Evaluation of potential health risk of arsenic-affected groundwater using indicator kriging and dose response model

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

A probabilistic approach to the estimation of radioactive contaminant inventories at a nuclear waste disposal site

Routine site inspections are often conducted to gather data on radiation contamination on the surface and below ground near nuclear waste disposal areas. These observations are used to calculate total radiation inventory and its spatial delineation. The statistical kriging approach is often used to spatially interpolate contamination data, and it generates predictions at unsampled sites that are then utilized to calculate the contaminated site's radiation inventory. The kriging output, however, creates a point estimate of the inventory that omits the potential uncertainties from other sources. This paper presents a method for assessing the uncertainty of radiation inventories based on the geostatistical conditional simulation method - a simulation methodology that takes into account the observations made at the sampled sites. The radiation inventories' histograms are generated by conducting many conditional simulations of the projection map using a fitted kriging model. A practical implementation of the suggested approach is shown by evaluating total beta inventories and their spatial delineation using groundwater monitoring data at a nuclear waste disposal site.

Drinking water and the implications for gender equity and empowerment: A systematic review of qualitative and quantitative evidence

BACKGROUND

Safe drinking water is a fundamental human right, yet more than 785 million people do not have access to it. The burden of water management disproportionately falls on women and young girls, and they suffer the health, psychosocial, political, educational, and economic effects. While water conditions and disease outcomes have been widely studied, few studies have summarized the research on drinking water and implications for gender equity and empowerment (GEE).

METHODS

A systematic review of primary literature published between 1980 and 2019 was conducted on drinking water exposures and management and the implications for GEE. Ten databases were utilized (EMBASE, PubMed, Web of Science, Cochrane, ProQuest, Campbell, the British Library for Development Studies, SSRN, 3ie International Initiative for Impact Evaluation, and clinicaltrials.gov). Drinking water studies with an all-female cohort or disaggregated findings according to gender were included.

RESULTS

A total of 1280 studies were included. GEE outcomes were summarized in five areas: health, psychosocial stress, political power and decision-making, social-educational conditions, and economic and time-use conditions. Water quality exposures and implications for women's health dominated the literature reviewed. Women experienced higher rates of bladder cancer when exposed to arsenic, trihalomethanes, and chlorine in drinking water and higher rates of breast cancer due to arsenic, trichloroethylene, and disinfection byproducts in drinking water, compared to men. Women that were exposed to arsenic experienced higher incidence rates of anemia and adverse pregnancy outcomes compared to those that were not exposed. Water-related skin diseases were associated with increased levels of psychosocial stress and social ostracization among women. Women had fewer decision-making responsibilities, economic independence, and employment opportunities around water compared to men.

CONCLUSION

This systematic review confirms the interconnected nature of gender and WaSH outcomes. With growing attention directed towards gender equity and empowerment within WaSH, this analysis provides key insights to inform future research and policy.

Comprehensive assessment of the impact of land use and hydrogeological properties on the groundwater quality in Taiwan using factor and cluster analyses

This study aims at making a comprehensive assessment of the impact of land use and the hydrogeological properties on groundwater quality. First, factor analysis (FA) is applied to reveal the main pollutant sources and hydrogeological processes controlling the groundwater quality. FA identifies the four most important factors. Factor 1 (seawater salinization) is characterized by a medium loading of land use type of aquaculture. It is recognized that the high scores for factor 1 in coastal areas are due to over-pumping from aquafarms. Focused land use management is required to prevent saline-water intrusion in coastal aquifers. Factor 3 (nitrate pollution) shows high correlations with the land use type of fruit farming and the gravel thickness in unsaturated layers. High scores for factor 3 are also found in the proximal area of the Chuoshui River Alluvial Fan and the northeastern mountain area in the Pingtung Plain. Fruit farmers should be educated to reduce the application of fertilizers and promote the organic fruit farming. The impacts of land use and the hydrogeological properties on both Factor 2 (arsenic enrichment) and Factor 4 (reductive dissolution of Fe²⁺ and Mn²⁺) are negligible. Second, cluster analysis (CA) is performed on computed scores of the four main factors to separates 123 monitoring wells into cluster 1 (low polluted zone), cluster 2 (nitrate polluted zone) and cluster 3 (hybrid polluted zone). The results obtained from CA provide practical applications such as reduce agrichemical use in the areas of cluster 2 and enforce intensive monitoring in the prioritizing areas of cluster 3. This study successively uses the FA and CA to extract the meaningful information present by geographical visualization of scores for 4 main factors and 3 distinct clusters zones. The results are essential for formulating sound groundwater resource and land use management policies to ensure groundwater sustainability.

Arsenic in the geo-environment: A review of sources, geochemical processes, toxicity and removal technologies

Impact of arsenic (As) contaminated groundwater on human health, through drinking and irrigation practices, is of grave-concern worldwide. This paper present the review of various sources, processes, health effects and treatment technologies available for the removal of As from arsenic contaminated water. Groundwater with high As concentration is detrimental to human health and incidents of As contamination in groundwater had been reported from different parts of the globe. More serious known As contamination problem as well as largest population at risk are found in Bangladesh, followed by West Bengal state in India along the Indo-Gangetic plains. Large scale natural As contamination of groundwater is found in two types of environment such as strongly reducing alluvial aquifers (ex. Bangladesh, India, China and Hungary) and inland basins in arid or semi-arid areas (ex. Argentina and Mexico). The provisional guideline of 10 ppb (0.0 l mg/l) has been adopted as the drinking water standard by World Health Organization (WHO). In the aquatic environment, the release, distribution and remobilization of As depend on temperature, redox potential, speciation, and interaction between liquid solution and solid phases. As predicaments in the environment is due to its mobilization under natural geogenic conditions as well as anthropogenic activities. Arsenic mineral is not present in As contaminated alluvial aquifer but As occurs adsorbed on hydrated ferric oxide (HFO) generally coat clastic grains derived from Himalayan mountains. As is released to the groundwater mainly by bio-remediated reductive dissolution of HFO with corresponding oxidation of organic matter. The development of strongly reductive dissolution of mineral oxides (Fe and Mn) at near-neutral pH may lead to desorption and ultimately release of As into the groundwater. As release through geochemical process is more important factor in alluvial aquifers causing As contamination rather than sources of arsenic. As is a toxin that dissolves in the bloodstream, rendering the victim susceptible to disease of the skin, bones, and also cancer of liver, kidney, gall bladder and the intestines. It is necessary to adopt highly successful technology to treat As contaminated water into the acceptable limit for human consumption. Universally accepted solutions are not developed/available even after the lapse of almost forty years since slow As poisoning identification in tens of millions of people especially in Bengal delta. The issue poses scientific, technical, health and societal problems even today.

A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan's Lanyang Plain

Groundwater resources are abundant and widely used in Taiwan’s Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization’s standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable spatial variability, which means that the associated risk to human health would also vary from region to region. This study aims to adapt a back-propagation neural network (BPNN) method to carry out more reliable spatial mapping of the As concentrations in the groundwater for comparison with the geostatistical ordinary kriging (OK) method results. Cross validation is performed to evaluate the prediction performance by dividing the As monitoring data into three sets. The cross-validation results show that the average determination coefficients (R²) for the As concentrations obtained with BPNN and OK are 0.55 and 0.49, whereas the average root mean square errors (RMSE) are 0.49 and 0.54, respectively. Given the better prediction performance of the BPNN, it is recommended as a more reliable tool for the spatial mapping of the groundwater As concentration. Subsequently, the As concentrations estimated obtained using the BPNN are applied to develop a spatial map illustrating the risk to human health associated with the ingestion of As-containing groundwater based on the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) standards established by the U.S. Environmental Protection Agency. Such maps can be used to demarcate the areas where residents are at higher risk due to the ingestion of As-containing groundwater, and prioritize the areas where more intensive monitoring of groundwater quality is required. The spatial mapping of As concentrations from the BPNN was also used to demarcate the regions where the groundwater is suitable for farmland and fishponds based on the water quality standards for As for irrigation and aquaculture.

An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by kriging and co-kriging models

Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of kriging-based models (ordinary kriging, simple kriging and universal kriging), which are constructed at Level 1 to predict nitrate concentrations; and a Multiple Co-Kriging (MCoK) model is used at Level 2 to enhance the accuracy of the predictions. Strategy 2 is also at two levels but employs Indicator Kriging (IK) at Level 1 as a probabilistic spatial model to predict areas at risk of exceeding two thresholds of 37.5 mg/L and 50 mg/L of nitrate concentration, and Multiple Co-Indicator Kriging (MCoIK) at Level 2 for a better accuracy. The improvements at Level 2 for both strategies are remarkable and hence they are used to gain an insight into inherent problems. The results of a study delineate areas with excessive nitrate concentrations, which are in the vicinity of urban areas and hence reflect poor planning practices since the 1990s. The results further reveal the patterns on sensitivities to seasonal variations driven by aquifer recharge and strong dilution processes in spring times; and on the role of pumpage impacting aquifers giving rise to possible hotspots of nitrate concentrations.

Employing indicator-based geostatistics and quantitative microbial risk analysis to assess the health risks of groundwater use for household demands on the Pingtung Plain, Taiwan

Because of the limited surface water on the Pingtung Plain, Taiwan, the plain's residents frequently extract groundwater to meet their daily household water demands. The residents may experience gastrointestinal infections due to incidental ingestion of groundwater with fecal pollution. This study used indicator kriging (IK) and quantitative microbial risk analysis (QMRA) to assess the health risks of using groundwater for household cleaning and horticultural irrigation on the Pingtung Plain. First, IK was employed to determine the conditional cumulative distribution function (CCDF) of groundwater Escherichia coli (E. coli). Nonparametric Monte Carlo simulation based on established CCDF was then adopted to characterize the distributions and uncertainty of groundwater E. coli. Finally, QMRA was employed to determine health risks of groundwater use for household cleaning and horticultural irrigation, and the 95th percentiles of the risk distributions were calculated to obtain a representative risk. The study results indicated that the health risks of groundwater use ranged from 3.95 × 10^-5 to 2.49 × 10^-2 infections/user/year and exceeded the acceptable level, 1 × 10^-4 infections/user/year, in most of the aquifers. Accordingly, residents of this plain should not directly extract groundwater for use in daily life.

Rice yield estimation based on forecasting the future condition of groundwater salinity in the Caspian coastal strip of Guilan Province, Iran

Irrigation water salinity is one of the factors that reduces agricultural production. Guilan Province is one of the most important rice-producing areas in Iran where groundwater is used for irrigation. The temporal and spatial variations of groundwater salinity were studied in the coastal strip covering 4285 km² of the province using data from 73 wells, as well as its estimated effect on the rice yield. Data on mean electrical conductivity (EC) for each 6-month period of 12 consecutive years, from the second half of 2002 until the end of 2014, were analyzed and resulted in 25 mean ECs. EC maps and maps of the probability of higher salinity areas were obtained by using ordinary kriging (OK) and indicator kriging (IK) in ArcGIS 9.3 software, respectively. Thereby, areas belonging to different salinity classes were outlined and places with higher salinity reducing the rice yield were identified. In addition, the Mann-Kendall test and Sen's slope were used to project future changes. The results indicated that due to the salinity of groundwater in the coastal strip area, the minimum and the maximum rice yields were 80% and 100%, respectively. Using the IK method, higher probability of groundwater salinity reducing the yield was found from the central parts toward the east. The Mann-Kendal test result showed significant temporal trends of the size of areas below the 100% yield (EC < 1 dS/m) and 90-100% yield (1 < EC < 1.34 dS/m) thresholds. The equations given by Sen's slope estimator indicated that the groundwater salinity will not be a limiting factor for achieving 100% rice yields from the year of 2021 onward in all of the Guilan coastal area. The trend of increasing precipitation in the area may be an important cause.

Spatial dynamic assessment of health risks for urban river cruises

River cruising ships move along river courses, and thus health risks to passengers may vary spatially due to the accidental exposure of river fecal pollution. This study performed a spatial dynamic assessment of health risks for river cruises in the highly urbanized Tamsui River Basin. First, the spatial distributions of river Escherichia coli (E. coli) were probabilistically characterized using indicator kriging (IK). Moreover, the current river cruise information was surveyed to obtain cruise routes and transit times. Then, to explore the parametric uncertainty of quantitative microbial risk assessment (QMRA), the ingestion rate (IR) for boating was determined using Monte Carlo simulation (MCS). Moreover, river E. coli distributions were estimated using nonparametric MCS according to multi-threshold IK estimates. Eventually, after combining the distribution of the joint probability of the IR and E. coli in QMRA, the β-Poisson dose-response function was adopted to analyze risks to river cruise passengers at discretized segments of cruise routes. Health risks to river cruise passengers were integrated at the discretized segments to explore suitable recreational strategies for river cruises. The research results indicate that all health risks do not exceed a daily target level of 8 illnesses per 1000 exposures for single-trip cruise routes. However, health risks to passengers can exceed this level for round-trip cruise routes along highly polluted urban river courses.

Potential health risk assessment through ingestion and dermal contact arsenic-contaminated groundwater in Jianghan Plain, China

Groundwater contamination with high arsenic (As) levels has caused serious health problem in Jianghan Plain. This study presents a framework to evaluate the results and their probable influencing factors of non-carcinogenic risk and carcinogenic risk in Shahu Village. An appropriate health risk assessment for residents exposing to As through ingestion and dermal contact pathways is also discussed in the paper. Hazard quotient (HQ) and target cancer risk (TR) are adopted to compute the non-carcinogenic and carcinogenic effects for residents, respectively. Monte Carlo simulation technique is used to quantify the uncertainty of the risk assessment. The assessment results show that the HQs and TRs of 10-m-deep and 25-m-deep wells exhibit seasonal variations with higher values in rainy season and lower values in dry season. The HQ values exceeding 1 at the depths of 10 (from 0.09 to 23.21 m) and 25 m (from 0.29 to 130.55 m) account for 61 and 94%, respectively, which associate with the As contents distribution in the aquifer sediments. The estimated TR values at the depths of 10 (from 3.86E-05 to 1.04E-02) and 25 m (from 1.32E-04 to 5.87E-02) exceeding the highest acceptable standard (10^-4) account for 95 and 100%, respectively. Comparison of the two exposure pathways, the ingestion exposure contributes much more than the dermal contact exposure for both non-carcinogenic risk and carcinogenic risk. The results of sensitivity analysis indicate that a more accurate measurement and better definition of probability distributions for As concentration in the groundwater can increase the accuracy of health risk assessment in Jianghan Plain. The findings demonstrate the importance of the drinking water safety, and the government should take measures to ensure the drinking water safety.

Spatial analysis of the risk to human health from exposure to arsenic contaminated groundwater: A kriging approach

A long-term groundwater quality survey in northeastern Taiwan's Lanyang Plain has revealed obvious contamination of the groundwater in some areas, with measured As concentrations in excess of the acceptable level of 10μg/L. Efforts for assessing the health risk associated with the intake of As through the drinking of contaminated groundwater are a necessary part of the important work of health risk management. However, the standard approach to assessing risks to human health does not adequately account for spatial heterogeneity in the measured As concentrations. Thus, this study applies two different kriging approaches to carry out a spatial analysis of the health risk associated with ingesting As through the drinking of groundwater in the Lanyang Plain. It is found that the indicator kriging (IK) approach, with occurrence probability threshold values of 0.4, 0.5 and 0.6 yields correct classification percentages of 75%, 68% and 61%, respectively, of unacceptable HQ zones. An HQ map prepared with the ordinary kriging (OK) approach shows a correct classification of unacceptable HQ zones of 80%. Considering that the OK approach does not require subjective selection of an occurrence probability threshold value as is the case with the IK approach and can yield a higher percentage of correct classification for unacceptable HQ zones, it is recommended as a more direct and reliable method for spatial analysis of human health risk due to arsenic exposure through the drinking of groundwater. The results show that the geographical distribution of unacceptable HQ zones is concentrated in the eastern part of the study area, which includes the high-population density townships. In other words, 34% of the people had access to groundwater where the HQ was >1. The results of this type of spatial health risk assessment can provide a basis for improving the decision-making process for health risk management.

Assessment and uncertainty analysis of groundwater risk

Groundwater with relatively stable quantity and quality is commonly used by human being. However, as the over-mining of groundwater, problems such as groundwater funnel, land subsidence and salt water intrusion have emerged. In order to avoid further deterioration of hydrogeological problems in over-mining regions, it is necessary to conduct the assessment of groundwater risk. In this paper, risks of shallow and deep groundwater in the water intake area of the South-to-North Water Transfer Project in Tianjin, China, were evaluated. Firstly, two sets of four-level evaluation index system were constructed based on the different characteristics of shallow and deep groundwater. Secondly, based on the normalized factor values and the synthetic weights, the risk values of shallow and deep groundwater were calculated. Lastly, the uncertainty of groundwater risk assessment was analyzed by indicator kriging method. The results meet the decision maker's demand for risk information, and overcome previous risk assessment results expressed in the form of deterministic point estimations, which ignore the uncertainty of risk assessment.

Human Health Risk Assessment Applied to Rural Populations Dependent on Unregulated Drinking Water Sources: A Scoping Review

Safe drinking water is a global challenge for rural populations dependent on unregulated water. A scoping review of research on human health risk assessments (HHRA) applied to this vulnerable population may be used to improve assessments applied by government and researchers. This review aims to summarize and describe the characteristics of HHRA methods, publications, and current literature gaps of HHRA studies on rural populations dependent on unregulated or unspecified drinking water. Peer-reviewed literature was systematically searched (January 2000 to May 2014) and identified at least one drinking water source as unregulated (21%) or unspecified (79%) in 100 studies. Only 7% of reviewed studies identified a rural community dependent on unregulated drinking water. Source water and hazards most frequently cited included groundwater (67%) and chemical water hazards (82%). Most HHRAs (86%) applied deterministic methods with 14% reporting probabilistic and stochastic methods. Publications increased over time with 57% set in Asia, and 47% of studies identified at least one literature gap in the areas of research, risk management, and community exposure. HHRAs applied to rural populations dependent on unregulated water are poorly represented in the literature even though almost half of the global population is rural.

Spatial Analysis of Human Health Risk Due to Arsenic Exposure through Drinking Groundwater in Taiwan's Pingtung Plain

Chronic arsenic (As) exposure continues to be a public health problem of major concern worldwide, affecting hundreds of millions of people. A long-term groundwater quality survey has revealed that 20% of the groundwater in southern Taiwan’s Pingtung Plain is clearly contaminated with a measured As concentration in excess of the maximum level of 10 µg/L recommended by the World Health Organization. The situation is further complicated by the fact that more than half of the inhabitants in this area continue to use groundwater for drinking. Efforts to assess the health risk associated with the ingestion of As from the contaminated drinking water are required in order to determine the priorities for health risk management. The conventional approach to conducting a human health risk assessment may be insufficient for this purpose, so this study adopts a geostatistical Kriging method to perform a spatial analysis of the health risk associated with ingesting As through drinking groundwater in the Pingtung Plain. The health risk is assessed based on the hazard quotient (HQ) and target cancer risk (TR) established by the U.S. Environmental Protection Agency. The results show that most areas where the HQ exceeds 1 are in the southwestern part of the study area. In addition, the high-population density townships of Daliao, Linyuan, Donggang, Linbian, Jiadong, and Fangliao presently have exceedingly high TR values that are two orders of magnitude higher than the acceptable standard. Thus, the use of groundwater for drinking in these townships should be strictly avoided. A map that delineates areas with high TR values and high population densities is provided. The findings broaden the scope of the spatial analysis of human health risk and provide a basis for improving the decision-making process.

Combining natural background levels (NBLs) assessment with indicator kriging analysis to improve groundwater quality data interpretation and management

The natural background level (NBL) concept is revisited and combined with indicator kriging method to analyze the spatial distribution of groundwater quality within a groundwater body (GWB). The aim is to provide a methodology to easily identify areas with the same probability of exceeding a given threshold (which may be a groundwater quality criteria, standards, or recommended limits for selected properties and constituents). Three case studies with different hydrogeological settings and located in two countries (Portugal and Italy) are used to derive NBL using the preselection method and validate the proposed methodology illustrating its main advantages over conventional statistical water quality analysis. Indicator kriging analysis was used to create probability maps of the three potential groundwater contaminants. The results clearly indicate the areas within a groundwater body that are potentially contaminated because the concentrations exceed the drinking water standards or even the local NBL, and cannot be justified by geogenic origin. The combined methodology developed facilitates the management of groundwater quality because it allows for the spatial interpretation of NBL values.

Geostatistical analysis of tritium, groundwater age and other noble gas derived parameters in California

Key characteristics of California groundwater systems related to aquifer vulnerability, sustainability, recharge locations and mechanisms, and anthropogenic impact on recharge are revealed in a spatial geostatistical analysis of a unique data set of tritium, noble gases and other isotopic analyses unprecedented in size at nearly 4000 samples. The correlation length of key groundwater residence time parameters varies between tens of kilometers ((3)H; age) to the order of a hundred kilometers ((4)Heter; (14)C; (3)Hetrit). The correlation length of parameters related to climate, topography and atmospheric processes is on the order of several hundred kilometers (recharge temperature; δ(18)O). Young groundwater ages that highlight regional recharge areas are located in the eastern San Joaquin Valley, in the southern Santa Clara Valley Basin, in the upper LA basin and along unlined canals carrying Colorado River water, showing that much of the recent recharge in central and southern California is dominated by river recharge and managed aquifer recharge. Modern groundwater is found in wells with the top open intervals below 60 m depth in the southeastern San Joaquin Valley, Santa Clara Valley and Los Angeles basin, as the result of intensive pumping and/or managed aquifer recharge operations.

Analysis of field-scale spatial correlations and variations of soil nutrients using geostatistics

Spatial correlations and soil nutrient variations are important for soil nutrient management. They help to reduce the negative impacts of agricultural nonpoint source pollution. Based on the sampled available nitrogen (AN), available phosphorus (AP), and available potassium (AK), soil nutrient data from 2010, the spatial correlation, was analyzed, and the probabilities of the nutrient's abundance or deficiency were discussed. This paper presents a statistical approach to spatial analysis, the spatial correlation analysis (SCA), which was originally developed for describing heterogeneity in the presence of correlated variation and based on ordinary kriging (OK) results. Indicator kriging (IK) was used to assess the susceptibility of excess of soil nutrients based on crop needs. The kriged results showed there was a distinct spatial variability in the concentration of all three soil nutrients. High concentrations of these three soil nutrients were found near Anzhou. As the distance from the center of town increased, the concentration of the soil nutrients gradually decreased. Spatially, the relationship between AN and AP was negative, and the relationship between AP and AK was not clear. The IK results showed that there were few areas with a risk of AN and AP overabundance. However, almost the entire study region was at risk of AK overabundance. Based on the soil nutrient distribution results, it is clear that the spatial variability of the soil nutrients differed throughout the study region. This spatial soil nutrient variability might be caused by different fertilizer types and different fertilizing practices.

Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran

Selection of appropriate interpolation methods for the conversion of discrete samples into continuous maps is a controversial issue in the environmental researches. The main objective of this study was to analyze the suitability of three interpolation methods for the discrimination of groundwater with respect to the water quality index (WQI). The groundwater quality data consisted of 17 variables associated with 65 wells located in Andimeshk-Shush Plain. Three spatial interpolation methods including ordinary kriging (OK), empirical Bayesian kriging (EBK), and inverse distance weighting (IDW) were utilized for modeling the groundwater contamination. In addition, different cross-validation indicators were applied to assess the performance of different interpolation methods. The results showed that the performance differed slightly among different methods, although the best performed interpolation method in this study was the empirical Bayesian kriging. Among the interpolation methods, IDW with weighting power of 4 estimated the most contaminated area, while OK estimated the lowest contaminated area. The weighting power of IDW had a significant influence on the estimation, meaning that the estimated contaminated area was increased when a greater weighting power was selected. The subtraction results indicated that there are slightly spatial differences among the contamination assessment results. Results of both standard deviation (SD) and coefficient of variation (CV) also showed that uncertainty was highest in the southern part of the study area, where the distribution of wells were more intensive than that of the northern part.

Organo-modified sericite in the remediation of an aquatic environment contaminated with As(III) or As(V)

The aim of this investigation was to obtain the hybrid material precursor to the naturally and abundantly available sericite, a mica-based clay; the materials were further employed in the remediation of arsenic from aqueous solutions. The study was intended to provide a cost-effective and environmentally benign treatment technology. The hybrid organo-modified sericite was obtained using hexadecyltrimethylammonium bromide (HDTMA) and alkyldimethylbenzylammonium chloride (AMBA) organic surfactants by introducing regulated doses of HDTMA or AMBA. The materials were characterized using infrared and X-ray diffraction analytical data, whereas the surface morphology was discussed by taking its SEM images. These materials were employed to assess the pre-concentration and speciation of As(III) and As(V) from aqueous solutions. The batch reactor data showed that increasing the sorptive concentration (from 1.0 to 15.0 mg/L) and pH (i.e., pH 2.0 to 10.0) caused the percent uptake of As(III) and As(V) to decrease significantly. The kinetic data showed that a sharp initial uptake of arsenic reached its equilibrium state within about 50 min of contact time, and the sorption kinetics followed a pseudo-second-order rate law both for As(III) and As(V) sorption. A 1,000 times increase in the background electrolyte concentration, i.e., NaNO3, caused a significant decrease in As(III) removal, whereas As(V) was almost unaffected, which inferred that As(III) was adsorbed, mainly by the van der Waals or even by the electrostatic attraction, whereas As(V) was adsorbed chemically and formed "inner-sphere" complexes at the solid/solution interface. The equilibrium state modeling studies indicated that the sorption data fitted well the Freundlich and Langmuir adsorption isotherms. Henceforth, the removal capacity was calculated under these equilibrium conditions. It was noted that organo-modified sericite possessed a significantly higher removal capacity compared to its virgin sericite. Between these two organo-modified sericite, the HDTMA-modified sericite possessed a higher removal capacity compared to the AMBA-modified sericite.

Evaluating the spatial distribution of quantitative risk and hazard level of arsenic exposure in groundwater, case study of Qorveh County, Kurdistan Iran

Regional distribution of quantitative risk and hazard levels due to arsenic poisoning in some parts of Iran’s Kurdistan province is considered. To investigate the potential risk and hazard level regarding arsenic-contaminated drinking water and further carcinogenic and non-carcinogenic effects on villagers, thirteen wells in rural areas of Qorveh County were considered for evaluation of arsenic concentration in water. Sampling campaign was performed in August 2010 and arsenic concentration was measured via the Silver Diethyldithiocarbamate method. The highest and lowest arsenic concentration are reported in Guilaklu and Qezeljakand villages with 420 and 67 μg/L, respectively. None of thirteen water samples met the maximum contaminant level issued by USEPA and Institute of Standards and Industrial Research of Iran (10 ppb). The highest arsenic concentration and consequently risk and hazard levels belong to villages situated alongside the eastern frontiers of the county. Existence of volcanic activities within the upper Miocene and Pleistocene in this part of the study area may be addressed as the main geopogenic source of arsenic pollution. Quantitative risk values are varying from 1.49E-03 in Qezeljakand to 8.92E-03 in Guilaklu and may be interpreted as very high when compared by similar studies in Iran. Regarding non-carcinogenic effects, all thirteen water samples are considered hazardous while all calculated chronic daily intakes are greater than arsenic reference dose. Such drinking water source has the potential to impose adverse carcinogenic and non-carcinogenic effects on villagers. Accordingly, an urgent decision must be made to substitute the current drinking water source with a safer one.

A comparison of multiple indicator kriging and area-to-point Poisson kriging for mapping patterns of herbivore species abundance in Kruger National Park, South Africa

Kruger National Park (KNP), South Africa, provides protected habitats for the unique animals of the African savannah. For the past 40 years, annual aerial surveys of herbivores have been conducted to aid management decisions based on (1) the spatial distribution of species throughout the park and (2) total species populations in a year. The surveys are extremely time consuming and costly. For many years, the whole park was surveyed, but in 1998 a transect survey approach was adopted. This is cheaper and less time consuming but leaves gaps in the data spatially. Also the distance method currently employed by the park only gives estimates of total species populations but not their spatial distribution. We compare the ability of multiple indicator kriging and area-to-point Poisson kriging to accurately map species distribution in the park. A leave-one-out cross-validation approach indicates that multiple indicator kriging makes poor estimates of the number of animals, particularly the few large counts, as the indicator variograms for such high thresholds are pure nugget. Poisson kriging was applied to the prediction of two types of abundance data: spatial density and proportion of a given species. Both Poisson approaches had standardized mean absolute errors (St. MAEs) of animal counts at least an order of magnitude lower than multiple indicator kriging. The spatial density, Poisson approach (1), gave the lowest St. MAEs for the most abundant species and the proportion, Poisson approach (2), did for the least abundant species. Incorporating environmental data into Poisson approach (2) further reduced St. MAEs.

Using indicator kriging for the evaluation of arsenic potential contamination in an abandoned mining area (Portugal)

Mining and mineral-processing activities can modify the environment in a variety of ways. Sulfide mineralization is notorious for producing waters with high metal contents. Arsenic is commonly associated with sulfide mineralization and is considered to be toxic in the environment at low levels. The studied abandoned mining area is located in central Portugal and the resulting tailings and rejected materials were deposited and exposed to the air and water for the last 50 years. Sixteen water sample-points were collected. One of these was collected outside the mining influence, with the aim of obtaining a reference background. The risk assessment, concerning the proximity to abandoned mineralized deposits, needs the evaluation of intrinsic and specific vulnerabilities aiming the quantification of the anthropogenic activities. In this study, two indicator variables were constructed. The first one (I(1)), a specific vulnerability, considers the arsenic water supply standard value (0.05 mg/L), and the probability of it being exceeded is dependent on the geologic and hydrological characteristics of the studied area and also on the anthropogenic activities. The second one (I(2)), an intrinsic vulnerability, considers arsenic background limit as cut-off value, and depends only on the geologic and hydro-geological characteristics of the studied area. At Segura, the arsenic water content found during December 2006 (1.190 mg/L) was higher than the arsenic water content detected in October 2006 (0.636 mg/L) which could be associated to the arsenic released from Fe oxy-hydroxide. At Segura abandoned mining area, the iso-probability maps of October 2006 and December 2006, show strong anomalies associated with the water drainage from abandoned mining activities. Near the village, the probability of exceeding the arsenic background value is high but lower than the probability of exceeding the arsenic water supply value. The arsenic anomalies indicate a high probability for water arsenic contamination and those waters should not be used for human consumption.

Human health risk from soil heavy metal contamination under different land uses near Dabaoshan Mine, Southern China

Soil heavy metal contamination is a major environmental concern, and the ecological risk associated with heavy metals is increasing. In this paper, we investigated heavy metal contamination near Dabaoshan Mine by: using sequential indicator simulation to delineate the spatial patterns of soil data; fitting multiple linear regression models for heavy metal uptake by crops; interpreting land uses from remote sensing images and integrating the spatial patterns, uptake models and land uses into a dose-response model for human health risks from heavy metals. The areas with elevated soil heavy metal concentrations are mainly located at the Dabaoshan Mine site and in the watershed basins of the Hengshi, Tielong and Chuandu rivers. The average concentrations of Cu, Zn, Cd and Pb in soil in the study area are all above the natural soil background levels, but Cd is the major contributor to human health risk in the area. Areas of low soil pH are also found throughout the watershed basins of the Hengshi, Tielong and Chuandu rivers. Of the different land use types in the study area, agricultural and residential land uses have the highest human health risk because ingestion is the dominant exposure pathway for heavy metals. The spatial patterns of the heavy metal concentrations and soil pH indicate that the areas with the highest human health risk regions do not directly coincide with the areas of highest heavy metal concentrations, but do coincide with the areas of lower soil pH. The contamination with high concentrations of heavy metals provides the risk source, but the combination of high heavy metal concentrations, low pH and agricultural or residential land use is required for human health risks to be present. The spatial pattern of the hazard quotients indicates that Cd is the most important pollutant contributing to the human health risk.

Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India

Two non-parametric kriging methods such as indicator kriging and probability kriging were compared and used to estimate the probability of concentrations of Cu, Fe, and Mn higher than a threshold value in groundwater. In indicator kriging, experimental semivariogram values were fitted well in spherical model for Fe and Mn. Exponential model was found to be best for all the metals in probability kriging and for Cu in indicator kriging. The probability maps of all the metals exhibited an increasing risk of pollution over the entire study area. Probability kriging estimator incorporates the information about order relations which the indicator kriging does not, has improved the accuracy of estimating the probability of metal concentrations in groundwater being higher than a threshold value. Evaluation of these two spatial interpolation methods through mean error (ME), mean square error (MSE), kriged reduced mean error (KRME), and kriged reduced mean square error (KRMSE) showed 3.52% better performance of probability kriging over indicator kriging. The combined result of these two kriging method indicated that on an average 26.34%, 65.36%, and 99.55% area for Cu, Fe, and Mn, respectively, are coming under the risk zone with probability of exceedance from a cutoff value is 0.6 or more. The groundwater quality map pictorially represents groundwater zones as "desirable" or "undesirable" for drinking. Thus the geostatistical approach is very much helpful for the planners and decision makers to devise policy guidelines for efficient management of the groundwater resources so as to enhance groundwater recharge and minimize the pollution level.

Application of geostatistics with Indicator Kriging for analyzing spatial variability of groundwater arsenic concentrations in Southwest Bangladesh

This article seeks to explore the spatial variability of groundwater arsenic (As) concentrations in Southwestern Bangladesh. Facts about spatial pattern of As are important to understand the complex processes of As concentrations and its spatial predictions in the unsampled areas of the study site. The relevant As data for this study were collected from Southwest Bangladesh and were analyzed with Flow Injection Hydride Generation Atomic Absorption Spectrometry (FI-HG-AAS). A geostatistical analysis with Indicator Kriging (IK) was employed to investigate the regionalized variation of As concentration. The IK prediction map shows a highly uneven spatial pattern of arsenic concentrations. The safe zones are mainly concentrated in the north, central and south part of the study area in a scattered manner, while the contamination zones are found to be concentrated in the west and northeast parts of the study area. The southwest part of the study area is contaminated with a highly irregular pattern. A Generalized Linear Model (GLM) was also used to investigate the relationship between As concentrations and aquifer depths. A negligible negative correlation between aquifer depth and arsenic concentrations was found in the study area. The fitted value with 95 % confidence interval shows a decreasing tendency of arsenic concentrations with the increase of aquifer depth. The adjusted mean smoothed lowess curve with a bandwidth of 0.8 shows an increasing trend of arsenic concentration up to a depth of 75 m, with some erratic fluctuations and regional variations at the depth between 30 m and 60 m. The borehole lithology was considered to analyze and map the pattern of As variability with aquifer depths. The study has performed an investigation of spatial pattern and variation of As concentrations.

Health risks for human intake of aquacultural fish: Arsenic bioaccumulation and contamination

Aquacultural tilapia (Oreochromis mossambicus L.) and shrimp (Penaeus monodon L.) from groundwater-cultured ponds in southwestern Taiwan were analyzed to estimate arsenic (As) bioaccumulation and the potential health risk to human intake. Most of aquacultural ponds exhibited higher arsenic than maximum allowed concentrations (50 μg L(-1)) in pond water of Taiwan. Arsenic levels in tilapia in Budai, Yichu and Beimen were 0.92 ± 0.52 μg g(-1), 0.93 ± 0.19 μg g(-1) and 0.76 ± 0.03 μg g(-1), respectively and in shrimp was 0.36 ± 0.01 μg g(-1) in Beimen. Total arsenic in tilapia is highly correlated (R(2) = 0.80) with total arsenic concentration of pond water. Total arsenic in fish showed high correlation with that in bone (R(2) = 0.98), head (R(2) = 0.97) and tissue (R(2) = 0.96). Organic arsenic species (DMA) was found higher relative to inorganic species of As(III) and As(V). The average percent contribution of inorganic arsenic to total arsenic in fish samples was 12.5% and ranged between 11.7 to 14.2%. Bioaccumulation factors (BAFs) for total arsenic in fish ranged from 10.3 to 22.1, whereas BAF for inorganic arsenic ranged from 1.33 to 2.82. The mean human health cancer risk associated with the ingestion of inorganic arsenic in the fish was estimated at 2.36 × 10(-4) ± 0.99 × 10(-4), which is over 200 times greater than a de Minimus cancer risk of 1 × 10(-6). The mean human health hazard quotient associated with ingesting inorganic arsenic in the fish was 1.22 ± 0.52, indicating that expected human exposure exceeds the reference dose for non-cancer health effects by 22%. These results suggest that the inhabitants in this region are being subjected to moderately elevated arsenic exposure through the consumption of tilapia and shrimp raised in aquaculture ponds.

Assessment of groundwater pollution in West Delhi, India using geostatistical approach

The exploration, exploitation, and unscientific management of groundwater resources in the National Capital Territory (NCT) of Delhi, India have posed a serious threat of reduction in quantity and deterioration of quality. The objective of the study is to determine the groundwater quality and to assess the risk of groundwater pollution at Najafgarh, NCT of Delhi. The groundwater quality parameters were analyzed from the existing wells of the Najafgarh and the thematic maps were generated using geostatistical concepts. Ordinary kriging and indicator kriging methods were used as geostatistical approach for preparation of thematic maps of the groundwater quality parameters such as bicarbonate, calcium, chloride, electrical conductivity (EC), magnesium, nitrate, sodium, and sulphate with concentrations equal or greater than their respective groundwater pollution cutoff value. Experimental semivariogram values were fitted well in spherical model for the water quality parameters, such as bicarbonate, chloride, EC, magnesium, sodium, and sulphate and in exponential model for calcium and nitrate. The thematic maps of all the groundwater quality parameters exhibited an increasing trend of pollution from the northern and western part of the study area towards the southern and eastern part. The concentration was highest at the southernmost part of the study area but it could not reflect correctly the groundwater pollution status. The indicator kriging method is useful to assess the risk of groundwater pollution by giving the conditional probability of concentrations of different chemical parameters exceeding their cutoff values. Thus, risk assessment of groundwater pollution is useful for proper management of groundwater resources and minimizing the pollution threat.

Spatial variability of groundwater depth and quality parameters in the National Capital Territory of Delhi

The groundwater quantity and quality scenario is of much concern in the National Capital Territory of Delhi, India, which necessitates an investigation to envisage the extent of spatial variability of groundwater depth and pollutant concentration levels in this region. Therefore, in this study, an effort was made to generate the spatial variability map of groundwater depth and quality parameters (viz. chloride, electrical conductivity, fluoride, magnesium, and nitrate). Ordinary kriging was used to analyze the spatial variability of groundwater depth and quality parameters, whereas indicator kriging was used to analyze groundwater quality parameters equal to or greater than the pollution threshold values. It was observed that the semivariogram parameters fitted well in the exponential model for water depth and in the spherical model for water quality parameters. The generated spatial variability maps indicated that in 43% of the study area, groundwater depth was within 20 m. The salinity level was higher than 2.5 dS m(-1) in 69% of the study area and the nitrate concentration exceeded 45 mg l(-1) in 36% of the area. The probability maps showed that about 24% of the area had the highest probability (0.8-1.0) of exceedence of the threshold electrical conductivity value and an area of 2% exhibited the highest probability of exceedence of the threshold value of nitrate concentration in the groundwater. The generated spatial variability and probability maps will assist water resource managers and policymakers in development of guidelines in judicious management of groundwater resources for agricultural and drinking purposes in the study area.

Spatial analysis of human health risk associated with ingesting manganese in Huangxing Town, Middle China

This paper spatially analyzed human health risk associated with ingesting manganese (Mn) contents in groundwater and vegetables irrigated with contaminated pond water in Huangxing Town, Middle China. The combination of monitoring data and sequential indicator simulation (SIS) was used to determine Mn exposure distributions in pond water and groundwater. Hazard quotient (HQ) associated with ingesting Mn was calculated to evaluate the risk to human health. Many HQs determined from risks exceed 1 in the region, indicating that the use of groundwater and pond water poses potential risk to human health. Lower risk areas are located in the northwest and partly southeast of the region. The probabilistic risk assessment formulated suitable references for pollution remedy and control in Huangxing Town. Safe areas in 75th percentile of HQ map are suggested to be safe for use and, the manganese residues in the unsafe areas of the 25th percentile of HQ map is to be treated firstly.

Application of indicator kriging to the complementary use of bioindicators at three trophic levels

The use of biological indicators is widespread in environmental monitoring, although it has long been recognised that each bioindicator is generally associated with a range of potential limitations and shortcomings. To circumvent this problem, this study adopted the complementary use of bioindicators representing different trophic levels and providing different type of information, in an innovative approach to integrate knowledge and to estimate the overall health state of ecosystems. The approach is illustrated using mercury contamination in primary producers (mosses), primary consumers (domestic pigeons and red-legged partridges) and top predators (Bonelli's eagles) in southern Portugal. Indicator kriging geostatistics was used to identify the areas where mercury concentration was higher than the median for each species, and to produce an index that combines mercury contamination across trophic levels. Spatial patterns of mercury contamination were consistent across species. The combined index provided a new level of information useful in incorporating measures of overall environmental contamination into pollution studies.

Health risks from arsenic-contaminated soil in Flin Flon-Creighton, Canada: integrating geostatistical simulation and dose-response model

Elevated concentrations of arsenic were detected in surface soils adjacent to a smelting complex in northern Canada. We evaluated the cancer risks caused by exposure to arsenic in two communities through combining geostatistical simulation with demographic data and dose-response models in a framework. Distribution of arsenic was first estimated using geostatistical circulant-embedding simulation method. We then evaluated the exposures from inadvertent ingestion, inhalation and dermal contact. Risks of skin cancer and three internal cancers were estimated at both grid scale and census-unit scale using parametric dose-response models. Results indicated that local residents could face non-negligible cancer risks (skin cancer and liver cancer mainly). Uncertainties of risk estimates were discussed from the aspects of arsenic concentrations, exposed population and dose-response model. Reducing uncertainties would require additional soil sampling, epidemic records as well as complementary studies on land use, demographic variation, outdoor activities and bioavailability of arsenic.

In field arsenic removal from natural water by zero-valent iron assisted by solar radiation

An in situ arsenic removal method applicable to highly contaminated water is presented. The method is based in the use of steel wool, lemon juice and solar radiation. The method was evaluated using water from the Camarones River, Atacama Desert in northern Chile, in which the arsenic concentration ranges between 1000 and 1300 microg L(-1). Response surface method analysis was used to optimize the amount of zero-valent iron (steel wool) and the citrate concentration (lemon juice) to be used. The optimal conditions when using solar radiation to remove arsenic from natural water from the Camarones river are: 1.3 g L(-1) of steel wool and one drop (ca. 0.04 mL) of lemon juice. Under these conditions, removal percentages are higher than 99.5% and the final arsenic concentration is below 10 microg L(-1). This highly effective arsenic removal method is easy to use and inexpensive to implement.