Timescale effect estimation in time-series studies of air pollution and health: A Singular Spectrum Analysis approach

TL;DR

This paper introduces a Singular Spectrum Analysis method to decompose air pollution time-series data into different timescales, aiding in understanding both immediate and long-term health effects of particulate matter exposure.

Contribution

The study applies SSA to differentiate timescales in air pollution data, providing a novel approach to analyze health impacts of particulate matter.

Findings

Decomposed PM10 data into multiple timescales.

Identified distinct short-term and long-term health effects.

Demonstrated methodology on urban air quality and health data.

Abstract

A wealth of epidemiological data suggests an association between mortality/morbidity from 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 $PM_{10}$ exposure on morbidity from respiratory causes within the urban area of Bari, Italy.