Derivation of Time-Activity Data Using Wearable Cameras and Measures of Personal Inhalation Exposure among Workers at an Informal Electronic-Waste Recovery Site in Ghana

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.

Effect of Particulate Matter Exposure on Respiratory Health of e-Waste Workers at Agbogbloshie, Accra, Ghana

“…Comprehensive description of PM determination can be found in [27]. Briefly, PM in the breathing zone of participants was measured using a MetOne Aerocet 831 particle mass profiler (MetOne Instruments Incorporated, Grants Pass, OR, USA) which simultaneously measured PM2.5 and PM10 levels per minute at a flow rate of 2.8 L/min.…”

Micronutrient-rich dietary intake is associated with a reduction in the effects of particulate matter on blood pressure among electronic waste recyclers at Agbogbloshie, Ghana

“…The e-waste recyclers were usually present at the worksite from about 7 am to 6 pm. A detailed study of their time-activity budgets revealed that they worked 6 to 7 days per week at an average of 10 h per day [47]. Our instruments were calibrated to sample for 4 h, and usually between the period of 8 am and 2 pm.…”

“…In wave III, the sampling duration was reduced to approximately 2 h due to the high levels of PM from the Harmattan winds. Further description of the sampling process is described in Laskaris et al [47].…”

Assessment of Micronutrient Status of Electronic Waste (E-waste) Recyclers at Agbogbloshie (Ghana) Using Dietary Information and Biomarker Data

“…However, in future studies, the use of repeated measurements of micronutrient biomarkers will be required to better characterise the micronutrient status of the recyclers and their effects on health outcomes. Further, we asked questions about job activities and present a simple interpretation here (3 general categories) though other studies have shown that e-waste recyclers tend to perform many tasks onsite (Laskaris et al, 2019). We tried to put the ndings into context by comparing values with "background populations" but there is a lack of understanding of such values from Africa, especially from vulnerable recycler populations based in low-and middleincome countries.…”