A1487
Title: Geographically weighted regression for air quality low-cost sensor calibration
Authors: Jean-Michel Poggi - University Paris-Saclay Orsay (France) [presenting]
Bruno Portier - INSA Rouen Normandie (France)
Emma Thulliez - INSA Rouen Normandie (France)
Abstract: The focus is on the use of Geographically Weighted Regression (GWR) to correct low-cost air quality sensor measurements. Those sensors are of major interest in the current era of high-resolution air quality monitoring at the urban scale, but require calibration using reference analyzers. The results for NO2 are provided along with comments on the estimated GWR model and the spatial content of the estimated coefficients. The study has been carried out using the publicly available SensEURCity dataset in Antwerp, which is particularly relevant because it includes 9 reference stations and 34 micro-sensors, all collocated and deployed within the city.
