Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate

Abstract

Satellite-derived aerosol optical depth (AOD) is popularly used to infer ground-level PM2.5 concentration due to its wide coverage. The fact that aerosols are largely confined in the atmospheric boundary layer makes boundary layer height (BLH) an important scale factor for AOD-based PM2.5 estimates. Our recent ground-based lidar observations, nevertheless, indicate that aerosol particles are heterogeneously mixed within the boundary layer, and even frequently reside above BLH, forming the residual layers (RL)-like pattern. To better sort out the underlying mechanism behind the above-mentioned phenomenon and the impact on hourly ground-level PM2.5 estimates from satellite-based AOD, we firstly propose a novel notion of haze layer height (HLH), which is calculated from Micro-Pulse Lidar (MPL) profile. Combined analysis of 3.5-year ground-based lidar profiles, CE-318 AOD, and PM2.5 measurements show that the coefficient of determination (R2) between PM2.5 and AOD normalized with HLH increases from 0.49 to 0.61 for 90% of the dataset. Second, we applied HLH to an Auto-encoder-based Deep Residual Network (ADRN) and tested the effect on satellite AOD-based PM2.5 estimation within a 300 km range surrounding the MPL station. With the aid of the AOD imputation technique, a similar improvement of using HLH instead is found on the regional scale PM2.5 estimation, which can be demonstrated by the comparison with air quality measurements and other machine learning models. The results show that using ADRN with HLH achieves the highest performance (mean R2 = 0.87, RMSE = 10.12 μg/m3) among 4 machine learning models. This new approach, largely combining active and passive remote sensing data through artificial neural networks (CAPTA), shows improved accuracy and coverage of hourly PM2.5 estimation with aerosol vertical information and AOD calculation under clouds. In addition, further analysis showed that the average difference between morning and daily PM2.5 concentration could equate to an accuracy of 0.19–2.57 yrs. in terms of life expectancy, indicating that our new approach to the determination of PM2.5 from space sheds new insight into the assessment of the aerosol impact on public health.