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A wide variety of tools are available, both parametric and nonparametric, for analyzing spatial data. However, it is not always clear how to translate statistical inferences into decision recommendations. This article explores the possibilities of estimating the effects of decision options using very direct manipulation of data, bypassing formal statistical analysis. We illustrate with the application that motivated this research, a study of arsenic in drinking water in nearly 5,000 wells in a small area in rural Bangladesh. We estimate the potential benefits of two possible remedial actions: (1) recommendations that people switch to nearby wells with lower arsenic levels; and (2) drilling new community wells. We use simple nonparametric clustering methods and estimate uncertainties using cross-validation. [ABSTRACT FROM AUTHOR]

A comparison of field and laboratory measurements of arsenic in groundwater of Araihazar, Bangladesh, indicates that the most widely used field kit correctly determined the status of 88% of 799 wells relative to the local standard of 50 μg/L As. Additional tests show that the inconsistencies, mainly underestimates in the 50-100 μg/L As range, can be avoided by increasing the reaction time from 20 to 40 min. Despite this limitation, the field data already compiled for millions of wells by the Bangladesh Arsenic Mitigation and Water Supply Project, in combination with information on well location and depth, should prove to be extremely useful to prioritize interventions in thousands of affected villages. [ABSTRACT FROM AUTHOR]

Investigates tube wells and households in Bangladesh to set the basis for a long-term epidemiological study of the consequences of chronic arsenic exposure. Variable distribution of arsenic in wells; Absence of indication of a seasonal cycle in arsenic concentrations coupled to the hydrological cycle; Proposal of well-switching to mitigate the arsenic crisis.

Presents a response by the authors to a letter to the editor about their article "Reliability of a Commercial Kit to Test Groundwater for Arsenic in Bangladesh," published in a previous issue.

The Bangladesh Arsenic Mitigation and Water Supply Program (BAMWSP) has compiled field-kit measurements of the arsenic content of groundwater for nearly five million wells. By comparing the spatial distribution of arsenic inferred from these field-kit measurements with geo-referenced laboratory data in a portion of Araihazar upazila, it is shown here that the BAMWSP data could be used for targeting safe aquifers for the installation of community wells in many villages of Bangladesh. Recent experiences with mobile-phone technology to access and update the BAMWSP data in the field are also described. It is shown that the technology, without guaranteeing success, could optimize interventions by guiding the choice of the drilling method that is likely to reach a safe aquifer and identifying those villages where exploratory drilling is needed.

A wide variety of tools are available, both parametric and nonparametric, for analyzing spatial data. However, it is not always clear how to translate statistical inferences into decision recommendations. This article explores the possibilities of estimating the effects of decision options using very direct manipulation of data, bypassing formal statistical analysis. We illustrate with the application that motivated this research, a study of arsenic in drinking water in nearly 5,000 wells in a small area in rural Bangladesh. We estimate the potential benefits of two possible remedial actions: (1) recommendations that people switch to nearby wells with lower arsenic levels; and (2) drilling new community wells. We use simple nonparametric clustering methods and estimate uncertainties using cross-validation.