Abstract
Abstract. The vertical distribution of aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter <2.5 µm (PM2.5). However, the traditional linear model (LM) cannot consider the influence of multiple meteorological variables sufficiently and then induce the low inversion accuracy. Generally, the machine learning (ML) algorithms can input multiple features which may provide us with a new way to solve this constraint. In this study, the surface aerosol EC and meteorological data from January 2014 to December 2017 were used to explore the conversion of aerosol EC to PM2.5 concentrations. Four ML algorithms 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 most suitable model for PM2.5 inversions from EC and meteorological data. Moreover, the sensitivity analysis of model input parameters was also conducted. All these results further indicated that it is necessary to consider the effect of meteorological variables when using EC to retrieve PM2.5 concentrations. Finally, the diurnal and seasonal variations of transport flux (TF) and PM2.5 profiles were analyzed based on the lidar data. The large PM2.5 concentration occurred at approximately 13:00–17:00 local time (LT) in 0.2–0.8 km. The diurnal variations of the TF show a clear conveyor belt at approximately 12:00–18:00 LT in 0.5–0.8 km. The results indicated that air pollutant transport over Wuhan mainly occurs at approximately 12:00–18:00 LT in 0.5–0.8 km. The TF near the 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 us important information on the atmospheric profile and provide us sufficient confidence to apply lidar in the study of air quality monitoring.
Highlights
Aerosol is a suspension of fine solid particles or liquid droplets in air (Hinds, 1999; Chen et al, 2014; Fan et al, 2019; Huang et al, 2019)
It can be understood that the machine learning (ML) models improve the prediction accuracy through meteorological factor correction
This study presents a comprehensive analysis to explore the conversion of aerosol extinction coefficient to PM2.5 concentrations based on the surface observation data from January 2014 to December 2017
Summary
Aerosol is a suspension of fine solid particles or liquid droplets in air (Hinds, 1999; Chen et al, 2014; Fan et al, 2019; Huang et al, 2019). With the anthropogenic aerosol emissions increasing in China, the concentration of fine particle matter with a diameter of less than 2.5 μm (PM2.5) in the atmosphere has increased significantly (Ding et al, 2016; Shi et al, 2020; Jin et al, 2021). The high concentrations of PM2.5 cause haze frequently and reduce atmospheric visibility, directly affecting the ecological environment and human health (Huang et al, 2014; He et al, 2020; Yin et al, 2021; Raaschou-Nielsen et al, 2013). It is necessary to carry out long-term continuous monitoring of the atmospheric environment, especially the spatial variation characteristics of PM2.5 concentrations
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