###### Abstract
For example, eGRID data are released as Microsoft Excel workbook (xlsx) files for all facilities, while TRI data are hosted as a series of comma separated value (csv) files and are modified with updates regularly. Furthermore, several USEPA inventories are available through Envirofacts, a RESTful web service. As the facility-level inventory data files are generally very large (millions of records), accessing and working with them can require specialized knowledge. Over time, these inventories have also been changing to meet programmatic demands and are also stored and provisioned with new technologies. These inventories provide critical information that helps to paint a national picture of environmental health and identify sources of potential environmental and human health issues related to pollution in the United States. They are widely used for modeling environmental conditions to assess environmental compliance (e.g., air quality modeling for meeting air quality standards) [5], determining needed capacity [6], developing benchmarks [7; 8; 9; 10], evaluating time trends [11], and many other purposes [12; 13]. Making these inventories easily usable may facilitate data compilation and modeling efforts. The Standardized Emission and Waste Inventories (StEWI) tool is a set of Python packages written to support rapid and transparent processing of these inventories. More broadly, StEWI is one tool within an ecosystem developed by the USEPA to support modeling in the realm of industrial ecology [14]. StEWI performs consistent, reproducible processing and combination of USEPA emissions, releases, and waste inventories and adheres to principles used and developed in various modeling efforts such as the U.S. Environmentally Extended Input-Output (USEEIO) model [15] and the USEPA rapid life cycle inventory (LCI) [1]. While some of these inventories contain additional information, such as data aggregated by region, StEWI solely compiles the facility-based release and waste generation data from the inventories. Additionally, StEWI compiles and harmonizes metadata for the facilities and the pollutants or wastes. StEWI is the first application of its kind known to the authors for rapid environmental data retrieval and combination from public sources for use in environmental modeling. Without StEWI, retrieving, processing, and understanding these inventories can be time consuming and prone to misinterpretation, particularly when applications require the use of multiple inventories. In this regard, SteWI fills an important gap to enable further applications of these valuable data sources. This may be directly useful to reconcile and harmonize emissions from common U.S. inventory sources, but the approach that SteWI embodies may also be valuable for other regions in which reconciling multi-sector, multi-pollutant inventories is also a challenge [16]. Furthermore, SteWI adds value to individual inventory data in a number of ways. For example, SteWI can remove overlapping releases when more than one inventory reports the same release type. SteWI generates metadata such as flow reliability scores based on the quality of the data described in the original source using methods previously developed by [PERSON] and [PERSON] [17]. SteWI also provides a resource that other entities can easily use to generate facility-level environmental information. The modeling effort used here may be instructive for other integrated modeling efforts that compile large environmental datasets. SteWI is actively maintained as an open-source tool on GitHub and will be updated as new inventory years are released or data are revised. The purpose of this article is to describe the structure of and data sources behind SteWI and to demonstrate the benefits of applying this novel package for analysis of a suite of facility-level emissions.