Data mining of the relationship between volatile organic components and transient high ozone formation

Abstract

The aim of this study is to identify relationships between volatile organic components (VOCs) and transient high ozone formation in the Houston area. The ozone is not emitted to the atmosphere directly but is formed by chemical reactions in the atmosphere. In Houston, short-term (1 h) sharp increases are observed followed by a rapid decrease back to typical concentrations. Automatic gas chromatographs (GCs) are operated at several sites which cryogenically collect VOCs during an hour and then the compounds are flash evaporated into the GC for analysis. Chromatographic data for more than 65 VOCs are stored in analysis report text files. A program has been developed to read the amount of each component in the measurements such that a data set is generated that includes the concentrations of each VOC for each hourly sample. A subset of the data is selected that corresponds to the period of the positive ozone transient and these data are used in the data mining (DM) process. Based on a chemical mass balance (CMB) analysis, a linear model was established between the subset of the VOCs data and the positive ozone transition. Non-negative least squares (NNLS) was used to calculate the regression coefficient of the VOCs that have the most significant positive relationship to the positive ozone transition. The results show that more attention might be paid to several unknown VOCs, which have significant relationships to the transient high ozone formation.