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The Representativeness of Outdoor Particulate Matter Concentrations for Estimating Personal Dose and Health Risk Assessment of School Children in Lisbon.

Eleftheria ChalvatzakiSofia Eirini ChatoutsidouSusana Marta AlmeidaLidia MorawskaMihalis Lazaridis
Published in: International journal of environmental research and public health (2023)
This study investigated the suitability of outdoor particulate matter data obtained from a fixed monitoring station in estimating the personal deposited dose. Outdoor data were retrieved from a station located within the urban area of Lisbon and simulations were performed involving school children. Two scenarios were applied: one where only outdoor data were used assuming an outdoor exposure scenario, and a second one where an actual exposure scenario was adopted using the actual microenvironment during typical school days. Personal PM 10 and PM 2.5 dose (actual exposure scenario) was 23.4% and 20.2% higher than the ambient (outdoor exposure scenario) PM 10 and PM 2.5 doses, respectively. The incorporation of the hygroscopic growth in the calculations increased the ambient dose of PM 10 and PM 2.5 by 8.8% and 21.7%, respectively. Regression analysis between the ambient and personal dose showed no linearity with R 2 at 0.07 for PM 10 and 0.22 for PM 2.5 . On the other hand, linear regression between the ambient and school indoor dose showed no linearity (R 2 = 0.01) for PM 10 but moderate (R 2 = 0.48) for PM 2.5 . These results demonstrate that ambient data must be used with caution for the representativeness of a realistic personal dose of PM 2.5 while for PM 10 the ambient data cannot be used as a surrogate of a realistic personal dose of school children.
Keyphrases
  • particulate matter
  • air pollution
  • electronic health record
  • stem cells
  • physical activity
  • big data
  • molecular dynamics
  • machine learning
  • high resolution
  • risk assessment