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
An Air Quality Data Analysis System for Interrelating Effects, Standards, and Needed Source Reductions: Part 9. Calculating Effective Ambient Air Quality Parameters
