关于确定大气混合层高度的几种方法

本文阐述了大气混合层高度研究在环境保护中的作用，论述了大气混合层高度确定的几种方法，分析了各种方法的特点及应用前景，同时也讨论了目前在我国大气混合层高度研究中提出的一些新方法。     

思小高速公路高填深挖改桥隧方案的环境影响分析

结合思茅一小勐养高速公路沿线具体的自然环境，通过分析高填深挖路段的具体情况，对公路选线过程中遇到的高填深挖与桥梁、隧道方案的比选从生态设计的理念方面进行了具体的环境影响分析。得出该公路的高填深挖改桥梁、隧道方案能够有效地减小公路建设对西双版纳热带雨林区的牛态环境影响，较好的与周围的环境协调，与自然景观融为一体。     

Emission inventory on company level: lessons from Russia

Developing a transparent,accurate greenhouse gas (GHG) emissionsinventory is the first step toward buildingan effective GHG management system. Todate, GHG inventories have been conductedprimarily at national levels. Theinternationally accepted inventorymethodology developed by theIntergovernmental Panel on Climate Change(IPCC) is oriented to countrywideinventories. The electricity company RAOUESR is the largest single corporateemitter of GHG in the Russian Federation. The company is responsible for about 1/3 ofRussia's CO₂ emissions; RAO's fossil fuelemissions are comparable to the fossil fuelemissions of the United Kingdom. The GHGinventory prepared by RAO is the first suchcorporate emissions inventory undertaken ina non-OECD country. In this article wepresent a detailed independent examinationof the methodology RAO applied for theinventory. We identify the most importantsources of uncertainty and we estimate theuncertainty. The main conclusion of theindependent review is that the methodologyutilized by RAO and the informationsupporting the methodology are reliable andpresent a reasonably accurate company-widepicture of RAO's CO₂ emissions. The shareof other greenhouse gases is negligiblysmall and we did not focus on this fractionof RAO's GHG emissions. As a next step, RAOmay wish to conduct more precisefacility-by-facility inventories in orderto create a robust GHG emission managementsystem.     

Assessment of Wet Sulfate Concentration Changes at CAPMoN Sites, Canada

This paper describes a statistical analysis of wet sulfate deposition data sampled by the Canadian Air and Precipitation Monitoring Network (CAPMoN) since 1988 till 1997. The goal of the investigation is to detect presence of prevailing significant changes in the probability distribution of annual samples collected by the network at each site. The considerations are based on a first order autoregression model with second order polynomial trend and methods used for analysis of variance and multiple comparison. Unlike studies suggesting existence of long term trends in the data, methods applied here indicate absence of any systematic changes in the observed annual concentration patterns at most of the sites.     

Uncertainty Assessment of Voc Emissions from Paint in the Netherlands Using the Nusap System

We demonstrate an innovative approach to uncertainty assessment known as the NUSAP system, to assess qualitative and quantitative uncertainty for the case of emissions of volatile organic compounds (VOC) from paint in The Netherlands. Using expert elicitation, we identified key sources of error, critical assumptions, and bias in the monitoring process. We assessed pedigree and probabilistic uncertainty of all input quantities. We used four pedigree criteria to assess the strength of the knowledge base: proxy representation, empirical basis, methodological rigour and degree of validation. Using Monte Carlo analysis, we assessed sensitivity and propagation of uncertainty. Results for sensitivity and pedigree were combined in a NUSAP Diagnostic Diagram, which effectively highlighted the assumption for VOC percentage of imported paint as the weakest spot in the monitoring of VOC emissions. We conclude that NUSAP facilitates systematic scrutinization of method and underlying assumptions and structures creative thinking on sources of error and bias. It provides a means to prioritise uncertainties and focus research efforts on the potentially most problematic parameters and assumptions, at the same time identifying specific weaknesses in the knowledge base. With NUSAP, nuances of meaning about quantities can be conveyed concisely and clearly, to a degree that is not possible with statistic methods only.     

基于博弈论的环保数据质量影响因素分析研究

污染源产排污数据是一切环保工作的基础,确保真实可信具有十分重要的意义.为此,分析了影响污染源产排污数据质量的主要因素:受经济利益驱动的自身因素;受政绩利益驱动的管理因素和受地方保护驱动的政府因素等.在新环保法未得到充分贯彻,排污单位、环保主管部门和地方政府三者违法成本均低于守法成本的条件下,以博奕论为分析工具讨论了三种影响因素产生负面作用的必然性,并根据分析结果针对性地提出了提高数据质量的应对策略.     

Cyberinfrastructure and Web Apps for Managing and Disseminating the National Water Model

Hydrologic modeling can be used to provide warnings before, and to support operations during and after floods. Recent technological advances have increased our ability to create hydrologic models over large areas. In the United States (U.S.), a new National Water Model (NWM) that generates hydrologic variables at a national scale was released in August 2016. This model represents a substantial step forward in our ability to predict hydrologic events in a consistent fashion across the entire U.S. Nevertheless, for these hydrologic results to be effectively communicated, they need to be put in context and be presented in a way that is straightforward and facilitates management‐related decisions. The large amounts of data produced by the NWM present one of the major challenges to fulfill this goal. We created a cyberinfrastructure to store NWM results, “accessibility” web applications to retrieve NWM results, and a REST API to access NWM results programmatically. To demonstrate the utility of this cyberinfrastructure, we created additional web apps that illustrate how to use our REST API and communicate hydrologic forecasts with the aid of dynamic flood maps. This work offers a starting point for the development of a more comprehensive toolset to validate the NWM while also improving the ability to access and visualize NWM forecasts, and develop additional national‐scale‐derived products such as flood maps.     

基于LLE-ELM的矿井瓦斯涌出量预测方法研究

基于局部线性嵌入(Locally Linear Embedding,LLE)算法和极限学习机(Extreme Learning Machine,ELM)神经网络建立矿井瓦斯涌出量预测模型,该预测模型运用LLE算法对矿井瓦斯涌出量影响因素样本进行数据挖掘,得到降维后的有效因子,再将这些有效因子作为ELM神经网络的输入层进行训练和预测。利用某矿井的实测数据进行实例分析,结果表明该预测模型预测速度快,精度高,能够用于矿井瓦斯涌出量预测。     

Advantages of Volunteer-Based Biodiversity Monitoring in Europe

Abstract:  Without robust and unbiased systems for monitoring, changes in natural systems will remain enigmatic for policy makers, leaving them without a clear idea of the consequences of any environmental policies they might adopt. Generally, biodiversity-monitoring activities are not integrated or evaluated across any large geographic region. The EuMon project conducted the first large-scale evaluation of monitoring practices in Europe through an on-line questionnaire and is reporting on the results of this survey. In September 2007 the EuMon project had documented 395 monitoring schemes for species, which represents a total annual cost of about €4 million, involving more than 46,000 persons devoting over 148,000 person-days/year to biodiversity-monitoring activities. Here we focused on the analysis of variations of monitoring practices across a set of taxonomic groups (birds, amphibians and reptiles, mammals, butterflies, plants, and other insects) and across 5 European countries (France, Germany, Hungary, Lithuania, and Poland). Our results suggest that the overall sampling effort of a scheme is linked with the proportion of volunteers involved in that scheme. Because precision is a function of the number of monitored sites and the number of sites is maximized by volunteer involvement, our results do not support the common belief that volunteer-based schemes are too noisy to be informative. Just the opposite, we believe volunteer-based schemes provide relatively reliable data, with state-of-the-art survey designs or data-analysis methods, and consequently can yield unbiased results. Quality of data collected by volunteers is more likely determined by survey design, analytical methodology, and communication skills within the schemes rather than by volunteer involvement per se.