Dataset Information

Personal exposure to particulate matter and heart rate variability among informal electronic waste workers at Agbogbloshie: a longitudinal study.

ABSTRACT:

Background

Informal electronic waste recycling activities are major contributors to ambient air pollution, yet studies assessing the effects or relationship between direct/continuous exposure of informal e-waste workers to particulate matter and cardiovascular function are rare.

Methods

Repeated measurements of fractions of PM_2.5, PM_10-2.5, and PM₁₀ in personal air of informal e-waste workers, (n = 142) and a comparable group (n = 65) were taken over a period of 20 months (March 2017 to November, 2018). Concurrently, 5-min resting electrocardiogram was performed on each participant to assess resting heart rate variability indices. Linear mixed-effects models were used to assess the association between PM fractions and cardiac function.

Results

SDNN, RMSSD, LF, HF and LH/HF ratio were all associated with PM. Significant associations were observed for PM_2.5 and Mean NN (p = 0.039), PM10 and SDNN (p = 0.035) and PM 10-2.5 and LH/HF (p = 0.039). A 10 μg/m³ increase in the concentrations of PM _2.5, PM_10-2.5, and PM₁₀ in personal air was associated with reduced HRV indices and increased resting HR. A 10 μg/m³ per interquartile (IQR) increase in PM_10-2.5 and PM₁₀, decreased SDNN by 11% [(95% CI: - 0.002- 0.000); (p = 0.187)] and 34% [(95% CI: - 0.002-0.001); (p = 0.035)] respectively. However, PM_2.5 increased SDNN by 34% (95% CI: - 1.32-0.64); (p = 0.493). Also, 10 μg/m³ increase in PM_2.5, PM_10-2.5 and PM₁₀ decreased RMSSD by 27% [(- 1.34-0.79); (p = 0.620)], 11% [(- 1.73, 0.95); (p = 0.846)] and 0.57% [(- 1.56-0.46); (p = 0.255%)].

Conclusion

Informal e-waste workers are at increased risk of developing cardiovascular disease from cardiac autonomic dysfunction as seen in reduced HRV and increased heart rate.

SUBMITTER: Amoabeng Nti AA

PROVIDER: S-EPMC8613947 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesApproximately 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.MethodsOne 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.ResultsBased 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.ConclusionsIn 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.