燃料电池汽车氢源生命周期分析

运用生命周期评价方法对使用不同氢源的燃料电池汽车进行了分析.经过目的与范围的确定、清单分析、影响评价和结果解释,表明电解制氢方案的污染排放明显高于甲醇重整和汽油重整方案,而甲醇重整和汽油重整方案又高于天然气制氢和煤制氢方案,对于相同的制氢方案,液氢方案的排放略高于气氢方案.      

广州市移动源现状及其污染控制管理对策

通过对广州市机动车辆构成状况和发展趋势、车流变化与氮氧化物变化关系以及各类车型污染排放分担率的分析,结果表明:在排放污染物的移动源中,小型车(出租车及其他小型车)和摩托车是一氧化碳、碳氢化合物的主要排放源;公共汽车等大型车是氮氧化物的主要排放源之一。并提出对广州市机动车污染控制应着重控制摩托车、小型车(特别是轿车,包括出租车)、公共汽车的污染排放。      

Providing the Spark: Impact of financial incentives on battery electric vehicle adoption

To overcome adoption barriers and promote battery electric vehicles (BEVs) as an energy efficient consumer transportation option, a number of states offer subsidies to consumers for BEVs. We use a national data set of vehicle registrations and state-level financial incentives to assess the impact of vehicle purchase subsidies on adoption using both difference-in-differences and synthetic controls methods. We find that incentives offered as direct purchase rebates generate increased levels of new BEV registrations at a rate of approximately 8 percent per thousand dollars of incentive offered. Between 2011 and 2015, vehicle rebate incentives are associated with an increase in overall BEV registrations of approximately 11 percent. Our findings indicate incentives offered as state income tax credits do not have a statistically significant effect on BEV adoptions, though we caution this may be a result of limited temporal variation in BEV incentives across our sample. Responses to rebate incentives do not differ significantly by the make of the vehicle purchased (i.e., Tesla and non-Tesla vehicles). We combine our results with recent assessments of marginal environmental costs of electric vehicle charging and measure net welfare effects of BEV subsidy programs. Our analysis indicates these programs are not welfare-improving if only considering benefits associated with avoided emissions. Additional benefits associated with long-term market growth, production cost savings, network externalities, or accelerated innovation could substantially impact the net welfare outcomes.     

广州市城区站点冬季PM2.5时间变化及碳氮同位素组成特征

于2018年冬季在广州城区磨碟沙站点开展细颗粒物(PM_2.5)样品采集,并获得PM_2.5中水溶性离子、含碳组分、稳定碳氮同位素的组成及时间变化特征,重点讨论了PM_2.5浓度升高时段的化学组成特征变化,进而利用稳定碳氮同位素变化特征探究了主要污染来源。结果表明：采样期间,研究站点PM_2.5平均质量浓度为22.1μg/m³,共出现两个PM_2.5浓度水平升高时段,所对应的平均质量浓度分别达46.0μg/m³和63.0μg/m³。风速降低、温度升高等不利气象条件是导致上述时段PM_2.5浓度上升的重要原因。在上述时段,伴随着PM_2.5浓度的升高,NO^-₃和NH⁺₄浓度均出现显著升高,NO^-₃与SO^2-₄的摩...     

天然气净化厂冷却塔风机驱动方式改造简论

文章介绍长庆油田第一天然气净化厂冷却塔风机驱动设备的改造情况。分析了长庆油田第一天然气净化厂冷却塔电机驱动方式存在的不足和原因,提出了改进对策和实施方案,论述了水轮机驱动方式改造工程在经济、节能降耗、环保、安全、高效等方面的优点,值得在油气田生产应用中推广。     

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.     

道路交通拥堵与城市雾霾污染的关系研究

道路拥堵与城市雾霾是机动车行驶带来的两个负溢出效应,大量文献揭示了城市机动车行驶对二者带来的影响,却鲜有文献关注道路拥堵程度与雾霾污染之间的内在联系。这其中的缘由在于,一则道路拥堵程度与雾霾污染之间互为因果,同时有共同的影响因素,由此带来的内生性难题难以有效解决;其次,采用统一标准来测度不同城市道路拥堵程度的数据难以获得。为此利用高德地图(Amap)根据机动车定位导航系统提供的城市拥堵延时的大数据,来捕获各省会城市每日道路交通的拥堵程度,同时运用各城市每日的燃油销售价格、国际市场原油价格以及上一周同一工作日道路的拥堵程度作为工具变量,通过两阶段最小二乘法(2SLS)估计道路拥堵程度对城市雾霾污染的影响。回归结果表明:①以城市燃油价格作为工具变量时,道路拥堵程度每增加1%,会导致省会城市PM2.5、PM10分别增加6.5%和6.7%;②以国际原油价格、上一周同一个工作日拥堵程度作为工具变量,以及改用GMM方法进行估计时,基准回归的结论仍然稳健,城市的治堵举措与治霾举措能够相互协同;③进一步以省会城市新增轨道交通来实现治堵和治霾的例子表明,发展轨道交通来实现治堵与治霾的协同效应,要以有效治堵作为前提,否则减排治霾的协同效果无法实现。     

Relating meteorological variables and trends in motor vehicle emissions to monthly urban carbon monoxide concentrations

A model which quantifies the relationship between the monthly time series for CO emissions, the monthly time series in ambient CO concentration, and meteorologically driven dispersion was developed. Fifteen cities representing a wide range of geographical and climatic conditions were selected. An eight-year time series (1984–1991 inclusive) of monthly averaged data were examined in each city. A new method of handling missing ambient concentration values which is designed to calculate city-wide average concentrations that follow the trend seen at individual monitor sites is presented. This method is general and can be used in other applications involving missing data. The model uses emissions estimates along with two meteorological variables (wind speed and mixing height) to estimate monthly averages of ambient air pollution concentrations. The model is shown to have a wide range of applicability; it works equally well for a wide range of cities that have very different temporal CO distributions. The model is suited for assessing long-term trends in ambient air pollutants and can also be used for estimating seasonal variations in concentration, estimation of trends in emissions, and for filling in gaps in the ambient concentration record.     

基于高阶递归神经网络的AUV鲁棒控制方法

目的 提出一种基于高阶递归神经网络的AUV鲁棒控制方法。方法 利用结构简单但逼近效果优越的高阶递归神经网络,对建模不确定性和外部未知干扰进行估计,并将其补偿到输入控制律中,以提高控制性能。之后,基于HJI理论和Lyapunov稳定性分析导出神经网络权重自适应更新律和AUV自适应控制律,设计反步滑模方法作为对比方法,并进行仿真实验。结果 设计的基于高阶递归神经网络的AUV鲁棒控制方法的跟踪误差、调节时间等控制指标均优于反步滑模方法。设计的鲁棒控制方法可以控制AUV精确跟踪目标轨迹,同时具有优秀的控制性能和鲁棒性。结论 这一研究为AUV轨迹跟踪控制领域提供了一种高效且有效的方法,有望在复杂、不确定的水下环境中得到应用。