Micropollutant biotransformation under different redox conditions in PhoRedox conventional activated sludge systems
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Abstract
The ecotoxicological safety of the water bodies relies on the reduction of micropollutant emissions from wastewater treatment plants (WWTP). The ecotoxicological safety of the water bodies relies on the reduction of micropollutant emissions from wastewater treatment plants (WWTP). Quantification of micropollutant removal at full-scale WWTP is scarce. To our knowledge, the anaerobic conversion rates determined at conventional activated sludge processes are, so far, scarcely available in the literature for most of the micropollutants. In this research, we quantified the biotransformation rate constants and the removal efficiencies of 16 micropollutants (4,5-methylbenzotriazole, azithromycin, benzotriazole, candesartan, carbamazepine, clarithromycin, diclofenac, gabapentin, hydrochlorothiazide, irbesartan, metoprolol, propranolol, sotalol, sulfamethoxazole, trimethoprim, and venlafaxine), under aerobic, anoxic, and anaerobic redox conditions; using as inoculum wastewater and biomass from a full-scale conventional activated sludge (CAS) system in the Netherlands. Clarithromycin was the compound that exhibited the highest aerobic (76%) and anaerobic (78%) removal efficiencies, while gabapentin showed the highest removal under anoxic conditions (91%). A preference for cometabolic biotransformation of the targeted micropollutants was observed. The highest biotransformation rate constants obtained were: at aerobic conditions clarithromycin with 1.46 L.gSS−1.d−1; at anoxic conditions, gabapentin with 2.36 L.gSS−1.d−1; and at anaerobic redox conditions clarithromycin with 1.87 L.gSS−1.d−1. The obtained results of biotransformation rates will allow further modelling of micropollutant removal in CAS systems, under various redox conditions. These biotransformation rates can be added to extended ASM models to predict effluent concentration and optimize targeted advanced oxidation processes allowing savings in the operational costs and increasing the process viability.