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.

本文使用称为相关向量机 (RVM) 的机器学习模型来量化冬季条件下臭氧 (O ₃ ) 和氮氧化物 (NO _x ) 的形成。实地研究测量基于 Olson 等人先前描述的工作。（2019 年），其中报告了犹他州冬季实地研究的连续测量结果。RVM 是使用 O ₃或二氧化氮 (NO ₂ ) 作为输出变量制定的。预测浓度和测量浓度之间的相关系数 (r ² )值对于 O ₃为 0.944，对于 NO ₂为 0.931. RVM 是根据观察到的测量结果构建的，并产生稀疏模型公式，这意味着仅使用数据的一个子集来近似整个数据集。在本研究中，以 O ₃作为输出变量的 RVM仅使用了 20% 的测量数据，而以 NO ₂为输出变量的 RVM使用了 16%。然后使用 RVM 作为预测模型来评估单个前体的重要性。使用 O ₃作为输出变量，三种物质的增加导致 O ₃浓度增加：过氧化氢 (H ₂ O ₂ )、五氧化二氮 (N ₂ O ₅ ) 和分子氯 (Cl ₂）。对于研究期间测量的两种终止产物，硝酸 (HNO ₃ ) 和甲酸 (CH ₂ O ₂ )，未观察到O ₃浓度的变化。使用NO ₂作为输出变量，仅N ₂ O ₅的增加导致NO ₂浓度增加。