Hydrogeochemistry and Water Quality Evaluation along the Flow Path in the Unconfined Aquifer of the Düzce Plain, North-western Turkey

The Diizce Plain has a multi-aquifer system, which consists of a near surface unconfined aquifer, along with first and second deeper confined aquifers. Hydrochemical evolution and water quality are related to infiltration of the precipitation, recharge from the formations surrounding the plain, flow path of groundwater and the relationship between surface and groundwater. The groundwater in the unconfined aquifer flows towards the Efteni Lake and the Biiyiik Melen River. Surface waters are divided into two main hydrochemical facies in the study area： （a） Ca2＋-HCO3-; and （b） Ca2＋, Mg2＋-HCOc-, SO4^2-. The groundwater has generally three main hydrochemical facies： （a） Ca2＋-HCO3-; （b） Ca2＋, Mg2＋-HCO3-; and （c） Ca2＋, Mg2＋-HCO3-, Cl-. The hydrochemical facies ＂a＂ and ＂b＂ dominate within shallow depths in recharge areas under rapid flow conditions, while hydrochemical facies ＂c＂ characterizes shallow and mixed groundwater, which dominate intermediate or discharge areas （near Efteni Lake and Biiyiik Melen River） during low flow conditions and agricultural contamination. Calcium and bicarbonate ions, total hardness and electrical conductivity of total dissolved solids （EC-TDS） values increase along the groundwater flow path; but these parameters remain within the limits specified by the standards set for industrial and agricultural usages.

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Water quality prediction is vital for solving water pollution and protecting the water environment.In terms of the characteristics of nonlinearity,instability,and randomness of water quality parameters,a short-term water quality prediction model was proposed based on variational mode decomposition(VMD)and improved grasshopper optimization algorithm(IGOA),so as to optimize long short-term memory neural network(LSTM).First,VMD was adopted to decompose the water quality data into a series of relatively stable components,with the aim to reduce the instability of the original data and increase the predictability,then each component was input into the iGOA-LSTM model for prediction.Finally,each component was added to obtain the predicted values.In this study,the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction.The experimental results showed that the prediction accuracy of the VMDIGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition(EEMD),the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),Nonlinear Autoregressive Network with Exogenous Inputs(NARX),Recurrent Neural Network(RNN),as well as other models,showing better performance in short-term prediction.The current study will provide a reliable solution for water quality prediction studies in other areas.

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

Water quality restoration in rivers requires identification of the locations and discharges of pollution sources,and a reliable mathematical model to accomplish this identification is essential.In this paper,an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge.The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method(MCMC).The methodological framework is tested using a designed case of a sudden wastewater spill incident(i.e.,source location,flow rate,and starting and ending times of the discharge)and a real case of multiple sewage inputs into a river(i.e.,locations and daily flows of sewage sources).The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites.It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space.In comparison,the inverse models based on the popular random walk Metropolis(RWM)algorithm and microbial genetic algorithm(MGA)do not produce reliable estimates for the two scenarios even after multiple simulation runs,and they fall into locally optimal solutions.Since much more water level data are available than water quality data,the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data,especially for rivers receiving significant sewage discharges.

A red water occurrence in drinking water distribution systems caused by changes in water source in Beijing, China： mechanism analysis and control measures

A red water phenomenon occurred in several communities few days after the change of water source in Beijing, China in 2008. In this study, the origin of this problem, the mechanism of iron release and various control measures were investigated. The results indicated that a significant increase in sulphate concentration as a result of the new water source was the cause of the red water phenomenon. The mechanism of iron release was found that the high-concentration sulphate in the new water source disrupted the stable shell of scale on the inner pipe and led to the release of iron compounds. Experiments showed that the iron release rate in the new source water within pipe section was over 11-fold higher than that occurring within the local source water. The recovery of tap water quality lasted several months despite ameliora- tive measures being implemented, including adding phosphate, reducing the overall proportion of the new water source, elevating the pH and alkalinity, and utilizing free chlorine as a disinfectant instead of chloramine. Adding phosphate was more effective and more practical than the other measures. The iron release rate was decreased after the addition of 1.5 mg. L-1 orthophosphate- P, tripolyphosphate-P and hexametaphosphate-P by 68%, 83% and 87%, respectively. Elevating the pH and alkalinity also reduced the iron release rate by 50%. However, the iron release rate did not decreased after replacing chloramine by 0.5-0.8 mg. L-1 of free chlorine as disinfectant.

Analysis of Sydney＇s recycled water schemes

Recycled water provides a viable opportunity to partially supplement fresh water supplies as well as substantially alleviate environmental loads. Currently, thousands of recycled water schemes have been success- fully conducted in a number of countries and Sydney is one of the leading cities, which has made massive effort to apply water reclamation, recycling and reuse. This study aims to make a comprehensive analysis of recycled water schemes in Sydney for a wide range of end uses such as landscape irrigation, industrial process uses and residential uses （e.g., golf course irrigation, industrial cooling water reuse, toilet flushing and clothes washing etc.）. For each representative recycled water scheme, this study investi- gates the involved wastewater treatment technologies, the effluent quality compared with specified guideline values and public attitudes toward different end uses. Based on these obtained data, multi criteria analysis （MCA） in terms of risk, cost-benefit, environmental and social aspects can be performed. Consequently, from the analytical results, the good prospects of further expansion and exploration of current and new end uses were identified toward the integrated water planning and management. The analyses could also help decision makers in making a sound judgment for future recycled water projects.