Early-Life Exposure to Traffic-Related Air Pollution and Risk of Development of Childhood Asthma: A Systematic Review and Meta-Analysis, a Novel Exposure Assessment Study and a Health Impact Assessment

Asthma is the most common chronic disease of childhood. In this thesis, I investigated whether there is an association between traffic-related air pollution (TRAP) and the development of childhood asthma, quantified the magnitude of this association and estimated its public health impact in Bradford, UK. For these purposes, I conducted a systematic review and a meta-analysis. I then developed a new vehicle emission model to estimate traffic NOx and compared it to the standard European model. Subsequently, I set up and validated two full-chain health impact assessment models; linking distinct traffic, emissions, atmospheric dispersion and health impact models. Each full-chain model was underlined by a different vehicle emission model, the new and the standard one, and as such I tested the sensitivity of final air quality and health impact estimates to the vehicle emission estimates. I estimated the childhood population exposure to NO2 and NOx at the smallest census tract level and quantified the annual number of asthma cases associated with these exposures, whilst disentangling the impacts of traffic-related NO2 and NOx, and also the impacts of traffic-related NO2 and NOx specifically from minor roads and cold starts. I compared the full-chain models’ estimates to estimates from commonly used land-use regression models which further provided exposure and health impact estimates for black carbon, PM2.5 and PM10. I quantified positive and statistically significant associations for black carbon, NO2, PM2.5, PM10 and risk of childhood asthma. The association with NOx was positive but not statistically significant. I showed that the new vehicle emission model, as compared to the standard model, resulted in different source apportionment and higher emissions at low average speeds. These differences, however, did not translate into meaningful differences in air quality or health impacts, partly due to limitations in the traffic data which underestimated congestion. The full-chain models estimated NO2 and NOx with satisfactory predictive power but resulted in lower exposures and health impacts as compared to land-use regression. I estimated that 15% to 38% of all asthma cases in Bradford may be attributable to air pollution. Up to 6% and 12% of all cases were specifically attributable to TRAP, with and without minor roads and cold starts, respectively, but this percentage was underestimated. Full-chain health impact modelling was demonstrated as a valuable but underutilized tool to estimate the burden of disease associated with TRAP and to test the impacts of specific policy scenarios with a temporal and/or spatial element. There is a further need to improve the feasibility, utility, resolution and validity of the supporting data and the full-chain modelling approach, especially by addressing its underestimation of TRAP, and consequently, the associated health impacts