La “Herramienta de Evaluación y Monitoreo del Desempeño Energético y Emisiones de Carbono” (ECAM) ofrece capacidades excepcionales para evaluar las emisiones de gases de efecto invernadero (GEI) y el consumo de
energía en sistemas de agua y saneamiento. Obtenga nuevas perspectivas al identificar áreas de oportunidad para reducir las emisiones de GEI, aumentar el ahorro de energía y mejorar la eficiencia general para reducir costos.
Puede encontrar más información sobre la ECAM 3.0 en la hoja informativa.
Esta Guía facilita la toma de decisiones preliminares sobre el proceso más apropiado para tratar las aguas residuales municipales. Esta Guía de selección de procesos se diseñó para respaldar la evaluación de proyectos de saneamiento desde un enfoque de mitigación del cambio climático que propicie el desarrollo sostenible, aunque sin descuidar los criterios de cumplimiento con las regulaciones ambientales mexicanas y la viabilidad técnica o económica.
Este folleto descriptivo y su libro complementario MS Excel © son herramientas de apoyo para evaluar las posibilidades de los trenes de tratamiento en las plantas de tratamiento de aguas residuales municipales. Los procesos recomendados y sus combinaciones reducen las emisiones gaseosas al máximo y muestran la viabilidad técnico-económica.
This paper provides a review of the changing nature of the water–energy nexus in urban water supply systems (UWSSs) due to the primary long-term drivers of climate change, population growth and technological development from the ‘energy for water’ perspective. We identify both the physical changes in UWSSs, as well as the changes in the attributes of the system, both of which contribute to the changing nature of the water–energy nexus. We provide an overview of responses to this change in the water–energy nexus through the lens of four application areas, namely long-term planning, system design, system operation and system rehabilitation, based on the review of 52 papers
Microalgae can synthesise the ozone depleting pollutant and greenhouse gas nitrous oxide (N2O). Consequently, significant N2O emissions have been recorded during real wastewater treatment in high rate algal ponds (HRAPs). While data scarcity and variability prevent meaningful assessment, the magnitude reported (0.13–0.57% of the influent nitrogen load) is within the range reported by the Intergovernmental Panel on Climate Change (IPCC) for direct N2O emissions during centralised aerobic wastewater treatment (0.016–4.5% of the influent nitrogen load).
An assessment was performed for elucidating the possible impact of different aeration strategies on the carbon footprint of a full-scale
wastewater treatment plant. Using a calibrated model, the impact of different aeration strategies was simulated. The ammonia controller
tested showed its ability in ensuring effluent ammonia concentrations compliant with regulation along with significant savings on aeration
energy, compared to fixed oxygen set point (DOsp) control strategies. At the same time, nitrous oxide emissions increased due to accumulation of nitrification intermediates. Nevertheless, when coupled with the carbon dioxide emissions due to electrical energy consumption for aeration, the overall carbon footprint was only marginally affected. Using the local average CO2 emission factor, ammonia control slightly reduced the carbon footprint with respect to the scenario where DOsp was fixed at 2 mg·L1. Conversely, no significant change could be detected when compared against the scenarios where the DOsp was fixed. Overall, the actual impact of ammonia control on the carbon footprint compared to other aeration strategies was found to be strictly connected to the sources of energy employed, where the larger amount of low CO2-emitting energy is, the higher the relative increase in the carbon footprint will be.
The strong greenhouse gas nitrous oxide (N2O) can be emitted from wastewater treatment systems as a byproduct of ammonium oxidation and as the last intermediate in the stepwise reduction of nitrate to N2 by denitrifying organisms. A potential strategy to reduce N2O emissions would be to enhance the activity of N2O reductase (NOS) in the denitrifying microbial community. A survey of existing literature on denitrification in wastewater treatment systems showed that the N2O reducing capacity (VmaxN2O/N2) exceeded the capacity to produce N2O (VmaxNO3/N2O) by a factor of 2e10. This suggests that denitrification can be an effective sink for N2O, potentially scavenging a fraction of the N2O produced by ammonium oxidation or abiotic reactions.
Climate change is likely to cause higher temperatures and alterations in precipitation patterns, with potential impacts on water resources. One important issue in this respect is inflow to drinking water reservoirs. Moreover, deteriorating infrastructures cause leakage in water distribution systems and urbanization augments water demand in cities. In this paper, a framework for assessing the combined impacts of multiple trends on water availability is proposed.
Cities in South Asia are experiencing storm water drainage problems due to a combination of urban sprawl, structural, hydrological, socioeconomic and climatic factors. The frequency of short duration, high-intensity rainfall is expected to increase in the future due to climate change. Given the limited capacity of drainage systems in South Asian cities, urban flooding and waterlogging is expected to intensify. The problem gets worse when low-lying areas are filled up for infrastructure development due to unplanned urban growth, reducing permeable areas.
Data Analytics is being deployed to predict the dissolved nitrous oxide (N2O) concentration in a full-scale sidestream sequence batch reactor (SBR) treating the anaerobic supernatant. On average, the N2O emissions are equal to 7.6% of the NH4eN load and can contribute up to 97% to the operational carbon footprint of the studied nitritation-denitritation and via-nitrite enhanced biological phosphorus removal
process (SCENA). The analysis showed that average aerobic dissolved N2O concentration could significantly vary under similar influent loads, dissolved oxygen (DO), pH and removal efficiencies. A combination of density-based clustering, support vector machine (SVM), and support vector regression (SVR) models were deployed to estimate the dissolved N2O concentration and behaviour in the different phases
of the SBR system.