Demonstrating PM2.5 and road-side dust pollution by heavy metals along Thika superhighway in Kenya, sub-Saharan Africa.

Abstract

This study assessed the level of heavy metal in roadside dust and PM2.5 mass concentrations along Thika superhighway in Kenya. Thika superhighway is one of the busiest roads in Kenya, linking Thika town with Nairobi. Triplicate road dust samples collected from 12 locations were analysed for lead (Pb), chromium (Cr), cadmium (Cd), nickel (Ni), zinc (Zn), and copper (Cu) using atomic absorption spectrophotometry (AAS). PM2.5 samples were collected on pre-weighed Teflon filters using a BGI personal sampler and the filters were then reweighed. The ranges of metal concentrations were 39-101 μg/g for Cu, 95-262 μg/g for Zn, 9-28 μg/g for Cd, 14-24 μg/g for Ni, 13-30 μg/g for Cr, and 20-80 μg/g for Pb. The concentrations of heavy metals were generally highly correlated, indicating a common anthropogenic source of the pollutants. The results showed that the majority of the measured heavy metals were above the background concentration, and in particular, Cd, Pb, and Zn levels indicated moderate to high contamination. Though not directly comparable due to different sampling timeframes (8 h in this study and 24 h for guideline values), PM2.5 for all sites exceeds the daily WHO PM2.5 guidelines of 25 μg/m3. This poses a health risk to people using and working close to Thika superhighway, for example, local residents, traffic police, street vendors, and people operating small businesses. PM2.5 levels were higher for sites closer to Nairobi which could be attributed to increased vehicular traffic towards Nairobi from Thika. This study provides some evidence of the air pollution problem arising from vehicular traffic in developing parts of the world and gives an indication of the potential health impacts. It also highlights the need for source apportionment studies to determine contributions of anthropogenic emissions to air pollution, as well as long-term sampling studies that can be used to fully understand spatiotemporal patterns in air pollution within developing regions.