Haze detection using persistent homology

Abstract

Haze is one of the environmental issues that greatly effects human health, economy and ecology. Particulate matter with aerodynamic size below 10 micrometers PM10 is the major pollutant during haze period. The existing methods are currently focusing on statistical analysis to provide quantitative analysis of PM10. Persistent homology is a tool in topological data analysis (TDA) that provides qualitative information known as topological features of data by detecting birth and death points that persist across multiple scales. One question arises in relating persistent homology and haze. Can persistent homology detect haze? This study addresses this question by providing qualitative structures of PM10 and detecting topological changes during haze episodes from 2000 until 2015 in Klang air quality monitoring station. This paper shows that, there are changes in topological features during haze episodes.