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

Abstract

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.

Development And Application of WRF(v4.1.2)-uEMEP(v5) Model at the City with the Highest Industrial Density: A Case Study of Foshan

“…The Modair4health system, which includes the online model WRF-Chem and the fluid mechanics model VADIS, uses linear and nonlinear methods to calculate morbidity and mortality in order to quantify the health effects of air pollution, and is an important multi-scale model tool for comprehensive air quality and health assessment in the city of Coimbra, Portugal (Silveira et al, 2023). KC-TRAQS combines the diffusion model and local air quality measurement datasets to estimate the relative contribution of air pollution in Kansas City, USA (Isakov et al, 2019), and similarly, following the processing and analysis of feasible data procedures, the LUR method is applied in many countries and cities, such as Hong Kong, Taipei-Keelung Metropolitan Area, Barcelona, Spain, and San Francisco, USA, but can only reflect the air pollution during the period of use (Bai et al, 2023;Criado et al, 2022;Li et al, 2021;Li et al, 2023). The GEM-MACH-PAH was used in Toronto, Canada, to explore the contribution of motor vehicles to benzene and polycyclic aromatic hydrocarbons in ambient air, but the uncertainty of atmospheric chemistry is difficult to assess (Whaley et al, 2020).…”