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Septic tanks in low- and middle-income countries are often not emptied for a long time, potentially resulting in poor pollutant removal efficiency and increased greenhouse gas emissions, including methane (CH4). We examined the impact of long emptying intervals (4.0-23 years) on the biochemical oxygen demand (BOD) removal efficiency of 15 blackwater septic tanks and the CH4 emission rates of 23 blackwater septic tanks in Hanoi. The average BOD removal efficiency was 37% (-2-65%), and the average CH4 emission rate was 10.9 (2.2-26.8) g/(cap·d). The emptying intervals were strongly negatively correlated with BOD removal efficiency (R = -0.676, p = 0.006) and positively correlated with CH4 emission rates (R = 0.614, p = 0.001). CH4 emission rates were positively correlated with sludge depth (R = 0.596, p = 0.002), but against expectation, negatively correlated with BOD removal efficiency (R = -0.219, p = 0.451). These results suggest that shortening the emptying interval improves the BOD removal efficiency and reduces the CH4 emission rate. Moreover, the CH4 emission estimation of the Intergovernmental Panel on Climate Change, which is a positive conversion of BOD removal, might be inaccurate for septic tanks with long emptying intervals. Our findings suggest that emptying intervals, sludge depth, and per-capita emission factors reflecting long emptying intervals are potential parameters for accurately estimating CH4 emissions from septic tanks.

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Widespread implementation of on-site water reuse is hindered by the limited availability of monitoring approaches that ensure microbial quality during operation. In this study, we developed a methodology for monitoring microbial water quality in on-site water reuse systems using inexpensive and commercially available online sensors. An extensive dataset containing sensor and microbial water quality data for six of the most critical types of disruptions in membrane bioreactors with chlorination was collected. We then tested the ability of three typological machine learning algorithms - logistic regression, support-vector machine, and random forest - to predict the microbial water quality as "safe" or "unsafe" for reuse. The main criteria for model optimization was to ensure a low false positive rate (FPR) - the percentage of safe predictions when the actual condition is unsafe - which is essential to protect users health. This resulted in enforcing a fixed FPR ≤ 2%. Maximizing the true positive rate (TPR) - the percentage of safe predictions when the actual condition is safe - was given second priority. Our results show that logistic-regression-based models using only two out of the six sensors (free chlorine and oxidation-reduction potential) achieved the highest TPR. Including sensor slopes as engineered features allowed to reach similar TPRs using only one sensor instead of two. Analysis of the occurrence of false predictions showed that these were mostly early alarms, a characteristic that could be regarded as an asset in alarm management. In conclusion, the simplest algorithm in combination with only one or two sensors performed best at predicting the microbial water quality. This result provides useful insights for water quality modeling or for applications where small datasets are a common challenge and a general advantage might be gained by using simpler models that reduce the risk of overfitting, allow better interpretability, and require less computational power.

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On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. We develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.

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Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.

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Lignosulfonate is a cheap material available in large quantities obtained as a byproduct of paper and cellulose. In this work, blends of polypropylene (PP) and sodium lignosulfonate (LGNa) were developed to evaluate the potential use of lignosulfonate as a lightweight, thermal insulation and flame retardant material. The blends were obtained by mixing in a torque rheometer and molded after compression. The blend proprieties were evaluated by physical, morphological, thermal, thermal conductivity, and flammability tests. The measured values were compared with theoretical models. The results indicated that a heterogeneous blend with a higher number of separated domains is formed when the LGNa content increases from 10 to 40 wt%. In addition, the density and thermal conductivity coefficient of the blends studied are not affected by the addition of LGNa. However, when the LGNa content in the blend exceeds 20 wt% the thermal stability and flame retardant proprieties are considerably reduced. The theoretical models based on the rule of mixtures showed a good agreement with the experimental values obtained from blend density, thermal conductivity, and thermal stability. In general, lignosulfonate tested in this work shows potential to be used as a reactive component in polymer blends.

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On-site wastewater treatment plants (OSTs) are usually unattended, so failures often remain undetected and lead to prolonged periods of reduced performance. To stabilize the performance of unattended plants, soft sensors could expose faults and failures to the operator. In a previous study, we developed soft sensors and showed that soft sensors with data from unmaintained physical sensors can be as accurate as soft sensors with data from maintained ones. The monitored variables were pH and dissolved oxygen (DO), and soft sensors were used to predict nitrification performance. In the present study, we use synthetic data and monitor three plants to test these soft sensors. We find that a long solids retention time and a moderate aeration rate improve the pH soft-sensor accuracy and that the aeration regime is the main operational parameter affecting the accuracy of the DO soft sensor. We demonstrate that integrated design of monitoring and control is necessary to achieve robustness when extrapolating from one OST to another in the absence of plant-specific fine-tuning. Additionally, we provide a unique labeled dataset for further feature and data-driven soft-sensor development. Our benchmarking results indicate that it is feasible to monitor OSTs with unmaintained sensors and without plant-specific tuning of the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.

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Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89-91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring small-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.

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The promise of collecting and utilizing large amounts of data has never been greater in the history of urban water management (UWM). This paper reviews several data-driven approaches which play a key role in bringing forward a sea change. It critically investigates whether data-driven UWM offers a promising foundation for addressing current challenges and supporting fundamental changes in UWM. We discuss the examples of better rain-data management, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation and management, integrated design and management, increasing water productivity, wastewater-based epidemiology and on-site water and wastewater treatment. The accumulated evidence from literature points toward a future UWM that offers significant potential benefits thanks to increased collection and utilization of data. The findings show that data-driven UWM allows us to develop and apply novel methods, to optimize the efficiency of the current network-based approach, and to extend functionality of today's systems. However, generic challenges related to data-driven approaches (e.g., data processing, data availability, data quality, data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access and ownership, current engineering practices and the difficulty of assessing the cost benefits of data-driven UWM.

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AbstractSolidago chilensis Meyen, Asteraceae, is traditionally used to treat inflammation. However, phytochemical and pharmacology investigations are lacking. This study evaluated the hypoglycemic and hypolipidemic effects of hydroalcoholic extract from S. chilensis aerial parts in rats. In oral glucose tolerance tests the rats received saline (0.5 ml/100 g) in control group (C), hydroalcoholic extract (125, 250 or 500 mg/kg p.o.; n = 6) or glibenclamide (10 mg/kg p.o.; n = 6). After 30 min, glucose (4 g/kg) was administered. Rats treated with hydroalcoholic extract 500 demonstrated decreased glucose levels at 180 min (-22.1%), when compared with group C, similar to glibenclamide. Moreover, treatment with hydroalcoholic extract 500 significantly increased the glycogen content in the liver and soleus muscle, and hydroalcoholic extract 250 specifically inhibited the enzyme maltase when compared with group C. Furthermore, all hyperglycemic rats treated with hydroalcoholic extract (125, 250 and 500) exhibited an accentuated decrease in total cholesterol levels (-36.8%, -36.7% and -41.3%, respectively). Our results suggest that hypoglycemic and hypolipidemic effects of hydroalcoholic extract could be associated with increased production and release of insulin as well as with insulinotropic and antioxidant effects.

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Este estudo avaliou a degradação oxidativa do herbicida pós-emergente Basagran(r) 600 por fotólise e fotocatálise, usando TiO2 como catalisador, comparando-se a eficiência de ambos os processos. Os experimentos foram realizados irradiando-se soluções aquosas do herbicida em um reator do tipo slurry. Os tratamentos foram acompanhados por espectrofotometria de absorção na região do UV-visível observando-se a banda centrada em 335 nm. O primeiro processo mostrou-se pouco efetivo, sendo observada mínima redução dos valores de absorvância. Já o segundo foi muito mais eficiente, sendo observado, após 270 min de irradiação, 100% de redução da absorvância da banda no comprimento de onda estudado. Este processo seguiu uma cinética de pseudo-primeira ordem e teve constante de velocidade (k) estimada de 0,0116 min-1.

This study evaluated the oxidative degradation of the post-emerging herbicide Basagran(r) 600 by photolysis and photocatalysis using TiO2 as catalyst, and both processes were compared in their efficiency. The experiments were done irradiating aqueous solutions of the herbicide in a slurry type reactor. The treatments were accompanied by spectrophotometry in the UV-visible range observing the band centered in 335 nm. The first process showed rather ineffective, being observed minimal reduction of the absorbance values. On the other hand, the second process was much more efficient, being observed, after 270 min of irradiation, 100% absorbance reduction of the band in the choose wavelength. This process followed pseudo-first-order kinetic and the estimated rate constant (k) was 0.0116 min-1.

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O principal objetivo do trabalho é propor um modelo de simulação de sistemas dinâmicos para a análise do manejo de pastagens visando a maximizar o consumo de forragem pelos animais. A forrageira utilizada foi o azevém anual, pastado por bovinos adultos. Foram consideradas diversas variáveis como: lotação animal, taxa de crescimento do pasto, taxa de apreensão da forragem pelos animais, primeira entrada dos animais na pastagem após a emergência (início do pastejo) e ciclo de rotação (soma dos dias de pastejo e de descanso do pasto). Para simular o crescimento do pasto, foi utilizada a equação de Verhulst. Simulações efetuadas em um modelo computacional formulado no aplicativo Berkeley Madonna indicaram que, quando a entrada dos animais no pasto foi mais precoce (até os 73 dias), o sistema rotativo proporcionou um maior consumo de matéria seca pelos animais que o sistema de pastejo contínuo. Já quando a entrada dos animais no pasto foi entre os 74 e os 95 dias, o sistema contínuo proporcionou um maior consumo de matéria seca pelos animais que o sistema de pastejo rotativo. As simulações indicaram também que, quando o pastejo rotativo é iniciado precocemente (60 dias), o ciclo de 30 dias (um dia de pastejo e 29 de descanso) proporcionou maior consumo de matéria seca pelos animais. Já quando o pastejo rotativo é iniciado tardiamente (75 dias), variações da extensão do ciclo de rotação tiveram pouca influência sobre o consumo de matéria seca pelos animais.

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