Wavelet Analysis to Characterize the Dependency of Vehicular Emissions on Road Roughness

Abstract

Decomposition and transformation algorithms are important tools in data analyses, including air pollution data processing, which has been a principal environmental concern for many decades. One of the main sources of air pollution is vehicular emissions, which contain harmful greenhouse gases. In addition to such factors as speed and acceleration affecting the quantity of emissions and fuel consumption, pavement roughness is an indirect factor. The portable emissions measurement system (PEMS) is used to collect on-road emissions data every second, and smartphone apps such as Roadroid are used to estimate road roughness. These data can be combined and correlated with vehicular activities such as speed, acceleration, and vehicle specific power and analyzed in detail to reveal the actual impact of roughness on emissions. However, analytical modeling can provide only an average estimation and does not retain local roughness and how it affects emissions. Such detailed information is necessary for pavement diagnosis and maintenance and emissions control. This paper presents a discrete wavelet transform (DWT) procedure that can offer local and supplementary relationships between pavement roughness and emissions that other simplified techniques cannot. PEMS data of four road segments in greater Houston, Texas, were considered to be decomposed by one-dimensional DWT for analysis of the wavelet subbands and their energies. The results of this study can be used in planning to minimize emissions with consideration for pavement conditions.