Timescale effect estimation in time-series studies of air pollution and health: A singular spectrum analysis approach

Abstract

A wealth of epidemiological data suggests an association between mortality/morbidityfrom pulmonary and cardiovascular adverse events and air pollution, but uncertainty remains as to the extent implied by those associations although the abundance of the data. In this paper we describe an SSA (Singular Spectrum Analysis) based approach in order to decompose the time-series of particulate matter concentration into a set of exposure variables, each one representing a different timescale. We implement our methodology to investigate both acute and long-term effects of PM10 exposure on morbidity from respiratory causes within the urban area of Bari, Italy. © 2008, Institute of Mathematical Statistics. All rights reserved.