Lichen - air quality association rule mining for urban environments in the tropics

Abstract

ABSTRACT There are significant gaps in air quality monitoring across many low- and middle-income countries, which can be filled by bioindicators like lichen. This study examined the links between lichen and air quality across urban environments in Nigeria. Lichen surveys and air quality monitoring were carried out across four major cities focusing on NO2, SO2, PM2.5, and PM10. Association rule mining was used to identify robust rules defining the association between lichen and air quality categories. For the maximal frequent set with Lichen in the antecedent, 9 and 5 rules were identified by A priori and Eclat, respectively. These indicated that three genera: Diorygma, Pyxine, and Physcia are the most commonly associated lichen with poor air quality particularly NO2 and SO2. This showed that these lichens are viable indicators of long-term air quality due to their consistent occurrence across the rules from different algorithms.