Using decision science to evaluate global biodiversity indices

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.     

Quantifying methane emission from fugitive sources by combining tracer release and downwind measurements – A sensitivity analysis based on multiple field surveys

Using a dual species methane/acetylene instrument based on cavity ring down spectroscopy (CRDS), the dynamic plume tracer dispersion method for quantifying the emission rate of methane was successfully tested in four measurement campaigns: (1) controlled methane and trace gas release with different trace gas configurations, (2) landfill with unknown emission source locations, (3) landfill with closely located emission sources, and (4) comparing with an Fourier transform infrared spectroscopy (FTIR) instrument using multiple trace gasses for source separation. The new real-time, high precision instrument can measure methane plumes more than 1.2 km away from small sources (about 5 kg h^?1) in urban areas with a measurement frequency allowing plume crossing at normal driving speed. The method can be used for quantification of total methane emissions from diffuse area sources down to 1 kg per hour and can be used to quantify individual sources with the right choice of wind direction and road distance. The placement of the trace gas is important for obtaining correct quantification and uncertainty of up to 36% can be incurred when the trace gas is not co-located with the methane source. Measurements made at greater distances are less sensitive to errors in trace gas placement and model calculations showed an uncertainty of less than 5% in both urban and open-country for placing the trace gas 100 m from the source, when measurements were done more than 3 km away. Using the ratio of the integrated plume concentrations of tracer gas and methane gives the most reliable results for measurements at various distances to the source, compared to the ratio of the highest concentration in the plume, the direct concentration ratio and using a Gaussian plume model. Under suitable weather and road conditions, the CRDS system can quantify the emission from different sources located close to each other using only one kind of trace gas due to the high time resolution, while the FTIR system can measure multiple trace gasses but with a lower time resolution.     

Applying artificial neural network methodology to ocean color remote sensing

Artificial neural networks (ANN) are widely used as continuous models to fit non-linear transfer functions. In this study we used ANN to retrieve chlorophyll pigments in the near-surface of oceans from Ocean Color measurements. This bio-optical inversion is established by analyzing concomitant sun-light spectral reflectances over the ocean surface and pigment concentration. The relationships are complex, non-linear, and their biological nature implies a significant variability. Moreover, the sun-light reflectances are usually measured by satellite radiometers flying at 800 km over the ocean surface, which affect the data by adding radiometric noise and atmospheric correction errors. By comparison with the polynomial fit usually employed to treat this problem, we show the advantages of neural function approximation like the association of non-linear complexity and noise filtering.     

Estimation of the CDF of a finite population in the presence of acalibration sample

We compare the performance of a number of estimators of the cumulative distribution function (CDF) for the following scenario: imperfect measurements are taken on an initial sample from afinite population and perfect measurements are obtained on a small calibration subset of the initial sample. The estimators we considered include two naive estimators using perfect and imperfect measurements; the ratio, difference and regression estimators for a two-phasesample; a minimum MSE estimator; Stefanski and Bay's SIMEX estimator (1996); and two proposed estimators. The proposed estimators take the form of a weighted average of perfect and imperfect measurements. They are constructed by minimizing variance among the class of weighted averages subject to an unbiasedness constraint. They differ in the manner of estimating the weight parameters. The first one uses direct sample estimates. The second one tunes the unknown parameters to an underlying normal distribution. We compare the root mean square error (RMSE) of the proposed estimator against other potential competitors through computer simulations. Our simulations show that our second estimator has the smallest RMSE among thenine compared and that the reduction in RMSE is substantial when the calibration sample is small and the error is medium or large.     

制冷剂二氟甲烷热解生成氟化氢的实验研究

空调制冷剂二氟甲烷(R32)是R22的替代制冷剂之一,其分子中含有氟元素,受热可产生剧毒物质氟化氢(HF),一旦泄漏分解,将会对人员造成极大危害.首次通过实验研究了R32泄漏后的受热分解情况,利用激光气体分析仪,测量了不同工况下HF的实时浓度,分析了R32受热分解的影响因素.结果表明R32泄漏后,在受热状态下很容易产生HF,HF浓度随着泄漏量和受热状态的变化而异,浓度可达1000ppm以上；研究结果可为含氟制冷剂的应用提供技术支持.     

肺炎疫情期间医护人员心理危机干预效果分析

为提升医护人员心理健康水平,该文对肺炎疫情期间医护人员实施心理危机干预并分析其效果,以参与2020年1月25日-2月25日湖北省武汉市新冠肺炎疫情期间救治工作的300名医护人员作为研究对象,平均划分为实验组和对照组,其中针对实验组实施统一的心理危机干预训练,此期间对照组未参与训练但日常生活与实验组统一,选取四种量表分别对干预前后的两组实施测评。测评结果表明,干预后实验组的SCL-90各因子得分、SDS得分及SAS得分较干预前均下降明显,且下降幅度超过对照组;实验组干预后的GQOLI总得分与各维度得分均较干预前显著升高,且升高幅度高于对照组。综上,心理危机干预可显著提升医护人员的心理健康水平,缓解其心理紧张等症状,能够在一定程度上降低肺炎疫情期间医护人员的心理危机感。     

基于拉曼散射的分布式光纤定温与差温探测方法

基于拉曼散射的分布式光纤测温系统应用于火灾监测有独特的优势,弥补了传统感温探测器的不足。介绍了基于拉曼散射的测温原理及基于光时域的空间定位原理,并进行了火灾预警实验,实验结果表明当建筑物高度在一定范围内,基于拉曼散射的分布式光纤测温系统可以很好地实现火灾预警功能。     

吸油烟机风量检测中可靠性分析

根据GB/T 17713-2011的要求,对外排式吸油烟机空气性能实验装置的板孔的设计进行了研究,结合板孔系数表,提出了与实际工作点最接近的计算方法,以设计的孔板测量外排式吸油烟机的风量。并根据实际的检测实例,找出了实际影响因素,提高了风量检测的可靠性。     

PM_(2.5)手工法与自动法比对差异及其与气象条件影响关系分析

通过在杭州、衢州和温州三个城市开展PM2.5手工法和自动法比对试验,分析PM2.5手工法和自动法的比对差异及其与气象条件的影响关系,以期为PM2.5自动监测的现场手工比对工作积累一定的经验,确保PM2.5自动监测数据的准确性。     

黑碳气溶胶的测量方法对比研究

大气黑碳气溶胶主要有光学分析法、热学分析法以及热光结合分析法。利用苏州南门国控子站的碳分析数据,对三种黑碳浓度测量方法进行对比并分析其差异来源。主要得出以下结论:黑碳仪衰减系数无普适标准,热学分析法使有机碳碳化、元素碳不完全分离,TOT法协议设置存在争议是三种方法存在差异的主要原因。