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Graphical tools for the planning and interpretation of polyurethane foam based passive air sampling campaigns.

Yuening LiJames M ArmitageFrank Wania
Published in: Environmental science. Processes & impacts (2022)
Due to low cost and easy handling during sampling and extraction, passive air samplers (PASs) using polyurethane foam (PUF) as a sorbent have become the most commonly deployed PASs for semi-volatile organic compounds (SVOCs). However, depending on the scenario, PUF-PAS may not always be operating in the linear uptake phase, which implies the need to consider how temperature, wind speed, deployment length and chemical properties interact to determine the amount of a target chemical taken up and the fraction of a depuration compound (DC) being lost during deployment. Guidance is, therefore, necessary to quantitatively interpret curvi-linear uptake in the PUF-PAS and avoid selection of DCs unsuited to the deployment conditions. In this study, the PAS-SIM model is used to generate graphical tools that aid in addressing important questions frequently arising during the use of PUF-PASs. Specifically, we generated five charts that display (i) the inherent sampling rate as a function of wind speed and a chemical's molecular diffusivity, (ii) the length of the linear uptake period as a function of chemical properties, temperature and the acceptable deviation from linearity, (iii) the time to 95% equilibrium as influenced by chemical properties, temperature and wind speed, (iv) the dependence of the fractional loss of DCs on chemical properties, temperature, wind speed and deployment length, and (v) the influence of chemical properties, temperature and the total suspended particle concentration on the extent of sorption to atmospheric particles. The charts also facilitate the assessment of the influence of parameter uncertainty. It is hoped that these charts assist with planning and interpreting sampling campaigns based on a mechanistic and quantitative understanding of uptake in PUF-based PASs.
Keyphrases
  • low cost
  • high resolution
  • dendritic cells
  • mass spectrometry
  • immune response
  • molecular dynamics
  • air pollution
  • neural network
  • carbon dioxide