Functional Time Series Models for Ultrafine Particle Distributions

• Activity Number: 349
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Heidi Fischer* and Robert E. Weiss and Yifang Zhu and Qunfang Zhang
• Companies: and UCLA and UCLA and UCLA
• Keywords: Bayesian Statistics ; Hierarchical Models ; Varying Coefficient Models ; Heteroskedasticity

Abstract

We propose Bayesian random effect functional time series models to model the impact of engine idling on ultrafine particle (UFP) counts inside school buses. UFPs are toxic to humans with health effects strongly linked to particle size. School engines emit particles primarily in the UFP size range and as school buses idle at bus stops, UFPs penetrate into cabins through cracks, doors, and windows. How UFP counts inside buses vary by particle size over time and under different idling conditions is not yet well understood. We model UFP counts at a given time with a cubic B-spline basis as a function of size and allow counts to increase over time at a size dependent rate once the engine turns on. We explore alternate parametric models for the engine-on increase which also vary smoothly over size. The log residual variance over size is modeled using a quadratic B-spline basis to account for heterogeneity and an autoregressive model is used for the residual. Model predictions are communicated graphically. These methods provide information needed for regulating vehicle emissions to minimize UFP exposure in the future.