Assessing the relative importance of surface ozone influential variables in regional-scale analysis

Abstract

The multilayer perceptron (MLP) model is frequently used to assess the relative importance (RI) of surface ozone influential variables when they are used to investigate ozone variation mechanisms. Previous studies, however, suffer from two limitations: 1) indentifying or searching the optimal MLP topology to avoid a biased RI assessment inevitably incurs a heavy computational burden, and 2) the model is suitable only for local-scale analysis of ozone variation mechanisms. To tackle both problems, we selected three typical air-quality monitoring sites in Hong Kong as our study targets, as the ozone variations at these sites are inhomogeneously affected by regional and local factors. An MLP model trained by automatic relevance determination (referred to as MLP-ARD), a Bayesian MLP approach, was employed to assess the RI for both regional and local ozone influential variables. The results indicated the following remarks: 1) The MLP-ARD model, due to its high degree of resistance to both the over-fitting and the under-fitting problems, is exempt from identifying or searching the optimal MLP topology when used to obtain an unbiased RI assessment and thus avoid the heavy computational burden. Furthermore, the RI assessment results obtained with the MLP-ARD method are comparable to those of the best assessment method in the literature. Based on these findings, decision-makers can scientifically promulgate a site-specific air pollution control strategy site; 2) Regional-scale analysis of ozone variation is indispensable, as taking the key regional ozone influential variables into account significantly improves the prediction accuracy of the MLP-ARD model, especially on peak ozone days.