RESUMO
This research investigates the environmental behavior of two widely prescribed cholesterol-lowering statin drugs that are expected to be present at significant concentrations in wastewater influents, namely: atorvastatin and simvastatin. Batch biodegradation experiments suggest that both statins are well degraded during secondary treatment, and removal rates exhibit a substrate-enhancement model reflecting elements of both first-order behavior and cometabolism. Resulting biodegradation parameters are used in conjunction with literature sorption parameters to construct a mass-balance model of statin concentrations during conventional treatment. Model results exhibit excellent accuracy compared to measurements from a medium-sized WWTP in the Southeastern USA. Influent concentrations of 1.56 µg L(-1) and 1.23 µg L(-1) were measured for atorvastatin and simvastatin. Results also suggest that 85-90% of each drug is removed during conventional treatment, with sorption accounting for less than 10% of overall removal. Expected effluent concentrations are orders of magnitude less than previously reported ecotoxicity thresholds for both drugs. Overall, results suggest statin active ingredients do not pose a significant environmental threat. It is recommended that future work characterize the fate of statin metabolites and that the same mass-balance modeling approach be used to assess other highly-prescribed pharmaceutical drugs.
Assuntos
Ácidos Heptanoicos/metabolismo , Pirróis/metabolismo , Sinvastatina/metabolismo , Eliminação de Resíduos Líquidos , Poluentes Químicos da Água/metabolismo , Atorvastatina , Biomassa , Biotransformação , Ácidos Heptanoicos/química , Modelos Estatísticos , Peso Molecular , Pirróis/química , Reprodutibilidade dos Testes , Esgotos/microbiologia , Sinvastatina/química , Poluentes Químicos da Água/químicaRESUMO
A mass balance model was developed to estimate prescription pharmaceutical loadings to municipal wastewater treatment plants via computation of influent concentrations (C(IN)). Model estimates of C(IN) were compared with published data and found to be accurate within an order of magnitude for some 90% of the drugs evaluated. The model was then used to rank prescription drugs on a per-capita mass loading basis for comparison with current research efforts and coupled with two EPISuite programs to generate estimates for environmental exposure. Results showed marked discrepancies between prescription numbers, loadings, exposures, and research conducted, as highlighted by the drug metformin, with the largest projected loading to treatment plants (>28 g per 1,000 people/day), yet only ranking 12th in terms of prescription numbers and no environmental research articles.
