Effects of In-Use Driving Conditions and Vehicle/Engine Operating Parameters on “Off-Cycle” Events: Comparison with Federal Test Procedure Conditions

Evolution of Vehicle Emission Factors in a Megacity Affected by Extensive Biofuel Use: Results of Tunnel Measurements in São Paulo, Brazil

Since 2001, four emission measurement campaigns have been conducted in multiple traffic tunnels in the megacity of São Paulo, Brazil, an area with a fleet of more than 7 million vehicles running on fuels with high biofuel contents: gasoline + ethanol for light-duty vehicles (LDVs) and diesel + biodiesel for heavy-duty vehicles (HDVs). Emission factors for LDVs and HDVs were calculated using a carbon balance method, the pollutants considered including nitrogen oxides (NO_x), carbon monoxide (CO), and sulfur dioxide, as well as carbon dioxide and ethanol. From 2001 to 2018, fleet-average emission factors for LDVs and HDVs, respectively, were found to decrease by 4.9 and 5.1% per year for CO and by 5.5 and 4.2% per year for NO_x. These reductions demonstrate that regulations for vehicle emissions adopted in Brazil in the last 30 years improved air quality in the megacity of São Paulo significantly, albeit with a clear delay. These findings, especially those for CO, indicate that official emission inventories underestimate vehicle emissions. Here, we demonstrated that the adoption of emission factors calculated under real-world conditions can dramatically improve air quality modeling in the region.

Estimation of bus emission models for different fuel types of buses under real conditions

Urban buses are heavy vehicles that move frequently throughout the day, and most of them are propelled by heavy-duty diesel engines. For these reasons, they have energy and environmental impacts that should not be ignored. Consequently, the primary objectives of this study were to compare the changes in bus speed, acceleration, and emissions between bus stops, intersections, and road sections by applying statistical methods; and to develop a vehicle specific power (VSP)-based artificial neural network (ANN) model to estimate emissions of CO, HC, NO_X, and CO₂ for four different fuel types of buses including gas-electric hybrid electric buses (GEHE bus), compressed natural gas buses (CNG bus), EURO 4 heavy-duty diesel engine buses (EURO 4 bus), and EURO 5 heavy-duty diesel engine buses (EURO 5 bus). The results of t-tests (with p-values varying between <0.001 and 0.026, which were not >0.050) showed that the differences in emissions between different locations and between different fuel types of buses were all statistically significant. In addition, to evaluate the performance of the proposed method, a polynomial regression model using linear, quadratic, and cubic terms of transient speed and acceleration was utilized for comparison. According to the results, the proposed method had more accurate and reliable estimation, which increased the lower 10% of absolute percentage error (Lower-10% APE) by 65.2%; reduced mean absolute percentage error (MAPE) by 41.4%, root mean squared error (RMSE) by 44.9%, and mean absolute error (MAE) by 43.5%; and increased R-squared from 0.659 to 0.781.

Variability in operation-based NO(x) emission factors with different test routes, and its effects on the real-driving emissions of light diesel vehicles

The objective of this study is to quantify the differences in NO(x) emissions between standard and non-standard driving and vehicle operating conditions, and to estimate by how much NO(x) emissions exceed the legislative emission limits under typical Korean road traffic conditions. Twelve Euro 3-5 light-duty diesel vehicles (LDDVs) manufactured in Korea were driven on a chassis dynamometer over the standard New European Driving Cycle (NEDC) and a representative Korean on-road driving cycle (KDC). NO(x) emissions, average speeds and accelerations were calculated for each 1-km trip segment, so called averaging windows. The results suggest that the NO(x) emissions of the tested vehicles are more susceptible to variations in the driving cycles than to those in the operating conditions. Even under comparable operating conditions, the NO(x) control capabilities of vehicles differ from each other, i.e., NO(x) control is weaker for the KDC than for the NEDC. The NO(x) emissions over the KDC for given vehicle operating conditions exceed those over the NEDC by more than a factor of 8. Consequently, on-road NO(x) emission factors are estimated here to exceed the Euro 5 emission limit by up to a factor of 8, 4 and 3 for typical Korean urban, rural, and motorway road traffic conditions, respectively. Our findings support the development of technical regulations for supplementary real-world emission tests for emission certification and the corresponding research actions taken by automotive industries.

Fuel-based fine particulate and black carbon emission factors from a railyard area in Atlanta

Railyards have the potential to influence localfine particulate matter (aerodynamic diameter < or = 2.5 microm; PM2.5) concentrations through emissions from diesel locomotives and supporting activities. This is of concern in urban regions where railyards are in proximity to residential areas. Northwest of Atlanta, Georgia, Inman and Tilford railyards are located beside residential neighborhoods, industries, and schools. The PM2.5 concentrations near the railyards is the highest measured amongst the state-run monitoring sites (Georgia Environmental Protection Division, 2012; http://www.georgiaair.org/amp/report.php). The authors estimated fuel-based black carbon (BC) and PM2.5 emission factors for these railyards in order to help determine the impact of railyard activities on PM2.5 concentrations, and for assessing the potential benefits of replacing current locomotive engines with cleaner technologies. High-time-resolution measurements of BC, PM2.5, CO2, and wind speed and direction were made at two locations, north and south of the railyards. Emissions factors (i.e., the mass of BC or PM2.5 per gallon of fuel burned) were estimated by using the downwind/upwind difference in concentrations, wavelet analysis, and an event-based approach. By the authors' estimates, diesel-electric engines used in the railyards have average emission factors of 2.8 +/- 0.2 g of BC and 6.0 +/- 0.5 g of PM2.5 per gallon of diesel fuel burned. A broader mix of railyard supporting activities appear to lead to average emission factors of 0.7 +/- 0.03 g of BC and 1.5 +/- 0.1 g of PM2.5 per gallon of diesel fuel burned. Railyard emissions appear to lead to average enhancements of approximately 1.7 +/- 0.1 microg/m3 of PM2.5 and approximately 0.8 +/- 0.01 microg/m3 of BC in neighboring areas on an annual average basis. Uncertainty not quantified in these results could arise mainly from variability in downwind/upwind differences, differences in emissions of the diverse zones within the railyards, and the influence of on-road mobile source emissions.

Modeling transit bus fuel consumption on the basis of cycle properties

A method exists to predict heavy-duty vehicle fuel economy and emissions over an "unseen" cycle or during unseen on-road activity on the basis of fuel consumption and emissions data from measured chassis dynamometer test cycles and properties (statistical parameters) of those cycles. No regression is required for the method, which relies solely on the linear association of vehicle performance with cycle properties. This method has been advanced and examined using previously published heavy-duty truck data gathered using the West Virginia University heavy-duty chassis dynamometer with the trucks exercised over limited test cycles. In this study, data were available from a Washington Metropolitan Area Transit Authority emission testing program conducted in 2006. Chassis dynamometer data from two conventional diesel buses, two compressed natural gas buses, and one hybrid diesel bus were evaluated using an expanded driving cycle set of 16 or 17 different driving cycles. Cycle properties and vehicle fuel consumption measurements from three baseline cycles were selected to generate a linear model and then to predict unseen fuel consumption over the remaining 13 or 14 cycles. Average velocity, average positive acceleration, and number of stops per distance were found to be the desired cycle properties for use in the model. The methodology allowed for the prediction of fuel consumption with an average error of 8.5% from vehicles operating on a diverse set of chassis dynamometer cycles on the basis of relatively few experimental measurements. It was found that the data used for prediction should be acquired from a set that must include an idle cycle along with a relatively slow transient cycle and a relatively high speed cycle. The method was also applied to oxides of nitrogen prediction and was found to have less predictive capability than for fuel consumption with an average error of 20.4%.

Total diesel exhaust particulate length measurements using a modified household smoke alarm ionization chamber

To evaluate the effectiveness of various means to combat the negative health effects of ultrafine particles emitted by internal combustion engines, a reliable, low-cost instrument for dynamic measurements of the exhaust emissions of ultrafine particulate matter (PM) is needed. In this study, an ordinary ionization-type building smoke detector was modified to serve as a measuring ionization chamber and utilized for dynamic measurements of PM emissions from diesel engines. When used with diluted exhaust, the readings show an excellent correlation with total particulate length. The instrument worked well with raw and diluted exhaust and with varying emission levels and is well suitable for on-board use.

Fuel use and emissions comparisons for alternative routes, time of day, road grade, and vehicles based on in-use measurements

The objective here is to quantify the variability in emissions of selected light duty gasoline vehicles by routes, time of day, road grade, and vehicle with a focus on the impact of routes and road grade. Field experiments using a portable emission measurement system were conducted under real-world driving cycles. The study area included two origin/destination pairs, each with three alternative routes. Total emissions varied from trip to trip and from route to route due to variations in average speed and travel time. On an average trip basis, the total NO emissions differed by 24% when comparing alternative routes and by 19% when comparing congested travel time with less congested traffic time. Positive road grades were associated with an approximately 20% increase in localized emissions rates, while negative road grades were associated with a similar relative decrease. The average vehicle-specific power based NO modal emission rates differed by more than 2 orders of magnitude when comparing different vehicles. The results demonstrate that alternative routing can significantly impact trip emissions. Furthermore, road grade should be taken into account for localized emissions estimation. Vehicle-specific models are needed to capture episodic effects of emissions for near-road short-term human exposure assessment.

Motorcycle emissions and fuel consumption in urban and rural driving conditions

This work reports sampling of motorcycle on-road driving cycles in actual urban and rural environments and the development of representative driving cycles using the principle of least total variance in individual regions. Based on the representative driving cycles in individual regions, emission factors for carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO(x)=NO+NO(2)) and carbon dioxide (CO(2)), as well as fuel consumption, were determined using a chassis dynamometer. The measurement results show that the representative driving cycles are almost identical in the three largest cities in Taiwan, but they differ significantly from the rural driving cycle. Irrespective of driving conditions, emission factors differ insignificantly between the urban and rural regions at a 95% confidence level. However, the fuel consumption in urban centers is approximately 30% higher than in the rural regions, with driving conditions in the former usually poor compared to the latter. Two-stroke motorcycles generally have considerably higher HC emissions and quite lower NO(x) emissions than those of four-stroke motorcycles. Comparisons with other studies suggest that factors such as road characteristics, traffic volume, vehicle type, driving conditions and driver behavior may affect motorcycle emission levels in real traffic situations.