Spatial Distribution of Particulate Matter (PM2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model

Abstract

Abstract Particulate matter is one of the life threathening pollutants that are harmful to human health. The aim of this study is to assess PM2.5 distribution using spatial interpolation techniques of inverse distance weighted (IDW) by predicting their concentrations at distinct unmonitored locations. The IDW interpolation was developed using Arcmap for Klang Valley area covering six districts in central Peninsular Malaysia. The Department of Environment (DOE) continous air quality monitoring stations (CAQMS) were installed at 6 districts in Klang Valley throughout period of 9 months between January and September 2022 for data collection. The results from IDW showed that PM2.5 concentrations were highest in February and lowest in March. The IDW spatial mapping demonstrated that PM2.5 distributed higher in Seremban and Petaling districts throughout 9 months while Kuala Lumpur and Putrajaya demonstrated contrary lower. The IDW cross-validation results showed an acceptable predictive accuracy with low RMSE values ranging from 1.790 to 5.073 and high R squared value with range from 0.0267 to 0.5081. The results showed a very good fit of the IDW model to the observed points, confirming that the results of these analyses can monitor and predict PM2.5 concentrations with high accuracy. The interpolation maps that result can help identify key regions that require air quality management mitigation strategies.