Air pollution problem is faced by many countries in the world. Ambient air quality studies and monitoring need a long time period of data to cover various atmospheric conditions, which create big data. A tool is needed to make easier and more effective to analyze big data. <strong>Aims: </strong>This study aims to analyze various application of <em>openair</em> model, which is available in open-source, for analyzing urban air quality data. <strong>Methodology and results: </strong>Each pollutant and meteorological data were collected through their sampling-analysis methods (active, passive or real-time) from a certain period of time. The data processed and imported in the <em>openair</em> model were presented in <em>comma separated value</em> (csv) format. The input data must consist of date-time, pollutant, and meteorological data. The analysis is done by selecting six functions: <em>theilSen</em> for trend analysis, <em>timeVariation</em> for temporal variations, <em>scatterPlot</em> for linear correlation analysis,<em> timePlot</em> for fluctuation analysis, <em>windRose</em> for wind rose creation, and <em>polarPlot</em> for creating pollution rose. Results from these functions are discussed. <strong>Conclusion, significance and impact study: </strong><em>Openair</em> model is capable of analyzing a long time air quality data. Application of <em>openair</em> model is possible to cities in Indonesia that already monitor ambient air quality but have not analyzed the data yet
K-Means Clustering of Ambient Air Quality Data of Uttarakhand, India during Lockdown Period of Covid-19 Pandemic
