Statistical modeling of global geogenic arsenic contamination in groundwater

Contamination of groundwaters with geogenic arsenic poses a major health risk to millions of people. Although the main geochemical mechanisms of arsenic mobilization are well understood, the worldwide scale of affected regions is still unknown. In this study we used a large database of measured arsenic concentration in groundwaters (around 20,000 data points) from around the world as well as digital maps of physical characteristics such as soil, geology, climate, and elevation to model probability maps of global arsenic contamination. A novel rule-based statistical procedure was used to combine the physical data and expert knowledge to delineate two process regions for arsenic mobilization: "reducing" and "high-pH/ oxidizing". Arsenic concentrations were modeled in each region using regression analysis and adaptive neuro-fuzzy inferencing followed by Latin hypercube sampling for uncertainty propagation to produce probability maps. The derived global arsenic models could benefit from more accurate geologic information and aquifer chemical/physical information. Using some proxy surface information, however, the models explained 77% of arsenic variation in reducing regions and 68% of arsenic variation in high-pH/oxidizing regions. The probability maps based on the above models correspond well with the known contaminated regions around the world and delineate new untested areas that have a high probability of arsenic contamination. Notable among these regions are South East and North West of China in Asia, Central Australia, New Zealand, Northern Afghanistan, and Northern Mali and Zambia in Africa.     

Fluoride removal by calcite: evidence for fluorite precipitation and surface adsorption

Fluoride contamination of groundwater, both anthropogenic and natural, is a major problem worldwide. In this study, fluoride removal by crushed limestone (99% pure calcite) was investigated by batch studies and surface-sensitive techniques from solutions with fluoride concentrations from 150 micromol/L (3 mg/L) to 110 mM (approximately 2100 mg/L). Surface-sensitive techniques, including atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS) as well as zeta potential measurements, confirm that, in addition to precipitation reactions, adsorption of fluoride also occurs. Results indicate that fluoride adsorption occurs immediately over the entire calcite surface with fluorite precipitating at step edges and kinks, where dissolved Ca2+ concentration is highest. The PHREEQ geochemical model was applied to the observed data and indicates that existing models, especially at low fluoride concentrations and high pH (>7.5) are not equipped to describe this complex system, largely because the PHREEQ model includes only precipitation reactions, whereas a combination of adsorption and precipitation parameters are required.     

Spatial modeling for groundwater arsenic levels in North Carolina

To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area.     

Physiological and structural events post mortem of importance for drip loss in pork

Early post mortem metabolism and structural changes from 3 to 24 h, together with pH, temperature and impedance Py development were investigated in 37 Duroc×Landrace×Large White (DLY) pigs covering a range of drip loss from 2.2 to 12.6%. Multivariate statistical analysis was used to assess the impacts of different metabolites, pH and temperature, impedance, cytoskeletal protein degradation and extracellular cross-sectional area on drip loss. Taken as single factors, the concentration of lactate could explain 80% of the variation in drip, inosine monophosphate (IMP) and adenosine triphosphate (ATP) concentration explained 71 and 68%, respectively, whereas inosin and glycogen levels explained only 59 and 60%. The extracellular area was found to explain 39% of the variation in drip. The area between fibres provided more significant information than did the area between fibre bundles. The degradation of the cytoskeletal proteins was not related to drip loss. Impedance Py development over 24 h could explain 66% of the variation in drip, whereas pH and temperature explained 85 and 87%, respectively. A model including all measured variables could explain 83% of the variation in drip. However, only pH, temperature, impedance, [ATP]_{1 h} and [lactate]_{1 h and 2 h} were significant in relation to drip. By reducing the variables in the multivariate analysis, 89% of the variation in drip could be explained by a model containing only pH_{2 h} and temperature_{1 min}. To explain variation in drip loss, pH and temperature measurements at significant time points were sufficient. Variation in post-mortem metabolites did, however, explain why variation in pH and temperature occurred. Development of drip channels was ruled by pH and temperature while impedance development was highly correlated to pH. This multi-faceted study shows those parameters, which can best be used to indicate or predict WHC, as well as those indicating the basic mechanism underlying variations in drip.     

Land use regression model for ultrafine particles in Amsterdam

There are currently no epidemiological studies on health effects of long-term exposure to ultrafine particles (UFP), largely because data on spatial exposure contrasts for UFP is lacking. The objective of this study was to develop a land use regression (LUR) model for UFP in the city of Amsterdam. Total particle number concentrations (PNC), PM10, PM2.5, and its soot content were measured directly outside 50 homes spread over the city of Amsterdam. Each home was measured during one week. Continuous measurements at a central urban background site were used to adjust the average concentration for temporal variation. Predictor variables (traffic, address density, land use) were obtained using geographic information systems. A model including the product of traffic intensity and the inverse distance to the nearest road squared, address density, and location near the port explained 67% of the variability in measured PNC. LUR models for PM2.5, soot, and coarse particles (PM10, PM2.5) explained 57%, 76%, and 37% of the variability in measured concentrations. Predictions from the PNC model correlated highly with predictions from LUR models for PM2.5, soot, and coarse particles. A LUR model for PNC has been developed, with similar validity as previous models for more commonly measured pollutants.     

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.     

Concentrations of fluoride in wines from the Canary Islands.

Potentiometry using an ion-selective electrode has widely been used for determining fluoride because of its simplicity and rapidity. The concentration of fluoride was determined (Gran's method) in 70 wines from the main wine-producing regions of the Canary Islands. The mean concentration of fluoride in wines from a region with a high concentration of fluoride in drinking waters was significantly (p < 0.05) higher than those mean concentrations obtained in the remaining wines. Non-important differences were found among the types of wine analysed. However, the fluoride concentrations of all the Canarian wines analysed here did not present a risk for the public health.     

N,O-羧化壳聚糖溶液的流动性、触变性以及动态粘弹性研究

通过分析25℃条件下N,O-羧化壳聚糖溶液的流动性、触变性以及动态粘弹性来研究其流变性能,并利用Power-law和Cross模型对其流动性进行了拟合分析。结果表明:低浓度的N,O-羧化壳聚糖溶液（0.01%¹%）表现为牛顿流体的特性,较高浓度的溶液体系（5%¹5%）表现为剪切变稀的假塑性流体的特性,其流动性符合流变学的Cross模型,且浓度越高,临界剪切速率越小;随着溶液浓度的增大,触变性越强,结构不易恢复,表明溶液体系对时间的依赖性越大;在整个动态测试中,低频率下,G″>G’,表现出类似于液体的粘弹性行为,随着频率的增大,溶液体系表现为类似于固体的弹性行为（G’>G″）,G″与G’交点的出现依赖于N,O-羧化壳聚糖溶液浓度的变化。      

Modeling the probability of arsenic in groundwater in New England as a tool for exposure assessment

We developed a process-based model to predict the probability of arsenic exceeding 5 microg/L in drinking water wells in New England bedrock aquifers. The model is being used for exposure assessment in an epidemiologic study of bladder cancer. One important study hypothesis that may explain increased bladder cancer risk is elevated concentrations of inorganic arsenic in drinking water. In eastern New England, 20-30% of private wells exceed the arsenic drinking water standard of 10 micrograms per liter. Our predictive model significantly improves the understanding of factors associated with arsenic contamination in New England. Specific rock types, high arsenic concentrations in stream sediments, geochemical factors related to areas of Pleistocene marine inundation and proximity to intrusive granitic plutons, and hydrologic and landscape variables relating to groundwater residence time increase the probability of arsenic occurrence in groundwater. Previous studies suggest that arsenic in bedrock groundwater may be partly from past arsenical pesticide use. Variables representing historic agricultural inputs do not improve the model, indicating that this source does not significantly contribute to current arsenic concentrations. Due to the complexity of the fractured bedrock aquifers in the region, well depth and related variables also are not significant predictors.     

Fate modeling of phenanthrene with regional variation in Tianjin,China

A multimedia fate model with spatially resolved air and soil phases was developed and evaluated. The model was used for calculation of phenanthrene concentrations in air, water, soil, and sediment in Tianjin area and transport fluxes between the adjacent bulk phases under steady-state assumption. Both air and soil phases were divided into 3113 individual compartments of 4 km2 each to assess the spatial variation of phenanthrene concentrations and fluxes. Independently measured phenanthrene concentrations in air, water, and soil were used for model validation. The spatial variation in soil was validated using a set of measured phenanthrene concentrations of 188 surface soil samples collected from the area. Most data used either for model calculation or for model validation were collected during the last 5 years. As the results of the model validation, the calculated mean values for phenanthrene concentrations in various bulk phases are in fair agreement with those independently observed and are very close to those calculated using the model without spatial variation. The absolute difference between the calculated and the measured mean concentrations are 0.14, 0.48, and 0.13 log-units (mol/m3) for air, water, and soil, respectively. The spatial distribution patterns of phenanthrene in both air and soil were well modeled. Spatially, however, the model overestimated the soil phenanthrene level at low concentration range and underestimated it at high concentration range. The calculated distribution of phenanthrene in the air matches well with the emission from fossil fuel combustion, while the calculated distribution pattern in the soil is similar to that observed.     

Verifiable metamodels for nitrate losses to drains and groundwater in the Corn Belt, USA

Nitrate leaching in the unsaturated zone poses a risk to groundwater, whereas nitrate in tile drainage is conveyed directly to streams. We developed metamodels (MMs) consisting of artificial neural networks to simplify and upscale mechanistic fate and transport models for prediction of nitrate losses by drains and leaching in the Corn Belt, USA. The two final MMs predicted nitrate concentration and flux, respectively, in the shallow subsurface. Because each MM considered both tile drainage and leaching, they represent an integrated approach to vulnerability assessment. The MMs used readily available data comprising farm fertilizer nitrogen (N), weather data, and soil properties as inputs; therefore, they were well suited for regional extrapolation. The MMs effectively related the outputs of the underlying mechanistic model (Root Zone Water Quality Model) to the inputs (R(2) = 0.986 for the nitrate concentration MM). Predicted nitrate concentration was compared with measured nitrate in 38 samples of recently recharged groundwater, yielding a Pearson's r of 0.466 (p = 0.003). Predicted nitrate generally was higher than that measured in groundwater, possibly as a result of the time-lag for modern recharge to reach well screens, denitrification in groundwater, or interception of recharge by tile drains. In a qualitative comparison, predicted nitrate concentration also compared favorably with results from a previous regression model that predicted total N in streams.     

Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing

An empirical model based on the regression between daily PM2.5 (particles with aerodynamic diameters of less than 2.5 microm) concentrations and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concentrations. The root-mean-square error of the model was 6.2 microg/m3 with a corresponding average PM2.5 concentration of 13.8 microg/m3. When PM2.5 concentrations greater than 40 microg/m3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geographical attributes of monitoring sites, were found to influence the association between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technology and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for determining ground-level particle concentrations.     

Screening analysis of human pharmaceutical compounds in U.S. surface waters

The PhATE (Pharmaceutical Assessment and Transport Evaluation) model presented in this paper was developed as a tool to estimate concentrations of active pharmaceutical ingredients (APIs) in U.S. surface waters that result from patient use (or consumption) of medicines. PhATE uses a mass balance approach to model predicted environmental concentrations (PECs) in 11 watersheds selected to be representative of most hydrologic regions of the United States. The model divides rivers into discrete segments. It estimates the mass of API that enters a segment from upstream or from publicly owned treatment works (POTW) and is subsequently lost from the segment via in-stream loss mechanisms or flow diversions (i.e., man-made withdrawals). POTW discharge loads are estimated based on the population served, the API use per capita, the potential loss of the compound associated with human use (e.g., metabolism), and the portion of the API mass removed in the POTW. Simulations using three surrogate compounds showthat PECs generated by PhATE are generally within an order of magnitude of measured concentrations and that the cumulative probability distribution of PECs for all watersheds included in PhATE is consistent with the nationwide distribution of measured concentrations of the surrogate compounds. Model simulations for 11 APIs yielded four categories of results. (1) PECs fit measured data for two compounds. (2) PECs are below analytical method detection limits and thus are consistent with measured data for three compounds. (3) PECs are higher than (i.e., not consistent with) measured data for three compounds. However, this may be the consequence of as yet unidentified depletion mechanisms. (4) PECs are several orders of magnitude below some measured data but consistentwith most measured data forthree compounds. For the fourth category, closer examination of sampling locations suggests that the field-measured concentrations for these compounds do not accurately reflect human use. Overall, these results demonstrate that PhATE may be used to predict screening-level concentrations of APIs and related compounds in the environment as well as to evaluate the suitability of existing fate information for an API.     

Surface complexation of ferrous iron and carbonate on ferrihydrite and the mobilization of arsenic

Surface complexation models are commonly used to predict the mobility of trace metals in aquifers. For arsenic in groundwater, surface complexation models cannot be used because the database is incomplete. Both carbonate and ferrous iron are often present at a high concentration in groundwater and will influence the sorption of arsenic, but the surface complexation constants are absent in the database of Dzombak and Morel. This paper presents the surface complexation constants for carbonate and ferrous iron on ferrihydrite as derived for the double-layer model. For ferrous iron the constants were obtained from published data supplemented by new experiments to determine the sorption on the strong sites of ferrihydrite. For carbonate the constants were derived from experiments by Zachara et al., who employed relatively low concentrations of carbonate. The double-layer model, optimized for low concentrations, was tested against sorption experiments of carbonate on goethite at higher concentration by Villalobos and Leckie, and reasonable agreement was found. Sorption was also estimated using linear free energy relations (LFER), and results compared well with our derived constants. Model calculations confirm that sorption of particularly carbonate at common soil and groundwater concentrations reduces the sorption capacity of arsenic on ferrihydrite significantly. The displacing effect of carbonate on sorbed arsenate and arsenite has been overlooked in many studies. It may be an important cause for the high concentrations of arsenic in groundwater in Bangladesh. Sediments containing high amounts of sorbed arsenic are deposited in surface water with low carbonate concentrations. Subsequently the sediments become exposed to groundwater with a high dissolved carbonate content, and arsenic is mobilized by displacement from the sediment surface.     

Influence of local human population on atmospheric polycyclic aromatic hydrocarbon concentrations

Literature values of atmospheric polycyclic aromatic hydrocarbon (PAH) concentrations from sampling sites around the world were found, and using a high-resolution human population grid, the population within a 25-km radius of each sampling site was calculated. A regression of concentration vs population revealed much about PAH concentration differences among regions as well as site locations within a continent. The best fit for the regression was for sampling locations in North America. A small amount of scatter was present for the regression of all developed countries indicating slight differences in emission regulations or energy usage. The regression from this plot was used as a benchmark for the expected relationship between PAHs and human population. Sites located within 25 km of a coasttended to have concentrations lower than expected, due to dilution with clean ocean air, while sites near industrial outputs or other point sources had higher than expected concentrations. Sites from developing countries typically had PAH concentrations that were far higher than those of the rest of the world.     

How do climate fluctuations affect persistent organic pollutant distribution in North America? Evidence from a decade of air monitoring

Interannual variations of persistent organic pollutant (POP) air concentrations from the Great Lakes region and the Arctic during the 1990s are strongly associated with atmospheric low-frequency fluctuations, notably the North Atlantic Oscillation (NAO), the El Ni?o-Southern Oscillation (ENSO), and the Pacific North American (PNA) pattern. This suggests interactions between climate variation and the global distribution of POPs. Atmospheric concentrations of hexachlorocyclohexanes (HCHs), hexachlorobenzene (HCB), and several lighter polychlorinated biphenyls (PCBs) measured around the Great Lakes basin increased during the positive phases of NAO and ENSO in the spring. This implies that anomalous high air temperatures associated with NAO and ENSO enhance volatilization of POPs from reservoirs on the Earth's surface accumulated in the past. These compounds are then available for transport from source regions to more pristine regions such as the Arctic under favorable flow patterns associated with global climate variations.     

Fluoride levels in wines of the Canary Islands (Spain)

Fluoride content in bottled wines of the different types and areas of the Canary Islands was studied by direct potentiometry with a fluoride specific electrode using the addition method. We found a mean concentration of 0.15 mg/l and a standard deviation of 0.068. The biggest concentration of fluoride ion (0.50 mg/l) was found in a white wine of Tacoronte-Acentejo. However, all wines analyzed presented concentrations of fluoride lower than the maximum allowed by the International Office of the Vineyard and the Wine. So, the contribution of the Canarian wines to the fluoride daily intake does not represent any risk of fluorosis. Also these concentrations have no influence on the production process of the wine.     

Probabilistic approach to estimating indoor air concentrations of chlorinated volatile organic compounds from contaminated groundwater: a case study in San Antonio, Texas

This paper describes a probabilistic model, based on the Johnson-Ettinger algorithm, developed to characterize the current and historic exposure to tricholorethylene (TCE) and tetrachlorethylene (PCE) in indoor air from plumes of groundwater contamination emanating from the former Kelly Air Force Base in San Antonio, Texas. We estimate indoor air concentration, house by house, in 30?101 homes and compare the estimated concentrations with measured values in a small subset of homes. We also compare two versions of the Johnson-Ettinger model: one used by the Environmental Protection Agency (EPA) and another based on an alternative parametrization. The modeled mean predicted PCE concentration historically exceeded PCE screening levels (0.41 ug/m(3)) in 5.5% of houses, and the 95th percentile of the predicted concentration exceeded screening levels in 85.3% of houses. For TCE, the mean concentration exceeded the screening level (0.25 ug/m(3)) in 49% of homes, and the 95th percentile of the predicted concentration exceeded the screening level in 99% of homes. The EPA model predicts slightly lower indoor concentrations than the alternative parametrization. Comparison with measured samples suggests both models, with the inputs selected, underestimate indoor concentrations and that the 95th percentiles of the predicted concentrations are closer to measured concentrations than predicted mean values.     

Symposium 2 Part 2: Food Production for a Growing World Population

ABSTRACT: Transition towards sustainable development is claimed as a common goal for the world society in many different documents. Within the agricultural sector, this transition is extremely urgent, since agricultural food production is fundamental to the survival of our civilization. Together with a rapidly growing world population, there are losses of arable land due to erosion and soil infertility caused by nonsustainable production methods. Along with such problems follow potential contamination of groundwater resources as well. Most of the nutrient and pesticide contamination of groundwater originates from agricultural soils. In this presentation, sound solutions to the major environmental issues of limiting contamination of soils and groundwater by modifying agricultural practices are discussed. The causes of pollution are briefly explained and existing measures for the reduction of agricultural non-point-source pollution of nutrients and pesticides are described, analyzed, and evaluated.     

Vulnerability of shallow groundwater and drinking-water wells to nitrate in the United States

Two nonlinear models were developed at the national scale to (1) predict contamination of shallow ground water (typically < 5 m deep) by nitrate from nonpoint sources and (2) to predict ambient nitrate concentration in deeper supplies used for drinking. The new models have several advantages over previous national-scale approaches. First, they predict nitrate concentration (rather than probability of occurrence), which can be directly compared with water-quality criteria. Second, the models share a mechanistic structure that segregates nitrogen (N) sources and physical factors that enhance or restrict nitrate transport and accumulation in ground water. Finally, data were spatially averaged to minimize small-scale variability so that the large-scale influences of N loading, climate, and aquifer characteristics could more readily be identified. Results indicate that areas with high N application, high water input, well-drained soils, fractured rocks or those with high effective porosity, and lack of attenuation processes have the highest predicted nitrate concentration. The shallow groundwater model (mean square error or MSE = 2.96) yielded a coefficient of determination (R(2)) of 0.801, indicating that much of the variation in nitrate concentration is explained by the model. Moderate to severe nitrate contamination is predicted to occur in the High Plains, northern Midwest, and selected other areas. The drinking-water model performed comparably (MSE = 2.00, R(2) = 0.767) and predicts that the number of users on private wells and residing in moderately contaminated areas (>5 to < or =10 mg/L nitrate) decreases by 12% when simulation depth increases from 10 to 50 m.