Keywords

Air pollution; urban traffic; geostatistics; GWR; ordinary kriging

Location

Session E2: Environmental Modeling of Human Health Effects from Global to Local Scale

Start Date

18-6-2014 2:00 PM

End Date

18-6-2014 3:20 PM

Description

Air pollution sources caused by increasing road traffic reduce air quality and affect people in urban areas. In order to improve living conditions in urban areas, predictions of effects on air pollution are needed for assessing exposures as part of epidemiological studies, and to inform urban air-quality policy and traffic management. A prediction system for estimation, analysis and visualization has been developed to model spatial patterns of traffic-related air pollution. In this study, several geostatistical techniques are used for prediction of NO2 and PM10. The primary data for geostatistical methods originate from sample points that are generated from a network of automatic monitoring stations and, in addition, complemented by other sample points estimated by geographically weighted regression (GWR). GWR is used to provide a local form of linear regression to explore spatially varying relationships between air pollution, as a dependent variable, and a number of explanatory variables, such as elevation, nearest distance to a major road and ratio of built-up sites in the local area (the circle with a diameter of 1 kilometre). The techniques used for spatial interpolation are based on geostatistical methods such as ordinary kriging. The attached case study is focused on the area of Prague, the capital and largest city of the Czech Republic. GWR and the prediction maps of air pollution by NO2 and PM10 show highly exposed sites that indicate the need for emergency measures in urban air-quality policy and traffic management.

 
Jun 18th, 2:00 PM Jun 18th, 3:20 PM

Using Geostatistical Tools for Mapping Traffic-Related Air Pollution in Urban Areas

Session E2: Environmental Modeling of Human Health Effects from Global to Local Scale

Air pollution sources caused by increasing road traffic reduce air quality and affect people in urban areas. In order to improve living conditions in urban areas, predictions of effects on air pollution are needed for assessing exposures as part of epidemiological studies, and to inform urban air-quality policy and traffic management. A prediction system for estimation, analysis and visualization has been developed to model spatial patterns of traffic-related air pollution. In this study, several geostatistical techniques are used for prediction of NO2 and PM10. The primary data for geostatistical methods originate from sample points that are generated from a network of automatic monitoring stations and, in addition, complemented by other sample points estimated by geographically weighted regression (GWR). GWR is used to provide a local form of linear regression to explore spatially varying relationships between air pollution, as a dependent variable, and a number of explanatory variables, such as elevation, nearest distance to a major road and ratio of built-up sites in the local area (the circle with a diameter of 1 kilometre). The techniques used for spatial interpolation are based on geostatistical methods such as ordinary kriging. The attached case study is focused on the area of Prague, the capital and largest city of the Czech Republic. GWR and the prediction maps of air pollution by NO2 and PM10 show highly exposed sites that indicate the need for emergency measures in urban air-quality policy and traffic management.