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In the past 20 years, considerable progress has been made to improve urban air quality in the EU. However, road traffic still contributes considerably to the deterioration of urban air quality to below standards, which requires a method to measure properly and model pollution levels resulting from road traffic. In order to visualize the geographical distribution of pollution concentration realistically, we applied the Land Use Regression (LUR) model to the urban area of Gothenburg. The NO 2 concentration was already obtained by 25 samplers through the urban area during 7-20 May, 2001. Predictive variables such as altitude, density, roads types, traffic and land use were estimated by geographic information system in buffers ranging 50 to 500 m-radii. Linear regression (α=5%) between NO 2 and every predictive variable was calculated, and the most robust variables and without collinearity variables were selected to the multivariate regression model. The final formula was applied using Kriging in a grid map to estimate NO 2 levels. The average of measurements was 23.5 μg/m³ (± 6.8 μg/m³) and 180 predictive variables were obtained. The final model explained 59.4% of the variance of NO 2 concentration with presence of altitude and sum of traffic within 150 m around the sampler sites as predictor variables. The correlation measured versus predicted levels of NO 2 was r = 0.77 (p < 0.001). These results highlight the contribution of traffic in air pollution concentration, although the model is not precise in regions outside the urban area (e.g. islands and rural area). Moreover, future analyses should include meteorological data to improve the LUR modelling.

BackgroundThe relative importance of different sources of air pollution for cardiovascular disease is unclear. The aims were to compare the associations between acute myocardial infarction (AMI) hospitalisations in Gothenburg, Sweden and 1) the long-range transported (LRT) particle fraction, 2) the remaining particle fraction, 3) geographical air mass origin, and 4) influence of local dispersion during 1985–2010.MethodsA case-crossover design was applied using lag0 (the exposure the same day as hospitalisation), lag1 (exposure one day prior hospitalisation) and 2-day cumulative average exposure (CA2) (mean of lag0 and lag1). The LRT fractions included PMion (sum of sulphate, nitrate and ammonium) and soot measured at a rural site. The difference between urban PM10 (particulate matter with an aerodynamic diameter smaller than 10 μm) and rural PMion was a proxy for locally generated PM10 (PMrest). The daily geographical origin of air mass was estimated as well as days with limited or effective local dispersion. The entire year was considered, as well as warm and cold periods, and different time periods.ResultsIn total 28 215 AMI hospitalisations occurred during 26 years. PM10, PMion, PMrest and soot did not influence AMI for the entire year. In the cold period, the association was somewhat stronger for PMrest than for urban PM10; the strongest associations were observed during 1990–2000 between AMI and CA2 of PMrest (6.6% per inter-quartile range (IQR), 95% confidence interval 2.1 to 11.4%) and PM10 (4.1%, 95% CI 0.2% − 8.2%). Regarding the geographical air mass origins there were few associations. Days with limited local dispersion showed an association with AMI in the cold period of 2001–2010 (6.7%, 95% CI 0.0% − 13.0%).ConclusionsIn the cold period, locally generated PM and days with limited local dispersion affected AMI hospitalisations, indicating importance of local emissions from e.g. traffic.

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