Abstract
PM2.5 exposure distributions of adult Helsinki citizens were simulated using a probabilistic simulation framework. Simulation results were compared to corresponding personal exposure distributions measured in the EXPOLIS study in Helsinki. The simpler models 1 and 2 (with two and three microenvironments, respectively) predict the general outline of the exposure distributions reasonably well. Compared to the observed exposure distribution, the mean is underestimated by less than 3 μg m-3 (20%) and the standard deviation by 23-35%. In the improved simulation models (3 and 4), the environmental tobacco smoke (ETS)-exposed subjects are excluded, the time-activity models of working and nonworking subpopulations are modeled separately, and the correlations of input concentration and time fraction variables have been accounted for. The output of these models was very close to the observed distributions; the differences in the means were less than 0.1 μg m-3 and the differences in standard deviation less than 1%. We conclude that when the required input data are available or can be reliably estimated, the target population PM2.5 exposure distributions can be predicted accurately enough for most practical purposes using this kind of a microenvironment model.
| Original language | English |
|---|---|
| Pages (from-to) | 74-85 |
| Number of pages | 12 |
| Journal | Journal of Exposure Analysis and Environmental Epidemiology |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2003 |
Bibliographical note
Funding Information:This work has been supported by EU Contract N ENV4-CT96-0202 (DG12-DTEE); Academy of Finland Contracts 36586, 40835, and 42610; and intramural funding by KTL, the National Public Health Institute of Finland, and other institutions in other centers.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
Funding
This work has been supported by EU Contract N ENV4-CT96-0202 (DG12-DTEE); Academy of Finland Contracts 36586, 40835, and 42610; and intramural funding by KTL, the National Public Health Institute of Finland, and other institutions in other centers.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Exposure
- Model
- Particles
- Population
- Simulation
- Validation
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