Monitoring and modeling of disinfection by-products (DBPs)

Optical properties of algogenic organic matter within the growth period of Chlorella sp. and predicting their disinfection by-product formation

Algogenic organic matter (AOM) in eutrophic waters is a well-known precursor to disinfection by-product (DBP) formation in drinking water. This purpose of this study is (i) to characterize the optical properties of AOM origins, including intra- (IOM) and extra-cellular organic matter (EOM), derived from Chlorella sp. growth as precursors to two major carbonaceous DBPs (C-DBPs), trihalomethanes (THMs) and haloacetic acids (HAAs) and (ii) to correlate these optical properties with THM and HAA formation potential (FP) in order to predict DBP formation. The results show that both EOM and IOM had low UV₂₅₄ and UV₂₈₀ absorbance during their entire growth phase. While IOM chiefly comprised of aromatic proteins and soluble microbial products-like substances (80% of average fluorescent intensity-AFI), EOM spectra were rich in humic- and fulvic-like substances (60% AFI). However, its chemical nature likely differed from terrestrial humics. In DBPFP tests, IOM was a higher-yielding precursor of THMs and HAAs compared to EOM, regardless its growth status. Consequently, C-DBPFP of IOM was always higher than EOM during four growth phases. Results from DBP tests also showed insignificant variation of EOM-derived THMFP and HAAFP during the algal growth phase, while the algal growth status strongly influenced the yields of IOM-derived THMFP and HAAFP. From correlation analysis, our results showed no correlation between UV absorbance with THMFP and HAAFP. Conversely, the regional AFI showed a good correlation with HAAFP and C-DBPFP. Predicting models based on AFI for the formation of HAAs and C-DBPs consequently yielded great predictability for laboratory AOM-containing water samples, with a coefficient of determination R²=0.879, p<0.01 and R²=0.846, p<0.01. This study indicates a promising application of fluorescent spectra for predicting DBPs derived from algae-rich water sources.

Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources

Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation; and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application.

Monthly survey of N-nitrosamine yield in a conventional water treatment plant in North China

A sampling campaign was conducted monthly to investigate the occurrence of N-nitrosamines at a conventional water treatment plant in one city in North China. The yield of N-nitrosamines in the treated water indicated precursors changed greatly after the source water switching. Average concentrations of N-nitrosodimethylamine (NDMA), N-nitrosomorpholine (NMOR), and N-nitrosopyrrolidine (NPYR) in the finished water were 6.9, 3.3, and 3.1ng/L, respectively, from June to October when the Luan River water was used as source water, while those of NDMA, N-nitrosomethylethylamine (NMEA), and NPYR in the finished water were 10.1, 4.9, and 4.7ng/L, respectively, from November to next April when the Yellow River was used. NDMA concentration in the finished water was frequently over the 10ng/L, i.e., the notification level of California, USA, which indicated a considerable threat to public health. Weak correlations were observed between N-nitrosamine yield and typical water quality parameters except for the dissolved organic nitrogen.

Trihalomethanes formation in Iranian water supply systems: predicting and modeling

Trihalomethanes (THMs) were the first disinfection by-products discovered in drinking water and are classified as probable carcinogens. This study measures and models THMs formation at two drinking water distribution systems (WDS1 and WDS2) in Ahvaz City, Iran. The investigation was based on field-scale investigations and an intensive 36-week sampling program, from January to September 2011. The results showed total THM concentrations in the range 17.4-174.8 μg/L and 18.9-99.5 μg/L in WDS1 and WDS2, respectively. Except in a few cases, the THM concentrations in WDS1 and WDS2 were lower than the maximum contaminant level values. Using two-tailed Pearson correlation test, the water temperature, dissolved organic carbon, UV254, bromide ion (Br-), free residual chlorine, and chlorine dose were identified as the significant parameters for THMs formation in WDS2. Water temperature was the only significant parameter for THMs formation in WDS1. Based on the correlation results, a predictive model for THMs formation was developed using a multiple regression approach. A multiple linear regression model showed the best fit according to the coefficients of determination (R2) obtained for WDS1 (R2=0.47) and WDS2 (R2=0.54). Further correlation studies and analysis focusing on THMs formation are necessary to assess THMs concentration using the predictive models.

Predictive model for disinfection by-product in Alexandria drinking water, northern west of Egypt

Chlorine has been utilized in the early stages of water treatment processes as disinfectant. Disinfection for drinking water reduces the risk of pathogenic infection but may pose a chemical threat to human health due to disinfection residues and their by-products (DBP) when the organic and inorganic precursors are present in water. In the last two decades, many modeling attempts have been made to predict the occurrence of DBP in drinking water. Models have been developed based on data generated in laboratory-scale and field-scale investigations. The objective of this paper is to develop a predictive model for DBP formation in the Alexandria governorate located at the northern west of Egypt based on field-scale investigations as well as laboratory-controlled experimentations. The present study showed that the correlation coefficient between trihalomethanes (THM) predicted and THM measured was R (2)=0.88 and the minimum deviation percentage between THM predicted and THM measured was 0.8 %, the maximum deviation percentage was 89.3 %, and the average deviation was 17.8 %, while the correlation coefficient between dichloroacetic acid (DCAA) predicted and DCAA measured was R (2)=0.98 and the minimum deviation percentage between DCAA predicted and DCAA measured was 1.3 %, the maximum deviation percentage was 47.2 %, and the average deviation was 16.6 %. In addition, the correlation coefficient between trichloroacetic acid (TCAA) predicted and TCAA measured was R (2)=0.98 and the minimum deviation percentage between TCAA predicted and TCAA measured was 4.9 %, the maximum deviation percentage was 43.0 %, and the average deviation was 16.0 %.

Predicting disinfection by-product formation potential in water

Formation of regulated and non-regulated disinfection by-products (DBPs) is an issue at both potable water and wastewater treatment plants (W/WWTPs). Water samples from W/WWTPs across the USA were collected and DBP formation potentials (DBPFPs) in the presence of free chlorine and chloramine were obtained for trihalomethane (THM), haloacetic acid (HAA), haloacetonitrile (HAN), and N-nitrosodimethylamine (NDMA). With nearly 200 samples covering a range of dissolved organic carbon (0.6-23 mg/L), ultraviolet absorbance (0.01-0.48 cm(-1) at 254 nm wavelength), and bromide (0-1.0 mg/L) levels, power function models were developed to predict the carbonaceous DBP (C-DBP) and nitrogenous DBP (N-DBP) precursors spanning 3 orders of magnitudes. The predicted THM and HAA formation potentials fitted well with the measured data (analytical variance of less than 22%). Inclusion of dissolved organic nitrogen (DON) into the HANFP model improved the predictions. NDMAFP was the most difficult one to predict based upon the selected water quality parameters, perhaps suggesting that bulk measurements such as DOC or UVA(254) were not appropriate for tracking NDMAFP. These are the first such DBPFP models for wastewater systems, and among the few models that consider both C-DBPs and N-DBPs formation potentials from the same water sources.

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 microl of the 10 mg l(-1) pentafluortoluene bromide solution was added to each vial. The extraction step was performed by adding 10 g of Na(2)SO(4) 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 microg 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.

The impact of water consumption, point-of-use filtration and exposure categorization on exposure misclassification of ingested drinking water contaminants

The use of population-level indices to estimate individual exposures is an important limitation of previous epidemiologic studies of disinfection by-products (DBPs). We examined exposure misclassification resulting from the use of system average DBP concentrations to estimate individual-level exposures. Data were simulated (n=1000 iterations) for 100 subjects across 10 water systems based on the following assumptions: DBP concentrations ranged from 0-99 microg/L with limited intra-system variability; water intake ranged from 0.5-2.5 L/day; 20% of subjects used bottled water exclusively; 20% of subjects used filtered tap water exclusively; DBP concentrations were reduced by 50% or 90% following filtration. DBP exposure percentiles were used to classify subjects into different exposure levels (e.g., low, intermediate, high and very high) for four classification approaches. Compared to estimates of DBP ingestion that considered daily consumption, source type (i.e., unfiltered tap, filtered tap, and bottled water), and filter efficiency (with 90% DBP removal), 48-62% of subjects were misclassified across one category based on system average concentrations. Average misclassification across at least two exposure categories (e.g., from high to low) ranged from 4-14%. The median classification strategy resulted in the least misclassification, and volume of water intake was the most influential modifier of ingestion exposures. These data illustrate the importance of individual water use information in minimizing exposure misclassification in epidemiologic studies of drinking water contaminants.

A sensitivity analysis of bias in relative risk estimates due to disinfection by-product exposure misclassification

We conducted a sensitivity analysis of relative risk estimates using local area mean disinfection by-product exposures. We used Monte Carlo simulations to generate data representing 100 towns, each with 100 births (n=10,000). Each town was assigned a mean total trihalomethane (TTHM) exposure value (mean=45, SD=28) based on a variable number of sampling locations (range 2-10). True maternal TTHM exposure was randomly assigned from a lognormal distribution using that town's true mean value. We compared the effect of a 20 microg/l increase in TTHM exposure on the risk of small-for-gestational age infancy using the true maternal exposure compared to various weighting measures of the town mean exposures. The exposure metrics included: (1) unweighted town mean, (2) town mean weighted by the inverse variance of the town mean, (3) town mean weighted by the inverse standard deviation of the town mean, (4) town mean weighted by 1-(standard deviation of sites per town/mean across all towns), and (5) a randomly selected value from one of the sites within the town of residence. To estimate the magnitude of misclassification bias from using the town mean concentrations, we compared the true exposure odds ratios (1.00, 1.20, 1.50, and 2.00) to the mean exposure odds ratios from the five exposure scenarios. Misclassification bias from the use of unweighted town mean exposures ranged from 19 to 39%, increasing in proportion to the size of the true effect estimates. Weighted town mean TTHM exposures were less biased than the unweighted estimates of maternal exposure, with bias ranging from 0 to 23%. The weighted town mean analyses showed that attenuation of the true effect of DBP exposure was diminished when town mean concentrations with large variability were downweighted. We observed a trade-off between bias and precision in the weighted exposure analyses, with the least biased effects estimates having the widest confidence intervals. Effect attenuation due to intrasystem variability was most evident in absolute and relative terms for larger odds ratios.

Assessing environmental attitudes and concerns about a contaminated site in a densely populated suburban environment

Considerable attention has been devoted to the concerns and perceptions of people residing around contaminated facilities, both brownfields in urban areas and others located in remote and lightly populated areas. This paper examines the concerns of recreationists and sportsmen residing near the Department of Energy's (DOE) Brookhaven National Laboratory, in central Long Island, one of the most densely populated regions in the United States, where tourism is of prime importance. On an open-ended question, the greatest concern was pollution, followed by environmental health as a global concern, and human health as a concern for Brookhaven. Accidents/spills, loss of public health, and loss of ecological health were rated highest among a list of concerns, and change in property values was rated lowest. When asked to rank seven concerns, protecting human health was ranked the highest, and economic interests were ranked the lowest. For future land use at Brookhaven, recreational uses were rated the highest, while building houses and factories, and storage of nuclear material were rated the lowest. These data can be used by managers, decision and policy makers, and the general public to assess and manage local and regional environmental concerns and to consider future land uses for decommissioned lands, such as those at Brookhaven.