Due to several anthropogenic activities, water bodies have been heavily impacted by contaminants identified in aquatic ecosystems, including pharmaceuticals, personal care products, agricultural and industrial chemicals. Risk assessment based on chemical mixtures is still default in many monitoring studies, with decisions being based solely on a chemical-by-chemical basis. The present study aimed to improve risk assessment procedures in water bodies by focusing on mixtures of chemical substances of different origins. The goal was to analyze potential interactions occurring at different complexity levels (binary and quaternary mixtures) using standardised toxicity assays. Mixture toxicity effects were assessed using Daphnia magna as the model organism and the compounds sodium fluoride, boric acid, ammonium hydroxide and acetaminophen as general representatives of contaminants in the aquatic ecosystem. The results revealed interactions between the compounds, mainly showing antagonism but also dose level and dose ratio-dependent deviations. Overall antagonism was the dominant deviation pattern, particularly at low doses, though synergism was also detected at higher doses or specific ratios. Synergism at low doses was found for the binary mixture of ammonium hydroxide and acetaminophen, two common pollutants, which denotes an enhanced risk to aquatic ecosystems. Independent Action provided more accurate predictions for the quaternary mixture, whereas Concentration Addition overestimated the toxicity of the mixture. Regarding the environmental risk assessment of water bodies, the interaction between chemicals in a mixture should not be neglected. The complexity of the mixture interactions found in the present study highlights the importance of complementing chemical screenings of water bodies with mixture toxicity data, particularly when considering chemicals of multiple origins whose joint action remains unknown. Display omitted •Complex interactions found in mixtures of multiple source substances.•Binary mixtures showed an overall antagonism.•IA model provided a more accurate prediction of the quaternary mixture.•Quaternary mixture elicited lower effects than predicted by the CA model.•Mixture toxicity data is a fundamental tool in predicting risk assessment.