Overall-effective Measures for Sustainable Water Resources Management in the Coastal Areas of Shandong Province, China——Case Study: the Huangshuihe River Catchment of Longkou City

In the coastal catchments of Shandong Province the water scarcity is aggravated due to saltwater intrusion, reducing the usability of water resources available. Such a situation calls for sustainable integrated water resources management (IWRM). The idea for the objectives and implementation of the IWRM are explained in this paper. The general objective of the planned project disscussed in the present study is to bring together German traditional expertise in water resources management and newer developments in the context of the European Water Framework Directive; the research efforts aim to relieve the desperate water scarcity situation in the costal area of Shandong Province.

Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’Theorem

Coupling Bayes’Theorem with a two-dimensional(2D)groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity(M),release location(0 X,0 Y)and release time(0 T),based on monitoring well data.To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters,a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy.To demonstrate how the model works,an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed.The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index.The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events.Based on the optimized monitoring well position and sampling frequency,the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach.The case study results show that the following parameters were obtained:1)the optimal monitoring well position(D)is at(445,200);and 2)the optimal monitoring frequency(Δt)is 7,providing that the monitoring events is set as 5 times.Employing the optimized monitoring well position and frequency,the mean errors of inverse modeling results in source parameters(M,X0,Y0,T0)were 9.20%,0.25%,0.0061%,and 0.33%,respectively.The optimized monitoring well position and sampling frequency canIt was also learnt that the improved Metropolis-Hastings algorithm(a Markov chain Monte Carlo method)can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization,which significantly improved the accuracy and numerical stability of the inverse modeling results.

Phosphorus recovery from municipal and fertilizer wastewater:China's potential and perspective

Phosphorus（P） is a limited resource, which can neither be synthesized nor substituted in its essential functions as nutrient. Currently explored and economically feasible global reserves may be depleted within generations. China is the largest phosphate fertilizer producing and consuming country in the world. China’s municipal wastewater contains up to 293,163 Mg year of phosphorus, which equals approximately 5.5% of the chemical fertilizer phosphorus consumed in China. Phosphorus in wastewater can be seen not only as a source of pollution to be reduced, but also as a limited resource to be recovered. Based upon existing phosphorus-recovery technologies and the current wastewater infrastructure in China, three options for phosphorus recovery from sewage sludge, sludge ash and the fertilizer industry were analyzed according to the specific conditions in China.

Influence of surface properties and antecedent environmental conditions on particulate-associated metals in surface runoff

Particulate-associated trace metals have been regarded as an important pollution source for urban surface runoff.Cd,Pb,Cu,Zn and total solids(TS)washed off two different surfaces(low-elevated facade and road surfaces)under two kinds of antecedent environmental conditions(dry and snow-melting)were determined in this study.Wet-vacuuming sweeping(WVS)and surface washing(SW)methods,representing the maximum pollution potential and common rainfall-induced wash-off condition respectively,were used to collect the particulate matters.The result shows that the wash-off concentrations of trace metals were found in the order of Cd(2.28±2.08 mg/l)<Pb(435.85±412.61 mg/l)<Cu(0.93±0.61 mg/l)<Zn(2.52±2.30 mg/l).The snow-melting process had a considerable influence on the wash-off concentrations of the trace metals on both road and facade surfaces.It reduced >38% and >79% of metals and TS concentrations in the facade surface and road surface runoff respectively.The wash-off concentrations of Cd,Cu,and Zn on the road surface 45e780% higher than those on the facade surfaces.The sensitivity analysis based on the Bayesian network indicates that the wash-off concentrations of metals were mainly dependent on the antecedent environmental conditions or the surface properties while the sampling methods had a minor influence.Therefore,to accurately model the pollutant migration in the surface runoff requires an improving method considering different surfaces and antecedent environment conditions.