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10.1016/j.apenergy.2020.115835 http://scihub22266oqcxt.onion/10.1016/j.apenergy.2020.115835 32952266!7486865!32952266     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32952266&cmd=llinks): Failed to open stream: HTTP request failed! 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The relationship between air pollution and COVID-19-related deaths: An application to three French cities |pdf=|usr=}}{{32952266}} gab.com Text The relationship between air pollution and COVID-19-related deaths: An application to three French cities JO: Appl Energy http://www.kidney.de/pget.php?&tw=1&pmid=32952266 #FOAvip Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM(2.5) and PM(10) linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM(2.5) and PM(10) connected to COVID-19: 17.4 microg/m(3) (PM(2.5)) and 29.6 microg/m(3) (PM(10)) for Paris; 15.6 microg/m(3) (PM(2.5)) and 20.6 microg/m(3) (PM(10)) for Lyon; 14.3 microg/m(3) (PM(2.5)) and 22.04 microg/m(3) (PM(10)) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings. Twit Text FOAVip The relationship between air pollution and COVID-19-related deaths: An application to three French cities ????Appl Energy http://www.kidney.de/pget.php?&tw=1&pmid=32952266 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32952266 #FOAvip English Wikipedia <ref>{{Cite pmid|32952266}}</ref> The relationship between air pollution and COVID-19-related deaths: An application to three French cities #MMPMID32952266 Magazzino C; Mele M; Schneider N Appl Energy 2020[Dec]; 279 (ä): 115835 PMID32952266show ga Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM(2.5) and PM(10) linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM(2.5) and PM(10) connected to COVID-19: 17.4 microg/m(3) (PM(2.5)) and 29.6 microg/m(3) (PM(10)) for Paris; 15.6 microg/m(3) (PM(2.5)) and 20.6 microg/m(3) (PM(10)) for Lyon; 14.3 microg/m(3) (PM(2.5)) and 22.04 microg/m(3) (PM(10)) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings. äThe relationship between air pollution and COVID-19-related deaths: An(epmc).pdf DeepDyve Pubget Overpricing