Short-term exposure to air pollution has been associated with exacerbation of respiratory diseases such as asthma. Substantial heterogeneity in effect estimates has been observed between previous studies. This study aims to quantify the local burden of daily asthma symptoms in asthmatic children in a medium-sized city. Air pollution exposure was estimated using the nearest sensor in a fine resolution urban air quality sensor network in the city of Eindhoven, the Netherlands. Bayesian estimates of the exposure response function were obtained by updating a priori information from a meta-analysis with data from a panel study using a daily diary. Five children participated in the panel study, resulting in a total of 400 daily diary records. Positive associations between NO2 and lower respiratory symptoms and medication use were observed. The odds ratio for any lower respiratory symptoms was 1.07 (95% C.I. 0.92, 1.28) expressed per 10 μg m−3 for current day NO2 concentration, using data from the panel study only (uninformative prior). Odds ratios for dry cough and phlegm were close to unity. The pattern of associations agreed well with the updated meta-analysis. The meta-analytic random effects summary estimate was 1.05 (1.02, 1.07) for LRS. Credible intervals substantially narrowed when adding prior information from the meta-analysis. The odds ratio for lower respiratory symptoms with an informative prior was 1.06 (0.99, 1.14). Burden of disease maps showed a strong spatial variability in the number of asthmatic symptoms associated with ambient NO2 derived from a regression kriging model. In total, 70 cases of asthmatic symptoms can daily be associated with NO2 exposure in the city of Eindhoven. We conclude that Bayesian estimates are useful in estimation of specific local air pollution effect estimates and subsequent local burden of disease calculations. With the fine resolution air quality network, neighborhood-specific burden of asthmatic symptoms was assessed.