Trends in onroad transportation energy and emissions

<p>Globally, 1.3 billion on-road vehicles consume 79 quadrillion BTU of energy, mostly gasoline and diesel fuels, emit 5.7 gigatonnes of CO₂, 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.</p> <p><i>Implications</i>: 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.</p

Integration of Coal Utilization and Environmental Control in IGCC Systems

Integrated gasification combined cycle (IGCC) systems are a new generation of coal-fueled power generation technologies which embody the concept of integrated environmental control. IGCC systems are capable of significantly lower discharge rates of gaseous, liquid, and solid wastes relative to conventional coal-based systems. However, because few IGCC concepts have been demonstrated at a commercial scale, there is significant uncertainty regarding the technical and environmental performance of many of these systems in full-scale applications. Examples of IGCC system concepts involving both cold and hot gas cleanup are evaluated probabilistically to provide insights into the resulting differences in plant performance, emissions, and cost

Modeling IGCC System Performance, Emissions, and Cost Using Probabilistic Engineering Models

Integrated gasification combined cycle (IGCC) systems are an emerging technology for the, clean and efficient utilization of coal. Because of the close interactions among plant performance, environmental control, and cost, assessments of IGCC technology must be based on integrated analysis of the entire system. The uncertain nature of the limited performance and cost data for the first generation systems, coupled with uncertainties associated with alternative process configurations, suggests a strong need for systematic :analysis of uncertainty in evaluating alternative designs or concepts. This paper will present results from a probabilistic case study of one innovative IGCC concept featuring "hot gas _cleanup." The case study will demonstrate the new types of insights that can be obtained from probabilistic analysis

Improved System Integration for Integrated Gasification Combined Cycle (IGCC) Systems

Integrated gasification combined cycle (IGCC) systems are a promising technology for power generation. They include an air separation unit (ASU), a gasification system, and a gas turbine combined cycle power block, and feature competitive efficiency and lower emissions compared to conventional power generation technology. IGCC systems are not yet in widespread commercial use and opportunities remain to improve system feasibility via improved process integration. A process simulation model was developed for IGCC systems with alternative types of ASU and gas turbine integration. The model is applied to evaluate integration schemes involving nitrogen injection, air extraction, and combinations of both, as well as different ASU pressure levels. The optimal nitrogen injection only case in combination with an elevated pressure ASU had the highest efficiency and power output and approximately the lowest emissions per unit output of all cases considered, and thus is a recommended design option. The optimal combination of air extraction coupled with nitrogen injection had slightly worse efficiency, power output, and emissions than the optimal nitrogen injection only case. Air extraction alone typically produced lower efficiency, lower power output, and higher emissions than all other cases. The recommended nitrogen injection only case is estimated to provide annualized cost savings compared to a nonintegrated design. Process simulation modeling is shown to be a useful tool for evaluation and screening of technology options

Probabilistic Analysis of Driving Cycle-Based Highway Vehicle Emission Factors

A probabilistic methodology for quantifying intervehicle variability and fleet average uncertainty in highway vehicle emission factors is developed. The methodology features the use of empirical distributions of emissions measurement data to characterize variability and the use of bootstrap simulation to characterize uncertainty. For the base emission rate as a function of mileage accumulation under standard conditions, a regression-based approach was employed in which the residual error terms were included in the probabilistic analysis. Probabilistic correction factors for different driving cycles, ambient temperature, and fuel Reid vapor pressure (RVP) were developed without interpolation or extrapolation of available data. The method was demonstrated for tailpipe carbon monoxide, hydrocarbon, and nitrogen oxides emissions for a selected light-duty gasoline vehicle technology. Intervehicle variability in emissions was found to span typically 2 or 3 orders of magnitude. The uncertainty in the fleet average emission factor was as low as ±10% for a 95% probability range, in the case of standard conditions, to as much as −90% to +280% when correction factors for alternative driving cycles, temperature, and RVP are applied. The implications of the results for method selection and for decision making are addressed

Optimization under Variability and Uncertainty: A Case Study for NO<i>_x</i> Emissions Control for a Gasification System

Methods for optimization of process technologies considering the distinction between variability and uncertainty are developed and applied to case studies of NOx control for Integrated Gasification Combined Cycle systems. Existing methods of stochastic optimization (SO) and stochastic programming (SP) are demonstrated. A comparison of SO and SP results provides the value of collecting additional information to reduce uncertainty. For example, an expected annual benefit of $240 000 is estimated if uncertainty can be reduced before a final design is chosen. SO and SP are typically applied to uncertainty. However, when applied to variability, the benefit of dynamic process control is obtained. For example, an annual savings of$1 million could be achieved if the system is adjusted to changes in process conditions. When variability and uncertainty are treated distinctively, a coupled stochastic optimization and programming method and a two-dimensional stochastic programming method are demonstrated via a case study. For the case study, the mean annual benefit of dynamic process control is estimated to be $700 000, with a 95$500 000 to $940 000. These methods are expected to be of greatest utility for problems involving a large commitment of resources, for which small differences in designs can produce large cost savings

Quantification of Variability and Uncertainty for Air Toxic Emission Inventories with Censored Emission Factor Data

Probabilistic emission inventories were developed for urban air toxic emissions of benzene, formaldehyde, chromium, and arsenic for the example of Houston. Variability and uncertainty in emission factors were quantified for 71−97% of total emissions, depending upon the pollutant and data availability. Parametric distributions for interunit variability were fit using maximum likelihood estimation (MLE), and uncertainty in mean emission factors was estimated using parametric bootstrap simulation. For data sets containing one or more nondetected values, empirical bootstrap simulation was used to randomly sample detection limits for nondetected values and observations for sample values, and parametric distributions for variability were fit using MLE estimators for censored data. The goodness-of-fit for censored data was evaluated by comparison of cumulative distributions of bootstrap confidence intervals and empirical data. The emission inventory 95% uncertainty ranges are as small as −25% to +42% for chromium to as large as −75% to +224% for arsenic with correlated surrogates. Uncertainty was dominated by only a few source categories. Recommendations are made for future improvements to the analysis

Variability in Light-Duty Gasoline Vehicle Emission Factors from Trip-Based Real-World Measurements

Using data obtained with portable emissions measurements systems (PEMS) on multiple routes for 100 gasoline vehicles, including passenger cars (PCs), passenger trucks (PTs), and hybrid electric vehicles (HEVs), variability in tailpipe emission rates was evaluated. Tier 2 emission standards are shown to be effective in lowering NO_<i>x</i>, CO, and HC emission rates. Although PTs are larger, heavier vehicles that consume more fuel and produce more CO₂ emissions, they do not necessarily produce more emissions of regulated pollutants compared to PCs. HEVs have very low emission rates compared to tier 2 vehicles under real-world driving. Emission factors vary with cycle average speed and road type, reflecting the combined impact of traffic control and traffic congestion. Compared to the slowest average speed and most congested cycles, optimal emission rates could be 50% lower for CO₂, as much as 70% lower for NO<i>_x</i>, 40% lower for CO, and 50% lower for HC. There is very high correlation among vehicles when comparing driving cycles. This has implications for how many cycles are needed to conduct comparisons between vehicles, such as when comparing fuels or technologies. Concordance between empirical and predicted emission rates using the U.S. Environmental Protection Agency’s MOVES model was also assessed

Propagation of Uncertainty in Hourly Utility NO<i>_x</i> Emissions through a Photochemical Grid Air Quality Model: A Case Study for the Charlotte, NC, Modeling Domain

One of the major hypothesized sources of uncertainties in air quality model inputs is the emission inventory. A probabilistic hourly NOx emission inventory for 32 units of nine coal-fired power plants in the Charlotte domain for the year 1995 was propagated through the Multiscale Air Quality Simulation Platform (MAQSIP). The inventory was developed using time series techniques. Time series for a 4-d episode were simulated and propagated through the air quality model 50 times in order to represent the ranges of uncertainty in hourly emissions and predicted ozone levels. Intra-unit autocorrelation in emissions and inter-unit dependence were accounted for. The range of uncertainty in predicted ozone was greater when inter-unit dependence was included as compared to when units were treated as statistically independent. Uncertainties in maximum ozone hourly or 8-h concentrations at a specific location could be attributed to a specific power plant based upon regression analysis. Out of 3969 grid cells in the modeling domain, there were 43 and 1654 grid cells with a probability greater than 0.9 of exceeding a 1-h 120 ppb standard and an 8-h 80 ppb standard, respectively. The time series of predicted ozone values had similar autocorrelation as compared to monitored data. The implications of these results for air quality management are addressed

Evaluation of Representativeness of Site-Specific Fuel-Based Vehicle Emission Factors for Route Average Emissions

An approach to evaluate the representativeness of site-specific fuel-based vehicle emission factors, such as would be obtained using Remote Sensing Devices (RSDs) is demonstrated based on real-world data for 23 selected light duty gasoline vehicles. Real time vehicle route-average emissions rates were measured using a Portable Emissions Measurement System (PEMS) for a variety of road types and traffic characteristics. Several hypothetical remote sensing sites were selected to estimate site-specific fuel-based emission factors. The average fuel-based emission factors increased with vehicle specific power (VSP) and varied by a factor of 3 and 4 for NO_<i>x</i> and CO, respectively. The route average emission factors varied by approximately 20% for either NO_<i>x</i> or CO. The site-specific emission factors varied among specific sites by 20 and 30% for NO_<i>x</i> and CO, respectively. Fuel-based HC emission rates had little variability with engine load, among routes, or between sites. Arbitrarily chosen sites can lead to potential biases for CO and NO_<i>x</i> if measured emission factors are used for route average rates and, therefore, for area-wide inventories. However, site-specific emission factors have the potential to be representative of area-wide emission rates if the distribution of positive VSP at the site is similar to that of routes or area-wide cycles of interest