Mapping urban air pollution using GIS: A regression-based approach

Briggs, DJ; Collins, S; Elliott, P; Fischer, P; Kingham, S; Lebret, E; Pryl, K; Van Reeuwijk, H; Smallbone, K; Van Der Veen, A

HERO ID

25950

Reference Type

Journal Article

Year

1997

Language

English

HERO ID 25950
In Press No
Year 1997
Title Mapping urban air pollution using GIS: A regression-based approach
Authors Briggs, DJ; Collins, S; Elliott, P; Fischer, P; Kingham, S; Lebret, E; Pryl, K; Van Reeuwijk, H; Smallbone, K; Van Der Veen, A
Journal International Journal of Geographical Information Science
Volume 11
Issue 7
Page Numbers 699-718
Abstract As part of the EU-funded SAVIAH project, a regression-based methodology for mapping traffic-related air pollution was developed within a GIS environment. Mapping was carried out for NO2 in Amsterdam, Huddersfield and Prague. In each centre, surveys of NO2, as a marker for traffic-related pollution, were conducted using passive diffusion tubes, exposed for four 2-week periods. A GIS was also established, containing data on monitored air pollution levels, road network, traffic volume, land cover, altitude and other, locally determined, features. Data from 80 of the monitoring sites were then used to construct a regression equation, on the basis of predictor environmental variables, and the resulting equation used to map air pollution across the study area. The accuracy of the map was then assessed by comparing predicted pollution levels with monitored levels at a range of independent reference sites. Results showed that the map produced extremely good predictions of monitored pollution levels, both for individual surveys and for the mean annual concentration, with r2 ~ 0À79-0À87 across 8-10 reference points, though the accuracy of predictions for individual survey periods was more variable. In Huddersfield and Amsterdam, further monitoring also showed that the pollution map provided reliable estimates of NO2 concentrations in the following year (r2~0À59-0À86 for n=20).
Doi 10.1080/136588197242158
Wosid CCC:A1997XY63700005
Is Certified Translation No
Dupe Override No
Is Public Yes
Language Text English