Statistical analysis of primary and secondary atmospheric formaldehyde

Regression models coupled with time series data were used to analyze the contribution of primary and secondary sources to formaldehyde (HCHO) concentrations, as determined by statistical analogy to primary (carbon monoxide, CO) and secondary (ozone, O₃) compounds measured simultaneously in Houston, TX. Time series analyses substantiated the need for statistical methods of analysis, given the complexity of the data and the rapid fluctuations that occur in atmospheric concentrations. A positive relationship was found for both the auto-correlation function (ACF) and partial auto-correlation function (PACF) of HCHO with either CO or O₃. Regression models used to distinguish primary and secondary contributions included a simple linear regression of the three compounds (one lag unit of time, 5min) on current HCHO concentrations, resulting in a ratio of secondary formation to primary emission of 1.7. A second, more robust model utilized auto-correlated error processes to approximate the true nature of the linear regression; this model also indicates the ratio of secondary to primary contribution at 1.7 as the mean of ten model simulations. From the error processes model, one lag unit of time was most significant for CO predicting HCHO, while simultaneous measurements (lag 0) were most significant for O₃ predicting HCHO. Outlying O₃ and HCHO concentrations were shown not to affect the results.