Background
Many different mechanisms are responsible for the formation of non-extractable residues (NER). The main types of interactions involved in the formation of NER are:
- sorption between the chemical and/or its transformation products and the matrix, e.g. organic matter, pH and total exchange capacity
- binding of a chemical and/or its transformation products to the matrix, e.g. covalent and ionic bonds
- physical sequestration of a chemical and/or its transformation products into the matrix
Generally, chemicals which are most strongly associated with sediment or soil (and least bio-accessible) are either covalently bound to the matrix, or physically sequestered and trapped in the pores of the matrix. Other interactions which have been shown to lead to NER or slowly desorbed residues included ionic binding and ligand exchange. A review of pesticide literature1 indicates that the degree of NER formation of compounds in soils and sediments is dependent on the presence of appropriate chemical groups and their respective binding strengths. Molecules with electron-rich functional groups, e.g. double bonded oxygen or sulphur in carbamates and dithiocarbamates result in higher NER formation. These reactive groups are generally polarised and are therefore more likely to interact with sediment or soil via electrostatic forces (ionic bonding) or hydrogen bonding. Conversely, the abundance of electronegative (electron withdrawing) functional groups within dinitroanilines and N-heteroatomic ring may reduce any polarising effect and result in lower NER formation. The presence of reactive groups, such as aniline or phenol, generally led to a higher percentage of NER formation.
Objectives
The project would develop appropriate rules to identify structural alerts. The formation of biogenic NER4 will also be considered within this project. If suitable data are identified from the literature, then the key parameters affecting NER formation will be used to develop polyparameter linear free energy relationships (ppLFERs)5-6 or artificial neural networks (ANNs)7-8 to design a prediction tool for identification of key structural alerts in NER formation.
Scope
The proposed RfP includes thorough researching of literature to identify most up-to-date information on the formation of NER in sediment and soil.
Related links
Download here the full version of the RfP LRI-ECO24.