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Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)

Background: the Lombardy region in Italy was the first area in Europe to record an outbreak of COVID-19 and one of the most affected worldwide. As this territory is strongly polluted, it was hypothesized that pollution had a role in facilitating the diffusion of the epidemic, but results are uncerta...

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Autores principales: Gianquintieri, Lorenzo, Brovelli, Maria Antonia, Pagliosa, Andrea, Bonora, Rodolfo, Sechi, Giuseppe Maria, Caiani, Enrico Gianluca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8617767/
https://www.ncbi.nlm.nih.gov/pubmed/34831909
http://dx.doi.org/10.3390/ijerph182212154
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author Gianquintieri, Lorenzo
Brovelli, Maria Antonia
Pagliosa, Andrea
Bonora, Rodolfo
Sechi, Giuseppe Maria
Caiani, Enrico Gianluca
author_facet Gianquintieri, Lorenzo
Brovelli, Maria Antonia
Pagliosa, Andrea
Bonora, Rodolfo
Sechi, Giuseppe Maria
Caiani, Enrico Gianluca
author_sort Gianquintieri, Lorenzo
collection PubMed
description Background: the Lombardy region in Italy was the first area in Europe to record an outbreak of COVID-19 and one of the most affected worldwide. As this territory is strongly polluted, it was hypothesized that pollution had a role in facilitating the diffusion of the epidemic, but results are uncertain. Aim: the paper explores the effect of air pollutants in the first spread of COVID-19 in Lombardy, with a novel geomatics approach addressing the possible confounding factors, the reliability of data, the measurement of diffusion speed, and the biasing effect of the lockdown measures. Methods and results: all municipalities were assigned to one of five possible territorial classes (TC) according to land-use and socio-economic status, and they were grouped into districts of 100,000 residents. For each district, the speed of COVID-19 diffusion was estimated from the ambulance dispatches and related to indicators of mean concentration of air pollutants over 1, 6, and 12 months, grouping districts in the same TC. Significant exponential correlations were found for ammonia (NH(3)) in both prevalently agricultural (R(2) = 0.565) and mildly urbanized (R(2) = 0.688) areas. Conclusions: this is the first study relating COVID-19 estimated speed of diffusion with indicators of exposure to NH(3). As NH(3) could induce oxidative stress, its role in creating a pre-existing fragility that could have facilitated SARS-CoV-2 replication and worsening of patient conditions could be speculated.
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spelling pubmed-86177672021-11-27 Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy) Gianquintieri, Lorenzo Brovelli, Maria Antonia Pagliosa, Andrea Bonora, Rodolfo Sechi, Giuseppe Maria Caiani, Enrico Gianluca Int J Environ Res Public Health Article Background: the Lombardy region in Italy was the first area in Europe to record an outbreak of COVID-19 and one of the most affected worldwide. As this territory is strongly polluted, it was hypothesized that pollution had a role in facilitating the diffusion of the epidemic, but results are uncertain. Aim: the paper explores the effect of air pollutants in the first spread of COVID-19 in Lombardy, with a novel geomatics approach addressing the possible confounding factors, the reliability of data, the measurement of diffusion speed, and the biasing effect of the lockdown measures. Methods and results: all municipalities were assigned to one of five possible territorial classes (TC) according to land-use and socio-economic status, and they were grouped into districts of 100,000 residents. For each district, the speed of COVID-19 diffusion was estimated from the ambulance dispatches and related to indicators of mean concentration of air pollutants over 1, 6, and 12 months, grouping districts in the same TC. Significant exponential correlations were found for ammonia (NH(3)) in both prevalently agricultural (R(2) = 0.565) and mildly urbanized (R(2) = 0.688) areas. Conclusions: this is the first study relating COVID-19 estimated speed of diffusion with indicators of exposure to NH(3). As NH(3) could induce oxidative stress, its role in creating a pre-existing fragility that could have facilitated SARS-CoV-2 replication and worsening of patient conditions could be speculated. MDPI 2021-11-19 /pmc/articles/PMC8617767/ /pubmed/34831909 http://dx.doi.org/10.3390/ijerph182212154 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gianquintieri, Lorenzo
Brovelli, Maria Antonia
Pagliosa, Andrea
Bonora, Rodolfo
Sechi, Giuseppe Maria
Caiani, Enrico Gianluca
Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)
title Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)
title_full Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)
title_fullStr Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)
title_full_unstemmed Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)
title_short Geospatial Correlation Analysis between Air Pollution Indicators and Estimated Speed of COVID-19 Diffusion in the Lombardy Region (Italy)
title_sort geospatial correlation analysis between air pollution indicators and estimated speed of covid-19 diffusion in the lombardy region (italy)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8617767/
https://www.ncbi.nlm.nih.gov/pubmed/34831909
http://dx.doi.org/10.3390/ijerph182212154
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