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

Use of Principal Components Analysis and Kriging to Predict Groundwater-Sourced Rural Drinking Water Quality in Saskatchewan

Groundwater drinking water supply surveillance data were accessed to summarize water quality delivered as public and private water supplies in southern Saskatchewan as part of an exposure assessment for epidemiologic analyses of associations between water quality and type 2 diabetes or cardiovascular disease. Arsenic in drinking water has been linked to a variety of chronic diseases and previous studies have identified multiple wells with arsenic above the drinking water standard of 0.01 mg/L; therefore, arsenic concentrations were of specific interest. Principal components analysis was applied to obtain principal component (PC) scores to summarize mixtures of correlated parameters identified as health standards and those identified as aesthetic objectives in the Saskatchewan Drinking Water Quality Standards and Objective. Ordinary, universal, and empirical Bayesian kriging were used to interpolate arsenic concentrations and PC scores in southern Saskatchewan, and the results were compared. Empirical Bayesian kriging performed best across all analyses, based on having the greatest number of variables for which the root mean square error was lowest. While all of the kriging methods appeared to underestimate high values of arsenic and PC scores, empirical Bayesian kriging was chosen to summarize large scale geographic trends in groundwater-sourced drinking water quality and assess exposure to mixtures of trace metals and ions.

Predicting arsenic concentrations in groundwater of San Luis Valley, Colorado: implications for individual-level lifetime exposure assessment

Consumption of inorganic arsenic in drinking water at high levels has been associated with chronic diseases. Risk is less clear at lower levels of arsenic, in part due to difficulties in estimating exposure. Herein we characterize spatial and temporal variability of arsenic concentrations and develop models for predicting aquifer arsenic concentrations in the San Luis Valley, Colorado, an area of moderately elevated arsenic in groundwater. This study included historical water samples with total arsenic concentrations from 595 unique well locations. A longitudinal analysis established temporal stability in arsenic levels in individual wells. The mean arsenic levels for a random sample of 535 wells were incorporated into five kriging models to predict groundwater arsenic concentrations at any point in time. A separate validation dataset (n = 60 wells) was used to identify the model with strongest predictability. Findings indicate that arsenic concentrations are temporally stable (r = 0.88; 95 % CI 0.83-0.92 for samples collected from the same well 15-25 years apart) and the spatial model created using ordinary kriging best predicted arsenic concentrations (ρ = 0.72 between predicted and observed validation data). These findings illustrate the value of geostatistical modeling of arsenic and suggest the San Luis Valley is a good region for conducting epidemiologic studies of groundwater metals because of the ability to accurately predict variation in groundwater arsenic concentrations.

Spatial modeling of ecological areas by fitting the limiting factors for As in the vicinity of mine, Serbia

Elevated arsenic (As) concentrations in soil are often found in the vicinity of certain mineral deposits that have been, or are currently, under exploitation, regardless of the target resource. Detailed study of such areas for safe agriculture requires considerable financial costs and long periods of time. Application of an appropriate spatial model that describes the behavior of arsenic in soil and plants can significantly ease the whole investigation process. This paper presents a model of ecological security of an area that, in the past, was an antimony mine and has a naturally high content of arsenic. For simulation and modeling the geographic information science (GIS) technology with the inserted predictors influencing the accessibility of As and its content in plants was used. The results obtained were the following: (1) a categorization of contaminated soils according to soil properties was developed; (2) the proposed methodology allows focusing on particular suspect area saving an energy and human resource input; and (3) new safe areas for growing crops in contaminated area were modeled. The application of the proposed model of As solubility to various crops grown around a former antimony mine near the village of Lisa, southwest Serbia showed that significant expansion of the areas suitable for growing potato, raspberry, and pasture was possible.

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.