Transforming data into knowledge for improved wastewater treatment operation : A critical review of techniques

The aim of this paper is to describe the state-of-the art computer-based techniques for data analysis to improve operation of wastewater treatment plants. A comprehensive review of peer-reviewed papers shows that European researchers have led academic computer-based method development during the last two decades. The most cited techniques are artificial neural networks, principal component analysis, fuzzy logic, clustering, independent component analysis and partial least squares regression. Even though there has been progress on techniques related to the development of environmental decision support systems, knowledge discovery and management, the research sector is still far from delivering systems that smoothly integrate several types of knowledge and different methods of reasoning. Several limitations that currently prevent the application of computer-based techniques in practice are highlighted

Using data from monitoring combined sewer overflows to assess, improve, and maintain combined sewer systems

Using low-cost sensors, data can be collected on the occurrence and duration of overflows in each combined sewer overflow (CSO) structure in a combined sewer system (CSS). The collection and analysis of real data can be used to assess, improve, and maintain CSSs in order to reduce the number and impact of overflows. The objective of this study was to develop a methodology to evaluate the performance of CSSs using low-cost monitoring. This methodology includes (1) assessing the capacity of a CSS using overflow duration and rain volume data, (2) characterizing the performance of CSO structures with statistics, (3) evaluating the compliance of a CSS with government guidelines, and (4) generating decision tree models to provide support to managers for making decisions about system maintenance. The methodology is demonstrated with a case study of a CSS in La Garriga, Spain. The rain volume breaking point from which CSO structures started to overflow ranged from 0.6. mm to 2.8. mm. The structures with the best and worst performance in terms of overflow (overflow probability, order, duration and CSO ranking) were characterized. Most of the obtained decision trees to predict overflows from rain data had accuracies ranging from 70% to 83%. The results obtained from the proposed methodology can greatly support managers and engineers dealing with real-world problems, improvements, and maintenance of CSSsSupport for this project was provided by the ENDERUS Project (CTM-2009-13018) and the predoctoral grant FPI BES-2010-039247, founded by the Spanish Ministry of Science and Innovation. Lluís Corominas received the postdoctoral Juan de la Cierva grant (JCI-2009-05604) from the Government of Spain and the Career Integration Grant (PCIG9-GA-2011-293535) from the EU. The authors also acknowledge the support from the Economy and Knowledge Department of the Catalan Government through the Consolidated Research Group (2014 SGR 291)–Catalan Institute for Water Researc

Monitoring waves of the COVID-19 pandemic: Inferences from WWTPs of different sizes.

Wastewater based epidemiology was employed to track the spread of SARS-CoV-2 within the sewershed areas of 10 wastewater treatment plants (WWTPs) in Catalonia, Spain. A total of 185 WWTPs inflow samples were collected over the period consisting of both the first wave (mid-March to June) and the second wave (July to November). Concentrations of SARS-CoV-2 RNA (N1 and N2 assays) were quantified in these wastewaters as well as those of Human adenoviruses (HAdV) and JC polyomavirus (JCPyV), as indicators of human faecal contamination. SARS-CoV-2 N gene daily loads strongly correlated with the number of cases diagnosed one week after sampling i.e. wastewater levels were a good predictor of cases to be diagnosed in the immediate future. The conditions present at small WWTPs relative to larger WWTPs influence the ability to follow the pandemic. Small WWTPs (<24,000 inhabitants) had lower median loads of SARS-CoV-2 despite similar incidence of infection within the municipalities served by the different WWTP (but not lower loads of HAdV and JCPyV). The lowest incidence resulting in quantifiable SARS-CoV-2 concentration in wastewater differed between WWTP sizes, being 0.11 and 0.82 cases/1000 inhabitants for the large and small sized WWTP respectively. © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/)

Rockfalls: analysis of the block fragmentation through field experiments

Abstract Fragmentation is a common feature of rockfall that exerts a strong control on the trajectories of the generated blocks, the impact energies, and the runout. In this paper, we present a set of four real-scale rockfall tests aimed at studying the fragmentation of the rocky blocks, from the global design of the field procedure to the data analysis and the main results. A total of 124 limestone, dacite, or granite blocks ranging between 0.2 and 5 m3 were dropped from different heights (8.5 to 23.6 m) onto four slopes with different shapes (single or double bench) and slope angles (42º to 71º). The characteristics of the blocks, in particular the size, surface texture and joint condition, were measured before the drops. The trajectories of the blocks and both the initial and the impact velocities were tracked and recorded by means of three high-speed video cameras. A total of 200 block-to-ground impacts have been studied. On average, 40% of the blocks broke upon impact on the slope or on the ground, making it necessary to measure the fragments. The initial and final sizes of the blocks/fragments were measured by hand with tape, though photogrammetric techniques (UAV and terrestrial) were also used for comparison purposes. The information gathered during the field tests provides a deep insight into the fragmentation processes. On the one hand, the high-resolution slow-motion videos help to describe when and how the block breakage takes place and the spatial distribution of the pieces. On the other hand, it is possible to compute the block trajectories, the velocities, and the energy losses using videogrammetry. The results include, for instance, a block average fragmentation of 54% and 14% for the limestone and granitoids, respectively; the systematic inventory of the size fragments, which may be used for fitting the power law distributions; and after each breakage, the total angle of aperture occupied by the fragments has been measured, with values in the range 25º–145º. To figure out the different behavior of the blocks in terms of breakage/no breakage, each block-to-ground impact has been characterized with a set of parameters describing the energy level, the robustness of the substrate, and the configuration of the block contact at the impact point, among others. All these terms are combined in a function F, which is used to adjust the field data. The adjustment has been carried out, first, for the whole 200 events and later for a subset of them. The procedure and the results are described in the paper. Although the discrimination capability of F is moderately satisfactory, it is very sensitive to the test site and setup. It must be highlighted that these field tests are a unique source of data to adjust the parameters of the numerical simulation models in use for rockfall studies and risk mitigation, especially when fragmentation during the propagation is considered