A Comparison of Data Sets Varying in Spatial Accuracy Used to Predict the Occurrence of Wildlife-Vehicle Collisions

Wildlife-vehicle collisions (WVCs) pose a significant safety and conservation concern in areas where high-traffic roads are situated adjacent to wildlife habitat. Improving transportation safety, accurately planning highway mitigation, and identifying key habitat linkage areas may all depend on the quality of WVC data collection. Two common approaches to describe the location of WVCs are spatially accurate data derived from global positioning systems (GPS) or vehicle odometer measurements and less accurate road-marker data derived from reference points (e.g., mile-markers or landmarks) along the roadside. In addition, there are two common variable types used to predict WVC locations: (1) field-derived, site-specific measurements and (2) geographic information system (GIS)-derived information. It is unclear whether these different approaches produce similar results when attempting to identify and explain the location of WVCs. Our first objective was to determine and compare the spatial error found in road-marker data (in our case the closest mile-marker) and landmark-referenced data. Our second objective was to evaluate the performance of models explaining high- and low-probability WVC locations, using congruent, spatially accurate (<3-m) and road-marker (<800-m) response variables in combination with field- and GIS-derived explanatory variables. Our WVC data sets were comprised of ungulate collisions and were located along five major roads in the central Canadian Rocky Mountains. We found that spatial error (mean ± SD) was higher for WVC data referenced to nearby landmarks (516 ± 808 m) than for data referenced to the closest mile-marker data (401 ± 219 m). The top-performing model using the spatially accurate WVC locations contained all explanatory variable types, whereas GIS-derived variables were only influential in the best road-marker model and the spatially accurate reduced model. Our study showed that spatial error and sample size, using road-marker data for ungulate species, are important to consider for model output interpretation, which will impact the appropriate scale on which to apply modeling results. Using road-marker references <1.6 km or GPS-derived data locations may represent an optimal compromise between data acquisition costs and analytical performance. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Measurement Of Nutrients In Green House Soil With Laser Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has been applied for the determination of nutrients in the green house soil samples. We determined appropriate spectral signatures of vital nutrients and calibrated the method to measure the nutrients in a naturally fertilized plot, cultivated with tomato and cucumber plants. From the calibration curves we predicted the concentrations of important nutrients such as Ca, K, P, Mg, Fe, S, Ni and Ba in the soil. Our measurements proved that the LIBS method rapidly and efficiently measures soil nutrients with excellent detection limits of 12, 9, 7, 9, 7, 10, 8 and 12~mg/kg for Ca, K, P, Mg, Fe, S, Ni and Ba respectively with a precision of 2%, The unique features of LIBS for rapid sample analysis demonstrated by this study suggests that this method offers promise for precision measurements of soil nutrients as compared to conventional methods in short span of time.     

Evaluating Kriging as a Tool to Improve Moderate Resolution Maps of Forest Biomass

The USDA Forest Service, Forest Inventory and Analysis program (FIA) recently produced a nationwide map of forest biomass by modeling biomass collected on forest inventory plots as nonparametric functions of moderate resolution satellite data and other environmental variables using Cubist software. Efforts are underway to develop methods to enhance this initial map. We explored the possibility of modeling spatial structure to make such improvements. Spatial structure in the field biomass data as well as in residuals from the map was investigated across 18 ecological zones in the Interior Western U.S. Exploratory tools included directional graphs of summary statistics, three dimensional maps, Moran’s I correlograms, and variograms. Where spatial pattern was present, field and residual biomass were kriged, and predictions made for an independent test set were evaluated for improvement over predictions in the initial biomass map. While kriging has some potential benefit when analyzing the field data and exploring spatial structure, kriging residuals resulted in little or no improvement in the initial biomass map developed using Cubist software. Stationarity assumptions, variogram behavior, and appropriate model fitting strategies are discussed.     

如何降低水中VOC分析的检测限

环境中挥发性有机污染物（ＶＯＣ）的存在很复杂,随着ＶＯＣ治理的进步,环境水中ＶＯＣ浓度有不断降低的趋势,要求ＶＯＣ分析具有更低的检测限。通过对环境水前处理过程采用优化吹扫捕集条件来扩大试样量,选择高灵敏度的检测器以及降低前处理过程的空白值,提纯分析用水;优化ＶＯＣ色谱分离条件,确保色谱峰良好分离来降低试样和空白值的标准偏差的研究,表明可以有效降低水中ＶＯＣ分析方法的检测限。     

通过频谱分析了解冷却塔噪声污染特性

目前,冷却塔噪声评价所采用的指标是A声级。而由于A声级并不能全面的反应冷却塔噪声污染的特性。本文通过对冷却塔噪声污染案例进行频谱监测,探讨了频谱分析方法对评价冷却塔噪声污染特性的可行性。     

六价铬环境监测分析方法探讨

通过对日常环境监测工作的分析,选定了最适合工作需要的六价铬监测分析方法。比较了冷藏保存一段时间后六价铬使用液绘制的曲线与原曲线差异,得出在一定显色条件下冷藏保存3个月后1．00mg／L六价铬使用液绘制的标准曲线可考虑作为备用曲线使用的结论。     

基于故障树的生态调水水质风险识别

以巢湖生态调水为例,将故障树分析应用于调水线路的水质风险识别。构建了输水区、受水区、排泄区水质风险故障树,确定了顶事件的概率,得到了各分区的最大风险事件,提出了风险防范措施,为生态调水的风险管理提供科学依据。     

间隔流动分析法测定水中的氨氮

充分利用间隔流动分析仪检测,速度快、自动化程度高的特点测定水中的氨氮,并将该方法与经典国标方法(纳氏试剂分光光度法)进行比对试验,F检验和t检验结果表明间隔流动分析方法与国标方法(纳氏试剂分光光度法)的精密度和准确度之间无显著性差异。     

叶绿素a与环境因子关系的研究——以三明地区某水源地为例

根据2009～2012年水源地水质自动监测所得数据,分析叶绿素a的动态变化与环境因子的相关关系,筛选影响叶绿素a含量变化的环境因子。分析表明:该水源地水库水质状况属贫营养型,其中叶绿素a含量变化主要受总磷、水温、高锰酸盐指数影响,以这3种影响因子建立起的多元回归方程能较好的预测水库叶绿素a的变化趋势。     

An imprecise Fault Tree Analysis for the estimation of the Rate of OCcurrence Of Failure (ROCOF)

The paper proposes an imprecise Fault Tree Analysis in order to characterize systems affected by the lack of reliability data. Differently from other research works, the paper introduces a classification of basic events into two categories, namely Initiators and Enablers. Actually, in real industrial systems some events refer to component failures or process parameter deviations from normal operating conditions (Initiators), whereas others refer to the functioning of safety barriers to be activated on demand (Enablers). As a consequence, the output parameter of interest is not the classical probability of occurrence of the top event, but its Rate of OCcurrence (ROCOF) over a stated period of time. In order to characterize the basic events, interval-valued information supplied by experts are properly aggregated and propagated to the top. To this purpose, the Dempster–Shafer Theory of evidence is proposed as a more appropriate mathematical framework than the classical probabilistic one. The proposed methodology, applied to a real industrial scenario, can be considered a helpful tool to support risk managers working in industrial plants.