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. Vietna...

Groundwater contamination with arsenic (As) is one of the major issues in the world, especially for Southeast Asian (SEA) countries where groundwater is the major drinking water source, especially in rural areas. Unfortunately, quantification of groundwater As contamination is another burden for those countries because it requires sophisticated equipment, expensive analysis, and well-trained technicians. Here, we collected approximately 350 groundwater samples from three different SEA countries, including Cambodia, Lao PDR, and Thailand, in an attempt to quantify total As concentrations and conventional water quality variables. After that, two machine learning models (i.e. artificial neural network (ANN) and support vector machine (SVM)) were applied to predict groundwater As contamination using conventional water quality parameters. Prior to modeling approaches, the pattern search algorithm in MATLAB software was used to optimize the ANN and SVM model parameters, attempting to find the best parameters set for modeling groundwater As concentrations. Overall, the SVM showed the superior prediction performance, giving higher Nash–Sutcliffe coefficients than ANN in both the training and validation periods. We hope that the model developed by this study could be a suitable quantification tool for groundwater As contamination in SEA countries.

The Chao Phraya River in Thailand has been greatly affected by climate change and the occurrence of extreme flood events, hindering its economic development. This study assessed the hydrological responses of the Chao Phraya River basin under several climate sensitivity and greenhouse gas emission scenarios. The Soil and Water Assessment Tool (SWAT) model was applied to simulate the streamflow using meteorological and observed data over a nine-year period from 2003 to 2011. The SWAT model produced an acceptable performance for calibration and validation, yielding Nash-Sutcliffe efficiency (NSE) values greater than 0.5. Precipitation scenarios yielded streamflow variations that corresponded to the change of rainfall intensity and amount of rainfall, while scenarios with increased air temperatures predicted future water shortages. High CO2 concentration scenarios incorporated plant responses that led to a dramatic increase in streamflow. The greenhouse gas emission scenarios increased the streamflow variations to 6.8%, 41.9%, and 38.4% from the reference period (2003–2011). This study also provided a framework upon which the peak flow can be managed to control the nonpoint sources during wet season. We hope that the future climate scenarios presented in this study could provide predictive information for the river basin.