Quantifying wintertime O₃ and NO_x formation with relevance vector machines

This paper uses a machine learning model called a relevance vector machine (RVM) to quantify ozone (O₃) and nitrogen oxides (NO_x) formation under wintertime conditions. Field study measurements were based on previous work described by Olson et al. (2019), where continuous measurements were reported from a wintertime field study in Utah. RVMs were formulated using either O₃ or nitrogen dioxide (NO₂) as the output variable. Values of the correlation coefficient (r²) between predicted and measured concentrations were 0.944 for O₃ and 0.931 for NO₂. RVMs are constructed from the observed measurements and result in sparse model formulations, meaning that only a subset of the data is used to approximate the entire dataset. For this study, the RVM with O₃ as the output variable used only 20% of the measurement data while the RVM with NO₂ used 16%. RVMs were then used as a predictive model to assess the importance of individual precursors. Using O₃ as the output variable, increases in three species resulted in increased O₃ concentrations: hydrogen peroxide (H₂O₂), dinitrogen pentoxide (N₂O₅), and molecular chlorine (Cl₂). For the two termination products measured during the study, nitric acid (HNO₃) and formic acid (CH₂O₂), no change in O₃ concentration was observed. Using NO₂ as the output variable, only increases in N₂O₅ resulted in increased NO₂ concentrations.