Behavior-weighted wearable passive sampling of personal airborne phthalate exposure among university students
Related Experiment Video
Updated: Nov 4, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
Published on: February 3, 2023
Hanci Chen1, Lixin Wang1, Zichen Guo1
1School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.
Abstract:
Phthalates (PAEs), widely used plasticizers in indoor materials, are semi-volatile organic compounds with potential adverse health effects. Conventional stationary sampling methods inadequately capture exposure variability linked to individual behaviors and personal microenvironments. This study introduces an activated carbon fiber-based wrist-worn passive sampler and establishes a behavior-weighted calibration framework that integrates static and dynamic conditions to reconstruct personal airborne PAE concentrations among university students. Sampling rates (SRs) were calibrated under static (horizontal placement) and dynamic (anterior-posterior and medio-lateral swinging) conditions to account for realistic behavioral patterns and wearing conditions, with behavioral weighting derived from wrist kinematics recorded via Xsens DOT sensors. Dynamic motion modified SRs in a compound-specific manner, with higher SRs observed for diisobutyl phthalate and di-n-butyl phthalate (DnBP), but not consistently for all PAEs. During a 21-day deployment among 27 students, the median concentration of Σ10PAEs was 252 ng/m3, dominated by di(2-ethylhexyl) phthalate (DEHP), DnBP, dimethyl phthalate, and diethyl phthalate. Females had higher median concentrations than males, and personal care product use frequency correlated positively with selected PAE concentrations. Monte Carlo simulation indicated that the estimated daily inhalation intake of Σ10PAEs was approximately 1.85 times higher among females than among males, with DEHP contributing the largest proportion in both groups. Sensitivity analysis identified accumulated pollutant mass as the dominant contributor to model output variance, whereas physiological parameters and wearing duration made secondary, compound-dependent contributions. These findings highlight the importance of incorporating individual activity patterns into passive sampling frameworks to obtain more realistic estimates of personal airborne exposure.
More Related Videos
09:33Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
Published on: December 23, 2022
09:50Real-World M3-BREATHE: Toward Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures for Treatment and Health Evaluation
Published on: June 5, 2026