Analysis of roadside air pollution in western province of Sri Lanka

Abstract

Air pollution is now one of the largest issues in the world that is leading to climate change, global warming, more frequent whether extremities, and respiratory related fatalities. Vehicle exhaust is one of the primary causes of air pollution and it emits CO2, NO2, SO2, and Particulate Matter (PM) among other air pollutants. Even though some of these targets and standards are being established by the World Health Organization (WHO) to limit the concentration levels of such toxic pollutants globally, no such clear system is yet visible to achieve such targets in Sri Lanka. As a starting point, the objective of this research is to analyze the level of roadside air pollutants in the Western Province of Sri Lanka and to identify the annual variation of roadside air quality parameters. Followed by the development of a model to understand the correlation between road traffic and roadside emissions with the intention of proposing mitigation measures to control air pollution. Air quality data from 40 fixed locations from 2013 to 2018 were collected and spatial analysis was carried out. NO2 and SO2 concentrations are analyzed using Arc GIS software and heat maps were generated visualizing the spatial distribution of pollutant concentration in the Western Province of Sri Lanka to identify critical locations with low air quality. Based on the spatial analysis results, it was observed that most of the urban areas have very high concentration levels of NO2 and SO2 which exceed more than twice the standard concentration level (WHO Air Quality Guideline-2005) during the period of measurements. It was also observed that from the year 2013 to 2017 there was a gradual decrease in NO2, and SO2 levels in the Western province although, in the year 2018, there was a sudden increase in NO2 and SO2 concentration levels. In addition, roadside air quality data was collected in conjunction with traffic data from 2022 to 2024 at eight selected locations to identify the distribution of pollutants away from the road and up to about 75m distance from the road edge. Based on the results of additional locations it is clear that NO2, PM2.5, and PM10 show a positive correlation with traffic data while CO2 shows some deviation during daytime at some locations