Quantitative fingerprinting of chemical contaminants in pollinating insects – Green unified chromatography and advanced detection techniques supported by machine learning

Pollinating insects are exposed to chemical products in the environment, both in agricultural areas (pesticides) and in private gardens and other urban areas (chemicals for private use).

However, biodiversity and sublethal effects in insects in connection with chemical exposure in gardens vs. agricultural areas have hardly been studied.

The purpose of the proposed project is to develop qualitative and quantitative methods for both known and unknown chemical contaminants in samples from gardens and agriculture-intense areas. We will screen for a wide range of different compounds including pesticides, additives, and pharmaceuticals.

The first challenge is to develop an efficient chromatography method enabling the separation of a large number of chemically diverse compounds with high resolution.

For this we will develop unified “green” chromatography methods based on a merge of supercritical fluid and liquid chromatography. The second challenge is to enable quantification of unknown compounds when chemical standards are lacking.

For this we will use machine learning tools to predict response factors in charged aerosol detection based on molecular descriptors, both calculated and experimentally determined by ion mobility high-resolution mass spectrometry.

We will collect samples such as pollinating insects and pollen to accurately develop the analytical methods, and thereby answer biologically relevant questions about exposure to chemical contaminants.