Characteristics of animal-related motor vehicle crashes in select National Park Service units—United States, 1990–2013

Objectives: Nationally, animal–motor vehicle crashes (AVCs) account for 4.4% of all types of motor vehicle crashes (MVCs). AVCs are a safety risk for drivers and animals and many National Park Service (NPS) units (e.g., national park, national monument, or national parkway) have known AVC risk factors, including rural locations and substantial animal densities. We sought to describe conditions and circumstances involving AVCs to guide traffic and wildlife management for prevention of AVCs in select NPS units.

Methods: We conducted an analysis using NPS law enforcement MVC data. An MVC is a collision involving an in-transit motor vehicle that occurred or began on a public roadway. An AVC is characterized as a collision between a motor vehicle and an animal. A non-AVC is a crash between a motor vehicle and any object other than an animal or noncollision event (e.g., rollover crash). The final data for analysis included 54,068 records from 51 NPS units during 1990–2013. Counts and proportions were calculated for categorical variables and medians and ranges were calculated for continuous variables. We used Pearson’s chi-square to compare circumstances of AVCs and non-AVCs. Data were compiled at the park regional level; NPS parks are assigned to 1 of 7 regions based on the park’s location.

Results: AVCs accounted for 10.4% (5,643 of 54,068) of all MVCs from 51 NPS units. The Northeast (2,021 of 5,643; 35.8%) and Intermountain (1,180 of 5,643; 20.9%) regions had the largest percentage of the total AVC burden. November was the peak month for AVCs across all regions (881 of 5,643; 15.6%); however, seasonality varied by park geographic regions. The highest counts of AVCs were reported during fall for the National Capital, Northeast/Southeast, and Northeast regions; winter for the Southeast region; and summer for Intermountain and Pacific West regions.

Conclusions: AVCs represent a public health and wildlife safety concern for NPS units. AVCs in select NPS units were approximately 2-fold higher than the national percentage for AVCs. The peak season for AVCs varied by NPS region. Knowledge of region-specific seasonality patterns for AVCs can help NPS staff develop mitigation strategies for use primarily during peak AVC months. Improving AVC data collection might provide NPS with a more complete understanding of risk factors and seasonal trends for specific NPS units. By collecting information concerning the animal species hit, park managers can better understand the impacts of AVC to wildlife population health.     

The need to improve information on road user type in National Vital Statistics System mortality data

Objectives: Both the National Vital Statistics System (NVSS) and the Fatality Analysis Reporting System (FARS) can be used to examine motor vehicle crash (MVC) deaths. These 2 data systems operate independently, using different methods to collect and code information about the type of vehicle (e.g., car, truck, bus) and road user (e.g., occupant, motorcyclist, pedestrian) involved in an MVC. A substantial proportion of MVC deaths in NVSS are coded as “unspecified” road user, which reduces the utility of the NVSS data for describing burden and identifying prevention measures. This study aimed to describe characteristics of unspecified road user deaths in NVSS to further our understanding of how these groups may be similar to occupant road user deaths.

Methods: Using data from 1999 to 2015, we compared NVSS and FARS MVC death counts by road user type, overall and by age group, gender, and year. In addition, we examined factors associated with the categorization of an MVC death as unspecified road user such as state of residence of decedent, type of medical death investigation system, and place of death.

Results: The number of MVC occupant deaths in NVSS was smaller than that in FARS in each year and the number of unspecified road user deaths in NVSS was greater than that in FARS. The sum of the number of occupant and unspecified road user deaths in NVSS, however, was approximately equal to the number of FARS occupant deaths. Age group and gender distributions were roughly equivalent for NVSS and FARS occupants and NVSS unspecified road users. Within NVSS, the number of MVC deaths listed as unspecified road user varied across states and over time. Other categories of road users (motorcyclists, pedal cyclists, and pedestrians) were consistent when comparing NVSS and FARS.

Conclusions: Our findings suggest that the unspecified road user MVC deaths in NVSS look similar to those of MVC occupants according to selected characteristics. Additional study is needed to identify documentation and reporting challenges in individual states and over time and to identify opportunities for improvement in the coding of road user type in NVSS.     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

Suicide by crashing into a heavy vehicle: Focus on professional drivers using in-depth crash data

Objective: Road traffic suicides typically involve a passenger car driver crashing his or her vehicle into a heavy vehicle, because death is almost certain due to the large mass difference between these vehicles. For the same reason, heavy-vehicle drivers typically suffer minor injuries, if any, and have thus received little attention in the research literature. In this study, we focused on heavy-vehicle drivers who were involved as the second party in road suicides in Finland.

Methods: We analyzed 138 road suicides (2011–2016) involving a passenger car crashing into a heavy vehicle. We used in-depth road crash investigation data from the Finnish Crash Data Institute.

Results: The results showed that all but 2 crashes were head-on collisions. Almost 30% of truck drivers were injured, but only a few suffered serious injuries. More than a quarter reported sick leave following their crash. Injury insurance compensation to heavy-vehicle drivers was just above €9,000 on average. Material damage to heavy vehicles was significant, with average insurance compensation paid being €70,500. Three out of 4 truck drivers reported that drivers committing suicide acted abruptly and left them little opportunity for preventive action.

Conclusions: Suicides by crashing into heavy vehicles can have an impact on drivers’ well-being; however, it is difficult to see how heavy-vehicle drivers could avoid a suicide attempt involving their vehicle.     

配置SCR的柴油货车道路运行NOx排放分析

为了研究重型柴油货车在不同道路运行工况下的NO_x排放特性,以一辆配置选择催化还原(Selective CatalyticReduction,SCR)净化系统的国V排放标准的重型柴油货车为研究对象,开展实际道路运行车载排放测试,通过车载排放测试系统实时采集车辆行驶速度、NO_x排放体积分数与排气温度等数据,分析车速、排气温度、路况等对NO_x排放的影响。结果表明,车速低于40 km/h,NO_x排放随车速增大稍有增加; 40～70 km/h,随车速增大NO_x排放降低;高于70 km/h,随车速增大NO_x排放显著降低。车速与排气温度呈线性正相关,排气温度高于150℃,SCR才能显示出对NO_x的净化效果。市区工况车速低,排温低于150℃,SCR不能有效工作;市郊、高速工况下排温高于150℃,SCR催化效率提高,车速增大,排温升高,NO_x排放降低,因此NO_x排放市郊工况低于市区工况,高速工况低于市郊工况。配置SCR的重型柴油货车NO_x高排放区主要集中在中低车速、加速区间内。     

南京市机动车排气污染控制和管理对策

简述了南京市机动车排气污染控制和管理对策。对新机动车排气控制实施了新车上牌的环保注册登记目录制,鼓励生产厂家采用先进的排放控制技术,达到国家制定的排放控制目标和排放标准;对在用机动车排气控制实施检测／维护(I／M)制度,对尾气排放不达标车辆进行正常的维修和保养,保证其发动机处于正常技术状况;对高排放污染机动车辆,安装尾气净化装置,以改善机动车排放水平。定期检测中各类型车辆维护前后排气污染物削减结果表明,85％的高排放污染车辆能达标,且CO的削减率最高,达50％左右;定期检测中各类型车辆安装机内净化器前后排气污染物削减结果表明,二次补气机内净化器对CO的净化率为22．5％～30．0％,对HC的净化率为9．7％～30．0％;高能电子点火机内净化器对CO的净化率为5．4％～22．2％,对HC的净化率为17．2％～30．8％。不定期检测中,一些高排放污染车辆安装三元催化反应器后,其净化率可达80％～90％。     

重庆市内环货车错时限行对空气质量的影响

在分析货车实施错时限行后内环车流量时段分布变化基础上,通过对PM_(2.5)、NO_2等指标的ADMS模型模拟和实际监测数据对比分析,探讨了内环货车错时限行对环境空气质量的影响。结果表明,货车错时限行后主城区环境空气中PM_(2.5)、NO_2小时平均质量浓度分别降低了9.4%和6.0%,峰值浓度明显降低,晚上出现峰值时间往后推移了2~3 h。经ADMS模型模拟计算,内环高峰时段机动车排放对主城区NO_2、PM、VOCs的浓度贡献分别降低了54.1%、56.3%、17.5%,CO浓度贡献不大。内环货车错时限行措施对重庆市主城区空气质量的改善有一定的积极作用。     

国家机动车排污控制管理数据库系统

介绍了“国家机动车排污控制管理系统”数据库的研制、特点及使用方法。该数据库存储了大量的城市环境概况、机动车排放因子、排放标准、燃油标准和排放控制对策等信息，能系统、准确的提供国家级相关数据及法律依据，使各级环保部门和政府决策部门能迅速、准确地掌握全国或某城市的有关机动车的静态或动态的污染排放综合情况     

Carbonyl emissions from heavy-duty diesel vehicle exhaust in China and the contribution to ozone formation potential

Fifteen heavy-duty diesel vehicles were tested on chassis dynamometer by using typical heavy duty driving cycle and fuel economy cycle. The air from the exhaust was sampled by 2,4- dinitrophenyhydrazine cartridge and 23 carbonyl compounds were analyzed by high performance liquid chromatography. The average emission factor of carbonyls was 97.2 mg/km, higher than that of light-duty diesel vehicles and gasoline-powered vehicles. Formaldehyde, acetaldehyde, acetone and propionaidehyde were the species with the highest emission factors. Main influencing factors for carbonyl emissions were vehicle type, average speed and regulated emission standard, and the impact of vehicle loading was not evident in this study. National emission of carbonyls from diesel vehicles exhaust was calculated for China, 2011, based on both vehicle miles traveled and fuel consumption. Carbonyl emission of diesel vehicle was estimated to be 45.8 Gg, and was comparable to gasolinepowered vehicles （58.4 Gg）. The emissions of formaldehyde, acetaldehyde and acetone were 12.6, 6.9, 3.8 Gg, respectively. The ozone formation potential of carbonyls from diesel vehicles exhaust was 537 mg O3/km, higher than 497 mg O3/km of none-methane hydrocarbons emitted from diesel vehicles.     

Identifying and modeling key trade-offs between hydrogen fuel cell and electric vehicles

Through a sensitivity analysis, the trade-off between vehicle range and CO₂ emissions is investigated as a function of electric emissions coefficient. Various powertrains were analysed for use in a small crossover sport utility vehicle. Gasoline, gasoline electric hybrid, diesel, fuel cell and battery electric vehicles (BEVs) were considered. Using various upstream fuel pathways and a model for vehicle performance, emissions and energy use were estimated. The hydrogen fuel cell vehicle was found preferable to BEVs under conditions of high CO₂ emissions per kW-hr and a high vehicle range requirement. The BEV was preferable for all other conditions.