出版社：Chinese Association for Aerosol Research in Taiwan
摘要：Dhaka, the capital of Bangladesh, is one of the megacities in the world with the worst airquality. 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 to2004 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 bymeteorology than PM2.5-10. Temperature, wind speed, and wind direction account for 94% of totalPM2.5 variability explained by the model, while relative humidity contributes to 75% of total PM2.5-10variability. 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 ofDhaka’s PM concentration, although overestimate the low concentrations during wet season(April–September). We validate these models using a recent dataset (2013–2017) from the samemonitoring site, in which modeled PM show strong positive correlations with observedconcentrations (r = 0.81 and 0.76 for PM2.5 and PM2.5-10 respectively). Models also exhibit strongpredictive power in forecasting PM levels of two other CAMSs in Dhaka. Thus, the developedmodels 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 Dhakawith reasonable accuracy if daily meteorological data and previous day’s PM concentration areavailable. The effect of climate change scenarios on air pollution dynamics of Dhaka can also beassessed using these models.
