Introduction

Now and in the future, the ever-growing demand for drinking water will lead many cities to implement indirect water reuse programs, where wastewater effluent becomes part of the drinking water sources. Pollution of those sources with emerging contaminants such as endocrine disrupting compounds (EDCs), pharmaceutically active compounds (PhACs) and personal care products (PCPs) is a fact known worldwide. The presence of these emerging contaminants is of increasing concern in drinking water treatment plants that recycle wastewater effluents or use wastewatercontaminated surface waters. Although the risks of PhACs, PCPs and EDCs polluting sources of water are partly recognized, interpretation of consequences are controversial; thus, the future effects of altered water with trace contaminants remains uncertain and may constitute a point of concern for human beings when potable water consumption is involved. Therefore, many drinking water utilities target as an important goal high-quality drinking water production to lessen quality considerations that may arise from the consumers. Nonetheless, in search of precautionary measures against the unforeseen consequences that those compounds may cause, the study of their removal through membrane treatment is presently of great scientific interest for the future, as water resources become scarce and demands for recycled water increase. Mainly reverse osmosis (RO) has been demonstrated to be an appropriate technology for removing a large number of emerging organic contaminants, but nanofiltration (NF) also constitutes a good option although believed to have certain limitations. Nonetheless, the performance of RO and NF can be questioned because there are limited tools that optimise quantification of the removal of contaminants. This is mainly because the achievement of a fundamental (theoretical) understanding to describe interactions occurring between membranes and organic solutes to quantify their performance can be a difficult task. Therefore, the use of alternative techniques (e.g. multivariate data analysis) can be more practical and effective to understand and model the separation of organic contaminants by membranes.