Citation:

Meyer, D., V. Mittal, W. Ingwersen, G. Ruiz-Mercado, W. Barrett, M. Gonzalez, J. Abraham, AND R. Smith. Purpose-Driven Reconciliation of Approaches to Estimate Chemical Releases. ACS Sustainable Chemistry & Engineering. American Chemical Society, Washington, DC, 7(1):1260-1270, (2019).

Impact/Purpose:

The purpose of this manuscript is to describe research in the Life Cycle and Human Exposure Modeling Project (CSS 18.03, "Release data to support chemical exposure modeling: Reconciliation of data mining and simulation approaches") examining methods to support source modeling in chemical assessment. The concept of fit-for-purpose reconciliation of source modeling methods is explored to help modelers determine the most efficient method that satisfies the decision need. As part of this work, a read-across approach via the use of regression tree analysis is introduced as a means to estimate chemical releases based on physicochemical properties and existing chemical release data in inventories maintained by U.S. EPA. A case study of cumene manufacturing emissions is presented to demonstrate how the potential strengths and weaknesses of top-down (data mining), bottom-up (simulation), and read-across (regression tree) approaches as they pertain to decision needs could be evaluated to inform the selection of an appropriate modeling approach for a given decision.

Description:

A framework is presented to address the toolbox of chemical release estimation methods available for manufacturing processes. Although scientists and engineers often strive for increased accuracy, the development of fit-for-purpose release estimates can speed results that could otherwise delay decisions important to protecting human health and the environment. A number of release estimation approaches are presented, with the newest using decision trees for regression and prediction. Each method is evaluated in a case study for cumene production to study the reconciliation of data quality concerns and requirements for time, resources, training, and knowledge. The evaluation of these decision support criteria and the lessons learned are used to develop a purpose-driven framework for estimating chemical releases.

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