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Human Exposure and Atmospheric Sciences

Air

A critical component of air quality research is to develop an understanding of how air pollutants from various sources impact ambient air and, in turn, how these concentrations relate to the air people actually breathe. EPA exposure scientists are developing laboratory and computer-based methods and models to understand the transport and transformation of air pollutants from various sources, and the effects it has on humans and ecosystems. This information provides a fundamental linkage for evaluating public health impacts and developing effective strategies to reduce air pollution and the resulting impacts on human exposures and health effects.

EPA's Air Sensor Toolbox for Citizen Scientists
EPA's Air Sensor Toolbox for Citizen Science provides information and guidance on new low-cost compact technologies for measuring air quality. The Toolbox provides information to help citizens more effectively and accurately collect air quality data in their community, including sampling methodologies, generalized calibration and validation approaches, and measurement methods options. Since citizens are interested in learning more about local air quality where they live, work and play, EPA scientists are collaborating with other federal, state, and non-governmental institutions to encourage the development of new sensor and app technologies for measuring air quality.

Investigating the Human Exposome
EPA scientists are evolving their research from a targeted "one exposure, one adverse effect" approach towards a broader "discovery" approach that incorporates the concept of the human "exposome" as the counterpart to the human genome. The human exposome is defined as all of the environmental chemicals, human metabolites, chemical metabolites, metabolic reaction products, as well as the byproducts and biochemistry of the symbiotic microorganisms in our gut. Unlike the human genome, which stays essentially unchanged from birth to death, the exposome changes constantly in response to the environment around you, your health state and activity, diet, and metabolic processes.

Computational atmospheric chemistry helps EPA scientists forecast future atmospheric conditions
EPA scientists have developed an innovative computational chemistry-based method called COMPCHEM that is used for predicting lifetimes and fates of atmospheric compounds. COMPCHEM consists of a set of well-established, state of the science, quantum chemistry and gas phase kinetic codes, all of which have been used in numerous studies reported in peer-reviewed literature. It is anticipated that COMPCHEM will be a cost effective tool for supplementing atmospheric chemistry data generated through laboratory studies.

Sophisticated test chambers used to simulate atmospheric conditions
EPA scientists are using experimental laboratory studies to gather and assess data on atmospheric gas phase and particulate phase chemistry in order to determine the effects that various source emissions have on humans and ecosystems. Researchers are also working on the construction and development of a new, mobile photochemical reaction chamber to be used to study the toxicity of combustion emission sources. Research results are anticipated improve understanding of how atmospheric reactions and transformations influence the toxicity of air pollutant mixtures.

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.

Apps & Sensors for use in Human Exposure & Community Monitoring Studies
Smart phone applications and hand-held monitoring devices, or Apps and Sensors, are being developed world-wide that have the capability of detecting various air pollutants. Through collaborations with outside developers, EPA exposure scientists are working on the development, improvement, and application of these newly emerging environmental Apps and Sensors. App and Sensor technology has the potential to advance the paradigm of how air quality measurements are gathered, while reducing the cost and expense of their collection.

Development of Federal Reference and Equivalent Methods for measuring key air pollutants
EPA researchers are continually evaluating potential new Federal Reference Methods and Federal Equivalent Methods to foster innovation and improved measurement of atmospheric pollutants. The methods are tested in the lab and field. The scientists keep up-to-date on current air pollution sensor technologies, including availability and commercialization of emerging technologies. Adopting new technologies improves EPA’s ability to measure air pollution in new ways and locations.

Air pollution near roadways
EPA researchers are partnering with the University of Michigan on a study of the impact of vehicle emissions on near-road air quality, human exposures, and potential health effects in asthmatic children. The Near-Road Exposures to Urban Air Pollutants Study (NEXUS) is being conducted as part of EPA’s larger research program on roadway air pollution and its potential health effects. The study design will help tease out the health effects of particulate matter from diesel-burning truck and car exhaust. Particulate matter and other pollutants are being measured immediately next to the roadways and at various distances from them, to study how pollutant concentrations change as distance from the roadway increases.

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.

Air Pollution Exposure Model for Individuals (EMI)
Air pollution health studies help scientists understand potential health risks people face from air pollution exposure. However, because of the cost and participant burden associated with indoor and personal air monitoring, health studies often estimate exposures using outdoor ambient measurements from central site air monitors. Unfortunately, these ambient concentration levels do not necessarily reflect personal exposures since indoor air pollutant levels can differ from ambient levels. This potential exposure error can increase the uncertainty of air pollution health risks estimated in health studies. To reduce this potential error, EPA scientists have developed an exposure model for individuals (EMI) who are participating in air pollution health studies.

Positive Matrix Factorization (PMF) Model
EPA’s Positive Matrix Factorization (PMF) Model is one of several receptor models developed by EPA scientists that provide scientific support for current ambient air quality standards and implementation of those standards by identifying and quantifying the relative contributions that various air pollution sources contribute to ambient air quality in a community or region. Users of EPA’s PMF model provide files of sample species concentrations and uncertainties which the model uses to calculate the number of sources types, profiles, relative contributions, and a time-series of contributions.

Unmix 6.0 Model
EPA’s Unmix 6.0 Model is one of several receptor models developed by EPA scientists that provide scientific support for current ambient air quality standards and implementation of those standards by identifying and quantifying the relative contributions that various air pollution sources contribute to ambient air quality in a community or region. Users of EPA’s PMF model provide files of sample species concentrations and uncertainties which the model uses to calculate the number of sources types, profiles, relative contributions, and a time-series of contributions.

Providing 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.

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.

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.

Understanding exposures in children’s environments
To protect children’s health, EPA scientists have conducted a series of studies to provide a better understanding of the chemical sources, pathways and routes of exposure, and other exposure factors that contribute most to children’s exposures to chemicals. EPA scientists are considering several factors in this research, including the influence of everyday environments, normal behaviors, and normal consumer product use in homes. Understanding these factors is necessary to inform key strategies for reducing children’s exposures to potentially harmful elements.

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