Abstract

BackgroundA vast number of chemical substances are released into the aquatic environment, leading to complex chemical mixtures in surface waters. Current water quality assessments, however, are based on the risk assessment of single substances. To consider potential mixture effects in water quality assessments, the North Rhine Westphalian State Agency for Nature, Environment and Consumer Protection (LANUV), Germany started a project assessing mixture toxicity in surface waters. This article summarises the mixture evaluation of chemical data collected by the Erftverband during a water sampling campaign in the Erft River in 2016/2017. Altogether, 153 substances were included in the analysis, of which 98 were detected. Two different approaches based on the concept of concentration addition were used to analyse the data. The results were compared to findings based on datasets from LANUV surveillance monitoring according to the EU Water Framework Directive.ResultsAcute and chronic mixture risk calculations indicated risks for 32% and up to 90% of the samples, respectively. The greatest acute toxic pressure was identified for the aquatic flora due to continuous exposure to varying pesticides, whereas the greatest chronic mixture risk was identified for fish as result of a ubiquitous presence of the pharmaceuticals diclofenac and ibuprofen. Overall, only a limited number of substances significantly contributed to the calculated mixture risks. However, these substances varied seasonally and regionally. When mixture risks were calculated based on different datasets, the monitoring design markedly affected the outcome of the mixture risk assessment. Data gaps of both ecotoxicological and exposure data lead to high uncertainties in the mixture risk assessment.ConclusionsEcotoxicological effects on aquatic organisms caused by chemical mixtures can be expected along the Erft River throughout the year. Both mixture risk assessment approaches can be used for a conservative assessment of mixture risks and characterise the aquatic pollution in the river more realistically than single substance assessments. For the integration of mixture risk evaluations into the assessment and management of the chemical water quality, a two-staged assessment combining both approaches is suggested. To improve future risk assessments, the accessibility and exchange of high-quality ecotoxicological data should be enhanced.

Highlights

  • A vast number of chemical substances are released into the aquatic environment, leading to complex chemical mixtures in surface waters

  • There are two mathematical models that have commonly been used for the assessment of ecotoxicological effects of defined chemical mixtures: (1) concentration addition (CA) assuming a similar mode of action (MoA) of the individual compounds of a mixture, and (2) independent action (IA) based on the assumption of dissimilar MoAs [10,11,12,13]

  • The upper part of the catchment area is mainly characterised by forest and grassland (43% and 29%, respectively), whereas the middle and lower reaches are increasingly influenced by intensive agriculture (47–57%) as well as urban and industrial areas (17–22%) [22, 23]

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Summary

Introduction

A vast number of chemical substances are released into the aquatic environment, leading to complex chemical mixtures in surface waters. To implement scientific approaches on mixture toxicity, and thereby, improve the current water quality assessments, the North Rhine Westphalian State Agency for Nature, Environment and Consumer Protection (LANUV) started a project assessing mixture toxicity in surface waters using data from chemical monitoring, on behalf of the Ministry for Environment, Agriculture, Conservation and Consumer Protection of the State of North Rhine-Westphalia (MULNV). There are two mathematical models that have commonly been used for the assessment of ecotoxicological effects of defined chemical mixtures: (1) concentration addition (CA) assuming a similar mode of action (MoA) of the individual compounds of a mixture, and (2) independent action (IA) based on the assumption of dissimilar MoAs [10,11,12,13] Both models predict mixture effects in a similar order of magnitude with CA being assumed to be a more conservative approach [14,15,16]. The CA model can be used as a pragmatic and protective model assessing the effects of environmental mixtures [11, 20, 21]

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