Purpose-Driven Reconciliation of Approaches to Estimate Chemical Releases.

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.

Formation of PFAAs in fish through biotransformation: A PBPK approach.

A physiologically-based pharmacokinetic (PBPK) model for perfluorinated alkyl acids (PFAAs) in rainbow trout has been updated to include formation of perfluorooctanoic acid (PFOA) from the biotransformation of 8:2 fluorotelomer carboxylic acid (8:2 FTCA). The updated model is dynamic and simulates both uptake and depuration phases. Two empirical studies are used to parameterize and test the model. In the first case, parameters related to fecal elimination and protein binding were optimized. In the second case, parameters were sourced either from literature or from optimized values based on the first study to test model performance. Optimization of parameters resulted in a decrease in the difference between experimental data and simulation results by 57 and 23 percent for the first and the second case, respectively, compared to the original case. Sensitivity analysis was performed to identify important parameters, and uncertainty in model prediction propagated by these parameters was assessed using Monte Carlo analysis. For each case, 80 and 89 percent, respectively, of median predicted values were within the limits of experimental error when comparing simulated and experimental data. This is the first toxicokinetic model that incorporates biotransformation of PFAA precursors and simultaneously predicts the distribution of the precursor and metabolite in different tissues. The model is mechanistic, and could be applied to simulate a variety of scenarios by using the organism-specific physiological properties compiled here with other chemical-specific parameters (e.g. protein interactions).

Toward Automated Inventory Modeling in Life Cycle Assessment: The Utility of Semantic Data Modeling to Predict Real-World Chemical Production.

A set of coupled semantic data models, i.e., ontologies, are presented to advance a methodology toward automated inventory modeling of chemical manufacturing in life cycle assessment. The cradle-to-gate life cycle inventory for chemical manufacturing is a detailed collection of the material and energy flows associated with a chemical's supply chain. Thus, there is a need to manage data describing both the lineage (or synthesis pathway) and processing conditions for a chemical. To this end, a Lineage ontology is proposed to reveal all the synthesis steps required to produce a chemical from raw materials, such as crude oil or biomaterials, while a Process ontology is developed to manage data describing the various unit processes associated with each synthesis step. The two ontologies are coupled such that process data, which is the basis for inventory modeling, is linked to lineage data through key concepts like the chemical reaction and reaction participants. To facilitate automated inventory modeling, a series of SPARQL queries, based on the concepts of ancestor and parent, are presented to generate a lineage for a chemical of interest from a set of reaction data. The proposed ontologies and SPARQL queries are evaluated and tested using a case study of nylon-6 production. Once a lineage is established, the process ontology can be used to guide inventory modeling based on both data mining (top-down) and simulation (bottom-up) approaches. The ability to generate a cradle-to-gate life cycle for a chemical represents a key achievement toward the ultimate goal of automated life cycle inventory modeling.