1. Introduction 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 the dispersion air quality model CALIOPE-Urban (Benavides et al. 2019) to obtain bias-corrected NO2 hourly maps at the street scale in the urban area of Barcelona for the year 2019. We use the Universal Kriging technique, which merges the dispersion model output with continuous hourly observations and uses a machine-learning-based microscale-Land Use Regression (LUR) model constrained with past short intensive passive dosimeter campaigns observations. Hourly NO2 observational data are obtained from the Catalan Atmospheric Pollution Surveillance Network (XVPCA) measurement points in the Barcelona urban and surrounding areas. The passive dosimeter campaigns are the xAire citizen science campaign (Perelló et al., 2021a, b), composed of 725 samplers and deployed between February 16th and March 15th, 2018, and the 2-week measurement campaign of the Institute of Environmental Assessment and Water Research - Spanish National Research Council (IDAEA-CSIC), that deployed 175 NO2 samplers across Barcelona during February and March 2017 (Benavides et al., 2019). We have discarded the xAire samplers related to playgrounds and classrooms, so we are using the remaining 669. In order to combine the xAire and IDAEA-CSIC campaigns, we have annualized both following the procedure described in Perelló et al. (2021b). For each station, an adjustment factor is computed as the ratio between the observed 2017 annual mean and the average over the period of the experimental campaign. Then, the average of this factor over all stations is used to scale all passive samplers to the 2017 annual mean. The final microscale-LUR model includes the following eight predictors: average building density; traffic buffers of 25 and 100 m, and a traffic scaled variable; all the annually averaged data from the regional CALIOPE modeling system (Baldasano Recio et al. (2011)); and the annual average NO2 from CALIOPE-Urban. The geometric variables are calculated from the Institut Cartogràfic i Geològic de Catalunya (ICGC) and Plan Nacional de Ortografía Aérea (PNOA (2020)). Traffic data are extracted from the road-link traffic network of the HERMESv3 bottom-up emission model (Guevara et al. (2020)). We also considered NO2, Planetary Boundary Layer height, and wind speed annual means from the regional air quality modeling system CALIOPE as potential predictors, together with the NO2 yearly mean from the air quality model CALIOPE-Urban. Our method includes uncertainty calculation based on the estimated error variance of the Universal Kriging, which is subsequently used to produce urban maps of the probability of exceeding hourly (200 micrograms per cubic meter) and annual (40 micrograms per cubic meter) NO2 average limits. With this methodology, we evaluate the statistical performance in Leave-One-Out Cross-Validation (LOOCV) using the hourly data available in 12 monitoring stations. Notice that this repository is linked with a journal article: Criado, A., Mateu Armengol, J., Petetin, H., Rodríguez-Rey, D., Benavides, J., Guevara, M., Pérez García-Pando, C., Soret, A., and Jorba, O.: Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona city: a case study with CALIOPE-Urban v1.0, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1147, 2022. 2. Code and data sets We present the source code and the results here, including the final kriging annual post-processed product (predicted concentrations, uncertainties, and exceedances) over the Barcelona urban area for 2019. The code is developed in R programming (R Core Team (2013)). Please, read carefully the README.md file attached to this Zenodo repository: About the code, the zip file UniversalKriging_acriado_jmateu.zip contains a folder called Code (with a unique sub-folder called src), and two archives, kriging_repository_final.R and config_file_final.yml. The main R script is kriging_repository_final.R, which invokes all the R scripts in the src folder necessary for its workflow. All journal article results that do not violate the confidentiality restrictions have been openly published in the folder Results journal article, including the microscale-LUR model basemap and the post-processed kriging values.