Dynamic Non-parametric Monitoring of Air-Pollution

Sotiris Bersimis () and Kostas Triantafyllopoulos ()
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Sotiris Bersimis: University of Piraeus

Methodology and Computing in Applied Probability, 2020, vol. 22, issue 4, 1457-1479

Abstract: Abstract Air pollution poses a major problem in modern cities, as it has a significant effect in poor quality of life of the general population. Many recent studies link excess levels of major air pollutants with health-related incidents, in particular respiratory-related diseases. This introduces the need for city pollution on-line monitoring to enable quick identification of deviations from “normal” pollution levels, and providing useful information to public authorities for public protection. This article considers dynamic monitoring of pollution data (output of multivariate processes) using Kalman filters and multivariate statistical process control techniques. A state space model is used to define the in-control process dynamics, involving trend and seasonality. Distribution-free monitoring of the residuals of that model is proposed, based on binomial-type and generalised binomial-type statistics as well as on rank statistics. We discuss the general problem of detecting a change in pollutant levels that affects either the entire city (globally) or specific sub-areas (locally). The proposed methodology is illustrated using data, consisting of ozone, nitrogen oxides and sulfur dioxide collected over the air-quality monitoring network of Athens.

Keywords: Multivariate statistical process control; Time series monitoring; Air surveillance; Air pollution; Non-parametric control chart; Generalised binomial-type statistics; Markov chain embedded variables; Primary: 62H12; Secondary: 62M10; 62P12 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11009-018-9661-0

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