Improved groundwater geogenic arsenic hazard map for Cambodia

C. Sovann, D. A. Polya

    Research output: Contribution to journalArticlepeer-review

    Abstract

    © 2014 CSIRO.Environmental context Groundwater arsenic is a major environmental risk to human health in many regions of the world, including Cambodia where groundwater is often used for drinking water. We present data for hitherto poorly sampled regions in Cambodia, notably around Tonle Sap and in the coastal provinces, and provide a geo-statistical model of arsenic in shallow groundwater for the whole country. Abstract Arsenic is a known environmental chemical hazard in shallow groundwaters of Cambodia and is increasingly recognised as a major problem for public health. Notwithstanding this, accurate arsenic data are not available for many wells in potentially arsenic-prone areas, particularly around the Tonle Sap Great Lake (TSL) and in the coastal provinces (CP). We present here new data for shallow groundwater (16-120-m depth) arsenic in the TSL and CP regions as well as an improved regression-kriging (RK) based groundwater arsenic hazard map for the whole country. High arsenic levels (up to 100gL-1) were found in shallow groundwaters from the TSL and CP regions of Cambodia, but despite strong compositional similarities (near neutral, reducing, Na-Mg-Ca-HCO3 dominated) with high arsenic level groundwaters near the Mekong and Bassac rivers, groundwater arsenic levels in both the TSL and CP regions were most commonly low (interquartile range 0.09-1.2gL-1). The RK geostatistical model was highly successful, accounting for over 50% of the observed variation in arsenic concentrations countrywide and represents a potentially useful tool for policymakers and those responsible and with the interest and authority to prepare arsenic mitigation and safe water supply plans.
    Original languageEnglish
    Pages (from-to)595-607
    Number of pages12
    JournalEnvironmental Chemistry
    Volume11
    Issue number5
    DOIs
    Publication statusPublished - 2014

    Keywords

    • geostatistics
    • regression kriging.

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