Air pollution index (API) is used in Malaysia to determine the level of air quality. API is based on the calculation consist of pollutants PM₁₀, O₃, CO₂, SO₂, and NO₂. Unhealthy air quality can harm human health and the environment as well as property. In view of this fact, a study of air pollution trend analysis in Malaysia from 2010 to 2015 was performed with the objective of determining the API trend in Malaysia from 2010 to 2015. A dataset of API value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). In this study, 19,872 datasets for all Malaysian air quality monitoring stations that had API value greater than 100 and a total of 52,584 datasets for Muar District in Johor were used. XLSTAT add-in 2014 was used to analyze the API hourly reading. Analysis shows that the air monitoring station at Sekolah Menengah Teknik Muar in Johor shows the highest value of API reading with 663 on 23 June 2013 (emergency level), where on that day Malaysia faced its worst air quality due to haze episodes. Other locations also show the worst air quality with API registering at unhealthy, very unhealthy, and hazardous levels.

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000–December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO₂, NO₂, O₃, CO and particulate matter with a diameter size of below 10 μm (PM₁₀). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.