Emerging contaminants are chemicals that are characterised by a lack of published (eco)toxicity data and/or lack adequate analytical methods to quantify the chemicals in environmental samples for their potential harm.
Prioritising chemicals of concern has been mainly done using existing hazard and exposure data and this process has been based on chemical and (eco)toxicity data gathered from literature or international databases (e.g. ECHA), as well as on exposure data (e.g. volume or monitoring data) to assess potential risks. Paradigms, such as the PBT (Persistent, Bioaccumulative, Toxic) paradigm have been widely used in identifying key chemical properties that contribute to emerging contaminants, particularly for organic contaminants. This paradigm works when we have enough information for a given chemical, and where persistence, bioaccumulation and toxicity are the key drivers for an emerging contaminant. Whilst PBT information is important, focusing just on these properties overlooks chemicals which have gaps in the data, and situations where multiple chemicals contribute to an emerging issue or chemical mixtures.
This paper proposes a cluster analysis model for assessing contaminants, rather than ranking chemicals. Using subsets of organic and inorganic chemicals, we include different types of data beyond PBT that are not conventionally used in chemical prioritisation into our model. Specifically, this method will a) consider the data gaps that exist for many chemicals (e.g. consider incorporating non-traditional ecotoxicity methods such as behavioural data), b) incorporate how chemicals contribute to emerging chemical issues (e.g. antimicrobial resistance), and c) consider the pathway in which a contaminant enters the environment. This enables us to identify and manage sensitive pathways which could contribute to emerging issues in the future. This model provides an important alternative way of considering emerging contaminants and contaminant issues beyond the PBT paradigm.