Nonlinear relationships between atmospheric aerosol and its gaseous precursors: Analysis of long-term air quality monitoring data by means of neural networks I. B. Konovalov Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia
Abstract. The nonlinear features of the relationships between concentrations of aerosol and volatile
organic compounds (VOC) and nitrogen oxides (NOx) in urban environments are revealed
directly from data of long-term routine measurements of NOx, VOC, and total suspended
particulate matter (PM). The main idea of the method is development of special empirical
models based on artificial neural networks. These models, that are basically, the nonlinear
extension of the commonly used linear statistical models provide the best fit for the real
(nonlinear) PM-NOx-VOC relationships under different atmospheric conditions. Such models
may be useful in the context of various scientific and practical problems related to
atmospheric aerosols. The method is demonstrated on an example of two empirical models
based on independent data-sets collected at two air quality monitoring stations at South Coast
Air Basin, California. It is shown that in spite of a rather large distance between the
monitoring stations (more than 50 km) and thus substantially different environmental
conditions, the empirical models demonstrate several common qualitative features.
Specifically, under definite conditions, a decrease in the level of NOx
or VOC may lead to an increase in mass concentration of aerosol. It is argued that these features are due to the
nonlinear dependence of hydroxyl radical on VOC and NOx.
Citation: Konovalov, I. B.: Nonlinear relationships between atmospheric aerosol and its gaseous precursors: Analysis of long-term air quality monitoring data by means of neural networks, Atmos. Chem. Phys., 3, 607-621, doi:10.5194/acp-3-607-2003, 2003.