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ECO31: Identifying strategies that will provide greater confidence in estimating the degradation rates of organic chemicals in water, soil and sediment

Principal Investigator

Prof Damian Helbling

Address:

School of Civil and Environmental Engineering

220 Hollister Hall

Ithaca, New York 14853

USA

Tel: +1 607 255 5146

E-mail: damian.helbling@cornell.edu

 

Collaborators

PD Dr. Kathrin Fenner, Senior Scientist, Eawag

Description

In the work described in this proposal, the project team aims to identify strategies that will provide greater confidence in estimating the degradation rates of organic chemicals in the environment. There are two main hypotheses that will be investigated:

  • Multivariate statistical approaches can be used to uncover key environmental factors that determine the range of chemical degradation rates for specific reaction types and in different environmental compartments; and
  • Data-driven, informed probability distributions can be generalised to represent uncertainty and variability in degradation rates for specific reaction types and in different environmental compartments

To address these hypotheses, the following three project objectives are defined:

  1. Identify the range of degradation rates reported for each reaction type in different environmental compartments. Conduct a literature review detailing regulatory and non-regulatory experimental and model-based approaches for estimating abiotic and biotic degradation rates in water, soil, and sediment. Collect abiotic and biotic degradation rate data and associated chemical and experimental metadata from experiments simulating each environmental compartment for a set of reference chemicals and collate into a database. Analyse dataset to describe the range of values reported for degradation rates as a function of chemical, reaction type, and environmental compartment. The analysis will define the range of degradation rates that have been reported for each reaction type and in each environmental compartment. Expectation. Reported degradation rates will range over orders of magnitude for specific chemicals, for specific reaction types, and in different environmental compartments.
  2. Identify key factors contributing to the range of degradation rates reported for each reaction type in different environmental compartments. Use multivariate statistics and hypothesis-testing to analyse dataset generated in Objective 1 to provide an evidence-based evaluation of the key factors influencing the magnitude or range of reported degradation rates for each reaction type and in each environmental compartment. Significance. The analysis will define the key variables contributing to the variability in reported degradation rates. Expectation. Key variables will be identified that determine the degradation rate of each reaction type. The result can be transferred to key variables that determine the degradation rate in different environmental compartments by considering which reactions are expected to occur in each compartment.
  3. Identify approaches to assign informed probability distributions to estimated degradation rates. Assign probability distributions to the degradation rate data collected in Objective 1 using a range of conventional and emerging techniques that can yield both informationless and informed probability distributions. Analyse the resulting set of probability distributions and discuss differences in the type and standard deviations of each distribution, the data requirements for informed probability distributions, and the generalisability of informed probability distributions to reaction types or environmental compartments. Significance. The analysis will identify a range of options to assign confidence to estimated degradation rates. Expectation. Informed probability distributions will provide greater confidence in estimated degradation rates, but will require additional testing data.

Timeline: June 2016 > May 2017

LRI funding: $ 109.000

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