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

Abstract

Abstract. The nonlinear features of the relationships between concentrations of aerosol and volatile organic compounds (VOC) and nitrogen oxides (NO x ) in urban environments are revealed directly from data of long-term routine measurements of NO x , 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-NO x -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 NO x 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 NO x .

Multi-annual changes of NO<sub>x</sub> emissions in megacity regions: nonlinear trend analysis of satellite measurement based estimates

“…polytechnique.fr/chimere/. The tropospheric NO 2 column amounts simulated by CHIMERE were evaluated against the SCIAMACHY and GOME measurements in earlier studies (Konovalov et al, 2005;Blond et al, 2007). In this study, the model's domain covers all of Europe, the Mediterranean and the Middle East with a horizontal resolution of 1 • ×1…”

“…Neural networks are extensively used in air pollution studies for approximation of unknown relationships between the observed quantities and forecasting (see, e.g. Gardner and Dorling, 1998;Konovalov 2002Konovalov , 2003Lary et al, 2004;Hooyberghs, 2005;Argiriou, 2007;Feister et al, 2008). Second, we follow the Bayesian probabilistic approach, which is commonly used in inverse modeling and data assimilation studies and is applied here to the estimation of weight coefficients of the neural network.…”

Inverse Modeling of Nitrogen Oxides Emissions from the 2010 Russian Wildfires by Using Satellite Measurements of Nitrogen Dioxide

“…Nitrogen oxides (NOx=NO+NO2) are important atmospheric constituents affecting climate processes and air quality specifically by serving as precursors of tropospheric ozone [1,2] and influencing secondary aerosol formation [3,4]. One of the significant sources of NOx on a global scale is associated with biomass burning (BB), including wildfires: it is estimated that biomass and biofuel burning provide around 15 % of NOx emissions globally [5].…”