Abstract
Faced with the global pandemic of Covid-19, we need to better understand the links between meteorological factors, air quality, and the virus. In the vein of a recent empirical literature, we reassess the impact of weather factors like temperatures, humidity, and air quality indicators on Covid-19 daily cases in China for both Wuhan and Beijing. Using a consistent number of observations (104), we compute, for the first time, correlations but also Granger causality and, above all, a spectral analysis using Wavelet methods. Our results go further of the previous studies and reveal the complexity of the studied relationships when both time and frequency domains are taken into account. Wavelet analysis enables us to go further of the usual correlation analysis. Though negative humidity impact on Covid-19 cases was expected to be relatively clear regarding previous literature based on correlations, we do not find evidence of such a result. The controversial effect of warmer temperatures on the Covid-19, often difficult to identify or sometimes identified as surprisingly positive, can negatively emerge via wavelet analysis for some periods only. This result is however clear-cut for the Hubei Province but for the Beijing one. Finally, our results reveal a bi-directional causality between air quality and the number of infected people. Short-run causality from Covid-19 to air quality (better induced air quality) via lockdown policies disappear in a medium-run and turns to become a significant causal link from induced air quality improvement to Covid-19 daily cases (reduction of infected people).
-
The study reassesses the link between local weather, air quality, and Covid-19 epidemic in China using time series (TS) with 104 observations for China.
-
Wavelet analysis enables us to go further of the usual correlations and TS analysis and reveal the complexity of the relationships.
-
Wavelet analysis about temperature and humidity effects shows that the relationship between weather and Covid-19 is complex and not uniform regarding time and frequency.
-
Humidity impact on Covid-19 epidemic seems to be not clear.
-
Temperature positive effect of Covid-19 epidemic turns to be negative for some periods only.
-
Short-run causality from Covid-19 via lockdown disappears in a medium-run and turns to become a significant causal link from induced air quality improvement to Covid-19 daily cases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Araujo, M. B., & Naimi, B. (2020). Spread of SARS-CoV-2 coronavirus likely to be constrained by climate. https://doi.org/10.1101/2020.03.12.20034728.
Baker, R. E., Yang, W., Vecchi, G. A., Metcalf, C. J. E., & Grenfell, B. T. (2020). Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic. Science, 369(6501), 315–319. https://doi.org/10.1126/science.abc2535(2020)
Bashir, M. F., et al. (2020). Correlation between climate indicators and COVID-19 pandemic in New York, USA. Science of the Total Environment, 728, 138835.
Bauer, R., Diaz-Sanchez, D., & Jaspers, D. (2012). Effects of air pollutants on innate immunity: The role of toll-like receptors and nucleotide-binding oligomerization domain-like receptors. The Journal of Allergy and Clinical Immunology, 129, 14–24.
Briz-Redón, A., & Serrano-Aroca, A. (2020). A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain. Science of the Total Environment, 728, 138811.
Bukhari, Q., & Jameel, Y. (2020). Will coronavirus pandemic diminish by summer? SSRN working paper 3556998.
Caren, L. (1981). Environmental pollutants: Effects on the immune system and resistance to infectious disease. Bioscience, 31, 582–586.
Casanova, L. M., Jeon, S., Rutala, W. A., Weber, D. J., & Sobsey, M. D. (2010). Effects of air temperature and relative humidity on coronavirus survival on surfaces. Applied and Environmental Microbiology, 76(9), 2712–2717.
Chan, K., Peiris, J., Lam, S., Poon, L., Yuen, K., & Seto, W. (2011). The effects of temperature and relative humidity on the viability of the SARS coronavirus. Advances in Virology, 2011, 734690.
Coccia, M. (2020). Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Science of the Total Environment, 729, 138474.
Dalziel, B. D., Kissler, S., Gog, J. R., Viboud, C., Bjornstad, O. N., Metcalf, C. J. E., & Grenfell, B. T. (2018). Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities. Science, 362(6410), 75–79.
Gallegati, M. (2018). A systematic wavelet-based exploratory analysis of climatic variables. Climatic Change, 148(1–2), 325–338.
Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.
Hougeven, M. J. (2020). Pollen likely seasonal factor in inhibiting flu-like epidemics. A Dutch study into the inverse relation between pollen counts, hay fever and flu-like incidence 2016–2019. Science of the Total Environment, 727, 138543.
Imai, C., Armstrong, B., Chalabi, Z., Pangtani, P., & Hashizume, M. (2015). Time series regression model for infectious disease and weather. Environmental Research, 142, 319–327.
Iqbal, N., Fareed, Z., Shahzad, F., He, X., Shahzad, U., & Ma, L. (2020). Nexus between COVID-19, temperature and exchange rate in Wuhan City: New findings from partial and multiple wavelet coherence. Science of the Total Environment, 729, 138916.
Jamil, T., Alam, I., Gojobori, T., & Duarte, C. M. (2020). No evidence for temperature-dependence of the COVID-19 epidemic. medRxiv. https://doi.org/10.1101/2020.03.29.20046706.
Kumar & Foufoula Georgiou. (1997). https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/97rg00427
Ma, Y., Zhao, Y., Liu, J., He, X., Wang, B., Fu, S., et al. (2020). Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Science of the Total Environment, 724, 138226.
Mohsen et al. (2020). https://www.sciencedirect.com/science/article/pii/S0048969720322221? via%3Dihub
Ogen, Y. (2020). Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality. Science of the Total Environment, 726, 138605.
Qi, H., Xiao, S., et al. (2020). COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis. Science of the Total Environment, 728, 138778.
Sahin, M. (2020). Impact of weather on COVID-19 pandemic in Turkey. Science of the Total Environment, 728, 138810.
Tobias et al. (2020). https://pubmed.ncbi.nlm.nih.gov/32330766/
Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61–78.
Wu, Y., Jing, W., Jue, L., Qiuyue, M., Jie, Y., Yaping, W., Min, D., & Min, L. (2020). Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Science of the Total Environment.
Xie, J., & Zhu, Y. (2020). Association between ambient temperature and COVID-19 infection in 122 cities from China. Science of the Total Environment, 724, 138201.
Yuan, J., Yun, H., & Lan,W., et al. (2006). A climatologic investigation of the SARS-CoV outbreak in Beijing, China. American Journal of Infection Control, 34(4), 234–236.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Damette, O., Goutte, S. (2021). Weather, Pollution, and Covid-19 Spread: A Time Series and Wavelet Reassessment. In: Belaïd, F., Cretì, A. (eds) Energy Transition, Climate Change, and COVID-19. Springer, Cham. https://doi.org/10.1007/978-3-030-79713-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-79713-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79712-6
Online ISBN: 978-3-030-79713-3
eBook Packages: Economics and FinanceEconomics and Finance (R0)