Abstract:
Objectives: Approximately 2 billion workers globally are employed in informal settings, which are
characterized by substantial risk from hazardous exposures and varying job tasks and schedules.
Existing methods for identifying occupational hazards must be adapted for unregulated and challenging
work environments. We designed and applied a method for objectively deriving time-activity
patterns from wearable camera data and matched images with continuous measurements of personal
inhalation exposure to size-specific particulate matter (PM) among workers at an informal
electronic-waste (e-waste) recovery site.
Methods: One hundred and forty-two workers at the Agbogbloshie e-waste site in Accra, Ghana,
wore sampling backpacks equipped with wearable cameras and real-time particle monitors during
a total of 171 shifts. Self-reported recall of time-activity (30-min resolution) was collected during the
end of shift interviews. Images (N = 35,588) and simultaneously measured PM2.5 were collected each
minute and processed to identify activities established through worker interviews, observation, and
existing literature. Descriptive statistics were generated for activity types, frequencies, and associated
PM2.5 exposures. A kappa statistic measured agreement between self-reported and image-based
time-activity data.Results: Based on image-based time-activity patterns, workers primarily dismantled, sorted/loaded,
burned, and transported e-waste materials for metal recovery with high variability in activity duration.
Image-based and self-reported time-activity data had poor agreement (kappa = 0.17). Most
measured exposures (90%) exceeded the World Health Organization (WHO) 24-h ambient PM2.5
target of 25 μg m−3. The average on-site PM2.5 was 81 μg m−3 (SD: 94). PM2.5 levels were highest
during burning, sorting/loading and dismantling (203, 89, 83 μg m−3, respectively). PM2.5 exposure
during long periods of non-work-related activities also exceeded the WHO standard in 88% of
measured data.
Conclusions: In complex, informal work environments, wearable cameras can improve occupational
exposure assessments and, in conjunction with monitoring equipment, identify activities associated
with high exposures to workplace hazards by providing high-resolution time-activity data.
