Nonlinear relationships between atmospheric aerosol and its gaseous precursors: Analysis of long-term air quality monitoring data by means of neural networks

Abstract

The nonlinear features of the relationships between concentrations of aerosol and volatile organic compounds (VOC) and oxides of nitrogen (NOx) in urban environments are derived directly from data of long-term routine measurements of NOx, VOC, and total suspended particulate matter (PM). The main idea of the method used for the analysis is creation of special empirical models based on artificial neural networks. These models which are in essence the nonlinear extension of commonly used linear statistical models are believed to provide the best fit for the real (nonlinear) PM-NOx-VOC relationships under different atmospheric conditions. It is believed that such models may be useful in context of various scientific and practical problems concerning atmospheric aerosols. The method is demonstrated by the example of two empirical models created with independent data-sets collected at two air quality monitoring stations at South Coast Air Basin, California. It is shown that in spite of considerable distance between the monitoring stations (more than 50 km) and thus substantially different environmental conditions, the empirical models manifest several common qualitative features. Specifically, it is found that, under definite conditions, the decrease of the level of NOx or VOC may lead to the increase of mass concentration of aerosol. It is argued that these features are caused by the nonlinear dependence of hydroxyl radical on VOC and NOx.