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Estimation of Surface Particulate Matter (PM2.5 and PM10) Mass Concentrations from Ceilometer Backscattered Profiles

Category: Aerosol Physics and Instrumentation

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DOI: 10.4209/aaqr.2019.08.0371

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To cite this article:
Parde, A.N., Ghude, S.D., Pithani, P., Dhangar, N.G., Nivdange, S., Krishna, G., Lal, D., Jenamani, R., Singh, P., Jena, C., Karumuri, R., Safai, P. and Chate, D. (2020). Estimation of Surface Particulate Matter (PM2.5 and PM10) Mass Concentrations from Ceilometer Backscattered Profiles. Aerosol Air Qual. Res., doi: 10.4209/aaqr.2019.08.0371.

Avinash N. Parde1, Sachin D. Ghude 1,2, Prakash Pithani2, Narendra G. Dhangar2, Sandip Nivdange3, Gopal Krishna2, D.M. Lal2, R. Jenamani4, Pankaj Singh5, Chinmay Jena2, Ramakrishna Karumuri2, P.D. Safai2, D.M. Chate2

  • 1 Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune 411007, India
  • 2 Indian Institute of Tropical Meteorology, Pune 411008, India
  • 3 Department of Environmental Science, Savitribai Phule Pune University, Pune 411007, India
  • 4 India Meteorological Department, New Delhi 110003, India
  • 5 Department of Physics, Deshbandhu College, University of Delhi, New Delhi 110019, India


  • Ceilometer profiles reveal information on the boundary layer structure over Delhi.
  • Backscattered signal correletes well with the surface PM mass concentration.
  • Regression model to estimate PM2.5/PM10 mass concentration using ceilometer.


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 PM2.5 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 PM2.5 and PM10 mass concentrations and the backscattered signal, the magnitude of the mean bias between the observed and the values for PM2.5 (–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 PM2.5 by 7% and underestimated the PM10 by 6% on clear days.


Pollution event PM2.5 PM10 Ceilometer backscatter

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