SIMONI (Smart Integrated Monitoring) as a novel bioanalytical strategy for water quality assessment: Part II-field feasibility survey.

Because it is impossible to chemically analyze all relevant micropollutants, the implementation of bioanalytical tools is essential to estimate ecological risks of chemical mixtures in regular water-monitoring programs. The first tier of the Smart Integrated Monitoring (SIMONI) strategy, which was described in part I, is based on the combination of passive sampling and bioanalytical measurements. Bioassay responses are compared with effect-based trigger values (EBT), and an overall SIMONI score on all bioassay data was designed to indicate environmental risks. The present study is focused on analyzing the feasibility of the hazard identification tier by evaluating results of 45 field campaigns at sites with different pollution profiles near the city of Amsterdam. A Daphnia assay was performed in situ, while silicon rubber or polar organic chemical integrative sampler (POCIS) extracts were tested with 4 nonspecific (daphnids, algae, bacteria, and cell culture) and 10 specific (9 Chemical Activated Luciferase Gene Expression [CALUX] assays and antibiotics scan) bioassays. Sensitivity analyses demonstrated the relevance of 2 classification variables in the SIMONI score formula on all bioanalytical data. The model indicated increased risks for the ecosystem at surface waters in greenhouse areas and undiluted wastewater-treatment plant (WWTP) effluents. The choice of testing specific bioassays on either polar or nonpolar passive sampling extracts is cost-effective and still provided meaningful insights on micropollutant risks. Statistical analyses revealed that the model provides a relevant overall impact assessment based on bioassay responses. Data analyses on the chemically determined mixture toxic pressure and bioanalytical methods provided similar insights in relative risk ranking of water bodies. The SIMONI combination of passive sampling and bioanalytical testing appears to be a feasible strategy to identify chemical hazards. Environ Toxicol Chem 2017;36:2400-2416. © 2017 SETAC.

SIMONI (smart integrated monitoring) as a novel bioanalytical strategy for water quality assessment: Part i-model design and effect-based trigger values.

It is virtually impossible to reliably assess water quality with target chemical analyses only. Therefore, a complementary effect-based risk assessment by bioanalyses on mixtures of bioavailable micropollutants is proposed: the Smart Integrated Monitoring (SIMONI) strategy. The goal of this strategy is to obtain more reliable information on the water quality to select optimum measures for improvement. The SIMONI strategy is 2-tiered. Tier 1 is a bioanalytical hazard identification of sites. A tier 2 ecological risk assessment is carried out only at a limited number of sites where increased hazards are detected in tier 1. Tier 2 will be customized, based on tier 1 evaluation and additional knowledge of the aquatic system. The present study focuses on the tier 1 bioanalytical hazard identification to distinguish "hot spots" of chemical pollution. First, a selection was made of relevant and cost-effective bioanalytical endpoints to cover a wide spectrum of micropollutant modes of action. Specific endpoints may indicate which classes of chemicals might cause adverse effects. Second, effect-based trigger values (EBT) were derived for these bioassays to indicate potential ecological risks. Comparison of EBT with bioassay responses should discriminate sites exhibiting different chemical hazards. Third, a model was designed to estimate the overall risks for aquatic ecosystems. The associated follow-up for risk management is a "toxicity traffic light" system: green, low hazard (no action required); orange, potential risk (further research needed); and red, high risk (mitigation measures). Thanks to cost-effectiveness, flexibility, and relevance, the SIMONI strategy has the potential to become the first bioanalytical tool to be applied in regular water quality monitoring programs. Environ Toxicol Chem 2017;36:2385-2399. © 2017 SETAC.