<p>Air pollution affects the economy, the environment, and public health. This is particularly relevant in dense urban areas due to their urban built, high traffic activity, and near-the-source population exposure. In the city of Barcelona, the 40 ug/m<sup>3</sup> nitrogen dioxide NO<sub>2</sub> annual limit value set up by the 2008/50/EC European Air Quality Directive (AQD) is systematically exceeded in traffic stations mainly due to the contribution of road transport. In the last Urban Mobility Plan (2019-2024), the city hall of Barcelona presented several traffic management strategies aiming to reduce on-road traffic emissions by both renewing and reducing the private motorized transport in the city. These measures include the application of tactical urban actions, green corridors and superblocks along with a Low Emission Zone, which together are expected to reduce the number of private vehicles circulating throughout the city by -25%. In parallel, the Port of Barcelona has recently announced a plan to electrify the docks and reduce emission from hotelling activities by -38%. To properly assess the impact of such measures, the AQD recommends the application of numerical models in combination with monitoring data. Following AQD recommendations, our study runs a coupled transport-emission model able to characterize traffic movement along the city and produce multiple scenarios that quantify the impact of the aforementioned measures on primary emissions. The resulting scenarios are then used to feed a multi-scale air quality modeling system to estimate NO<sub>2</sub> concentration values at very high resolution (20m, hourly). To reduce the uncertainty typically associated with modeling results, the estimated values are corrected with a data-fusion methodology using observations from the official monitoring network and several measurement campaigns. Our results show that the implementation of all mobility restrictions and electrification of the Port will allow Barcelona to comply with the current legislated NO<sub>2</sub> air quality standards at the traffic monitoring stations, with reductions up to -24.7% and -12 ug/m<sup>3</sup>. However, the resulting NO<sub>2</sub> levels achieved at these locations would still fail to meet the new 2021 WHO guideline (10 ug/m<sup>3</sup>) and the recent proposal for a revision of the EU AQD (20 ug/m<sup>3</sup>). Also, despite the estimated NO<sub>2</sub> reductions, several areas in the city would still be above the current legal limit of 40 ug/m<sup>3</sup>, including 16.7% of schools and 19.7% of hospitals and healthcare facilities. All in all, our results suggest the planned measures are steps in the right direction, yet still insufficient to ensure healthy AQ values across the entire city.</p>
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Abstract. Comprehensive monitoring of NO2 exceedances is imperative for protecting human health, especially in trafficked urban areas. However, accurate spatial characterization of exceedances is challenging due to the typically low density of air quality monitoring stations and the inherent uncertainties of urban air quality models. We study how observational data from different sources and time scales can be combined with a dispersion air quality model to obtain bias-corrected NO2 hourly maps at the street scale. We present a kriging-based data-fusion workflow that merges a dispersion model output with continuous hourly observations, and uses a machine-learning-based Land Use Regression (LUR) model constrained with past short intensive passive dosimeter campaigns observations. While the hourly observations allow to bias-adjust the temporal variability of the dispersion model, the microscale-LUR model adds information on the NO2 spatial patterns. Our method includes uncertainty calculation based on the estimated error variance of the Universal Kriging technique, which is subsequently used to produce urban maps of probability of exceeding the 200 µg /m3 hourly and the 40 µg /m3 NO2 annual average limits. We assess the statistical performance of this approach in the city of Barcelona for the year 2019. Our results show that simply merging the monitoring stations with the model output already significantly increases the correlation coefficient (r) by +29 % and decreases the Root Mean Square Error (RMSE) by -32 %. When adding the time-invariant LUR model in the data-fusion workflow, the improvement is even more remarkable: +46 % and -48 % for the r and RMSE, respectively. Our work highlights the usefulness of high-resolution spatial information in data-fusion methods to estimate exceedances at the street scale better.
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