Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra.

Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously used to identify compounds based on SERS spectral data. However, utilization of SERS to quantify concentrations, with or without machine learning, has been difficult due to the spectral intensity being sensitive to confounding factors such as the substrate parameters, orientation of the analyte, and sample preparation technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from SERS spectra using machine learning. Frequency domain transform methods, including the Fourier and Walsh-Hadamard transforms, are applied to spectral data sets of three analytes (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of the sample pollutants is predicted with >80% cross-validation accuracy from raw SERS data. A cross-validation accuracy of 85% was achieved using deep learning for a moderately sized data set (∼100 spectra), and 70-80% was achieved for small data sets (∼50 spectra). Performance can be maintained within this range even when combining various sample preparation techniques and environmental media interference. Additionally, as a spectral pretreatment, the Fourier and Hadamard transforms are shown to consistently improve prediction accuracy across multiple data sets. Finally, standard models were shown to accurately identify characteristic peaks of compounds via analysis of their importance scores, further verifying their predictive value.

Evidencing anthropogenic pollution of surface waters in a tropical region: a case study of the Culiacan River basin.

Nowadays, one of the most critical challenges is reduced access to water. Climate change, industrialization, and population growth have caused many countries to suffer from water crises, especially in arid and semi-arid areas. The Culiacan River basin in Sinaloa is a region of great importance in Mexico due to its intensive agricultural activity. Hence, water quality assessment has become a necessity to ensure sustainable water use. This study describes the spatiotemporal water quality features of the Humaya, Tamazula, and Culiacan Rivers within the Culiacan River basin and their sources of contamination. Twenty-two water quality parameters were analyzed from samples taken every 6 months from 2012 to 2020 at 19 sampling sites in the basin. A multivariate statistical analysis revealed significant correlations (r > 0.85) between the water quality parameters. The modified Integrated Water Quality Index (IWQI) identified severe pollution in samples from the urban river section of the basin, while good water quality conditions were found upstream. Severe contamination was observed in 26.32% of the samples, whereas only 13.45% evidenced good water quality. The Water Quality Index (WQI) indicated that 94.74% of the samples presented fair water quality, suggesting that the surface waters of the Culiacan River Basin are suitable for agricultural irrigation. This study provides insights into the current water quality status of the surface waters in the Culiacan River Basin, identifying significant pollution sources and areas of concern. The spatiotemporal dynamics of water quality in the Culiacan River basin revealed the importance of continuous monitoring and effective water management practices to improve water quality and achieve sustainable agricultural practices.

Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants' Activities and Properties.

In this study, we introduce the count-based Morgan fingerprint (C-MF) to represent chemical structures of contaminants and develop machine learning (ML)-based predictive models for their activities and properties. Compared with the binary Morgan fingerprint (B-MF), C-MF not only qualifies the presence or absence of an atom group but also quantifies its counts in a molecule. We employ six different ML algorithms (ridge regression, SVM, KNN, RF, XGBoost, and CatBoost) to develop models on 10 contaminant-related data sets based on C-MF and B-MF to compare them in terms of the model's predictive performance, interpretation, and applicability domain (AD). Our results show that C-MF outperforms B-MF in nine of 10 data sets in terms of model predictive performance. The advantage of C-MF over B-MF is dependent on the ML algorithm, and the performance enhancements are proportional to the difference in the chemical diversity of data sets calculated by B-MF and C-MF. Model interpretation results show that the C-MF-based model can elucidate the effect of atom group counts on the target and have a wider range of SHAP values. AD analysis shows that C-MF-based models have an AD similar to that of B-MF-based ones. Finally, we developed a "ContaminaNET" platform to deploy these C-MF-based models for free use.

Metal organic frameworks for electrochemical sensor applications: A review.

Metal-organic frameworks (MOFs) are broadly known as porous coordination polymers, synthesized by metal-based nodes and organic linkers. MOFs are used in various fields like catalysis, energy storage, sensors, drug delivery etc., due to their versatile properties (tailorable pore size, high surface area, and exposed active sites). This review presents a detailed discussion of MOFs as an electrochemical sensor and their enhancement in the selectivity and sensitivity of the sensor. These sensors are used for the detection of heavy metal ions like Cd2+, Pb2+, Hg2+, and Cu2+ from groundwater. Various types of organic pollutants are also detected from the water bodies using MOFs. Furthermore, electrochemical sensing of antibiotics, phenolic compounds, and pesticides has been explored. In addition to this, there is also a detailed discussion of metal nano-particles and metal-oxide based composites which can sense various compounds like glucose, amino acids, uric acid etc. The review will be helpful for young researchers, and an inspiration to future research as challenges and future opportunities of MOF-based electrochemical sensors are also reported.

A Review of Nanocomposite-Modified Electrochemical Sensors for Water Quality Monitoring.

Electrochemical sensors play a significant role in detecting chemical ions, molecules, and pathogens in water and other applications. These sensors are sensitive, portable, fast, inexpensive, and suitable for online and in-situ measurements compared to other methods. They can provide the detection for any compound that can undergo certain transformations within a potential window. It enables applications in multiple ion detection, mainly since these sensors are primarily non-specific. In this paper, we provide a survey of electrochemical sensors for the detection of water contaminants, i.e., pesticides, nitrate, nitrite, phosphorus, water hardeners, disinfectant, and other emergent contaminants (phenol, estrogen, gallic acid etc.). We focus on the influence of surface modification of the working electrodes by carbon nanomaterials, metallic nanostructures, imprinted polymers and evaluate the corresponding sensing performance. Especially for pesticides, which are challenging and need special care, we highlight biosensors, such as enzymatic sensors, immunobiosensor, aptasensors, and biomimetic sensors. We discuss the sensors' overall performance, especially concerning real-sample performance and the capability for actual field application.

Model development for evidence-based prioritisation of policy action on emerging chemical and microbial drinking water risks.

While the burden of disease from well-studied drinking water contaminants is declining, risks from emerging chemical and microbial contaminants arise because of social, technological, demographic and climatological developments. At present, emerging chemical and microbial drinking water contaminants are not assessed in a systematic way, but reactively and incidence based. Furthermore, they are assessed separately despite similar pollution sources. As a result, risks might be addressed ineffectively. Integrated risk assessment approaches are thus needed that elucidate the uncertainties in the risk evaluation of emerging drinking water contaminants, while considering risk assessors' values. This study therefore aimed to (1) construct an assessment hierarchy for the integrated evaluation of the potential risks from emerging chemical and microbial contaminants in drinking water and (2) develop a decision support tool, based on the agreed assessment hierarchy, to quantify (uncertain) risk scores. A multi-actor approach was used to construct the assessment hierarchy, involving chemical and microbial risk assessors, drinking water experts and members of responsible authorities. The concept of value-focused thinking was applied to guide the problem-structuring and model-building process. The development of the decision support tool was done using Decisi-o-rama, an open-source Python library. With the developed decision support tool (uncertain) risk scores can be calculated for emerging chemical and microbial drinking water contaminants, which can be used for the evidence-based prioritisation of actions on emerging chemical and microbial drinking water risks. The decision support tool improves existing prioritisation approaches as it combines uncertain indicator levels with a multi-stakeholder approach and integrated the risk assessment of chemical and microbial contaminants. By applying the concept of value-focused thinking, this study addressed difficulties in evidence-based decision-making related to emerging drinking water contaminants. Suggestions to improve the model were made to guide future research in assisting policy makers to effectively protect public health from emerging drinking water risks.

A generalized predictive model for TiO2-Catalyzed photo-degradation rate constants of water contaminants through artificial neural network.

Titanium dioxide (TiO2) is a well-known photocatalyst in the applications of water contaminant treatment. Traditionally, the kinetics of photo-degradation rates are obtained from experiments, which consumes enormous labor and experimental investments. Here, a generalized predictive model was developed for prediction of the photo-degradation rate constants of organic contaminants in the presence of TiO2 nanoparticles and ultraviolet irradiation in aqueous solution. This model combines an artificial neural network (ANN) with a variety of factors that affect the photo-degradation performance, i.e., ultraviolet intensity, TiO2 dosage, organic contaminant type and initial concentration in water, and initial pH of the solution. The molecular fingerprints (MF) were used to interpret the organic contaminants as binary vectors, a format that is machine-readable in computational linguistics. A dataset of 446 data points for training and testing was collected from the literature. This predictive model shows a good accuracy with a root mean square error (RMSE) of 0.173.

Development of a Low-Cost UV-Vis Spectrophotometer and Its Application for the Detection of Mercuric Ions Assisted by Chemosensors.

Detection of an environmental contaminant requires the use of expensive measurement equipment, which limits the realization of in situ tests because of their high cost, their limited portability, or the extended time duration of the tests. This paper presents in detail the development of a portable low-cost spectrophotometer which, by using a specialized chemosensor, allows detection of mercuric ions (Hg2+), providing effective and accurate results. Design specifications for all the stages assembling the spectrophotometer and the elements selected to build them are presented along with the process to synthesize the chemosensor and the tests developed to validate its performance in comparison with a high-precision commercial laboratory spectrophotometer.

An assessment of the impact of multi-route co-exposures on human variability in toxicokinetics: A case study with binary and quaternary mixtures of volatile drinking water contaminants.

This study aimed to assess the impact of multi-route co-exposures to chemicals on interindividual variability in toxicokinetics. Probabilistic physiologically based pharmacokinetic multi-route interaction models were developed for adults and four younger subpopulations. Drinking water-mediated multi-route exposures were simulated for benzene alone or in co-exposure with toluene, ethylbenzene and m-xylene, for trichloroethylene or vinyl chloride (VC), alone and in mixture. These simulations were performed for "low" and "high" exposure scenarios, involving respectively the US EPA's short-term drinking water health advisories, and 10 times these advisory values. Distributions of relevant internal dose metrics for benzene, trichloroethylene and VC were obtained using Monte Carlo simulations. Intergroup variability indexes (VI) were computed for the "low" (VIL ) and "high" (VIH ) exposure scenarios, as the ratio between the 95th percentile in each subpopulation over the median in adults. Thus, for benzene, parent compound's area under the curve-based VIL for single exposures vs. co-exposures correspondingly varied between 1.7 (teenagers) and 2.8 (infants) vs. 1.9 and 3.1 respectively. VIH varied between 2.5 and 3.5 vs. 2.9 and 4.1. Inversely, VIL and VIH for the amount of benzene metabolized via CYP2E1 pathway decreased in co-exposure compared to single exposure. For VC and trichloroethylene, similar results were obtained for the "high" exposure, but "low" co-exposures did not impact the toxicokinetics of individual substances. In conclusion, multi-route co-exposures can have an impact on the toxicokinetics of individual substances, but to an extent, that does not seem to challenge the default values attributed to the factors deemed at reflecting interindividual or child/adult differences in toxicokinetics.

Braid solid-phase microextraction of polycyclic aromatic hydrocarbons by using fibers coated with silver-based nanomaterials in combination with HPLC with fluorometric detection.

Authors propose a novel braid support configuration for use in solid-phase microextraction (SPME) fibers. Two different braided supports (double and triple) were prepared and compared with the conventional single support configuration. Three kinds of silver-based nanomaterials that serve as coatings on these supports are described. They included silver dendrites, silver nanoparticles (AgNPs), and silver dendrites decorated with AgNPs (Ag-dendrites@AgNPs). They were prepared by electrodeposition, a layer-by-layer (LBL) method, and a hybrid strategy, respectively. Fibers were used in the direct-immersion (DI) mode of SPME. Five polycyclic aromatic hydrocarbons (PAHs) were studied as model analytes by DI-SPME when analyzing (spiked) underground waters. PAHs were further determined with high-performance liquid chromatography (HPLC) and fluorescence detection. The analytical performance of the fibers was compared to that of the commercial polydimethylsiloxane (PDMS) fiber of 100 µm thickness. AgNPs obtained by LBL was the best coating and the double braid was the best support configuration. The configuration of the SPME support always played an important role independently on the coating material, being always beneficial the use of double-braids. Despite the low coatings volumes of the silver-based fibers compared to that of PDMS, the analytical features of the method were adequate. Figures of merit include: (a) limits of detection down to 20 ng·L-1; (b) intra-day, inter-day, and inter-fiber precisions (expressed as RSDs) of <13%, <12%, and < 13%, respectively; and (c) adequate operational lifetime (>60 extractions). Graphical abstract Schematic presentation of braided solid-phase microextraction support configurations together with different silver-based nanomaterials as coatings.

Metal-organic frameworks/carbon-based materials for environmental remediation: A state-of-the-art mini-review.

In recent years, many research groups started to study the combination of metal-organic frameworks (MOFs) with nanocarbon materials, which showed the excellent improved performances than MOFs alone. The addition of carbon materials such as graphene oxides (GOs) and carbon nanotubes (CNTs) into MOFs can improve the physico-chemical properties of parent MOFs with excellent chemical robustness, high mechanical and distinguished electronic thermal robustness. These advantages facilitate the wider applications of MOFs/carbon materials (MOFs-C) in more research fields. This paper is devoted to reviewing the recent studies about the preparation and applications of MOFs-C in environmental remediation. This paper discusses the efficient adsorptive removal of a wide range of pollutants by MOFs-C, including organic contaminants and heavy metals from water as well as VOCs and some other toxic gases from atmospheric environment. Additionally, the catalytic performance of these nanocomposites for photocatalysis and Fenton-like oxidation of water pollutants is discussed in details. Meanwhile, the significant roles of nanocarbons and in-depth mechanisms for improved adsorption or catalysis are summarized. Finally, future perspectives on the development and application of MOFs-C composites for pollution remediation are presented at the end of this paper.

Graphene-Based Catalysts for Ozone Processes to Decontaminate Water.

The use of graphene-based materials as catalysts in both ozone and ozone/radiation processes is creating interest among researchers devoted to the study of advanced oxidation processes (AOPs) for the degradation of organic pollutants in water. In this review, detailed explanations of catalytic and photocatalytic ozonation processes mediated by graphene-based materials are presented, focusing on aspects related to the preparation and characterization of catalysts, the nature of the water pollutants treated, the type of reactors and radiation sources applied, the influence of the main operating variables, catalyst activity and stability, and kinetics and mechanisms.

Ingested nitrate and nitrite, disinfection by-products, and pancreatic cancer risk in postmenopausal women.

Nitrate and nitrite are precursors of N-nitroso compounds (NOC), probable human carcinogens that cause pancreatic tumors in animals. Disinfection by-products (DBP) exposures have also been linked with digestive system cancers, but few studies have evaluated relationships with pancreatic cancer. We investigated the association of pancreatic cancer with these drinking water contaminants and dietary nitrate/nitrite in a cohort of postmenopausal women in Iowa (1986-2011). We used historical monitoring and treatment data to estimate levels of long-term average nitrate and total trihalomethanes (TTHM; the sum of the most prevalent DBP class) and the duration exceeding one-half the maximum contaminant level (>½ MCL; 5 mg/L nitrate-nitrogen, 40 µg/L TTHM) among participants on public water supplies (PWS) >10 years. We estimated dietary nitrate and nitrite intakes using a food frequency questionnaire. We computed hazard ratios (HR) and 95% confidence intervals (CI) using Cox regression and evaluated nitrate interactions with smoking and vitamin C intake. We identified 313 cases among 34,242 women, including 152 with >10 years PWS use (N = 15,710). Multivariable models of average nitrate showed no association with pancreatic cancer (HRp95vs. Q1 = 1.16, 95% CI: 0.51-2.64). Associations with average TTHM levels were also null (HRQ4vs. Q1 = 0.70, 95% CI:0.42-1.18). We observed no trend with increasing years of exposure to either contaminant at levels >½ MCL. Positive associations were suggested in the highest dietary nitrite intake from processed meat (HRp95vs. Q1 = 1.66, 95% CI 1.00-2.75;ptrend = 0.05). We found no interactions of nitrate with known modifiers of endogenous NOC formation. Our results suggest that nitrite intake from processed meat may be a risk factor for pancreatic cancer.

A community-based evaluation of proximity to unconventional oil and gas wells, drinking water contaminants, and health symptoms in Ohio.

Over 4 million Americans live within 1.6 km of an unconventional oil and gas (UO&G) well, potentially placing them in the path of toxic releases. We evaluated relationships between residential proximity to UO&G wells and (1) water contamination and (2) health symptoms in an exploratory study. We analyzed drinking water samples from 66 Ohio households for 13 UO&G-related volatile organic compounds (VOCs) (e.g., benzene, disinfection byproducts [DBPs]), gasoline-range organics (GRO), and diesel-range organics. We interviewed participants about health symptoms and calculated metrics capturing proximity to UO&G wells. Based on multivariable logistic regression, odds of detection of bromoform and dibromochloromethane in surface water decreased significantly as distance to nearest UO&G well increased (odds ratios [OR]: 0.28-0.29 per km). Similarly, distance to nearest well was significantly negatively correlated with concentrations of GRO and toluene in ground water (rSpearman: -0.40 to -0.44) and with concentrations of bromoform and dibromochloromethane in surface water (rSpearman: -0.48 to -0.50). In our study population, those with higher inverse-distance-squared-weighted UO&G well counts within 5 km around the home were more likely to report experiencing general health symptoms (e.g. stress, fatigue) (OR: 1.52, 95%CI: 1.02-2.26). This exploratory study, though limited by small sample size and self-reported health symptoms, suggests that those in closer proximity to multiple UO&G wells may be more likely to experience environmental health impacts. Further, presence of brominated DBPs (linked to UO&G wastewater) raises the question of whether UO&G activities are impacting drinking water sources in the region. The findings from this study support expanded studies to advance knowledge of the potential for water quality and human health impacts; such studies could include a greater number of sampling sites, more detailed chemical analyses to examine source attribution, and objective health assessments.

A decision tree approach to screen drinking water contaminants for multiroute exposure potential in developing guideline values.

The consideration of inhalation and dermal routes of exposures in developing guideline values for drinking water contaminants is important. However, there is no guidance for determining the eligibility of a drinking water contaminant for its multiroute exposure potential. The objective of the present study was to develop a 4-step framework to screen chemicals for their dermal and inhalation exposure potential in the process of developing guideline values. The proposed framework emphasizes the importance of considering basic physicochemical properties prior to detailed assessment of dermal and inhalation routes of exposure to drinking water contaminants in setting guideline values.

Solar or UVA-Visible Photocatalytic Ozonation of Water Contaminants.

An incipient advanced oxidation process, solar photocatalytic ozonation (SPO), is reviewed in this paper with the aim of clarifying the importance of this process as a more sustainable water technology to remove priority or emerging contaminants from water. The synergism between ozonation and photocatalytic oxidation is well known to increase the oxidation rate of water contaminants, but this has mainly been studied in photocatalytic ozonation systems with lamps of different radiation wavelength, especially of ultraviolet nature (UVC, UVB, UVA). Nowadays, process sustainability is critical in environmental technologies including water treatment and reuse; the application of SPO systems falls into this category, and contributes to saving energy and water. In this review, we summarized works published on photocatalytic ozonation where the radiation source is the Sun or simulated solar light, specifically, lamps emitting radiation to cover the UVA and visible light spectra. The main aspects of the review include photoreactors used and radiation sources applied, synthesis and characterization of catalysts applied, influence of main process variables (ozone, catalyst, and pollutant concentrations, light intensity), type of water, biodegradability and ecotoxicity, mechanism and kinetics, and finally catalyst activity and stability.

Nanomaterial-enabled Rapid Detection of Water Contaminants.

Water contaminants, e.g., inorganic chemicals and microorganisms, are critical metrics for water quality monitoring and have significant impacts on human health and plants/organisms living in water. The scope and focus of this review is nanomaterial-based optical, electronic, and electrochemical sensors for rapid detection of water contaminants, e.g., heavy metals, anions, and bacteria. These contaminants are commonly found in different water systems. The importance of water quality monitoring and control demands significant advancement in the detection of contaminants in water because current sensing technologies for water contaminants have limitations. The advantages of nanomaterial-based sensing technologies are highlighted and recent progress on nanomaterial-based sensors for rapid water contaminant detection is discussed. An outlook for future research into this rapidly growing field is also provided.

Nitrate in drinking water and bladder cancer risk in Spain.

BACKGROUND: Nitrate is a widespread contaminant in drinking water and ingested nitrate under conditions resulting in endogenous nitrosation is suspected to be carcinogenic. However, the suggested association between nitrate in drinking water and bladder cancer remains inconsistent. We evaluated the long-term exposure to drinking water nitrate as a risk factor for bladder cancer, considering endogenous nitrosation modifiers and other covariables. METHODS: We conducted a hospital-based case-control study of bladder cancer in Spain (1998-2001). Residential histories and water consumption information were ascertained through personal interviews. Historical nitrate levels (1940-2000) were estimated in study municipalities based on monitoring records and water source. Residential histories of study subjects were linked with nitrate estimates by year and municipality to calculate individual exposure from age 18 to recruitment. We calculated odds ratios (OR) and 95% confidence intervals (CI) for bladder cancer among 531 cases and 556 controls with reliable interviews and nitrate exposure information covering at least 70% of years from age 18 to interview. RESULTS: Average residential levels ranged from 2.1mg/L to 12.0mg/L among regions. Adjusted OR (95%CI) for average residential levels relative to ≤ 5 mg/L were 1.2 (0.7-2.0) for >5-10mg/L and 1.1 (0.6-1.9) for >10mg/L. The OR for subjects with longest exposure duration (>20 years) to highest levels (>9.5mg/L) was 1.4 (0.9-2.3). Stratification by intake of vitamin C, vitamin E, meat, and gastric ulcer diagnosis did not modify these results. A non-significant negative association was found with waterborne ingested nitrate with an OR of 0.7 (0.4-1.0) for >8 vs. ≤ 4 mg/day. Adjustment for several covariables showed similar results to crude analyses. CONCLUSION: Bladder cancer risk was inconsistently associated with chronic exposure to drinking water nitrate at levels below the current regulatory limit. Elevated risk is suggested only among subjects with longest exposure duration to the highest levels. No evidence of interaction with endogenous nitrosation modifiers was observed.

Assessing the water quality in a World Heritage Site using biomarkers in top fish predators.

The use of biomarkers in fish for biomonitoring is a valuable approach to reveal effects of human impacts on biota health. Top predator fish are effective models for monitoring human activities' impacts on aquatic ecosystems. The Guaraguaçu River is the largest river-system on coastal region of South Brazil and a World Heritage site. The river receives contaminants from disorderly urban growth, including discharges of domestic sewage and small fishery boats, particularly during the tourist season. Our study aimed to assess impact of anthropogenic activities on water quality in the Guaraguaçu River by analyzing environmental contamination biomarkers in the top fish predator Hoplias malabaricus. Fish were collected using a fyke net trap across sectors representing a gradient of anthropic impact: sector 1 - pristine; sector 2 - impacted; and sector 3 - less impacted. Water samples were collected to analyze the presence of trace elements and pesticide. Biomarkers of the antioxidant system, histopathology, genotoxicity, neurotoxicity, and concentration of trace elements were analyzed in fish tissues. In water samples Al, Fe and Mn were detected, but no pesticides were found. In fish muscle, zinc and iron were detected. Brain acetylcholinesterase activity decreased in impacted sectors, indicating neurotoxic effects. The antioxidant system increased activity in gills and liver, and damage from lipoperoxidation was observed, particularly in sector 2 when compared to sector 1, suggesting oxidative stress. Histopathological biomarkers revealed lesions in the liver and gills of fish in impacted sectors. Micronuclei, a genotoxicity biomarker, were observed in organisms from all sectors. Our results demonstrate detrimental effects of poor water quality on biota health, even when contaminants are not detected in water.

Lifetime exposure to brominated trihalomethanes in drinking water and swimming pool attendance are associated with chronic lymphocytic leukemia: a Multicase-Control Study in Spain (MCC-Spain).

BACKGROUND: Chronic lymphocytic leukemia (CLL) etiology is poorly understood, and carcinogenic chemicals in drinking and recreational water are candidates. OBJECTIVE: To evaluate the association between drinking-water exposure to trihalomethanes (THMs) and nitrate as well as lifetime swimming pool attendance and CLL. METHODS: During 2010-2013, hospital-based CLL cases and population-based controls were recruited in Spain, providing information on residential histories, type of water consumed and swimming pool attendance. Average THMs and nitrate levels in drinking water were linked to lifetime water consumption. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using mixed models. RESULTS: Final samples for residential tap water analyses and swimming pool attendance analyses were 144 cases/1230 controls and 157 cases/1240 controls, respectively. Mean (SD) values for average lifetime residential brominated THMs and chloroform in tap water (µg/L), and ingested nitrate (mg/day) were 48.1 (35.6), 18.5 (6.7) and 13.7 (9.6) respectively in controls; and 72.9 (40.7), 17.9 (5.4), and 14.1 (8.8) in CLL cases. For each 10 µg/L increase of brominated THMs and chloroform lifetime-average levels, the ORs (95% CI) were 1.22 (1.14, 1.31) and 0.54 (0.34, 0.87), respectively. For each 5 mg/day increase of ingested nitrate, the OR of CLL was 0.91 (0.80, 1.04). The OR of lifetime pool users (vs. non-users) was 2.38 (1.61, 3.52). Upon performing annual frequency of attending pools analysis through categorization, the second and third categories showed an ORs of 2.36 (1.49, 3.72) and 2.40 (1.51, 3.83), respectively, and P-trend of 0.001. IMPACT STATEMENT: This study identifies an association of long-term exposure to THMs in drinking water, at concentrations below the regulatory thresholds and WHO guidelines, and swimming pool attendance, with chronic lymphocytic leukemia (CLL). These unprecedented findings are highly relevant since CLL is an incurable cancer with still unknown etiology and because the widespread exposure to chlorination by-products that remain in drinking and recreational water worldwide. Despite the demonstrated carcinogenicity in animals of several chlorination by-products, little is known about their potential risks on human health. This study makes a significant contribution to the search for environmental factors involved in the etiology of CLL and to the evidence of the health impact of these high prevalent water contaminants.