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

Changes in heart rate variability of healthy subjects shortly exposed to printing shop particles and the effect of air purifier intervention

Particulate matter (PM) released by printers may cause airway inflammation and cardiac electrophysiological changes. We conducted a two-stage crossover study to examine the association between short-term exposure to printing shop particles (PSPs) and the heart rate variability (HRV) among healthy volunteers, as well as to evaluate the effect of air purifier intervention. The on-site assessments of PSPs and individual HRV parameters of the volunteers were used to analyze the influence of size-fractionated PSPs and air purifier intervention on HRV at different lag times after PSPs exposure (0 min, 5 min, 15 min, and 30 min) by using the linear mixed-effects models. We observed that changes in 6 HRV parameters were associated with particle mass concentration (PMC) of PSPs, and changes in 8 HRV parameters were associated with particle number concentration (PNC) of PSPs. The sensitive HRV parameters were the square root of the mean of the sum of the squares of differences between adjacent NN intervals (rMSSD), NN50 count divided by the total number of all NN intervals (pNN50), normalized high frequency power (nHF), very high frequency power (VHF), normalized low frequency power (nLF), and the ratio of low frequency power to high frequency power (LF/HF). Most HRV parameters exhibited the strongest effect associated with PMC and PNC at a lag time of 30 min. The air purifier intervention markedly reduced the PNC and PMC of size-fractionated PSPs, enhanced 5 HRV parameters, and decreased the nLF and LF/HF. Our study suggests that short-term exposure to PSPs can affect HRV parameters, reflecting changes in cardiac autonomic nervous activity, and the use of an air purifier can reduce the concentration of PSPs and improve the autonomic nervous system activity of the exposed individuals.

Health Risks Associated with Informal Electronic Waste Recycling in Africa: A Systematic Review

Informal electronic waste (e-waste) recycling in Africa has become a major public health concern. This review examined studies that report on the association between e-waste exposure and adverse human health outcomes in Africa. The review was conducted following the updated version of the Preferred Items for Systematic Review and Meta-analysis (PRISMA 2020) statement checklist. We included papers that were original peer-reviewed epidemiological studies and conference papers, written in English, and reported on e-waste exposure among human populations and any health-related outcome in the context of Africa. Our results from the evaluation of 17 studies found an association between informal e-waste recycling methods and musculoskeletal disease (MSD) symptoms and physical injuries such as back pains, lacerations, eye problems, skin burns, and noise-induced hearing loss (NIHL). In addition, the generation and release of particulate matter (PM) of various sizes, and toxic and essential metals such as cadmium (Cd), lead (Pb), zinc (Zn), etc., during the recycling process are associated with adverse systemic intermediate health outcomes including cardiopulmonary function and DNA damage. This systematic review concludes that the methods used by e-waste recyclers in Africa expose them to increased risk of adverse health outcomes. However, there is a need for more rigorous research that moves past single pollutant analysis.

Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales

The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues about the underlying biophysical mechanisms. In this prototype study, we demonstrate an experimental paradigm to holistically capture and evaluate the interactions between an environmental context and physiological markers of an individual operating that environment. A cyclist equipped with a biometric sensing suite is followed by an environmental survey vehicle during outdoor bike rides. The interactions between environment and physiology are then evaluated though the development of empirical machine learning models, which estimate particulate matter concentrations from biometric variables alone. Here, we show biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body. This work sets the stage for future investigations of these interactions for a larger number of factors, e.g., black carbon, CO2, NO/NO2/NOx, and ozone. By tapping into our body's 'built-in' sensing abilities, we can gain insights into how our environment influences our physical health and cognitive performance.