Trends of Air Pollutants in Colombo City and Relationship with Meteorological Variables

Authors : Anusha JEGATHESAN, Erandathie LOKUPİTİYA, Sarath PREMASİRİ

Pages : 93-98

View : 7 | Download : 7

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :Colombo, the commercial capital of Sri Lanka, has to deal with air pollutants such as Sulphur dioxide insert ignore into journalissuearticles values(SO2);, Nitrogen dioxide insert ignore into journalissuearticles values(NO2);, Particulate matter insert ignore into journalissuearticles values(PM2.5 and PM10);. The main objective was to study the trends of NO2 and SO2 concentrations during the period 2013-2019 and predict the future air quality of Colombo by modelling the monthly time series of those pollutants. The data used in this research was secondary, obtained from the National Building Research Organization insert ignore into journalissuearticles values(NBRO); and the Department of Meteorology of Sri Lanka. The SO2 and NO2 exponential smoothing models fitted had R- squared values of 66.40% and 68.90% respectively. Significant correlation results were obtained between the predicted insert ignore into journalissuearticles values(2020-2021); and the observed values. The NO2 levels displayed a significant correlation insert ignore into journalissuearticles values(r = 0.86, p < 0.05);. The multiple regression models fitted for NO2 and SO2 with the weather parameters indicated a good fit. A comparison of air pollutant levels recorded before the pandemic period insert ignore into journalissuearticles values(2013 - 2019); with the air pollutant levels after the pandemic insert ignore into journalissuearticles values(2020 - 2021); had a significant difference insert ignore into journalissuearticles values(p < 0.05);. Statistically significant negative correlations were found between SO2 levels with relative humidity insert ignore into journalissuearticles values(r = -0.27; p < 0.05); and between NO2 levels with temperature insert ignore into journalissuearticles values(r = -0.23; p < 0.05);, and relative humidity insert ignore into journalissuearticles values(r = -0.36; p < 0.05);. Similarly, Air Quality Index insert ignore into journalissuearticles values(AQI); values determined from PM2.5 showed a significant negative correlation with rainfall, relative humidity, and wind speed insert ignore into journalissuearticles values(p < 0.05); while AQI values of PM10 showed a significant negative correlation with rainfall and relative humidity insert ignore into journalissuearticles values(p < 0.05);. Thus, increased levels of meteorological variables such as precipitation, humidity, and wind speed seem to reduce the atmospheric concentrations of the above pollutants.
Keywords : Air Polllution, Time Series Modelling, Forecasting