Development of land use regression model to estimate particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Peninsular Malaysia

Nowadays, exposure modelling has become the preferred method for assessing human air pollution exposure due to its capability to predict air pollution under various conditions. The land use regression model (LUR) is a widely conducted model utilized to estimate air pollutants especially in unmonitored locations. However, the application of the model is still lacking in developing countries, especially in the Southeast Asia region. Therefore, this study was conducted to develop the LUR model to estimate PM10 and NO2 concentrations in Peninsular Malaysia. Multiple linear regression with a supervised forward stepwise was used to develop the models, and the models were validated using the leave-out-one cross-validation (LOOCV) approach. Results showed that the LUR model of PM10 explained 58.5% variation, while the NO2 LUR model described 86.8% variation. The difference value of PM10 model R2 and LOOCV R2 were between 0.1% and 1.2 %, and the NO2 models were between 0.01% and 0.08% depicting the robust stability of the models. Both models indicated that increased road and industrial areas significantly influence PM10 and NO2 concentrations. Nevertheless, more studies on the LUR model should be conducted in developing countries to assess the model's applicability in the region.