Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 2--A case study in Beijing

A grid-based, bottom-up method has been proposed by combining a vehicle emission model and a travel demand model to develop a high-resolution vehicular emission inventory for Chinese cities. Beijing is used as a case study in which the focus is on fuel consumption and emissions from hot-stabilized activities of light-duty gasoline vehicles (LGVs) in 2005. The total quantity of emissions, emission intensity, and spatial distribution of emissions at 1- by 1-km resolution are presented and compared with results from other inventory methods commonly used in China. The results show that the total daily fuel consumption and vehicular emissions of carbon dioxide, carbon monoxide, hydrocarbons, and oxides of nitrogen from LGVs in the Beijing urban area in 2005 were 1.95 x 10(7) L, 4.28 x 10(4) t, 1.97 x 10(3) t, 0.28 x 10(3) t, and 0.14 x 10(3) t, respectively. Vehicular fuel consumption and emissions show spatial variations that are consistent with the traffic characteristics. The grid-based inventory developed in this study reflects the influence of traffic conditions on vehicle emissions at the microscale and may be applied to evaluate the effectiveness of traffic-related measures on emission control in China.     

Determining gaseous emission factors and driver's particle exposures during traffic congestion by vehicle-following measurement techniques

Vehicle gaseous emissions (NO, CO, CO2, and hydrocarbon [HC]) and driver's particle exposures (particulate matter < 1 microm [PM1], < 2.5 microm [PM2.5], and < 10 microm [PM10]) were measured using a mobile laboratory to follow a wide variety of vehicles during very heavy traffic congestion in Macao, Special Administrative Region, People's Republic of China, an urban area having one of the highest population densities in the world. The measurements were taken with high time resolution so that fluctuations in the emissions can be seen readily during vehicle acceleration, cruising, deceleration, and idling. The tests were conducted in close proximity to the vehicles, with the inlet of a five-gas analyzer mounted on the front bumper of the mobile laboratory, and the distance between the vehicles was usually within several meters. To measure the driver's particle exposures, the inlets of the particle analyzers were mounted at the height of the driver's breathing position in the mobile laboratory, with the driver's window open. A total of 178 and 113 vehicles were followed individually to determine the gaseous emission factor and the driver's particle exposures, respectively, for motorcycle, passenger car, taxi, truck, and bus. The gaseous emission factors were used to model the roadside air quality, and good correlations between the modeled and monitored CO, NO2, and nitrogen oxide (NO(x)) verified the reliability of the experiments. Compared with petrol passenger cars and petrol trucks, diesel taxies and diesel trucks emitted less CO but more NO(x). The impact of urban canyons is shown to cause a significant increase in the PM1 peak. The background concentrations contributed a significant amount of the driver's particle exposures.     

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.     

Emission factors for high-emitting vehicles based on on-road measurements of individual vehicle exhaust with a mobile measurement platform

Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO(x)), and particle mass with aerodynamic diameter below 2.5 microm (PM2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low- and high-speed acceleration, low- and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO(x), black carbon (BC), PM2.5, and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO(x) and CO fleet-average emission factors and more than 60% of PM2.5, UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM2.5, and NO(x) tended to cluster among the same vehicles.     

Developing a High-Resolution Vehicular Emission Inventory by Integrating an Emission Model and a Traffic Model: Part 2—A Case Study in Beijing

Abstract

A grid-based, bottom-up method has been proposed by combining a vehicle emission model and a travel demand model to develop a high-resolution vehicular emission inventory for Chinese cities. Beijing is used as a case study in which the focus is on fuel consumption and emissions from hot-stabilized activities of light-duty gasoline vehicles (LGVs) in 2005. The total quantity of emissions, emission intensity, and spatial distribution of emissions at 1- by 1-km resolution are presented and compared with results from other inventory methods commonly used in China. The results show that the total daily fuel consumption and vehicular emissions of carbon dioxide, carbon monoxide, hydrocarbons, and oxides of nitrogen from LGVs in the Beijing urban area in 2005 were 1.95 × 10⁷ L, 4.28 × 10⁴ t, 1.97 × 10³ t, 0.28 × 10³ t, and 0.14 × 10³ t, respectively. Vehicular fuel consumption and emissions show spatial variations that are consistent with the traffic characteristics. The grid-based inventory developed in this study reflects the influence of traffic conditions on vehicle emissions at the microscale and may be applied to evaluate the effectiveness of traffic-related measures on emission control in China.     

Idle emissions from heavy-duty diesel and natural gas vehicles at high altitude

Idle emissions of total hydrocarbon (THC), CO, NOx, and particulate matter (PM) were measured from 24 heavy-duty diesel-fueled (12 trucks and 12 buses) and 4 heavy-duty compressed natural gas (CNG)-fueled vehicles. The volatile organic fraction (VOF) of PM and aldehyde emissions were also measured for many of the diesel vehicles. Experiments were conducted at 1609 m above sea level using a full exhaust flow dilution tunnel method identical to that used for heavy-duty engine Federal Test Procedure (FTP) testing. Diesel trucks averaged 0.170 g/min THC, 1.183 g/min CO, 1.416 g/min NOx, and 0.030 g/min PM. Diesel buses averaged 0.137 g/min THC, 1.326 g/min CO, 2.015 g/min NOx, and 0.048 g/min PM. Results are compared to idle emission factors from the MOBILE5 and PART5 inventory models. The models significantly (45-75%) overestimate emissions of THC and CO in comparison with results measured from the fleet of vehicles examined in this study. Measured NOx emissions were significantly higher (30-100%) than model predictions. For the pre-1999 (pre-consent decree) truck engines examined in this study, idle NOx emissions increased with model year with a linear fit (r2 = 0.6). PART5 nationwide fleet average emissions are within 1 order of magnitude of emissions for the group of vehicles tested in this study. Aldehyde emissions for bus idling averaged 6 mg/min. The VOF averaged 19% of total PM for buses and 49% for trucks. CNG vehicle idle emissions averaged 1.435 g/min for THC, 1.119 g/min for CO, 0.267 g/min for NOx, and 0.003 g/min for PM. The g/min PM emissions are only a small fraction of g/min PM emissions during vehicle driving. However, idle emissions of NOx, CO, and THC are significant in comparison with driving emissions.     

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.     

我国氮氧化物排放因子的修正和排放量计算:2000年

根据我国城市的发展状况 ,采用城市分类的方法 ,将我国 2 6 1个地级市按照人口数量分为 5个类别。每类城市选取一个典型城市进行实地调查 ,对我国燃烧锅炉和机动车的NOx 的排放因子进行了修正 ,提出了适合我国目前排放水平的各类城市的固定源和移动源的排放因子。并依据 2 0 0 0年中国大陆地区的电站锅炉、工业锅炉和民用炉具的燃料消耗量和机动车保有量 ,以地级市为基本单位 ,估算了 2 0 0 0年我国各地区的NOx 排放量 ,分析了分地区、分行业、分燃料类型的NOx 排放特征。 2 0 0 0年我国NOx 排放总量为 11.12Mt,其中固定源占 6 0 .8% ;移动源占 39.2 %。NOx 排放在地域、行业和燃料类型上分布均不平衡。NOx 的排放主要集中在华东和华北地区 ,其排放量占全国排放量的一半以上。燃煤为最重要的NOx 排放源 ,其排放量占燃料型NOx 排放量的 72 .3%左右。     

Improvements to lawn and garden equipment emissions estimates for Baltimore, Maryland

Lawn and garden equipment are a significant source of emissions of volatile organic compounds (VOCs) and other pollutants in suburban and urban areas. Emission estimates for this source category are typically prepared using default equipment populations and activity data contained in emissions models such as the U.S. Environmental Protection Agency's (EPA) NONROAD model or the California Air Resources Board's (CARB) OFFROAD model. Although such default data may represent national or state averages, these data are unlikely to reflect regional or local differences in equipment usage patterns because of variations in climate, lot sizes, and other variables. To assess potential errors in lawn and garden equipment emission estimates produced by the NONROAD model and to demonstrate methods that can be used by local planning agencies to improve those emission estimates, this study used bottom-up data collection techniques in the Baltimore metropolitan area to develop local equipment population, activity, and temporal data for lawn and garden equipment in the area. Results of this study show that emission estimates of VOCs, particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen oxides (NO(x)) for the Baltimore area that are based on local data collected through surveys of residential and commercial lawn and garden equipment users are 24-56% lower than estimates produced using NONROAD default data, largely because of a difference in equipment populations for high-usage commercial applications. Survey-derived emission estimates of PM and VOCs are 24 and 26% lower than NONROAD default estimates, respectively, whereas survey-derived emission estimates for CO, CO2, and NO(x) are more than 40% lower than NONROAD default estimates. In addition, study results show that the temporal allocation factors applied to residential lawn and garden equipment in the NONROAD model underestimated weekend activity levels by 30% compared with survey-derived temporal profiles.     

Comparison of air pollutant emissions among mega-cities

Ambient measurements of hydrocarbons, carbon monoxide and nitrogen oxides from three mega-cities (Beijing, Mexico City, Tokyo) are compared with similar measurements from US cities in the mid-1980s and the early 2000s. The common hydrocarbon pattern seen in all data sets suggests that emissions associated with gasoline-fueled vehicles dominate in all of these cities. This commonality suggests that it will be efficient and, ultimately, cost effective to proceed with vehicular emission controls in most emerging mega-cities, while proceeding with development of more locally appropriate air quality control strategies through emissions inventory development and ambient air monitoring. Over the three decades covered by the US data sets, the hydrocarbon emissions decreased by a significant factor (something like an order of magnitude), which is greater than suggested by emission inventories, particularly the EDGAR international inventory. The ambient hydrocarbon and CO concentrations reported for the three non-US mega-cities are higher than present US ambient concentrations, but lower than those observed in the 1980s in the US. The one exception to the preceding statement is the high concentrations of CO observed in Beijing, which apparently have a large regional contribution.     

Size and composition distributions of particulate matter emissions: part 2--heavy-duty diesel vehicles

Particulate matter (PM) emissions from heavy-duty diesel vehicles (HDDVs) were collected using a chassis dynamometer/dilution sampling system that employed filter-based samplers, cascade impactors, and scanning mobility particle size (SMPS) measurements. Four diesel vehicles with different engine and emission control technologies were tested using the California Air Resources Board Heavy Heavy-Duty Diesel Truck (HHDDT) 5 mode driving cycle. Vehicles were tested using a simulated inertial weight of either 56,000 or 66,000 lb. Exhaust particles were then analyzed for total carbon, elemental carbon (EC), organic matter (OM), and water-soluble ions. HDDV fine (< or =1.8 microm aerodynamic diameter; PM1.8) and ultrafine (0.056-0.1 microm aerodynamic diameter; PM0.1) PM emission rates ranged from 181-581 mg/km and 25-72 mg/km, respectively, with the highest emission rates in both size fractions associated with the oldest vehicle tested. Older diesel vehicles produced fine and ultrafine exhaust particles with higher EC/OM ratios than newer vehicles. Transient modes produced very high EC/OM ratios whereas idle and creep modes produced very low EC/OM ratios. Calcium was the most abundant water-soluble ion with smaller amounts of magnesium, sodium, ammonium ion, and sulfate also detected. Particle mass distributions emitted during the full 5-mode HDDV tests peaked between 100-180 nm and their shapes were not a function of vehicle age. In contrast, particle mass distributions emitted during the idle and creep driving modes from the newest diesel vehicle had a peak diameter of approximately 70 nm, whereas mass distributions emitted from older vehicles had a peak diameter larger than 100 nm for both the idle and creep modes. Increasing inertial loads reduced the OM emissions, causing the residual EC emissions to shift to smaller sizes. The same HDDV tested at 56,000 and 66,000 lb had higher PM0.1 EC emissions (+22%) and lower PM0.1 OM emissions (-38%) at the higher load condition.     

The impact of transportation control measures on emission reductions during the 2008 Olympic Games in Beijing,China

Traffic congestion and air pollution were two major challenges for the planners of the 2008 Olympic Games in Beijing. The Beijing municipal government implemented a package of temporary transportation control measures during the event. In this paper, we report the results of a recent research project that investigated the effects of these measures on urban motor vehicle emissions in Beijing. Bottom–up methodology has been used to develop grid-based emission inventories with micro-scale vehicle activities and speed-dependent emission factors. The urban traffic emissions of volatile organic compounds (VOC), carbon monoxide (CO), nitrogen oxides (NO_x) and particulate matter with an aerodynamic diameter of 10 μm or less (PM₁₀) during the 2008 Olympics were reduced by 55.5%, 56.8%, 45.7% and 51.6%, respectively, as compared to the grid-based emission inventory before the Olympics. Emission intensity was derived from curbside air quality monitoring at the North 4th Ring Road site, located about 7 km from the National Stadium. Comparison between the emission intensity before and during the 2008 Olympics shows a reduction of 44.5% and 49.0% in daily CO and NO_x emission from motor vehicles. The results suggest that reasonable traffic system improvement strategies along with vehicle technology improvements can contribute to controlling total motor vehicle emissions in Beijing after the Olympic Games.     

Idle emissions from heavy-duty diesel vehicles: review and recent data

Heavy-duty diesel vehicle idling consumes fuel and reduces atmospheric quality, but its restriction cannot simply be proscribed, because cab heat or air-conditioning provides essential driver comfort. A comprehensive tailpipe emissions database to describe idling impacts is not yet available. This paper presents a substantial data set that incorporates results from the West Virginia University transient engine test cell, the E-55/59 Study and the Gasoline/Diesel PM Split Study. It covered 75 heavy-duty diesel engines and trucks, which were divided into two groups: vehicles with mechanical fuel injection (MFI) and vehicles with electronic fuel injection (EFI). Idle emissions of CO, hydrocarbon (HC), oxides of nitrogen (NOx), particulate matter (PM), and carbon dioxide (CO2) have been reported. Idle CO2 emissions allowed the projection of fuel consumption during idling. Test-to-test variations were observed for repeat idle tests on the same vehicle because of measurement variation, accessory loads, and ambient conditions. Vehicles fitted with EFI, on average, emitted approximately 20 g/hr of CO, 6 g/hr of HC, 86 g/hr of NOx, 1 g/hr of PM, and 4636 g/hr of CO2 during idle. MFI equipped vehicles emitted approximately 35 g/hr of CO, 23 g/hr of HC, 48 g/hr of NOx, 4 g/hr of PM, and 4484 g/hr of CO2, on average, during idle. Vehicles with EFI emitted less idle CO, HC, and PM, which could be attributed to the efficient combustion and superior fuel atomization in EFI systems. Idle NOx, however, increased with EFI, which corresponds with the advancing of timing to improve idle combustion. Fuel injection management did not have any effect on CO2 and, hence, fuel consumption. Use of air conditioning without increasing engine speed increased idle CO2, NOx, PM, HC, and fuel consumption by 25% on average. When the engine speed was elevated from 600 to 1100 revolutions per minute, CO2 and NOx emissions and fuel consumption increased by >150%, whereas PM and HC emissions increased by approximately 100% and 70%, respectively. Six Detroit Diesel Corp. (DDC) Series 60 engines in engine test cell were found to emit less CO, NOx, and PM emissions and consumed fuel at only 75% of the level found in the chassis dynamometer data. This is because fan and compressor loads were absent in the engine test cell.     

Total versus urban: Well-to-wheels assessment of criteria pollutant emissions from various vehicle/fuel systems

The potential impact on the environment of alternative vehicle/fuel systems needs to be evaluated, especially with respect to human health effects resulting from air pollution. We used the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model to examine the well-to-wheels (WTW) emissions of five criteria pollutants (VOCs, NO_x, PM₁₀, PM_2.5, and CO) for nine vehicle/fuel systems: (1) conventional gasoline vehicles; (2) conventional diesel vehicles; (3) ethanol (E85) flexible-fuel vehicles (FFVs) fueled with corn-based ethanol; (4) E85 FFVs fueled with switchgrass-based ethanol; (5) gasoline hybrid vehicles (HEVs); (6) diesel HEVs; (7) electric vehicles (EVs) charged using the average U.S. generation mix; (8) EVs charged using the California generation mix; and (9) hydrogen fuel cell vehicles (FCVs). Pollutant emissions were separated into total and urban emissions to differentiate the locations of emissions, and emissions were presented by sources. The results show that WTW emissions of the vehicle/fuel systems differ significantly, in terms of not only the amounts but also with respect to locations and sources, both of which are important in evaluating alternative vehicle/fuel systems. E85 FFVs increase total emissions but reduce urban emissions by up to 30% because the majority of emissions are released from farming equipment, fertilizer manufacture, and ethanol plants, all of which are located in rural areas. HEVs reduce both total and urban emissions because of the improved fuel economy and lower emissions. While EVs significantly reduce total emissions of VOCs and CO by more than 90%, they increase total emissions of PM₁₀ and PM_2.5 by 35–325%. However, EVs can reduce urban PM emissions by more than 40%. FCVs reduce VOCs, CO, and NO_x emissions, but they increase both total and urban PM emissions because of the high process emissions that occur during hydrogen production. This study emphasizes the importance of specifying a thorough life-cycle emissions inventory that can account for both the locations and sources of the emissions to assist in achieving a fair comparison of alternative vehicle/fuel options in terms of their environmental impacts.     

Trends in vehicular emissions in China's mega cities from 1995 to 2005

Multiyear inventories of vehicular emissions in Beijing, Shanghai and Guangzhou from 1995 through 2005 have been developed in this paper to study the vehicle emissions trends in China's mega cities during the past decade. The results show that the vehicular emissions of CO, HC, NO_x and PM₁₀ have begun to slow their growth rates and perhaps even to decline in recent years due to the implementation of measures to control vehicular emissions in these cities. However, vehicular CO₂ emissions have substantially increased and still continue to grow due to little fuel economy improvement. Passenger cars and large vehicles (including heavy duty trucks and buses) are the major sources of vehicular CO₂ and CO emissions while large vehicles were responsible for nearly 70% and 80% of the vehicular NO_x and PM₁₀ emissions in these mega cities. Motorcycles are also important contributors to vehicular emissions in Guangzhou and Shanghai.     

Size and composition distributions of particulate matter emissions: part 1--light-duty gasoline vehicles

Size-resolved particulate matter (PM) emitted from light-duty gasoline vehicles (LDGVs) was characterized using filter-based samplers, cascade impactors, and scanning mobility particle size measurements in the summer 2002. Thirty LDGVs, with different engine and emissions control technologies (model years 1965-2003; odometer readings 1264-207,104 mi), were tested on a chassis dynamometer using the federal test procedure (FTP), the unified cycle (UC), and the correction cycle (CC). LDGV PM emissions were strongly correlated with vehicle age and emissions control technology. The oldest models had average ultrafine PM0.1 (0.056- to 0.1-microm aerodynamic diameter) and fine PM1.8 (< or =1.8-microm aerodynamic diameter) emission rates of 9.6 mg/km and 213 mg/km, respectively. The newest vehicles had PM0.1 and PM1.8 emissions of 51 microg/km and 371 microg/km, respectively. Light duty trucks and sport utility vehicles had PM0.1 and PM1.8 emissions nearly double the corresponding emission rates from passenger cars. Higher PM emissions were associated with cold starts and hard accelerations. The FTP driving cycle produced the lowest emissions, followed by the UC and the CC. PM mass distributions peaked between 0.1- and 0.18-microm particle diameter for all vehicles except those emitting visible smoke, which peaked between 0.18 and 0.32 microm. The majority of the PM was composed of carbonaceous material, with only trace amounts of water-soluble ions. Elemental carbon (EC) and organic matter (OM) had similar size distributions, but the EC/OM ratio in LDGV exhaust particles was a strong function of the adopted emissions control technology and of vehicle maintenance. Exhaust from LDGV classes with lower PM emissions generally had higher EC/OM ratios. LDGVs adopting newer technologies were characterized by the highest EC/OM ratios, whereas OM dominated PM emissions from older vehicles. Driving cycles with cold starts and hard accelerations produced higher EC/OM ratios in ultrafine particles.     

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.     

Modeling vehicle emissions in different types of Chinese cities: importance of vehicle fleet and local features

We propose a method to simulate vehicle emissions in Chinese cities of different sizes and development stages. Twenty two cities are examined in this study. The target year is 2007. Among the cities, the vehicle emission factors were remarkably different (the highest is 50-90% higher than the lowest) owing to their distinct local features and vehicle technology levels, and the major contributors to total vehicle emissions were also different. A substantial increase in vehicle emissions is foreseeable unless stronger measures are implemented because the benefit of current policies can be quickly offset by the vehicle growth. Major efforts should be focused on all cities, especially developing cities where the requirements are lenient. This work aims a better understanding of vehicle emissions in all types of Chinese cities. The proposed method could benefit national emission inventory studies in improving accuracy and help in designing national and local policies for vehicle emission control.     

Estimates of black carbon and size-resolved particle number emission factors from residential wood burning based on ambient monitoring and model simulations

In this paper we derive typical emission factors for coarse particulate matter (PM(10)), oxides of nitrogen (NO(x)), black carbon (BC), and number particle size distributions based on a combination of measurements and air quality dispersion modeling. The advantage of this approach is that the emission factors represent integrated emissions from several vehicle types and different types of wood stoves. Normally it is very difficult to estimate the total emissions in cities on the basis of laboratory measurements on single vehicles or stoves because of the large variability in conditions. The measurements were made in Temuco, Chile, between April 18 and June 15, 2005 at two sites. The first one was located in a residential area relatively far from major roads. The second site was located in a busy street in downtown Temuco where wood consumption is low. The measurements support the assumption that the monitoring sites represent the impact of different emission sources, namely traffic and residential wood combustion (RWC). Fitting model results to the available measurements, emission factors were obtained for PM(10) (RWC = 2160 +/- 100 mg/kg; traffic = 610 +/- 51 mg/veh-km), NO(x) (RWC = 800 +/- 100 mg/kg; traffic = 4400 +/- 100 mg/veh-km), BC (RWC = 74 +/- 6 mg/kg; traffic = 60 +/- 3 mg/veh-km) and particle number (N) with size distribution between 25 and 600 nm (N(25-600)) (RWC = 8.9 +/- 1 x 10(14) pt/kg; traffic = 6.7 +/- 0.5 x 10(14) pt/veh-km). The obtained emission factors are comparable to results reported in the literature. The size distribution of the N emission factors for traffic was shown to be different than for RWC. The main difference is that although traffic emissions show a bimodal size distribution with a main mode below 30 nm and a secondary one around 100 nm, RWC emissions show the main mode slightly below 100 nm and a smaller nucleation mode below 50 nm.     

Particulate emissions from U.S. Department of Defense artillery backblast testing

There is a dearth of information on dust emissions from sources that are unique to the U.S. Department of Defense testing and training activities. However, accurate emissions factors are needed for these sources so that military installations can prepare accurate particulate matter (PM) emission inventories. One such source, coarse and fine PM (PM10 and PM2.5) emissions from artillery backblast testing on improved gun positions, was characterized at the Yuma Proving Ground near Yuma, AZ, in October 2005. Fugitive emissions are created by the shockwave from artillery pieces, which ejects dust from the surface on which the artillery is resting. Other contributions of PM can be attributed to the combustion of the propellants. For a 155-mm howitzer firing a range of propellant charges or zones, amounts of emitted PM10 ranged from -19 g of PM10 per firing event for a zone 1 charge to 92 g of PM10 per firing event for a zone 5. The corresponding rates for PM2.5 were approximately 9 g of PM2.5 and 49 g of PM2.5 per firing. The average measured emission rates for PM1o and PM2.5 appear to scale with the zone charge value. The measurements show that the estimated annual contributions of PM10 (52.2 t) and PM2.5 (28.5 t) from artillery backblast are insignificant in the context of the 2002 U.S. Environment Protection Agency (EPA) PM emission inventory. Using national-level activity data for artillery fire, the most conservative estimate is that backblast would contribute the equivalent of 5 x 10(-4) % and 1.6 x 10(-3)% of the annual total PM10 and PM2.5 fugitive dust contributions, respectively, based on 2002 EPA inventory data.