Impact/Purpose:

The primary objective of this research is to improve current PM population exposure models to more accurately predict exposures for the general population and susceptible sub-populations. Through model improvements, a better understanding of the major factors controlling exposure to PM will be achieved. Specific objectives of this research are to:

- predict total personal exposure to PM10 and PM2.5 for the general and for susceptible sub-populations residing in different urban environments

- estimate the contribution of ambient PM to predicted total PM exposures

- determine what factors are of primary importance in determining PM exposures, including an analysis of the effects of time spent in various microenvironments and the importance of spatial variability in ambient PM concentrations

- determine what factors contribute the greatest uncertainty to model predictions and make recommendations for measurement and modeling studies to reduce these uncertainties

- predict daily and annual average exposures using single or multi-day time-activity diaries

- incorporate state-of-the-art dosimetric models of the lung into PM population exposure and dose models

- evaluate models against measured data from PM panel and other exposure measurement studies

- develop exposure and dose metrics applicable to acute and chronic environmental epidemiology studies

Description:

The overall objective of this study is the development of a refined probabilistic exposure and dose model for particulate matter (PM) suitable for predicting PM10 and PM2.5 population exposures. This modeling research will be conducted both in-house by EPA scientists and through collaborative agreements with two different university consortia: (a) Environmental and Occupational Health Sciences Institute (EOHSI) and (b) Lawrence Berkeley National Laboratory (LBNL)/UC Berkeley (see also Task #3957). This model will be used to predict exposures (the magnitude, frequency, and duration of exposures) to PM of ambient origin for the general population and susceptible subpopulations. Specifically, this model will include human activity pattern data to account for the way people interact with their environment as a function of time, location and ambient conditions. New statistical methods will be used that will incorporate both the variability and the uncertainty in the relevant exposure factors. This model will also be linked to atmospheric models (from source to airshed and from airshed to neighborhood stationary monitors) and to respiratory tract dose models. The ultimate product will be a scientifically robust exposure modeling system to analyze the relationship between PM sources, ambient air PM concentrations, and personal exposures and dose. The development of an improved, user-friendly PM exposure modeling system will give EPA better capability to set more rational and defensible National Ambient Air Quality Standards (NAAQS) for particulate matter, PM10 and PM2.5. Moreover, this work will also be valuable for improving the power of epidemiological studies of the impact of PM pollution on mortality and morbidity.

Record Details:

Record Type:PROJECT

Start Date:10/01/1998

Completion Date:09/01/2002

Record ID: 15831

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Project Information:

Progress :Development of the first-generation SHEDS-PM model began in FY99 using available PM10 concentration data and model inputs for Vancouver, BC. The first-generation SHEDS-PM model was used to predict the population distribution of daily-average total PM10 exposures using two 12-hour periods (daytime/nighttime). An analysis of the importance of microenvironmental concentrations and human activities on total PM exposure using the Vancouver PM10 case study was presented at a scientific conference in FY00.
Development of inputs and modifications to the model code for a Philadelphia PM2.5 case study was initiated during FY00 and completed in FY01. The model code was improved to allow for prediction of the ambient PM contribution to total PM exposures, and to include eight specific microenvironments (outdoors, in vehicles, and indoors at residence, office, school, stores, restaurants, and bars). A literature review was conducted in FY00 to determine indoor/outdoor relationships for PM in the non-residential microenvironments and the data were summarized in a journal article (Zufall et al., 2000, JAWMA-submitted). Results of the first-generation SHEDS-PM model Philadelphia PM2.5 case study were published in a journal article (Burke et al., 2001, JEAEE-accepted).
Measurements of personal PM2.5 exposures in Philadelphia were not available for comparison with the case study predictions. However, an evaluation of the SHEDS-PM was performed in FY01 using newly available data from NERL's RTP Panel Study. The SHEDS-PM model was used to predict the distribution of daily-average PM2.5 exposures for the population in the RTP Panel study (non-smokers over the age of 55 living in detached single-family homes in the RTP area). Good agreement was observed between the distributions of predicted exposures and the measured personal PM2.5 mass.
Development of Version 2 of the SHEDS-PM model was also initiated in FY01 to allow for prediction of the population distribution of hourly-average total and ambient PM exposures. The model code was modified to calculate total and ambient PM exposures sequentially based on hourly input data, thereby producing a time series of exposures for each individual over a 24-hour period. This modification to the structure of the exposure calculation was required in order to incorporate estimates of intake dose in the SHEDS-PM model which will be added in FY02. In addition to these refinements, initial steps were made toward making the model more flexible and user-friendly.

Also during FY01, NERL and EOHSI began collaborating on a demonstration case study of the source-to-dose modeling framework that will continue into FY02. The models used included Models3/CMAQ to predict gridded concentration fields for PM2.5 mass in Philadelphia for 2 week episode in July 1999, a spatial/temporal interpolation model (STRF) to predict concentrations at each census tract, and NERL's CHAD database of human activity data and SHEDS-PM model to predict the population distributions of exposures and intake dose.

The university consortia have also developed additional model components and analyses, and have generated the publications and presentations listed below.

Publications:

A Population Exposure Model for Particulate Matter: Case Study Results for PM2.5 in Philadelphia, PA (Burke et al., 2001, JEAEE)

Growth and Deposition of Hygroscopic Particulate Matter in the Human Lung (Broday and Georgopoulos, 2001, Aerosol Sci. Tech.).

An Integrated Exposure and Dose Modeling and Analysis System - Part III: Deposition of Inhaled Particles in the Human Respiratory Tract. (Lazaridis et al., 2001 Env. Sci. Tech).

Airborne Particulate Matter Concentrations in Non-residential Micro-environments: A Review (Zufall et al., 2000, JAWMA-submitted)

Interpolating Vancouver's Daily Ambient PM10 Field (Sun, et al., 2000, Environmentrics)

Reducing Un

Relevance :This research effort will provide a population-oriented exposure model for PM that can be linked to other models developed by ORD, including emissions-based atmospheric dispersion models developed by NERL (AMD) and respiratory tract dose models developed by NHEERL. The ultimate product will be a scientifically robust exposure modeling system to analyze the relationship between PM sources, ambient air PM concentrations, and personal exposures and dose. The development of an improved, user-friendly PM exposure modeling system will give EPA (OAQPS and NCEA) better capability to set rational and defensible National Ambient Air Quality Standards (NAAQS) for PM. Case-study application of the modeling system will identify the major factors influencing PM exposures, including the relative contributions of outdoor and indoor PM concentrations to personal exposure and dose, as well as the effects of human activities, housing characteristics and seasonal factors (i.e. meteorology). These applications will also help NERL's human exposure measurement program prioritize and direct studies toward the data needed for reducing uncertainty in the model predictions. The greater understanding of personal exposure to PM obtained through application of this source-to-dose modeling system will also be valuable for improving the power of epidemiological studies of the impact of PM pollution on mortality and morbidity.

Clients :ORD, NERL, OAQPS, Scientific community

Project IDs:

ID Code :5732

Project type :OMIS