Groundwater Modeling in Agricultural Watershed under Different Recharge and Discharge Scenarios for Quaternary Aquifer Eastern Nile Delta,Egypt

Different scenarios of recharge and discharge were assessed for sustainable management of groundwater in Quaternary aquifer east of Nile Delta. MODFLOW was utilized to investigate the effect of land use change and damming construction in the upstream of the Nile River on the current and short-term groundwater management strategies. The interpretive transient simulation was performed between 2004 and 2016 after steady-state calibration in 2004, and transient state from 2004 to 2013 with different irrigation recharges associated with land use change in this period. Sensitivity analysis was performed for hydraulic conductivities, recharge, and conductance parameters. The predictive transient simulation was run till 2023 under three scenarios of increasing pumping rates by 15, 30, and 50% for agriculture expansion and specified head reduction of Port Said Canal by 0.2, 0.4, and 0.6 m associated with the reduction of Nile water levels after Grand Ethiopian Residence Dam, GERD operation in 2017. Results from the in- and out-flow budgets showed that groundwater aquifer is stable at the current rate of pumping till 2023. Groundwater heads decreased by 0.2 and 0.42 m in the southern section, and a slight increase in the northern part was noticed for the first and second scenarios, respectively. When additional pumping stress is applied (50% increase), groundwater head dropped by 0.66 m, and the storage is no longer able to maintain the aquifer capacity after 2020 (worst-case scenario).     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

A Mathematical Model for the Effects of Fertilization on Nitrogen Concentrations in Unsaturated Soil on Blueberry Farms in Southern New Jersey

Nitrogen is commonly known as a food source for crops. However, the nitrogen compounds used in crop fertilizers, most commonly nitrate (NO₃) and ammonium (NH₄), are not widely understood. Blueberry plants do not take up these compounds as efficiently as organic nitrogen so varying amounts of leaching into the soil and groundwater will occur. A biogeochemical model consisting of ordinary and partial differential equations is implemented to computationally predict the concentrations of nitrate and ammonium in unsaturated soil of blueberry plants, specifically in the southern region of New Jersey. The model takes into account the type of soil of the region, the nitrate uptake of the plant, the water content in the roots region, the pressure heads in the soil pores, and the application rates of fertilizers containing nitrate, ammonium, and organic nitrogen. Computational simulations demonstrate that the model accounts for natural processes and, in addition, show that commonly used fertilizer application rates cause unnecessarily high concentrations of both nitrate and ammonium in the unsaturated soil level. Further, simulations show that decreasing nitrate fertilizer applications by 85.7% in annual and 91.8% in bi-annual schedules provides an optimal system for safe reapplication.     

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.     

Multivariate analyses of the effect of an urban wastewater treatment plant on spatial and temporal variation of water quality and nutrient distribution of a tropical mid-order river

Freshwater resources are increasingly scarce due to human activities, and the understanding of water quality variations at different spatial and temporal scales is necessary for adequate management. Here, we analyze the hypotheses that (1) the presence of a wastewater treatment plant (WWTP) and (2) a polluted tributary that drains downstream from the WWTP change the spatial patterns of physicochemical variables (pH, turbidity, dissolved oxygen, and electrical conductivity) and nutrient concentrations (reactive soluble phosphorus, total phosphorus, nitrogen series, total nitrogen, and total dissolved carbon) along a mid-order river in SE Brazil and that these effects depend on rainfall regime. Six study sites were sampled along almost 4 years to evaluate the impacts of human activities, including sites upstream (1–3) and downstream (5–6) from the WWTP. The impacts were observed presenting an increasing trend from the source (site 1) towards Água Quente stream (site 4, the polluted tributary), with signs of attenuation at site 5 (downstream from both WWTP and site 4) and the river mouth (site 6). Input of nutrients by rural and urban runoff was observed mainly at sites 2 and 3, respectively. At sites 4 and 5, the inputs of both untreated and treated wastewaters increased nutrient concentrations and changed physicochemical variables, with significant impacts to Monjolinho River. Seasonal variations in the measured values were also observed, in agreement with the pluviometric indexes of the region. Univariate analyses suggested no effect of the WWTP for most variables, with continued impacts at sites downstream, but non-parametric multivariate analysis indicated that these sites were recovering to chemical characteristics similar to upstream sites, apparently due to autodepuration. Therefore, multivariate methods that allow rigorous tests of multifactor hypotheses can greatly contribute to determine effects of both point and non-point sources in river systems, thus contributing to freshwater monitoring and management.     

Monitoring saltwater intrusion in Rupert Bay,Québec,Canada, after the partial diversion of a major tributary

As part of a large hydroelectric project in northern Québec (Canada), a portion of the flow of the Rupert River was diverted toward the existing La Grande hydroelectric complex. As a result of the partial diversion, the discharge of the Rupert River at its mouth is reduced by an average of 50% annually. This corresponds to an 18% decrease in the total freshwater inflow into the bay and, thus, to a shift of the upstream limit of the saltwater intrusion in Rupert Bay. Changes in saltwater intrusion had been predicted numerically as part of the project’s environmental impact assessment (EIA). In the project’s conditions of authorization, monitoring the hydraulic conditions and the extent of saltwater intrusion in the Rupert Bay was required by government authorities. The objective of this paper is to present the results of this environmental monitoring and, more specifically, to validate the modifications predicted in the EIA in terms of both saltwater intrusion limit and hydraulic conditions in the Rupert Bay. Results obtained during 2 years of monitoring are within the predicted trends and order of magnitude of changes anticipated in the EIA. The results, thus, confirm that the shift of the upstream limit of the saltwater front along the channels of the bay was conservatively predicted by numerical modeling.     

Effects of coal spoil amendment on heavy metal accumulation and physiological aspects of ryegrass (<Emphasis Type="Italic">Lolium perenne</Emphasis> L.) growing in copper mine tailings

Copper mine tailings pose many threats to the surrounding environment and human health, and thus, their remediation is fundamental. Coal spoil is the waste by-product of coal mining and characterized by low levels of metals, high content of organic matter, and many essential microelements. This study was designed to evaluate the role of coal spoil on heavy uptake and physiological responses of Lolium perenne L. grown in copper mine tailings amended with coal spoil at rates of 0, 0.5, 1, 5, 10, and 20%. The results showed that applying coal spoil to copper mine tailings decreased the diethylenetriaminepentaacetic acid (DTPA)-extractable Cd, Cu, Pb, and Zn contents in tailings and reduced those metal contents in both roots and shoots of the plant. However, application of coal spoil increased the DTPA-extractable Cr concentration in tailings and also increased Cr uptake and accumulation by Lolium perenne L. The statistical analysis of physiological parameters indicated that chlorophyll and carotenoid increased at the lower amendments of coal spoil followed by a decrease compared to their respective controls. Protein content was enhanced at all the coal spoil amendments. When treated with coal spoil, the activities of superoxide dismutases (SOD), peroxidase (POD), and catalase (CAT) responded differently. CAT activity was inhibited, but POD activity was increased with increasing amendment ratio of coal spoil. SOD activity increased up to 1% coal spoil followed by a decrease. Overall, the addition of coal spoil decreased the oxidative stress in Lolium perenne L., reflected by the reduction in malondialdehyde (MDA) contents in the plant. It is concluded that coal spoil has the potential to stabilize most metals studied in copper mine tailings and ameliorate the harmful effects in Lolium perenne L. through changing the physiological attributes of the plant grown in copper mine tailings.     

Effect of multi-factors interaction on trace lead equilibrium during municipal solid waste incineration

The thermodynamic equilibrium of trace lead during the waste incineration was calculated on the basis of the minimization of the total Gibbs energy. The effect of incineration condition and MSW components on Pb distribution was investigated mainly in the view of the interaction of related elements. In the oxygen-rich condition, incineration temperature affects Pb distribution by the interaction of Cl, Ca and Na. In the fuel-rich condition, incineration temperature affects Pb distribution directly by the thermal transition of PbS(s) to PbCl(g) and the thermal transition of PbCl(g) to Pb(g). Air ratio has significant effect on Pb distribution by the interaction of H, O and Cl. The liberated Cl in oxidizing condition is far less than that in reducing condition. Na has the top priority to bond with Cl, than Ca only at low temperature and H only at high temperature, so the effect of Cl on Pb distribution depends on the content of Na and Ca. S promotes Pb volatilization by the interaction with Na in oxygen-rich and chlorine-poor condition and depresses Pb volatilization by the formation of PbS(s) directly without interaction with other elements in fuel-rich condition.     

Path analysis of factors influencing household solid waste generation: a case study of Xiamen Island,China

Comprehensive study of the factors influencing household solid waste (HSW) generation is crucial and fundamental for exploring the generation mechanism and forecasting future dynamics of HSW. A case study of Xiamen Island, China was employed to reveal the direct and indirect effects of demographic/socioeconomic factors on solid waste generation at the urban household scale. Based on a face-to-face questionnaire and two-stage survey of solid waste generation, a path analysis model was built. Results showed that the proposed path model exhibited good fit indices. Family size and dinning-at-home rate (DR), whose coefficients were ?0.40 and 0.43, respectively, were the two major factors influencing HSW directly. Moreover, family size, education level, employment rate and age structure played different degrees of indirect effects on HSW generation through respective paths, which should not be ignored. In terms of total effects, coefficients of family size, DR and employment rate were ?0.46, 0.43 and ?0.37, respectively, which were three most dominant factors influencing HSW generation. As for waste composition, organic waste was the most representative of HSW dynamics, and was the most sensitive to impact by the factors studied. Quantitative results of this study have important policy implications for sustainable municipal solid waste management.     

Recycling of combined coal-biomass ash from electric power plant waste as a cementitious material: characteristics and improvement

Combined coal-biomass ash has an enormous impact on environmental quality near electric power plants. This paper describes an alternative to disposal in which the ash is used to produce cementitious materials. Ash was obtained from combustion of coal and biomass containing four mass ratios of anthracite, bitumen, rice husks, and eucalyptus bark. The cement-forming properties were systematically characterized including compressive strength development, durability, and expansion in water. The ash samples were ground to increase the specific surface area, and then used to partially replace ASTM Type I Portland cement in mixtures containing 15, 30, or 45 % ash by mass. The water-binder material's (Portland cement with or without combined coal-biomass ash) ratios (w/c) were held constant at 45, 55, or 65 % by mass. Types A, B, and D ash behaved similarly, while the properties of type C ash were slightly different. Increasing the ash fraction in Portland cement mixtures increased the water requirement and resulted in lower compressive strength. Thorough mechanical grinding reduced the porosity and significantly enhanced the material properties.