Exploring the role of water chemistry on metal accumulation in biofilms from streams in mining areas

International audienceBiofilms play a key role in aquatic ecosystems. They are ubiquitous, even in the most contaminated ecosystems, and have great potential as biomonitors of exposure to contaminants such as metals. Freshwater biofilms and surface waters were sampled in two active mining areas of Canada: in the northern part of Nunavik (Quebec) and in the Greater Sudbury area (Ontario). Significant linear relationships were found between both total dissolved and free metal ion concentrations with biofilm metal contents for Cu and Ni, but not for Cd. When pH was below 6, biofilms accumulated less metals than at higher pHs. These results confirm that protons have a protective effect, leading to lower internalized metal concentrations. When considering only the sites where pH was above 6, the linear relationships between metal concentrations in water and in biofilms were improved for all three studied metals. The presence of metal ions could also modify the internalization of a given metal. To further study the role of cations as competitors to Cu, Ni and Cd uptake, relationships between the ratio of biofilm metal contents (Cu, Ni and Cd) on the ambient free metal ion concentrations were built as a function of potential cation competitors, such as major cations and metals. Surprisingly, our data suggest that calcium plays a minor role in preventing metal accumulation as compared to magnesium and possibly other metals. At a global scale, metal accumulation remained highly consistent between the two studied regions and over the sampling period, despite differences in ambient physicochemical water characteristics, climate or types of ecosystems. Metal bioaccumulation is thus a promising biomarker to assess metal bioavailability in a mining context. Nevertheless, more data are still required to further highlight the contribution of each competitor in metal accumulation by biofilms and to be able to build a unifying predictive model