An evaluation of air quality during the Covid-19 pandemic
Singh, Madhu (2022)
Singh, Madhu
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202201261692
https://urn.fi/URN:NBN:fi:amk-202201261692
Tiivistelmä
This study provides an overview of strikingly changed air quality during the present pandemic same was the motivation for this research, aims and limitations. A machine learning method to analyze the situation is proposed. COVID-19 and air quality are linked to respiratory disease caused by SARS-Cov2 virus and was declared a pandemic by WHO from 11- March-2020. The pandemic infection spreads through human-to-human contact SARS-Cov2 virus. Communicate, and prognosis are potentially affected by many factors, including air quality. An Air Quality Index (AQI) is used by government agencies to communicate to the public how polluted the air currently is or how polluted it is forecast to become. AQI is a higher risk to human health that impacts the global and human body organs; unfortunately, the massive risk of infection is the severity of the disease.
AQI is paramount to maintain a sustainable environment. Otherwise, it will result in life-long individual and society problems and cause infection within the human race. Declining AQI intensified pandemic mortalities. During the past pandemics, Spanish flu in 1918 and SARS-CoV-1 in 2003, increased mortality and virulence of respiratory infections and decreased viral clearance the study into the impact between AQI and SARS -Cov2 infection risk. If AQI particle level plays a significant role in COVID-19 incidence, it has a strong relationship for the mitigation strategy required to prevents spreading.
This paper uses a regression model to find during pandemic AQI level and a time series model to predict future year's prediction of the transmission of the exponentially growing current pandemic and air quality. Time-series is to check lockdown line air quality rises or not. Auto-Regressive Integrated Moving Average (ARIMA) model prediction performed on PM25 air particles PM25 and CO level in air quality. The paper highlights the changes in air quality during the pandemic noticed in European countries. The impact of the environment in the pandemic due to changes in air quality is studied. Also, analyze and compare the COVID- 19 air quality situation to previous year pandemics like Spanish flu and Black Death.
AQI is paramount to maintain a sustainable environment. Otherwise, it will result in life-long individual and society problems and cause infection within the human race. Declining AQI intensified pandemic mortalities. During the past pandemics, Spanish flu in 1918 and SARS-CoV-1 in 2003, increased mortality and virulence of respiratory infections and decreased viral clearance the study into the impact between AQI and SARS -Cov2 infection risk. If AQI particle level plays a significant role in COVID-19 incidence, it has a strong relationship for the mitigation strategy required to prevents spreading.
This paper uses a regression model to find during pandemic AQI level and a time series model to predict future year's prediction of the transmission of the exponentially growing current pandemic and air quality. Time-series is to check lockdown line air quality rises or not. Auto-Regressive Integrated Moving Average (ARIMA) model prediction performed on PM25 air particles PM25 and CO level in air quality. The paper highlights the changes in air quality during the pandemic noticed in European countries. The impact of the environment in the pandemic due to changes in air quality is studied. Also, analyze and compare the COVID- 19 air quality situation to previous year pandemics like Spanish flu and Black Death.