Urban flood risk assessment using sewer flooding databases

Sustainable water management is a global challenge for the 21st century. One key aspect remains protection against urban flooding. The main objective is to ensure or maintain an adequate level of service for all inhabitants. However, level of service is still difficult to assess and the high-risk locations difficult to identify. In this article, we propose a methodology, which (i) allows water managers to measure the service provided by the urban drainage system with regard to protection against urban flooding; and (ii) helps stakeholders to determine effective strategies for improving the service provided. One key aspect of this work is to use a database of sewer flood event records to assess flood risk. Our methodology helps urban water managers to assess the risk of sewer flooding; this approach does not seek to predict flooding but rather to inform decision makers on the current level of risk and on actions which need to be taken to reduce the risk. This work is based on a comprehensive definition of risk, including territorial vulnerability and perceptions of urban water stakeholders. This paper presents the results and the methodological contributions from implementing the methodology on two case studies: the cities of Lyon and Mulhouse.     

Elaboration of the hydroxyapatite with different precursors and application for the retention of the lead

Carbonate hydroxyapatite (CHAP) was synthesized from different precursors; synthetic (CaCO3 and Ca(OH)2) and natural (egg shell before and after calcinations at 900 degrees C) under different conditions and characterized by using TG/DTG analysis, X-ray powder diffraction (XRD) method and Fourier transform infrared (FT-IR) spectroscopy techniques. The results of these analyses indicate that the four powders present the same structure of hydroxyapatite. Furthermore the four powders obtained were used for the retention of lead. The results obtained indicated that all powders present high adsorption capacity for lead, but from environmental and economic views, the hydroxyapatite synthesized from eggshell no calcined (HA2) is most advantageous. The influence of different sorption parameters, such as: initial metal concentration, equilibration time, solution pH and sorbent dosage was studied and discussed.     

Dual-color fluorescence lifetime correlation spectroscopy to quantify protein-protein interactions in live cell

Dual-color fluorescence correlation spectroscopy is an interesting method to quantify protein interaction in living cells. But, when performing these experiments, one must compensate for a known spectral bleed through artifact that corrupts cross-correlation data. In this article, problems with crosstalk were overcome with an approach based on fluorescence lifetime correlation spectroscopy (FLCS). We show that FLCS applied to dual-color EGFP and mCherry cross-correlation allows the determination of protein-protein interactions in living cells without the need of spectral bleed through calibration. The methodology was validated by using EGFP-mCherry tandem in comparison with coexpressed EGFP and mCherry in live cell. The dual-color FLCS experimental procedure where the different laser intensities do not have to be controlled during experiment is really very helpful to study quantitatively protein interactions in live sample.     

The application of model predictive control of ammonia nitrogen in an activated sludge process

In this paper a model predictive controller (MPC) for ammonia nitrogen is presented and evaluated in a real activated sludge process. A reduced nonlinear mathematical model based on mass balances is used to model the ammonia nitrogen in the activated sludge plant. An MPC algorithm that minimises only the control error at the end of the prediction interval is applied. The results of the ammonia MPC were compared with the results of the ammonia feedforward-PI and ammonia PI controllers from our previous study. The ammonia MPC and ammonia feedforward-PI controller give better results in terms of ammonia removal and aeration energy consumption than the ammonia PI controller because of the measurable disturbances used. On the other hand, with the ammonia MPC, comparable or even slightly poorer results than with the ammonia feedforward-PI controller are obtained. Further improvements to the MPC could be possible with an improved accuracy of the nonlinear reduced model of the ammonia nitrogen, more sophisticated control criteria used inside the controller and the extension of the problem from univariable ammonia to multivariable total nitrogen control.     

Effective degradation of para-chloronitrobenzene through a sequential treatment using zero-valent iron reduction and Fenton oxidation

In this study, zero-valent iron (ZVI) was used to pretreat para-chloronitrobenzene (p-CNB), and the major product was para-chloroaniline (p-CAN). By adding H(2)O(2) directly, further p-CAN degradation can be attributed to Fenton oxidation because ferrous ions (Fe(2+)) released during the ZVI corrosion could be used as an activator for H(2)O(2) decomposition. In the reduction process, the reduction efficiency of p-CNB as well as Fe(2+) concentration increased with increasing iron dosage and decreasing solution pH. Under the optimal conditions, 25 mg L(-1) of p-CNB could be transformed in 3 h when initial solution pH was 3.0 and ZVI dosage was 2.0 g L(-1). A sufficient amount of Fe(2+) (50.4 mg L(-1)) was obtained after the above reaction to activate H(2)O(2). In the Fenton process, the oxidization of p-CAN was also more effective in acidic conditions and it increased with increasing H(2)O(2) concentration. The control experiments showed that the sequential treatment was more effective than Fenton oxidation alone in treating p-CNB wastewater since the removal rate of total organic carbon (TOC) was improved by about 34%. It suggested that the amino function group is more susceptible to oxidative radical attack than the nitro function group. Therefore, sequential treatment using zero-valent iron reduction followed by Fenton oxidation is a promising method for p-CNB degradation.     

Samplinghelper a web-based tool to assess the reliability of sampling strategies in sewers and receiving waters

Sampling is a key step in the analysis of chemical compounds. It is particularly important in the environmental field, for example for wastewater effluents, wet-weather discharges or streams in which the flows and concentrations vary greatly over time. In contrast to the improvements that have occurred in analytical measurement, developments in the field of sampling are less active. However, sampling errors may exceed by an order of magnitude those related to analytical processes. We proposed an Internet-based application based on a sampling theory to identify and quantify the errors in the process of taking samples. This general theory of sampling, already applied to different areas, helps to answer questions related to the number of samples, their volume, their representativeness, etc. The use of the internet to host this application facilitates use of theoretical tools and raise awareness of the uncertainties related to sampling. An example is presented, which highlights the importance of the sampling step in the quality of analytical results.     

Understanding urban residential end uses of water

The 'end use' of water is a breakdown of the total household water usage such as water used for toilets, showers, washing machines, taps, lawn watering, etc. Understanding end uses of water will enable water planners, water authorities and household owners determine where water is used/wasted, how much and how often. This paper describes the end uses of water from a number of single-family homes in Greater Melbourne, Australia. The study involves the analysis of water consumption data recorded at 5-s intervals from logged households collected by Yarra Valley Water in Melbourne in 2004. The study determines how much water is used for outdoor and indoor purposes in a single-family home in Melbourne and compares the water usage during winter and summer. Hourly patterns of major end uses of water are also developed. The aim of this study is to improve the understanding of the end uses of water and to assist where to focus water conservation efforts that would be most effective financially and environmentally, and be acceptable to everyone.     

Spectrophotometric characterization of human impacted surface waters in the Moselle watershed

In order to characterize the pollution discharged into the Moselle River and some of its tributaries, spectroscopic techniques, namely UV-vis spectroscopy and synchronous fluorescence spectroscopy, have been combined. UV-visible spectra were analysed using the maximum of the second derivative at 225 nm (related to nitrates), the SUVA254 and E2/E3 indices (related to the nature of organic matter). Synchronous fluorescence spectra (delta lambda = 50 nm) presented different shapes depending upon the type of pollution. The pollution results from anthropogenic activities: untreated domestic sewage due to misconnections in a periurban river, effluent from urban WWTPS, agricultural runoff (nitrates) in several streams, discharge from a paper mill (humic-like substances due to wood processing) and from steel mills (PAHs).     

俄罗斯兴建基斯罗古布斯卡亚潮汐电站的经验

以基斯罗古布斯卡亚（Kislogubskaya）潮汐电站为例,综述了俄罗斯潮汐电站的发展历程。从修建该电站所采用的建筑材料、建筑方法以及发电机组的选型等方面,对过去40多年的运行经验进行了总结。     

Nonlinear model predictive control with relevance vector regression and particle swarm optimization

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.