The performance of a particulate filter is determined by multi-scale properties that span the macro, meso and atomic scale. Traditionally, the primary role of a GPF is to reduce solid particles and liquid droplets. At the macroscale, transport of gas through a filter’s channels and interconnecting pores act as main transport arteries for catalytically active sites. At the mesoscale, the micropore structure is important for ensuring that there are enough active sites that are accessible for the gas to reach the catalyst nanoparticles. Whereas at the atomic scale, the structure of the catalyst material determines the performance and selectivity within the filter. Understanding all length scales requires a correlative approach but this is often quite difficult to achieve due to the number of software packages a scientist has to deal with. We demonstrate how current state of the art approaches in the field can be combined into a streamlined pipeline to characterise particulate filters by digitally reconstructing the sample, analysing it at high throughput, and eventually used as an input for gas flow simulations and better product design.