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

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.

Keywords:

• Arsenic
• cokriging
• spatial interpolation
• risk mapping
• Bangladesh

Acknowledgments

The senior author would like to express his sincere thanks to the Commonwealth Commission for funding this research. He also wishes to express his thanks to Professor Dipankar Chakraborti of the SOES, Jadavpur University, Kolkata, India for his cooperation in the laboratory analysis of the water samples.

Notes

•People's preferences were given priority. They want to collect their drinking water from any arsenic-free tubewell.

▪Figure in the parentheses indicate the percent of buffer area in hectares.

*Note: Figures in the parentheses indicate the percent of total population.