Literature DB >> 17952773

Arsenic risk mapping in Bangladesh: a simulation technique of cokriging estimation from regional count data.

M Manzurul Hassan1, Peter J Atkins.   

Abstract

Risk analysis with spatial interpolation methods from a regional database on to a continuous surface is of contemporary interest. Groundwater arsenic poisoning in Bangladesh and its impact on human health has been one of the "biggest environmental health disasters" in current years. It is ironic that so many tubewells have been installed in recent times for pathogen-free drinking water but the water pumped is often contaminated with toxic levels of arsenic. This paper seeks to analyse the spatial pattern of arsenic risk by mapping composite "problem regions" in southwest Bangladesh. It also examines the cokriging interpolation method in analysing the suitability of isopleth maps for different risk areas. GIS-based data processing and spatial analysis were used for this research, along with state-of-the-art decision-making techniques. Apart from the GIS-based buffering and overlay mapping operations, a cokriging interpolation method was adopted because of its exact interpolation capacity. The paper presents an interpolation of regional estimates of arsenic data for spatial risk mapping that overcomes the areal bias problem for administrative boundaries. Moreover, the functionality of the cokriging method demonstrates the suitability of isopleth maps that are easy to read.

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Year:  2007        PMID: 17952773     DOI: 10.1080/10934520701564210

Source DB:  PubMed          Journal:  J Environ Sci Health A Tox Hazard Subst Environ Eng        ISSN: 1093-4529            Impact factor:   2.269


  2 in total

1.  Health risk estimates for groundwater and soil contamination in the Slovak Republic: a convenient tool for identification and mapping of risk areas.

Authors:  K Fajčíková; V Cvečková; A Stewart; S Rapant
Journal:  Environ Geochem Health       Date:  2014-04-13       Impact factor: 4.609

2.  Hotspot analysis of spatial environmental pollutants using kernel density estimation and geostatistical techniques.

Authors:  Yu-Pin Lin; Hone-Jay Chu; Chen-Fa Wu; Tsun-Kuo Chang; Chiu-Yang Chen
Journal:  Int J Environ Res Public Health       Date:  2010-12-30       Impact factor: 3.390

  2 in total

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