Evaluating the efficiency of green vehicles and diesel vehicles

One of the major interests of governments in the area of environmental protection is to reduce greenhouse gas (GHG) emissions and air pollutants’ emissions (HC, CO, NO_x, PM, and SO_x) from vehicles. This paper aims at evaluating the efficiency of green and diesel vehicles. Two hundred fifty vehicles were selected for comparison study. The results show that the efficiency of each type of vehicle hardly differed in 2008 and 2009, but the gap between green and diesel vehicles became significant in 2010–2012. The results also indicate that larger automotive companies and smaller green vehicles show higher efficiency in recent years, whereas the type of green vehicles and the origin of automotive companies are not significantly related to the efficiency score of green vehicles. These findings provide more information for understanding differences in green vehicles.     

Comparison of ANN,MVR, and SWR models for computing thermal efficiency of a solar still

In this paper, the viability of modeling the instantaneous thermal efficiency (η_ith) of a solar still was determined using meteorological and operational data with an artificial neural network (ANN), multivariate regression (MVR), and stepwise regression (SWR). This study used meteorological and operational variables to hypothesize the effect of solar still performance. In the ANN model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, feed water temperature, brine water temperature, total dissolved solids of feed water, and total dissolved solids of brine water. The η_ith was represented by one node in the output layer. The same parameters were used in the MVR and SWR models. The advantages and disadvantages were discussed to provide different points of view for the models. The performance evaluation criteria indicated that the ANN model was better than the MVR and SWR models. The mean coefficient of determination for the ANN model was about 13% and14% more accurate than those of the MVR and SWR models, respectively. In addition, the mean root mean square error values of 6.534% and 6.589% for the MVR and SWR models, respectively, were almost double that of the mean values for the ANN model. Although both MVR and SWR models provided similar results, those for the MVR were comparatively better. The relative errors of predicted η_ith values for the ANN model were mostly in the vicinity of ±10%. Consequently, the use of the ANN model is preferred, due to its high precision in predicting η_ith compared to the MVR and SWR models. This study should be extremely beneficial to those coping with the design of solar stills.     

Monitoring of the Endangered Pinna nobilis Linné, 1758 in the Mar Grande of Taranto (Ionian Sea, Italy)

Two methods were used to calculate the meteorologically adjusted ground level ozone trends in southern Taiwan. The first method utilized is a robust linear regression method. The second approach uses a multilayer perceptron (MLP) artificial neural network (ANN) method. The observations obtained from 16 monitoring stations were analyzed and divided into six groups by hierarchical divisive clustering procedure. The daily maximum 1 and 8 h ozone concentrations for each group are then calculated. The meteorologically adjusted trends obtained by linear regression and MLP methods are smaller than the unadjusted trends for all groups and average time. It indicts that the meteorological conditions in Taiwan tend to increase ambient ozone concentrations in recent years.     

硬岩金属矿山开采活动与矿山地震活动的关系研究

以冬瓜山微震监测的数据为基础,结合开采、地质条件等因素,运用统计学和线形拟合方法,研究了开采活动与矿山地震活动的关系特征。其结果发现:累积矿震事件数与累积采掘体积、爆破药量存在着双对数线性的正比例关系;而累积矿震能量却与开采体积存在着斜率为负的双对数线性关系;较大矿震事件主要发生在开采爆破活动之后的时间内,震级大于0的矿震事件在时间上要超前爆破事件,矿震活动在开采活动之后呈现从强到弱再从弱到强的变化过程。该结果对硬岩金属矿山的矿震活动研究有一定的参考价值。     

金属材料高温疲劳-蠕变寿命预测方法研究进展

从线性累积损伤、应力参量控制模式、应变参量控制模式、损伤力学、断裂力学、多元统计以及神经网络等7个方面研讨了国内外金属材料在高温条件下疲劳-蠕变寿命预测方法的主要研究进展与成果。其中,线性累积损伤主要应用于高温蠕变——低周疲劳的寿命预测;应变控制模式的预测方法成功地把蠕变因素引入到疲劳寿命预测模型中;多元统计以及神经网络是金属材料进行寿命预测的新方法。     

Impact of psychological risk factors on disaster rescue operations: the case of Italian volunteers

This paper reports on research into the characteristics of emergency rescue workers, with a focus on psychological distress. We present the results of a study of volunteers from the Italian national civil protection programme. A questionnaire was administered to 2,576 subjects from all over Italy. Most respondents were men, mostly unemployed, whose average level of education was higher than that among most Italian volunteers. Many were poorly endowed with basic skills, with large differences according to geography. Many expected to convert their volunteer activities into permanent jobs. Using this data, we employed a logistical regression model to analyse the risk of low, moderate and severe psychological distress symptoms, including anxiety and panic attacks, during rescue operations. The results indicate that some rescuers are particularly vulnerable to distress. The highest risk factors pertained to volunteers from southern Italy whose training is inadequate, who have little experience of emergency work and who play subordinate roles in small teams that lack psychological support services.     

Modeling and risk assessment for soil temperatures beneath prescribed forest fires

Prescribed fire is a management tool used by wildland resource management organizations in many ecosystems to reduce hazardous fuels and to achieve a host of other objectives. To study the effects of fire in naturally accumulating fuel conditions, the ambient soil temperature is monitored beneath prescribed burns. In this study we developed a stochastic model for temperature profiles (values at 15 minute intervals) recorded at four depths beneath the soil during a large prescribed burn study. The model was used to assess the temporal fit of the data to particular solutions of the heat equation. We used a random effects model to assess the effects of observed site characteristics on maximum temperatures and to estimate risks of temperatures exceeding critical levels in future similar prescribed fires. Contour plots of estimated risks of temperatures exceeding 60°C for a range of fuel levels and soil depths indicated high risks of occurrence, especially when the moisture levels are low. However, the natural variability among sites seems to be large, even after controlling fuel and moisture levels, resulting in large standard errors of predicted risks.     

Formation of Disinfection Byproducts (DBPs) in Surface Water Sources: Differential Ultraviolet (UV) Absorbance Approach

Chlorination for drinking water forms various disinfection byproducts (DBPs) of trihalomethanes (THMs) and haloacetic acids (HAAs). Chlorination has been attributed to the destruction of activated aromatic sites of the natural organic matter (NOM) predominantly at electron rich sites. Experiments with Istanbul surface waters showed that the magnitude of the decrease in the ultraviolet (UV) absorbance at 272 nm (UV₂₇₂) was an excellent indicator of destruction of these sites by chlorine. The main objective of the present study is to develop the differential UV₂₇₂ absorbance (ΔUV₂₇₂) related models for the prediction of the formation of THM, HAA, and their species in raw water samples from Terkos, Buyukcekmece, and Omerli lakes under different chlorination conditions. Significant factors affecting DBP formation in the raw waters were identified through numerical and graphical techniques. The R² values of the models varied between 0.94 and 0.97, indicating excellent predictive ability for THM₄ and HAA₉ in the raw waters. The models were validated using additional data. The results of this study concluded that addition of ΔUV₂₇₂ parameter into THM₄ and HAA₉ models make the prediction of these DBP compounds more precisely than those of DBP models developed in the past. A better understanding of these modeling systems will help the water treatment plant operators to minimize the DBP formation, providing a healthier and better drinking water quality as well as identifying strategies to improve water treatment and disinfection processes.     

Source Apportionment of Particulate Matter (PM10) and Indoor Dust in a University Building

A study on source apportionment of indoor dust and particulate matter (PM₁₀) composition was conducted in a university building by using chemometrics. The objective of this study was to investigate the potential sources of selected heavy metals and ionic species in PM₁₀ and indoor dust. PM₁₀ samples were collected using a low-volume sampler (LVS) and indoor dust was collected using a soft brush. Inductively coupled plasma spectrometry (ICP-MS) was used to determine the concentration of heavy metals, while the concentration of cations and anions was determined by atomic absorption spectrometer (AAS) and ion chromatography (IC), respectively. The concentration of PM₁₀ recorded in the building throughout the sampling period ranged from 20 ± 10 μgm^?3 to 80 ± 33 μgm^?3. The composition of heavy metals in PM₁₀ and indoor dust were dominated by zinc (Zn), followed by lead (Pb), copper (Cu), and cadmium (Cd). Principle component analysis (PCA) and multiple linear regression (MLR) showed that the main sources of pollutants in PM₁₀ came from indoor renovations (73.83%), vehicle emissions (16.38%), earth crust sources (9.68%), and other outdoor sources (0.11%). For indoor dust, the pollutant source was mainly earth crust. This study suggests that chemometrics can be used for forensic investigation to determine the possible sources of indoor contaminants within a public building.     

Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India

Forest fires constitute one of the most serious environmental problems in several forested regions of India. In the Indian sub-continent, relatively few studies have focused on the assessment of biophysical and anthropogenic controls of forest fires at a landscape scale and the spatial aspects of these relationships. In this study, we used fire count data sets from satellite remote sensing data covering 78 districts over four different states of the Deccan Plateau, India, for assessing the underlying causes of fires. Spatial data for explanatory variables of fires pertaining to topography, vegetation, climate, anthropogenic and accessibility factors have been gathered corresponding with fire presence/absence. A logistic regression model was used to estimate the probability of the presence of fires as a function of the explanatory variables. Results for fire area estimates suggested that, of the total fires covering 47,043km(2) that occurred during the year 2000 for the entire Indian region, 29.0% occurred in the Deccan Plateau, with Andhra Pradesh having 13.5%, Karnataka 14.7%, Kerala 0.1%, and Tamilnadu 1.15%. Results from the logistic regression suggest that the strongest influences on the fire occurrences were the amount of forest area, biomass densities, rural population density (PD), average precipitation of the warmest quarter, elevation (ELE) and mean annual temperature (MAT). Among these variables, biomass density (BD) and average precipitation of the warmest quarter had the highest significance, followed by others. These results on the best predictors of forest fires can be used both as a strategic planning tool to address broad scale fire risk concerns, and also as a tactical guide to help forest managers to design fire mitigation measures at the district level.