Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM 2.5 Concentrations across Great Britain. Remote Sens. 2020, 12 , 3803.

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Title: Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM 2.5 Concentrations across Great Britain. Remote Sens. 2020, 12 , 3803.
Authors: Schneider, Rochelle1,2,3 (AUTHOR) francesco.sera@lshtm.ac.uk, Vicedo-Cabrera, Ana M.4,5 (AUTHOR) ana.vicedo-cabrera@lshtm.ac.uk, Sera, Francesco1 (AUTHOR) pierre.masselot@lshtm.ac.uk, Masselot, Pierre1 (AUTHOR) Antonio.Gasparrini@lshtm.ac.uk, Stafoggia, Massimo6 (AUTHOR) m.stafoggia@deplazio.it, de Hoogh, Kees7,8 (AUTHOR) c.dehoogh@swisstph.ch, Kloog, Itai9 (AUTHOR) ikloog@bgu.ac.il, Reis, Stefan10,11 (AUTHOR) srei@ceh.ac.uk, Vieno, Massimo10 (AUTHOR) mvi@ceh.ac.uk, Gasparrini, Antonio1,2,12 (AUTHOR)
Source: Remote Sensing. Sep2021, Vol. 13 Issue 18, p3588. 1p.
Subjects: Machine learning
Geographic Terms: United Kingdom
Abstract: Reference 1 Schneider R., Vicedo-Cabrera A.M., Sera F., Masselot P., Stafoggia M., de Hoogh K., Kloog I., Reis S., Vieno M., Gasparrini A. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM 2.5 Concentrations across Great Britain. Figure Graph: Figure 3 Stage-4 predicted PM2.5 concentrations across Great Britain (Top) and London (Bottom) for 2008, 2013, and 2018 aggregated by annual means. [Extracted from the article]
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Abstract:Reference 1 Schneider R., Vicedo-Cabrera A.M., Sera F., Masselot P., Stafoggia M., de Hoogh K., Kloog I., Reis S., Vieno M., Gasparrini A. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM 2.5 Concentrations across Great Britain. Figure Graph: Figure 3 Stage-4 predicted PM2.5 concentrations across Great Britain (Top) and London (Bottom) for 2008, 2013, and 2018 aggregated by annual means. [Extracted from the article]
ISSN:20724292
DOI:10.3390/rs13183588