Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan
Motor vehicle emissions are the primary air pollution source in cities worldwide. Changes in traffic flow in a city can drastically change overall levels of air pollution. The level of air pollution may vary significantly in some street segments compared to others, and a small number of stationary a...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Lomonosov Moscow State University
2022-10-01
|
| Series: | Geography, Environment, Sustainability |
| Subjects: | |
| Online Access: | https://ges.rgo.ru/jour/article/view/2603 |
| _version_ | 1797878382061420544 |
|---|---|
| author | Mahesh Senarathna Sajith Priyankara Rohan Jayaratne Rohan Weerasooriya Lidia Morawska Gayan Bowatte |
| author_facet | Mahesh Senarathna Sajith Priyankara Rohan Jayaratne Rohan Weerasooriya Lidia Morawska Gayan Bowatte |
| author_sort | Mahesh Senarathna |
| collection | DOAJ |
| description | Motor vehicle emissions are the primary air pollution source in cities worldwide. Changes in traffic flow in a city can drastically change overall levels of air pollution. The level of air pollution may vary significantly in some street segments compared to others, and a small number of stationary ambient air pollution monitors may not capture this variation. This study aimed to evaluate air pollution before and during a new traffic plan established in March 2019 in the city of Kandy, Sri Lanka, using smart sensor technology. Street level air pollution data (PM2.5 and NO2 ) was acquired using a mobile air quality sensor unit before and during the implementation of the new traffic plan. The sensor unit was mounted on a police traffic motorcycle that travelled through the city four times per day. Air pollution in selected road segments was compared before and during the new traffic plan, and the trends at different times of the day were compared using data from a stationary smart sensor. Both PM2.5 and NO2 levels were well above the World Health Organization (WHO) 24-hour guidelines during the monitoring period, regardless of the traffic plan period. Most of the road segments had comparatively higher air pollution levels during compared to before the new traffic plan. For any given time (morning, midday, afternoon, evening), day of the week, and period (before or during the new traffic plan), the highest PM2.5 and NO2 concentrations were observed at the road segment from Girls High School to Kandy Railway Station. The mobile air pollution monitoring data provided evidence that the mean concentration of PM2.5 during the new traffic plan (116.7 µg m-3) was significantly higher than before the new traffic plan (92.3 µg m-3) (p < 0.007). Increasing spatial coverage can provide much better information on human exposure to air pollutants, which is essential to control traffic related air pollution. Before implementing a new traffic plan, careful planning and improvement of road network infrastructure could reduce air pollution in urban areas. |
| first_indexed | 2024-04-10T02:32:18Z |
| format | Article |
| id | doaj.art-3496b74258d04fcebca7519d83faaccd |
| institution | Directory Open Access Journal |
| issn | 2071-9388 2542-1565 |
| language | English |
| last_indexed | 2024-04-10T02:32:18Z |
| publishDate | 2022-10-01 |
| publisher | Lomonosov Moscow State University |
| record_format | Article |
| series | Geography, Environment, Sustainability |
| spelling | doaj.art-3496b74258d04fcebca7519d83faaccd2023-03-13T07:52:35ZengLomonosov Moscow State UniversityGeography, Environment, Sustainability2071-93882542-15652022-10-01153273610.24057/2071-9388-2022-011617Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic PlanMahesh Senarathna0Sajith Priyankara1Rohan Jayaratne2Rohan Weerasooriya3Lidia Morawska4Gayan Bowatte5Postgraduate Institute of Science, University of Peradeniya; National Institute of Fundamental StudiesDepartment of Mathematics & Statistics, Texas Tech UniversityInternational Laboratory for Air Quality and Health, Queensland University of TechnologyNational Institute of Fundamental Studies, Hantana RoadInternational Laboratory for Air Quality and Health, Queensland University of TechnologyNational Institute of Fundamental Studies, Hantana Road; Allergy and Lung Health Unit, Melbourne School of Population and Global Health, University of Melbourne; Department of Basic Sciences, Faculty of Allied Health Sciences, University of PeradeniyaMotor vehicle emissions are the primary air pollution source in cities worldwide. Changes in traffic flow in a city can drastically change overall levels of air pollution. The level of air pollution may vary significantly in some street segments compared to others, and a small number of stationary ambient air pollution monitors may not capture this variation. This study aimed to evaluate air pollution before and during a new traffic plan established in March 2019 in the city of Kandy, Sri Lanka, using smart sensor technology. Street level air pollution data (PM2.5 and NO2 ) was acquired using a mobile air quality sensor unit before and during the implementation of the new traffic plan. The sensor unit was mounted on a police traffic motorcycle that travelled through the city four times per day. Air pollution in selected road segments was compared before and during the new traffic plan, and the trends at different times of the day were compared using data from a stationary smart sensor. Both PM2.5 and NO2 levels were well above the World Health Organization (WHO) 24-hour guidelines during the monitoring period, regardless of the traffic plan period. Most of the road segments had comparatively higher air pollution levels during compared to before the new traffic plan. For any given time (morning, midday, afternoon, evening), day of the week, and period (before or during the new traffic plan), the highest PM2.5 and NO2 concentrations were observed at the road segment from Girls High School to Kandy Railway Station. The mobile air pollution monitoring data provided evidence that the mean concentration of PM2.5 during the new traffic plan (116.7 µg m-3) was significantly higher than before the new traffic plan (92.3 µg m-3) (p < 0.007). Increasing spatial coverage can provide much better information on human exposure to air pollutants, which is essential to control traffic related air pollution. Before implementing a new traffic plan, careful planning and improvement of road network infrastructure could reduce air pollution in urban areas.https://ges.rgo.ru/jour/article/view/2603air qualitymobile air quality sensorsparticulate matterroad traffic |
| spellingShingle | Mahesh Senarathna Sajith Priyankara Rohan Jayaratne Rohan Weerasooriya Lidia Morawska Gayan Bowatte Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan Geography, Environment, Sustainability air quality mobile air quality sensors particulate matter road traffic |
| title | Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan |
| title_full | Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan |
| title_fullStr | Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan |
| title_full_unstemmed | Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan |
| title_short | Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan |
| title_sort | measuring trafficrelated air pollution using smart sensors in sri lanka before and during a new traffic plan |
| topic | air quality mobile air quality sensors particulate matter road traffic |
| url | https://ges.rgo.ru/jour/article/view/2603 |
| work_keys_str_mv | AT maheshsenarathna measuringtrafficrelatedairpollutionusingsmartsensorsinsrilankabeforeandduringanewtrafficplan AT sajithpriyankara measuringtrafficrelatedairpollutionusingsmartsensorsinsrilankabeforeandduringanewtrafficplan AT rohanjayaratne measuringtrafficrelatedairpollutionusingsmartsensorsinsrilankabeforeandduringanewtrafficplan AT rohanweerasooriya measuringtrafficrelatedairpollutionusingsmartsensorsinsrilankabeforeandduringanewtrafficplan AT lidiamorawska measuringtrafficrelatedairpollutionusingsmartsensorsinsrilankabeforeandduringanewtrafficplan AT gayanbowatte measuringtrafficrelatedairpollutionusingsmartsensorsinsrilankabeforeandduringanewtrafficplan |