Preprints
https://doi.org/10.5194/egusphere-2022-1147
https://doi.org/10.5194/egusphere-2022-1147
09 Nov 2022
 | 09 Nov 2022

Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona city: a case study with CALIOPE-Urban v1.0

Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodríguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba

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.

Journal article(s) based on this preprint

21 Apr 2023
Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodriguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
Geosci. Model Dev., 16, 2193–2213, https://doi.org/10.5194/gmd-16-2193-2023,https://doi.org/10.5194/gmd-16-2193-2023, 2023
Short summary

Alvaro Criado et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1147', Anonymous Referee #1, 05 Dec 2022
    • AC1: 'Reply on RC1', Alvaro Criado, 08 Mar 2023
  • RC2: 'Comment on egusphere-2022-1147', Anonymous Referee #2, 16 Dec 2022
    • AC2: 'Reply on RC2', Alvaro Criado, 08 Mar 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1147', Anonymous Referee #1, 05 Dec 2022
    • AC1: 'Reply on RC1', Alvaro Criado, 08 Mar 2023
  • RC2: 'Comment on egusphere-2022-1147', Anonymous Referee #2, 16 Dec 2022
    • AC2: 'Reply on RC2', Alvaro Criado, 08 Mar 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Alvaro Criado on behalf of the Authors (08 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (09 Mar 2023) by Christoph Knote
RR by Anonymous Referee #1 (13 Mar 2023)
RR by Anonymous Referee #2 (13 Mar 2023)
ED: Publish as is (21 Mar 2023) by Christoph Knote
AR by Alvaro Criado on behalf of the Authors (30 Mar 2023)  Author's response   Manuscript 

Journal article(s) based on this preprint

21 Apr 2023
Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodriguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
Geosci. Model Dev., 16, 2193–2213, https://doi.org/10.5194/gmd-16-2193-2023,https://doi.org/10.5194/gmd-16-2193-2023, 2023
Short summary

Alvaro Criado et al.

Alvaro Criado et al.

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Short summary
The goal of this work is to derive and evaluate a general statistical post-processing tool specifically designed for the street scale that can be applied to any urban air quality system. Our data-fusion methodology corrects NO2 fields based on continuous hourly observations and experimental campaigns. This study able us to obtain exceedance probability maps of air quality standards. In 2019, 13 % of the Barcelona area had a 70 % or higher probability of exceeding the annual legal NO2 limit.