Spatio-temporal models with space-time interaction and their applications to air pollution data

TL;DR

This paper develops a novel spatio-temporal model with space-time interaction components to analyze and forecast air pollution levels, specifically PM2.5, across multiple monitoring stations in Taiwan.

Contribution

It introduces a new spatio-temporal modeling approach incorporating space-time interaction and random effects for air pollution data analysis.

Findings

Model effectively captures space-time dynamics of PM2.5

Provides accurate short-term air quality forecasts

Enhances understanding of pollution spatial-temporal patterns

Abstract

It is of utmost importance to have a clear understanding of the status of air pollution and to provide forecasts and insights about the air quality to the general public and researchers in environmental studies. Previous studies of spatio-temporal models showed that even a short-term exposure to high concentrations of atmospheric fine particulate matters can be hazardous to the health of ordinary people. In this study, we develop a spatio-temporal model with space-time interaction for air pollution data. The proposed model uses a parametric space-time interaction component along with the spatial and temporal components in the mean structure, and introduces a random-effects component specified in the form of zero-mean spatio-temporal processes. For application, we analyze the air pollution data (PM2.5) from 66 monitoring stations across Taiwan.