Estimation of surface particulate matter (Pm_2.5 and pm₁₀) mass concentrations from ceilometer backscattered profiles

In this study, we used remotely sensed backscattered profiles from a ceilometer to characterize the vertical and horizontal mixing of aerosols in the polluted planetary boundary layer (PBL). These profiles revealed the structure of the boundary layer, which included the mixed layer, the nocturnal residual layer and the elevated aerosol layer far above the mixed layer over Delhi. The accumulation of aerosols near the surface during feeble turbulence and the mixing of aerosols from the residual layer into the surface layer during convection was captured very well by a ceilometer. The backscattered signal from a height of 45 m above the ground was strongly correlated (82%) with the observed surface PM_2.5 and PM₁₀ mass concentrations. We developed an empirical regression model based on this relationship, which was then tested and validated against independent measurements of the concentrations from November 2018. Although local meteorological conditions, particularly cloudiness and rain, influenced the strength of the correlation between the observed PM_2.5 and PM₁₀ mass concentrations and the backscattered signal, the magnitude of the mean bias between the observed and the values for PM_2.5 (–21 µg m^–3, RMSE = 75) and PM₁₀ (31 µg m^–3, RMSE = 118) indicated that the predicted values were fairly accurate. The model overestimated the PM_2.5 by 7% and underestimated the PM₁₀ by 6% on clear days.