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

Fugitive greenhouse gas (GHG) emissions in the form of nitrous oxide (N 2 O) and methane (CH 4 ) have been reported from many different wastewater treatment plants.However, the majority of the current literature only reports emissions during short periods of time and only focuses on one of the two GHGs. In this study, N 2 O and CH 4 emissions from the aerated parts of a plug-flow full-scale bioreactor treating municipal wastewater were studied over five months from November through March. A multiple gas hood collection system was used to simultaneously monitor the first three aerated compartments of the plug-flow bioreactor. Results show temporal variations in N 2 O emissions with N 2 O detected during November, no emissions during December and January, and a recovery of emissions from February onwards. In addition, different spatial emissions were found across the three aerated zones, with the highest N 2 O emissions detected in the second aerated zone. A daily N 2 O emission pattern was characterised by an N 2 O peak correlated with the ammonium that arrived in the monitored zone. However, CH 4 emissions occurred during the whole monitored period and showed a spatial variability inside the plug-flow bioreactor, presenting the highest emissions in the first aerated zone and then decreasing in the two subsequent zones. In addition, the dynamic carbon footprint (C-footprint

Models of microcontaminant fate and transport in wastewater treatment plants (WWTPs) and rivers have been developed and used to assist decision-making in the field of water management. These models come with parameter uncertainties that must be properly incorporated in the decision-making process. The main goal of this study is to evaluate how the magnitudes of key model parameter uncertainties influence the selection of end-of-pipe interventions (at WWTPs) designed to reduce the microcontaminant loads in rivers. We developed a model that describes the fate and removal of pharmaceuticals in WWTPs and the river network based on 3 key parameters: human pharmaceutical consumption and excretion (F) and the pharmaceutical degradation constants in WWTPs (k) and rivers (k). We modelled the fate and transport of diclofenac in the Llobregat River basin (NE Spain). We calibrated the model using a Bayesian approach, which resulted in an accurate prediction of measured diclofenac loads at 9 locations along the Llobregat River and at the influents and effluents of 2 WWTPs (R = 0.95). Using different scenarios, we evaluated three levels of uncertainty in the key model parameters. The first level of uncertainty corresponded to the reference distributions obtained from the Bayesian calibration. Then, for each parameter, we generated a narrower PDF (decreased uncertainty with respect to the reference) and a wider PDF (increased uncertainty). For each level of uncertainty, we evaluated increasing removal efficiencies of diclofenac at the WWTPs, from 38% to 98%. We assumed that removal efficiencies of up to 75% can be achieved by upgrading secondary treatment; beyond 75%, tertiary treatment is needed. The scenario analysis showed that achieving diclofenac removal efficiencies corresponding to tertiary treatment results in apparent concentration reductions (statistically significant differences relative to the reference situation), regardless of the level of uncertainty applied to the model parameters. However, upgrades in the secondary treatment resulted in apparent reductions only in the case of reduced uncertainty. We concluded that model uncertainty greatly influences the decisions that river basin authorities must make to reduce the microcontaminant loads released by WWTPs into rivers. In addition, we discussed research priorities to help reduce model uncertainty and thereby make more appropriate decisions.