Daphne gnidium, a possible native host plant of the European grapevine moth Lobesia botrana, stimulates its oviposition. Is a host shift relevant?

Summary. We tested the hypothesis that Daphne gnidium is an ancestral host plant of Lobesia botrana. To this end, we studied the oviposition response of this moth to various aerial organs of this plant. Our results show that females prefer to lay eggs on daphne berries rather than on grapes (cv. chasselas) and that polar surface extracts of daphne berries and leaves strongly stimulate oviposition in a dosedependant way, whereas flower extracts are weakly stimulant and branch extracts are inactive or deterrent for oviposition. Furthermore, a total extract of daphne berries stimulates oviposition in semi-natural conditions when applied onto fresh grapes. Oviposition stimulants from berries were isolated by HPLC coupled with a dual-choice oviposition bioassay. The ecological value of D. gnidium for L. botrana is discussed and the possible use of oviposition stimulants from daphne, contrasting with the oviposition signal of the cultivated host plant, is proposed in the perspective of developing behavioural manipulation methods such as stimulo-deterrent diversionary strategies compatible with IPM programs.     

Proximate composition of household waste and applicability of waste management technologies by source separation in Hanoi,Vietnam

The organic fraction of municipal solid wastes in Southeast Asia, which has a high moisture content, accounts for a large proportion of total waste. Local governments need to pay adequate attention to the composition of wastes to determine alternative waste management technologies. This study proposed the use of a triangle diagram to describe changes in proximate composition and rates of successful source separation of municipal solid waste and to identify technical challenges about alternative waste management technologies such as incineration, composting, and refuse-derived fuel production based on physical and proximate composition analysis of household waste sampled in Hanoi, Vietnam, as a case study. The analysis indicated the effectiveness of different types of source separation as well as different levels of successful achievement of source separation as an adjustment mechanism for the proximate composition of waste. Proper categorization of wastes for source separation is necessary for the appropriate use of alternative waste management technologies. The results showed that, at a source separation rate of just greater than 0.52 in a three-way separation scheme, the waste separated as combustible waste would be suitable for incineration with energy recovery. Based on well-designed schemes of source separation, alternative waste management technologies can be applied.     

Controlling various contaminants in wastewater effluent through membranes and engineered wetland

For effective wastewater reclamation and water recovery, the treatment of natural and effluent organic matters (NOM and EfOM), toxic anions, and micropollutants was considered in this work. Two different NOM (humic acid of the Suwannee River, and NOM of US and Youngsan River, Korea), and one EfOM from the Damyang wastewater treatment plant, Korea, were selected for investigating the removal efficiencies of tight nanofiltration (NF) and ultrafiltration (UF) membranes with different properties. Nitrate, bromate, and perchlorate were selected as target toxic anions due to their well known high toxicities. Tri-(2-chloroethyl)-phosphate (TCEP), oxybenzone, and caffeine, due to their different K _ow and pK _a values, were selected as target micropollutants. As expected, the NF membranes provided high removal efficiencies in terms of all the tested contaminants, and the UF membrane provided fairly high removal efficiencies for anions (except for nitrate) and the relatively hydrophobic micropollutant, oxybenzon. Through the wetlands, nitrate was successfully removed. Therefore, a fair process of combining membranes with an engineered wetland could be proposed for sustainable wastewater reclamation and optimum control of contaminats.     

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.     

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.     

Fractal geometry and applicability to biological simulation shapes for sustainable architecture design in Vietnam

Environmental Science and Pollution Research - Sustainability is the biggest goal that all areas including building architecture aim at. Sustainability is created by the harmony of buildings to the...     

Toward successful implementation of conservation research: A case study from Vietnam

A number of different approaches have been used to explain the successes and failures of biodiversity conservation strategies in developing countries. However, to date, little attention has been paid toward assessing the influence of knowledge transfer between science, policy, and conservation practices in the implementation of these strategies. Vietnam’s Pu Luong Cuc Phuong Conservation Area is a globally important ecosystem, situated within a limestone landscape and inhabited by hundreds of local communities. Biodiversity conservation has become an important part of sustainable development in this area. This study analyzes three conservation strategies employed in the Pu Luong Cuc Phuong Conservation Area by applying the Research–Integration–Utilization (RIU) model of scientific knowledge transfer. Our analyses reveal weaknesses in scientific knowledge transfer arising from low-quality research and poor integration strategies. Based on our results, we developed recommendations to improve research and integration in an effort to enhance science-based policy support.     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.