Air pollution in Scania, southern Sweden

Background: The environment where we humans live provides the fundamental requirements we need to survive – food to eat, water to drink, and air to breathe. The quality of these elements has a major impact on human health, as they can contain substances that are detrimental to health. These we call environmental pollutants. This thesis explores the effects of exposure to air pollutants in particular. A large portion of the earth’s population is exposed to high levels of air pollution, and 7 million premature deaths worldwide are estimated to be attributed to air pollution. In order to study relationships between exposure to air pollution and health outcomes and to quantify associations, epidemiologic research is needed. From these, exposure-response functions, or in this case air pollution concentration-response functions, are established for diseases and mortality, which form a foundation for quantitative health impact assessments (HIA). HIAs then help stakeholders and the public understand health risks and make, broadly accepted, informed decisions about interventions needed to improve public health. Aim: To explore and evaluate methods to assess air pollution exposure in Scania for application in epidemiologic studies as well as health impact assessments. Methods: Using a detailed emission database covering Scania, dispersion modelling of concentrations of particles and nitrogen oxides was conducted at high temporal and spatial resolutions. Modelled concentrations were evaluated against measurements (Papers I and II) and subsequently used as exposure indicators in a health impact assessment (Paper III) on premature mortality, asthma, dementia, autism spectrum disorders, preeclampsia (PE) and low birth weight. The last paper included (Paper IV) is an epidemiological study on air pollution and preeclampsia. Results: Modelling of nitrogen dioxide (NO2) showed a correlation of RS = 0.8 with measurements at residence facades with a mean difference of 1.08 μg/m3. Results were poorer for modelled versus measured personal exposure. Efforts to compensate for time spent at workplace did not improve the results much. Modelling of particle concentrations also showed correlations with monitor measurements. However, a large proportion of particle concentrations in Scania consists of long-range background emissions, which likely results in a high correlation between different monitors themselves, which likely contributes to the high correlation with modelled concentrations. With a mean population exposure to patricles with aerodynamic size of 2.5 μm or less (PM2.5) of 11.9 μg/m3, Scania experiences relatively low exposure levels from an international perspective. Still, we estimated 6% of premature deaths and 11% of low birth weight (LBW) births to be attributed to PM2.5. Reaching a maximum PM2.5 exposure of 10 μg/m3 for all residents would reduce deaths and LBW substantially but could not be achieved only by removing local emissions of PM2.5. Additional results include a positive association between air pollution exposure and preeclampsia among pregnant women. Conclusions: Dispersion modelling is a useful tool for assessing outdoor concentrations of ambient air pollution. It should be noted that concentrations recorded outdoors at the residence of study persons do not equal someone’s total personal exposure. Several alternative approaches exist, and future research will help demonstrate their respective strengths and weaknesses. It is likely that combined methods including remote sensing will prove favourable. Further, our results indicate substantial benefits for public health if the air pollution levels in Scania were reduced despite being comparatively low from an internal perspective.