Sadia Afrin1,2, Mohammad Maksimul Islam This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Tanvir Ahmed1

1 Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka - 1000, Bangladesh
2 Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA


Received: July 3, 2020
Revised: October 1, 2020
Accepted: October 13, 2020

 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.2020.07.0371  


Cite this article:

Afrin, S., Islam, M.M. and Ahmed, T. (2020). A Meteorology Based Particulate Matter Prediction Model for Megacity Dhaka . Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.2020.07.0371


HIGHLIGHTS

  • PM prediction models can capture temporal and spatial variability within Dhaka.
  • Meteorology with lag PM2.5 can explain 73% variability in PM2.5 concentration.
  • Wind speed and direction seem to influence only the fine PM concentration.
  • Modeled concentrations show strong positive correlation with observed concentration.
 

ABSTRACT 


Dhaka, the capital of Bangladesh, is one of the megacities in the world with the worst air quality. In this study, we develop statistical models for predicting particulate matter (PM) concentration in ambient air of Dhaka using meteorological and air quality data from 2002 to 2004 of a continuous air quality monitoring station (CAMS). Model for finer fraction of PM (PM2.5) explains up to 57% variability of daily PM2.5 concentration, whereas model for coarser fraction (PM2.5-10) explains up to 35% of its variability, indicating that PM2.5 is influenced more by meteorology than PM2.5-10. Temperature, wind speed, and wind direction account for 94% of total PM2.5 variability explained by the model, while relative humidity contributes to 75% of total PM2.5-10 variability. Inclusion of PM lag effect increases models’ predictive power by 4-16%. In general, our developed models show promising performance in capturing the seasonal variability of Dhaka’s PM concentration, although overestimate the low concentrations during wet season (April to September). We validate these models using a recent dataset (2013-2017) from the same monitoring site, in which modeled PM show strong positive correlations with observed concentrations (r = 0.81 and 0.76 for PM2.5 and PM2.5-10 respectively). Models also exhibit strong predictive power in forecasting PM levels of two other CAMSs in Dhaka. Thus, the developed models have potentials to explain the temporal and spatial variability of daily PM within Dhaka. These models can be helpful to policymakers as they can predict daily PM at any location of Dhaka with reasonable accuracy if daily meteorological data and previous day’s PM concentration are available. The effect of climate change scenarios on air pollution dynamics of Dhaka can also be assessed using these models.


Keywords: Air quality; Particulate matter; Regression model; Seasonal variability.




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