Application of WRF-Chem to simulate air quality over Northern Vietnam

Environmental Science and Pollution Research - The WRF-Chem (Weather Research and Forecasting with Chemistry) model is implemented and validated against ground-based observations for meteorological...     

Production of plant growth–promoting bacteria inoculants from composting leachate to develop durable agricultural ecosystems

Environmental Science and Pollution Research - Composting process of residual organic material generates considerable amounts of liquid leachate which contains high organic load. This waste stream...     

A state-of-the-art review on emission characteristics of SI and CI engines fueled with 2,5-dimethylfuran biofuel

Environmental Science and Pollution Research - Currently, the considerable decline in fossil fuel resources and the high rise in vehicle emissions have prompted researchers and governments to...     

IAEA programme on the quality of marine radioactivity data

Society's growing interest in environmental issues requires the production of reliable information for policy-makers, stakeholders and society in general. This information must be based on accurate data produced by qualified laboratories and data need to be comparable between numerous laboratories for joint assessments, e.g. in International Conventions. The Marine Environment Laboratories of the International Atomic Energy Agency has been providing Quality Assurance services to laboratories involved in marine radioactivity studies for the past 30 years. This has included training, organization of laboratory intercomparison exercises, production of Reference Materials (RMs) and more recently, production of Certified Reference Materials (CRMs) that comply with relevant ISO standards. Here, the overall process of Certification of Reference Materials is reviewed, past work summarized and future needs of marine radioactivity laboratories briefly discussed.     

Optimization of Fenton oxidation pre-treatment for B. thuringiensis – Based production of value added products from wastewater sludge

Fenton oxidation pretreatment was investigated for enhancement of biodegradability of wastewater sludge (WWS) which was subsequently used as substrate for the production of value- added products. The Response surface method with fractional factorial and central composite designs was applied to determine the effects of Fenton parameters on solubilization and biodegradability of sludge and the optimization of the Fenton process. Maximum solubilization and biodegradability were obtained as 70% and 74%, respectively at the optimal conditions: 0.01 ml H₂O₂/g SS, 150 [H₂O₂]₀/[Fe²⁺]₀, 25 g/L TS, at 25 °C and 60 min duration. Further, these optimal conditions were tested for the production of a value added product, Bacillus thuringiensis (Bt) which is being used as a biopesticide in the agriculture and forestry sector. It was observed that Bt growth using Fenton oxidized sludge as a substrate was improved with a maximum total cell count of 1.63 × 10⁹ CFU ml^?1 and 96% sporulation after 48 h of fermentation. The results were also tested against ultrasonication treatment and the total cell count was found to be 4.08 × 10⁸ CFU ml^?1 with a sporulation of 90%. Hence, classic Fenton oxidation was demonstrated to be a rather more promising chemical pre-treatment for Bt - based biopesticide production using WWS when compared to ultrasonication as a physical pre-treatment.     

Application of GIS and modelling in health risk assessment for urban road mobility

Transport is an essential sector in modern societies. It connects economic sectors and industries. Next to its contribution to economic development and social interconnection, it also causes adverse impacts on the environment and results in health hazards. Transport is a major source of ground air pollution, especially in urban areas, and therefore contributes to the health problems, such as cardiovascular and respiratory diseases, cancer and physical injuries. This paper presents the results of a health risk assessment that quantifies the mortality and the diseases associated with particulate matter pollution resulting from urban road transport in Haiphong City, Vietnam. The focus is on the integration of modelling and geographic information system approaches in the exposure analysis to increase the accuracy of the assessment and to produce timely and consistent assessment results. The modelling was done to estimate traffic conditions and concentrations of particulate matters based on geo-referenced data. The study shows that health burdens due to particulate matter in Haiphong include 1,200 extra deaths for the situation in 2007. This figure can double by 2020 as the result of the fast economic development the city pursues. In addition, 51,000 extra hospital admissions and more than 850,000 restricted activity days are expected by 2020.     

Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

ABSTRACT

Time-series and machine-learning methods are being strongly exploited to improve the accuracy of short-term load forecasting (STLF) results. In developing countries, power consumption behaviors could be suddenly changed by different customers, e.g. industrial customers, residential customers, so the load-demand dataset is often unstable. Therefore, reliability assessment of the load-demand dataset is obviously necessary for STLF models. Hence, this paper proposes a novel and unified statistical data-filtering method with the best confidence interval to eliminate unexpected noises/outliers of the input dataset before performing various short-term load forecasting models. This proposed novel data-filtering method, so-called the data pre-processing method, is also compared to other existing data-filtering methods (e.g. Kalman filter, Density-Based Spatial Clustering of Applications with Noise, Wavelet transform, and Singular Spectrum Analysis). By using an SCADA system?-based database of a typical 22kV distribution network in Vietnam, NYISO database, and PJM-RTO database, case studies of short-term load forecasting have been conducted with a conventional ARIMA model, an ANN forecasting model, an LSTM-RNN model, an LSTM-CNN combined model, a deep auto-encoder (DAE) network, a Wavenet-based model, a Wavenet and LSTM hybrid model, and a Wavelet Neural Network (WNN) model, which are to validate the novel and unified statistical data-filtering method proposed. The achieved numerical results demonstrate which the accuracy of the aforementioned STLF models can be significantly improved due to the proposed statistical data-filtering method with the best confidence interval of the input load dataset. The proposed statistical data-filtering method can considerably outperform the existing data-filtering methods.     

Extraction,chemical compositions,and cytotoxic activities of essential oils of Thevetia peruviana

Abstract

In this study, The essential oil of flowers, fruits, and leaves of Thevetia peruviana, which were collected in Vietnam, were being reported for the first time. The essential oil of flowers was extracted by different methods: n-hexane extraction, distillation water, and supercritical CO₂ extraction. The compositions of essential oil of different parts of Thevetia peruviana were analyzed by GC-FID and GC/MS systems. Major chemical compositions of essential oil were identified as monoterpenes, sesquiterpenes, terpenoids, and sterol. The activities of total essential oil extracts of the Thevetia peruviana exhibit inhibitory activities against five cancer cell lines.     

Influencing factors on sustainable development: a case study in Quang Tri province,Vietnam

Background Sustainable development (SD) is a common concept. Knowledge and attitudes are essential in the SD process. This study assesses the knowledge, attitudes, and practices (KAP) of local people about SD. Aim To study the factors that influence the understanding of the concept, contents, and indicators of different aspects affecting the health and environmental issues. Methods A cross-sectional study was carried out from June to July 2007 among 546 households in the Quang Tri province. Data were gathered on basis of socio-demographic variables, namely age, gender, education, occupation, income, and region. Chi square tests and multivariate analysis were performed on the obtained data. The data were cleaned and analysed using SPSS 15.0 for windows. Results Occupation is related to knowledge, attitude, or practice. Income is related to knowledge or practice. Gender related to only attitude. Lastly, region is related to attitude or practice. The proportion of wrong understanding about SD is 2.0 times (95% CI: 1.3; 3.1, p < 0.001) higher than that of the people who have good understanding about it. The rate of willingness to do any related SD programmes of the people who understanding is 2.1 times (95% CI: 1.4; 3.2, p < 0.001) higher than that of the people who have bad one. Conclusions This study shows that knowledge on sustainability of the local communities is low. Occupation and income influence understanding of SD more than region, age, gender, and education. Most of the local people who do not understand SD in general, do not want to participate or act in SD programmes.