Arsenic in groundwater and sediment in the Mekong River delta, Vietnam

A study of groundwater and sediment during 2007-2008 in the Mekong River delta in Vietnam (MDVN) revealed that 26%, 74%, and 50% of groundwater samples were above the US EPA drinking water guidelines for As (10 μg/L), Mn (0.05 mg/L), and Fe (0.3 mg/L). The range of As, Fe, and Mn concentrations in the MDVN were <0.1-1351 μg/L, <0.01-38 mg/L, and <0.01−14 mg/L, respectively. Elevated levels of As were found in groundwater at sampling sites close to the Mekong River and in wells less than 60−70 m deep. An inverse relationship was found between As and Mn concentrations in groundwater. Sediment samples from An Giang and Dong Thap had the highest As concentrations (18 mg/kg and 38 mg/kg, respectively). Arsenic sediment occurred mainly in the poorly crystalline Fe oxide phases. Reductive dissolution of the Fe oxide phase is not necessarily the dominant mechanism of As release to groundwater.     

Sustainability indicators for clean development mechanism projects in Vietnam

The Clean Development Mechanism (CDM) is one of the flexible instruments of the Kyoto Protocol designed to combat climate change so as to bring advantages to developing countries and developed countries alike. Indeed, CDM projects have a two-fold objective: to offset greenhouse gas emissions and to contribute to sustainable development in the host country. However in many cases, the latter objective appears to be marginalized. This is at least partly due to the difficulties surrounding the definition and the measurement of sustainability, in particular in a developing country context. To assess CDM projects’ contribution to sustainable development in the host country, scholars and practitioners need adapted indicator sets. A set of indicators were developed by way of an iterative Delphi approach amongst selected Vietnamese experts. The Delphi approach allowed a systematic collection of the experts’ judgements on the sustainability indicators through a set of sequentially applied questionnaires, interspersed with feedback from earlier responses. This exercise resulted in the selection of a set of 36 indicators, which emphasise economic efficiency, public health and pollution issues. The exercise yielded a locally supported and context-specific set of sustainability indicators that will allow Vietnamese decision-makers to enhance the sustainability of the approved CDM projects. In the future this set should be continually improved through real-life application and further participation from local stakeholders. This study is a first step in a long-term process towards developing an adapted toolkit for sustainability assessment of CDM projects in Vietnam.     

Mercury in air in an area impacted by strong industrial activities

The concentrations of total gaseous mercury (TGM) and its relevant environmental parameters were measured at a highly industrialized area in the Ban Wall industrial complex (BWIC) in An San city, Korea from March to May 2005. The mean concentrations of Hg measured during the entire study period were computed to be 6.32 ± 8.56 ng m⁻³ (range of 2.32–181 ng m⁻³; N = 1160). Due to the effects of strong man-made activities, the significantly high Hg concentration levels (e.g., at or above 10 ng m⁻³) comprised about 7.5% of all data with the mean of 21.8 ± 26.3 ng m⁻³ (N = 87). By separating the data into daytime and nighttime periods, the Hg values exhibited a notable daytime enhancement possibly due to strong man-made activities during working hours. The results of the correlation analysis indicated the possible relationship between the Hg concentration and the temperature as well as several pollutant species (e.g., NO₂ and NO_x). Evaluation of the Hg data in relation with the air mass transport pattern confirms that the Hg concentration levels in this industrial area are affected most eminently by local, rather than distant, pollution sources.     

Response surface method for modeling the removal of carbon dioxide from a simulated gas using water absorption enhanced with a liquid-film-forming device

This paper presents the results from using a physical absorption process to absorb gaseous CO_2mixed with N_2using water by producing tiny bubbles via a liquid-film-forming device(LFFD)that improves the solubility of CO_2in water.The influence of various parameters—pressure,initial CO_2concentration,gas-to-liquid ratios,and temperature—on the CO_2removal efficiency and its absorption rate in water were investigated and estimated thoroughly by statistical polynomial models obtained by the utilization of the response surface method(RSM)with a central composite design(CCD).Based on the analysis,a high efficiency of CO_2capture can be reached in conditions such as low pressure,high CO_2concentration at the inlet,low gas/liquid ratio,and low temperature.For instance,the highest removal efficiency in the RSM–CCD experimental matrix of nearly 80%occurred for run number 20,which was conducted at 0.30 MPa,CO_2concentration of 35%,gas/liquid ratio of 0.71,and temperature of 15°C.Furthermore,the coefficients of determination,R~2,were 0.996 for the removal rate and 0.982 for the absorption rate,implying that the predicted values computed by the constructed models correlate strongly and fit well with the experimental values.The results obtained provide essential information for implementing this method properly and effectively and contribute a promising approach to the problem of CO_2capture in air pollution treatment.     

Statistical analysis of regulatory ecotoxicity tests.

ANOVA-type data analysis, i.e.. determination of lowest-observed-effect concentrations (LOECs), and no-observed-effect concentrations (NOECs), has been widely used for statistical analysis of chronic ecotoxicity data. However, it is more and more criticised for several reasons, among which the most important is probably the fact that the NOEC depends on the choice of test concentrations and number of replications and rewards poor experiments, i.e., high variability, with high NOEC values. Thus, a recent OECD workshop concluded that the use of the NOEC should be phased out and that a regression-based estimation procedure should be used. Following this workshop, a working group was established at the French level between government, academia and industry representatives. Twenty-seven sets of chronic data (algae, daphnia, fish) were collected and analysed by ANOVA and regression procedures. Several regression models were compared and relations between NOECs and ECx, for different values of x, were established in order to find an alternative summary parameter to the NOEC. Biological arguments are scarce to help in defining a negligible level of effect x for the ECx. With regard to their use in the risk assessment procedures, a convenient methodology would be to choose x so that ECx are on average similar to the present NOEC. This would lead to no major change in the risk assessment procedure. However, experimental data show that the ECx depend on the regression models and that their accuracy decreases in the low effect zone. This disadvantage could probably be reduced by adapting existing experimental protocols but it could mean more experimental effort and higher cost. ECx (derived with existing test guidelines, e.g., regarding the number of replicates) whose lowest bounds of the confidence interval are on average similar to present NOEC would improve this approach by a priori encouraging more precise experiments. However, narrow confidence intervals are not only linked to good experimental practices, but also depend on the distance between the best model fit and experimental data. At least, these approaches still use the NOEC as a reference although this reference is statistically not correct. On the contrary, EC50 are the most precise values to estimate on a concentration response curve, but they are clearly different from the NOEC and their use would require a modification of existing assessment factors.     

Measuring the association of environmental,corporate, financial,and social CSR: evidence from fuzzy TOPSIS nexus in emerging economies

Environmental Science and Pollution Research - The purpose of this research is to measure the combing impact of corporate social responsibility on company performance and to conduct a comparative...     

A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed for motorcycles

ABSTRACT

Using the laboratory-based fuel consumption models for predicting real-world fuel consumption requires the measurement of data under certain conditions to obtain high accuracy of predicted result. Therefore, it is necessary to develop a logging device for measuring the real-time fuel consumption and speed of vehicle on the road. This article presents a study on developing the on-board data logging device to collect real-world data of fuel consumption and speed for motorcycles with the update rate of 1 Hz. The instantaneous speed of the motorcycle was determined based on the rotational speed of the wheel and the wheel radius. Another module was used to determine the instantaneous fuel consumption rate (FR) though measuring the duration injection pulse. The relationship between the duration injection pulse and the injected amount of fuel was established with high correlation coefficient of 0.997. In addition, a filter was designed to remove noise in the dataset collected using the data logging device. The random errors in the speed and the FR profiles were detected and replaced, the percentage of these errors is 1.8% and 2.4%, respectively. The developed method is a precise one for transient fuel consumption and speed measurement. In chassis dynamometer test, the average deviation between steady speed measured by the chassis and the data logging device is only approximately 0.35%. At transient state, the biggest deviation between these two datasets is less than 3.5%. The average FR at steady speed measured by the developed method is slightly different from the one measured by the carbon balance method. The difference is 0.9%, 2.5%, and 0.25% at the speeds of 30 km/h, 50 km/h, and 70 km/h, respectively. Following the transient test cycle, the fuel consumption measured by the developed method is 4.35% lower than that determined by the carbon balance method.

Implications: A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed was developed for motorcycles. The proposed method can record well the real-world driving data for motorcycles, including the fuel consumption and speed, with the update rate of 1 Hz. The filter was designed to minimize noise while maintaining data integrity of the collected dataset, the percentage of errors in the the speed and the FR profiles is 1.8% and 2.4%, respectively. The proposed method, therefore, can be used as effective tools for future studies relating to the fuel consumption and emission of motorcycles on the road.     

Using a social learning configuration to increase Vietnamese smallholder farmers’ adaptive capacity to respond to climate change

Social learning is crucial for local smallholder farmers in developing countries to improve their adaptive capacity and to adapt to the current and projected impacts of climate change. While it is widely acknowledged that social learning is a necessary condition for adaptation, few studies have systematically investigated under which conditions particular forms of social learning are most successful in improving adaptive capacity of the most vulnerable groups. This study aims to design, implement and evaluate a social learning configuration in a coastal community in Vietnam. We make use of various methods during four workshop-based interventions with local smallholder farmers: interviews with key farmers and commune leaders, farmer-to-farmer learning, participatory observations and focus group discussions. The methods for evaluation of social learning configuration include in-depth interviews, focus group discussions and structured survey interviews. Our findings show that the social learning configuration used in this study leads to an increased problem ownership, an enhanced knowledge-base with regard to climate change impacts and production adaptation options, improved ability to see connections and interdependencies and finally, strengthened relationships and social cohesion. The results suggest that increased social learning in the community leads to increase in adaptive capacity of smallholder farmers and improves both their economic and environmental sustainability. We discuss the key lessons for designing learning configurations that can successfully enhance adaptive capacity and smallholder farmers’ agency and responsiveness to the challenges posed by climate change impacts.