International Journal of Environmental Pollution and Environmental Modelling

International Journal of Environmental Pollution and Environmental Modelling

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

Yazarlar: ["Anusha JEGATHESAN", "Erandathie LOKUPİTİYA", "Sarath PREMASİRİ"]

Cilt - , Sayı Cilt: 5 Sayı: 2 , 2022 , Sayfalar -


Anahtar Kelimeler:Air Polllution,Time Series Modelling,Forecasting

Özet: Colombo, the commercial capital of Sri Lanka, has to deal with air pollutants such as Sulphur dioxide (SO2), Nitrogen dioxide (NO2), Particulate matter (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 (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 (2020-2021) and the observed values. The NO2 levels displayed a significant correlation (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 (2013 - 2019) with the air pollutant levels after the pandemic (2020 - 2021) had a significant difference (p < 0.05). Statistically significant negative correlations were found between SO2 levels with relative humidity (r = -0.27 p < 0.05) and between NO2 levels with temperature (r = -0.23 p < 0.05), and relative humidity (r = -0.36 p < 0.05). Similarly, Air Quality Index (AQI) values determined from PM2.5 showed a significant negative correlation with rainfall, relative humidity, and wind speed (p < 0.05) while AQI values of PM10 showed a significant negative correlation with rainfall and relative humidity (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.

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