Statistical analysis of a large set of semi-quantitative GC-MS screening data to evaluate and prioritize organic contaminants in surface and drinking water of the Netherlands.

Statistical analysis of a large set of semi-quantitative GC-MS screening data to evaluate and prioritize organic contaminants in surface and drinking water of the Netherlands.

Houtman, Corine J;Kroesbergen, Jan;Baggelaar, Paul K;van Lieverloo, J Hein M;
The Science of the total environment 2019 Vol. 697 pp. 133806
260
houtman2019statisticalthe

Abstract

Due to anthropogenic activities in the catchments, surface waters are contaminated with a large variety of chemical compounds. Drinking water companies in the Netherlands use surface water from the rivers Rhine, and Meuse, Lake IJssel and water from a reclaimed land area as sources for the production of drinking water. Samples from the abstraction points and the produced drinking waters were investigated using chemical screening with gas chromatography coupled to mass spectrometry to detect an as wide as possible range of organic contaminants, generating enormous data sets. This study aimed to evaluate and interpret five and a half years of screening data to get insight in the variety of known and new less polar compounds in surface and drinking waters, and to investigate if there were spatial patterns in the detection of compounds. Compounds from a wide variety of applications were detected. The vast majority of detected compounds was found only in a few samples. Certain compounds, however, e.g. organophosphate flame retardants, were detected with prevalences up to 100% per location. Most compounds were detected in samples from the rivers Rhine and Meuse, less in those from Lake IJssel and the reclaimed land area and only few in drinking water. Principal component and Hierarchical Cluster Analyses helped to detect patterns in the presence of contaminants on particular locations and to prioritize compounds for further investigation of their emission sources, and -in case of unknown compounds - their identification.

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