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Estimation of Surface Particulate Matter (PM2.5 and PM10) Mass Concentration from the 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. (2019). Estimation of Surface Particulate Matter (PM2.5 and PM10) Mass Concentration from the Ceilometer Backscattered Profiles. Aerosol Air Qual. Res., doi: 10.4209/aaqr.2019.08.0371.

Avinash N. Parde1, Sachin D. Ghude 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

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 the ceilometer to understand the vertical and horizontal mixing of aerosols in the polluted planetary boundary layer (PBL). Backscattered profiles from the ceilometer were able to reveal information on the boundary layer structure including mixed layer, nocturnal residual layer and aerosol elevated layer far above the mixed layer over Delhi. Backscattered profiles also showed the accumulation of aerosols near the surface under the feeble turbulent conditions and mixing of aerosols from the residual layer to the surface layer under the convective conditions. We found that the ceilometer backscattered signal from a height of 45 m above the ground was strongly correlated (82%) with the surface PM2.5 and PM10 mass concentration. We developed an empirical model based on regression between the ceilometer backscattered signal and surface PM2.5 and PM10 observations in Delhi. The regression model was then tested and validated against the independent measurement of surface PM2.5 and PM10 observations during winter 2019. Local meteorological conditions, particularly cloudy and rainy conditions, were found to influence the quality of the relationship between the PM2.5 and PM10 mass concentration and backscattered signal. The performance statistics indicated that the magnitude of mean bias between observed and estimated PM2.5 (-21 µg m-3, RMSE = 75) and PM10 (31 µg m-3, RMSE = 118) was significantly close to the observations. On clear days, estimated PM2.5 and PM10 mass concentration using the empirical model was overestimated by 7% and underestimated by 6%, respectively.

Keywords

Pollution event PM2.5 PM10 Ceilometer backscatter


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