The Human Exposure Database System (HEDS) provides public access to data sets, documents, and metadata from EPA on human exposure. It is primarily intended for scientists involved in human exposure studies or work requiring such data.

We introduce a method that identifies from earnings conference calls the attention paid by financial analysts to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. The measures are useful in predicting important real outcomes related to the net-zero transition, notably job creation in disruptive green technologies and green patenting, and they contain information that is priced in options and equity markets. Updates [2024-08-17]: We have updated our data to 2023Q4. Updates [2023-11-21]: We have updated our data to 2022Q4. Updates [2023-02-15]: We have updated our data to ensure that the topic measures have zero values when CCExposure=0. Updates [2022-03-11]: We have updated our data to 2021Q4. Updates [2022-02-25]: We have expanded the number of variables provided in the datasets (we re-run the bigram searching algorithm so the original scores change but remain highly correlated with the legacy version.). Updates [2021-05-14]: We have updated our data to 2020Q4. Updates [2021-04-03]: Last update missed 2019 Q3 and Q4. We added the data of these two quarters in the latest version. Updates [2021-01-19]: We have updated our data to 2020Q3.

National Exposure Database (NEDB) holds information for assisting HSE for the following reasons: (i) informed policy making such as occupational exposure limits (OELs); (ii) developing benchmarking for enforcement; (iii) HSE initiatives such as Safety Awareness days (SHADS); (iv) developing exposure trend models; (v) preparing guidance and simple tools such as COSHH (Control of Substances Hazardous to Health) Essentials, Health Risks at Work for small and medium sized enterprises (SMEs); (vi) supporting our negotiating position in EU policymaking (REACH – Regulation Evaluation Authorisation and Restriction of Chemicals, IOELVs – Indicative Occupational Exposure Limit Value) in relation to Substances Hazardous to Health (SHH). NEDB has approximately 19,000 individual visit records on SHH. The majority of visit records contain sampling data points. The number of sampling data points held in NEDB is about a million. The information relates to air concentration, breathing zone exposure levels and total exposure burden (contribution through inhalation, skin and ingestion) information using biological samples - exhaled breath, urine and/or blood as appropriate. NEDB does not store names of individuals who were subjected to sampling. However, a given anonymised air sampling data can be linked to an individual’s name through the original visit record held in HSE’s internal electronic records system. The recorded information includes: Occupier name and Address, Date of visit, Industry, process and job, Substances sampled and their concentration, Sample type (breathing zone; static or biological), Exposure modifier information.

The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure predictions. The database currently includes biomonitoring exposure data from three studies: the American Healthy Homes Survey, the First National Environmental Health Survey of Child Care Centers and the Children's Total Exposure to Persistent Pesticides and Other Persistent Organic Pollutants study. Data include the amounts of chemicals found in food, drinking water, air, dust indoor surfaces and urine. The database will eventually include high-throughput exposure predictions for thousands of chemicals based on manufacture and use information. EPA researchers developed high-throughput exposure models to predict exposures for 1,763 chemicals using production volume, environmental fate and transport models, and a simple indicator of consumer product use.The model is being improved by adding more refined indoor and consumer use information since these are also large determinants of exposure. As these models are refined and more exposure data is collected, it will be added to ExpoCastDB.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

description: The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure predictions. The database currently includes biomonitoring exposure data from three studies: the American Healthy Homes Survey, the First National Environmental Health Survey of Child Care Centers and the Children's Total Exposure to Persistent Pesticides and Other Persistent Organic Pollutants study. Data include the amounts of chemicals found in food, drinking water, air, dust indoor surfaces and urine. The database will eventually include high-throughput exposure predictions for thousands of chemicals based on manufacture and use information. EPA researchers developed high-throughput exposure models to predict exposures for 1,763 chemicals using production volume, environmental fate and transport models, and a simple indicator of consumer product use.The model is being improved by adding more refined indoor and consumer use information since these are also large determinants of exposure. As these models are refined and more exposure data is collected, it will be added to ExpoCastDB.; abstract: The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure predictions. The database currently includes biomonitoring exposure data from three studies: the American Healthy Homes Survey, the First National Environmental Health Survey of Child Care Centers and the Children's Total Exposure to Persistent Pesticides and Other Persistent Organic Pollutants study. Data include the amounts of chemicals found in food, drinking water, air, dust indoor surfaces and urine. The database will eventually include high-throughput exposure predictions for thousands of chemicals based on manufacture and use information. EPA researchers developed high-throughput exposure models to predict exposures for 1,763 chemicals using production volume, environmental fate and transport models, and a simple indicator of consumer product use.The model is being improved by adding more refined indoor and consumer use information since these are also large determinants of exposure. As these models are refined and more exposure data is collected, it will be added to ExpoCastDB.

The dataset contains workplace lead measurement results collected during health hazards evaluation surveys from 1991 to 2015 for over 1,900 area lead exposure assessment. The data about exposure are estimates of lead concentration in air and on working area surfaces and are accompanied by description of location, industry, working area, the activity that generates exposure, as well as other variables.

Exposure data (excel file) and metadata description for all hazards, thresholds, categories, and volcanoes

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The dataset contains workplace noise measurement results collected during health hazards evaluation surveys from 1996 to 2013 for over 580 area noise level assessments. The collected data about exposure are based on OSHA and NIOSH assessment criteria and are accompanied by description of location, industry, working area, the activity that generates exposure, as well as other variables.

Understanding the health outcomes of military exposures is of critical importance for Veterans, their health care team, and national leaders. Approximately 43% of Veterans report military exposure concerns to their VA providers. Understanding the causal influences of environmental exposures on health is a complex exposure science task and often requires interpreting multiple data sources; particularly when exposure pathways and multi-exposure interactions are ill-defined, as is the case for complex and emerging military service exposures. Thus, there is a need to standardize clinically meaningful exposure metrics from different data sources to guide clinicians and researchers with a consistent model for investigating and communicating exposure risk profiles. The Linked Exposures Across Databases (LEAD) framework provides a unifying model for characterizing exposures from different exposure databases with a focus on providing clinically relevant exposure metrics. Application of LEAD is demonstrated through comparison of different military exposure data sources: Veteran Military Occupational and Environmental Exposure Assessment Tool (VMOAT), Individual Longitudinal Exposure Record (ILER) database, and a military incident report database, the Explosive Ordnance Disposal Information Management System (EODIMS). This cohesive method for evaluating military exposures leverages established information with new sources of data and has the potential to influence how military exposure data is integrated into exposure health care and investigational models.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information This file contains information reported to EPA under the Inventory Update Rule (IUR). Please note that no information claimed as TSCA Confidential Business Information by an IUR reporter is contained in this file. This database includes information about chemicals manufactured or imported in quantities of 10,000 pounds or more at a single site during calendar years or corporate fiscal years 2001, 1997, 1993, 1989, and 1985. Processing and use information was not required to be reported and only organic chemicals were required to be reported for all cycles prior to 2006. For a listing of changes to the reported data between 2002 and 2006, please see the Downloadable 2006 IUR Public Database page. This file requires the database application program Microsoft Access. This ACCDB file is downloaded in compressed (ZIP) file format. After downloading the file to your preferred location, double-click on the file to extract the ACCDB file. You may filter or search the 1998-2002 IUR database tables (“IUR-98_NonCBI” and “IUR-02_NonCBI”) by each available reported field, for example, by: Company Name, Site Name/Location, CAS Registry Number, or Chemical Name. You may filter or search the 1986-2006 IUR nationally aggregated production volume (PV) table (“IURALLPVs_1986-2006_NonCBI”) by CAS Registry Number, Chemical Name, or PV range. [Quote from: https://www.epa.gov/chemical-data-reporting/downloadable-2002-1986-iur-public-database] If you have questions about the underlying data stored here, please contact U.S. Environmental Protection Agency. TSCA-Hotline@epa.gov is said to answer questions on chemical data reporting requirements for the current version of the program that generated this historical data. It's unclear if they would have much information on this particular data set. But they might be able to redirect a query. For questions about this extracted data and metadata card contact CAFE at climatecafe@bu.edu

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The information provided in the Contaminant Exposure and Effects-Terrestrial Vertebrates (CEE-TV) Database profiles available geo-referenced information on contaminant exposure and effects in terrestrial vertebrates along the U. S. Atlantic, Gulf, and Pacific coasts (including Hawaii and Alaska) and the Great Lakes.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

This is the dataset used for the U.S. lead exposure risk hotspot analysis in Zartarian et al., 2024, ES&T. The data dictionary files explain the contents of the 2 included zipped data folders. The Figures 1&2 zipped folder contains the data for Figures 1 and 2, the Supplement A figures, and the data for all tables in the paper. The Supplement B zipped folder contains the Random Forest modeling methodology in Supplement B and corresponding data. This folder also includes the full national dataset version of Random Forest model version 1 and 2 used in the analysis (in .csv format). This dataset is associated with the following publication: Zartarian Morrison, V., J. Xue, A. Poulakos, R. Tornero-Velez, L. Stanek, E. Snyder, V. Helms Garrison, K. Egan, and J. Courtney. A U.S. Lead Exposure Hotspots Analysis. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 7(7): 3311-3321, (2024).