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This project is to develop machine learning based methods to normalise the responses of electrochemical and solid state gas sensors used in air quality monitoring for transport, health and agriculture. The proposed models use a wide range of environmental parameters and reference grade trace gas analysers as well as the responses of the electrochemical and solid state sensors as the training sets. The intended result is low cost gas sensors that will report data that with smaller uncertainties. Therefore better data at lower cost.