On-road remote sensing has the ability to operate in real-time, and under real world conditions, making it an ideal candidate for detecting gross polluters on major freeways and thoroughfares. In this study, remote sensing was employed to detect carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxide (NO). On-road remote sensing data taken from measurements performed in six states, (Arizona, California, Colorado, Illinois, Texas, and Utah) were cleaned and analyzed. Data mining and exploration were first undertaken in order to search for relationships among variables such as make, year, engine type, vehicle weight, and location. Descriptive statistics were obtained for the three pollutants of interest. The data were found to have non-normal distributions. Applied transformations were ineffective, and nonparametric tests were applied. Due to the extremely large sample size of the dataset (508,617 records), nonparametric tests resulted in "p" values that demonstrated "significance." The general linear model was selected due to its ability to handle data with non-normal distributions. The general linear model was run on each pollutant with output producing descriptive statistics, profile plots, between-subjects effects, and estimated marginal means. Due to insufficient data within certain cells, results were not obtained for gross vehicle weight and engine type. The "year" variable was not directly analyzed in the GLM because "year" was employed in a weighted least squares transformation. "Year" was found to be a source of heteroscedasticity; and therefore, the basis of a least-squares transformation. Grouped-years were analyzed using medians, and the results were displayed graphically. Based on the GLM results and descriptives, Japanese vehicles typically had the lowest CO, HC, and NO emissions, while American vehicles ranked high for the three. Illinois, ranked lowest for CO, while Texas ranked highest. Illinois and Colorado were lowest for HC emissions, while Utah and California were highest. For NO, Colorado ranked highest with Texas and Arizona, lowest.
