title: Description and demonstration of the EXPOLIS simulation model: two examples of modeling population exposure to particulate matter.
abstract: As a part of the EXPOLIS study, a stochastic exposure-modeling framework was developed. The framework is useful to compare exposure distributions of different (sub-) populations or different scenarios, and to gain insight into population exposure distributions and exposure determinants. It was implemented in an MS-Excel workbook using @Risk add-on software. Basic concept of the framework is that time-weighted average exposure is a sum of partial exposures in the visited microenvironments. Partial exposure is determined by the concentration and the time spent in the microenvironment. In the absence of data, indoor concentrations are derived as a function of ambient concentrations, effective penetration rates and contribution of indoor sources. Framework input parameters are described by probability distributions. A lognormal distribution is assumed for the microenvironment concentrations and for the contribution of indoor sources, and a beta distribution for the time spent in a microenvironment and for the penetration factor. Mean and standard deviation values parameterize the distributions. In this paper, Latin Hypercube sampling is used for the input distributions. The outcome of the framework is an estimate of the population exposure distribution for the selected air pollutant. The framework is best suited for averaging times from 24 h upwards. Sensitivity analyses can be performed to determine the most influential factors of exposure. The application of the framework is illustrated in two examples. The EXPOLIS PM(2.5) example uses microenvironment measurement and time-activity data from the EXPOLIS study to model PM(2.5) population exposure distributions in four European cities. The results are compared to the observed personal exposure distributions from the same study. The Dutch PM(10) example uses input data from several (Dutch) databases and from literature, and shows a more complex application of the framework for comparison of scenarios and subpopulations.
authors.affiliation: National Institute of Public Health and the Environment, PO Box 1, 3720 BA Bilthoven, The Netherlands. Hanneke.Kruize@rivm.nl
authors.forename: Hanneke
authors.initials: H
authors.lastname: Kruize
authors.forename: Otto
authors.initials: O
authors.lastname: Hänninen
authors.forename: Oscar
authors.initials: O
authors.lastname: Breugelmans
authors.forename: Erik
authors.initials: E
authors.lastname: Lebret
authors.forename: Matti
authors.initials: M
authors.lastname: Jantunen
mesh_terms: D000393:Air Pollutants / Q000009:adverse effects*; D004781:Environmental Exposure; D006801:Humans; D008962:Models, Theoretical; D010316:Particle Size; D011159:Population Surveillance; D011634:Public Health; D018570:Risk Assessment
chemical_list: D000393:Air Pollutants