Title: Modelling Arsenic Hazard in Groundwater in Cambodia: A Geostatistical Approach Using Ancillary Data
Authors: RODRIGUEZ LADO LUISPOLYA DavidWINKEL LennyBERG MichaelHEGAN Aimee
Citation: APPLIED GEOCHEMISTRY vol. 23 no. 11 p. 3010-3018
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Publication Year: 2008
JRC N°: JRC42044
ISSN: 0883-2927
URI: http://dx.doi.org/10.1016/j.apgeochem.2008.06.028
http://publications.jrc.ec.europa.eu/repository/handle/JRC42044
DOI: 10.1016/j.apgeochem.2008.06.028
Type: Articles in periodicals and books
Abstract: The As concentration in shallow groundwater in Cambodia was estimated using 1329 georeferenced water samples collected during the period 1986¿2004 from wells between 16¿100 m depth. Arsenic concentrations were estimated using block regression-kriging on the log transformed As measurements. Auxiliary raster maps (DEM-parameters, remote sensing images and geology) were converted to 16 principal components that were used to explain the distribution of As over the study area. The regression-kriging model was validated using an external set of 276 samples, and the results were compared to those obtained by ordinary block kriging. The regression analysis revealed that there is a good correlation between topographic environmental variables and the content of As in groundwater. This result is broadly consistent with the findings of previous studies and is not unexpected given models of microbial mediated As mobilization in recent low lying sediments. Kândal, Prey Vêng and Kâmpóng Cham are the provinces with the highest potential As hazard, indicating the requirement for development and implementation of policy control measures. The regression-kriging model explained 48% of the variability in the validation set. However, the model does not show good results for the prediction of high As concentration. This points to the existence of local environmental factors, not captured by this model, that highly influence the mobilization of As in groundwater. Even if the results of the validation of regression-kriging and ordinary kriging are similar, the regression kriging approach provides a more realistic description of the distribution of As since it also captures the large-scale variation of As in the study area.
JRC Directorate:Sustainable Resources

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