You are here:
Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data
Citation:
Meyer, David E., S. Cashman, AND A. Gaglione. Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data. JOURNAL OF INDUSTRIAL ECOLOGY. Berkeley Electronic Press, Berkeley, CA, 25(1):20-35, (2021). https://doi.org/10.1111/jiec.13044
Impact/Purpose:
The purpose of this manuscript is to describe a set of research activities in the Life Cycle and Human Exposure Modeling Project (CSS 18.03). The outcomes of the research are methods to apply small-scale data mining to EPA chemical information data sources and model life cycle releases of chemicals, with initial focus on manufacturing. The list of EPA data sources includes the National Emissions Inventory, the Toxics Release Inventory, the Electronic Greenhouse Gas Reporting Tool, the Discharge Monitoring Report, the RCRAinfo Biennial Report on hazardous waste, and the Chemical Data Reporting database. The proposed methods can be used to reduce the time and resources required to develop source information for chemical assessments.
Description:
This study proposes methods to improve data mining workflows for modeling chemical manufacturing life cycle inventory. Secondary data sources can provide valuable information about environmental releases during chemical manufacturing. However, the often facility‐level nature of the data challenges their utility for modeling specific processes and can impact the quality of the resulting inventory. First, a thorough data source analysis is performed to establish data quality scoring and create filtering rules to resolve data selection issues when source and species overlaps arise. A method is then introduced to develop context‐based filter rules that leverage process metadata within data sources to improve how facility air releases are attributed to specific processes and increase the technological correlation and completeness of the inventory. Finally, a sanitization method is demonstrated to improve data quality by minimizing the exclusion of confidential business information (CBI).
URLs/Downloads:
DOI: Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data
Free access through PubMed Central