Using functional data analysis of diurnal ozone and NOx cycles to inform transportation emissions control

Abstract

Ozone pollution is caused by photochemical reactions of precursor volatile organic compounds and nitrogen oxides, of which transportation emissions are the single major source. Understanding the complexity of diurnal ozone processes and their relationships with precursors is thus critical for identifying optimal transportation emissions control strategies. This paper uses functional data analysis methods to model the dynamics of diurnal ozone and nitrogen oxides cycles, their interrelationships, and the multilevel spatio-temporal variations of ozone and nitrogen oxides measurements from Southern California. Functional data analysis accounts for the continuous nature of diurnal ozone/nitrogen oxides processes by converting discrete observations into functional diurnal curves. Results show important, but low-dimensional, spatio-temporal variations in diurnal ozone/nitrogen oxides cycles. Representative summer diurnal ozone profiles are constructed using functional clustering. Variability in hourly distribution of traffic activities and emissions is also discussed.