Curated input data for regression tree modeling, links for EPA sources of data, and glossary of terms used in the data are presented in a spreadsheet. The data set has production volumes, emissions, and physicochemical properties for chemicals. This dataset is associated with the following publication: 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, USA, 7(1): 1260-1270, (2019).

The EPA GitHub repository PAU4ChemAs as described in the README.md file, contains Python scripts written to build the PAU dataset modules (technologies, capital and operating costs, and chemical prices) for tracking chemical flows transfers, releases estimation, and identification of potential occupation exposure scenarios in pollution abatement units (PAUs). These PAUs are employed for on-site chemical end-of-life management. The folder datasets contains the outputs for each framework step. The Chemicals_in_categories.csv contains the chemicals for the TRI chemical categories. The EPA GitHub repository PAU_case_study as described in its readme.md entry, contains the Python scripts to run the manuscript case study for designing the PAUs, the data-driven models, and the decision-making module for chemicals of concern and tracking flow transfers at the end-of-life stage. The data was obtained by means of data engineering using different publicly-available databases. The properties of chemicals were obtained using the GitHub repository Properties_Scraper, while the PAU dataset using the repository PAU4Chem. Finally, the EPA GitHub repository Properties_Scraper contains a Python script to massively gather information about exposure limits and physical properties from different publicly-available sources: EPA, NOAA, OSHA, and the institute for Occupational Safety and Health of the German Social Accident Insurance (IFA). Also, all GitHub repositories describe the Python libraries required for running their code, how to use them, the obtained outputs files after running the Python script modules, and the corresponding EPA Disclaimer. This dataset is associated with the following publication: Hernandez-Betancur, J.D., M. Martin, and G.J. Ruiz-Mercado. A data engineering framework for on-site end-of-life industrial operations. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 327: 129514, (2021).

These data are fully described in the associated EPA report. The Import Shares (ISs) are provided in a single Excel file covering all years. The Import Emission Factors (IEFs) are provided in csv files by year and by level of commodity classification, with names starting with "US_detail_import_factors" for detail-level commodity classification and "US_summary_import_factors" for summary-level commodity classification. USEEIO models (v2.3) with these IEFs were built using useeior v1.6.0 and written out to Excel files. Import emission factors are incorporated into these USEEIO models where they are further transformed into producer price and found in the M_n sheet of the model Excel files. Table 6 in the report shows year and level of commodity classification for each model along with which IEF file is used. The files with model names ending in *.yml are the model specification files for each of the published models. Correspondence files are provided that are used to (1) map EXIOBASE commodities to USEEIO commodities, (2) map BEA service category data to USEEIO sectors, and (3) map EXIOBASE Country/Region to BEA Service, Census Goods and TiVA trade regions. Data dictionaries for file types: 1. USEEIO models (Excel) - https://github.com/USEPA/useeior/blob/v1.6.0/format_specs/Model.md 2. Model spec files https://github.com/USEPA/useeior/blob/v1.6.0/format_specs/ModelSpecification.md 3. Import shares - Data dictionary found in file 4. Import Emission Factors (csv) - https://github.com/USEPA/USEEIO/blob/6cdd903fe5be58941c833f4cf585313f7e40d2a7/import_factors_exio/README.md 5. Correspondence files - https://github.com/USEPA/USEEIO/blob/fe48b5bc79ca994624838ce3b8171b9c65b691e2/import_factors_exio/concordances/README.md

Model input data in InputData.xls. Table and figure data in stored_results.xls. This dataset is associated with the following publication: Burkhardt, J., B. Burkhart, and F. Shang. Modeling Nicotine-Induced Chlorine Loss in Drinking Water Using Updated EPANET-MSX. JOURNAL OF ENVIRONMENTAL ENGINEERING. American Society of Civil Engineers (ASCE), Reston, VA, USA, 149(12): 04023086, (2023).

2017년~2023년도 7월의 환경부, 한국환경산업기술원, 생활화학제품 기업, 시민단체가 "생활화학제품 안전관리 자발적 협약"을 통해 민관사 공동으로 생활화학제품 전성분 공개를 추진한 제품 정보(기업명, 제품명, 품목, 자가검사번호(신고번호)) 입니다.

기업 자체적으로 탄소배출량 산정 및 계산시 지원하고자 개발한 제품 탄소배출량 산정 작성지침 매뉴얼(2014년) 자료