Concentrations of pesticides in water and sediment in sub-Saharan Africa and the implications for honey bees

Abstract

The global challenge of inadequate access to clean and safe drinking water affects over a billion individuals, of whom more than 800 million are in sub-Saharan Africa. Evaluation of water quality is an integral part of effective and sustainable management of water sources that ensures its suitability for human consumption and environmental stability. Agrochemicals are a major source of surface water contamination. Long-term water quality monitoring is essential to track changes in water quality across time and space to make informed decisions regarding water management practices that protect the water sources in sub-Saharan Africa. In this study, we demonstrate the alarming fragmented nature and lack of long-term water quality monitoring data for sub-Saharan Africa by reviewing the level of contamination of water sources with pesticides that are widely used in the cultivation of the staple crop maize, specifically, atrazine, glyphosate, imidacloprid and metolachlor. Honey bees and beehive products are excellent indicators of environmental contamination and provide both qualitative and quantitative data. Long-term environmental monitoring programmes using managed honey bees as biomonitors have proven to be highly successful. We discuss the toxicity of atrazine, glyphosate, imidacloprid and metolachlor to honey bees and the feasibility of using honey bees as a bioindicator species for early detection of pesticide contamination of water sources in remote and rural areas in sub-Saharan Africa.

Significance:

Water is essential for life, yet millions of people across sub-Saharan Africa lack access to safe water. Long-term water quality monitoring is an essential part of water management practices that protect water sources and prevents deterioration of water quality. This review illustrates a lack of standardised and long-term water quality monitoring data in sub-Saharan Africa. Existing data provide a limited view of water quality in this region, potentially leading to an inaccurate perception of the true level of contamination. We suggest a low-cost, sustainable sampling model for remote, rural areas as part of developing an efficient, long-term water quality monitoring programme.