First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019-2021

Abstract

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM_2.5, PM₁₀, and O₃, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM_2.5 and PM₁₀. The concentrations and AQI values for PM_2.5 ranged from 0 to 93.6 μg/m³ and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM₁₀ ranged from 0.1 to 149.27 μg/m³ and 2-98 μg/m³, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM_2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM₁₀ and O₃ explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

Keywords: Air quality index (AQI); Autoregressive integrated moving average (ARIMA); Multiple linear regression (MLR); Ozone; Particulate matter.