Spatiotemporal modelling of PM 2.5 concentrations in Lombardy (Italy): a comparative study

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Philipp Otto
  • Alessandro Fusta Moro
  • Jacopo Rodeschini
  • Qendrim Shaboviq
  • Rosaria Ignaccolo
  • Natalia Golini
  • Michela Cameletti
  • Paolo Maranzano
  • Francesco Finazzi
  • Alessandro Fassò

Externe Organisationen

  • Universität Bergamo (UniBg)
  • Università di Torino
  • University of Milano-Bicocca
  • Fondazione Eni Enrico Mattei (FEEM)
  • University of Glasgow
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)245-272
Seitenumfang28
FachzeitschriftEnvironmental and Ecological Statistics
Jahrgang31
Ausgabenummer2
Frühes Online-Datum1 Feb. 2024
PublikationsstatusVeröffentlicht - Juni 2024

Abstract

This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models (RFSTK). These models are evaluated for their effectiveness in predicting PM 2.5 concentrations in Lombardy (North Italy) from 2016 to 2020. Despite differing methodologies, all models demonstrate proficient capture of spatiotemporal patterns within air pollution data with similar out-of-sample performance. Furthermore, the study delves into station-specific analyses, revealing variable model performance contingent on localised conditions. Model interpretation, facilitated by parametric coefficient analysis and partial dependence plots, unveils consistent associations between predictor variables and PM 2.5 concentrations. Despite nuanced variations in modelling spatiotemporal correlations, all models effectively accounted for the underlying dependence. In summary, this study underscores the efficacy of conventional techniques in modelling correlated spatiotemporal data, concurrently highlighting the complementary potential of Machine Learning and classical statistical approaches.

ASJC Scopus Sachgebiete

Zitieren

Spatiotemporal modelling of PM 2.5 concentrations in Lombardy (Italy): a comparative study. / Otto, Philipp; Fusta Moro, Alessandro; Rodeschini, Jacopo et al.
in: Environmental and Ecological Statistics, Jahrgang 31, Nr. 2, 06.2024, S. 245-272.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Otto, P, Fusta Moro, A, Rodeschini, J, Shaboviq, Q, Ignaccolo, R, Golini, N, Cameletti, M, Maranzano, P, Finazzi, F & Fassò, A 2024, 'Spatiotemporal modelling of PM 2.5 concentrations in Lombardy (Italy): a comparative study', Environmental and Ecological Statistics, Jg. 31, Nr. 2, S. 245-272. https://doi.org/10.1007/s10651-023-00589-0
Otto, P., Fusta Moro, A., Rodeschini, J., Shaboviq, Q., Ignaccolo, R., Golini, N., Cameletti, M., Maranzano, P., Finazzi, F., & Fassò, A. (2024). Spatiotemporal modelling of PM 2.5 concentrations in Lombardy (Italy): a comparative study. Environmental and Ecological Statistics, 31(2), 245-272. https://doi.org/10.1007/s10651-023-00589-0
Otto P, Fusta Moro A, Rodeschini J, Shaboviq Q, Ignaccolo R, Golini N et al. Spatiotemporal modelling of PM 2.5 concentrations in Lombardy (Italy): a comparative study. Environmental and Ecological Statistics. 2024 Jun;31(2):245-272. Epub 2024 Feb 1. doi: 10.1007/s10651-023-00589-0
Otto, Philipp ; Fusta Moro, Alessandro ; Rodeschini, Jacopo et al. / Spatiotemporal modelling of PM 2.5 concentrations in Lombardy (Italy) : a comparative study. in: Environmental and Ecological Statistics. 2024 ; Jahrgang 31, Nr. 2. S. 245-272.
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