ABSTRACT
During the pandemic, Italy experienced several phases of lockdown with different types of restrictions. Starting on February 23rd 2020, 11 municipalities in northern Italy suspended activities in schools, universities, museums, cultural venues, and all public initiatives. The ordinance announcing the national emergency was released on March 11th, stabilising the first lockdown period for the whole of Italy, which lasted until the second half of May. After a phase of cushioned restrictions during the summer, the so-called 'Second Wave' began forcing anew ordinance on October 13th with more stringent restrictions as the number of infections increased. On November 3rd, the "colour system" was introduced with three risk bands-red, orange and yellow-assigned weekly to the regions based on monitoring indicators. The main objective of the present study is to assess the impact of the meteorological and air quality conditions on COVID-19 cases in the region of Emilia-Romagna in Italy during the lockdown periods. Several pollutant time series from the Copernicus Atmosphere Monitoring Service were joined with meteorological data from the daily gridded land-only observational dataset over Europe and then compared with the total number of infections, hospitalisations and deaths. Data provided by the two monitoring systems were processed through an algorithm and organised by provinces and municipalities in Emilia-Romagna, Italy. The explorative analysis, conducted using both time series and seasonally adjusted time series, shows that pollutants most affected by lockdown phases are CO, NO2, PM10, PM2.5 and SO2. The findings in this study may help further studies better understand the variations 2020 and 2021 and the correlation with COVID-19 variables. © British Crown Copyright (2022)
ABSTRACT
The requirement for an effective handling and management of heterogeneous and possibly confidential data continuously increases within multiple scientific domains. PLANET (Pollution Lake ANalysis for Effective Therapy) is a INFN-funded research initiative aiming to implement an observational study to assess a possible statistical association between environmental pollution and Covid19 infection, symptoms and course. PLANET is built on a "data-centric" based approach that takes into account clinical components, environmental and pollution conditions, complementing primary data and many eventual confounding factors such as population density, commuter density, socio-economic metrics and more. Besides the scientific one, the main technical challenge of the project is about collecting, indexing, storing and managing many types of datasets while guaranteeing FAIRness as well as adherence to the prescribed regulatory frameworks, such as those granted by the General Data Protection Regulation, GDPR. In this contribution we describe the developed open-source DataLake platform, detailing its key features: the event-based storage system provided by MinIO, which allows automatic metadata processing;the data-ingestion pipeline implemented via Argo Workflows;the GraphQL interface to query object metadata;finally, the seamless integration of the platform within a compute multi-user environment, showing how all these frameworks are integrated in the Enhanced PrIvacy and Compliance (EPIC) Cloud partition of the INFN Cloud federation. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)