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2022-02

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cc-by (c) Platikanov, Stefan., 2022
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/197489

Understanding temporal and spatial changes of O3 or NO2 concentrations combining multivariate data analysis methods and air quality transport models

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https://doi.org/10.1016/j.scitotenv.2021.150923

Abstract

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O3 and NO2 concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km2, Iberian Peninsula at 4 × 4 km2 and Catalonia at 1 × 1 km2). Results obtained in the analysis of these different data sets allowed a better understanding of O3 and NO2 concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O3 and NO2 concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO2 predictions, showed in general a better performance than O3 predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources.

Subject

Qualitat de l'aire, Quimiometria, Ozó

Subject (English)

Air quality, Chemometrics, Ozone

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Articles publicats en revistes (Genètica, Microbiologia i Estadística)

Citation

PLATIKANOV, Stefan, et al. Understanding temporal and spatial changes of O3 or NO2 concentrations combining multivariate data analysis methods and air quality transport models. Science of the Total Environment. 2022. Vol. 806, num. 150923, pags. 1-13. ISSN 0048-9697. [consulted: 18 of August of 2026]. Available at: https://hdl.handle.net/2445/197489

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