Objective: We describe a methodological framework to estimate potential cost savings in Belgium for a decrease in cardiovascular emergency admissions (ischemic heart disease (IHD), heart rhythm disturbances (HRD), and heart failure) due to a reduction in air pollution. Methods: Hospital discharge data on emergency admissions from an academic hospital were used to identify cases, derive risk functions, and estimate hospital costs. Risk functions were derived with case-crossover analyses with weekly average PM10, PM2.5, and NO2 exposures. The risk functions were subsequently used in a microcosting analysis approach. Annual hospital cost savings for Belgium were estimated for two scenarios on the decrease of air pollution: 1) 10% reduction in each of the pollutants and 2) reduction towards annual WHO guidelines. Results: Emergency admissions for IHD and HRD were significantly associated with PM10, PM2.5, and NO2 exposures the week before admission. The estimated risk reduction for IHD admissions was 2.44% [95% confidence interval (CI): 0.33%-4.50%], 2.34% [95% CI: 0.62%-4.03%], and 3.93% [95% CI: 1.14%-6.65%] for a 10% reduction in PM10, PM2.5, and NO2 respectively. For Belgium, the associated annual cost savings were estimated at (sic)5.2 million, _ 5.0 million, and _ 8.4 million respectively. For HRD, admission risk could be reduced by 2.16% [95% CI: 0.14%-4.15%], 2.08% [95% CI: 0.42%-3.70%], and 3.46% [95% CI: 0.84%-6.01%] for a 10% reduction in PM10, PM2.5, and NO2 respectively. This corresponds with a potential annual hospital cost saving in Belgium of (sic)3.7 million, (sic)3.6 million, and (sic)5.9 million respectively. If WHO annual guidelines for PM10 and PM2.5 are met, more than triple these amounts would be saved. Discussion: This study demonstrates that a model chain of case-crossover and micro-costing analyses can be applied in order to obtain estimates on the impact of air pollution on hospital costs. (C) 2015 Elsevier B.V. All rights reserved. ; Vrije Universiteit Brussel [IRP DEFIS 42028] ; air pollution; hospital costs; cardiovascular diseases; case-crossover analysis; micro-costing analysis approach