A data fusion uncertainty-enabled method to map street-scale hourly NO2: a case study in Barcelona

Considering that air pollution is the leading global environmental risk factor according to the WHO,  characterizing NO₂ levels holds crucial significance, particularly in heavily trafficked urban areas where NO₂ legal limits and health guidelines are frequently exceeded. Obtaining accurate and comprehensive NO₂ datasets on a city level is especially challenging due to the inherent uncertainties associated with urban air quality models, and the scarcity of air quality monitoring stations. An alternative method to describe NO₂ levels involves developing short-term experimental campaigns using indicative measurements, although they report period-averaged results and do not have full spatial coverage. 

Taking advantage of the three mentioned approaches,  this work proposes a data-fusion method that combines i) near-real-time hourly observations obtained from the official air quality monitoring network, ii) the output of an urban air quality model (CALIOPE-Urban) that operates at high spatial (up to 20m x 20m) and temporal (hourly) resolutions, and iii) a microscale Land-Use-Regression (LUR) model based on machine learning. The microscale-LUR model includes different urban datasets such as traffic flow or average building density and two NO₂ experimental campaigns. 

While the hourly observations enable the temporal variability adjustment in the dispersion model, the microscale-LUR model provides additional insights into the spatial characteristics of NO₂ distribution. Our data-fusion approach was implemented on an hourly basis over the metropolitan area of Barcelona in 2019. Besides the bias-corrected NO₂ hourly maps, this method also computes the uncertainty associated with the variance of the estimated error during the correction process. By integrating both corrected NO₂ values and their associated uncertainty, it produces maps that show the probability of exceeding the hourly 200 µg/m3 and the annual 40 µg/m3 NO₂ legal thresholds over Barcelona. 

Cross-validated results at the monitoring stations demonstrate that the spatial bias correction increases the correlation coefficient (r) by +46 % and decreases the root mean square error (RMSE) by −48 %, compared to the model output. This research emphasizes the importance of highly detailed spatial data within data-fusion techniques, enhancing the accuracy of predicting exceedances at the street level.