Volcanic Ash as a precursor for SARS-CoV-2 infection among susceptible populations in Ecuador: A satellite Imaging and excess mortality-based analysis

The global COVID-19 pandemic has altered entire nations and their health systems. The greatest impact of the pandemic has been seen among vulnerable populations such as those with comorbidities like heart diseases, kidney failure, obesity or those with worst health determinants like unemployment and poverty. In the current study, we are proposing previous exposure to fine-grained volcanic ashes as a risk factor for developing COVID-19. Based on several previous studies it has been known since the mid-eight-tees of the last century that volcanic ash is most likely an accelerating factor to suffer from different types of cancer including lung or thyroid cancer. Our study postulates, that people who are most likely to be infected during a SARS-CoV-2 widespread wave will be those with comorbidities that are related to previous exposure to volcanic ashes. We have explored 8,703 satellite images from the last 21 years of available data from the NOAA database and correlated them with the data from the national institute of health statistics in Ecuador. Additionally, we provide more realistic numbers of fatalities due to the virus based on excess mortality data of 2020-2021, when compared to previous years. This study would be a very first of its kind combining social and spatial distribution of COVID-19 infections and volcanic ash distribution. The results and implications of our study will also help countries to identify such aforementioned vulnerable parts of the society, if the given geodynamic and volcanic settings are similar.

A New Algorithm for the Retrieval of Volcanic Ash Cloud Properties using MSG-SEVIRI and Artificial Neural Networks

<p>Artificial neural networks (ANNs) have been successfully applied to various remote sensing problems. Here we use ANNs to detect and analyze volcanic ash clouds pixelwise in MSG-SEVIRI images. Therefore, radiative transfer calculations based on realistic ash properties and atmospheric profiles covering a wide range of possible atmospheric states are performed, and their results are used for the training of the ANNs.</p><p>With respect to the volcanic ash properties the role of the complex refractive index (RI) is highlighted: While it can vary strongly between different eruptions, some models use a limited set of RI measurements. Here we sketch a novel method to calculate the RI of volcanic ashes for wavelengths from 5 to 15 µm from measurements of their individual components (i.e. minerals, glasses, gas bubbles) based on generic petrological ash compositions. A comprehensive data set of RIs for volcanic glasses and bulk volcanic ashes of different chemical compositions is derived and used for the ANNs training data set.</p><p>The final ANNs with specific tasks (classification, retrieval of optical depth, cloud top height and particle effective radius) are validated against an unseen simulated test data set. This allows us to systematically investigate strengths and weaknesses of the retrievals with respect to cloud properties (e.g. optical thickness), geographic and meteorological conditions. To prove real-world applicability case studies for volcanic ash clouds produced by Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered, and comparisons with lidar and in situ measurements show overall good agreement. As for the training only homogeneous single layer ash clouds were assumed, a sensitivity study was carried out to investigate the impact of the vertical mass profile, multiple layers and the geometrical extent of the clouds on the retrieval results.</p><p>Finally, a comparison with a precursor algorithm running operationally at the German weather service (DWD) since 2015 shows that in the case of the Eyjafjallajökull 2010 eruption the new algorithm detects more as well as higher concentrated volcanic ash clouds.</p>