随机性混合数据评价技术的方法研究

在城市和环境规划等方案评价中,如何综合利用定性和定量信息是人们较为关注的问题。本文首先将方案评价中常见的定性信息分为三大类,并用数学式来描述各类定性信息,在此基础上,运用随机处理的方法对定性信息进行处理后与定量信息进行综合,以求得各个方案的优劣排序概率,并由此概率确定最优方案。这种方法克服了以往同类方法只能利用定性信息中的排序信息的缺点,从而使得方案评价更客观。最后,本文给出了方法应用的实例。     

Norwegian Meteorological Institute’s real-time dispersion model snap (Severe Nuclear Accident Program): Runs for ETEX and ATMES II experiments with different meteorological input

The Norwegian Meteorological Institute (DNMI) has developed and implemented for operational use a real-time dispersion model Severe Nuclear Accident Program (SNAP) with capability for predicting concentrations and depositions of the radioactive debris from large accidental releases. SNAP has been closely linked to DNMI’s operational numerical weather prediction (NWP) models.How good are these predictions? Participation in ETEX has partly answered this question. DNMI used SNAP with LAM50S giving meteorological input for these real-time dispersion calculations. LAM50S Limited Area Model with 50 km grid squareswas DNMI’s operational NWP model in 1994 when ETEX took place.In this article we report on how SNAP performed in the first of the ETEX releases in near-real-time mode, using LAM50S—and in hindcast mode for ATMES II, using “ECMWF 1995: ETEX Data set (ATMES II)”as meteorological input data. These two input data sets came from NWP models with quite different characteristics but with similar resolution in time and space.The results from these dispersion simulations matched closely. Deviations early in the simulation period shrank to insignificant differences later on. Since both input data sets were based on “weather analysis” and had similar resolution in space and time, SNAP described the dispersion of the released material very similar in these two simulations.     

Addressing data challenges in riverine nutrient load modeling of an intensively managed agro-industrial watershed

Data limitations often challenge the reliability of water quality models, especially in intensively managed watersheds. While numerous studies report successful hydrological model setup and calibration, few have addressed in detail the data challenges for multisite and multivariable model calibration to an intensively managed watershed. In this study, we address some of these challenges based on our reflective experience calibrating the Soil and Water Assessment Tool (SWAT) to the Upper Sangamon River Watershed in central Illinois based on daily flow, annual crop yield, and monthly sediment, nitrate, and total phosphorus loads. We highlight some challenges in SWAT calibration processes due to data errors and inconsistencies, and insufficient precipitation and water quality observations. Following, we demonstrate the merits of additional weather and water quality observations that could help reduce input uncertainties, and we provide suggestions for selecting appropriate observations for the model calibration. After dealing with the data issues, we show that the SWAT model could be calibrated with acceptable results for the case study watershed.     

推动生态环境规划数字化转型的研究与思考

生态环境规划在国家生态环境治理体系中发挥了越来越重要的作用。数字化技术是生态环境规划的基础工具,对于提升生态环境规划编制与实施的系统性和科学性发挥重要的作用。本文在回顾生态环境规划相关数字技术发展历程的基础上,指出了未来生态环境规划数字化转型的重要意义和必然趋势,分析了传统生态环境规划存在的一些技术方法落后、与信息化技术融合有待深入、信息化基础建设薄弱等问题和挑战,提出了生态环境规划未来数字化转型的若干建议,包括加强数值模拟、大数据分析、数字空间分析、虚拟现实等数字化技术的应用。     

The influence of heterogeneous environmental regulation on the green development of the mining industry: empirical analysis based on the system GMM and dynamic panel data model

The intensity of environmental regulation (ERI) affects the short-term effect of the level of green mining (GML), and which structure determines the long-term mechanism. Based on the panel data from 2001 to 2015, with the dynamic panel model and system GMM estimation method were employed to test the influence of heterogeneous environmental regulation on green mining and its transmission mechanism. The results show that, there is a “U” type nonlinear relationship between the ERI and GML. The direct effect of command-control-based (CAC) and the market incentive-based (MBI) environmental regulation on green development of mining shows the characteristics of inhibition and promotion. There is a “U” type of indirectly moderating effect between technological innovation and the energy consumption structure on the GML. The technological innovation promotes the green development of the mining industry only after pass the inflection point of MBI, while the CAC plays a significant guiding role in upgrading of the energy consumption structure. There is an inhibition and promotion effect of MBI on the GML in the southeast coastal area, and the CAC is not significantly. Meanwhile, both of the ERI shows no positive effects in the central and western inland region.     

Using medico-legal data to investigate fatal older road user crash circumstances and risk factors

Objective: This study used medico-legal data to investigate fatal older road user (ORU, aged 65 years and older) crash circumstances and risk factors relating to 4 key components of the Safe System approach (e.g., roads and roadsides, vehicles, road users, and speeds) to identify areas of priority for targeted prevention activity.

Method: The Coroners' Court of Victoria's (CCOV) Surveillance Database was searched to identify and describe the frequency and rate per 100,000 population of fatal ORU crashes in the Australian state of Victoria for 2013–2014. Information relating to the deceased ORU, crash characteristics and circumstances, and risk factors was extracted and analyzed.

Results: One hundred and thirty-eight unintentional fatal ORU crashes were identified in the CCOV Surveillance Database. Of these fatal ORU crashes, most involved older drivers (44%), followed by older pedestrians (32%), older passengers (17%), older pedal cyclists (4%), older motorcyclists (1%), and older mobility scooter users (1%). The average annual rate of fatal ORU crashes per 100,000 population was 8.1 (95% confidence interval [CI], 6.0–10.2). In terms of the crash characteristics and circumstances, most fatal ORU crashes involved a counterpart (98%), of which the majority were passenger cars (50%) or fixed/stationary objects (25%), including trees (46%) or embankments (23%). In addition, most fatal ORU crashes occurred close to home (73%), on-road (87%), on roads that were paved (94%), on roads with light traffic volume (37%), and during low-risk conditions: between 12 p.m. and 6 p.m. (44%), on weekdays (80%), during daylight (75%), and under dry/clear conditions (81%). Road user (RU) error was identified by the police and/or the coroner for the majority of fatal crashes (55%), with a significant proportion of deceased ORUs deemed to have failed to yield (54%) or misjudged (41%).

Conclusions: RU error was the most significant factor identified in fatal ORU crashes, which suggests that there is a limited capacity of the road system to fully accommodate RU errors. Initiatives related to safer roads and roadsides, vehicles, speed zones, as well as behavioral approaches are key areas of priority for targeted activity to prevent fatal ORU crashes in the future.     

灾害多发地域社会关系网络建构路径的实证研究——以云南省大关县翠华镇为例

为了更好地构建典型灾害多发地域的社会关系网络,进而为相关应急管理工作提供支撑,采用灾害多发地域档案数据与问卷数据验证相结合的方法,提出灾害多发地域社会关系网络的建构路径;选取位于古滑坡带上的云南省大关县翠华镇为例进行说明,通过地理信息系统（Arcgis）分析当地档案数据与问卷获取数据的空间对象分布特征,并以问卷调查的局部数据验证相关档案数据的可靠性,最后,在翠华镇社会关系网络基础上,分析该灾害多发地域的应急预警信息传播特点,提出“分区域预警,以户为单位传播预警信息“的措施建议。研究结果表明:通过档案数据与问卷数据相结合方法构建灾害多发地域社会关系网络的方法可行,能够为相关应急管理措施提供参考和支撑。     

基于大数据的特种设备宏观安全风险预警方法研究

特种设备监管机构在监督检验过程中积累了大量数据资料,为充分挖掘其中的潜在价值,增强监管针对性,提高检验效率,应用并优化关联规则挖掘、社区发现、可视化等数据挖掘技术,构建1种宏观安全风险预警方法。该方法以特种设备安全监督检验大数据为基础,通过挖掘单台设备微观因素间的关联关系,实现整类特种设备宏观安全风险的识别与预警;以2008—2016年全国长管拖车检验数据为例,进行应用实践分析。研究结果表明:该方法可以对区域范围、缺陷类别等多种宏观指标进行预警,指导针对性监管与检验。     

基于电网防台抗台系统的应急主题数据仓库分析与设计

为解决电网防台抗台过程中缺少应急辅助支持的问题,提出基于现有电网防台抗台系统中的业务数据,采用星型结构分析与设计“台风气象灾情”、“电网因灾（台风）损失”、“电网因灾（台风）应急资源投入”等应急主题数据仓库,充分利用、开发相关的数据探查与ETL工具实现相关的数据集成,构建相关应急主题下的多维数据模型,并开展联机分析处理（OLAP）研究工作,为相关企业和部门开展电网应急管理工作提供应急辅助支持。     

大数据在环境执法与监测中的应用创新

当前背景下,大数据的有效应用以及普及,其很大程度上推动了各个领域创新发展的进程,尤其是在一些企业中,其通过对大数据的应用,实现了对数据的分析以及统计,有效优化了生产工艺,也满足了现阶段节能降耗的要求。因此,在进行环境执法与监测工作阶段,为了可以更好地提升监测水平,也应该积极的对大数据技术进行应用,合理创新。