Theodore Younglove

Modal Emissions Model for Heavy-Duty Diesel Vehicles

There have been significant improvements in recent years in transportation and emissions modeling to allow better evaluations of transportation operational effects and associated vehicle emissions. In particular, instantaneous or modal emissions models have been developed for a variety of light-duty vehicles. To date, most of the effort has focused primarily on developing these models for light-duty vehicles with less effort devoted to heavy-duty diesel (HDD) vehicles. Although HDD vehicles currently make up only a fraction of the total vehicle population, they are major contributors to the emissions inventory. A description is provided of an HDD truck model that is part of a larger comprehensive modal emissions modeling (CMEM) program developed at the University of California (UC), Riverside. Several HDD truck submodels have been developed in the CMEM framework, each corresponding to a distinctive vehicle-technology category. The developed models use a parameterized physical approach in which the entire emission process is broken down into different components that correspond to physical phenomena associated with vehicle operation and emission production. A variety of trucks were extensively tested under a wide range of operating conditions at UC Riversides Mobile Emissions Research Laboratory. The collected data were then used to calibrate the HDD models. Particular care was taken to investigate and implement the effects of varying grade and the use of variable fuel injection strategies. Results show good estimates for fuel use and the regulated emission species including nitrogen oxides, one of the key targets for HDD vehicles.

Recent Validation Efforts for a Comprehensive Modal Emissions Model

Mobile source emissions estimation techniques play a critical role for regional planning and development of emission control strategies. The primary models for mobile source emissions estimation have been the U.S. Environmental Protection Agency’s MOBILE model and the California Air Resources Board’s EMFAC model. These models work well for large regional areas but are not as well suited for “microscale” evaluation. Over the last several years, the College of Engineering–Center for Environmental Research and Technology (CE-CERT) has been evaluating in-use, light-duty vehicles as part of NCHRP Project 25-11, resulting in the development of a Comprehensive Modal Emissions Model (CMEM). An essential part of any model development process is validating the model. Various validation techniques have been applied to CMEM. This paper describes some of the latest validation work carried out in comparing CMEM results to independent emission testing results (independent in both vehicles and driving cycles). Further, CMEM has been compared with the latest versions of EMFAC and MOBILE. In general, compared with the independent emission measurements, CMEM predicts well. It has been found that CMEM is consistent with MOBILE and EMFAC at low to medium speeds. Greater deviations were found at very low speeds and very high speeds. At high speeds, CMEM tends to predict higher hydrocarbon (HC) emissions and lower oxides of nitrogen (NOx) emissions. At the very low speeds, CMEM tends to predict lower than EMFAC and MOBILE for all emissions. These comparisons are part of an ongoing validation process for development of CMEM.

Estimating Emissions and Fuel Consumption for Different Levels of Freeway Congestion

To improve upon the speed correction factor methodology used by conventional emission models (i.e., MOBILE and EMFAC), the Environmental Protection Agency is introducing in its latest version of MOBILE (version 6) a new set of facility-specific driving cycles. These cycles represent driving patterns for different facility types (e.g., highway and arterial) and congestion conditions. Using a state-of-the-art comprehensive modal emissions model developed under NCHRP Project 25-11, one is able to predict the integrated emissions and fuel use values for these cycles for a wide variety of vehicle-technology categories. These facility-congestion results are then compared with steady-state emissions-fuel use measurements that were made in deriving the modal model. Furthermore, cruise modes that have mild speed perturbations are also investigated. All of these results are then compared with the speed correction equations used in the conventional emissions factor models. It is found that the mild acceleration perturbations at high speeds can lead to significantly higher emissions compared with the steady-state values. Because of this, the new high-speed freeway driving cycles (representing higher levels of service) in many cases have (modeled) emissions higher than those for the cycles that represent lower levels of service. Fuel consumption by speed does not change drastically in the comparisons.

Analysis of Modal Emissions From Diverse In-Use Vehicle Fleet

The initial phase of a long-term project with national implications for the improvement of transportation and air quality is described. The overall objective of the research is to develop and verify a computer model that accurately estimates the impacts of a vehicle’s operating mode on emissions. This model improves on current emission models by allowing for the prediction of how traffic changes affect vehicle emissions. Results are presented that address the following points: vehicle recruitment, preliminary estimates of reproducibility, preliminary estimates of air conditioner effects, and preliminary estimates of changes in emissions relative to speed. As part of the development of a comprehensive modal emission model for light-duty vehicles, 28 distinct vehicle/technology categories have been identified based on vehicle class, emission control technology, fuel system, emission standard level, power-to-weight ratio, and emitter level (i.e., normal versus high emitter). These categories and the sampling proportions in a large-scale emissions testing program (over 300 vehicles to be tested) have been chosen in part based on emissions contribution. As part of the initial model development, a specific modal emissions testing protocol has been developed that reflects both real-world and specific modal events associated with different levels of emissions. This testing protocol has thus far been applied to an initial fleet of 30 vehicles, where at least 1 vehicle falls into each defined vehicle/technology category. The different vehicle/technology categories, the emissions testing protocol, and preliminary analysis that has been performed on the initial vehicle fleet are described.

Designing On-Road Vehicle Test Programs for the Development of Effective Vehicle Emission Models

Mobile source emission models for years have depended on laboratory-based dynamometer data. Recently, however, portable emission measurement systems (PEMS) have become commercially available and in widespread use, and make on-road real-world measurements possible. As a result, the newest mobile source emission models (e.g., U.S. Environmental Protection Agency’s mobile vehicle emission simulator) are becoming increasingly dependent on PEMS data. Although on-road measurements are made under more realistic conditions than laboratory-based dynamometer test cycles, they introduce influencing variables that must be carefully measured for properly developed emission models. Further, test programs that simply measure in-use driving patterns of randomly selected vehicles will result in models that can effectively predict current-year emission inventories for typical driving conditions. However, when predicting more aggressive transportation operations than current typical operations (e.g., higher speeds, accelerations), the model predictions will be less certain. In this paper, various issues associated with on-road emission measurements and modeling are presented. Further, an example on-road emission data set and the reduction in estimation error through the addition of a short aggressive driving test to the in-use data are examined. On the basis of these results, recommendations are made on how to improve the on-road test programs for developing more robust emission models.

Selecting ozone exposure statistics for determining crop yield loss from air pollutants

Numerous ozone exposure statistics were calculated using hourly ozone data from crop yield loss experiments previously conducted for alfalfa, fresh market and processing tomatoes, cotton, and dry beans in an ambient ozone gradient near Los Angeles, California. Exposure statistics examined included peak (maximum daily hourly) and mean concentrations above specific threshold levels, and concentrations during specific time periods of the day. Peak and mean statistics weighted for ozone concentration and time period statistics weighted for hour of the day were also determined. Polynomial regression analysis was used to relate each of 163 ozone statistics to crop yield. Performance of the various statistics was rated by comparing residual mean square (RMS) values. The analyses demonstrated that no single statistic was best for all crop species. Ozone statistics with a threshold level performed well for most crops, but optimum threshold level was dependent upon crop species and varied with the particular statistics calculated. The data indicated that daily hours of exposure above a critical high-concentration threshold related well to crop yield for alfalfa, market tomatoes, and dry beans. The best statistic for cotton yield was an average of all daily peak ozone concentrations. Several different types of ozone statistics performed similarly for processing tomatoes. These analyses suggest that several ozone summary statistics should be examined in assessing the relationship of ambient ozone exposure to crop yield. Where no clear statistical preference is indicated among several statistics, those most biologically relevant should be selected.

An Analysis Of 190 Cases Of Suspected Pesticide Illness

Background: The diagnosis of mild to moderate pesticide exposure presents a challenge because the signs and symptoms of exposure are similar to those of many other diseases. We reviewed all alleged pesticide injuries seen in a single office during a 6-year period to determine which findings were useful in discriminating between a pesticide-related illness and other causes. Methods: We reviewed retrospectively the charts of 190 patients alleging pesticide illness who were treated in a standardized manner. Results: One hundred sixteen (116) patients (61.1 percent) were found to have pesticide illness. Important predictors of pesticide illness were anxiety, vertigo, nausea, vomiting, tearing, and weakness. Seventy-four patients (38.9 percent) were found to have nonpesticide-related illness, with nonspecific irritant contact dermatitis and scabies the most common diagnoses. Rash was the only significant predictor of nonpesticide related illness. Conclusions: It is difficult to relate signs and symptoms to pesticide poisoning, and exposure history is very important. Alternative diagnoses need to be considered. Laboratory tests are not nearly as valuable as many might expect, and skin rash is not a common finding in mild to moderate pesticide poisoning.

Mobile Source Emissions: Analysis of Spatial Variability in Vehicle Activity Patterns and Vehicle Fleet Distributions

Accurately estimating mobile-source emissions requires a good understanding of vehicle activity and the characteristics of the on-road vehicle fleet. Spatial variability in vehicle activity patterns and vehicle fleet composition can have significant effects on the overall emissions inventory. Simply determining total vehicle miles traveled is insufficient for emissions inventory calculations from the new-generation models of mobile-source emissions. Improvements in emissions-control technology over the past 20 years have led to large decreases in the emissions of light-duty cars and trucks, resulting in large variations in vehicle emissions depending on model year and technology type. In addition, research indicates that the accurate characterization of vehicle activity is necessary in conjunction with better spatial resolution of vehicle fleet characteristics because of the differing modal behavior of the vehicles within various vehicle and technology groups. Vehicle activity and vehicle fleet data were collected in the South Coast Air Basin in southern California. Vehicle activity was characterized primarily using a large second-by-second speed and acceleration data set collected from probe vehicles operated within the flow of traffic. In addition, three sets of vehicle fleet data were collected and used for spatial comparison. The results of the analysis show spatial and temporal differences in vehicle activity patterns and vehicle fleet characteristics; differences in speed and congestion affect the speed–acceleration profiles as well as associated emissions.

Intelligent off-board management of vehicle operating parameters

Todays modern vehicle has a number of on-board embedded control systems that perform a number of functions. These systems provide for a higher user convenience (e.g., cruise control), better safety (e.g., air bag systems), and lower pollutant emissions (e.g., energy and emission control systems). These systems generally operate autonomously, relying on internal state information. In this paper, we describe how these embedded systems can be improved upon through the use of external information provided through ITS telematic capability. There are several examples of this, such as dynamic intelligent speed adaptation, energy management strategies for hybrid electric and fuel-cell vehicles, and dynamically controlled emission control systems. These different application areas and methods are described herein. Further, a number of simulation experiments have been carried for the case of controlling the vehicle operating parameters of a heavy-duty vehicles emission control system. It has been shown that by simply changing the emission control strategy based on location can result in significantly lower emissions in critical areas at a relatively low cost.

The use of a dry combustion infrared instrumental technique to determine total and organic carbon in California soils

Abstract A simple, reliable, method for determining both total and organic carbon in California soils using a dry combustion technique has been developed. Five soils of widely varying carbon contents were selected from locations in California and their total carbon and organic carbon content determined using a modified Dohrmann Carbon Analyzer (Model No. DC‐85A).The carbon analyzer was modified by the inclusion of an expansion chamber, which allowed for the determination of > 160 μg total carbon. A temperature differential method was developed to identify both the organic and, by difference, carbonate‐carbon components of each soil sample. Data obtained by this method compared favorably with determinations of organic carbon derived from a rapid dichromate oxidationlltralion technique.