济南市机动车排放因子的确定

对机动车典型排放因子进行分散分析 ,并引用各地的典型因子 ,采取算术平均和加权平均的方法来确定济南市的各类机动车的排放因子。     

PAHs pollution from traffic sources in air of Hangzhou, China: Trend and influencing factors

Introduction China is experiencing rapid economic and populationgrowth. The rapid growth and increasing fossil fuel energyconsumption have resulted in large amounts of pollutants suchas TSP, SO2, NOx and hydrocarbons to be emitted intoambient atmosphere. Nowadays, national routine monitor haspaid much attention to TSP, SO2 and NOx. Highconcentrations of SO2 and NOx can lead to an increase ofregional levels of acid deposition and tropospheric ozon, thu…     

机动车尾气的产生及对人群健康的影响和防治对策

较详细的介绍了机动车燃料和尾气的关系，全面论述了尾气中主要污染物对人体健康的危害，并对防治尾气的污染提出了建议。     

汽车尾气净化催化剂研究进展

介绍了汽车尾气净化催化剂的发展过程、三效催化剂的净化原理以及催化剂的组成等.     

A remote sensing system of vehicle emissions based on tunable diode laser technology

As being an effective real-time method of monitoring vehicle emissions on-road, a remote sensing system based on the tunable diode laser （TDL） technology was presented, and the key technologies were discussed. A field test in Guangzhou（Guangdong, China） was performed and was found that the factors, such as slope, instantaneous speed and acceleration, had significant influence on the detectable rate of the system. Based on the results, the proposal choice of testing site was presented.     

北大园区室内挥发性有机物(VOCs)的研究

1997年3~5月,对北京大学校园区内多处师生住所及公共场所室内空气中的挥发性有机物(VOCs)进行了调查研究。结果表明,大多数房屋内总VOCs浓度在220~2000μg/m3范围内;通风条件、季节变化、人为活动对室内VOCs浓度水平起着重要影响。室外空气质量也直接影响着室内VOCs浓度高低:室内多种芳香烃和烷烃主要来自于室外汽车尾气的排放,其贡献率为76%~92%。      

我国汽车排气污染现状与发展

从汽车保有量,油口消耗、污染物排放和城市大气环境质量等方面分析了我国汽车污染现状及２０００年和２０１０年２个时段的发展趋势,从节能,净化和管理的角度初步分析了削减汽车污染物排放的综合对策。     

天津市道路车流量特征分析

车流量是移动源和道路扬尘排放清单计算的一个重要参数,直接影响排放清单的不确定性.为了解天津市道路车流量特征,通过人工摄录法获取天津市一年四季5种不同类型共9条道路的车流量及车型分布信息,并对其特征进行分析.结果表明:天津市不同类型道路之间车流量总体上呈快速路（8 790辆/h）>主干道（4 077辆/h）>外环线（3 335辆/h）>次干道（2 461辆/h）>支路（1 507辆/h）的特征;冬季车流量最大,而春、夏两季相对较小.各类型道路车流量占比最大的车型均为轻型车（76.88%~90.71%）,中型车（5.31%~11.80%）次之,重型车（3.20%~11.31%）最少;快速路和外环线工作日的车流量大于非工作日,并且差异具有统计学意义,呈"周末效应".研究显示:次干道和支路车流量与主干道车流量呈线性相关,在后续进行道路扬尘和移动源清单编制时,可以考虑通过将主干道车流量与次干道、支路拟合的方式减小工作量;此外,主干道、次干道和支路的早高峰,上、下午平峰,中午和晚高峰的车流量差异不大,可将一天划分为白天、夜晚和凌晨3个时段进行统计,从而节约时间和成本.      

Injury Severity Score alone predicts mortality when compared to EMS scene time and transport time for motor vehicle trauma patients who arrive alive to hospital

ABSTRACT

Objective: This study aims to identify the association, if any, between prehospital scene time, prehospital transport time, and Injury Severity Score (ISS) with in-hospital mortality.

Methods: A retrospective analysis was performed on patients at least 18 years of age who arrived to the hospital alive via emergency medical services (EMS) after a motor vehicle collision (MVC) between 1992 and 2016. These patients were divided into groups based on minutes spent at the scene and in transport. The ISS of the in-hospital mortalities, as well as the entire patient sample for each time frame, was collected. Patients without documented scene time, transport time, or ISS were excluded.

Results: Four thousand one hundred ninety-four patients were captured when analyzing scene time, though only 3,980 met inclusion criteria. In addition, 4,177 patients were captured when analyzing transport time, though only 3,979 met inclusion criteria. Scene time and transport time were not statistically significant predictors of in-hospital mortality (P = .31 and P = .458, respectively). ISS was found to be a statistically significant predictor of in-hospital mortality (P < .001).

Conclusions: ISS predicts mortality independent of scene time or transport time for patients who arrive to the hospital alive following an MVC at Guthrie Robert Packer Hospital. Limitations of our study include inability to capture prehospital deaths and inability to correlate ISS with prehospital injury severity scores.     

基于HMM和DT的无人机异常检测方法

为了实时检测无人机异常状态,提出基于隐马尔可夫模型(Hidden Markov Model,HMM)和决策树(Decision Tree,DT)的无人机异常检测方法(HMMDT).首先根据异常致因将无人机异常分为干扰异常和硬件异常;然后结合HMM和DT建立无人机异常检测模型,定义无人机异常度衡量异常状态的严重程度,确定...