Using electrochemical sensors on a mobile robot is very promising for a broad range of applications. However, gas distribution mapping and localisation of a static gas source are intricate tasks, mainly due to the turbulent nature of gas transport under natural conditions. Regarding the gas distribution mapping problem, this work introduces an algorithm to create a representation of the time-constant properties of a concentration field from sparse point samples, which stores belief about the average relative concentration of a detected gas in a grid structure. Next, based on extensive experimental data, various solutions to the problem of gas source localisation that do not rely on information about wind direction and wind speed are analysed. Ultimately, suitable features to recognise a gas source are discussed and an investigation of the possibility to classify an object as being a gas source or not from a learned sensor response pattern is presented.