Formation and modeling of disinfection by-products in drinking water of six cities in China

Water quality parameters including TOC, UV(254), pH, chlorine dosage, bromide concentration and disinfection by-products were measured in water samples from 41 water treatment plants of six selected cities in China. Chloroform, bromodichloromethane, dibromochloromethane, dichloroacetic acid and trichloroacetic acid were the major disinfection by-products in the drinking water of China. Bromoform and dibromoacetic acid were also detected in many water samples. Higher concentrations of trihalomethanes and haloacetic acids were measured in summer compared to winter. The geographical variations in DBPs showed that TTHM levels were higher in Zhengzhou and Tianjin than other selected cities. And the HAA5 levels were highest in Changsha and Tianjin. The modeling procedure that predicts disinfection by-products formation was studied and developed using artificial neural networks. The performance of the artificial neural networks model was excellent (r > 0.84).     

Monitoring and modeling of disinfection by-products (DBPs)

In the United States, the newly promulgateddisinfectant/disinfection by-product (D/DBP) regulationsforce water treatment utilities to be more concerned withfinished and distributed water qualities. In this study,monitoring of DBP formation was conducted from three Frenchwater treatment plants trying to assess DBP variationsthrough time and space. Compared to the in-plant totaltrihalomethanes (TTHM) levels, TTHM levels in thedistribution system increased from less than 150% to morethan 300%. Significant variations for TTHM and bromate(BrO₃ ^-) levels throughout the seasons were alsoobserved; generally higher levels in the summer and lowerlevels in the winter. Combining chemical DBP models(empirical power functional models) and hydraulicsimulations, DBPs including TTHM and BrO₃ ^- weresuccessfully simulated from the full-scale monitoring data,indicating that empirical DBP model can be a potential toolto access DBP formation in actual plants. This study alsoprovides the protocols to assess DBP simulations in thewater treatment systems.     

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.     

Ecological risk analysis of polycyclic aromatic hydrocarbons (PAHs) in surface sediments from Laizhou Bay

The current research aims at developing predictive models for trihalomethane (THM) formation in Lebanon based on field-scale investigations as well as laboratory controlled experimentations. Statistical analysis on field data revealed significant correlations for TTHM with chlorine dose, Specific UV-A, and UV(254) absorbing organics. Simulated distribution system-THM tests showed significant correlations with applied chlorine dose, total organic carbon, bromides, contact time, and temperature. Predictive models, formulated using multiple regression approaches, exhibiting the highest coefficients of determination were quadratic for the directly after chlorination (DAC; r(2) = 0.39, p < 0.036) and network (r(2) = 0.33, p < 0.064) THM databases, and logarithmic for the laboratory simulated THM database (r(2) = 0.70, p < 0.001). Computed r(2) values implied low correlations for the DAC and network THM database, and high correlation for the laboratory simulated THM database. Significance of the models were at the 0.05 level for DAC database, 0.10 level for the network database, and very high (<0.01 level) for the laboratory simulated THM database. It is noteworthy to mention that no previous attempts to assess, monitor, and predict THM concentrations in public drinking water have been reported for the country although a large fraction of the population consumes chlorinated public drinking water.     

Nitrate,sulphate and chloride contents in public drinking water supplies in Sicily,Italy

Water samples collected from public drinking water supplies in Sicily were analysed for electric conductivity and for their chloride, sulphate and nitrate contents. The samples were collected as uniformly as possible from throughout the Sicilian territory, with an average sampling density of about one sample for every 7,600 inhabitants. Chloride contents that ranged from 5.53 to 1,302 mg/l were correlated strongly with electric conductivity, a parameter used as a proxy for water salinity. The highest values are attributable to seawater contamination along the coasts of the island. High chloride and sulphate values attributable to evaporitic rock dissolution were found in the central part of Sicily. The nitrate concentrations ranged from 0.05 to 296 mg/l, with 31 samples (4.7% of the total) exceeding the maximum admissible concentration of 50 mg/l. Anomalous samples always came from areas of intensive agricultural usage, indicating a clear anthropogenic origin. The same parameters were also measured in bottled water sold in Sicily, and they all were within the ranges for public drinking water supplies. The calculated mean nitrate intake from consuming public water supplies (16.1 mg/l) did not differ significantly from that of bottled water (15.2 mg/l). Although the quality of public water supplies needs to be improved by eliminating those that do not comply with the current drinking water limits, at present it does not justify the high consumption of bottled water (at least for nitrate contents).     

测定游离氯和总氯样品保存方法研究

建立了游离氯和总氯样品保存方法。用氢氧化钠溶液作为固定剂,现场固定含有游离氯和总氯的水样,使水样pH>12。结果表明,样品经4℃低温避光保存,5 d内测定,测量结果没有显著变化,方法检出限(以Cl2计)为0.004mg/L,加标回收率为96.7%～104%。     

Economics of place-based monitoring under the safe drinking water act, part II: Design and development of place-based monitoring strategies

The goals of environmental legislation and associated regulations are to protect public health, natural resources, and ecosystems. In this context, monitoring programs should provide timely and relevant information so that the regulatory community can implement legislation in a cost-effective and efficient manner. The Safe Drinking Water Act (SDWA) of 1974 attempts to ensure that public water systems (PWSs) supply safe water to its consumers. As is the case with many other federal environmental statutes, SDWA monitoring has been implemented in relatively uniform fashion across the United States. In this three part series, spatial and temporal patterns in water quality data are utilized to develop, compare, and evaluate the economic performance of alternative place-based monitoring approaches to current monitoring practice. Part II: Several factors affect the performance of monitoring strategies, including: measurable objectives, required precision in estimates, acceptable confidence levels of such estimates, available budget for sampling. In this paper, we develop place-based monitoring strategies based on extensive analysis of available historical water quality data (1960-1994) of 19 Iowa community water systems. These systems supply potable water to over 350,000 people. In the context of drinking water, the objective is to protect public health by utilizing monitoring resources to characterize contaminants that are detectable, and are close to exceeding health standards. A place-based monitoring strategy was developed in which contaminants were selected based on their historical occurrence, rather than their appearance on the SDWA contaminant list. In a subset of the water systems, the temporal frequency of monitoring for one ubiquitous contaminant, nitrate, was tailored to patterns in its historical occurrence and concentration. Three sampling allocation models (linear, quadratic, and cubic) based on historic patterns in peak occurrence were developed and evaluated. Random and fixed-interval sampling strategies within the context of such models were also developed and evaluated. Strategies were configured to incorporate a variety of options for frequency and number of samples (depending on budget and the desired precision in estimate of peak concentrations).     

Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed

Nitrate-nitrogen concentrations in rivers represent challenges for water supplies that use surface water sources. Nitrate concentrations are often modeled using time-series approaches, but previous efforts have typically relied on monthly time steps. In this study, we developed a dynamic regression model of daily nitrate concentrations in the Raccoon River, Iowa, that incorporated contemporaneous and lags of precipitation and discharge occurring at several locations around the basin. Results suggested that 95 % of the variation in daily nitrate concentrations measured at the outlet of a large agricultural watershed can be explained by time-series patterns of precipitation and discharge occurring in the basin. Discharge was found to be a more important regression variable than precipitation in our model but both regression parameters were strongly correlated with nitrate concentrations. The time-series model was consistent with known patterns of nitrate behavior in the watershed, successfully identifying contemporaneous dilution mechanisms from higher relief and urban areas of the basin while incorporating the delayed contribution of nitrate from tile-drained regions in a lagged response. The first difference of the model errors were modeled as an AR(16) process and suggest that daily nitrate concentration changes remain temporally correlated for more than 2 weeks although temporal correlation was stronger in the first few days before tapering off. Consequently, daily nitrate concentrations are non-stationary, i.e. of strong memory. Using time-series models to reliably forecast daily nitrate concentrations in a river based on patterns of precipitation and discharge occurring in its basin may be of great interest to water suppliers.     

Comparison of Nitrate Levels in Raw Water and Finished Water from Historical Monitoring Data on Iowa Municipal Drinking Water Supplies

Nitrate contamination of water sources is a concern where large amounts of nitrogen fertilizers are regularly applied to soils. Ingested nitrate from dietary sources and drinking water can be converted to nitrite and ultimately to N-nitroso compounds, many of which are known carcinogens. Epidemiologic studies of drinking water nitrate and cancer report mixed findings; a criticism is the use of nitrate concentrations from retrospective drinking water data to assign exposure levels. Residential point-of-use nitrate data are scarce; gaps in historical data for municipal supply finished water hamper exposure classification efforts. We used generalized linear regression models to estimate and compare historical raw water and finished water nitrate levels (1960s--1990s) in single source Iowa municipal supplies to determine whether raw water monitoring data could supplement finished water data to improve exposure assessment. Comparison of raw water and finished water samples (same sampling date) showed a significant difference in nitrate levels in municipalities using rivers; municipalities using other surface water or alluvial groundwater had no difference in nitrate levels. A regional aggregation of alluvial groundwater municipalities was constructed based on results from a previous study showing regional differences in nitrate contamination of private wells; results from this analysis were mixed, dependent upon region and decade. These analyses demonstrate using historical raw water nitrate monitoring data to supplement finished water data for exposure assessment is appropriate for individual Iowa municipal supplies using alluvial groundwater, lakes or reservoirs. Using alluvial raw water data on a regional basis is dependent on region and decade.     

Fuzzy Logic Model to Estimate Seasonal Pseudo Steady State Chlorophyll-A Concentrations in Reservoirs

A fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO₄ phosphorus, NO₃ nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.     

Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r² = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional stream condition can be made, and it can be used to locate potentially impaired stream segments. Further, the model results can be displayed visually, which allows results to be presented to a wide variety of audiences easily.     

Verification of a depth-integrated sample arm as a means to reduce solids stratification bias in urban stormwater sampling

A new water sample collection system was developed to improve representation of solids entrained in urban stormwater by integrating water-quality samples from the entire water column, rather than a single, fixed point. The depth-integrated sample arm (DISA) was better able to characterize suspended-sediment concentration and particle size distribution compared to fixed-point methods when tested in a controlled laboratory environment. Median suspended-sediment concentrations overestimated the actual concentration by 49 and 7% when sampling the water column at 3- and 4-points spaced vertically throughout the water column, respectively. Comparatively, sampling only at the bottom of the pipe, the fixed-point overestimated the actual concentration by 96%. The fixed-point sampler also showed a coarser particle size distribution compared to the DISA which was better able to reproduce the average distribution of particles in the water column over a range of hydraulic conditions. These results emphasize the need for a water sample collection system that integrates the entire water column, rather than a single, fixed point to properly characterize the concentration and distribution of particles entrained in stormwater pipe flow.     

Modeling rhizofiltration: heavy-metal uptake by plant roots

The discovery of phytoaccumulation potential of plant species has led to its application for remediation of heavy-metal-contaminated soil and wastewater, which is termed as phytoextraction/rhizofiltration. For prediction, analysis, planning and cost-effective design of such systems, mathematical models not only are used as a screening tool but also provide optimal parameters like harvesting time, irrigation schedule, etc. Several laboratory and field scale studies have been carried out in the past, and mathematical expressions have been developed by various researchers for different phenomena like metal adsorption in soil, plant root growth with time, moisture and metal uptake by plant root, moisture movement in unsaturated zone, soil moisture relationship, etc. The complete design of any such phytoremediation program would require the knowledge of behavior of heavy-metal movement in soil, water and plant root system. In this paper, a model for simulating heavy-metal dynamics in soil, water and plant root system is developed and discussed. The governing non-linear partial differential equation is solved numerically by implicit finite difference method using Picard's iterative technique, and the formulation has been illustrated using a characteristic example. The source code is written in MATLAB.     

Monitoring the Trihalomethanes Present in Water After Treatment with Chlorine Under Laboratory Condition

In this work assays involving chlorinated water samples, which were previous spiked with humic substances or algae blue green and following the production of the THMs for 30 days is described. To implement the assays, five portions of 1,000 ml of water were stored in glass bottles. The water samples were treated with solutions containing 2, 3, 4 and 5 mg l⁻¹ chlorine. The samples aliquots (60 ml) were transferred into the glass vials, 10 ml were removed to have a headspace and 100 μl of the 10 mg l⁻¹ pentafluortoluene bromide solution was added to each vial. The extraction step was performed by adding 10 g of Na₂SO₄ followed by 5 ml of n-pentane. The vials were stopped with a TFE-faced septum and sealed with aluminum caps. The generated THMs were determined by gas chromatography with electron capture detector using reference solutions with concentration ranging from 8 to 120 μg l⁻¹ THMs. Three assays were monitored during 30 days and chloroform was the predominant compound found in the water samples, while other species of THMs were not detected. The results showed that when the chlorine concentration was increased in water samples containing algae the concentration of THM varied randomly. Nevertheless, in water samples containing humic substances the increase of the THM concentration presented a relationship with the chlorine concentration. It was also observed that chloroform concentration increased with the elapsed time up to one and six days to water samples spiked with humic substances and algae blue green, respectively and decreased along 30 days. By other hand, assays performed using water samples containing decanted algae material showed that THM was not generated by the chlorine addition.     

Seasonal change and correlation with environmental parameters for 2-MIB in Feng-Shen Reservoir, Taiwan

Two common drinking water earthy/musty odorants, 2-methyl-isoborneol (2-MIB) and trans-1,10-dimethyl-trans-9-decalol (geosmin), were monitored and analyzed for their correlation with environmental conditions in Feng-Shen Reservoir (FSR), Taiwan. Long-term monitoring results, from December 2000 to July 2003, indicated that 2-MIB was present in the reservoir at concentrations between 10 and 200 ng L(-1), and those of geosmin were always smaller than 10 ng L(-1). The 2-MIB concentrations followed a trend of higher concentrations during the warm seasons. After being analyzed with 10 water quality and three meteorological parameters, 2-MIB concentration was found to correlate with corresponding air and water temperatures. An analysis of air temperature history versus 2-MIB concentration in FSR suggested that 2-MIB concentration was best correlated with the mean of average daily temperature from 1 to 9 days before sampling, with R (2) = 0.90. This correlation was further employed to predict the 2-MIB concentration observed during another sampling period in FSR. A similar degree of fit was observed between predictions and experimental data, suggesting the potential applicability of the approach. In addition to the effect of temperature, heavy rainfalls that occurred before the sampling time may also reduce the 2-MIB concentration in the reservoir. A short-term continuous monitoring of the odorants for 32 h showed that both geosmin and 2-MIB concentrations in the reservoir remained almost constant, with only about a 10% difference during the sampling period. This may suggest that the odorants were uniformly distributed in the water near the sampling location in the reservoir.     

A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes

Chlorophyll-a (chl-a) concentrations are often used as a proxy for water quality problems as well as phytoplankton blooms. Available chl-a models range from simple phosphorus loading models to complex regression and dynamic models. A comparison of multiple regression models was made with genetic programming (GP) techniques to predict chl-a concentrations over a large range of 104 Swedish lakes. Independent variables used were lake area, mean depth, iron, latitude, ammonium, nitrogen + nitrate, pH, phosphate, secchi depth, silicon, temperature, total phosphorus, total nitrogen and total organic carbon. GP is a method based on the Darwinian evolution theory. This implies that a program will be able to test different mathematical equations, iterating and improving each equation using fundamental ideas from evolution theory to increase the predictive power. A good correspondence was found between the multiple regression and the GP modelling approach. No significant improvement of the predictive power was found using GP, and it is therefore recommended that multiple regression methods should be preferred when predicting chl-a concentrations as these models tend to be less complex and the modelling approach is easier to use. Results from GP were in some cases more accurate compared to multiple regressions; however, the best model was created by multiple regressions which used concentrations of total phosphorus, total nitrogen and latitude as independent variables. These findings will be an important note for limnologists and modelling managers when developing future models of chl-a concentrations in lakes.     

Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications

Degradation of water quality is a major problem worldwide and often leads to serious environmental impacts and concerns about public health. In this study, the water quality monitoring and assessment of the Koumoundourou Lake, a brackish urban shallow lake located in the northeastern part of Elefsis Bay (Greece), were evaluated. A number of water quality parameters (pH, temperature, dissolved oxygen concentration, electrical conductivity, turbidity, nutrients, and chlorophyll-a concentration) were analyzed in water samples collected bimonthly over a 1-year period from five stations throughout the lake. Moreover, biological quality elements were analysed seasonally over the 1-year period (benthic fauna). Statistical analysis was performed in order to evaluate the water quality of the lake and distinguish sources of variation measured in the samples. Furthermore, the chemical and trophic status of the lake was evaluated according to the most widely applicable classification schemes. Satellite images of Landsat 5 Thematic Mapper were used in order for algorithms to be developed and calculate the concentration of chlorophyll-a (Chl-a). The trophic status of the lake was characterized as oligotrophic based on phosphorus and as mesotrophic–eutrophic based on Chl-a concentrations. The results of the remote sensing application indicated a relatively high coefficient of determination (R ²) among point sampling results and the remotely sensed data, which implies that the selected algorithm is reliable and could be used for the monitoring of Chl-a concentration in the particular water body when no field data are available.     

Modeling the Formation of Chlorination By-Products During Enhanced Coagulation

Because of increasing need to balance health risks for pathogen control and disinfection by-products (DBP) formation in drinking water supplies, water utilities are forced to closely examine and optimize their disinfection practices. This research was designed to investigate the effects of independent variables of dissolved organic carbon (DOC), ferric chloride dosage, chlorine dose, and reaction time on trihalomethanes (THMs) formation in Terkos Lake Water (TLW) of Istanbul City. A statistically-based empirical model was developed for predicting THM formation during enhanced coagulation. The R ² and F value of model were 0.762 and 460, respectively. The model was found to be statistically significant for all four variables, and model predictions appear to be most accurate for this study. A multiple linear model exhibited the best fit of data. It was observed that THM formation depended primarily on DOC removal. Model calibration, testing and validation were accomplished by using independent data set.     

Use of neural networks for monitoring surface water quality changes in a neotropical urban stream

This paper reports the using of neural networks for water quality analysis in a tropical urban stream before (2002) and after sewerage building and the completion of point-source control-based sanitation program (2003). Mathematical modeling divided water quality data in two categories: (a) input of some in situ water quality variables (temperature, pH, O2 concentration, O2 saturation and electrical conductivity) and (b) water chemical composition (N-NO2(-); N-NO3(-); N-NH4(+) Total-N; P-PO4(3-); K+; Ca2+; Mg+2; Cu2+; Zn2+ and Fe+3) as the output from tested models. Stream water data come from fortnightly sampling in five points along the Ipanema stream (Southeast Brazil, Minas Gerais state) plus two points downstream and upstream Ipanema discharge into Doce River. Once the best models are consistent with variables behavior we suggest that neural networking shows potential as a methodology to enhance guidelines for urban streams restoration, conservation and management.     

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.