1 Introduction

The recent unprecedented pandemic crisis is so large, that the scientific community has been tempted to investigate the possible links and feedbacks between COVID-19 and the environment (Gautam and Hens 2020). Obviously, the pandemic has many negative effects influencing our lives, health and the economy (Wang et al. 2020). However, COVID-19 had also one positive effect for the atmospheric environment, namely the reduction of air pollution at several parts of the world due to the lockdown measures (Liu et al. 2020a, b; Muhammad et al. 2020). Paradoxically, this improvement in air quality may be beneficiary for the health and well-being of the local populations despite the pandemic risk. On the other hand, most of the reduction in anthropogenic emissions was associated with the transport sector, while industrial pollution sources and adverse weather conditions were still found to increase the concentrations of air pollutants at specific areas (e.g., Wang et al. 2020). Significant improvements in air-quality conditions have already been reported for India (Gautam et al. 2020; Gautam 2020a, b), China (Dutheil et al. 2020; Gupta et al. 2020) and for specific cities like Delhi, London, Paris and Wuhan (Bherwani et al. 2020).

In this study, we focus on the links and feedbacks between COVID-19 and the environment both during and after the lockdown period based on station measurements over Europe. In this manner, we examine both the effects of the COVID-19 lockdown measures on the atmospheric environment and vice versa, i.e., the possible effects of atmospheric variables on pandemic spread. We present air quality measurements of NO2, CO, O3, and PM2.5 concentrations in Europe during the lockdown phase (March–April 2020) and during the recovery phase (May–June 2020). To the best of our knowledge, this is the first study to report the variability in air-quality conditions, before, during, and after the lockdown period, based on station measurements over the entire Europe. This “anthropopause” period imposed by COVID-19 measures provided a formidable opportunity to study air pollutants in Europe under the conditions of reduced anthropogenic activity. Air quality measurements are discussed in the first part of this work where we found out that air pollutants have been reduced at levels normally anticipated not before 2050 if the European Union Green Agenda is fully implemented. Next this work tackles important questions related to the alleged interrelations between environmental conditions (temperature, moisture, PM2.5 concentrations) and the spread of COVID-19 cases. The last part of the paper examines a hypothesis, according to which the observed climate warming at high latitudes and the thawing of permafrost could facilitate the release of pathogens in the atmosphere.

2 Air quality during the anthropopause period in Europe

A global anthropopause period was imposed through the lockdown due to the COVID-19 pandemic outbreak. During March–April 2020, the lockdown of transport and various businesses related activities have resulted in significant reduction of emissions. We have used the Eionet platform (https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm) air quality data provided by the European Environmental Agency (EEA), to study the anomalies (in percentage) of 4 selected air pollutants. Τhe percentage anomalies for NO2, CO, O3 and PM2.5 are shown in Fig. 1. In this figure, the anomalies were calculated in percent from the mean of the 5-year period 2015–2019. As can be seen from Fig. 1, during the lockdown of transport and other activities the concentrations of basic air pollutants such as NO2, CO, PM2.5 have all dropped in the range from − 10 up to − 60 or − 70%. As expected, surface O3 levels have increased by more than 10% in most parts of Europe, which is observed in the most polluted regions of the continent and at urban stations due to the reduction in the main ozone destruction mechanism in urban environments (NO titration) following the substantial decrease of NOx emissions (Finlayson-Pitts and Pitts 1997). These changes were unprecedented and so coherent spatially, that the Europe as a whole had air quality characteristics foreseen to occur only after the possible implementation of European decisions that will take place until 2050 towards a neutral carbon environment. Figure 2 shows the time series of percent anomalies for NO2 during the period June 2015–June 2020, based on the Eionet network. The points in the timeseries represent biweekly NO2 concentration percentage departures from the corresponding 5-year (2015–2019) mean during the period 2015–2020. The horizontal dashed lines indicate ± 2σ and 3σ confidence levels. From that figure one can easily see the overall European response of NO2 concentration on a biweekly basis from July 2015 to July 2020. The abrupt and highly significant drop in NO2 concentration is evident during the antropopause period (March and April 2020) followed by a recovery phase in May and June 2020. It is important to mention here that the observed changes to the concentrations of the above short-lived pollutants are mostly relative to local air-quality considerations. Any possible impacts on the actual global climate would require much longer periods of emission limitations before any expected effect can be detected.

Fig. 1
figure 1

March–April 2020 percentage anomalies from 2015 to 2019 climatology based on Eionet network

Fig. 2
figure 2

Time series of percent anomalies for biweekly mean NO2 concentrations in Europe during the period 2015–2020, based on the Eionet network

3 The large-scale spread of infections and the alleged role of atmospheric parameters in Europe

The COVID-19 outbreak has caught by surprise China’s Wuhan area before Christmas 2019 where stringent measures forced people to stay at home. The population in the Western world started being affected much later close to February 2020. Stringent measures were not taken worldwide and in fact WHO has characterized COVID-19 as pandemic on 12 March 2020 (World Health Organization—WHO 2020). In Europe the first cases followed a day to day increase in geometrical progression throughout the month of March 2020 which also marks the beginning of the declining phase in China. Some countries took early measures (such as Greece), while others implemented measures later on. Some took no measures at all, such as Sweden. A detailed table which includes the measures taken by different countries is provided in Appendix Table 4.

Here we examine the alleged interrelationship between atmospheric temperature, humidity, particulate matter concentrations and the mean daily number of COVID-19 cases at individual European countries and in Europe as a total. Although the global spread of the infection occurs primarily through social contact, traveling, aviation, trade, etc., the variability of meteorological conditions could also be related to the changes in the daily reported number of cases. The role of environmental conditions on infection spread has been previously reported for pandemics ( Hemmes et al. 1960; Kissler et al. 2020; Lin et al. 2006). Recent publications address the relation of COVID-19 cases with the meteorological conditions either on a global scale (Huang et al. 2020; Lal et al. 2020; Sobral et al. 2020) or at specific cities and countries Benedetti et al. 2020; Briz-Redón and Serrano-Aroca 2020; Demongeot et al. 2020; Gunthe et al. 2020; Menebo 2020; Şahin 2020). Other papers have studied separately the effects of meteorology from those of air pollution on the number of COVID-19 infections or deaths in Europe (Ogen 2020; Zoran et al. 2020a, b) and elsewhere (Wang et al. 2020).

In this context, we have re-examined the relationship that was alleged in some of the above papers to exist between air temperature, humidity and daily number of new COVID-19 cases. In that sense, we have correlated the day-to-day variability of COVID-19 cases with major environmental parameters such as the daily mean temperature and relative humidity and the daily mean concentrations of suspended particulates with diameter less than 2.5 μm. Meteorological and air quality data for the regression analysis were taken from Copernicus ERA5 reanalysis (Hersbach et al. 2020) and CAMS NRT Global, respectively (https://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=sfc/). The sources for pandemic COVID-19 cases are shown in Appendix Table 5 (last update July 2020). In view of the difficulties involved in reporting the cases on time for every day, we decided to calculate the 5-day average for each dataset which pertains to a given region and country in Europe. The number of regions for each country are shown in Tables 1 and 2 which also show the number of pairs entering each correlation. For each 5-day period, we calculated the average number of new reported COVID-19 cases normalized by the population of each region which were afterwards correlated with the climatic anomalies of the atmospheric parameters (T, RH) together with PM2.5 daily mean concentrations. The correlations were calculated for 18 European countries as shown in both Tables 1 and 2, where the Balkans include Slovenia, Croatia, Bosnia-Herzegovina, Montenegro, Albania and Greece. We should note here that different countries have different testing rates. Low testing rates may well mask the true infection numbers. However the large number of pairs entering our correlations (in Tables 1 and 2) ensures that statistically speaking, the significance of the correlations ensures the percentage of variance explained by environmental parameters without refering to the true infection numbers. The last column in Tables 1 and 2 shows the correlations for Europe as a whole between COVID-19 cases, temperature, relative humidity, PM2.5. More specifically, Table 1 shows the above-mentioned correlations for individual months (March, April, May, June), while Table 2 shows the correlations for the 2 bimonthly samples (March–April and May–June) as well as the 4-month period (March–June 2020). Statistically significant correlations at better than the 99% confidence level are shown by an asterisk next to the correlation coefficent. Both Tables 1 and 2 show in parentheses for each country and for the Europe as a whole, the number of regions from which data were provided and in parenthesis the number of pairs entering each correlation.

Table 1 Correlation coefficients between the number of new COVID-19 cases during a 5-day period and the mean 5 days temperature, relative humidity, PM2.5 and CVI for each month from March to June 2020
Table 2 Correlation coefficients between the number of new COVID-19 cases during a 5-day period and the mean 5 days temperature, relative humidity, PM2.5 and CVI for the bi-monthly periods March–April, May–June and the 4-month period March–June 2020

In both tables, we have calculated an index (COVID-19 Index, hereafter CVI) to describe the interrelationship between the atmospheric variables discussed above and the number of COVID-19 cases. In order to do so, we use a multiple regression analysis in which the dependent variable COVID-19 index (CVI) is the mean daily number of new cases in each group and the independent variables are the temperature (°C), the relative humidity (%), and the daily anomalies of PM2.5 concentrations (in μg m−3) as shown in Eq. 1. The CVI index is correlated with the number of new cases in Europe explaining about 9% of their total variance during March and April, 4% during May and 0% during June (Table 1).

$$CVI = a_{0} + a_{1} T + a_{2} \left( {{\text{RH}}} \right) + a_{3} {\text{PM}}2.5$$

The multiple regression analysis for Eq. (1) in Europe takes the form:

$${\text{CVI}} = 0.302 - 0.041T - 0.004\left( {{\text{RH}}} \right) + 0.059\left( {{\text{PM}}2.5} \right)$$
$$a_{0} = \, 0.302 \pm 0.023, \, a_{1} = 0.041 \pm 0.005; \, a_{2} = 0.004 \pm 0.001{\text{ and }}a_{3} = 0.059 \pm 0.006$$

To summarize, part of the variance of the reported 5-day averaged number of COVID-19 cases in Europe for March and April can be explained by an index based on the atmospheric variables used. More specifically, temperature and humidity are anti-correlated with the number of COVID-19 cases, while PM2.5 is positively correlated. So, the number of new COVID-19 cases shows an increase at lower temperature (cold), lower humidity (dry) and high concentrations of particulates only during March and April. The correlation between particulates and CVI can be expected in view of the fact that smaller particulates have negative effects on respiratory diseases (Dockery and Pope 1994).

However when we repeated the above calculations for May and June, the correlations proved to be insignificant. More specifically the CVI correlation coefficient for the entire 4-month period March–June in Europe drops to 0.05 (Table 2). These results raise a question on whether the spread of COVID-19 disease depends on atmospheric conditions, or not. Based on the contradicting results between the two different periods (the anthropopause and post-anthropopause), the existence of such a relationship cannot be supported and the above analysis remains inconclusive. The spread of the disease is affected primarily by social distancing and the implementation of lockdown measures (e.g., Bherwani et al. 2020). In fact, we have been witnessing an increase in COVID-19 cases during the summer months of 2020 globally.

4 A working hypothesis on acceleration of climate change in Siberia and the Arctic and the possible implications for the emergence of pathogens from the permafrost

An additional mechanism connecting climate change with epidemic infections could be that viruses originating from melting glacier or thawing permafrost in the arctic can be transferred both by the wind and the migratory birds toward lower latitudes, eventually enhancing the complexity for infection dynamics. Similar hypotheses on the release of deadly infection vectors from exposed carcasses due to thawing of permafrost in arctic regions have been discussed for anthrax (Revich and Podolnaya 2011; Revich et al. 2012; Hueffer et al. 2020). The viruses could be hidden in the melting ice and thawing of permafrost regions in the Arctic in view of the evolving global warming and its acceleration in this part of the world (IPCC Intergovernmental Panel on Climate Change 2013). Pathogenic viruses and microbes have been found to survive very long periods of time buried in permafrost regions (Tumpey et al. 2005; Biagini et al. 2012; Legendre et al. 2014). For example Legendre et al. 2014 (Legendre et al. 2014) found a still infectious 30,000-year-old virus (named Pithovirus sibericum) in a Siberian permafrost sample. Tumpey et al., 2005 managed to reconstruct the Spanish influenza virus from a victim that was buried in Alaska since 1918 and Biagini et al. 2012 detected a smallpox related virus (variola virus) in mummies buried in Siberian permafrost from the late 17th to early 18th.

Permafrost is the soil layer that remains permanently frozen throughout the year under various permafrost types at higher latitudes (i.e., continuous, discontinuous, sporadic and isolated) (Brown et al. 1997). In Central Asia, several mixed types of permafrost start from NW China and Mongolia and extend northward toward Siberia. Long-term measurements of deep permafrost temperatures at depths of 10–200 m in Central Asia and Russia (Romanovsky et al. 2010; Zhao et al. 2010) have shown a continuous warming trend over the last decades (1972–2009). Climate warming leads also in thickening of the active soil layer, i.e., the upper soil region that responds to the seasonal ambient conditions (temperature and precipitation) (Streletskiy et al. 2015). The consecutive melting of permafrost layers from year to year due to climate warming could eventually result to the exposure of gradually deeper permafrost layers and thus increase the possibility for exposure of contaminating sources such as buried carcasses, cemetery graves and fossils along the migrating birds’ pathways and stopover sites (Clairbaux et al. 2019). Lower latitude permafrost areas such as the regions of north Mongolia and south Russia (e.g., Irkutsk, Lake Baikal) are more susceptible to inter-annual temperature changes and to global warming (Hueffer et al. 2020). Following this hypothesis, the acceleration of temperature increase in the Arctic and the melting of permafrost in these areas could be related to the release of present and future “unknown” viruses. That scenario is horrifying, particularly if one considers that migrating bird pathways and wintering areas can also be modified in view of global warming continuation (Romanovsky et al. 2010). From the climatological point of view, Central Asia is one of the most vulnerable regions for manmade climate change. The 140 years (1880–2020) continuous record of mean temperature at the station of Irkutsk (52.27° N, 104.32° E) exhibits a heating of almost 3 °C in this period (Fig. 3). Most of this warming (about 2.5 °C) occurs after 1970 as seen from the right regression line in Fig. 3.

Fig. 3
figure 3

Mean monthly temperature anomalies for Irkutsk (1880–2019)

Warming of the arctic is also clearly evident by the National Snow and Ice Data Center (NSIDC) analysis of satellite observations showing the changes in the arctic sea-ice since 1979 (Fig. 4). The annual minimum of the arctic sea-ice area shrinks from about 7 million km2 before 2000 to 4.5 million km2 after 2000. The red numbers in Fig. 4 correspond to specific pandemics listed in Table 3. It is worth noticing that as seen in Table 3, seven out of ten major influenza and coronavirus outbreaks in the past 130 years originated in Southeast Asia (Drosten et al. 2003; Ksiazek et al. 2003; Doshi 2011; Zaki et al. 2012; Saunders-Hastings and Krewski 2016; Paraskevis et al. 2020; Wu et al. 2020a, b).

Fig. 4
figure 4

Annual minimum extent of the Arctic Sea Ice (in million km2) from 1979 to 2019. Important pandemics during this period are also shown in the graph with red numbers corresponding to Table 3

Table 3 Historical Pandemics in the northern hemisphere from 1889 to 2019

The continuous decrease in arctic ice cover in the summer months (Fig. 4) will eventually lead to an ice-free polar ocean by the end of this century. This could modify the behavior of several local species including migrating birds. Emerging of new stopover sites previously covered by ice and the ability to prey on the open sea might alter the birds’ habits such as migrating routes and wintering areas (Clairbaux et al. 2019). To emphasize the complexity of this hypothesis, climate warming and consequent changes in permafrost are more evident near lakes and wetlands accompanied also by geological deformations such as landslides along river and lake banks due to permafrost thawing and increase of the active layer depth (Tyszkowski et al. 2015). Such water and wetland ecological complexes are natural resting biotopes for a variety of species like Anseriformes and Gruiformes especially at the south parts of Siberia (Sivay et al. 2012). In a recent study by Sharshov et al. (2017), the virus of influenza was detected in 185 birds from a total of 2300 samples obtained from wild migratory birds in the south of Western Siberia during 2007–2014. Certain viruses including influenza persist in environmental ice (glaciers, snow, permafrost) for years, centuries, millennia, or even longer (Rogers et al. 2004). Therefore, it is possible that a continuous alteration of wetland biotopes landscapes due to climate warming and the consecutive melting of soil layers after centuries of remaining at permafrost stage could assist the re-emergence of ancient virus agents in the environment. The virus agents remain frozen in birds’ feces and are exposed back in spring and summer through melting and thawing processes to generate a seasonal infection cycle (Yu et al. 2010). For the interested reader, we provide in Appendix calculations of back trajectories for the prevailing air masses up to 10 days before the onset of major pandemics. Finally, the extremely rapid urbanization of large regions of biotopes and brutal changes of landscape in the past few decades in China may have also played a role to enhance the genetic diversity of certain pathogens.

5 Conclusions

The findings of our study can be summarized as follows:

  1. 1.

    The COVID-19 anthropopause period resulted in a remarkable drop of atmospheric pollutant concentrations in Europe, resembling the anticipated 2050 Green Deal conditions. The comparisons are presented as percentage anomalies from the 2015–2019 average values. The reduction in NO2 concentrations is pronounced over the entire continent ranging between − 10 and − 20% in the south and east Europe and − 50% in central Europe. This reduction in NO2 is accompanied by an increase of up to 30% in O3 as an expected result (titration). The reduction of CO concentrations is also found to be more than − 30% especially in Italy and central Europe with only a few stations presenting a statistical increase (mainly in the Iberian Peninsula). Particulate matter (PM2.5) concentrations also present significant reduction of − 10 to − 20% at most stations.

  2. 2.

    The improvement of air quality in Europe lasted only for 2 months during the lockdown (March and April 2020). As soon as the lockdown measures were waived, the concentrations of pollutants in the atmosphere quickly recovered to pre-pandemic levels. The recovery phase took little more than two months. No climatic implications can be justified from this short-term perturbation of anthropogenic emissions.

  3. 3.

    Our analysis on the possible correlation between meteorology, air quality and the number of daily reported COVID-19 cases in Europe resulted in the construction of a tentative statistical index that explained more than 9% of the total variance in the reported COVID-19 cases for March and April 2020. However, the correlations dropped to insignificant levels when we repeated our calculations for May and June 2020. This shows that any suggested relation between temperature, humidity and virus spread cannot be justified and most likely the global spread of the infection occurs primarily through social contact, traveling, aviation, trade, etc.

  4. 4.

    Seven out of ten influenza and coronavirus pandemics from 1889 to the present originated in China. Significant climate warming, thawing of permafrost soil and glacier retreat is evident in Siberia and the Arctic in coincidence with the increase of epidemics in Southeast Asia. The possible release of frozen viruses at these areas and the possible changes in migrating birds’ routes due to climate change cannot be excluded in studying past and future epidemic hazards.

The significant correlation that was found between virus spread, colder temperatures and lower relative humidity levels during the first two months of the pandemic (March and April 2020) is in accordance with previous correlation studies on the connections between COVID-19 and meteorology for China (Liu et al. 2020a, b; Wang et al. 2020), Iran (Ahmadi et al. 2020), US and Italian regions (Livadiotis 2020) and at global scale (Wu et al. 2020a, b) during the same period. Contradictory results have been reported by a correlation study in Brazil (Auler et al. 2020) where high temperatures and intermediate relative humidity are found to favor the spread of COVID-19. On the other hand, the correlation between the increased PM levels and virus infections is also in agreement with previous COVID-19 studies (Comunian et al. 2020; Fattorini and Regoli 2020; Frontera et al. 2020; Sasidharan et al. 2020). However, as shown in our analysis, such correlations can be misleading because these relations are not robust when repeated for a longer period of time. The persistence of the infection at the north hemisphere during the summer months is also an indication that the variability of COVID-19 cases is not significantly correlated with air temperature variability.

The findings of this paper bring us before new challenges and directions in climate change research. Glacier retreating and thawing of permafrost layers expected in the forthcoming decades may result in new and unknown epidemics posing new horrifying threats to mankind. It is important that combined atmospheric and epidemiological studies as well as detailed scientific expeditions should be organized to investigate such health-related aspects of climate change. Species like bats and other exotic mammals are believed to be the largest reservoir for SARS-CoV-like viruses (Cheng et al. 2007; Cupertino et al. 2020; Fan et al. 2019) and therefore the humanity needs to revisit certain social customs and traditional diets which can threaten our lives and result in pandemic disasters like the one we experience at present.