Data fusion for air quality mapping using low-cost sensor observations
Résumé
The recent technological developments and the strong increased interest for public information lead to a fast-growing use of low-cost sensors (LCS) for air quality monitoring. In Nantes (a French city), LCS have been installed in the city center and deployed on driving school cars, ambulances and service vehicles to measure PM concentrations. We used the large amount of PM10 observations provided by the sensors for air quality mapping to show the potential added-value with respect to the dispersion model (ADMS-Urban) calculations (Gressent et al., 2020). Data fusion was performed by combining the preprocessed fixed and mobile LCS observations in November 2018 and the 2016 annual average of the ADMS-Urban outputs. The measurement uncertainty related to the LCS and the dispersion of the data are considered in data fusion that is achieved at hourly resolution. Results show that considering the model alone implies 8% bias whereas including the LCS observations reduces the bias to 2.5%. However, the fusion tends to smooth the PM10 peaks. In addition, the effect of the measurement uncertainty has been investigated by doubling it or reducing it to the reference station measurement uncertainty. The sensitivity study demonstrates that the performance is increasing by reducing the uncertainty. This highlights the importance to estimate accurately the measurement uncertainty of the devices to ensure relevant air quality mapping. The method efficiency is also quite limited by the low correlation between the sensor observations and the model used as external drift in the kriging that may be explained by the remaining bias on LCS data. This has been addressed in Rollin et al., in prep, for PM2.5 by developing a new methodology to identify “rendez-vous” between sensors that relies on a graph approach linking measurements of sensors close to each other’s that allows characterizing and correcting the measurement bias.