Identification of contamination source in water distribution network based on consumer complaints

A new methodology was proposed for contamination source identification using information provided by consumer complaints from a probabilistic view.Due to the high uncertainties of information derived from users,the objective of the proposed methodology doesn't aim to capture a unique solution,but to minimize the number of possible contamination sources.In the proposed methodology,all the possible pollution nodes are identified through the CSA methodology firstly.And then based on the principle of total probability formula,the probability of each possible contamination node is obtained through a series of calculation.According to magnitude of the probability,the number of possible pollution nodes is minimized.The effectiveness and feasibility of the methodology is demonstrated through an application to a real case of ZJ City.Four scenarios were designed to investigate the influence of different uncertainties on the results in this case.The results show that pollutant concentration,injection duration,the number of consumer complaints nodes used for calculation and the prior probability with which consumers would complaint have no particular effect on the identification of contamination source.Three nodes were selected as the most possible pollution sources in water pipe network of ZJ City which includes more than 3 000 nodes.The results show the potential of the proposed method to identify contamination source through consumer complaints.

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.

Viral Contamination Source in Clinical Microbiology Laboratory

To understand the potential causes of laboratory-acquired infections and to provide possible solutions that would protect laboratory personnel, samples from a viral laboratory were screened to determine the main sources of contamination with six subtypes of Rhinovirus. Rhinovirus contamination was found in the gloves, cuffs of protective wear, inner surface of biological safety cabinet （BSC） windows, and trash handles. Remarkably, high contamination was found on the inner walls of the centrifuge and the inner surface of centrifuge tube casing in the rotor. Spilling infectious medium on the surface of centrifuge tubes was found to contribute to contamination of centrifuge surfaces. Exposure to sodium hypochlorite containing no less than 0.2 g/L available chlorine decontaminated the surface of the centrifuRe tubes from Rhinovirus after 2 min.

Distribution and Source of Main Contaminants in Surface Sediments of Tidal Flats in the Northern Shandong Province

Twenty-nine samples of surface sediments from tidal flats in the Northern Shandong Province were collected for grain size, heavy metal(Hg, Cu, Pb, Zn, Cd, and Cr), and oil pollution analyses. The geoaccumulation index(Igeo) and factor analysis were introduced to evaluate sediment quality and source of contaminants. The mean concentrations of Hg, Cu, Pb, Zn, Cd, Cr, and oil in the surface sediments in the study area are 0.033, 17.756, 19.121, 55.700, 0.291, 59.563, and 14.213 μg g-1, respectively. The heavy metal contamination in the old delta lobe is slightly higher than that in the abandoned delta lobe; however, the opposite was observed for oil pollution. The Igeo results revealed that the overall quality of the surface sediments in the study area is in good condition. The heavy metal pollution levels show a descending order: Cd> Hg> Cr> Cu> Zn> Pb, Cd being the main pollutant. The contamination level for in the study area is relatively lower than those for China's other tidal flats. Heavy metals are mainly derived from natural sources of rock weathering and erosion, partly influenced by industrial and agricultural discharge. However, oil pollution is mainly from runoff input, motorized fishing boat sewage, and oil exploitation.

A procedure for design of hydrogen networks with multiple contaminants

It is necessary to reduce hydrogen consumption to meet increasingly strict environmental and product-quality regulations for refinery plants. In this paper, the concentration potential concepts proposed for design of water-using networks are extended to synthesis of hydrogen networks with multiple contaminants. In the design procedure, the precedence of processes is determined by the values of concentration potential of demands.The usage of complementary source pair(s) to reduce utility consumption is investigated. Three case studies are presented to illustrate the effectiveness of the method. It is shown that the design procedure has clear engineering meaning.

Rapid identification of single constant contaminant source by considering characteristics of real sensors

For the release of hazardous contaminant indoors, source identification is critical for developing effective response measures. A method which can quickly and accurately identify the position, emission rate, and release time of a single constant contaminant source by using real sensors was presented. The method was numerically demonstrated and validated by a case study of contaminant release in a three-dimensional office. The effects of the measurement errors and total sampling period of sensor on the performance of source identification were thoroughly studied. The results indicate that the adverse effects of the measurement errors can be mitigated by extending the total sampling period. For reaching a desirable accuracy of source identification, the total sampling period should exceed a certain threshold, which can be determined by repeatedly running the identification method tmtil the results tend to be stable. The method presented can contribute to develop an onsite source identification system for protecting occupants from indoor releases.

Chemical Profile of Lebanon＇s Potential Contaminated Coastal Water

Lebanese marine water is exposed to multi-point source contaminants. Risk assessment associated with deteriorative water quality profile is not only affected by the disposed quantities of specific contaminant, but also its chemical speciated forms. The objective of this study is to quantify the Lebanese chemical marine water profile. Sea water samples were collected based on GIS map. The chemical water quality was assessed by measuring the macro water parameters and potential existing metals. The microbiological profile of water was evaluated by fecal coliform. Results showed contamination by fecal coliform and high levels of toxic metals. However, the actual projected impact of the detected metals on human health were assessed using geochemical models ＂FREEQCE＂ and ＂Mineq＋＂ that predict the metal species forms, mainly, the free aqua-metal ion, most toxic form. Thus, these results necessitate the proposal of integrated intervention plans for the protection of this marine source and consistent with international treaties.

Groundwater contaminant source identification considering unknown boundary condition based on an automated machine learning surrogate

Groundwater contamination source identification(GCSI)is a prerequisite for contamination risk evaluation and efficient groundwater contamination remediation programs.The boundary condition generally is set as known variables in previous GCSI studies.However,in many practical cases,the boundary condition is complicated and cannot be estimated accurately in advance.Setting the boundary condition as known variables may seriously deviate from the actual situation and lead to distorted identification results.And the results of GCSI are affected by multiple factors,including contaminant source information,model parameters,boundary condition,etc.Therefore,if the boundary condition is not estimated accurately,other factors will also be estimated inaccurately.This study focuses on the unknown boundary condition and proposed to identify three types of unknown variables(contaminant source information,model parameters and boundary condition)innovatively.When simulation-optimization(S-O)method is applied to GCSI,the huge computational load is usually reduced by building surrogate models.However,when building surrogate models,the researchers need to select the models and optimize the hyperparameters to make the model powerful,which can be a lengthy process.The automated machine learning(AutoML)method was used to build surrogate model,which automates the model selection and hyperparameter optimization in machine learning engineering,largely reducing human operations and saving time.The accuracy of AutoML surrogate model is compared with the surrogate model used in eXtreme Gradient Boosting method(XGBoost),random forest method(RF),extra trees regressor method(ETR)and elasticnet method(EN)respectively,which are automatically selected in AutoML engineering.The results show that the surrogate model constructed by AutoML method has the best accuracy compared with the other four methods.This study provides reliable and strong support for GCSI.

Advances in strategies to assure the microbial safety of food-associated ice

Ice has been extensively employed as a general coolant or ingredient in the food industry.However,evident indicates that food-associated ice can be easily contaminated by pathogenic bacteria,such as Escherichia coli,Staphylococcus aureus,and Salmonella spp.,among others.The contamination sources are attributed to the use of unhygienic water,improper handling,and poor hygiene conditions of ice-making machines.The contaminated ice might be a vesicle for the transmission of pathogens to food and human,posing potential risks to public health.This review overviewed the microbial contamination of food-associated ice,including the microbial contamination levels and the main microbial contaminants.In addition,a systematical introduction was constructed on the recent advances and applications of state-of-the-art methods to manufacture safe ices,illustrating ozone,electrolyzed water,cold plasma,essential oils,and ultraviolet light-emitting diode irradiation.The validities and limitations of ice treated by these techniques on the food quality were then discussed in detail.Various studies were also organized to demonstrate the potential of the mentioned treated ice,for ensuring the microbial safety and control quality deterioration during the storage of aquatic products.Finally,the direction for future study was given.

Risk Assessment of Metal Contamination in Soil and Groundwater in Asia： A Review of Recent Trends as well as Existing Environmental Laws and Regulations

Asia is experiencing a more rapid economic growth compared to any other regions. The contamination of soil and groundwater with metals can mainly be attributed to human activities; therefore, risk assessments to characterize the nature and magnitude of risks to humans and ecological receptors from contaminants are important. Risk assessments are often iterative processes, which involve identification and filling data gap. Experimental samplings, geostatistical and multivariate statistical methods as well as multimedia risk assessment modeling are the three major methodologies used in the assessment of metal contamination in soil and groundwater.This review highlights a number of measurements for improving risk calculation methods and expounds scientific approaches that involve the identification of the major source of contamination, exposure pathways and bioavailability of metals. In general, risk assessments of metals in soil and groundwater worldwide are mainly focused on the levels of contamination, identification of exposure pathways, and prediction of the probability of contamination. To date, very limited studies have reported the development of relevant environmental laws and policies in the regulation of soil and groundwater contamination in Asia. The development, variations and limitations in the regulations of soil and groundwater contamination among developed countries may provide helpful guidance for the developing countries in Asia.

Occurrence of polycyclic aromatic hydrocarbons in the estuarine sediments of the Taihu Lake and their associated toxic effects on aquatic organisms

Polycyclic aromatic hydrocarbons(PAHs)are ubiquitous toxic organic pollutants in the natural environment that are strongly associated with socioeconomic activities.Exploring the distribution,sources,and ecological toxicity of PAHs is essential to abate their pollution and biological risks.The 16 priority PAHs in different lakeside city estuarine sediments in the northern Taihu Lake in China were determined using gas chromatography-mass spectrometry.Results showed that total concentrations of PAHs(ΣPAHs)ranged from 672.07 to 5858.34 ng g^(^(-1)),with a mean value of 2121.37 ng g^(^(-1)).High-molecular-weight PAHs(4-6 rings)were dominant,accounting for 85%of theΣPAHs detected.Due to the barrier of gate/dam in the estuarine area,the concentrations of PAHs in the sediments were significantly different between the river mouth and lake side.Changes in total organic carbon(TOC)content and the spatial distribution of PAHs in the sediments were consistent.Sediment pollution assessment explored using the fuzzy evaluation model indicated 75%of slight PAH pollution.Some estuarine sediments(22%)concentrated in the east of the Wuli Lake in the Meiliang bay of the Taihu Lake were moderately or heavily polluted.The PAHs may lead to occasional detrimental biological consequences in the area.Diagnostic ratios and principal component analysis-multiple linear regression suggested vehicle emission and natural gas combustion as the primary PAH contributors(81%).

Occurrence and distribution of pharmaceuticals in surface water and sediment of Buffalo and Sundays River estuaries, South Africa and their ecological risk assessment

This study investigated the presence of pharmaceuticals in the water and sediment of Buffalo and Sundays River Estuary,South Africa.Extraction for water and sediment samples was achieved with solidphase extraction and ultrasonic extraction methods,respectively.Quantification of the target analytes was done with UPLC-QTOF-MS.The pH of surface water in all the sampling sites(8.18-10.1)were above the WHO guideline(6.5).A good correlation was observed between the concentrations of analytes with organic matter and organic carbon.Results showed that carbamazepine exhibited the highest concentration(8.75±1.45 mg/kg)of all the pharmaceuticals,while trimethoprim has the highest detection frequency with the highest concentration of 1.62±0.83 mg/kg in the sediment samples.Sulfamethoxazole was below the detection limit in both estuaries.Calculated risk quotients revealed that carbamazepine and trimethoprim could be of potential risk,while caffeine may pose a very low risk to the aquatic organisms investigated.Proper monitoring of the runoff entering Buffalo Estuary is required,being the point of discharge of Buffalo River to the Indian Ocean.Also,the domestic waste discharge from residences in the surroundings of Sundays Estuary should be monitored.

Geant4 applications for modeling molecular transport in complex vacuum geometries

We discuss a novel use of the Geant4 simulation toolkit to model molecular transport in a vacuum environment,in the molecular flow regime.The Geant4 toolkit was originally developed by the high energy physics community to simulate the interactions of elemen-tary particles within complex detector systems.Here its capabilities are utilized to model molecular vacuum transport in geometries where other techniques are impractical.The techniques are verified with an application representing a simple vacuum geometry that has been studied previously both analytically and by basic Monte Carlo simulation.We discuss the use of an application with a very complicated geometry,that of the Large Synoptic Survey Telescope camera cryostat,to determine probabilities of transport of contaminant molecules to optical surfaces where control of contamination is crucial.