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Exposure Research

Exposure Research: Air

Methods, Models, Tools and Databases

Methods

  • Federal Reference Methods
    EPA Federal Reference Methods (FRM) are used to measure compliance with National Ambient Air Quality Standards (NAAQS) for criteria air pollutants. ORD conducts research to develop and evaluate methods as potential FRMs.

Models

  • CMAQ Model
    CMAQ is an air quality model and software suite designed to model multiple pollutants at multiple scales. CMAQ allows regulatory agencies and state governments to evaluate the impact of air quality management decisions, and gives scientists the ability to probe, simulate, and understand chemical and physical interactions in the atmosphere.
  • AERMOD Modeling System
    A steady-state plume model that incorporates air dispersion based on planetary boundary layer turbulence structure and scaling concepts, including treatment of both surface and elevated sources, and both simple and complex terrain.
  • Exposure Model for Individuals (EMI)
    The EMI is an air pollution exposure model for individuals in cohort health studies. The EMI is being developed to predict individual exposures for multiple air pollutants from ambient concentrations, meteorology, and questionnaire information such as building characteristics, occupant behavior related to building operation, and time-activity patterns. The overall goal of this research is to provide a modeling tool to enhance the ability to conduct air pollution human exposure assessments with greater certainty.
    This research is being conducted in response to the needs of numerous air pollution cohort health studies. The EMI applications for client-specific needs will serve to strengthen the science behind regulatory decision-making on setting the National Ambient Air Quality Standards.
  • Positive Matrix Factorization 3.0 (EPA PMF 3.0)
    EPA PMF is a receptor model being developed and freely distributed by ORD. The model uses ambient measurements and estimated uncertainties in those measurements to infer emission sources likely impacting a monitor and by how much. The solution is based on a constrained weighted least squares approach. The software has a graphical user interface (GUI) that makes entering data and viewing modeling results easy. The current versions of EPA PMF (versions 1.x) solve the relatively simple bilinear model.
  • Unmix 6.0
    EPA Unmix is a receptor model that is freely distributed by ORD. The model uses ambient measurements to determine the number of source types and their impacts at a monitoring site. The Unmix algorithm is based on defining feasible solution space for the input data. This space is derived using edges or sample groups with relatively low impacts. These edges define source types which account for the majority of variability in the data. The software has a graphical user interface (GUI) that makes entering data and viewing modeling results easy.
  • Fused Air Quality Surfaces using Downscaling
    Based on statistical modeling research in the development of fused space-time predictive surfaces for air quality, this web page provides access to the most recent daily O3 and PM2.5 surfaces. As new and improved statistical models become available, we plan to continually update these surfaces.
  • Fused Discrete Air Quality Surfaces
    This web page provides access to discrete, daily O3 and PM2.5 predictive surfaces. Here, a space-time hierachical Bayesian model (see HBMetadataAir for reference and model description) is used to fuse daily ozone (8-hr maximum) monitoring data from the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) with gridded output from the Models-3/Community Multi-Scale Air Quality Model (CMAQ).
  • Stochastic Human Exposure and Dose Simulation Model (SHEDS)
    EPA's Stochastic Human Exposure and Dose Simulation model (known as SHEDS) allows scientists to estimate total exposures and risks people face from chemicals encountered in everyday activities. SHEDS can estimate the range of total chemical exposures in a population from different exposure pathways (inhalation, skin contact, dietary and non-dietary ingestion) over different time periods, given a set of demographic characteristics. The model enhances estimates of exposure in many different contexts, and has been used to inform EPA human health risk assessments and risk management decisions.
  • MicroTrac - Personal time-activity modeling
    EPA scientists have developed MicroTrac, a computer model that uses GPS data on location and speed to estimate the time people spend in various "microenvironments" such as inside and outside their home, school, workplace, and motor vehicle. Using MicroTrac with personal GPS devices, accelerometers, and health monitors in exposure and health effects studies will allow scientists to link the location and activities of study participants with air pollution measurements and measures of health effects during a study.

Tools

  • Atmospheric Model Evaluation Tool
    The Atmospheric Model Evaluation Tool (AMET) was developed to aid in the evaluation of meteorological and air quality simulations.
  • Watershed Deposition Mapping Tool
    The Watershed Deposition Tool (WDT) was developed by EPA to provide an easy to use tool for mapping the deposition estimates from CMAQ to watersheds to provide the linkage of air and water needed for TMDL (Total Maximum Daily Load) and related nonpoint-source watershed analyses.
  • VERDI
    VERDI is a flexible, modular, Java-based program for visualizing multivariate gridded meteorology, emissions, and air quality modeling data created by environmental modeling systems such as the Community Multiscale Air Quality (CMAQ) model and the Weather Research and Forecasting (WRF) model.
  • Remote Sensing Information Gateway (RSIG)
    The Remote Sensing Information Gateway (RSIG) offers a new way for users to get the multi-terabyte, environmental datasets they want via an interactive, Web browser-based application. A file download and parsing process that now takes months will be reduced via RSIG to minutes.
  • Spatial Allocator
    The Spatial Allocator was developed by the Institute for the Environment at the University of North Carolina at Chapel Hill for the U.S. Environmental Protection Agency to provide tools that could be used by the air quality modeling community to perform commonly needed spatial tasks without requiring the use of a commercial Geographic Information System (GIS).
  • Probabilistic Reverse dOsimetry Estimating Exposure Distribution (PROcEED)
    Probabilistic Reverse dOsimetry Estimating Exposure Distribution (PROcEED) is a web-based application used to conduct probabilistic reverse dosimetry calculations. The tool is used for estimating a distribution of exposure concentrations likely to have produced biomarker concentrations measured in a population.
  • Innovative Approaches for Air Quality Monitoring

Databases

  • Watershed Deposition Tool Data
  • Air quality data for CDC's National Environmental Public Health Tracking Network
    EPA scientists are collaborating with the Centers for Disease Control and Prevention (CDC) on a CDC initiative to build a National Environmental Public Health Tracking (EPHT) network. Working with state, local and federal air pollution and health agencies, the EPHT program is facilitating the collection, integration, analysis, interpretation, and dissemination of data from environmental hazard monitoring, and from human exposure and health effects surveillance. These data provide scientific information to develop surveillance indicators, and to investigate possible relationships between environmental exposures, chronic disease, and other diseases, that can lead to interventions to reduce the burden of these illnesses. An important part of the initiative is air quality modeling estimates and air quality monitoring data, combined through Bayesian modeling, that can be linked with health outcome data.
  • MULTIMED
    A one-dimensional, steady-state model used to predict the concentration of contaminants migrating from a waste disposal facility via the subsurface, surface water, and air pathways to receptor sites.
  • Fused Air Quality Surfaces Using Downscaling
    This web page provides access to the most recent O3 and PM2.5 surfaces datasets using downscaling.
  • Fused Discrete Air Quality Surfaces
    This web page provides access to discrete, daily O3 and PM2.5 predictive surfaces. Here, a space-time hierachical Bayesian model (see HBMetadataAir for reference and model description) is used to fuse daily ozone (8-hr maximum) monitoring data from the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) with gridded output from the Models-3/Community Multi-Scale Air Quality Model (CMAQ).
  • Consolidated Human Activity Database (CHAD)
    EPA scientists have compiled detailed data on human behavior from 19 separate studies into EPA's Consolidated Human Activity Database (CHAD). The database includes a total of more than 30,000 individual study days of detailed human behavior, with each day broken down into individual hours and activity types. The data also include demographic information which allows researchers to examine specific groups within the general population and how their unique behavior patterns influence their exposures to chemicals. Scientists at EPA, other government agencies, academia, and the private sector routinely use CHAD data in human exposure and health studies, and in models used for exposure and risk assessments that protect human health.
  • Chlordane Pesticide Dataset
    EPA scientists have developed a dataset that compiles chlordane measurements from published literature in one place. The dataset provides researchers with a useful resource that taps into peer reviewed published results, summarizing and organizing the data into a user friendly tool. The dataset compiles about 2,400 enantiomer-specific measurements for five pairs of chlordane enantiomers. It consolidates information that may be useful for scientists interested in studying trends, estimating exposure and toxicity of mixtures, developing methods, and modeling enantiomers.
  • Consolidated Pesticide Information Dataset (CPI)
    EPA scientists have developed a dataset of basic information on approximately 1,700 pesticides. The dataset was gathered from multiple sources and is in spreadsheet format. It contains a total of twenty fields, including chemical names, identification numbers, structures, and pesticide use class — such as insecticide, herbicide, and fungicide. The CPI dataset will serve as a valuable tool for those interested in pesticide mixtures, green or sustainable pesticides, development of methods and models, and other areas of pesticide research.

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