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Estimation of Exposure to Organic Flame Retardants via Hand Wipe, Surface Wipe, and Dust: Comparability of Different Assessment Strategies.

Xiaotu LiuZhiguo CaoGang YuMin WuXiaoxiao LiYacai ZhangBin WangJun Huang
Published in: Environmental science & technology (2018)
This study aimed to investigate the exposure of three occupational populations (i.e., office worker, taxi drivers, and security guards) to flame retardants by comparing different sampling approaches (i.e., hand wipe, surface wipe, and dust). Hand wipe samples were collected from 68 participants from three populations in Beijing, China. Dust and/or surface wipes were also sampled from their respective occupational workplaces. Ten phosphorus flame retardants (PFRs), two novel brominated flame retardants (NBFRs) and eight polybrominated diphenyl ethers (PBDEs) were analyzed. BDE209, decabromodiphenylethane (DBDPE), tris(chloropropyl) phosphate isomers (∑TCPP), tris(2-chloroethyl) phosphate (TCEP) and triphenyl phosphate (TPHP) were detected in at least 95% of the samples, collectively accounting for over 90% of the total concentrations in each type of samples. Concentrations and composition profiles of flame retardants differed in hand wipes of the three populations with summed level of all target compounds (∑FRs) ranked as taxi drivers > office workers > security guards. Most FRs in hand wipes were significantly correlated with those in surface wipes, whereas the correlations between hand wipes and dust are weak. Estimated exposure to FRs via dust ingestion and dermal absorption for each population varied when using different types of samples for exposure assessment, suggesting the importance of sampling strategy selection. Estimation via hand wipes indicated that taxi drivers were subjected to greater exposure to PFRs among three populations, while office workers were subjected to greater BFR exposure. Our data suggest hand wipes have the potential of being standardized into a noninvasive method for evaluating human exposure to environmental contaminants across different populations.
Keyphrases
  • human health
  • health risk
  • health risk assessment
  • polycyclic aromatic hydrocarbons
  • drinking water
  • air pollution
  • public health
  • machine learning
  • genetic diversity
  • deep learning
  • induced pluripotent stem cells