Animal houses should be optimally ventilated to ensure a proper indoor climate for production. Natural ventilation is potentially the most sustainable way to accomplish this and is considered the preferred BAT for intensive pig farming. Benefits include lower energy costs and noise reduction (thus diminishing animal stress). The application of natural ventilation is still limited due to the lack of a reliable measuring and control technique for the ventilation rate. Consequently, emissions from naturally ventilated housing systems cannot be measured adequately. This hinders innovations and the implementation of natural ventilation, since new housing systems are legally required to be certified as ammonia emission low. The main goal of this research is to assess the ventilation rate by using a combination of strategic velocity measurements and validated algorithms. In order to accurately predict the flow rate across the ventilation openings, the optimal sampling positions and sampling sequence must be determined. For practical use, also a real life measuring technique (PMT) will be developed for the ventilation rate and related emissions. To accomplish these main targets, two Reference Sensor Frames (RSF) are developed: one for the ridge and one for side ventilation openings. For the latter, also an automatic measuring frame is developed to increase the versatility of the measurements. The PMT will be installed in a full scale naturally ventilated test construction (L×W×H = 12×5×4 m) in the field. Yearlong ventilation and emission experiments will be conducted. The results will be analyzed and modeled with neural networks to assess the ventilation and emission rate. These findings will also be verified in a CFD approach.