Official air quality monitoring networks are often scarce and unevenly spatially distributed. In recent years, the use of low-cost sensors next to official networks is increasing. These additional networks provide measurements of high spatial and temporal resolution and potentially reveal patterns and emissions sources that are hard to detect with conventional methods. In this work, the data assimilation method implemented around the LOTOS-EUROS chemistry transport model is employed to assimilate measurements from heterogenous low-cost sensor networks around the city of Eindhoven in the Netherlands in November 2021. Three data assimilation experiments are performed and evaluated against a free run of the model. In the first one, measurements from the low-cost Innovatief Lucht Meetsysteem (ILM) network are exploited. In the second one, the citizen science network SamenMeten is used and in the third one, a combination of both datasets is applied. In the assimilation experiments at a domain around the city of Eindhoven, it is shown to be essential to use boundary conditions from an assimilation on a larger domain to account for the variability in pollution that originates from sources outside the domain of interest. Such an improvement in boundary conditions counts for a decrease in the initial free run negative biases of 45% for PM₁₀ and 23% for PM_2.5 in the city of Eindhoven. The assimilation of low-cost measurements in the region after the correction of the boundary conditions decreases the absolute PM₁₀ biases in the 3 independent official stations over the city of Eindhoven further from −4.4 μg m⁻³ to about 0.8 μg m⁻³ averaged over the three experiments. Also, the correlation coefficient is increased from 0.75 to 0.89 and the normalized root mean square is decreased from 0.47 to 0.25. We conclude that the improved boundary conditions and assimilation of observations from dense low-cost networks are able to improve the LOTOS-EUROS simulations at urban scale.

Skoulidou, I., Segers, A., Henzing, B., Zhang, J., Goudriaan, R., Koukouli, M.-E., Balis, D.

Atmospheric Environment, 333, art. no. 120652,2024