Browsing by SDGs "[SDGs]SDG6"

Although rainwater harvesting system (RHS) is an effective alternative to water supply, its efficiency is often heavily influenced by temporal distribution of rainfall and water demand. Since natural precipitation is a random process and has probabilistic characteristics, it will be more appropriate to describe these probabilistic features of rainfall and its relationship with design storage capacity as well as supply deficit of RHS. This paper aims at developing a methodology for establishing the probabilistic relationship between storage capacities and deficit rates of RHS. A simulation model was built to simulate the input rainfall and water release in RHS. Historical rainfall records were then used as input for simulation and the results were used in probabilistic analysis for establishing the relationships between storage capacities and water supply deficits. The city of Taipei was used as study area for demonstration of this methodology and probabilistic distribution curves for storage capacity and deficit rate relationships were presented. As a result, a set of curves describing the continuous relationships between storage capacities and deficit rates under different exceedance probabilities were generated as references to RHS storage design. At a chose exceedance probability of failure, the engineer can decide from the curve on the storage size under a preset deficit rate. Crown Copyright © 2009.

Chlorinated nitroaromatic antibiotic chloramphenicol (CAP) is a priority pollutant in wastewaters. A fed-batch bioelectrochemical system (BES) with biocathode with applied voltage of 0.5 V (served as extracellular electron donor) and glucose as intracellular electron donor was applied to reduce CAP to amine product (AMCl2). The biocathode BES converted 87.1 ± 4.2% of 32 mg/L CAP in 4 h, and the removal efficiency reached 96.0 ± 0.9% within 24 h. Conversely, the removal efficiency of CAP in BES with an abiotic cathode was only 73.0 ± 3.2% after 24 h. When the biocathode was disconnected (no electrochemical reaction but in the presence of microbial activities), the CAP removal rate was dropped to 62.0% of that with biocathode BES. Acetylation of one hydroxyl of CAP was noted exclusive in the biocatalyzed process, while toxic intermediates, hydroxylamino (HOAM), and nitroso (NO), from CAP reduction were observed only in the abiotic cathode BES. Electrochemical hydrodechlorination and dehalogenase were responsible for dechlorination of AMCl2 to AMCl in abiotic and microbial cathode BES, respectively. The cyclic voltammetry (CV) highlighted higher peak currents and lower overpotentials for CAP reduction at the biocathode compared with abiotic cathode. With the biocathode BES, antibacterial activity of CAP was completely removed and nitro group reduction combined with dechlorination reaction enhanced detoxication efficiency of CAP. The CAP cathodic transformation pathway was proposed based on intermediates analysis. Bacterial community analysis indicated that the dominate bacteria on the biocathode were belonging to α, β, and γ-Proteobacteria. The biocathode BES could serve as a potential treatment process for CAP-containing wastewater. © 2013 American Chemical Society.

In the face of increasing socio-economic and climatic pressures in growing cities, it is rational for managers to consider multiple approaches for securing water availability. One often disregarded option is the promotion of reforestation in source regions supplying important quantities of atmospheric moisture transported over long distances through aerial rivers, affecting water resources of a city via precipitation and runoff (‘smart reforestation’). Here we present a case demonstrating smart reforestation's potential as a water management option. Using numerical moisture back-tracking models, we identify important upwind regions contributing to the aerial river of Santa Cruz de la Sierra (Bolivia). Simulating the effect of reforestation in the identified regions, annual precipitation and runoff reception in the city was found to increase by 1.25% and 2.30% respectively, while runoff gain during the dry season reached 26.93%. Given the city's population growth scenarios, the increase of the renewable water resource by smart reforestation could cover 22–59% of the additional demand by 2030. Building on the findings, we argue for a more systematic consideration of aerial river connections between regions in reforestation and land planning for future challenges.

Aerobic granules are large biological aggregates with compact interiors that can be used in efficient wastewater treatment. This mini-review presents new researches on the development of aerobic granular processes, extended treatments for complicated pollutants, granulation mechanisms and enhancements of granule stability in long-term operation or storage, and the reuse of waste biomass as renewable resources. A discussion on the challenges of, and prospects for, the commercialization of aerobic granular process is provided. © 2016 Elsevier Ltd.

Aerobic granulation, a novel environmental biotechnological process, was increasingly drawing interest of researchers engaging in work in the area of biological wastewater treatment. Developed about one decade ago, it was exciting research work that explored beyond the limits of aerobic wastewater treatment such as treatment of high strength organic wastewaters, bioremediation of toxic aromatic pollutants including phenol, toluene, pyridine and textile dyes, removal of nitrogen, phosphate, sulphate and nuclear waste and adsorption of heavy metals. Despite this intensive research the mechanisms responsible for aerobic granulation and the strategy to expedite the formation of granular sludge, and effects of different operational and environmental factors have not yet been clearly described. This paper provides an up-to-date review on recent research development in aerobic biogranulation technology and applications in treating toxic industrial and municipal wastewaters. Factors affecting granulation, granule characterization, granulation hypotheses, effects of different operational parameters on aerobic granulation, response of aerobic granules to different environmental conditions, their applications in bioremediations, and possible future trends were delineated. The review attempts to shed light on the fundamental understanding in aerobic granulation by newly employed confocal laser scanning microscopic techniques and microscopic observations of granules. © 2008 Elsevier B.V. All rights reserved.

Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs. © 2015 Elsevier B.V.

Estrogens are among the most concerned emerging contaminants in the wastewater treatment effluent due to their sexual disruption in aquatic wildlife. The use of microalgae for secondary wastewater effluent polishing is a promising approach due to the economic benefit and value-added products. In this study, three microalgae species, including Selenastrum capricornutum, Scenedesmus quadricauda and Chlorella vulgaris were selected to conduct batch experiments to examine important mechanisms, especially the role of algal extracellular organic matter (AEOM) on two selected estrogens (17β-estradiol, E2 and 17α-ethynylestradiol, EE2) removal. Results showed that estrogens could not be significantly degraded under visible light irradiation and adsorption of estrogens by microalgae was negligible. All three living microalgae cultures have ability to remove E2 and EE2, and Selenastrum capricornutum showed the highest E2 and EE2 removal efficiency of 91% and 83%, respectively, corresponding to the reduction of predicted estrogenic activity of 86%. AEOM from three microalgae cultures could induce photodegradation of estrogens, and AEOM from Selenastrum capricornutum and Chlorella vulgaris achieved 100% of E2 and EE2 removal under visible light irradiation. Fluorescence excitation–emission matrix spectroscopy identified humic/fulvic-like substances in AEOM from three microalgae cultures, which might be responsible for inducing the indirect photolysis of E2 and EE2. Therefore, in the living microalgae cultures, the major estrogens removal mechanisms should include biotransformation as well as AEOM meditated photocatalytic degradation. Since removal rates through photodegradation could be faster than biotransformation, the AEOM mediated photocatalytic degradation can play a potential role to remove emerging contaminants when using microalgae technology for wastewater effluent treatment. © 2020 The Authors

Water quality modeling has been shown to be a useful tool in strategic water quality management. The present study combines the Qual2K model with the HEC-RAS model to assess the water quality of a tidal river in northern Taiwan. The contaminant loadings of biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), total phosphorus (TP), and sediment oxygen demand (SOD) are utilized in the Qual2K simulation. The HEC-RAS model is used to: (i) estimate the hydraulic constants for atmospheric re-aeration constant calculation; and (ii) calculate the water level profile variation to account for concentration changes as a result of tidal effect. The results show that HEC-RAS-assisted Qual2K simulations taking tidal effect into consideration produce water quality indices that, in general, agree with the monitoring data of the river. Comparisons of simulations with different combinations of contaminant loadings demonstrate that BOD is the most import contaminant. Streeter-Phelps simulation (in combination with HEC-RAS) is also performed for comparison, and the results show excellent agreement with the observed data. This paper is the first report of the innovative use of a combination of the HEC-RAS model and the Qual2K model (or Streeter-Phelps equation) to simulate water quality in a tidal river. The combination is shown to provide an alternative for water quality simulation of a tidal river when available dynamic-monitoring data are insufficient to assess the tidal effect of the river. © 2008 Elsevier Ltd. All rights reserved.

The process of anaerobic co-digestion is vital importance to resource recovery from organic solid wastes such as food waste and municipal sludge. However, its application is hindered by the limited understanding on the complex substrates-products transformation reactions and mechanisms therein. In this study, food waste (FW) and excess sludge (ES) from municipal wastewater treatment were mixed at various ratios (ES/FW 5:0, 4:1, 2:1, 1:1, 1:2, 1:4, w/w), and the co-digestion process was studied in a batch test. The consumption of substrates including soluble proteins and carbohydrates, the variation in the intermediates such as various volatile fatty acids, and the production of hydrogen and methane gases were monitored. The results suggested that 4:1 was likely the optimal ratio where substrates were consumed and biogas generated efficiently, whereas 1:2 and 1:4 caused severe inhibition. Fermentation of ES alone produced mainly acetic and propionic acid, while the addition of FW led to butyric acid type fermentation. Intermediates in the fermentation liquid were tentatively identified, and the levels of NADH quantified using 3D-excitation/emission fluorescence spectrometry. One class of the intermediates, tryptophan-like proteins were correlated to the butyric acid accumulation in ES/FW mixtures, and NADH level was proposed as an indicator of VFAs production activities. © 2018 Elsevier Ltd

Three cost functions of wastewater treatment plants in industrial districts were analyzed and discussed. From the results, the effluent charge rate can be proposed in order to internalize parts of the external cost of wastewater pollution. The charge base of the effluent charge shall be the total pollutant amounts in the effluent. The charge rate shall be set up according to the results of cost functions for various treatment efficiencies. The marginal cost of NT$790 (US$30.4) per noxiousness unit (F = 1000 m3/day, Q = 5000 noxiousness units/year, treatment efficiency = 90%) was recommended to be the initial charge rate. This cost should be updated regularly and the charge rate should be raised according annually.Three cost functions of wastewater treatment plants in industrial districts were analyzed and discussed. From the results, the effluent charge rate can be proposed in order to internalize parts of the external cost of wastewater pollution. The charge base of the effluent charge shall be the total pollutant amounts in the effluent. The charge rate shall be set up according to the results of cost functions for various treatment efficiencies. The marginal cost of NT$790 (US$30.4) per noxiousness unit (F = 1,000 m3/day, Q = 5,000 noxiousness units/year, treatment efficiency = 90%) was recommended to be the initial charge rate. This cost should be updated regularly and the charge rate should be raised according annually.

Understanding local precipitation patterns is essential to water resource management and flood mitigation. Precipitation patterns can vary in space and time depending on factors from different spatial scales such as local topographical changes and macroscopic atmospheric circulation. This study applied the two-stage classification method to distinguish the space–time patterns of local precipitations in the two identified distinct synoptic conditions, i.e. summer and autumn, from 24 gauges during 1996–2008 in Yilan County, Taiwan. The proposed method classifies the synoptic and local conditions for the space–time rainfall patterns by using K-means coupled with empirical orthogonal function analysis, and hierarchical ascending clustering method respectively. The proposed two-stage classification method considers not only the magnitude and the space–time distribution of rainfall events, but also the associated synoptic conditions. The results identified three primary patterns of extreme and two patterns of normal events in both seasons. Regarding the extreme events from typhoons, wind directions and the frontal accompanied effect are major contributors to the magnitude and spatial distribution of rainfall events in the summer and autumn, respectively. Spatiotemporal covariance structures are used to characterize the variability of normal events, showing the increasing frequency of wide spatial and temporal ranges from the summer to autumn. In summary, the proposed classification analysis provides patterns associated with distinct underlying physical mechanisms and space–time characteristics. The general characteristics of rainfall patterns can provide insights for the hydrological modeling of local catchments under different climatic scenarios. © 2014, Springer-Verlag Berlin Heidelberg.

Meandering through the most densely populated metropolitan areas of Taipei, Taiwan, the Danshui River and its tributaries have undergone the construction of 14 wetlands since 2004, as a means to improve water quality. This study was conducted to examine the functional capabilities associated with treating non-point source pollution through these riparian wetlands. Trend analysis was used to differentiate dissolved oxygen, biochemical oxygen demand, suspended solids, ammonia, and Escherichia coli, among 13 sampling sites using both functions of a Mann-Kendall test and a seasonal Mann-Kendall test. The results show that water quality in Taipei metropolitan rivers has been improving since increasing the number of constructed wetlands and connecting households to the public sewage system. The concentration of pollutants such as those influencing biochemical oxygen demand have gradually declined in drought seasons because riparian wetlands contribute a base flow to dilute riverine pollutants. This paper indicates that the creation of treatment systems influences dissolved oxygen conditions at the municipal scale, suggesting that constructed wetlands could stabilize water quality during extreme hydrological events and improve water quality particularly in times of drought. © IWA Publishing 2011.

The terrestrial water storage anomaly (TWSA) is a critical component of the global water cycle where improved spatiotemporal dynamics would enhance exploration of weather- and climate-linked processes. Thus, correctly simulating TWSA is essential not only for water-resource management but also for assessing feedbacks to climate through land-atmosphere interactions. Here we evaluate simulated TWSA from 25 climate models (from phase 6 of the Climate Model Intercomparison Project) through comparison with TWSA from GRACE satellite data (2003-14) in 14 river basins globally and assess causes of discrepancies by examining precipitation (P), evapotranspiration (ET), and runoff (Roff) fluxes during recharge (increasing TWS) and discharge (decreasing TWS) cycles. Most models show consistent biases in seasonal amplitudes of TWS anomalies relative to GRACE output: higher modeled amplitudes in river basins in high northern latitudes and the Parana and Congo basins, and lower amplitudes in most midlatitude basins and other tropical basins. This TWSA systematic bias also exists in the previous CMIP5 simulations. Models overestimate P compared to observed P datasets in 7 out of 14 basins, which increases (decreases) seasonal storage amplitude relative to GRACE in the recharge (discharge) cycle. Overestimation (underestimation) of runoff is another common contributing factor in the discharge phase that increases (decreases) TWSA amplitudes relative to GRACE in five river basins. The results provide a comprehensive assessment of the reliability of the simulated annual range in TWSA through comparison with GRACE data that can be used to guide future model development. ? 2021 American Meteorological Society.

Climate change leads to increasing intensity and frequency of extreme rainfalls, especially in Taiwan with steep slopes and rapid currents. Heavy rainfalls trigger serious erosion and landslides on hillslopes, which increase sand concentration in rivers, and thus affect the water quality of reservoirs and the ecohydrological functions of rivers. We take the Zhuoshui River basin as an example and applied the modified Soil Water Assessment Tool (SWAT) model, SWAT-Twn, to simulate sediment in the basin. In SWAT-Twn, estimation of sediment yield is carried out by integrating the Taiwan Universal Soil Loss Equation (TUSLE) and the landslide simulation. Results of daily streamflow simulation showed that the model performances were above the satisfactory level, while simulations of daily sediment loads showed that the SWAT-Twn model performed better than the official SWAT (SWAT664), in terms of PBIAS of ? 46.6 to 16.0% (SWAT-Twn) and ? 1.2 to ? 107.0% (SWAT664). Two scenarios of land use/cover, scenario 1 with fixed land use/cover and scenario 2 with updated land use/cover in each year, were applied to simulate annual sediment in the river basin for investigating the effects of landslide area variation on sediments. Results of sediment simulation under the two scenarios showed that although updating landslide area may facilitate sediment yield simulation at the subbasin level, the sediment transport equation, Bagnold equation, does not reflect the variation in sediment loads in the watershed. With further modifications, SWAT-Twn is expected to be an effective tool for simulating the impacts of landslide on sediment loads in the watersheds with rainfall-induced landslide. ? 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Water quality analysis and modeling in natural rivers has often been accomplished by monitoring programs and examination of the resulting series for long- and short-term tendencies, seasonality and the relationship of the cause and effect of human activities. This paper introduces the HHT method to the environmental engineering field for the analysis and prediction of non-stationary and nonlinear time series. The HHT combines two distinct analytical methods to decompose observed data into independent Intrinsic Mode Functions (IMFs), the Empirical Mode Decomposition (EMD) method and to transform these time-dependant functions into time-frequency functions, the Hilbert Transform method. The HHT is introduced to the field of water quality analysis for evaluation of trends and assessment of impacts of external physical factors for more sustainable river water quality management. A case study using the HHT method for data analysis and for simulation of time series of Chrysene concentrations in the Niagara River is presented. © 2008 ASCE.

The pollution of irrigation water leads to the pollution of farmlands directly or indirectly, which will further cast impacts on crop quality. Therefore, accurate predictions of future pollution events are essential for management of irrigation water. The aim of our study is to predict the potential occurrence of future abrupt pollution events by historical and real time monitoring water quality data. The 12 basic water quality monitoring stations and 2 heavy metal monitoring stations are selected in this study. We then use SVM and RF methods to predict whether the water quality might exceed normal standard in the near future. Our result shows that both of the methods received high credibility in predicting the standard-exceeding conditions of irrigation water. In addition, our study takes water level as well as precipitation factors into the models for a better precision in predicting of major standard-exceeding concentration of heavy metal, copper, in the irrigation water of study area. The result indicates that the prediction ability increased after water level factor was added, but not in the case of precipitation factor. Additionally, by making water quality data resemble the actual conditions, data segmentation should be conducted based on time series while analyzing the data instead of random selection. The accuracy of SVM model can be increased to 99.7% and 85.18% in the validation and test data set. By predicting potential occurring time of pollution events via historical as well as water monitoring data, it is possible to take necessary preventions to lower the risks of crops being polluted, which is a major issue in agricultural production nowadays. © 2020.

Climate change might potentially cause extreme weather events to become more frequent and intense. It could also enhance water scarcity and reduce food security. More efficient water management techniques are thus required to ensure a stable food supply and quality. Maintaining proper soil water content and soil temperature is necessary for efficient water management in agricultural practices. The usage of water and fertilizers can be significantly improved with a precise water content prediction tool. In this study, we proposed a new framework that combines weather forecast data, numerical models, and machine learning methods to simulate and predict the soil temperature and volumetric water content in a greenhouse. To test the framework, we performed greenhouse experiments with cherry tomatoes. The numerical models and machine learning methods we selected were Newton's law of cooling, HYDRUS-1D, the random forest model, and the ICON (inferring connections of networks) model. The measured air temperature, soil temperature, and volumetric water content during the cultivation period were used for model calibration and validation. We compared the performances of the models for soil temperature and volumetric water content predictions. The results showed that the random forest model performed a more accurate prediction than other methods under the limited information provided from greenhouse experiments. This approach provides a framework that can potentially learn best water management practices from experienced farmers and provide intelligent information for smart greenhouse management. © 2020 by the authors.

This study designs a multipurpose urban shallow artificial lake, including water supply, flood detention, and water environment preservation. It is expected to not only preserve a healthy water environment but to also retain water conservation and flood detention. This study adopts system dynamics (SD) to analyze the relationship between different purposes of water resources utilization. Furthermore, different operation strategies effects can be simulated by SD through a proposed urban multipurpose shallow artificial lake system. The results demonstrate the dynamic effects of strategies managers propose such as demand analysis, inflow control, and water quality improvement in this case study for Taiwan. SD aids lake system prediction and understanding temporally in sequential planning for water supply, environmental preservation, and flood detention. The SD model will hopefully serve as a reference to study different features before artificial lakes constructing. ? 2009 Springer Science+Business Media B.V.

A procedure to apply genetic algorithm to optimize operation rules is proposed and applied to the LiYuTan Reservoir in Taiwan. The designed operation rules are operation zones with discount rates of water supply. The first step of the procedure is to predefine the shape of boundary curves of operation zones according to reservoir storage routing. Then, relatively fewer variables are used to describe the curves, and a last genetic algorithm (GA) is applied to optimize the curves. The procedure is applied to the newly built LiYuTan Reservoir for increasing domestic water demands. Shortage index is used to evaluate the performance of operation zones. A year is divided into 36 operational periods, with each month containing three operational periods. The shortage indexes calculated in operational periods are 9.81, 8.27, and 7.13, respectively, for the reservoir without operation rules, applying operation zones optimized by GA with encoding 36 storage levels for each curve, and adopting operation zones optimized by GA with encoding the curves with predefined shape. The average deficits for the three cases are 77.2, 43.6, and 33.3 (104 m3/day), respectively. The results indicate that operation zones optimized by the proposed procedure have smaller shortage indexes and lower average deficits. In addition, the optimized operation zones have less variation and thus are more practical for operation. Conclusively, the proposed procedure utilizing GA to optimize operation zones with predefined shape can provide better and realistic outcomes through limited iterations.

This work presents a model based on the on-farm irrigation scheduling and the simulated annealing (SA) optimization method for agricultural water resource management. The proposed model is applied to an irrigation project located in Delta, Utah of 394.6 ha area for optimizing economic profits, simulating the water demand and crop yields and estimating the related crop area percentages with specified water supply and planted area constraints. The application of SA to irrigated project planning in this study can be divided into nine steps: (1) to receive the output from the on-farm irrigation scheduling module; (2) to enter three simulated annealing parameters; (3) to define the design 'chromosome' representing the problem; (4) to generate the random initial design 'chromosome'; (5) to decode the design 'chromosome' into a real number; (6) to apply constraints; (7) to apply an objective function and a fitness value; (8) to implement the annealing schedule by the Boltzmann probability; and (9) to set the 'cooling rate' and criterion for termination. The irrigation water requirements from the on-farm irrigation scheduling module are: (1) 1067.9, 441.7, and 471.8 mm for alfalfa, barley and maize, respectively, in one unit command area; and (2) 1039.5, 531.4, 490.9, and 539.4 mm for alfalfa, barley, maize and wheat, respectively, in the other unit command area. The simulation results demonstrate that the most appropriate parameters of SA for this study are as follows: (1) initial simulation 'temperature' of 1000; (2) number of moves equal to 90; and (3) 'cooling rate' of 0.95. © 2001 Silsoe Research Institute.