Endocrine disrupting chemicals (EDCs) are compounds of mainly anthropogenic origin that interfere with the endocrine system of animals and humans thus causing a series of disorders. Wastewater treatment plants are one of the major routes for transporting such chemicals to the water courses. In the context of this study, several chlorination batch tests were performed in order to assess the effectiveness of chlorination to remove bisphenol A (BPA), triclosan (TCS), nonylphenol (NP) and its ethoxylates (NP1EO and NP2EO) from secondary effluent. According to the results, an appreciable removal of NP, BPA and TCS to the order of 60-84% was observed as an effect of moderate chlorination doses. This was not the case for NP1EO and NP2EO as even at high chlorine doses, removal efficiencies were lower (37% for NP1EO and 52% for NP2EO). Removal efficiencies of NP, BPA and TCS are practically independent of contact time, although this was not the case for NP1EO and NP2EO. Based on toxicity experiments, it is anticipated that following chlorination of the target chemicals, production of more toxic metabolites is taking place. Therefore the effectiveness of chlorination to remove EDCs is questionable and more research is needed to guarantee safe wastewater reuse.
While several application domains are exploiting the added-value of analytics over various datasets to obtain actionable insights and drive decision making, the public policy management domain has not yet taken advantage of the full potential of the aforementioned analytics and data models. Diverse and heterogeneous datasets are being generated from various sources, which could be utilized across the complete policies lifecycle (i.e. modelling, creation, evaluation and optimization) to realize efficient policy management. To this end, in this paper we present an overall architecture of a cloud-based environment that facilitates
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