Computer Analysis of the California Cycle

The current trend toward greater instrumentation in analysis of automobile exhaust gases is sure to increase in future years. This will come about as a result of increased activity to find a solution to air pollution problems resulting from automobile exhaust gases. Owing to the complex driving cycle needed to evaluate vehicle emission, considerable time has been required to analyze and interpret the records from any run. To alleviate this analytic problem, an automatic data logger was obtained that records on magnetic tape the signal from a number of analytical instruments each second (in binary coded decimal form). These records are then analyzed on a computer to give integrated and averaged results over the specified driving cycle. There is no longer any need for manual evaluation of the data. In addition to the standard analyses already mentioned, the use of the data logger permits a much more complete analysis of what is happening in a vehicle. For example, the analytical instruments used include an oxygen analyzer in addition to carbon dioxide, carbon monoxide, and hydrocarbons. With these measurements the air-fuel ratio of any or all driving conditions can be calculated.     

Evolutionary algorithm for vehicle driving cycle generation

Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of "microtrips", which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of "microtrips" isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate "microtrips" is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.     

At the Crossroads

ABSTRACT

Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of “microtrips”, which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of “microtrips” isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate “microtrips” is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.

IMPLICATIONS It is expected that the paper will provide a useful tool for modeling and analysis of vehicle fuel economy and emissions and for the design, optimization, and analysis of driving cycles for testing and vehicle fleet management.     

Emissions from an automobile fire

The emissions from automobile fires have been investigated. The main gas phase components were analysed in small-scale tests with representative material from an automobile. A more detailed investigation of full-scale simulated automobile fires was also conducted, including the characterisation of gas phase components, particulates and run-off water from extinguishing activities. Three separate full scale fire tests have been characterised: a fire ignited and developed in the engine compartment; a fire ignited inside the coupé, that was extinguished in the early stages; and a similar fire ignited inside the coupé that was allowed to spread until the entire vehicle was involved in the fire. The quantitative analysis of the smoke gases from the full-scale fires showed that emissions with a potentially negative impact on the environment, or chronic toxic effect on humans, were produced in significant quantities. These emissions included HCl, SO2, VOCs (e.g. benzene), PAHs, and PCDDs/PCDFs. Analysis of run-off water indicated that it was severely contaminated, containing elevated levels of both organic compounds and metals. Comparison with data from other vehicle fires found in the literature shows that contamination by lead, copper, zinc, and antimony appears to be significant in water run-off from these types of fires.     

On-road motor vehicle emissions and fuel consumption in urban driving conditions

This paper reports on the analysis of on-road vehicle speed, emission, and fuel consumption data collected by four instrumented vehicles. Time-, distance-, and fuel-based average fuel consumption, as well as CO, HC, NOx, and soot emission factors, were derived. The influences of instantaneous vehicle speed on emissions and fuel consumption were studied. It was found that the fuel-based emission factors varied much less than the time- and distance-based emission factors as instantaneous speed changed. The trends are similar to the results obtained from laboratory tests. The low driving speed contributed to a significant portion of the total emissions over a trip. Furthermore, the on-road data were analyzed using the modal approach. The four standard driving modes are acceleration, cruising, deceleration, and idling. It was found that the transient driving modes (i.e., acceleration and deceleration) were more polluting than the steady-speed driving modes (i.e., cruising and idling) in terms of g/km and g/sec. These results indicated that the on-road emission measurement is feasible in deriving vehicle emissions and fuel consumption factors in urban driving conditions.     

A Characterization of Emissions from an Early Model Flexible-Fuel Vehicle

An emission study was conducted on a 1987 Ford Crown Victoria flexible-fuel vehicle, an early prototype which had been driven about 25,000 miles. The vehicle was run on both gasoline and a blend of 85 percent methanol and 15 percent gasoline. Emission rates of regulated pollutants (hydrocarbons, carbon monoxide, nitrogen oxides, formaldehyde, and methanol) and nonregulated pollutants (speciated organic materials) were determined for both exhaust and evaporative emissions. Tests were run varying the driving cycle, ambient temperature and catalytic converter. In general, hydrocarbon composition of exhaust emissions was significantly affected by catalyst replacement and cold starts, slightly affected by driving schedule, and unaffected by ambient temperature and test fuel. Hydrocarbon composition of evaporative emissions was only sensitive to the type of evaporative test being performed: diurnal tests typically had larger fractions of lower molecular weight paraffins than hot soak tests.     

On-Road Motor Vehicle Emissions and Fuel Consumption in Urban Driving Conditions

ABSTRACT

This paper reports on the analysis of on-road vehicle speed, emission, and fuel consumption data collected by four instrumented vehicles. Time-, distance-, and fuel-based average fuel consumption, as well as CO, HC, NO_x, and soot emission factors, were derived. The influences of instantaneous vehicle speed on emissions and fuel consumption were studied. It was found that the fuel-based emission factors varied much less than the time- and distance-based emission factors as instantaneous speed changed. The trends are similar to the results obtained from laboratory tests. The low driving speed contributed to a significant portion of the total emissions over a trip. Furthermore, the on-road data were analyzed using the modal approach. The four standard driving modes are acceleration, cruising, deceleration, and idling. It was found that the transient driving modes (i.e., acceleration and deceleration) were more polluting than the steady-speed driving modes (i.e., cruising and idling) in terms of g/km and g/ sec. These results indicated that the on-road emission measurement is feasible in deriving vehicle emissions and fuel consumption factors in urban driving conditions.     

Temperature effects on particulate emissions from DPF-equipped diesel trucks operating on conventional and biodiesel fuels

Emissions tests were conducted on two medium heavy-duty diesel trucks equipped with a particulate filter (DPF), with one vehicle using a NOx absorber and the other a selective catalytic reduction (SCR) system for control of nitrogen oxides (NOx). Both vehicles were tested with two different fuels (ultra-low-sulfur diesel [ULSD] and biodiesel [B20]) and ambient temperatures (70ºF and 20ºF), while the truck with the NOx absorber was also operated at two loads (a heavy weight and a light weight). The test procedure included three driving cycles, a cold start with low transients (CSLT), the federal heavy-duty urban dynamometer driving schedule (UDDS), and a warm start with low transients (WSLT). Particulate matter (PM) emissions were measured second-by-second using an Aethalometer for black carbon (BC) concentrations and an engine exhaust particle sizer (EEPS) for particle count measurements between 5.6 and 560 nm. The DPF/NOx absorber vehicle experienced increased BC and particle number concentrations during cold starts under cold ambient conditions, with concentrations two to three times higher than under warm starts at higher ambient temperatures. The average particle count for the UDDS showed an opposite trend, with an approximately 27% decrease when ambient temperatures decreased from 70ºF to 20ºF. This vehicle experienced decreased emissions when going from ULSD to B20. The DPF/SCR vehicle tested had much lower emissions, with many of the BC and particle number measurements below detectable limits. However, both vehicles did experience elevated emissions caused by DPF regeneration. All regeneration events occurred during the UDDS cycle. Slight increases in emissions were measured during the WSLT cycles after the regeneration. However, the day after a regeneration occurred, both vehicles showed significant increases in particle number and BC for the CSLT drive cycle, with increases from 93 to 1380% for PM number emissions compared with tests following a day with no regeneration.

Implications:?The use of diesel particulate filters (DPFs) on trucks is becoming more common throughout the world. Understanding how DPFs affect air pollution emissions under varying operating conditions will be critical in implementing effective air quality standards. This study evaluated particulate matter (PM) and black carbon (BC) emissions with two DPF-equipped heavy-duty diesel trucks operating on conventional fuel and a biodiesel fuel blend at varying ambient temperatures, loads, and drive cycles.     

Ammonia emissions from a representative in-use fleet of light and medium-duty vehicles in the California South Coast Air Basin

We used Fourier Transform Infrared Spectroscopy (FTIR) to measure tailpipe ammonia emissions from a representative fleet of 41 light and medium-duty vehicles recruited in the California South Coast Air Basin. A total of 121 chassis dynamometer emissions tests were conducted on these vehicles and the test results were examined to determine the effects of several key variables on ammonia emissions. Variables included vehicle type, driving cycle, emissions technology, ammonia precursor emissions (i.e. CO and NOx) and odometer readings/model year as a proxy for catalyst age. The mean ammonia emissions factor was 46 mg km^?1 (σ = 48 mg km^?1) for the vehicle fleet. Average emission factors for specific vehicle groups are also reported in this study. Results of this study suggest vehicles with the highest ammonia emission rates possess the following characteristics: medium-duty vehicles, older emissions technologies, mid-range odometer readings, and higher CO emissions. In addition, vehicles subjected to aggressive driving conditions are likely to be higher ammonia emitters. Since the vehicles we studied were representative of recent model year vehicles and technologies in urban airsheds, the results of our study will be useful for developing ammonia emissions inventories in Los Angeles and other urban areas where California-certified vehicles are driven. However, efforts should also be made to continue emissions testing on in-use vehicles to ensure greater confidence in the ammonia emission factors reported here.     

Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.     

Measurement of fine particles in diesel emissions using a real-time aerosol monitor

A real-time monitoring methodology to determine diesel fine particles in diesel emissions has been evaluated. The range of particle size captured by the monitor was approximately 0.1 microm to 1 microm. DustTrak real-time monitors were connected to the dilution tunnel of the vehicle exhaust to measure the emissions during the vehicle tests under both dynamic and steady-state driving conditions, and concentration data were recorded every 5 sec. Test variation of the real-time monitoring among different test days was similar to that measured by traditional filter-based gravimetric method, whereas the repeatability of the monitor data within the same-day tests was better than that of gravimetric method. Correlations between the two methods were established for different fuels tested on a single light duty vehicle. When the emissions from the reference fuel was used to convert the monitor's response to diesel fuels, the levels determined by the real-time monitor were consistent with those measured by gravimetric method among different fuels tested. Use of the real-time monitor could provide information on the levels of fine particles that is more relevant to the public health than the total particles.     

Participate and Gaseous Emissions from Natural Gas Furnaces and Water Heaters

Three furnaces and one hot water heater were tested for particulate and gaseous emissions. The effects of fuel, stoichiometry, operating conditions, and appliance type on emission levels were studied. The filterable particulate levels from a properly operating furnace were very low. However, condensable particulate emissions were considerably greater, approximately the same as predicted by EPA estimates of furnace particulate emissions. Carbon emissions comprised about 12% of filterable particulate emissions. However, when operated highly fuel-rich, copious amounts of elemental carbon particles were emitted with a mass median diameter of less than 0.4 μm. Gaseous emissions were dependent on cycling of the furnace and stoichiometry.

An estimate was made of the daily furnace emissions compared to daily emissions from a 1980 catalyst-equipped automobile. While gaseous emissions were less than the corresponding vehicle emissions, the particle emissions from the furnace were three times greater than particle emissions from a 1980 vehicle.     

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

Wireless communication systems have been broadly applied in various complicated traffic operations to improve mobility and safety on roads, which may raise a concern about the implication of the new technology on vehicle emissions. This paper explores how the wireless communication systems improve drivers’ driving behaviors and its contributions to the emission reduction, in terms of Operating Mode (OpMode) IDs distribution used in emission estimation. A simulated work zone with completed traffic operation was selected as a test bed. Sixty subjects were recruited for the tests, whose demographic distribution was based on the Census data in Houston, Texas. A scene of a pedestrian’s crossing in the work zone was designed for the driving test. Meanwhile, a wireless communication system called Drivers Smart Advisory System (DSAS) was proposed and introduced in the driving simulation, which provided drivers with warning messages in the work zone. Two scenarios were designed for a leading vehicle as well as for a following vehicle driving through the work zone, which included a base test without any wireless communication systems, and a driving test with the trigger of the DSAS. Subjects’ driving behaviors in the simulation were recorded to evaluate safety and estimate the vehicle emission using the Environmental Protection Agency (EPA) released emission model MOVES. The correlation between drivers’ driving behavior and the distribution of the OpMode ID during each scenario was investigated. Results show that the DSAS was able to induce drivers to accelerate smoothly, keep longer headway distance and stop earlier for a hazardous situation in the work zone, which driving behaviors result in statistically significant reduction in vehicle emissions for almost all studied air pollutants (p-values range from 4.10E-51 to 2.18E-03). The emission reduction was achieved by the switching the distribution of the OpMode IDs from higher emission zones to lower emission zones.

Implications: Transportation section is a significant source of greenhouse gas emissions. Many studies demonstrate that the wireless communication system dedicated for safety and mobility issues may contribute to the induction in vehicle emissions through changing driving behaviors. An insight into the correlation between the driving behaviors and the distribution of Operating Mode (OpMode) IDs is essential to enhance the emission reduction. The result of this study shows that with a Drivers Smart Advisory System (DSAS) drivers accelerated smoothly and stopped earlier for a hazardous situation, which induce the switch of the OpMode IDs from high emission zones to lower emission zones.     

Further Validation of Artificial Neural Network-Based Emissions Simulation Models for Conventional and Hybrid Electric Vehicles

Abstract

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO₂] and oxides of nitrogen [NO_x]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO₂ and NO_x in the case of a class-8 truck but were more accurate as the truck weight increased.     

Measurement of Fine Particles in Diesel Emissions Using a Real-Time Aerosol Monitor

Abstract

A real-time monitoring methodology to determine diesel fine particles in diesel emissions has been evaluated. The range of particle size captured by the monitor was ～0.1 μm to 1 μm. DustTrak real-time monitors were connected to the dilution tunnel of the vehicle exhaust to measure the emissions during the vehicle tests under both dynamic and steady-state driving conditions, and concentration data were recorded every 5 sec. Test variation of the real-time monitoring among different test days was similar to that measured by traditional filter-based gravi-metric method, whereas the repeatability of the monitor data within the same-day tests was better than that of gravimetric method. Correlations between the two methods were established for different fuels tested on a single light duty vehicle. When the emissions from the reference fuel was used to convert the monitor’s response to diesel fuels, the levels determined by the real-time monitor were consistent with those measured by gravimetric method among different fuels tested. Use of the real-time monitor could provide information on the levels of fine particles that is more relevant to the public health than the total particles.     

Oxidative DNA damage estimated by urinary 8-hydroxydeoxyguanosine: influence of taxi driving,smoking and areca chewing

Nitrogen oxides (NOx) and polycyclic aromatic hydrocarbons are common air pollutants generated from automobile exhaust and cigarette smoke. This study was to investigate urinary 8-hydroxydeoxyguanosine (8-OHdG) as an effective biomarker on DNA damage from traffic exhaust and/or smoking in exposed and non-exposed individuals. With subject consents, the levels of plasma NOx, urinary 1-hydroxypyrene (1-OHP) and 8-OHdG were determined for 95 male taxi drivers and 75 male community residents as the reference group. After adjusting for associate variables, there was a significant correlation between the levels of urinary 8-OHdG and 1-OHP but not NOx. The average level of urinary 8-OHdG was significantly higher in drivers than in community men (13.4+/-4.7 vs. 11.5+/-4.7 microg/g creatinine in mean+/-standard deviation). Compared with non-smoking community men, the multivariate logistic regression showed that the odds ratios (OR) of having elevated levels of urinary 8-OHdG (greater than the overall median value, 12.1 microg/g creatinine) were 6.6 (95% confidence interval (CI)=2.1-20.8) for smoking community men, 5.0 (95% CI=1.7-14.7) for non-smoking taxi drivers and 4.6 (95% CI=1.4-15.0) for smoking taxi drivers. Higher risk was also observed for areca quid chewers compared with non-chewers (OR=1.6; 95% CI=1.1-3.6). In conclusion, taxi driving and smoking may contribute independently to elevated DNA damage using urinary 8-OHdG levels as a sensitive biomarker. This effect is most potent on heavy smokers.     

Modeling vehicle interior noise exposure dose on freeways: Considering weaving segment designs and engine operation

Vehicle interior noise functions at the dominant frequencies of 500 Hz below and around 800 Hz, which fall into the bands that may impair hearing. Recent studies demonstrated that freeway commuters are chronically exposed to vehicle interior noise, bearing the risk of hearing impairment. The interior noise evaluation process is mostly conducted in a laboratory environment. The test results and the developed noise models may underestimate or ignore the noise effects from dynamic traffic and road conditions and configuration. However, the interior noise is highly associated with vehicle maneuvering. The vehicle maneuvering on a freeway weaving segment is more complex because of its nature of conflicting areas. This research is intended to explore the risk of the interior noise exposure on freeway weaving segments for freeway commuters and to improve the interior noise estimation by constructing a decision tree learning–based noise exposure dose (NED) model, considering weaving segment designs and engine operation. On-road driving tests were conducted on 12 subjects on State Highway 288 in Houston, Texas. On-board Diagnosis (OBD) II, a smartphone-based roughness app, and a digital sound meter were used to collect vehicle maneuvering and engine information, International Roughness Index, and interior noise levels, respectively. Eleven variables were obtainable from the driving tests, including the length and type of a weaving segment, serving as predictors. The importance of the predictors was estimated by their out-of-bag–permuted predictor delta errors. The hazardous exposure level of the interior noise on weaving segments was quantified to hazard quotient, NED, and daily noise exposure level, respectively. Results showed that the risk of hearing impairment on freeway is acceptable; the interior noise level is the most sensitive to the pavement roughness and is subject to freeway configuration and traffic conditions. The constructed NED model shows high predictive power (R = 0.93, normalized root-mean-square error [NRMSE] < 6.7%).

Implications: Vehicle interior noise is usually ignored in the public, and its modeling and evaluation are generally conducted in a laboratory environment, regardless of the interior noise effects from dynamic traffic, road conditions, and road configuration. This study quantified the interior exposure dose on freeway weaving segments, which provides freeway commuters with a sense of interior noise exposure risk. In addition, a bagged decision tree–based interior noise exposure dose model was constructed, considering vehicle maneuvering, vehicle engine operational information, pavement roughness, and weaving segment configuration. The constructed model could significantly improve the interior noise estimation for road engineers and vehicle manufactures.     

enforcement of diesel smoke emissions for the state of new jersey

This paper reports on the research program undertaken by the State of New Jersey to determine those tests and instruments which can be used by authorized state agencies for the enforcement of diesel smoke emissions. The state agencies under consideration for the enforcement of diesel smoke emissions are the following: (1) The Division of Motor Vehicles, in its system of state owned inspection stations, will be able to inspect all diesel-powered trucks, and tractors which are registered in New Jersey. (2) The Public Utilities Commission will be able to inspect at their home garages all buses registered in the State. (3) The New Jersey State Police will be able to inspect diesel-powered vehicles on the road. It was decided that the maximum inspection time for each vehicle was not to exceed one minute. On the basis of the one minute per vehicle requirement, eight different tests were evaluated to determine which ones correlated well with normal vehicle operation. These tests included acceleration of a fixed external inertia, free acceleration of only the moving parts of the engine, three ramp tests, a test in which a heavy vehicle was towed, and a driving test in which the vehicle being tested was actually accelerated. The results of tests demonstrated that the modified free acceleration method correlates reasonably well with a loaded steady state cycle, distinguishes the high emitters and is simple to perform. Consequently, the free acceleration test method is recommended for use in inspection stations and on the road. Finally, nearly two hundred vehicles have been tested by this procedure to determine present and potential levels of diesel smoke emissions. Another phase of the program consisted of the determination of smoke measuring techniques and instrumentation. The use and design of smokemeters were extensively investigated, as well as the use of the visual and photographic techniques. Of the various smokemeters tested for this application, several measured smoke satisfactorily in the laboratory, but none were found adequate for field use; they either lacked portability or were unstable due to the deposition of soot on the optics. At the time of writing, specifications for the necessary smokemeter have been drafted and published for bid to interested manufacturers.     

杭州市机动车尾气污染和控制对策

随关经济的快速发展,城市道路交通和机动车尾气污染成为人们关注的热点,通过对杭州市机动车行驶现状及路检情况调查,分析了机动车排污分担率及尾气排放的潜在影响,提出控制机动车尾气大大气污染的对策。     

Characterization of Emissions from Vehicles Using Methanol and Methanol-Gasoline Blended Fuels

Exhaust and evaporative emissions were examined from vehicles fueled with methanol or a gasoline-methanol blend. Regulated automobile pollutants, as well as detailed hydrocarbons, methanol, and aldehydes were measured, and exhaust emission trends were obtained for vehicle operation over five different driving cycles. Results indicated that use of the blended fuel does not generally have any significant effect on base-line exhaust emission rates of regulated pollutants; however, emission rates of aldehydes increased during the Federal Test Procedure. Aldehyde emissions from the methanol-fueled car were roughly an order of magnitude higher than those resulting from blended fuel usage. The hydrocarbon composition of evaporative emissions with the blended fuel was similar to that with the base-line fuel except when canister breakthrough occurred. Evaporative emissions during breakthrough were comprised chiefly of N-butane.