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The nonlinear features of the relationships between concentrations of aerosol and volatileorganic compounds (VOC) and nitrogen oxides (NOx) in urban environments are revealeddirectly from data of long-term routine measurements of NOx, VOC, and total suspendedparticulate matter (PM). The main idea of the method is development of special empiricalmodels based on artificial neural networks. These models, that are basically, the nonlinearextension of the commonly used linear statistical models provide the best fit for the real(nonlinear) PM-NOx-VOC relationships under different atmospheric conditions. Such modelsmay be useful in the context of various scientific and practical problems related toatmospheric aerosols. The method is demonstrated on an example of two empirical modelsbased on independent data-sets collected at two air quality monitoring stations at South CoastAir Basin, California. It is shown that in spite of a rather large distance between themonitoring stations (more than 50 km) and thus substantially different environmentalconditions, the empirical models demonstrate several common qualitative features.Specifically, under definite conditions, a decrease in the level of NOxor VOC may lead to an increase in mass concentration of aerosol. It is argued that these features are due to thenonlinear dependence of hydroxyl radical on VOC and NOx

atmospheric chemistry and physics

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