Variations in trihalomethane levels in three French water distribution systems and the development of a predictive model

Epidemiological studies have demonstrated that chlorination by-products in drinking water may cause some types of cancer in humans. However, due to differences in methodology between the various studies, it is not possible to establish a dose-response relationship. This shortcoming is due primarily to uncertainties about how exposure is measured—made difficult by the great number of compounds present—the exposure routes involved and the variation in concentrations in water distribution systems. This is especially true for trihalomethanes for which concentrations can double between the water treatment plant and the consumer tap.The aim of this study is to describe the behaviour of trihalomethanes in three French water distribution systems and develop a mathematical model to predict concentrations in the water distribution system using data collected from treated water at the plant (i.e. the entrance of the distribution system).In 2006 and 2007, samples were taken successively from treated water at the plant and at several points in the water distribution system in three French cities. In addition to the concentrations of the four trihalomethanes (chloroform, dichlorobromomethane, chlorodibromomethane, bromoform), many other parameters involved in their formation that affect their concentration were also measured.The average trihalomethane concentration in the three water distribution systems ranged from 21.6 μg/L to 59.9 μg/L. The increase in trihalomethanes between the treated water at the plant and a given point in the water distribution system varied by a factor of 1.1-5.7 over all of the samples. A log-log linear regression model was constructed to predict THM concentrations in the water distribution system. The five variables used were trihalomethane concentration and free residual chlorine for treated water at the plant, two variables that characterize the reactivity of organic matter (specific UV absorbance (SUVA), an indicator developed for the free chlorine consumption in the treatment plant before distribution δ) and water residence time in the distribution system.French regulations impose a minimum trihalomethane level for drinking water and most tests are performed on treated water at the plant. Applied in this context, the model developed here helps better to understand trihalomethane exposure in the French population, particularly useful for epidemiological studies.     

Trihalomethanes aqueous solutions sono-oxidation

Ultrasonic (US) irradiation, hydrogen peroxide (H(2)O(2)), Fenton's oxidation and the combination of the processes were investigated for destruction and removal of the following trihalomethanes (THMs) compounds from aqueous solutions: CHCl(3), CHBrCl(2), CHBr(2)Cl, CHBr(3), and CHI(3). H(2)O(2) had no significant effect on the THMs sonodegradation. The coupled US and Fenton processes did not affect the CHCl(3), CHBrCl(2), and CHBr(2)Cl sonolysis efficiency. Nevertheless, the sonodegradation of CHBr(3) was enhanced. CHI(3) was degraded by Fenton's oxidation rather than by the US irradiation during the sonication/Fenton treatment. The combination of sonication with H(2)O(2) or Fenton's reagent did not affect the mineralization of the THMs aqueous mixture.     

Multivariate statistical relationships between routine water plant data and trihalomethane levels. Implications for studies of human health

Routine water treatment plant data were used to construct a mathematical/statistical model of trihalomethane formation during lime-soda ash softening. Chemical characteristics of the raw water, such as temperature and color, and several treatment parameters, including chlorine dose, were significant predictors for chloroform and total trihalomethanes. The pattern of prediction was notably different for the brominated species. The results of this preliminary study support the view that routine water plant data can be used to estimate retrospectively, and with accuracy, trihalomethane levels for past time periods in which only the routine plant data are available. Possible limitations of the approach and the prospects for improving epidemiologic health effects studies of trihalomethanes in drinking water are discussed.     

Trihalomethane formation in whirlpool SPAs

In a survey of 25 whirlpool spas using halogen disinfectants, the only organohalide contaminant observed in the water and in the air at concentrations in excess of 1 μg l⁻¹ (water) and 1 μg m⁻³ (air) was the trihalomethane corresponding to the disinfectant used. The levels of trihalomethane observed in the water in thermal spas were comparable to levels reported for swimming pools. We conclude that the heat, agitation and aeration of the spas do not produce higher residual levels of trihalomethanes in the water and do not promote the formation of novel volatile organohalides. The concentration of trihalomethane in the water appeared to be related to the combination of high total dissolved solids and high disinfectant levels. The trihalomethane levels in the air though dependent on the concentration of trihalomethane in the water were modified by the variety of room sizes, ventilation rates, water surface areas and aeration rates associated with each individual spa.     

Formation of brominated products in irradiated titanium dioxide suspensions containing bromide and dissolved organic carbon

We report the irradiation of TiO(2) suspensions containing Br(-) and dissolved organic carbon (DOC). In the absence of DOC, we found no evidence for the formation of BrO(3)(-) upon irradiation of 1gL(-1) P25 suspensions with UV light for initial Br(-) concentrations up to 10mgL(-1). In the presence of DOC (Lake Hohloh, Germany and salicylic acid), we found no evidence for the formation of either BrO(3)(-) or trihalomethanes (THMs). However, small amounts of adsorbable organic halogen (AOX) were formed at high bromide concentrations (3mgL(-1)). When irradiating P25 suspensions containing bromide and 2,4-dihydroxybenzoic acid (DHBA, high bromoform formation potential), we observed the formation of significant amounts of bromoform (up to 10microgL(-1)). Bromoform appeared only after the DHBA had been degraded.     

Hard-rock LHD cost estimation using single and multiple regressions based on principal component analysis

In feasibility studies and mine planning, accurate and effective tools and methods facilitating cost estimation play an important role. Load-Haul-Dump (LHD) machines are a key loading and haulage equipment in most of the underground metal mines and hard rock tunnels. In this paper, a cost estimation model of these vehicles has been presented in the form of single and multivariable functions. These functions have been provided on the basis of costs types (i.e. capital and operating costs) and motor types (diesel and electric). Independent variables, in the single regression analysis is bucket capacity and in Multiple Linear Regression (MLR) analysis include bucket capacity, overall width, overall machine height and horse power (HP). The MLR is conducted in three steps. First, with the help of Principal Component Analysis (PCA), correlation between independent variables is omitted. Thereafter, significant PCs are selected and used as independent variables in the MLR functions. Finally, the cost relationships are established as functions of initial LHD variables. The mean absolute error rates are 11.59% and 6.87% for the single and multiple linear regression functions, respectively.     

Analysis of selected trace organics in advanced wastewater treatment systems

Effluents through four different pilot tertiary wastewater treatment systems were monitored for selected trace organic compounds. The effects of using ozone, free and combined chlorine residuals in these systems were also studied. Advanced treatment of secondary effluent using various combinations of flocculation (alum and polymer), dual media filtration, and carbon adsorption were evaluated for production/removal of volatile halogenated organics, polynuclear aromatics, chlorinated pesticides, and polychlorinated biphenyls. Gas chromatographic methods were used for the analysis of these different classes of compounds: specific techniques and analytical parameters are described. Salient results included: drastic increases in trihalomethane production using free chlorine residuals: disinfection with combined chlorine species does not produce significant levels of trihalomethanes: approximately 90% reduction in trihalomethane levels by carbon adsorption: absence of detectable quantities of polynuclear aromatics: significant decreases in pesticide and PCB levels by carbon adsorption and chlorination. Statistical dependence of trihalomethane production on soluble COD, suspended solids and chloramine levels was evident from multiple linear regression calculations.     

Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River, Shanghai, China

We applied cluster analysis and principal component analysis (PCA) with multivariate linear regression (MLR) to apportion sources of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River in Shanghai, China, based on the measured PAH concentrations of 32 samples collected at eight sites in four seasons in 2006. The results indicate that petrogenic and pyrogenic sources are the important sources of PAHs. Further analysis shows that the contributions of coal combustion, traffic-related pollution and spills of oil products (petrogenic) are 40%, 36% and 24% using PCA/MLR, respectively. Pyrogenic sources (coal combustion and traffic related pollution) contribute 76% of anthropogenic PAHs to sediments, which indicates that energy consumption is a predominant factor of PAH pollution in Shanghai. Rainfall, the monsoon and temperature play important roles in the distinct seasonal variation of PAH pollution, such that the contamination level of PAHs in spring is significantly higher than in the other seasons.

Brief

We apportion PAHs in surface sediments of the Huangpu River and show that coal combustion, traffic-related pollution, and petroleum spillage are the major sources.     

Use of three-dimensional excitation and emission matrix fluorescence spectroscopy for predicting the disinfection by-product formation potential of reclaimed water

This study was undertaken to demonstrate the feasibility of using three-dimensional excitation-emission matrix (3DEEM) fluorescence spectroscopy for the determination of chlorination disinfection by-product (DBP) precursors and the disinfection by-product formation potential (DBPFP) of reclaimed water samples. Two major DBP precursors were examined in this study, including humic acid (HA) and fulvic acid (FA). The 3DEEM fluorescence results obtained from various reclaimed water samples indicated that the reclaimed water samples were rich in fulvic acid-like substances that were associated with two main peaks (Ex/Em = 235-245/420-440 nm, and Ex/Em = 330-340/410-430 nm) in the fluorescence spectrum. The results also illustrated that the wavelength location of peak fluorescence intensity of a reclaimed water sample was independent of the influent water quality and the wastewater treatment process used in the reclamation plant. As a result, the peak fluorescence intensity and the wavelength location of the peak were used to identify the species of DBP precursors and their concentrations in the reclaimed water sample. Four regression models were then developed to relate the peak fluorescence intensity of the water sample to its DBPFP, including the formation potential of trihalomethane (THMFP) and the formation potential of haloacetic acid (HAAFP). The regression models were verified using the measured DBPFP results of a series of reclaimed water samples. It was found that the regression modeling results matched the measured DBPFP values well, with prediction errors below 10%. Therefore, the use of 3DEEM fluorescence spectroscopy together with the developed regression models in this study can provide a reliable and rapid tool for monitoring the quality of reclaimed water. Using this method, water quality could be monitored online, without utilizing the lengthy conventional DBPFP measurement.     

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ) - a direct classification approach - for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards.     

The Use of Bone Charcoal in the Treatment of Rural Water Supplies

As a result of the Water Act of 1989 on the quality of water intended for human consumption, a number of small spring sources in Yorkshire were the subject of legal undertakings for remedial action to reduce the concentration of trihalomethanes in the distribution system. The trihalomethanes are formed when the colour in these waters, which is made up of predominantly fulvic and humic acids, is chlorinated. Therefore, in order to solve the problem of trihalomethanes in treated water, colour removal was necessary.
The paper describes the use of bone charcoal in slow sand filters to remove colour from rural water supplies. It covers six months pilot-plant work which was undertaken at Marsett water-treatment works (near Richmond), and the implementation at other plants within Yorkshire. low-rate filtration through bone charcoal ensured that the colour and trihalomethane concentrations in filtered water complied with EC standards, and the material coped well with rapid changes in raw water quality.     

Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network

The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. As groundwater resources are one of most important freshwater sources for water supplies in Southeast Asian countries, it is important to investigate the spatial distribution of As contamination and evaluate the health risk of As for these countries. The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. Therefore, modeling approaches for As concentration using conventional on-site measurement data can be an alternative to quantify the As contamination. The objective of this study is to evaluate the predictive performance of four different models; specifically, multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), and the combination of principal components and an artificial neural network (PC-ANN) in the prediction of As concentration, and to provide assessment tools for Southeast Asian countries including Cambodia, Laos, and Thailand. The modeling results show that the prediction accuracy of PC-ANN (Nash-Sutcliffe model efficiency coefficients: 0.98 (traning step) and 0.71 (validation step)) is superior among the four different models. This finding can be explained by the fact that the PC-ANN not only solves the problem of collinearity of input variables, but also reflects the presence of high variability in observed As concentrations. We expect that the model developed in this work can be used to predict As concentrations using conventional water quality data obtained from on-site measurements, and can further provide reliable and predictive information for public health management policies.     

Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: A case study of the Yeongsan Reservoir, Korea

Statistical regression models involve linear equations, which often lead to significant prediction errors due to poor statistical stability and accuracy. This concern arises from multicollinearity in the models, which may drastically affect model performance in terms of a trade-off scenario for effective water resource management logistics. In this paper, we propose a new methodology for improving the statistical stability and accuracy of regression models, and then show how to cope with pitfalls in the models and determine optimal parameters with a decreased number of predictive variables. Here, a comparison of the predictive performance was made using four types of multiple linear regression (MLR) and principal component regression (PCR) models in the prediction of chlorophyll-a (chl-a) concentration in the Yeongsan (YS) Reservoir, Korea, an estuarine reservoir that historically suffers from high levels of nutrient input. During a 3-year water quality monitoring period, results showed that PCRs could be a compact solution for improving the accuracy of the models, as in each case MLR could not accurately produce reliable predictions due to a persistent collinearity problem. Furthermore, based on R² (goodness of fit) and F-overall number (confidence of regression), and the number of explanatory variables (R-F-N) curve, it was revealed that PCR-F(7) was the best model among the four regression models in predicting chl-a, having the fewest explanatory variables (seven) and the lowest uncertainty. Seven PCs were identified as significant variables, related to eight water quality parameters: pH, 5-day biochemical oxygen demand, total coliform, fecal indicator bacteria, chemical oxygen demand, ammonia-nitrogen, total nitrogen, and dissolved oxygen. Overall, the results not only demonstrated that the models employed successfully simulated chl-a in a reservoir in both the test and validation periods, but also suggested that the optimal parameters should cautiously be considered in the design of regression models.     

Chemometrics in monitoring spatial and temporal variations in drinking water quality

This case study reports multivariate techniques applied for the evaluation of temporal/spatial variations and interpretation of monitoring data obtained by the determination of chloro/bromo disinfection by-products in drinking water at 12 locations in the Gdańsk area (Poland), over the period 1993-2000. The complex data matrix (1756 observations) was treated with various multivariate techniques. Cluster analysis (CA) was successful, yielding two different groups of similarity reflecting different types of drinking water supplied (surface and groundwater). The locations supplied in general with groundwater could be further classified into two subgroups, depending on whether the groundwater was mixed with surface water or not. Analysis of variance (ANOVA) was used to classify and thus confirm the groups found by means of cluster analysis and proved the existence of statistically significant differences between the concentration levels of CHCl3, CHBrCl2+C2HCl3, CHBr2Cl, and CH2Cl2 in the samples collected. Of all the variables evaluated, only three were characterized by statistically significant correlations (CHCl3, CHBrCl2+C2HCl3, CHBr2Cl). The analysis of correlation coefficients revealed that chloroform formed as the main chlorinated disinfection by-product and, furthermore, the natural presence of bromide in water (both ground and surface) results in the formation of brominated disinfection by-products (DBPs). Temporal variations of volatile organic chlorinated compounds (VOCls) were also evaluated by multidimensional ANOVA. Observation of temporal changes in the concentration of VOCls at the location supplied with both surface and groundwater reveals a steady improvement in drinking water quality. In general, the study shows the importance of drinking water monitoring in connection with simple but powerful statistical tools to better understand spatial and temporal variations in water quality.     

Modeling of bromate formation by ozonation of surface waters in drinking water treatment

The main objective of this paper is to try to develop statistically and chemically rational models for bromate formation by ozonation of clarified surface waters. The results presented here show that bromate formation by ozonation of natural waters in drinking water treatment is directly proportional to the "Ct" value ("Ctau" in this study). Moreover, this proportionality strongly depends on many parameters: increasing of pH, temperature and bromide level leading to an increase of bromate formation; ammonia and dissolved organic carbon concentrations causing a reverse effect. Taking into account limitation of theoretical modeling, we proposed to predict bromate formation by stochastic simulations (multi-linear regression and artificial neural networks methods) from 40 experiments (BrO(3)(-) vs. "Ctau") carried out with three sand filtered waters sampled on three different waterworks. With seven selected variables we used a simple architecture of neural networks, optimized by "neural connection" of SPSS Inc./Recognition Inc. The bromate modeling by artificial neural networks gives better result than multi-linear regression. The artificial neural networks model allowed us classifying variables by decreasing order of influence (for the studied cases in our variables scale): "Ctau", [N-NH(4)(+)], [Br(-)], pH, temperature, DOC, alkalinity.     

Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R² value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.     

城市污水再生水氯消毒副产物比较研究

比较分析了对城市污水再生水采用液氯和二氧化氯消毒前后消毒副产物的变化。结果表明:城市污水再生水本身含有少量的氯代有机物,经液氯消毒后二氯甲烷、三氯甲烷含量大幅增加,为进水的3～4倍,同时产生了新的消毒副产物二氯一溴甲烷;经二氧化氯消毒后未出现新的消毒副产物,且原有副产物浓度基本不变。因此,再生水使用二氧化氯消毒具有明显优势。     

Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models

In the present study, artificial neural networks (ANNs), neuro-fuzzy (NF), multi linear regression (MLR) and conventional sediment rating curve (SRC) models are considered for time series modeling of suspended sediment concentration (SSC) in rivers. As for the artificial intelligence systems, feed forward back propagation (FFBP) method and Sugeno inference system are used for ANNs and NF models, respectively. The models are trained using daily river discharge and SSC data belonging to Little Black River and Salt River gauging stations in the USA.Obtained results demonstrate that ANN and NF models are in good agreement with the observed SSC values; while they depict better results than MLR and SRC methods. For example, in Little Black River station, the determination coefficient is 0.697 for NF model, while it is 0.457, 0.257 and 0.225 for ANN, MLR and SRC models, respectively. The values of cumulative suspended sediment load estimated by ANN and NF models are closer to the observed data than the other models. In general, the results illustrate that NF model presents better performance in SSC prediction in compression to other models.     

Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki

Forecasting models based on stepwise multiple linear regression (MLR) have been developed for Athens and Helsinki. The predictor variables were the hourly concentrations of pollutants (NO, NO₂, NO_x, CO, O₃, PM_2.5 and PM₁₀) and the meteorological variables (ambient temperature, wind speed/direction, and relative humidity) and in case of Helsinki also Monin-Obukhov length and mixing height of the present day. The variables to be forecasted are the maximum hourly concentrations of PM₁₀ and NO_x, and the daily average PM₁₀ concentrations of the next day. The meteorological pre-processing model MPP-FMI was used for computing the Monin-Obukhov length and the mixing height. The limitations of such statistical models include the persistence of both the meteorological and air quality situation; the model cannot account for rapid changes (on a temporal scale of hours or less than a day) that are commonly associated, e.g., with meteorological fronts, or episodes of a long-range transport origin. We have selected the input data for the model from one urban background and one urban traffic station both in Athens and Helsinki, in 2005. We have used various statistical evaluation parameters to analyze the performance of the models, and inter-compared the performance of the predictions for both cities. Forecasts from the MLR model were also compared to those from an Artificial Neural Network model (ANN) to investigate, if there are substantial gains that might justify the additional computational effort. The best predictor variables for both cities were the concentrations of NO_x and PM₁₀ during the evening hours as well as wind speed, and the Monin-Obukhov length. In Athens, the index of agreement (IA) for NO_x ranged from 0.77 to 0.84 and from 0.69 to 0.72, in the warm and cold periods of the year. In Helsinki, the corresponding values of IA ranged from 0.32 to 0.82 and from 0.67 to 0.86 for the warm and cold periods. In case of Helsinki the model accuracy was expectedly better on the average, when Monin-Obukhov length and mixing height were included as predictor variables. The models provide better forecasts of the daily average concentration, compared with the maximum hourly concentration for PM₁₀. The results derived by the ANN model where only slightly better than the ones derived by the MLR methodology. The results therefore suggest that the MLR methodology is a useful and fairly accurate tool for regulatory purposes.     

A fault detection technique for air-source heat pump water chiller/heaters

This paper describes a fault detection method and system to detect the faults in air-source heat pump water chiller/heaters. Principal component analysis (PCA) approach is used to extract the correlation of variables in heat pump unit and reduce the dimension of measured data. A PCA model is built to determine the thresholds of statistics and calculate square prediction errors (SPE) of new observations, which are used to check if a fault occurs in heat pump unit. The fault detection system consists of a PCA-based fault detection code, a backpack computer, a digital logger and eight easy-to-install temperature sensors. A real air-source heat pump water chiller/heater for the air-conditioning system of an office building provides the realistic test platform for the validation of fault detection method. The measured data from the heat pump unit under normal condition shows that the PCA model can capture the major correlation and variance among the test variables. Two levels of artificial condenser fouling fault are successfully detected. The results show that the PCA-based fault detection method is applicable and effective for air-source heat pump water chiller/heater.