Increasing airborne particulate matter (PM) concentration in Kenya is an unfortunate consequence of rapid urbanization, coupled with a lack of strict implementation of air quality regulations. This has led to detrimental effects on human health, environment and local climate. To gain an indepth understanding of these effects, there is a need for a detailed characterization of PM in terms of abundance, sources, and properties, especially over the less characterized areas such as The Republic of Kenya (Kenya). This study presents long-term (1980-2020) spatial-temporal distributions and trends of PM2.5 over Kenya retrieved from the MERRA-2 model. The spatial patterns of annual mean PM2.5 loading were generally characterized by low (<7 μg•m-3), moderate (7 - 9 μg•m-3), and high (>11 μg•m-3) PM2.5 concentrations indicating distinct features of PM2.5 load. High (>11 μg•m-3) PM2.5 concentrations were observed over the arid and semiarid areas of the Northwest part of the country dominated by dust. Whereas, low (<7 μg•m-3) PM concentrations were observed over the Central and South Western parts of the country, with high vegetation and relatively high altitudes and precipitation. The seasonal mean PM2.5 over Kenya was found to be high (low) during the local dry (wet) seasons with mean values of >12 μg•m-3 and <6 μg•m-3, respectively. The magnitude of interannual variability in PM and its components over Kenya was found to be influenced by changes in emissions and local meteorology. The major PM2.5 emissions components were natu-ral dust emissions over the arid and semiarid areas in Northern Kenya with low annual precipitation. Linear trend analysis revealed an increase in PM2.5 over the years. Furthermore, the annual spatial trends revealed a general increase in PM2.5 over Kenya, being positive and significant over the dust-dominated areas of Northern Kenya. Later the spatial correlation between PM2.5 and its components revealed areas of similarities/dissimilarities and the magnitude of a correlation coefficient. PM2.5 correlated positively with dust in most parts of the country, followed by Sulphate (SO4), showing the significant contribution of the two components to PM2.5. On the other hand, a low (<2.5) correlation was observed between PM2.5 and Black Carbon (BC) and Organic Carbon (OC). Further analysis of annual and seasonal spatial variation, linear trends, and correlation of PM2.5 and components revealed dust as the major component of PM2.5 concentrations over the study domain. The study has improved the understanding of PM2.5 concentrations over the domain. It could provide significant information suitable for policy-making on air quality regula-tions in Kenya, especially on dust reduction mechanisms over the dominant areas.
Cite this paper
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