Modeling threshold exceedance probabilities of spatially correlated time series

TL;DR

This paper introduces a two-step method combining temporal smoothing and spatial interpolation to estimate the probability of exceeding air pollution limits, demonstrated on PM10 data in North Italy.

Contribution

It presents a novel approach for modeling exceedance probabilities of pollutants using combined temporal and spatial analysis techniques.

Findings

Effective estimation of exceedance probabilities for PM10 in North Italy.

Method demonstrates potential for environmental health risk assessment.

Applicable to other pollutants and regions with spatially correlated time series data.

Abstract

The Commission of the European Union, as well the United States Environmental Protection Agency, have set limit values for some pollutants in the ambient air that have been shown to have adverse effects on human and environmental health. It is therefore important to identify regions where the probability of exceeding those limits is high. We propose a two-step procedure for estimating the probability of exceeding the legal limits that combines smoothing in the time domain with spatial interpolation. For illustration, we show an application to particulate matter with diameter less than 10 microns (PM$_{10}$) in the North-Italian region Piemonte.