Special Issue on 2019 Asian Aerosol Conference (AAC)

Avinash N. Parde1, Sachin D. Ghude This email address is being protected from spambots. You need JavaScript enabled to view it.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

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


 

Received: August 22, 2019
Revised: October 9, 2019
Accepted: November 4, 2019

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.2019.08.0371 

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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. 20: 1640–1650. https://doi.org/10.4209/aaqr.2019.08.0371


HIGHLIGHTS

  • 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.
 

ABSTRACT


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.


Keywords: Pollution event; PM2.5 and PM10; Ceilometer backscatter.



Aerosol Air Qual. Res. 20:1640-1650. https://doi.org/10.4209/aaqr.2019.08.0371 


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