深圳市混合动力巴士污染物排放特性的实验研究

利用高精度的车载排放测试仪,对使用同种发动机的普通柴油巴士和混合动力巴士进行城市典型道路工况下的排放测试,对比2种车型的污染物排放特征。通过对2种车型基于不同车速及比功率下的排放特性分析,发现混合动力巴士有效减少了CO和颗粒物(PM)的排放,CO、PM的排放量分别为普通柴油巴士的42.4%、28.7%;然而由于混合动力巴士的匹配控制系统相对复杂,车身总质量较大,导致其NO_x、碳氢化合物(HC)排放明显高于普通柴油巴士,NO_x、HC排放量分别为普通巴士的167.5%、931.4%。     

基于QAR数据的民航发动机尾气污染物排放量估算

对现有航空发动机尾气污染物排放估算模型进行总结与分析,重点对一阶逼近3.0尾气颗粒污染物(particulate matter,PM)估算方法和ICAO尾气气体污染物估算方法进行了详细的研究。借助QAR记录的发动机相关性能数据,利用ICAO公布的排放指数,用一阶逼近3.0方法来估算CFM56民航发动机一个LTO阶段尾气颗粒污染物的排放情况,用ICAO气体排放模型估算CFM56民航发动机一个LTO阶段NO_x、CO、HC气体的排放情况。最终得到CFM56发动机一个典型航班LTO阶段的尾气污染物排放总体情况。实例计算得到CFM56发动机一个典型航班LTO阶段的排放情况为:NO_x,5 683.61 g;CO,3 821.89 g;HC,208.946 g;PM,121.287 g。本文提出的单机尾气污染物排放量估算方法可推广应用到估算整个机队LTO阶段的污染物排放量,以及整个机队某个时间段的排放情况,对评估机场附近区域大气环境提供了重要参考数据。     

基于COPERT 4模型的杭州市机动车排气污染特征研究

以2015年为基准年,利用COPERT 4模型计算了杭州市分车型分排放标准下的机动车排气污染物(CO、碳氢化合物(HC)、NO_x、PM_(2.5))的排放因子,并估算了各污染物排放量及分车型分排放标准下的各污染物分担率。结果表明,随着排放标准的提升,机动车排气污染物排放因子总体呈现下降的趋势。汽油车的CO和HC排放因子高于柴油车,而柴油车的NO_x和PM_(2.5)排放因子高于汽油车;天然气车的各污染物排放因子基本接近汽油车,而汽油电混动车的各污染物排放因子则明显低于其他动力车;各污染物排放因子随车型的增大(重)而增大。2015年杭州市机动车排气污染物CO、NO_x、HC和PM_(2.5)排放量分别为48 923.0、44 713.7、7 014.7、837.9t,其中汽油车CO和HC分担率较高主要是因为小型汽油客车CO和HC分担率高,并且其保有量占比也高,应重点控制小型汽油客车的保有量;柴油车NOx和PM_(2.5)分担率较高主要是因为重型柴油货车NO_x和PM_(2.5)分担率高,但其保有量占比不高,应重点控制重型柴油货车的排放因子。     

青岛市私家车排放清单及其分布特征

为掌握青岛市私家车排放尾气污染现状,利用路网数据和实时交通信息,结合实地调研数据和排放因子,编制主城区私家车污染物排放清单。结果表明:(1)在2017年10—12月的工作日,青岛市主城区的私家车CO、碳氢化合物(HC)、NO_x、PM_(2.5)日排放量分别为17.18、9.36、11.77、8.51t/d。(2)在时间分布上,污染物排放呈现"双峰"特点。早高峰排放量峰值高、高峰持续时间相对较短,晚高峰的峰值略低、高峰持续时间相对较长,高峰期污染物排放量占40%以上。(3)污染物排放主要集中在城区多个商圈的结合处,道路交叉口排放量较高。同时,并不是路网密度越大,空间排放分布值越高。     

我国移动源主要大气污染物排放量的估算

在考虑我国移动源主要大气污染物排放标准变化的基础上,分别对我国2000—2012年道路移动源和非道路移动源主要大气污染物(CO、NOx、HC、PM2.5)的排放量进行了估算。研究表明:2000—2012年间,我国移动源主要大气污染物排放总量呈现先增后减的趋势,2005年达到最大值,为4 233万t,其中道路移动源的排放量占80%以上;各类大气污染物的排放量的差异性较大,CO和NOx的排放量较多,占排放总量的87%以上,从整体趋势上来看,CO的排放量逐年较少,NOx的排放量逐年增大,而HC和PM2.5变化不大;摩托车和重型柴油货车是道路移动源主要排放源,农业机械是非道路移动源的主要排放源;移动源排放的主要大气污染物在地区间的分布极不平衡,2012年排放量最高的5个省份依次是山东、河北、河南、广东和江苏;排放强度较大的地区主要集中在环渤海经济圈、长三角地区和珠三角地区,其中又以上海、北京、天津3个直辖市的排放强度最大。     

在用轻型汽油车排放随行驶里程劣化规律分析

基于佛山市3.5万条简易稳态工况(ASM)下的尾气排放检测数据,通过分类统计和线性拟合方法分析在用轻型汽油车的污染物排放浓度随行驶里程的劣化规律。分析结果表明,该地区轻型汽油车污染物排放浓度主要分布于低值区间,超过85%的样本数据低于最低排放限值;车辆排放劣化特征随行驶里程呈规律性变化,行驶里程在0~5万km时污染物排放缓慢增长,5万~16万km时呈快速线性增长,16万km后震荡缓慢增长;行驶里程在16万km前,不同车型的排放特征存在一定差异,其中轻型货车和轻型客车的排放浓度高、劣化速度快;CO、HC、NO随行驶里程的劣化规律可用线性增长模型表示。本研究结论可为预测机动车污染变化趋势、完善在用车检查/维护制度、高排放车辆识别和淘汰等方面提供理论支持。     

大型城市客车加速模拟工况排放特性的实验研究

通过对258辆大型城市客车进行加速模拟工况排放测试,研究了城市客车的排放特性,比较了压缩天然气(CNG)车辆与汽油车辆的排放特性,分析了车辆车龄与排放的关系、发动机燃油供给方式与排放的关系,以及车辆总质量与排放的关系。研究结果表明：燃用CNG的车辆其CO、NOx排放较汽油低许多,尤其是CO,但HC排放较汽油高;电喷车辆的CO、NOx排放比化油器车低,但HC排放值高于化油器车;车龄增长,车辆的CO、NOx排放值增大,但HC变化不明显;车辆总质量增加,排放呈下降趋势。     

双燃料发动机瞬态工况HC排放特征

采用双燃料增压中冷发动机,模拟了不同转速、转矩、进气流量以及进气温度对HC排放的影响.结果表明,在一定范围内HC排放会随着进气流量和转速增加而升高,随转矩和进气温度的增加而减小.1 600 r/min时HC排放水平高于其他各转速,此转速对应的最高HC排放水平进气流量、转矩和进气温度分别为80 kg/h、30N·m和28℃.对1 600 r/min下各参数进行拟合得出了最大HC排放水平对应的进气流量、转矩和进气温度,拟合效果较好.通过相关性分析,进气温度、转速和进气流量对HC排放影响显著,转矩对HC的排放影响不明显.     

低温环境中乙醇汽油和普通汽油的冷凝水与污染物排放

对低温环境中乙醇汽油和普通汽油的冷凝水、CO、HC、NOx和CO2排放特性进行了研究,并对5种排放物的形成机理和排放趋势进行了分析。ECE工况(-20、-10和0℃)和怠速工况(-30、-20、-10和0℃)下,乙醇汽油和普通汽油的冷凝水排放量主要受含氢量、车辆构造和外界环境的共同影响。ECE工况中冷凝水的总体排放趋势是随着温度降低而增加,乙醇汽油的总排水量持平或略低于普通汽油。-10℃时乙醇汽油的高含氧量能促进燃烧速度和燃烧效率,减少CO和HC排放,增加NOx排放;0℃时低温环境和乙醇的高汽化潜热会影响可燃混合气形成和燃烧速度,降低缸壁温度,增加CO和HC排放。     

行驶速度对机动车尾气排放的影响

利用COPERTIV模型计算和车载尾气测量系统实测得到不同行驶速度下的机动车尾气排放因子,并分析不同车型不同排放标准等级车辆的行驶速度对排放的影响。调查研究北京市城区路网早高峰、平峰、晚高峰和夜间的车流量、车型构成、行驶速度,基于Arc GIS建立平均车速和行驶里程的网格分布数据库,并对比车速修正前后不同道路类型不同污染物的排放强度。结果表明,基于COPERT IV模型和车载测量系统计算的小客车NOx和HC排放因子随车速的变化趋势类似,均随车速的增加呈现U型分布;柴油公交车与柴油卡车NOx和HC排放因子随着车速的升高而减小。4个时间段平均车速大小排序为:夜间(44 km·h~(-1))晚高峰(34 km·h~(-1))平峰(32 km·h~(-1))早高峰(28 km·h~(-1))。车速修正后CO和HC的排放量上升,上升幅度分别为10.6%~11.8%和8.8%~9.2%,NOx和PM排放量下降,下降幅度分别为22.1%~23.3%和12.7%~13.5%。     

Multispecies remote sensing measurements of vehicle emissions on Sherman Way in Van Nuys,California

As part of the 2010 Van Nuys tunnel study, researchers from the University of Denver measured on-road fuel-specific light-duty vehicle emissions from nearly 13,000 vehicles on Sherman Way (0.4 miles west of the tunnel) in Van Nuys, California, with its multispecies Fuel Efficiency Automobile Test (FEAT) remote sensor a week ahead of the tunnel measurements. The remote sensing mean gram per kilogram carbon monoxide (CO), hydrocarbon (HC), and oxide of nitrogen (NO_x) measurements are 8.9% lower, 41% higher, and 24% higher than the tunnel measurements, respectively. The remote sensing CO/NO_x and HC/NO_x mass ratios are 28% lower and 20% higher than the comparable tunnel ratios. Comparisons with the historical tunnel measurements show large reductions in CO, HC, and NO_x over the past 23 yr, but little change in the HC/NO_x mass ratio since 1995. The fleet CO and HC emissions are increasingly dominated by a few gross emitters, with more than a third of the total emissions being contributed by less than 1% of the fleet. An example of this is a 1995 vehicle measured three times with an average HC emission of 419 g/kg fuel (two-stroke snowmobiles average 475 g/kg fuel), responsible for 4% of the total HC emissions. The 2008 economic downturn dramatically reduced the number of new vehicles entering the fleet, leading to an age increase (>1 model year) of the Sherman Way fleet that has increased the fleet's ammonia (NH₃) emissions. The mean NH₃ levels appear little changed from previous measurements collected in the Van Nuys tunnel in 1993. Comparisons between weekday and weekend data show few fleet differences, although the fraction of light-duty diesel vehicles decreased from the weekday (1.7%) to Saturday (1.2%) and Sunday (0.6%).

Implications: On-road remote sensing emission measurements of light-duty vehicles on Sherman Way in Van Nuys, California, show large historical emission reductions for CO and HC emissions despite an older fleet arising from the 2008 economic downturn. Fleet CO and HC emissions are increasingly dominated by a few gross emitters, with a single 1995 vehicle measured being responsible for 4% of the entire fleet's HC emissions. Finding and repairing and/or scrapping as little as 2% of the fleet would reduce on-road tailpipe emissions by as much as 50%. Ammonia emissions have locally increased with the increasing fleet age.     

Remote Sensing Data and a Potential Model of Vehicle Exhaust Emissions

In June 1991, General Motors Research and Development Center (GMR&D) participated in a remote sensing study conducted by the California Air Resources Board and the U. S. Environmental Protection Agency. During this study, the GMR&D remote sensor was used to measure the carbon monoxide (CO) and hydrocarbon (HC) emissions from approximately 15,000 vehicles. The vehicle type (passenger car, light-duty truck, or medium/heavy-duty truck), manufacturer, and model year were identified for each vehicle by acquiring registration data from the state of California. Analyses were performed separately for each vehicle type and for passenger cars by separate model years. The data indicate that the passenger cars with the highest 10% of CO emissions generated approximately 58% of the total CO from all cars. Similarly, the 10% highest HC-emitting cars generated 65% of the total HC from cars. It was found that for each model year of vehicle, the distribution of emission concentrations followed a logarithmic relationship. The logarithmic functions that describe these relationships can be used to estimate the fraction of vehicles that emitted at or above any given concentration of CO or HC. However, these logarithmic functions only describe measured distributions for vehicles emitting more than 1% CO and 0.015% HC.     

The effects of deterioration and technological levels on pollutant emission factors for gasoline light-duty trucks

Vehicle deterioration and technological change influence emission factors (EFs). In this study, the impacts of vehicle deterioration and emission standards on EFs of regulated pollutants (carbon monoxide [CO], hydrocarbon [HC], and nitrogen oxides [NO_x]) for gasoline light-duty trucks (LDTs) were investigated according to the inspection and maintenance (I/M) data using a chassis dynamometer method. Pollutant EFs for LDTs markedly varied with accumulated mileages and emission standards, and the trends of EFs are associated with accumulated mileages. In addition, the study also found that in most cases, the median EFs of CO, HC, and NO_x are higher than those of basic EFs in the International Vehicle Emissions (IVE) model; therefore, the present study provides correction factors for the IVE model relative to the corresponding emission standards and mileages.

Implications: Currently, vehicle emissions are great contributors to air pollution in cities, especially in developing countries. Emission factors play a key role in creating emission inventory and estimating emissions. Deterioration represented by vehicle age and accumulated mileage and changes of emission standards markedly influence emission factors. In addition, the results provide collection factors for implication in the IVE model in the region levels.     

A Fuel-Based Motor Vehicle Emission Inventory

Abstract

A fuel-based methodology for calculating motor vehicle emission inventories is presented. In the fuel-based method, emission factors are normalized to fuel consumption and expressed as grams of pollutant emitted per gallon of gasoline burned. Fleet-average emission factors are calculated from the measured on-road emissions of a large, random sample of vehicles. Gasoline use is known at the state level from sales tax data, and may be disaggregated to individual air basins. A fuel-based motor vehicle CO inventory was calculated for the South Coast Air Basin in California for summer 1991. Emission factors were calculated from remote sensing measurements of more than 70,000 in-use vehicles. Stabilized exhaust emissions of CO were estimated to be 4400 tons/day for cars and 1500 tons/day for light-duty and medium- duty trucks, with an estimated uncertainty of ±20% for cars and ±30% for trucks. Total motor vehicle CO emissions, including incremental start emissions and emissions from heavy-duty vehicles were estimated to be 7900 tons/day. Fuelbased inventory estimates were greater than those of California's MVEI 7F model by factors of 2.2 for cars and 2.6 for trucks. A draft version of California's MVEI 7G model, which includes increased contributions from high-emitting vehicles and off-cycle emissions, predicted CO emissions which closely matched the fuel-based inventory. An analysis of CO mass emissions as a function of vehicle age revealed that cars and trucks which were ten or more years old were responsible for 58% of stabilized exhaust CO emissions from all cars and trucks.     

On-road remote sensing measurements and fuel-based motor vehicle emission inventory in Hangzhou,China

Motor vehicles are one of the largest sources of air pollutants worldwide. Despite their importance, motor vehicle emissions are inadequately understood and quantified, esp. in developing countries. In this study, the real-world emissions of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxide (NO) were measured using an on-road remote sensing system at five sites in Hangzhou, China in 2004 and 2005. Average emission factors of CO, HC and NO_x for petrol vehicles of different model year, technology class and vehicle type were calculated in grams of pollutant per unit of fuel use (g l⁻¹) from approximately 32,260 petrol vehicles. Because the availability of data used in traditional on-road mobile source estimation methodologies is limited in China, fuel-based approach was implemented to estimate motor vehicle emissions using fuel sales as a measure of vehicle activity, and exhaust emissions factors from remote sensing measurements. The fuel-based exhaust emission inventories were also compared with the results from the recent international vehicle emission (IVE) model. Results show that petrol vehicle fleet in Hangzhou has significantly high CO emissions, relatively high HC and low NO_x, with the average emission factors of 193.07±15.63, 9.51±2.40 and 5.53±0.48 g l⁻¹, respectively. For year 2005 petrol vehicles exhaust emissions contributed with 182,013±16,936, 9107±2255 and 5050±480 metric ton yr⁻¹ of CO, HC and NO_x, respectively. The inventories are 45.5% higher, 6.6% higher and 53.7% lower for CO, HC and NO_x, respectively, than the estimates using IVE travel-based model. In addition, a number of insights about the emission distributions and formation mechanisms have been obtained from an in-depth analysis of these results.     

Assessment of vehicular pollution in China

As the motor vehicle population in China continues to increase at an annual rate of approximately 15%, air pollution related to vehicular emissions has become the focus of attention, especially in large cities. There is an urgent need to identify the severity of this pollution in China. Based on an investigation into vehicle service characteristics, this study used a series of driving cycle tests of in-use Chinese motor vehicles for their emission factors in laboratories, which indicated that CO and HC emission factors are 5-10 times higher, and NOx 2-5 times higher, than levels in developed countries. The MOBILE5 model was adapted to the Chinese situation and used to calculate the emission of pollutants from motor vehicles. Results show that vehicle emission is concentrated in major cities, such as Beijing, Guangzhou, Shanghai, and Tianjin. Motor vehicle emissions contribute a significant proportion of pollutants in those cities, with contribution rates of CO and NOx greater than 80% and 40%, respectively, in Beijing and Guangzhou. Urban air quality is far worse than the national ambient air quality standard. In conclusion, although China has a relatively small number of motor vehicles, most of them are concentrated within metropolitan areas, and their emissions are closely related to urban air pollution problems in large cities.     

On-road remote sensing of liquefied petroleum gas (LPG) vehicle emissions measurement and emission factors estimation

In the present study, the real-world on-road liquefied petroleum gas (LPG) vehicle/taxi emissions of carbon monoxide (CO), hydrocarbon (HC) and nitric oxide (NO) were investigated. A regression analysis approach based on the measured LPG vehicle emission data was also used to estimate the on-road LPG vehicle emission factors of CO, HC and NO with respect to the effects of instantaneous vehicle speed and acceleration/deceleration profiles for local urban driving patterns. The results show that the LPG vehicle model years and driving patterns have a strong correlation to their emission factors. A unique correlation of LPG vehicle emission factors (i.e., g km⁻¹ and g l⁻¹) on different model years for urban driving patterns has been established. Finally, a comparison was made between the average LPG, and petrol [Chan, T.L., Ning, Z., Leung, C.W., Cheung, C.S., Hung, W.T., Dong, G., 2004. On-road remote sensing of petrol vehicle emissions measurement and emission factors estimation in Hong Kong. Atmospheric Environment 38, 2055–2066 and 3541] and diesel [Chan, T.L., Ning, Z., 2005. On-road remote sensing of diesel vehicle emissions measurement and emission factors estimation in Hong Kong. Atmospheric Environment 39, 6843–6856] vehicle emission factors. It has shown that the introduction of the replacement of diesel taxis to LPG taxis has alleviated effectively the urban street air pollution. However, it has demonstrated that proper maintenance on the aged LPG taxis should also be taken into consideration.     

Effects of improved spatial and temporal modeling of on-road vehicle emissions

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NOx) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDV activity by time of day, day of week, and road type. More HDDV activity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5-15% higher NOx and PM emission rates during the weekdays and 15-40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NOx and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled.     

负载对实际道路重型柴油车排放的影响研究

利用车载测试系统对重型柴油货车空载、50％负载和100％负载不同负载情况下在实际道路的排放进行测试,基于测试数据分析负载对重型柴油货车排放CO、HC、NOx和微小颗粒物(PM)等4种污染物的影响.不同速度区间和行驶模式下负载对排放的影响分析表明,在有负载时,大多数工况下4种污染物排放呈现增加趋势,但各速度区间和行驶模式下的增幅不尽相同,部分工况出现下降.空载时测试柴油车基于新欧洲行驶循环测试(NEDC)工况的标准化CO、HC、NOx和PM排放因子分别为3.38、0.39、6.27、0.39 g/km.对于柴油车重点污染物NOx和PM而言,与空载相比,50％负载时分别增加43％和59％,100％负载时分别增加62％和44％.     

Characterization of in-use light-duty gasoline vehicle emissions by remote sensing in Beijing: impact of recent control measures

China's national government and Beijing city authorities have adopted additional control measures to reduce the negative impact of vehicle emissions on Beijing's air quality. An evaluation of the effectiveness of these measures may provide guidance for future vehicle emission control strategy development. In-use emissions from light-duty gasoline vehicles (LDGVs) were investigated at five sites in Beijing with remote sensing instrumentation. Distance-based mass emission factors were derived with fuel consumption modeled on real world data. The results show that the recently implemented aggressive control strategies are significantly reducing the emissions of on-road vehicles. Older vehicles are contributing substantially to the total fleet emissions. An earlier program to retrofit pre-Euro cars with three-way catalysts produced little emission reduction. The impact of model year and driving conditions on the average mass emission factors indicates that the durability of vehicles emission controls may be inadequate in Beijing.