Globally, 1.3 billion on-road vehicles consume 79 quadrillion BTU of energy, mostly gasoline and diesel fuels, emit 5.7 gigatonnes of CO
, and emit other pollutants to which approximately 200,000 ...annual premature deaths are attributed. Improved vehicle energy efficiency and emission controls have helped offset growth in vehicle activity. New technologies are diffusing into the vehicle fleet in response to fuel efficiency and emission standards. Empirical assessment of vehicle emissions is challenging because of myriad fuels and technologies, intervehicle variability, multiple emission processes, variability in operating conditions, and varying capabilities of measurement methods. Fuel economy and emissions regulations have been effective in reducing total emissions of key pollutants. Real-world fuel use and emissions are consistent with official values in the United States but not in Europe or countries that adopt European standards. Portable emission measurements systems, which uncovered a recent emissions cheating scandal, have a key role in regulatory programs to ensure conformity between "real driving emissions" and emission standards. The global vehicle fleet will experience tremendous growth, especially in Asia. Although existing data and modeling tools are useful, they are often based on convenience samples, small sample sizes, large variability, and unquantified uncertainty. Vehicles emit precursors to several important secondary pollutants, including ozone and secondary organic aerosols, which requires a multipollutant emissions and air quality management strategy. Gasoline and diesel are likely to persist as key energy sources to mid-century. Adoption of electric vehicles is not a panacea with regard to greenhouse gas emissions unless coupled with policies to change the power generation mix. Depending on how they are actually implemented and used, autonomous vehicles could lead to very large reductions or increases in energy consumption. Numerous other trends are addressed with regard to technology, emissions controls, vehicle operations, emission measurements, impacts on exposure, and impacts on public health.
Without specific policies to the contrary, fossil fuels are likely to continue to be the major source of on-road vehicle energy consumption. Fuel economy and emission standards are generally effective in achieving reductions per unit of vehicle activity. However, the number of vehicles and miles traveled will increase. Total energy use and emissions depend on factors such as fuels, technologies, land use, demographics, economics, road design, vehicle operation, societal values, and others that affect demand for transportation, mode choice, energy use, and emissions. Thus, there are many opportunities to influence future trends in vehicle energy use and emissions.
•Real-world emissions compared with U.S. standards and certification levels.•Comparison not previously reported for U.S. light duty gasoline vehicles.•Portable Emission Measurement Systems (PEMS) ...used for real-world measurement.•Certification levels underestimate real-world emission rates.•Cold start inclusive real-world emission rates are as high as the standards.
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U.S. light duty vehicles are subject to the U.S. Environmental Protection Agency (EPA) emission standards. Emission compliance is determined by certification testing of selected emissions from representative vehicles on standard driving cycles using chassis dynamometers. Test results are also used in many emission inventories. The dynamometer based emission rates are adjusted to provide the certification levels (CL), which must be lower than the standards for compliance. Although standard driving cycles are based on specific observations of real-world driving, they are not necessarily real-world representative. A systematic comparison of the real-world emission rates of U.S. light duty gasoline vehicles (LDGVs) versus CL, and emission standards has not been previously reported. The purpose of this work is to compare regulatory limits (both CLs and emission standards) and the real-world emissions of LDGVs. The sensitivity of the comparisons to cold start emission was assessed.
Portable Emission Measurement Systems (PEMS) were used to measure hot stabilized exhaust emissions of 122 LDGVs on a specified 110 mile test route. Cold start emissions were measured with PEMS for a selected vehicle sample of 32 vehicles. Emissions were measured for carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx). For each vehicle, a Vehicle Specific Power (VSP) modal emission rate model was developed. The VSP modal rates were weighted by the standard driving cycles and real-world driving cycles to estimate the respective cycle average emission rates (CAERs). Measured vehicles were matched with certification test vehicles for comparison. For systematic trends in comparison, vehicles were classified into four groups based on the Tier 1 and Tier 2 emission regulation, and the vehicle type such as passenger car and passenger truck.
Depending on the cycle-pollutant and the vehicle groups, hot stabilized CAERs are on average either statistically significantly higher than or significantly not different from the CLs, with the exception of CO on the US06 cycle, for which real-world rates are lower than CLs. Compared to the emission standards, hot stabilized CAERs are on average significantly lower. However, comparisons of CAERs and standards are sensitive to cold start emissions. For some combinations of pollutants and vehicle groups, cold start inclusive CAERs are higher than the corresponding CLs and as high as the standards. The CLs, which are based on standard driving cycles, tend to underestimate real-world emission rates. Therefore, emission inventory estimates using certification test results are potentially underestimated.
The objectives of this work are to model spatially resolved passenger locomotive fuel use and emission rates, locate emissions hotspots, and identify strategies to reduce trip train fuel use and ...emissions. Train fuel use and emission rates, speed, acceleration, track grade, and track curvature were quantified based on over-the-rail measurements, using portable emission measurement systems, for diesel and biodiesel passenger rail service on the Amtrak-operated Piedmont route. Measurements included 66 one-way trips and 12 combinations of locomotives, consists, and fuels. A locomotive power demand (LPD) based emissions model was developed based on the physics of resistive forces opposing train motion, taking into account factors such as speed, acceleration, track grade, and curvature. The model was applied to locate spatially-resolved locomotive emissions hotspots on a passenger rail route, and also identify train speed trajectories with low trip fuel use and emissions. Results show that acceleration, grade, and drag are the major resistive forces affecting LPD. Hotspot track segments have 3 to 10 times higher emission rates than non-hotspot segments. Real-world trajectories are identified that reduce trip fuel use and emissions by 13 % to 49 % compared to the average. Strategies for reducing trip fuel use and emissions include dispatching energy-efficient and low-emitting locomotives, using a 20 % blend of biodiesel, and operating on low-LPD trajectories. Implementing these strategies will not only decrease trip fuel use and emissions but reduce the number and intensity of hotspots and, thus, lowering the potential for exposure to train-generated pollution near railroad tracks. This work provides insights on reducing railroad energy use and emissions, which would lead to a more sustainable and environmental-friendly rail transportation system.
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•A model was developed to estimate locomotive emission rates on 400-m track segments.•Locomotive power demand (LPD) is mostly affected by acceleration, grade, and drag.•Trajectories with low trip average LPD have low trip fuel use and emissions.•Hotspot segments have 93 % higher locomotive power demand than non-hotspot segments.•Hotspot segments have 3–10 times higher emission rates than non-hotspot segments.
The World Health Organization estimates 3.7 million deaths in 2012 in low- and middle-income Asian countries due to outdoor air pollution. However, these estimates do not account for the higher ...exposures of specific particulate matter (PM) components – including fine particles (PM2.5), ultrafine particles (UFP) and black carbon (BC) – typical of transport microenvironments (TMEs). With the rapidly growing number of on-road vehicles in Asia, human exposure to PM is an increasing concern. The aim of this review article is to comprehensively assess studies of PM2.5, UFP, and BC in Asian TMEs in order to better understand the extent of exposure, the underlying factors leading to exposure, and how Asian exposures compare to those found in Europe and the United States of America (USA). The health impacts of exposure to PM2.5, UFP, and BC are described and the key factors that influence personal exposure in TMEs (i.e., walk, cycle, car, and bus) are identified. Instrumentation and measurement methods, exposure modeling techniques, and regulation are reviewed for PM2.5, UFP, and BC. Relatively few studies have been carried out in urban Asian TMEs where PM2.5, UFP, and BC had generally higher concentrations compared to Europe and USA. Based on available data, PM2.5 concentrations while walking were 1.6 and 1.2 times higher in Asian cities (average 42 μg m−3) compared to cities in Europe (26 μg m−3) and the USA (35 μg m−3), respectively. Likewise, average PM2.5 concentrations in car (74 μg m−3) and bus (76 μg m−3) modes in Asian cities were approximately two to three times higher than in Europe and American cities. UFP exposures in Asian cities were twice as high for pedestrians and up to ∼9-times as high in cars than in cities in Europe or the USA. Asian pedestrians were exposed to ∼7-times higher BC concentrations compared with pedestrians in the USA. Stochastic population-based models have yet to be applied widely in Asia but can be used to quantify inter-individual and inter-regional variability in exposures and to assess the contribution of TMEs to total exposures for multiple pollutants. The review also highlights specific gaps in the Asian TME data set that need to be filled since UFP and BC studies were rare as were studies of pedestrian and cyclist exposure.
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•PM, UFP and BC exposures are assessed for Asian transport microenvironments.•Rare data on cyclist and motorcyclist exposure despite substantial use in Asian cities.•Cyclists were relatively the least exposed while bus users showing highest exposure.•Car users in Asia receive up to 9-times higher exposure to UFP to Europe and USA.•Need more studies in diverse meteorological and traffic microenvironments in Asia.
Spatially varying diesel locomotive fuel use and emission rates (FUERs) are needed to accurately quantify local emission hotspots and their health impacts. However, existing locomotive FUER data are ...typically not spatially resolved or representative of real-world locomotive operation. Therefore, existing data are of limited use in quantifying the spatial variability in real-world FUERs. The objectives of this work are to quantify spatial variability in locomotive FUERs and identify factors differentiating hotspots from non-hotspots. FUERs were measured based on real-world measurements conducted for the Piedmont passenger rail service using a portable emission measurement system. FUERs were quantified based on 0.25 mile track segments on the Piedmont route. Hotspots were defined as segments in the top quintile of segment-average FUERs. On average, hotspots contributed 40–50% to trip fuel use and emissions. Hotspots were typically associated with low-to-medium speed, and high acceleration and grade. In contrast, non-hotspots were associated with high speed, and low acceleration and grade. Hotspots were typically located near populated areas and, thus, may exacerbate air pollutant exposure. The method demonstrated here can be applied to other passenger train services to assess key trends in hotspot locations and factors that explain the occurrence of hotspots.
Real-world vehicle fuel use and emission rates depend on engine load, which is quantified in terms of Vehicle Specific Power (VSP). VSP depends on vehicle speed, acceleration, and road grade. There ...is not a standard method for measuring road grade from a moving vehicle. A method for quantifying grade is evaluated based on statistical analysis of multiple runs using low cost consumer grade Global Positioning System (GPS) receivers with in-built Barometric Altimeter (GPS/BA). The average grade precision is ±0.71, ±0.46, and ±0.31 percentage points, for sample sizes of 9, 18, and 36 GPS/BA runs, respectively, among 2213 individual 0.08 km road segments. In addition, 4 sets of repeated measurements were performed on the same routes using a high cost, high accuracy Differential GPS (DGPS). Both sets of GPS-based grade estimates compared well with those derived from LIght Detection And Ranging (LIDAR) data. GPS/BA and DGPS grade estimates were similar, except for high magnitude grades of 8–10 percent for which DGPS estimates are more accurate. DGPS is more sensitive to loss of signal; thus, a hybrid approach for substituting GPS/BA data for missing DGPS data at specific locations along a route is demonstrated. The local and overall effects of road grade on fuel use and emission rates are investigated for an example light duty gasoline vehicle.
•A method for estimating road grade based on combining multiple GPS runs is introduced.•We compare road grade estimates from consumer grade and mapping grade GPS data.•We demonstrate that road grade can be precisely and accurately measured using GPS data.•We illustrate the effect of road grade on real-world vehicle fuel use and emission rates.
Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food‐safety risk assessment ...models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food‐safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.
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.
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•Real-world hot-stabilized fuel use and exhaust emission rates were measured.•E0, regular and premium E10 with different octane ratings, and E27 were tested.•All five vehicles, ...including four non-flexible fuel vehicles, adapted to each fuel.•E27 can increase engine efficiency and reduce CO and PM emissions.
Differences in fuel use and emission rates of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxide (NOx), and particulate matter (PM) were quantified for three gasoline-ethanol blends and neat gasoline measured for one flexible-fuel vehicle (FFV) and four non-FFVs using a portable emission measurement system (PEMS). The purpose was to determine if non-FFVs can adapt to a mid-level blend and to compare the fuel use and emission rates among the fuels. Each vehicle was measured on neat gasoline (E0), 10% ethanol by volume (E10) “regular” (E10R) and “premium” (E10P), and 27% ethanol by volume (E27). Four real-world cycles were repeated for each vehicle with each fuel. Second-by-second fuel use and emission rates were binned into Vehicle Specific Power (VSP) modes. The modes were weighted according to real-world standard driving cycles. All vehicles, including the non-FFVs, were able to adapt to E27. Octane-induced efficiency gain was observed for higher octane fuels (E10P and E27) versus lower octane fuels (E0 and E10R). E27 tends to lower PM emission rates compared to E10R and E10P and CO emission rates compared to the other three fuels. HC emission rates for E27 were comparable to those of E10R and E10P. No significant difference was found in NOx emission rates for E27 versus the other fuels. Intervehicle variability in fuel use and emission rates was observed. Lessons learned regarding study design, vehicle selection, and sample size, and their implications are discussed.
Light-duty gasoline vehicle (LDGV) tailpipe emission rates can be quantified based on pollutant concentrations measured using portable emission measurement systems (PEMS). Emission rates depend on ...exhaust flow. For simplified and micro-PEMS, exhaust flow is inferred from engine mass air flow (MAF) and air-to-fuel ratio. For many LDGVs, MAF is broadcast via the on-board diagnostic (OBD) interface. For some vehicles, only indirect indicators of MAF are broadcast. In such cases, MAF can be estimated using the speed-density method (SDM). The SDM requires an estimate of the engine volumetric efficiency (VE), which is the ratio of actual to theoretical MAF. VE is affected by intra-vehicle variability in the engine load and inter-vehicle variability in engine characteristics (e.g., the type of valvetrain). The suitability of SDM-based estimates of MAF in conjunction with simplified and micro-PEMS has not been adequately evaluated. Therefore, the objectives are to: (1) quantify VE accounting for intra- and inter-vehicle variability; and (2) evaluate the accuracy of SDM-based vehicle emission rate estimation approaches. Seventy-seven naturally-aspirated LDGVs were measured using PEMS. For each vehicle, VE was estimated using three approaches: (1) constant VE calibrated to actual fuel use; (2) average estimates of VE for Vehicle Specific Power modes imputed from OBD data; and (3) modeled VE using multilinear regression (MLR). The MLR models were developed based on engine load and engine characteristics. The best model was selected based on various statistical diagnostics. When engines were under load, variability in VE was most sensitive to variations in engine load. During idling, VE differed between engines depending on engine characteristics. The constant and modeled VE estimation approaches enable the accurate estimation of microscale and mesoscale emission rates, with errors typically within ±10% compared to values imputed from OBD data. Thus, accurate emission rates can be obtained from simplified and micro-PEMS.
Implications: Simplified and micro portable emission measurement systems (PEMS) enable widespread measurement of vehicle exhaust emission. As a cost saving measure, they estimate exhaust flow indirectly rather than via measurement, typically based on engine mass air flow (MAF). For some vehicles, MAF is not reported by the on-board diagnostic (OBD) system but can be inferred from other reported variables and volumetric efficiency (VE). However, VE is typically proprietary. Methods demonstrated here for estimating VE enable accurate quantification of emission rates, thereby enabling use of these PEMS for policy-relevant applications such as technology assessments, trends analysis, and emissions inventories.
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