Use of Microsensor Data for Urban-Scale Air Quality Modelling and Mapping
Résumé
The recent technological developments and the strong 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 new low-cost instruments by deploying fixed sensors (on top of buildings, on street lights, or on reference stations) and/or mobile sensors (on top of cars, bikes, or carried by citizens). These experiments allow for the first time to measure pollutant concentrations at very high resolution in space and time. The large amount of collected information offers new opportunities of developments in air quality modeling and mapping at urban scale This work aims to take the best of these sensors despite the very high related uncertainty to contribute to i) the public awareness, ii) the monitoring of air quality, iii) the assessment of the individual exposure and iv) the improvement of modeling and emission inventories. A geostatistical methodology (external drift kriging or data fusion) is presented for air quality mapping at urban scale that uses reference station and sensor observations as well as dispersion model outputs and GIS information. This development is associated with new challenges such as the consideration of i) the quick change of the sensor location if it is mobile, ii) the temporal variability of the measurements, iii) the analysis of numerous and heterogeneous data (big data science), iv) the spatial representativeness of the measurements, and v) the measurement uncertainties. The approach is applied to PM2.5, PM10 and NO2 pollutants on French urban areas in close collaboration with the AASQA (Association Agréées pour la surveillance de la Qualitée de l'Air) and the other data producers. The use of microsensor data, as a whole, in the air quality mapping should improve significantly the pollutant concentration estimates in urban areas. Efforts still need to be done on the sampling design to ensure the spatial representativeness of the observations and on the optimization of the sensor deployment to get more accurate and consistent estimates.