Impact of renewable energy on economic growth? Novel evidence from developing countries through MMQR estimations

Environmental Science and Pollution Research - This study aims to examine the association between economic growth and energy consumption (renewable and nonrenewable). The data was collected from 80...     

Calculating nuclear accident probabilities from empirical frequencies

Since there is no authoritative, comprehensive and public historical record of nuclear power plant accidents, we reconstructed a nuclear accident data set from peer-reviewed and other literature. We found that, in a sample of five random years, the worldwide historical frequency of a nuclear major accident, defined as an INES level 7 event, is 14 %. The probability of at least one nuclear accident rated at level ≥4 on the INES scale is 67 %. These numbers are subject to uncertainties because of the fuzziness of the definition of a nuclear accident.     

Numerical Modeling of Flow Fields and Dispersion of Passive Pollutants in the Vicinity of the Temelín Nuclear Power Plant

This study deals with numerical simulation of flow fields and dispersion of model passive admixtures in the planetary boundary layer in a 10 × 10 km square area, centered on the Czech Republic’s Temelín Nuclear Power Plant. Numerical calculations of three-dimensional flow fields with eight basic wind directions given for the inlet boundary of the computational domain are performed using the FLUENT CFD code with the standard k – ε turbulence model. The resultant modeling of the flow fields provides information as to probable local directions and velocities of flow vectors on a horizontal scale of 100 m, which are consistent with the data given for the boundaries of the calculation area in the framework of a scale of 10 km. The modeled flow fields generate the input data for related Lagrangian simulation of the ground concentration and deposition fields of passive particles assumedly emitted at the site of the Temelín plant. Simulated plumes describe eight cases of potential ground-level distribution of model passive admixtures in the area.     

Concentrations,profiles, emission inventory,and risk assessment of chlorinated benzenes in bottom ash and fly ash of municipal and medical waste incinerators in northern Vietnam

Environmental Science and Pollution Research - Concentrations and congener profiles of seven di- to hexachlorinated benzenes (CBzs) were characterized in bottom ash and fly ash samples collected...     

A socio-economic analysis of rice production systems in the uplands of northern Vietnam

Production of upland rice under shifting cultivation system is an important economic activity in the mountainous areas of northern Vietnam. A comparative study of two districts with differential market access and population pressure was conducted to highlight the effect of these variables on upland rice systems in northern Vietnam. Farmers in the district with a greater population pressure have a shorter cropping and a shorter fallowing cycle than in the district with lower population pressure. Farmers reported a high incidence of food shortage in both districts. Income from livestock and wages are important for food purchases, especially in the district with a better access to market. Even in these upland districts, access to lowland fields is a critical determinant of food security.     

VOCs in representative canadian residences

Stored extracts of passive samplers exposed in 757 randomly selected Canadian residences provided a unique opportunity for retrospective determination of the occurrence of airborne volatile organic compounds (VOCs). Aliquots of the individual extracts were pooled to form a composite exposure sample and a corresponding blank sample. To identify and quantitate potentially hazardous organics in the samples, GC-MS analyses were conducted by several approaches. The amounts of 52 target compounds in the the composite sample were estimated based on selected ion monitoring (SIM) results, extraction recoveries, average air volume sampled, and 3M OVM 3500 passive sampling rates. Forty of the organics were detected and were present in amounts equivalent to airborne concentrations ranging from <1 to 104 μg m⁻³. Several other compounds were also tentatively identified by full scan analysis. Many of the detected organics have been reported to be associated with activities such as tobacco smoking and the presence of consumer products and plastic materials indoors. The analytical results have been useful in risk assessments and establishment of a new Canadian priority substances list (PSL).     

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.     

Impact of minimum winter temperatures on the population dynamics of Dendroctonus frontalis.

Predicting population dynamics is a fundamental problem in applied ecology. Temperature is a potential driver of short-term population dynamics, and temperature data are widely available, but we generally lack validated models to predict dynamics based upon temperatures. A generalized approach involves estimating the temperatures experienced by a population, characterizing the demographic consequences of physiological responses to temperature, and testing for predicted effects on abundance. We employed this approach to test whether minimum winter temperatures are a meaningful driver of pestilence from Dendroctonus frontalis (the southern pine beetle) across the southeastern United States. A distance-weighted interpolation model provided good, spatially explicit, predictions of minimum winter air temperatures (a putative driver of beetle survival). A Newtonian heat transfer model with empirical cooling constants indicated that beetles within host trees are buffered from the lowest air temperatures by approximately 1-4 degrees C (depending on tree diameter and duration of cold bout). The life stage structure of beetles in the most northerly outbreak in recent times (New Jersey) were dominated by prepupae, which were more cold tolerant (by >3 degrees C) than other life stages. Analyses of beetle abundance data from 1987 to 2005 showed that minimum winter air temperature only explained 1.5% of the variance in interannual growth rates of beetle populations, indicating that it is but a weak driver of population dynamics in the southeastern United States as a whole. However, average population growth rate matched theoretical predictions of a process-based model of winter mortality from low temperatures; apparently our knowledge of population effects from winter temperatures is satisfactory, and may help to predict dynamics of northern populations, even while adding little to population predictions in southern forests. Recent episodes of D. frontalis outbreaks in northern forests may have been allowed by a warming trend from 1960 to 2004 of 3.3 degrees C in minimum winter air temperatures in the southeastern United States. Studies that combine climatic analyses, physiological experiments, and spatially replicated time series of population abundance can improve population predictions, contribute to a synthesis of population and physiological ecology, and aid in assessing the ecological consequences of climatic trends.     

UNFCCC Kyoto Protocol Clean Development Mechanism Baseline Construction for Vietnam National Electricity Grid

For projects under the UNFCCC Kyoto Protocol Clean Development Mechanism (CDM), a baseline has to be set to allow calculation of the greenhouse gas emissions reductions achieved. An important obstacle to CDM project development is the lack of data for baseline definition; often project developers do not have access to data and therefore incur high transaction costs to collect them. The government of Vietnam has set up all necessary institutions for CDM, wants to promote CDM projects and thus is interested to reduce transaction costs. We calculate emission factors of the Vietnam electricity grid according to the rules defined by the CDM Executive Board for small scale projects and for large renewable electricity generation projects. The emission factors lie between 365 and 899 g CO₂/kWh depending on the specification. The weighted operating and build margin reaches 600 g for 2003, while grid average reaches 399 g. Using three-year averages, a combined build and operating margin of 705 g is calculated. We hope that these data facilitate CDM project development in the electricity supply and energy efficiency improvement in Vietnam.     

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.