Modeling the Effects of Meteorological Factors on SO₂ and PM10 Concentrations with Statistical Approaches

Abstract

Abstract Air quality has been deteriorated seriously in urban areas as a result of increasing anthropogenic activities. Meteorological conditions affect air pollution levels in the urban atmosphere significantly due to their important role in transport and dilution of the pollutants. This paper aims to investigate usability of some promising statistical methods for examining the impacts of metrological factors on SO 2 and PM10 levels. Data were collected from city centre of Kocaeli in winter periods from 2007 to 2010 as pollutant concentrations increase in winters due to expanding combustion facilities. Results of bivariate correlation analysis showed that humidity and rainfall have remarkable negative correlations with the pollutants. Multiple linear regression models and artificial neural network (ANN) models were used to predict next day's PM10 and SO 2 levels. In regression models calculated R 2 values were 0.89 and 0.75 for PM10 and SO 2 , respectively. Among the various architectures, single layer networks provided better performance in ANN applications. Highest R 2 values were obtained as 0.89 and 0.69 for PM10 and SO 2 , respectively, by using appropriate networks.