Sewer-mining: A water reuse option supporting circular economy, public service provision and entrepreneurship.

Water scarcity, either due to increased urbanisation or climatic variability, has motivated societies to reduce pressure on water resources mainly by reducing water demand. However, this practice alone is not sufficient to guarantee the quality of life that high quality water services underpin, especially within a context of increased urbanisation. As such, the idea of water reuse has been gaining momentum for some time and has recently found a more general context within the idea of the Circular Economy. This paper is set within the context of an ongoing discussion between centralized and decentralized water reuse techniques and the investigation of trade-offs between efficiency and economic viability of reuse at different scales. Specifically, we argue for an intermediate scale of a water reuse option termed 'sewer-mining', which could be considered a reuse scheme at the neighbourhood scale. We suggest that sewer mining (a) provides a feasible alternative reuse option when the geography of the wastewater treatment plant is problematic, (b) relies on mature treatment technologies and (c) presents an opportunity for Small Medium Enterprises (SME) to be involved in the water market, securing environmental, social and economic benefits. To support this argument, we report on a pilot sewer-mining application in Athens, Greece. The pilot, integrates two subsystems: a packaged treatment unit and an information and communications technology (ICT) infrastructure. The paper reports on the pilot's overall performance and critically evaluates the potential of the sewer-mining idea to become a significant piece of the circular economy puzzle for water.

Treated water quality assurance and description of distribution networks by multivariate chemometrics.

Throughout the year 2007, 89 treated water samples from three water treatment plants (WTPs) of the Athens Water Supply and Sewerage Company (EYDAP S.A.) and 180 samples from network tanks (NWTs) were analyzed for electrical conductivity (EC), alkalinity (TA), pH, aluminium (Al), total hardness (TH), chloride (Cl(-)), residual chlorine (free Cl), calcium (Ca(2+)) and magnesium (Mg(2+)). The results regarding the WTPs were subjected to a principal component analysis (PCA) with 75% of the total variance being explained. A stepwise linear discriminant analysis (LDA) model constructed from the 89 treated water samples was used to predict class membership of the samples from the NWTs with a view to estimating the propagation of a possible water quality deterioration originating from the WTPs. The model utilized Cl(-), Al and EC and yielded a 96% correct classification of the training dataset, whereas the cross-validation yielded a 94% correct classification. Network tank samples were 95% correctly classified with regard to their theoretically expected origin. The stepwise discriminant analysis based on separate covariance matrices of the canonical discriminant functions yielded a 98% correct classification of both the training dataset and the network tank samples. The classification and regression tree (C&RT) algorithm showed that the main parameters used in the discrimination of the WTP samples were EC and Al. The post-hoc classification of the training dataset was 99%, whereas 88% of NWT samples were correctly classified.