitioning and bioaccumulation parameters. Unfortunately, there is considerable variability among tabulated K ow values for many compounds of concern. This paper assesses the implications of using various values of K ow to calculate health-protective polychlorinated biphenyl (PCB) sediment quality objectives (SQOs) in a case study using a simplified food chain model and the range of K ow values available from or recommended by the U. S. EPA. For the site and K ow values considered in this study, which are a snapshot of values available in the spring of 2004, the SQOs differ by as much as a factor of 5. This range of SQOs is estimated to correspond to a difference in remediation costs of $48 million.

Selected physicochemical aspects of poly- and perfluoroalkylated substances relevant to performance, environment and sustainability—Part one

“…A recent extensive compilation of experimental data and in silico predictions is available that includes aqueous solubilities, vapour pressures, pK a values for acids, partition coefficients between air and water, octanol and air, organic carbon and water, bioconcentration, bioaccumulation and biomagnification factors for environmentally relevant PFASs (Ding and Peijnenburg, 2013). These data are limited, often inaccurate and discrepancies can reach several orders of magnitude (Linkov et al, 2005;Arp et al, 2006;Goss et al, 2006;Ding and Peijnenburg, 2013). These discrepancies may originate from purity or solubility problems, colloidal aggregation or adsorption behaviour, matrix effect, unsuitable analytical methods or models, lack of standardization of procedures, inadequate validation, etc.…”

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

“…With the need for a fair apportionment, growing public interest, and the evolution of legislative frameworks, the continuously evolving fraction and composition of the chemicals reaching the water bodies presents a challenge in itself. Some of these chemicals can be present at concentrations less than current detection limits, or they can accumulate in the environment, while others can pose a yet unknown risk for humans and/or the environment [4,[45][46][47][48]. For example, the PBDE limit proposed by the EU of 0.000000049 µg/L is several orders of magnitude below the levels achievable by even advanced treatment [5].…”

A systematic review of methods of uncertainty analysis and their applications in the assessment of chemical exposures, effects, and risks

“…Such data are often assumed to be robust/of "good" quality, and thus requiring no further quality assessment before using them. Yet, that assumption might well prove to be wrong, as Linkov et al (2005) demonstrated regarding K ow values for PCBs in the US EPA databases, in particular for Aroclor 1254. The K ow values available cover three to four orders of magnitude, which cannot be explained solely by measurement variability.…”