COVID-19 lockdown and its latency in Northern Italy: seismic evidence and socio-economic interpretation.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 10 2020
05 10 2020
Historique:
received:
21
06
2020
accepted:
11
09
2020
entrez:
6
10
2020
pubmed:
7
10
2020
medline:
21
10
2020
Statut:
epublish
Résumé
The Italian Government has decreed a series of progressive restrictions to delay the COVID-19 pandemic diffusion in Italy since March 10, 2020, including limitation in individual mobility and the closure of social, cultural, economic and industrial activities. Here we show the lockdown effect in Northern Italy, the COVID-19 most affected area, as revealed by noise variation at seismic stations. The reaction to lockdown was slow and not homogeneous with spots of negligible noise reduction, especially in the first week. A fresh interpretation of seismic noise variations in terms of socio-economic indicators sheds new light on the lockdown efficacy pointing to the causes of such delay: the noise reduction is significant where non strategic activities prevails, while it is small or negligible where dense population and strategic activities are present. These results are crucial for the a posteriori interpretation of the pandemic diffusion and the efficacy of differently targeted political actions.
Identifiants
pubmed: 33020508
doi: 10.1038/s41598-020-73102-3
pii: 10.1038/s41598-020-73102-3
pmc: PMC7536181
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16487Références
Fratti, G. E. The nature of high frequency earth noise spectra. Geophysics 28(4), 547–562 (1963).
doi: 10.1190/1.1439228
Stutzmann, E., Roult, G. & Astiz, L. GEOSCOPE station noise levels. Bull. Seismol. Soc. Am. 90, 690–701 (2000).
doi: 10.1785/0119990025
Bonnefoy-Claudet, S., Cotton, F. & Bard, P. The nature of noise wavefield and its applications for site effects studies. Earth Sci. Rev. 79, 205–227 (2006).
doi: 10.1016/j.earscirev.2006.07.004
Diaz, J., Schimmel, M., Ruiz, M. & Carbonell, R. Seismometers within cities: a tool to connect Earth Sciences and society. Front. Earth Sci. 8, 9 (2020).
doi: 10.3389/feart.2020.00009
Demuth, J. L., Morss, R. E., Lazo, J. K. & Trumbo, C. The effects of past hurricane experiences on evacuation intentions through risk perception and efficacy beliefs: a mediation analysis. Weather Clim. Soc. 8, 327–344 (2016).
doi: 10.1175/WCAS-D-15-0074.1
Gerstoft, P. & Bromirski, P. D. “Weather bomb” induced seismic signals. Science 353, 869–870 (2016).
doi: 10.1126/science.aag1616
Kanai, K. & Tanaka, T. On microtremors. VIII Bull. Earthq. Res. Inst. 39, 97–114 (1961).
Marzorati, S. & Bindi, D. Ambient noise levels in north central Italy. Geochem. Geophys. Geosyst. 7, Q09010 (2006).
doi: 10.1029/2006GC001256
Hong, T.-K., Lee, J., Lee, G., Lee, J. & Park, S. Correlation between ambient seismic noises and economic growth. Seismol. Res. Lett. 91, 2343–2354 (2020).
doi: 10.1785/0220190369
Somala, S. N. Seismic noise changes during COVID-19 pandemic: a case study of Shillong, India. Nat. Hazards 103, 1623–1628. https://doi.org/10.1007/s11069-020-04045-1 (2020).
doi: 10.1007/s11069-020-04045-1
Poli, P. et al. The 2020 coronavirus lockdown and seismic monitoring of anthropic activities in Northern Italy. Sci. Rep. 10, 9404. https://doi.org/10.1038/s41598-020-66368-0 (2020).
doi: 10.1038/s41598-020-66368-0
pubmed: 32523080
pmcid: 7287089
Lecocq, T. et al. Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures. Science https://doi.org/10.1126/science.abd2438 (2020).
doi: 10.1126/science.abd2438
pubmed: 32703907
Arpalombardia. https://www.arpalombardia.it . Accessed 11 May 2020.
Allerta Meteo Emilia-Romagna. https://allertameteo.regione.emilia-romagna.it/web/guest/singola-allerta/-/asset_publisher/FZPQSb6AzKtJ/Allerta-Bollettino/id/1096287#.XqJ3sJMzYWo . Accessed 11 May 2020.
Ufficio Studi Confcommercio. https://www.confcommercio.it/documents/20126/2678762/Congiuntura+Confcommercio+%28CC%29+4-2020.pdf/86ca9f76-1e00-9757-e039-85169341296c . Accessed 11 May 2020.
Saccorotti, G., Piccinini, D., Cauchie, L. & Fiori, I. Seismic noise by wind farms: a case study from the Virgo gravitational wave observatory, Italy. Bull. Seismol. Soc. Am. 101, 568–578 (2011).
doi: 10.1785/0120100203
Brenguier, F. et al. Train traffic as a powerful noise source for monitoring active faults with seismic interferometry. Geophys. Res. Lett. 46, 9529–9536 (2019).
doi: 10.1029/2019GL083438
Gatto, M. et al. Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures. Proc. Natl. Acad. Sci. 117, 10484–10491 (2020).
doi: 10.1073/pnas.2004978117
Nielsen, MarketTrack. https://www.nielsen.com/us/en/insights/article/2020/covid-19-tracking-the-impact-on-fmcg-and-retail/ . Accessed 11 May 2020.
INGV Seismological Data Centre. Rete Sismica Nazionale (RSN). Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy. https://doi.org/10.13127/SD/X0FXNH7QFY (2006).
Geological Survey-Provincia Autonoma Di Trento. Trentino Seismic Network. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/ST (1981).
OGS (Istituto Nazionale Di Oceanografia E Di Geofisica Sperimentale). North-East Italy Seismic Network. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/OX (2016).
University Of Genova. Regional Seismic Network of North Western Italy. International Federation of Digital Seismograph Networks. https://doi.org/10.7914/SN/GU (1967).
Nakata, N., Snieder, R., Tsuji, T., Larner, K. & Matsuoka, T. Shear-wave imaging from traffic noise using seismic interferometry by cross-coherence. Geophysics 76, SA97–SA106 (2011).
doi: 10.1190/geo2010-0188.1
Riahi, N. & Gerstoft, P. The seismic traffic footprint: tracking trains, aircraft, and cars seismically. Geophys. Res. Lett. 42, 2674–2681 (2015).
doi: 10.1002/2015GL063558
Bormann, P. & Wielandt, E. Seismic Signals and Noise, Version June 2013 (2013).
Holub, K. Some made man sources of the seismic noise. Acta Montana IRSM AS CR Ser. A 12(107), 83–98 (1998).
Groos, J. C. & Ritter, J. R. R. Time domain classification and quantification of seismic noise in an urban environment. Geophys. J. Int. 179, 1213–1231 (2009).
doi: 10.1111/j.1365-246X.2009.04343.x
Lott, F. F., Ritter, J. R. R. & Al-Qaryouti, M. On the analysis of wind-induced noise in seismological recordings. Pure Appl. Geophys. 174, 1453–1470 (2017).
doi: 10.1007/s00024-017-1477-2
McNamara, D. E. & Buland, R. P. Ambient noise levels in the continental United States. Bull. Seismol. Soc. Am. 94, 1517–1527 (2004).
doi: 10.1785/012003001
Brassel, K. E. & Reif, D. A procedure to generate Thiessen polygons. Geograph. Anal. 11, 289–303 (1979).
doi: 10.1111/j.1538-4632.1979.tb00695.x
Olivieri, M. & Spada, G. Spatial sea-level reconstruction in the Baltic Sea and in the pacific Ocean from tide gauges observations. Ann. Geophys. 59, 0323 (2016).
Manni, F., Guerard, E. & Heyer, E. Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by using Monmonier’s algorithm. Hum. Biol. 76, 173–190 (2004).
doi: 10.1353/hub.2004.0034
Wessel, P., Smith, W. H. F., Scharroo, R., Luis, J. F. & Wobbe, F. Generic mapping tools: improved version released. EOS Trans. AGU 94, 409–410 (2013).
doi: 10.1002/2013EO450001
Taylor, J. R. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements (University Science Books, Mill Valley, 1997).
Istat (Istituto Nazionale di Statistica).Online data warehouse. https://www.istat.it . Accessed 23 April 2020.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://doi.org/10.5258/SOTON/WP00645 .
Greene, W. H. Econometric Analysis 8th edn. (Pearson, London, 2018).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2013). https://www.R-project.org/ .
Eurostat. https://ec.europa.eu/eurostat/documents/3859598/5902521/KS-RA-07-015-EN.PDF . Accessed 11 May 2020.
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55 (2007).
doi: 10.1109/MCSE.2007.55