Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value

In Nantes, low-cost sensors were installed in the city center and deployed on driving school cars, ambulances and service vehicles to measure PM₁₀ concentrations. This work aims to use the large amount of observations provided by the sensors for air quality mapping at the urban scale in order to show the potential added-value with respect to the dispersion model (ADMS-Urban) calculations. A preprocessing is applied to the raw sensor dataset to remove the unreliable observations based on an outlier detection technique and to compensate for the measurement drift by adjusting the estimated daily variation of the underlying background PM₁₀ concentrations with the reference stations. Then, data fusion is performed by combining the preprocessed fixed and mobile low-cost sensor observations and the 2016 annual average of the ADMS-Urban outputs. The measurement uncertainty related to the low-cost sensors and the dispersion of the data are considered in data fusion as the Variance of Measurement Error (VME). The spatial interpolation is achieved at hourly resolution and results are presented for November 29th, 2018 from 7 am to 7 pm. Hourly fused maps show disparate responses to data fusion mainly depending on the variability of the sensor data and the correlation between the sensor observations and the drift. The data fusion performance has been investigated by comparing the daily average of the estimated concentrations, the reference observations and the hourly model outputs at each station of the Air Pays de la Loire network. Results show that considering the model alone implies 8% bias whereas including the LCS observations reduces the bias to 2.5%. However, the concentration distributions related to the data fusion are characterized by a lower dispersion than the reference observations and the model estimation. Thus, the fusion smooths 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. Efforts on this issue might increase the performance of the spatial interpolation.