降雨影响噪声自动监测数据的有效性判定分析

选取北京市近5年夏半年（4—9月）夜间的降雨数据及相关噪声自动监测小时等效声级,对小时雨量与噪声自动监测数据进行数学统计分析,找出影响噪声监测数据的小时雨量值及不同声级受雨噪声影响的雨量限值,作为降雨对噪声自动监测小时等效声级的有效性判定条件。     

浅谈福建武夷山大气背景监测站的运行维护

大气背景监测站多位于高山且相对清洁地区,在运行维护方面和城市点位有很大不同,低量程仪器和其他自动监测仪器在运行维护上也有所不同。国家大气背景监测福建武夷山站是国内14个大气背景监测站之一,该站于2011年3月4日运行,文章总结该站运行以来的维护经验,从运行条件、日常维护、简单故障判断、数据处理等几个方面详细叙述,给出维护经验和注意事项,以期为其他类似背景站的运行维护提供参考。     

岛津TNP-4110在线分析仪的日常维护

从数据比对、硬件维护两个方面探讨岛津TNP-4110在线仪的日常维护问题。通过分析各种干扰因素来查找造成数据比对差异的原因并提出相应解决措施。在仪器日常维护上设立不同周期保养计划、共享设备维护经验,结合实践制定了合适的检修流程图。     

水环境监测信息集成、共享与决策支持平台构建

以水环境监测数据可靠传输交换与集成、信息规范化处理分析、高效共享与决策支持为应用主线,对水环境监测信息集成、共享与决策支持平台的建设目标、总体架构、主要功能进行了研究,设计了平台的总体架构及主要功能,以实现涉水部门水环境信息的采集、传输、交换、存储、分析、发布、共享、展现等,为环境管理与决策人员、为公众、为各级(国家级流域级省级地市级县级)环境监测业务人员提供信息支撑。     

大数据解析技术在大气环境监测中的应用研究

针对近年来新兴的大数据及挖掘、分析技术,对大数据解析技术在环境科学研究中的应用进行了分析和探析。以城市局部地区大气细颗粒污染物PM2.5浓度计算为例,筛选了PM2.5浓度历史数据、气象条件、交通状况、人群活动情况、网格道路状况等数据为影响特征量,分析了用神经网络法进行大数据解析和推演的原理和数学过程,是用数学、计算机、统计等方法研究解决多元、非线性复杂环境问题的一种新的探索。     

地表水水质自动监测数据技术评估

对地表水水质自动监测站取水口直接采样和模拟水样,用自动监测和实验室方法监测相应的指标,对两者的监测结果进行比较,研究和分析两者之间差异的原因,评估自动监测数据,提出建议。     

基于平衡计分卡的大气环境监测数据共享网站绩效评估研究

基于平衡计分卡原理,结合网站特点,建立了大气环境监测数据共享网站绩效评估模型。以共享网站的战略愿景为模型框架的中心,以财务、顾客、内部程序、学习与成长等4个基本维度为一级指标,构建了绩效评估指标体系,并采用层次分析法确定了各指标的权重。利用该模型对长三角区域空气质量预测预报系统和宁波市空气质量预报预警平台的大气数据共享模块进行实证研究,绩效评估结果均为良好。     

土压平衡矩形顶管施工引起的地表沉降规律研究

为了分析与掌握矩形顶管施工引起的地表沉降变形规律,提出基于MIDAS-GTS有限元软件的分析方法,首先,通过工程实例验证MIDAS-GTS有限元软件在分析顶管施工引起地表沉降变形的有效性;其次,通过影响因素的敏感性分析确定不同影响因素作用下的地表沉降变形特性,这些影响因素主要包括开挖面支护压力、侧摩阻力、地层结构特性等;最后,通过地表沉降变形特性分析得出矩形顶管的适用范围。研究结果表明:基于MIDAS-GTS有限元软件的地表沉降规律分析方法和结果,可为矩形顶管施工过程提供相关参考和借鉴。     

Safe speed limits for a safe system: The relationship between speed limit and fatal crash rate for different crash types

Objective: The objective of this article is to provide empirical evidence for safe speed limits that will meet the objectives of the Safe System by examining the relationship between speed limit and injury severity for different crash types, using police-reported crash data.

Method: Police-reported crashes from 2 Australian jurisdictions were used to calculate a fatal crash rate by speed limit and crash type. Example safe speed limits were defined using threshold risk levels.

Results: A positive exponential relationship between speed limit and fatality rate was found. For an example fatality rate threshold of 1 in 100 crashes it was found that safe speed limits are 40 km/h for pedestrian crashes; 50 km/h for head-on crashes; 60 km/h for hit fixed object crashes; 80 km/h for right angle, right turn, and left road/rollover crashes; and 110 km/h or more for rear-end crashes.

Conclusions: The positive exponential relationship between speed limit and fatal crash rate is consistent with prior research into speed and crash risk. The results indicate that speed zones of 100 km/h or more only meet the objectives of the Safe System, with regard to fatal crashes, where all crash types except rear-end crashes are exceedingly rare, such as on a high standard restricted access highway with a safe roadside design.     

Injury risk functions for frontal oblique collisions

Objective: The objective of this article was the construction of injury risk functions (IRFs) for front row occupants in oblique frontal crashes and a comparison to IRF of nonoblique frontal crashes from the same data set.

Method: Crashes of modern vehicles from GIDAS (German In-Depth Accident Study) were used as the basis for the construction of a logistic injury risk model. Static deformation, measured via displaced voxels on the postcrash vehicles, was used to calculate the energy dissipated in the crash. This measure of accident severity was termed objective equivalent speed (oEES) because it does not depend on the accident reconstruction and thus eliminates reconstruction biases like impact direction and vehicle model year. Imputation from property damage cases was used to describe underrepresented low-severity crashes―a known shortcoming of GIDAS. Binary logistic regression was used to relate the stimuli (oEES) to the binary outcome variable (injured or not injured).

Results: IRFs for the oblique frontal impact and nonoblique frontal impact were computed for the Maximum Abbreviated Injury Scale (MAIS) 2+ and 3+ levels for adults (18–64 years). For a given stimulus, the probability of injury for a belted driver was higher in oblique crashes than in nonoblique frontal crashes. For the 25% injury risk at MAIS 2+ level, the corresponding stimulus for oblique crashes was 40 km/h but it was 64 km/h for nonoblique frontal crashes.

Conclusions: The risk of obtaining MAIS 2+ injuries is significantly higher in oblique crashes than in nonoblique crashes. In the real world, most MAIS 2+ injuries occur in an oEES range from 30 to 60 km/h.