| Successful ozone control strategies require an improved understanding of ozone formation, scavenging, and transport dynamics, which are reflected in the variability associated with diurnal ozone cycles. This study contributes to our understanding of ozone cycles by conducting functional data analysis (FDA) of daily ozone/NOx curves. FDA of diurnal ozone and NOx curves provides a means for converting raw hourly ozone/NOx data into functional data objects, which facilitate statistical analyses but retain variability. Functional principal component analysis (FPCA) is used to characterize the complexity and the dimensionality of the variability in daily ozone and NOx cycles and functional analysis of variance (FANOVA) is used to statistically examine day of week effects on ozone/NOx cycles, particularly the ozone weekend effect (OWE). The OWE is then directly linked to identifying efficient ozone control strategies by statistically examining weekly patterns in the timing, magnitude and fleet mix of traffic activities, the dominant sources to ozone precursors emissions. We conduct nonparametric factorial analyses of light-duty vehicle (LDV) and heavy-duty truck (HDT) volumes observed at 27 weigh-in-motion traffic monitoring stations in southern CA to examine the implications of their weekly and spatial patterns for the OWE. The results indicate statistically significant variations in daily total as well period-based traffic volumes by day of week, vehicle group (LDV vs. HDT) and WIM locale with respect to LA metro (central vs. peripheral). These statistically tested weekly patterns in traffic, translated into weekly variations in running exhaust emissions, support five of seven California Air Resource Board's OWE hypotheses: NOx-Reduction, NOx-timing, carryover near the ground, aerosol and UV radiation and ozone quenching hypotheses. The results of this dissertation research will significantly help in the development of State Implementation Plans (SIP), the evaluation of traffic control measures (TCMs), and the interaction between transportation planning and air quality modeling, which are mandated by the Clean Air Act Amendment (1991) and the Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991, now TEA-21. |
