Otto, P. , Fusta Moro, A., Rodeschini, J., Shaboviq, Q., Ignaccolo, R., Golini, N., Cameletti, M., Maranzano, P., Finazzi, F. and Fassò, A. (2024) Spatiotemporal modelling of PM2.5 concentrations in Lombardy (Italy): a comparative study. Environmental and Ecological Statistics, (doi: 10.1007/s10651-023-00589-0) (Early Online Publication)
![[img]](https://eprints.gla.ac.uk/style/images/fileicons/text.png) |
Text
315797.pdf - Published Version Available under License Creative Commons Attribution. 3MB |
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 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 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.
| Item Type: | Articles |
|---|---|
| Additional Information: | This research was funded by Fondazione Cariplo under the grant 2020–4066 “AgrImOnIA: the impact of agriculture on air quality and the COVID-19 pandemic” from the “Data Science for science and society” program. |
| Status: | Early Online Publication |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Finazzi, Dr Francesco and Otto, Dr Philipp |
| Authors: | Otto, P., Fusta Moro, A., Rodeschini, J., Shaboviq, Q., Ignaccolo, R., Golini, N., Cameletti, M., Maranzano, P., Finazzi, F., and Fassò, A. |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Environmental and Ecological Statistics |
| Publisher: | Springer |
| ISSN: | 1352-8505 |
| ISSN (Online): | 1573-3009 |
| Published Online: | 01 February 2024 |
| Copyright Holders: | Copyright © 2024 The Author(s) |
| First Published: | First published in Environmental and Ecological Statistics 2024 |
| Publisher Policy: | Reproduced under a Creative Commons license |
University Staff: Request a correction | Enlighten Editors: Update this record
Deposit and Record Details
Deposit and Record Details