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10.1016/j.uclim.2020.100664 http://scihub22266oqcxt.onion/10.1016/j.uclim.2020.100664 C7332952!7332952!32834964     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32834964&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Urban+Clim 2020 ; 33 (ä): 100664 Nephropedia Template TP {{tp|p=32834964|t=2020. Gaussian approach for probability and correlation between the number of COVID-19 cases and the air pollution in Lima |pdf=|usr=}}{{32834964}} gab.com Text Gaussian approach for probability and correlation between the number of COVID-19 cases and the air pollution in Lima JO: Urban Clim http://www.kidney.de/pget.php?&tw=1&pmid=32834964 #FOAvip At the end of February 2020, Peru started the first cases of pneumonia associated with coronavirus (COVID-19), they were reported in Lima, Peru (Rodriguez-Morales et al., 2020). Therefore, the first week on March started with 72 infected people, the government published new law for a national crisis by COVID-19 pandemic (Vizcarra et al., 2020), with a quarantine in each city of Peru. Our analysis has considered March and April 2020, for air quality measurement and infections in Lima, the data collected on 6 meteorological stations with CO (carbon monoxide), NO2 (nitrogen oxide), O3 (ozone), SO2 (sulfur dioxide), PM10 and PM2.5 (particle matter with diameter aerodynamic less than 2.5 and 10?m respectively). As a result, the average of these concentrations and the hospital information is recollected per hour. This analysis is executed during the quarantine an important correlation is discovered in the zone with highest infection by COVID-19, NO2 and PM10, even though in a reduction of air pollution in Lima. In this paper, we proposed a classification model by Reduced-Space Gaussian Process Regression for air pollution and infections; with technological and environmental dynamics and global change associated COVID-19. An evaluation of zones in Lima city, results have demonstrated influence of industrial influence in air pollution and infections by COVID-19 before and after quarantine during the last 28?days since the first infection in Peru; the problems relating to data management were validated with a successful classification and cluster analysis for future works in COVID-19 influence by environmental conditions. Twit Text FOAVip Gaussian approach for probability and correlation between the number of COVID-19 cases and the air pollution in Lima ????Urban Clim http://www.kidney.de/pget.php?&tw=1&pmid=32834964 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834964 #FOAvip English Wikipedia <ref>{{Cite pmid|32834964}}</ref> Gaussian approach for probability and correlation between the number of COVID-19 cases and the air pollution in Lima #MMPMID32834964 Arias Velásquez RM; Mejía Lara JV Urban Clim 2020[Sep]; 33 (ä): 100664 PMID32834964show ga At the end of February 2020, Peru started the first cases of pneumonia associated with coronavirus (COVID-19), they were reported in Lima, Peru (Rodriguez-Morales et al., 2020). Therefore, the first week on March started with 72 infected people, the government published new law for a national crisis by COVID-19 pandemic (Vizcarra et al., 2020), with a quarantine in each city of Peru. Our analysis has considered March and April 2020, for air quality measurement and infections in Lima, the data collected on 6 meteorological stations with CO (carbon monoxide), NO2 (nitrogen oxide), O3 (ozone), SO2 (sulfur dioxide), PM10 and PM2.5 (particle matter with diameter aerodynamic less than 2.5 and 10?m respectively). As a result, the average of these concentrations and the hospital information is recollected per hour. This analysis is executed during the quarantine an important correlation is discovered in the zone with highest infection by COVID-19, NO2 and PM10, even though in a reduction of air pollution in Lima. In this paper, we proposed a classification model by Reduced-Space Gaussian Process Regression for air pollution and infections; with technological and environmental dynamics and global change associated COVID-19. An evaluation of zones in Lima city, results have demonstrated influence of industrial influence in air pollution and infections by COVID-19 before and after quarantine during the last 28?days since the first infection in Peru; the problems relating to data management were validated with a successful classification and cluster analysis for future works in COVID-19 influence by environmental conditions. äGaussian approach for probability and correlation between the number o(epmc).pdf DeepDyve Pubget Overpricing