Analysis of Air Quality in Indonesia Using Tensor Decompositon-Based Clustering

Abstract

Indonesia faces severe challenges in addressing high levels of air pollution. To anticipate and address the effect of air pollution on human health and the environment, all parties need concerted efforts. Grouping regions based on similarities in air quality will help the government tailor policies more effectively. This research aims to conduct a cluster-based analysis of air quality in terms of not only its pollutant compound but also its spatial- and temporal context using tensor decomposition. The cluster elements were then analyzed with similar occurrence patterns to obtain potential factors. Air quality data containing time, region, and air quality parameters are processed to construct a tensor. Air quality parameters being addressed are carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter (PM10 and PM2.5). The data was retrieved from 31 representative monitoring stations across provinces in Indonesia from October 2023 to December 2023. Tensor decomposition with three latent features in each dimension was used for k-medoid clustering using the silhouette and elbow evaluation. Tucker decomposition is used for the general method in tensor decomposition because of its ability to control the decomposition rank. This means regulating the number of latent features or components to be extracted during the decomposition process. The analysis resulted optimal cluster numbers for region, time, and pollutant dimensions. It suggested that four clusters were identified based on region dimension. Meanwhile, in terms of temporal patterns, three clusters resulted. Furthermore, three clusters were identified based on pollutant types. The most concerning region cluster, including Palembang and Pontianak, have PM10 and PM2.5 concentrations above the safe level.