NOVEL DATA ANALYSIS TECHNIQUE TO EVALUATE FIELD NOx AND CO2 CONTINUOUS EMISSION DATA, BASED ON THE EVALUATION OF: (1) AN OFF-ROAD DIESEL COMPACTOR RUNNING ON THREE FUEL TYPES AND (2) TWO COMPACTORS RUNNING ON DIESEL FUEL

Abstract

In spite of being few in number, off-road vehicles have a significant contribution to air pollutants such as NOx and CO2. Engine dynamometer test cycles have been developed in an effort to better characterize the emissions from off-road vehicles. However, these test cycles may not accurately represent the emission profiles under normal operating conditions. The current study seeks to: (1) collect real-world NOx and CO2 emission profiles from an off-road diesel vehicle; (2) analyze NOx and CO2 emission profiles for a diesel off-road vehicle running on no. 2 diesel, 20% biodiesel mix (B20) and ultra-low sulfur diesel (ULSD) fuels to determine potential emission reductions; (3) test the effect that temporal factors exert on NOx and CO2 emission profiles; (4) evaluate the emission variability between two pieces of equipment of the same model; and (5) develop a standard, systematic analysis for handling large emission data sets. The study is based on the tailpipe emission sampling of a diesel fueled 525-horsepower Trashmaster 3-90E trash compactor operated at the N.R. Hamm Landfill facility located near the city of Perry in Jefferson County, Kansas. The sampling instrument used for the study is the Simple, Portable, On-vehicle Testing (SPOT) system manufactured by Analytical Engineering Inc. The SPOT is able to collect second-by-second data for total exhaust mass flow, relative humidity, engine speed, and NOx and CO2 emissions among other parameters. The fuel types used include regular no. 2 diesel, B20 and ULSD. The sampling campaign took place in two stages: (1) running the compactor with regular no. 2 diesel from August 28 to September 1 and with B20 and ULSD fuels from September 12 to September 15, 2005, and (2) running a second compactor of the same model with no. 2 diesel. The purpose of the first stage of the project was to determine the possible emission reductions from the use of B20 and ULSD. The purpose of the second stage was to test the emission variability between two compactors of the same model. This is relevant since it is commonly assumed that the emission profile from one engine is representative for all engines of the same type and family. Initial data analysis showed a significant autocorrelation in the NOx and CO2 data observations. Autocorrelation is inherent in continuous data sets where sequential observations are too close together to be independent from each other and must be resolved so that a robust statistical analysis may ensue. By using a time interval data reduction technique a set of quasi-independent observations was produced. This technique allowed for a valid use of the general linear model (GLM) with engine speed as the covariate factor to test day, fuel type and compactor factors. For the first stage of the project the results from the GLM showed that neither day nor fuel type factors were statistically significant on NOx and CO2 emissions. These results suggest that NOx and CO2 emissions are not dependent on the day in which they were collected or on the fuel type used. The second stage of the project involved the comparison of NOx and CO2 emissions from two compactors of the same model while running on no. 2 diesel fuel. The results from the temporal analysis indicated that the day factor was not statistically significant for either of the two pollutants. Results from the compactor analysis showed that compactor was not a statistically significant factor on NOx emissions. However, the interaction of compactor and engine speed factors was found to be statistically significant on NOx emissions. For CO2 emissions the results indicated that compactor was a statistically significant factor. These results suggest that the there is a statistically significant difference between the NOx and CO2 emissions obtained from each of the two compactors. However, this difference is expressed differently in each of the two data sets. In addition to the GLM analyses, a data fitting model analysis was also completed for NOx and CO2. The results showed that the linear and the cubic models do a good job of fitting the NOx and CO2 data and they both have high R2 values. These data fitting technique may be used to estimate NOx and CO2 emissions based solely on engine speed after an emission profile has been collected. This information can be of great import to obtain more accurate emission estimates from off-road diesel vehicles. This study makes three main contributions including the development of a data handling technique to deal with autocorrelation in continuous data. This study also showed that the three fuel types evaluated had no significant effect on NOx and CO2 emissions. Finally, the evaluation of two Trashmaster 3-90E compactors showed that NOx and CO2 emissions are significantly different between the compactors.