This study aims to identify high emissions of CO2 and NOX from gasoline passenger cars based on PEMS data by introducing a concept of emission state, and investigate their correlations with driving behaviors. The clustering approach of K-means++ was employed to classify the instantaneous mass emission value and emission rate under various road types, respectively. A novel identification indicator (i.e., the ratio of change rate and growth rate of instantaneous emissions) was proposed as the basis for dividing each emission state. Subsequently, three matrices (i.e., probability matrix, value matrix, and identification matrix) were constructed to reflect relationships between emission states and emission rates under each road type. Moreover, driving scenarios of CO2 and NOX high emission were investigated and compared by machine learning models with SHAP explanation and ordered logistic models. The empirical results indicate that the identification indicators of CO2 and NOX high emissions are 2.49 g/s2 and 3.66 mg/s2 on the freeway, 2.98 g/s2 and 2.12 mg/s2 on the primary road, and 2.77 g/s2 and 2.05 mg/s2 on the secondary road. Within the same ranges of driving behavior parameters on the freeway, the occurrence probability of CO2 high emission state is higher than that of relatively high emission state, while an opposite trend is observed for NOX emissions. Interestingly, despite NOX and CO2 show similar emission characteristics on the primary and secondary road, the driving behaviors corresponding to high emissions of NOX and CO2 present significant disparities. Generally, the acceleration is the primary determinant of CO2 high emissions, while both acceleration and deceleration are significant contributors to NOX high emissions. The findings of this study recommend that long periods of high-speed travelling should be avoided on the freeway. Frequent and abrupt changes in acceleration and deceleration should be minimized on the primary and secondary road, respectively.
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