As part of the EU-funded SAVIAH project, a regression-based methodology for mapping tra c-related air pollution was developed within a GIS environment. Mapping was carried out for NO2 in Amsterdam, Hudders® eld and Prague. In each centre, surveys of NO2 , as a marker for tra c-related pollution, were conducted using passive di usion tubes, exposed for four 2-week periods. A GIS was also established, containing data on monitored air pollution levels, road network, tra c 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 r 2~0´7 9± 0´87 across 8± 10 reference points, though the accuracy of predictions for individual survey periods was more variable. In Hudders® eld and Amsterdam, further monitoring also showed that the pollution map provided reliable estimates of NO2 concentrations in the following year (r 2~0´5 9± 0´86 for n=20).

Neighbourhood green space was related to better cardiovascular and mental health in a New Zealand Health Survey, independent of individual risk factors. Although physical activity was higher in greener neighbourhoods, it did not fully explain the green space and health relationship.

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