Assessment Air Quality Using ES-SOFM Hybrid Model in Xi’an, China

Abstract

On the basis of six kinds of air pollutant data provided by the Xi’an Environment Protection Bureau, Two kinds of air assessment model were presented in this paper. Firstly, air quality index (AQI), which has been adopted as a part of national standard in China, was used to assess the air quality of Xi’an in 2013 winter. We also introduced a fuzzy self-organizing feature map (SOFM) model to classify air quality in an unsupervised and comprehensive way. As a further research, a hybrid model was put forward based on Evolutionary Strategy (ES) and SOFM. With SOFM neural networks embedded into ES, the sensitivity of SOFM neural networks to the initial weight matrix and sequence of exemplar input is overcome by the global optimization of ES. The results of our work demonstrate that the ES-SOFM Hybrid Model is quite appropriate techniques for air quality assessment. Unlike AQI method, SOFM’s result is decided by all pollutant instead of only the most serious one. No matter what kind of method, all assessment results show the very serious air pollution in Xi’an. The government and every citizen must take steps at once to prevent air quality from further depravation.