Data fusion for air quality mapping using sensor data: feasibility and added-value through an application in Nantes

The recent technological developments and the increased interest for public information lead to a fast-growing use of microsensors for air quality monitoring. Measurement campaigns are conducted to assess the potential of these low-cost instruments by deploying fixed sensors (e.g. on top of buildings, street lights or reference stations) and/or mobile sensors (e.g. on top of cars, bikes, or carried by citizens). These experiments allow to measure pollutant concentrations at high resolution in space and time. The large amount of collected information offers new opportunities of developments in air quality modelling and mapping. This work aims to take the best of these sensors despite the related measurement uncertainty to produce urban air pollution maps at fine spatial and temporal resolution. A geostatistical methodology (data fusion) is presented, which uses sensor observations as well as dispersion model outputs. It is applied to PM10 data in the French city of Nantes. It involves new challenges such as the consideration of the quick change of the sensor location if it is mobile, the temporal variability of the measurements, the analysis of numerous and heterogeneous data, the spatial representativeness of the measurements and the measurement uncertainties. Also, efforts still need to be done on the sampling design to ensure appropriate spatial coverage of the considered domain and get more accurate estimates.