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March 2017 Functional time series models for ultrafine particle distributions
Heidi J. Fischer, Qunfang Zhang, Yifang Zhu, Robert E. Weiss
Ann. Appl. Stat. 11(1): 297-319 (March 2017). DOI: 10.1214/16-AOAS1004

Abstract

We propose Bayesian functional mixed effect time series models to explain the impact of engine idling on ultrafine particle (UFP) counts inside school buses. UFPs are toxic to humans and school engines emit particles primarily in the UFP size range. As school buses idle at bus stops, UFPs penetrate into cabins through cracks, doors, and windows. Counts increase over time at a size dependent rate once the engine turns on. 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 using a mixed effect model with a cubic B-spline basis as a function of size. The log residual variance over size is modeled using a quadratic B-spline basis to account for heterogeneity in error across size bin, and errors are autoregressive over time. Model predictions are communicated graphically. These methods provide information needed to quantify UFP counts by size and possibly minimize UFP exposure in the future.

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Heidi J. Fischer. Qunfang Zhang. Yifang Zhu. Robert E. Weiss. "Functional time series models for ultrafine particle distributions." Ann. Appl. Stat. 11 (1) 297 - 319, March 2017. https://doi.org/10.1214/16-AOAS1004

Information

Received: 1 December 2014; Revised: 1 November 2016; Published: March 2017
First available in Project Euclid: 8 April 2017

MathSciNet: MR3634325
Digital Object Identifier: 10.1214/16-AOAS1004

Rights: Copyright © 2017 Institute of Mathematical Statistics

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Vol.11 • No. 1 • March 2017
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