Untargeted identification and quantification of forever chemicals and other emerging substances of concern within pesticide formulations.

Plant protection products (PPPs) are designed and formulated specifically to protect plants from harm caused by pests, disease and weeds. They contain at least one active ingredient which has the controlling effect on the target issue and can exist in many forms. PPPs are manufactured in a variety of formulation types to mix the active ingredients with co-formulants that aide the distribution, absorption and stability of the product.

These co-formulants are intended to remain inert within the formulation, however it is not uncommon for co-formulants to either have a harmful effect on human, animal and environmental health or for reactions to occur between components of the product mix that create harmful manufacturing by-products known as impurities. This can change the efficacy of the product and can cause the product to contain levels of restricted chemicals that are outside of the specifications provided by the European Union (EU) and/or the Chemicals Regulation Division (CRD) of the UK.

Certain formulation types are more likely to contain harmful impurities and co-formulants because of the nature of their manufacture. In April 2024, 'forever chemicals' known as poly- and perfluoroalkyl substances (PFAS), chemicals containing strong, man-made fluorine-carbon bonds that are environmentally persistent and a risk to human health, made headlines due to their occurrence at dangerous levels in food sold on the UK market. The vast number of PFAS chemicals is growing constantly, and includes twenty-five pesticide active ingredients.

Liquid chromatography with mass spectrometry (LC MS/MS) is the industry standard for identification and quantification of these low abundant molecules, yet most work in this area is on the targeted quantification of known PFAS. This project will improve sample preparation protocols, and use ultra performance LC with ultra-high resolution MS analysis to classify and characterise all components in each single analysis, culminating in data outputs that can be easily understood by non-experts.