Effects of soil erosion on water quality and water uses in the upper Phong watershed.

The main objective of this paper is to simulate the effects of soil erosion on river water quality and on agricultural production as a result of the transformation of forestlands in the catchment of the upstream Phong River. Suspended solids carry down attached nutrients and agricultural chemicals causing water pollution in the downstream. There are four different types of land use in this simulation, namely forestlands, flatland and highland sugarcane plantation areas, and paddy fields. The highest mean annual amount of soil erosion is from paddy fields (585,700 tons/year), followed by highland (73,800 tons/year) and flatland (63,950 tons/year) sugarcane plantation areas and forestlands (41,800 tons/year), respectively. However, as most of paddy fields are located in a low land and are wet type cultivations, the soil erosion occurred has less impact on river water quality and its production compared to the soil erosion from the steeper slopes of highland plantation areas. Under the resource-based agriculture, the sugarcane production is mainly increased by expanding the plantation areas leading to a significant loss of topsoil and a considerable reduction of agricultural production. Soil erosion contributes to an increase in the average annual suspended solids concentration by 72 mg/l.     

Health risk assessment of inorganic arsenic intake of Cambodia residents through groundwater drinking pathway

In order to compare the magnitudes and health impacts of arsenic and other toxic trace elements in well water, groundwater and hair samples were collected from three areas with different arsenic exposure scenarios in the Mekong River basin of Cambodia. Ampil commune in Kampong Cham province was selected as an uncontaminated area, Khsarch Andaet commune in Kratie province was selected as a moderately contaminated area, and Kampong Kong commune in Kandal Province was selected as an extremely contaminated area. Results of ICP-MS analyses of the groundwater samples revealed that As, Mn, Fe and Ba concentrations were significantly different among the three study areas (Kruskal-Wallis test, p < 0.0001). Out of 46 observed wells in the Kandal province study area, 100% detected As > 50 μg L⁻¹ and Fe > 300 μg L⁻¹; 52.17% had Mn > 400 μg L⁻¹ and 73.91% found Ba > 700 μg L⁻¹. In the Kratie province study area (n = 12), 25% of wells showed elevated arsenic levels above 10 μg L⁻¹ and 25% had Mn > 400 μg L⁻¹, whereas samples from Kampong Cham province study area (n = 18) were relatively clean, with As < 10 μg L⁻¹. A health risk assessment model derived from the USEPA was applied to calculate individual risks resulting from drinking groundwater. Computational results indicated that residents from Kandal Province study area (n = 297) confronted significantly higher non-carcinogenic and carcinogenic risks than those in Kratie (n = 89) and Kampong Cham (n = 184) province study areas (Kruskal-Wallis test, p < 0.0001). 98.65% of respondents from the Kandal province study area were at risk for the potential non-cancer effect and an average cancer risk index was found to be 5 in 1000 exposure. The calculations also indicated that, in the Kratie province study area, 13.48% of respondents were affected by non-cancer health risks and 33.71% were threatened by cancer, whereas none of respondents in the Kampong Cham province study area appeared to have non-carcinogenic effect. Positively significant correlations of the arsenic content in scalp hair (As_h) with both arsenic levels in groundwater (As_w) (r_s (304) = 0.757, p < 0.0001) and individual average daily doses (ADD) of arsenic (r_s (304) = 0.763, p < 0.0001) undoubtedly indicated that arsenic accumulation in the bodies of Cambodia residents in the Mekong River basin was mainly through a groundwater drinking pathway. To the best of our knowledge, this is the first comprehensive report comparing individual health risk assessments of arsenic exposure through a groundwater drinking pathway to enriched arsenic levels from groundwater in the Mekong River basin, Cambodia. This study indicates that elevated arsenic concentrations in groundwater may lead to thousands of cases of arsenicosis in the near future if mitigating actions are not taken.     

Development of Water Quality Indexes to Identify Pollutants in Vietnam’s Surface Water

This study presents the first water quality indexes developed to evaluate surface water in Vietnam. The basic water quality index (WQIB) can be effectively used to evaluate the spatial and temporal variations of surface water quality as well as to identify water pollutants. The overall water quality index (WQIO) can provide additional information, particularly on toxic substances contributing to water pollution. The water quality indexes developed for this paper were applied to the national surface-water quality monitoring data taken from 1999 to 2007. Water pollutants were classified into three subcategories: organic and nutrients, particulates, and bacteria. Surface water in northern and central Vietnam was poor in quality and contained organic matter, nutrients, and bacteria. Water in the southern part was mainly polluted by bacteria. Trend analysis results reveal a deterioration in water quality in those provinces under pressure from rapid population growth, urbanization, and industrialization. Vietnam has established an official policy to ensure comprehensive nationwide water quality monitoring by 2020. The implementation of water quality indexes may provide the guiding data for sustainable water-resources management in Vietnam.     

Daily intake and human risk assessment of organochlorine pesticides (OCPs) based on Cambodian market basket data

To assess organochlorine pesticide (OCP) contaminations and its possible adverse health impacts, different food samples were collected from three areas of Cambodia, one of the poorest countries in the world. The ∑OCP concentrations in Kampong Cham, Kratie and Kandal provinces ranged from 1.28 to 188 (median 3.11), 1.06 to 25.1 (5.59) and 2.20 to 103 (20.6) ng g⁻¹, respectively. The dichlorodiphenyltrichloroethanes (DDTs) were the predominant OCPs and accounted for 62.2% (median) among all foodstuffs. Congener profile analyses suggested that there were new input sources of DDTs and hexachlorocyclohexanes (HCHs) in Cambodia, particularly in Kandal province. The estimated daily intake of OCPs (330 ng kg⁻¹ day⁻¹) for residents in Kandal province ranked No. 1 among the 13 compared countries or regions. On the basis of 95th percentile concentrations, the carcinogenic hazard ratios (HRs) of most investigated individual OCPs in vegetable and fish in Cambodia exceeding unity. Particularly for α-HCH in vegetable, the 95th HR was as high as 186. The data revealed that there is a great cancer risk for the local residents with life time consumption of OCP contaminated vegetable and fish. To our knowledge, this the first study to evaluate the daily intakes of OCPs in Cambodia.     

Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network

The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. As groundwater resources are one of most important freshwater sources for water supplies in Southeast Asian countries, it is important to investigate the spatial distribution of As contamination and evaluate the health risk of As for these countries. The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. Therefore, modeling approaches for As concentration using conventional on-site measurement data can be an alternative to quantify the As contamination. The objective of this study is to evaluate the predictive performance of four different models; specifically, multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), and the combination of principal components and an artificial neural network (PC-ANN) in the prediction of As concentration, and to provide assessment tools for Southeast Asian countries including Cambodia, Laos, and Thailand. The modeling results show that the prediction accuracy of PC-ANN (Nash-Sutcliffe model efficiency coefficients: 0.98 (traning step) and 0.71 (validation step)) is superior among the four different models. This finding can be explained by the fact that the PC-ANN not only solves the problem of collinearity of input variables, but also reflects the presence of high variability in observed As concentrations. We expect that the model developed in this work can be used to predict As concentrations using conventional water quality data obtained from on-site measurements, and can further provide reliable and predictive information for public health management policies.