水中亚硝胺类消毒副产物的污染现状、形成与控制

综述了水中亚硝胺类消毒副产物的来源、浓度水平及限定标准,以及亚硝胺类物质(NAs)的生成机制、分析检测技术、控制与去除方法。指出,随着人们对饮用水安全消毒的重视和污水消毒的推广,应增加大范围水中NAs污染水平的调查工作,以及对复杂污水体系中NAs生成前体物的研究,优化和改进消毒工艺,减少NAs的生成,针对复杂水样建立准确可靠的NAs检测方法。     

Study of personal–indoor–ambient fine particulate matters among school communities in mixed urban–industrial environment in India

A sampling program was conducted to investigate the formation of disinfection by-products (DBPs) and dissolved organic carbon (DOC) at two advanced water treatment plants in Kaohsiung City, Taiwan. The results in this study can be used as a reference for the operational control of water treatment plants and the setting of regulations in Taiwan. Samples of drinking water were collected from two advanced water treatment plants from June 2007 to April 2008. Changes in the concentration of dissolved organic carbon, the trihalomethane formation potential, and the haloacetic acids formation potential were measured in raw water samples. Variations in the concentrations of trihalomethanes (THMs) and haloacetic acids (HAA₅) in finished drinking water were evaluated. The major species of HAA₅ were in the order of dichloroacetic acid and trichloroacetic acid and the THM was of trichloromethane. DOC was strongly related to DBPs in raw water. In this investigation, the removal efficiency of DBPs in Plant A (ultrafiltration/reverse osmosis system) exceeded that in Plant B (ozonation/biological activated carbon system). Both advanced water treatment plants greatly improved the quality of drinking water.     

饮用水消毒副产物检测技术应用进展

饮用水消毒副产物是水体进行消毒过程中天然有机物与消毒剂反应生成的副产物。毒理学和流行病学相关研究表明,饮用水消毒副产物会对人体健康产生不利影响。笔者综述了传统与新兴消毒副产物检测过程中的前处理技术与分析技术,总结了不同前处理技术和分析技术的原理、优缺点及应用情况,并预测和展望了消毒副产物检测技术的发展前景。     

Assessing regulatory violations of disinfection by-products in water distribution networks using a non-compliance potential index

Inactivating pathogens is essential to eradicate waterborne diseases. However, disinfection forms undesirable disinfection by-products (DBPs) in the presence of natural organic matter. Many regulations and guidelines exist to limit DBP exposure for eliminating possible health impacts such as bladder cancer, reproductive effects, and child development effects. In this paper, an index named non-compliance potential (NCP) index is proposed to evaluate regulatory violations by DBPs. The index can serve to evaluate water quality in distribution networks using the Bayesian Belief Network (BBN). BBN is a graphical model to represent contributing variables and their probabilistic relationships. Total trihalomethanes (TTHM), haloacetic acids (HAA5), and free residual chlorine (FRC) are selected as the variables to predict the NCP index. A methodology has been proposed to implement the index using either monitored data, empirical model results (e.g., multiple linear regression), and disinfectant kinetics through EPANET simulations. The index’s usefulness is demonstrated through two case studies on municipal distribution systems using both full-scale monitoring and modeled data. The proposed approach can be implemented for data-sparse conditions, making it especially useful for smaller municipal drinking water systems.     

Disinfection by-product formation and mutagenic assay caused by preozonation of groundwater containing bromide

Brominated organic and inorganic by-products are generated during ozonation of groundwater containing high bromide concentrations. This study measured concentrations of bromate, bromoform, bromoacetic acids, bromoacetonitriles, bromoacetone, 2,4-dibromophenol and aldehyde generated by ozonation. The potential mutagenicity of ozonated waters was assessed using the Ames and Microtox tests. Test results for the 18 ozonated groundwater samples demonstrate that bromate formation is associated with high pH, bromide and alkalinity content, low levels of dissolved organic carbon (DOC) and ammonia, and low alkalinity. Brominated organic by-products were correlated with high bromide ion and natural organic matter content, and low ammonia concentrations. The Ames test results demonstrate that all extracts from ozonated water have mutagenic activity; however, the 18 raw groundwater samples had no mutagenicity. The Microtox test results also show that the ozonated water samples were highly toxic. Generally, both bromide and DOC content promoted the formation of ozonation by-products and mutagenicity. Controlling of bromide and DOC concentrations is an effective method of reducing potential by-product formation and eliminating mutagenicity problems associated with groundwater ozonation.     

Spatial and temporal occurrence of N-nitrosamines in seven drinking water supply systems

The spatiotemporal presence of eight N-nitrosamines in the water of seven supply systems in Quebec considered to be susceptible to these emerging disinfection by-products was evaluated. This is the first study on the presence of N-nitrosamines in drinking water utilities in Quebec. Seven sampling campaigns were carried out at several sampling points in each of the systems over a period of 1 year. The results show that N-nitrosamines, primarily N-nitrosodimethylamine (NDMA), were not commonly detected in the water of the facilities under study (10 % of samples). The concentrations measured were lower than those reported in recent North American studies. None of the 195 samples taken exceeded the Ontario standard of 9 ng/L for NDMA (maximum value observed of 3.3 ng/L). N-nitrosomethylethylamine and N-nitrosopiperidine were detected once, with concentrations of 3.7 and 6.0 ng/L, respectively. Chloramination was identified as being the main risk factor regarding the presence of N-nitrosamines, but water quality and some operating parameters, in particular disinfectant residual, also seem to be related to their presence. NDMA concentrations at the end of the distribution systems were generally higher than water leaving the plant. No seasonal trends were observed for the formation of N-nitrosamines in the investigated supply systems. Finally, an association between the presence of N-nitrosamines and the levels of trihalomethanes and haloacetic acids was observed in some facilities.     

Occurrence of haloacetic acids (HAAs) and trihalomethanes (THMs) in drinking water of Taiwan

In this study, water samples were collected from 86 water treatment plants for analysis of haloacetic acids (HAAs) and trihalomethanes (THMs) from February to March, 2007 and from July to August, 2007. Both seasonal and geographical variations of disinfection by-products (DBPs) in drinking water of Taiwan were presented. The results showed that the five HAA concentrations (HAA5) were 1.0–38.9 μg/L in the winter and 0.2–46.7 μg/L in the summer; and the total THMs were ND-99.4 μg/L in the winter and ND-133.2 μg/L in the summer. For samples taken from the main Taiwan island, dichloroacetic acid (29.4–31.7%) and trichloroacetic acid (25.3–27.6%) were the two major HAA species, and trichloromethane was the major THM species (49.9–62.2%) in finished water. For water treatment plants located on the offshore islands outside of Taiwan, high bromide concentration was found in raw water, and higher percentage of brominated THMs and HAAs were formed in the overall formation. A statistically significant (P?<?0.005) logarithmic linear regression model was found to be useful to describe the correlations between TTHM and HAA5 or nine HAAs (HAA5?=?1.219 ×TTHM ^0.754, R ²?=?0.658; HAA9?=?1.824 ×TTHM ^0.735, R ²?=?0.678). No apparent difference was observed for DBPs concentrations between finished water and distribution samples in this study.     

Modelling geosmin concentrations in three sources of raw water in Quebec, Canada

The presence of off-flavour compounds such as geosmin, often found in raw water, significantly reduces the organoleptic quality of distributed water and diverts the consumer from its use. To adapt water treatment processes to eliminate these compounds, it is necessary to be able to identify them quickly. Routine analysis could be considered a solution, but it is expensive and delays associated with obtaining the results of analysis are often important, thereby constituting a serious disadvantage. The development of decision-making tools such as predictive models seems to be an economic and feasible solution to counterbalance the limitations of analytical methods. Among these tools, multi-linear regression and principal component regression are easy to implement. However, due to certain disadvantages inherent in these methods (multicollinearity or non-linearity of the processes), the use of emergent models involving artificial neurons networks such as multi-layer perceptron could prove to be an interesting alternative. In a previous paper (Parinet et al., Water Res 44: 5847-5856, 2010), the possible parameters that affect the variability of taste and odour compounds were investigated using principal component analysis. In the present study, we expand the research by comparing the performance of three tools using different modelling scenarios (multi-linear regression, principal component regression and multi-layer perceptron) to model geosmin in drinking water sources using 38 microbiological and physicochemical parameters. Three very different sources of water, in terms of quality, were selected for the study. These sources supply drinking water to the Québec City area (Canada) and its vicinity, and were monitored three times per month over a 1-year period. Seven different modelling methods were tested for predicting geosmin in these sources. The comparison of the seven different models showed that simple models based on multi-linear regression provide sufficient predictive capacity with performance levels comparable to those obtained with artificial neural networks. The multi-linear regression model (R ²?=?0.657, <0.001) used only four variables (phaeophytin, sum of green algae, chlorophyll-a and potential Redox) in comparison with ten variables (potassium, heterotrophic bacteria, organic nitrogen, total nitrogen, phaeophytin, total organic carbon, sum of green algae, potential Redox, UV absorbance at 254 nm and atypical bacteria) for the best model obtained with artificial neural networks (R ²?=?0.843).     

Drinking water treatment is not associated with an observed increase in neural tube defects in mice

Disinfection by-products (DBPs) arise when natural organic matter in source water reacts with disinfectants used in the water treatment process. Studies have suggested an association between DBPs and birth defects. Neural tube defects (NTDs) in embryos of untreated control mice were first observed in-house in May 2006 and have continued to date. The source of the NTD-inducing agent was previously determined to be a component of drinking water. Tap water samples from a variety of sources were analyzed for trihalomethanes (THMs) to determine if they were causing the malformations. NTDs were observed in CD-1 mice provided with treated and untreated surface water. Occurrence of NTDs varied by water source and treatment regimens. THMs were detected in tap water derived from surface water but not detected in tap water derived from a groundwater source. THMs were absent in untreated river water and laboratory purified waters, yet the percentage of NTDs in untreated river water were similar to the treated water counterpart. These findings indicate that THMs were not the primary cause of NTDs in the mice since the occurrence of NTDs was unrelated to drinking water disinfection.     

Laboratory-scale chlorination to estimate the levels of halogenated DBPs in full-scale distribution systems

Occurrence of halogenated disinfection by-products (DBPs) (trihalomethanes –THMs– and haloacetic acids –HAAs–) in the waters of two utilities in Quebec City (Canada) was investigated using two approaches: experimental chlorination studies and full-scale sampling within distribution systems. Experimental studies were designed to reproduce treatment plant and distribution system conditions (chlorine dose, water temperature, pH and water residence time). Differences in halogenated DBPs in the two distribution systems under study were significant and comparable to those observed in experimental laboratory studies. For the waters of both utilities, chlorination studies better reproduced the occurrence of halogenated DBPs in points of the distribution system located near the treatment plant (low residence time of water) than in other points. Multivariate regression models for THMs, HAAs and their species were developed using the data from experimental studies in order to predict halogenated DBP levels measured in the distribution system. Models were all statistically significant, but showed low ability to predict full-scale halogenated DBPs, particularly in points located at distribution system extremities. Specifically, experimental chlorination-based models are not able to simulate the decrease of HAA levels. Results of this research suggest that the use of experimental data to predict halogenated DBP levels in full-scale distribution systems – for operational, regulatory and epidemiological purposes – must be done with caution.     

Investigations on boron levels in drinking water sources in China

To evaluate boron contamination of public drinking water in China, both dissolved and total boron contents in 98 public drinking water sources from 49 cities, 42 brands of bottled water samples from supermarkets in several cities, and 58 water samples from boron industrial area were measured by inductively coupled plasma-mass spectrometry (ICP-MS). Our experimental results showed that boron existed in public drinking water sources mainly in dissolved status with total concentrations ranging from 0.003 to 0.337 mg/L (mean = 0.046 mg/L). The mean boron concentrations in mineral and pure bottled water were 0.052 and 0.028 mg/L, respectively. The results obtained in this work showed that there was no health risk on view of boron in public drinking water sources and bottled water. In boron industrial area, boron concentrations in surface water and ground water were 1.28 mg/L (range = 0.007–3.8 mg/L) and 18.3 mg/L (range = 0.015–140 mg/L), respectively, which indicated that boron industry caused boron pollution in local water system.     

Trihalomethane formation potential in treated water supplies in urban metro city

Trihalomethane (THM) formation potential (TFP) is very useful test to assess the level of the formation of trihalomethanes in worst case scenario. Organics in water have the potential to generate harmful disinfection by-products (DBPs) such as THMs, as a result of their reaction with disinfectant chlorine used in drinking water. DBPs are increasingly recognized as cancerous agents. TFP of postchlorinated treated water were investigated at six water treatment plants (WTPs) in Delhi City (India). The present paper presents the current trends of TFP so that prevention and control measures can be initiated by the regulating agencies responsible for drinking water supply. Liquid–liquid extraction method, followed by qualitative and quantitative estimation by gas chromatograph equipped with electron capture detector, had been used for the determination of THMs in water samples collected at the outlet just before supplying to the consumers during 2000–2007. The TFP values from 2004 onward of all WTPs did not exceed the WHO guideline value of ≤1.     

人工神经网络在深圳市水库富营养化评价中的应用

对富营养化评价标准进行插值获取大量的样本,建立了基于BP人工神经网络的富营养化评价模型。将模型应用于评价深圳市13座主要水库的富营养化状况,对其成因进行分析,并提出了对策与建议。研究结果表明,石岩水库与深圳水库为轻度富营养化,占评价水库总数的15.4%;西丽水库等11座水库为中营养,占评价水库总数的84.6%。人工神经网络用于建立湖库富营养评价模型是适合的。     

Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability

For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.     

The occurrence of the herbicide dalapon (2,2-dichloropropionate) in potable water as a disinfection by-product

Salts of 2,2-dichloropropionic acid, such as dalapon, are well known as herbicides and are regulated as such in potable water in Australia and elsewhere. It is also an identified disinfection by-product (DBP), but little is known about the compound's formation and typical levels from this source. This work presents results from a sampling campaign where 2,2-dichloropropionate was found at levels between 0.1 and 0.5 μg l(-1) in potable water samples from a major treatment plant in South East Queensland, Australia. However, levels were below the reporting limit (0.01 μg l(-1)) in the immediate source water for the plant. Also, temporal trends in 2,2-dichloropropionate observed in treated water during sampling mirrored those of trihalomethanes albeit at much lower concentrations, suggesting that the occurrence is due to in situ formation as a DBP. This could present a regulatory dilemma in some jurisdictions.     

Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.     

Survey on fluoride, bromide and chloride contents in public drinking water supplies in Sicily (Italy)

Six hundred and sixty-seven water samples were collected from public drinking water supplies in Sicily and analysed for electric conductivity and for their Cl(-), Br(-) and F(-) contents. The samples were, as far as possible, collected evenly over the entire territory with an average sampling density of about one sample for every 7,600 inhabitants. The contents of Cl(-) and Br(-), ranging between 5.53 and 1,302 mg/l and between <0.025 and 4.76 mg/l respectively, correlated well with the electric conductivity, a parameter used as a proxy for water salinity. The highest values were found both along the NW and SE coasts, which we attributed to seawater contamination, and in the central part of Sicily, which we attributed to evaporitic rock dissolution. The fluoride concentrations ranged from 0.023 to 3.28 mg/l, while the highest values (only three exceeding the maximum admissible concentration of 1.5 mg/l) generally correlated either with the presence in the area of crystalline (volcanic or metamorphic) or evaporitic rocks or with contamination from hydrothermal activity. Apart from these limited cases of exceeding F(-) levels, the waters of public drinking water supplies in Sicily can be considered safe for human consumption for the analysed parameters. Some limited concern could arise from the intake of bromide-rich waters (about 3% exceeding 1 mg/l) because of the potential formation of dangerous disinfection by-products.     

Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.     

Size and resin fractionations of dissolved organic matter and trihalomethane precursors from four typical source waters in China

Dissolved organic matter (DOM) and its potential to form disinfection by-products (DBPs) during drinking water treatment raise challenges to water quality control. Understanding both chemical and physical characteristics of DOM in source waters is key to better water treatment. In this study, the DOM from four typical source waters in China was fractionated by XAD resin adsorption (RA) and ultrafiltration (UF) techniques. The trihalomethane formation potential (THMFP) of all fractions in the DOM were investigated to reveal the major THM precursors. The fraction distributions of DOM could be related to their geographical origins in a certain extent. The dominant chemical fraction as THM precursors in the DOM from south waters (East-Lake reservoir in Shenzhen and Peal rivers in Guangzhou) was hydrophobic acid (HoA). The size fraction with molecular weight (MW) <1 kDa in both south waters had the highest THMFP. The results of cluster analysis showed that the parameters of fractions including DOC percentage (DOC%), UV₂₅₄%, SUVA₂₅₄ (specific UV₂₅₄ absorbance) and THMFP were better for representing the differences of DOM from the studied waters than specific THMFP (STHMFP). The weak correlation between SUVA₂₅₄ and STHMFP for either size or XAD fractions suggests that whether SUVA₂₅₄ can be used as an indicator for the reactivity of THM formation is highly dependent on the nature of organic matter.