Functional Time Series Models for Ultrafine Particle Distributions

TL;DR

This paper introduces Bayesian functional time series models to analyze how ultrafine particle counts inside school buses vary over time, size, and engine idling conditions, aiding in emission regulation strategies.

Contribution

It develops novel Bayesian functional models incorporating size-dependent effects, heterogeneity, and autoregressive residuals for UFP count analysis.

Findings

Model captures size-dependent UFP dynamics

Provides graphical predictions for UFP counts

Supports emission regulation policies

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.