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. The vertical distribution of aerosol extinctioncoefficient (EC) measured by lidar systems has been used to retrieve theprofile of particle matter with a diameter &lt;2.5 µm(PM2.5). However, the traditional linear model (LM) cannot consider theinfluence of multiple meteorological variables sufficiently and theninduce the low inversion accuracy. Generally, the machine learning (ML)algorithms can input multiple features which may provide us with a new wayto solve this constraint. In this study, the surface aerosol EC andmeteorological data from January 2014 to December 2017 were used to explorethe conversion of aerosol EC to PM2.5 concentrations. Four MLalgorithms were used to train the PM2.5 prediction models:random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and extreme gradient boosting decision tree (XGB). The mean absolute error(root mean square error) of LM, RF, KNN, SVM and XGB models were 11.66(15.68), 5.35 (7.96), 7.95 (11.54), 6.96 (11.18) and 5.62 (8.27) µg/m3, respectively. This result shows that the RF model is the mostsuitable model for PM2.5 inversions from EC and meteorological data.Moreover, the sensitivity analysis of model input parameters was alsoconducted. All these results further indicated that it is necessary toconsider the effect of meteorological variables when using EC to retrievePM2.5 concentrations. Finally, the diurnal and seasonal variations oftransport flux (TF) and PM2.5 profiles were analyzed based on the lidardata. The large PM2.5 concentration occurred at approximately13:00–17:00 local time (LT) in 0.2–0.8 km. The diurnal variations ofthe TF show a clear conveyor belt at approximately 12:00–18:00 LT in0.5–0.8 km. The results indicated that air pollutant transport over Wuhanmainly occurs at approximately 12:00–18:00 LT in 0.5–0.8 km. The TF nearthe ground usually has the highest value in winter (0.26 mg/m2 s),followed by the autumn and summer (0.2 and 0.19 mg/m2 s, respectively),and the lowest value in spring (0.14 mg/m2 s). These findings give usimportant information on the atmospheric profile and provide us sufficientconfidence to apply lidar in the study of air quality monitoring.

atmospheric chemistry and physics

Estimation of the vertical distribution of particle matter (PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt;) concentration and its transport flux from lidar measurements based on machine learning algorithms