Institutional Repository
Technical University of Crete
Technical University of Crete
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EL
| URI: | http://purl.tuc.gr/dl/dias/43C1D240-265A-4D65-9681-4D365962A549 | ||
| Year | 2021 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | A. Korhonen, H. Relvas, A. I. Miranda, J. Ferreira, D. Lopes, S. Rafael, S. M. Almeida, T. Faria, V. Martins, N. Canha, E. Diapouli, K. Eleftheriadis, E. Chalvatzaki, M. Lazaridis, H. Lehtomäki, I. Rumrich, and O. Hänninen, “Analysis of spatial factors, time-activity and infiltration on outdoor generated PM2.5 exposures of school children in five European cities,” Sci. Total Environ., vol. 785, Sep. 2021, doi: 10.1016/j.scitotenv.2021.147111. https://doi.org/10.1016/j.scitotenv.2021.147111 | ||
| Appears in Collections |
Atmospheric particles are a major environmental health risk. Assessments of air pollution related health burden are often based on outdoor concentrations estimated at residential locations, ignoring spatial mobility, time-activity patterns, and indoor exposures. The aim of this work is to quantify impacts of these factors on outdoor-originated fine particle exposures of school children.We apply nested WRF-CAMx modelling of PM2.5 concentrations, gridded population, and school location data. Infiltration and enrichment factors were collected and applied to Athens, Kuopio, Lisbon, Porto, and Treviso. Exposures of school children were calculated for residential and school outdoor and indoor, other indoor, and traffic microenvironments. Combined with time-activity patterns six exposure models were created. Model complexity was increased incrementally starting from residential and school outdoor exposures.Even though levels in traffic and outdoors were considerably higher, 80–84% of the exposure to outdoor particles occurred in indoor environments. The simplest and also commonly used approach of using residential outdoor concentrations as population exposure descriptor (model 1), led on average to 26% higher estimates (15.7 μg/m3) compared with the most complex model (# 6) including home and school outdoor and indoor, other indoor and traffic microenvironments (12.5 μg/m3). These results emphasize the importance of including spatial mobility, time-activity and infiltration to reduce bias in exposure estimates.
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