Modeling contaminant concentration distributions in China's centralized source waters

Characterizing contaminant occurrences in China's centralized source waters can provide an understanding of source water quality for stakeholders. The single-factor (i.e., worst contaminant) water-quality assessment method, commonly used in Chinese official analysis and publications, provides a qualitative summary of the country's water-quality status but does not specify the extent and degree of specific contaminant occurrences at the national level. Such information is needed for developing scientifically sound management strategies. This article presents a Bayesian hierarchical modeling approach for estimating contaminant concentration distributions in China's centralized source waters using arsenic and fluoride as examples. The data used are from the most recent national census of centralized source waters in 2006. The article uses three commonly used source water stratification methods to establish alternative hierarchical structures reflecting alternative model assumptions as well as competing management needs in characterizing pollutant occurrences. The results indicate that the probability of arsenic exceeding the standard of 0.05 mg/L is about 0.96-1.68% and the probability of fluoride exceeding 1 mg/L is about 9.56-9.96% nationally, both with strong spatial patterns. The article also discusses the use of the Bayesian approach for establishing a source water-quality information management system as well as other applications of our methods.     

What's next after 40 years of drinking water regulations?

The quality of drinking water in the United States has continued to improve over the past 40 years. The formation of the U.S. Environmental Protection Agency (USEPA) in 1971, the passage of the initial Safe Drinking Water Act (SDWA, PL 93-523) in 1974, and the passage of the 1996 SDWA Amendments (PL 104-208) represent significant progress in drinking water quality. While the widespread adoption of filtration and disinfection in the early 1900s virtually eliminated waterborne typhoid fever, some residual risks still remained 40 years ago. These national regulatory developments compelled USEPA and the drinking water community to address these remaining risks in drinking water and optimize risk reduction for the public.     

Characterization of background concentrations of contaminants using a mixture of normal distributions

We present a Bayesian approach for characterizing background contaminant concentration distributions using data from sites that may have been contaminated. Our method, focused on estimation, resolves several technical problems of the existing methods sanctioned by the U.S. Environmental Protection Agency (USEPA) (a hypothesis testing based method), resulting in a simple and quick procedure for estimating background contaminant concentrations. The proposed Bayesian method is applied to two data sets from a federal facility regulated under the Resource Conservation and Restoration Act. The results are compared to background distributions identified using existing methods recommended by the USEPA. The two data sets represent low and moderate levels of censorship in the data. Although an unbiased estimator is elusive, we show that the proposed Bayesian estimation method will have a smaller bias than the EPA recommended method.     

Daily dietary intake of fluoride by Slovenian Military based on analysis of total fluorine in total diet samples using fluoride ion selective electrode

The purpose of this study was to develop an analytical procedure for determination of the amount of total fluoride in total diet samples, including drinking water and beverages. Samples were taken by the duplicate portion technique and decomposed by alkali carbonate fusion using KNaCO₃, and the amount of fluoride in solution was determined by fluoride ion selective electrode using the multiple known addition technique. The mean amount of total fluoride determined in 20 total diet samples obtained from the Slovenian Military was 1.84 ± 0.70 mg/kg on a dry matter basis. Accordingly the estimated daily intake was 1.50 ± 0.56 mg.     

Demographic and behavioral modifiers of arsenic exposure pathways: a Bayesian hierarchical analysis of NHEXAS data

We introduce a Bayesian hierarchical statistical model that describes subpopulation-specific pathways of exposure to arsenic. Our model is fitted to data collected as part of the National Human Exposure Assessment Survey (NHEXAS) and builds on the structural-equation-based analysis of the same data by Clayton et al. (Journal of Exposure Analysis and Environmental Epidemiology, 2002, 12, 29-43). Using demographic information (e.g., gender or age) and surrogates for environmental exposure (e.g., tobacco usage or the average number of minutes spent in an enclosed workshop), we identify subgroup differences in exposure routes. Missing and censored data, as well as uncertainty due to measurement error, are handled systematically in the Bayesian framework. Our analysis indicates that household size, amount of time spent at home, use of tapwater for drinking and cooking, number of glasses of water drunk, use of central air conditioning, and use of gas equipment significantly modify the arsenic exposure pathways.     

Quantification of variability and uncertainty for air toxic emission inventories with censored emission factor data

Probabilistic emission inventories were developed for urban air toxic emissions of benzene, formaldehyde, chromium, and arsenic for the example of Houston. Variability and uncertainty in emission factors were quantified for 71-97% of total emissions, depending upon the pollutant and data availability. Parametric distributions for interunit variability were fit using maximum likelihood estimation (MLE), and uncertainty in mean emission factors was estimated using parametric bootstrap simulation. For data sets containing one or more nondetected values, empirical bootstrap simulation was used to randomly sample detection limits for nondetected values and observations for sample values, and parametric distributions for variability were fit using MLE estimators for censored data. The goodness-of-fit for censored data was evaluated by comparison of cumulative distributions of bootstrap confidence intervals and empirical data. The emission inventory 95% uncertainty ranges are as small as -25% to +42% for chromium to as large as -75% to +224% for arsenic with correlated surrogates. Uncertainty was dominated by only a few source categories. Recommendations are made for future improvements to the analysis.     

Modelling homogeneous and heterogeneous microbial contaminations in a powdered food product

The actual physical distribution of microorganisms within a batch of food influences quantification of microorganisms in the batch, resulting from sampling and enumeration by microbiological tests. Quantification may be most accurate for batches in which microorganisms are distributed homogeneously. However, when the distribution is non-homogeneous, quantification may result in an under-, or overestimation. In the case of pathogens being non-homogeneously distributed, this heterogeneity will impact on public health. Enumeration data are commonly modelled by the Lognormal distribution. Although the Lognormal distribution can model heterogeneity, it does not allow for complete absence of microorganisms. Studies that validate the appropriateness of using Lognormal or other statistical distributions are scarce. This study systematically investigated laboratory and industrial scale batches of powdered infant formula, modelled the enumeration data using a range of statistical distributions, and assessed the appropriateness of individual models. For laboratory scale experiments, batches of milk powder were contaminated by distributing similar numbers of cells of Cronobacter sakazakii either homogeneously throughout a batch of milk powder or by distributing the cells in a localised part of the batch. Each batch was then systematically sampled and the distribution determined by enumerating the samples. By also enumerating the remainder of the batch, a balance could be made of the total number of microorganisms added and of the number retrieved from a batch. Discrete, as well as continuous statistical distributions, were fitted to enumeration data and the parameters estimated by Maximum Likelihood. The data were fitted both as censored and uncensored data. Enumeration data obtained for an industrial batch of powdered infant formula were investigated in this way as well. It was found that Normal, Poisson and Zero-Inflated Poisson distributions fitted the data sets very poorly. In case of homogeneous contamination, there was not a notable difference between the ability of Negative Binomial, Poisson-Lognormal, Weibull, Gamma, and Lognormal distributions to model the data. Overall, either the Negative Binomial distribution or the Poisson-Lognormal distribution fitted the data best in the 10 batches studied, especially when part of a data set contained zeros and/or the numbers were low. The Negative Binomial fitted the laboratory batches best and the Poisson-Lognormal fitted the industrial batch best.     

Artificial sweetener sucralose in U.S. drinking water systems

The artificial sweetener sucralose has recently been shown to be a widespread of contaminant of wastewater, surface water, and groundwater. In order to understand its occurrence in drinking water systems, water samples from 19 United States (U.S.) drinking water treatment plants (DWTPs) serving more than 28 million people were analyzed for sucralose using liquid chromatography tandem mass spectrometry (LC-MS/MS). Sucralose was found to be present in source water of 15 out of 19 DWTPs (47-2900 ng/L), finished water of 13 out of 17 DWTPs (49-2400 ng/L) and distribution system water of 8 out of the 12 DWTPs (48-2400 ng/L) tested. Sucralose was only found to be present in source waters with known wastewater influence and/or recreational usage, and displayed low removal (12% average) in the DWTPs where finished water was sampled. Further, in the subset of DWTPs with distribution system water sampled, the compound was found to persist regardless of the presence of residual chlorine or chloramines. In order to understand intra-DWTP consistency, sucralose was monitored at one drinking water treatment plant over an 11 month period from March 2010 through January 2011, and averaged 440 ng/L in the source water and 350 ng/L in the finished water. The results of this study confirm that sucralose will function well as an indicator compound for anthropogenic influence on source, finished drinking and distribution system (i.e., tap) water, as well as an indicator compound for the presence of other recalcitrant compounds in finished drinking water in the U.S.     

Perchlorate, nitrate, and iodide intake through tap water

Perchlorate is ubiquitous in the environment, leading to human exposure and potential impact on thyroid function. Nitrate can also competitively inhibit iodide uptake at the sodium-iodide symporter and thus reduce thyroid hormone production. This study investigates the intake of perchlorate, nitrate, and iodide attributable to direct and indirect tap water consumption. The National Health and Nutrition Examination Survey collected tap water samples and consumption data from 3262 U.S. residents during the years 2005-2006. The median perchlorate, nitrate, and iodide levels measured in tap water were 1.16, 758, and 4.55 μg/L, respectively. Measured perchlorate levels were below the United States Environmental Protection Agency (U.S. EPA) drinking water equivalent level for perchlorate (24.5 μg/L). Significant correlations were found between iodide and nitrate levels (r = 0.17, p < 0.0001) and perchlorate and nitrate levels (r = 0.25, p < 0.0001). On the basis of 24 h recall, 47% of the study participants reported drinking tap water; 89% reported either direct or indirect consumption of tap water. For the adult population (age ≥ 20 yrs) the median tap water consumption rate was 11.6 mL/kg-day. Using individual tap water consumption data and body weight, we estimated the median perchlorate, nitrate, and iodide dose attributable to tap water as 9.11, 11300, and 43.3 ng/kg-day, respectively, for U.S. adults. This perchlorate exposure dose from tap water is relatively small compared to the total perchlorate exposure dose previously characterized for the U.S. adults (median 64 ng/kg-day) and the U.S. EPA reference dose (700 ng/kg-day).     

Estimating correlation with multiply censored data arising from the adjustment of singly censored data

Environmental data frequently are left censored due to detection limits of laboratory assay procedures. Left censored means that some of the observations are known only to fall below a censoring point (detection limit). This presents difficulties in statistical analysis of the data. In this paper, we examine methods for estimating the correlation between variables each of which is censored at multiple points. Multiple censoring frequently arises due to adjustment of singly censored laboratory results for physical sample size. We discuss maximum likelihood (ML) estimation of the correlation and introduce a new method (cp.mle2) that, instead of using the multiply censored data directly, relies on ML estimates of the covariance of the singly censored laboratory data. We compare the ML methods with Kendall's tau-b (ck.taub) which is a modification Kendall's tau adjusted for ties, and several commonly used simple substitution methods: correlations estimated with nondetects set to the detection limit divided by 2 and correlations based on detects only (cs.det) with nondetects setto missing. The methods are compared based on simulations and real data. In the simulations, censoring levels are varied from 0 to 90%, p from -0.8 to 0.8, and v (variance of physical sample size) is set to 0 and 0.5, for a total of 550 parameter combinations with 1000 replications at each combination. We find that with increasing levels of censoring most of the correlation methods are highly biased. The simple substitution methods in general tend toward zero if singly censored and one if multiply censored. ck.taub tends toward zero. Least biased is cp.mle2, however, it has higher variance than some of the other estimators. Overall, cs.det performs the worst and cp.mle2 the best.     

Risk of viral acute gastrointestinal illness from nondisinfected drinking water distribution systems

Acute gastrointestinal illness (AGI) resulting from pathogens directly entering the piping of drinking water distribution systems is insufficiently understood. Here, we estimate AGI incidence from virus intrusions into the distribution systems of 14 nondisinfecting, groundwater-source, community water systems. Water samples for virus quantification were collected monthly at wells and households during four 12-week periods in 2006-2007. Ultraviolet (UV) disinfection was installed on the communities' wellheads during one study year; UV was absent the other year. UV was intended to eliminate virus contributions from the wells and without residual disinfectant present in these systems, any increase in virus concentration downstream at household taps represented virus contributions from the distribution system (Approach 1). During no-UV periods, distribution system viruses were estimated by the difference between well water and household tap virus concentrations (Approach 2). For both approaches, a Monte Carlo risk assessment framework was used to estimate AGI risk from distribution systems using study-specific exposure-response relationships. Depending on the exposure-response relationship selected, AGI risk from the distribution systems was 0.0180-0.0661 and 0.001-0.1047 episodes/person-year estimated by Approaches 1 and 2, respectively. These values represented 0.1-4.9% of AGI risk from all exposure routes, and 1.6-67.8% of risk related to drinking water exposure. Virus intrusions into nondisinfected drinking water distribution systems can contribute to sporadic AGI.     

Uncertainty analysis of life cycle greenhouse gas emissions from petroleum-based fuels and impacts on low carbon fuel policies

The climate change impacts of U.S. petroleum-based fuels consumption have contributed to the development of legislation supporting the introduction of low carbon alternatives, such as biofuels. However, the potential greenhouse gas (GHG) emissions reductions estimated for these policies using life cycle assessment methods are predominantly based on deterministic approaches that do not account for any uncertainty in outcomes. This may lead to unreliable and expensive decision making. In this study, the uncertainty in life cycle GHG emissions associated with petroleum-based fuels consumed in the U.S. is determined using a process-based framework and statistical modeling methods. Probability distributions fitted to available data were used to represent uncertain parameters in the life cycle model. Where data were not readily available, a partial least-squares (PLS) regression model based on existing data was developed. This was used in conjunction with probability mixture models to select appropriate distributions for specific life cycle stages. Finally, a Monte Carlo simulation was performed to generate sample output distributions. As an example of results from using these methods, the uncertainty range in life cycle GHG emissions from gasoline was shown to be 13%-higher than the typical 10% minimum emissions reductions targets specified by low carbon fuel policies.     

瓶装饮用天然矿泉水水质净化及运行管理

介绍了瓶装饮用天然矿泉水的国家标准,推荐了满足这些标准所需要的水质净化典型工艺流程,分析了水质净化系统运行、维护、管理中的问题及解决途径。     

Boron determination in a multi element national water monitoring program: the absence of legal limits

Directive 98/83/EC concerning the drinking water quality and Directive 80/777/EC for Natural Mineral Water demand strict control and monitoring for the presence of metals. The State General Laboratory as the official control laboratory (Accredited by ISO 17025:2005) implements a national monitoring program in order to ensure that the drinking and natural mineral water quality satisfy the requirements of the respective Directives. The National Monitoring program covers mainly metals such as Pb, Cd, Cr, Ni, As, Se, Sb, Hg, Mn, Cu, Fe, Al and B in water supplied for human consumption either by distribution networks, vending machines, mobile water containers, ground water intended for human consumption as well as bottled water. The determination of metals in water by Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) is a technique that successfully meets the requirements of the above Directives as it is a very powerful tool for the measurement of metals at very low concentrations with high accuracy and precision. The results obtained indicate that metal concentrations in drinking and bottled water examined were by far, below the acceptable legal limits and even below the relevant detection limits. However, in samples of bottled natural mineral water, high boron concentration were determined and risk assessment was performed due to the absence of relevant legal limits. The present paper demonstrates the steps undertaken by the General Water Analysis Laboratory of the SGL for the validated method used by ICP-MS in the determination of trace metals including boron in drinking and bottled water.     

Water use on nonirrigated pasture-based dairy farms: Combining detailed monitoring and modeling to set benchmarks

Water use in intensively managed, confinement dairy systems has been widely studied, but few reports exist regarding water use on pasture-based dairy farms. The objective of this study was to quantify the seasonal pattern of water use to develop a prediction model of water use for pasture-based dairy farms. Stock drinking, milking parlor, and total water use was measured on 35 pasture-based, seasonal calving dairy farms in New Zealand over 2 yr. Average stock drinking water was 60 L/cow per day, with peak use in summer. We estimated that, on average, 26% of stock drinking water was lost through leakage from water-distribution systems. Average corrected stock drinking water (equivalent to voluntary water intake) was 36 L/cow per day, and peak water consumption was 72 L/cow per day in summer. Milking parlor water use increased sharply at the start of lactation (July) and plateaued (August) until summer (February), after which it decreased with decreasing milk production. Average milking parlor water use was 58 L/cow per day (between September and February). Water requirements were affected by parlor type, with rotary milking parlor water use greater than herringbone parlor water use. Regression models were developed to predict stock drinking and milking parlor water use. The models included a range of climate, farm, and milk production variables. The main drivers of stock drinking water use were maximum daily temperature, potential evapotranspiration, radiation, and yield of milk and milk components. The main drivers for milking parlor water use were average per cow milk production and milking frequency. These models of water use are similar to those used in confinement dairy systems, where milk yield is commonly used as a variable. The models presented fit the measured data more accurately than other published models and are easier to use on pasture-based dairy farms, as they do not include feed and variables that are difficult to measure on pasture-based farms.     

Boron determination in a multi element national water monitoring program: the absence of legal limits

Directive 98/83/EC concerning the drinking water quality and Directive 80/777/EC for Natural Mineral Water demand strict control and monitoring for the presence of metals. The State General Laboratory as the official control laboratory (Accredited by ISO 17025:2005) implements a national monitoring program in order to ensure that the drinking and natural mineral water quality satisfy the requirements of the respective Directives. The National Monitoring program covers mainly metals such as Pb, Cd, Cr, Ni, As, Se, Sb, Hg, Mn, Cu, Fe, Al and B in water supplied for human consumption either by distribution networks, vending machines, mobile water containers, ground water intended for human consumption as well as bottled water. The determination of metals in water by Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) is a technique that successfully meets the requirements of the above Directives as it is a very powerful tool for the measurement of metals at very low concentrations with high accuracy and precision. The results obtained indicate that metal concentrations in drinking and bottled water examined were by far, below the acceptable legal limits and even below the relevant detection limits. However, in samples of bottled natural mineral water, high boron concentration were determined and risk assessment was performed due to the absence of relevant legal limits. The present paper demonstrates the steps undertaken by the General Water Analysis Laboratory of the SGL for the validated method used by ICP-MS in the determination of trace metals including boron in drinking and bottled water.     

Estimation of fugitive lead emission rates from secondary lead facilities using hierarchical Bayesian models

Fugitive emissions from secondary lead recovery facilities are difficult to estimate and can vary significantly from site to site. A methodology is presented for estimating fugitive emissions using back inference from observed ambient concentrations at nearby monitors, in conjunction with an atmospheric transport and dispersion model. Observed concentrations are regressed against unit source-monitor transfer terms computed by the model, and the fitted parameters of the regression equation include the background ambient lead concentration, the fugitive lead emission rate, and (when stack emissions are assumed to be unknown) the stack lead emission rate. The methodology is implemented at three sites, one each in Florida, Texas, and New York. A hierarchical Bayesian method is used to estimate the parameters of the model, allowing inferences to be made for both site-specific values and multisite (national) distributions of fugitive emissions and background concentrations. Informed prior distributions must be specified for the background lead concentrations and for fugitive and stack emission rates in order to obtain stable estimates. Sensitivity analyses with alternative priors indicate that posterior estimates of background concentrations and fugitive emission rates are relatively insensitive to the assumed priors, although estimated stack emission rates can vary with alternative priors, especially for the New York facility, where the stack emission rate is highly uncertain and poorly resolved by the model. The fugitive lead emission rates estimated for the sites are comparable to, or in some cases (especially Texas and New York) likely larger than the stack emissions that are determined for these facilities. An aggregate predictive distribution is derived for the average fugitive lead emission rate from secondary lead smelting facilities, with a median value of 9.2 x 10(-7) g Pb/m2/sec, and a 90% credible interval from 2.1 x 10(-7)-5.3 x 10(-6) g Pb/m2/sec. This wide range reflects both the variation in fugitive lead emissions from site to site and the high degree of uncertainty resulting from an estimate based on only a very small sample of sites. As such, the primary contribution of this study is methodological, demonstrating how information from multiple sites can be combined and considered simultaneously for the estimation of fugitive emission rates, but recognizing that additional sites must be included to obtain a more precise characterization.     

Prediction of consumer acceptability of potato chips with various moisture contents using survival analysis

Consumer acceptability of potato chips with different moisture contents (MC) was evaluated using survival analysis. Nine different humidity conditions (30–70%) at 25°C were used. MC values of samples ranged from 2.2–9.2% after treatment. Panelists (50) were asked to rate chip acceptability. The chip MC was predicted using non-linear models. Panelist-generated data were categorized as left, interval, and right censored. A total of 18 panelists were left censored and 30 were interval censored. Weibull and lognormal distributions were used to fit the censored data. The mean MC of chips rejected by 50% of consumers were 3.54% (95% confidence interval, 3.14–3.99%) and 3.60% (CI 3.23–4.01%) for the Weibull and lognormal distribution, respectively. These values corresponded to 5.7 and 5.6 overall consumer liking values (Weibull and lognormal distributions, respectively). MC of 3.5–3.6% was necessary to attain consumer acceptability values of 5.6 and product acceptance by 50% of consumers.     

Role of detection limits in drinking water regulation

Some commentators on environmental science and policy have claimed that advances in analytical chemistry, reflected by an ability to detect contaminants at ever-decreasing concentrations, lead to regulations stricter than justified by available toxicological data. We evaluate this claim in the context of drinking water regulation, with respect to contaminants regulated under the Safe Drinking Water Act (SDWA). We examine the relationships between historical and present maximum contaminant levels and goals in the greater context of detection capability and evaluate the extent to which different aspects of the regulatory apparatus (i.e., analytical capability, cost-benefit analysis, analysis of competing risks, and available toxicological data) influence the regulatory process. Our findings do not support the claim that decreases in detection limit lead to more stringent regulation in the context of drinking water regulation in the United States. Further, based on our analysis of the National Primary Drinking Water Regulation and existing United States Environmental Protection Agency approaches to establishing the practical quantifiable level, we conclude that in the absence of changes to the underlying toxicological model, regulatory revision is unlikely.     

Hierarchical Bayesian analysis of censored microbiological contamination data for use in risk assessment and mitigation

Microbiological contamination data often is censored because of the presence of non-detects or because measurement outcomes are known only to be smaller than, greater than, or between certain boundary values imposed by the laboratory procedures. Therefore, it is not straightforward to fit distributions that summarize contamination data for use in quantitative microbiological risk assessment, especially when variability and uncertainty are to be characterized separately. In this paper, distributions are fit using Bayesian analysis, and results are compared to results obtained with a methodology based on maximum likelihood estimation and the non-parametric bootstrap method. The Bayesian model is also extended hierarchically to estimate the effects of the individual elements of a covariate such as, for example, on a national level, the food processing company where the analyzed food samples were processed, or, on an international level, the geographical origin of contamination data. Including this extra information allows a risk assessor to differentiate between several scenario’s and increase the specificity of the estimate of risk of illness, or compare different scenario’s to each other. Furthermore, inference is made on the predictive importance of several different covariates while taking into account uncertainty, allowing to indicate which covariates are influential factors determining contamination.