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

Abstract

Nowadays, the rise in air pollution can be seen around the world especially in urban city. Studying the distribution of air pollution becomes one of the essential ways to know the characteristics and nature of air pollutants for air pollution mitigation actions around the world. In Myanmar, there are very few studies focusing on this area and these very few studies were carried out for short term periods. Therefore, this study was aimed to investigate the temporal distribution of air pollutants (PM2.5, PM10 and O3) following by MLR modeling for the influence of meteorological factors on air pollutant concentrations and Time series modeling for the prediction of AQI. Hourly air pollutant concentration was collected by US Embassy monitoring station and NGO monitoring station. Daily meteorological data was collected from department of Meteorological and Hydrology, Yangon, Myanmar. Pearson's correlation analysis was applied for checking the direction and magnitude of association between all meteorological data in order to include in regression models. Seasonal (summer, monsoon and winter) and annual multi linear regression models were analyzed to examine the associated factors for air pollution during study periods. Moreover, AQI for PM2.5, PM10 and O3 was calculated by the standard formula of USEPA. Finally, AQI prediction of PM10 and PM2.5 for 2022 was done by ARIMA of Time Series Modeling. Excel and R studio software were used for all the statistical analysis contained in this study. Overall, both air pollutants concentration and AQI of PM2.5 were 0-93.6 µg/m3 and 0-171 which exceeded the acceptable level by WHO which are 35.15 µg/m3 and under 50. Similarly, air pollutants concentration and AQI of PM10 were 0.1-149.27 µg/m3 and 2-98 which exceeded the acceptable level by WHO which are 50 µg/m3 and under 50. Particulate matter pollution is the worst especially in summer and winter. AQI of ozone during study period was 1-56 which was at a safe level. Dew point temperature, relative humidity and rainfall had significant negative association for all pollutants while, ambient temperature had significant positive association with all pollutants in this study. Winter models for particulate matters had the best model performance and explained majority of variation in particulate matters. 60%, 45% and 45% of variation in PM2.5, PM10 and Ozone were successfully explained by relative humidity in annual model. Time series modeling forecasted AQI of particulate matters and saw an increasing trend in the year 2022. In this study, it can be clearly seen that AQI of PM2.5 and PM10 are really high in Yangon city, Myanmar and the influence factors for distribution of air pollutants were identified. Therefore, urgent mitigation actions for air pollution should implement in the Yangon city, Myanmar for the health concerns of residents living in the city. The accurate and precise results from this study are aimed to give required information as a reference in setting national ambient air quality standard, in regional policy making process and also for giving awareness for the public through the publication.