Quantification of pharmaceuticals in hospital effluent: Weighted ranking of environmental risk using a fuzzy hybrid...

An analytical method for quantification of seventeen pharmaceuticals and one metabolite was validated and applied in the analysis of hospital effluent samples. Two different sampling strategies were used: seasonal sampling, with 7 samples collected bimonthly; and hourly sampling, with 12 samples collected during 12 h. Thus, the variability was both seasonal and within the same day. High variability was observed in the measured concentrations of the pharmaceuticals and the metabolite. The quantification method, performed using weighted linear regression model, demonstrated results of average concentrations in seasonal samples ranged between 0.19 μgL^-1 (carbamazepine) and higher than 61.56 μgL^-1 (acetaminophen), while the hourly samples showed average concentrations between 0.07 μgL^-1 (diazepam) and higher than 54.91 μgL^-1 (acetaminophen). It is described as higher because the maximum concentration of the calibration curve took into account the dilution factor provided by DLLME. The diurnal results showed a trend towards higher concentrations in the first and last hours of sampling. The risk quotient (RQ) was calculated using organisms from three different trophic levels, for all the analytes quantified in the samples. Additionally, in order to understand the level of importance of each RQ, an expert panel was established, with contributions from 23 specialists in the area. The results were analyzed using a hybrid decision-making approach based on a Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, in order to rank the compounds by environmental risk priority. The compounds of greatest concern were losartan, acetaminophen, 4-aminoantipyrine, sulfamethoxazole, and metoclopramide. Comparison of the environmental risk priority ranking with the potential human health risk was performed by applying the same multicriteria approach, with the prediction of endpoints using in silico (Q)SAR models. The results obtained suggested that sulfamethoxazole and acetaminophen were the most important analytes to be considered for monitoring.