| Literature DB >> 33297239 |
Abstract
Corona virus is highly uncertain and complex in space and time. Atmospheric parameters such as type of pollutants and local weather play an important role in COVID-19 cases and mortality. Many studies were carried out to understand the impact of weather on spread and severity of COVID-19 and vice-versa. A review study is conducted to understand the impact of weather and atmospheric pollution on morbidity and mortality. Studies show that aerosols containing corona virus generated by sneezes and coughs are major route for spread of virus. Viability and virulence of SARS-CoV-2 stuck on the surface of particulate matter is not yet confirmed. Studies found that an increase in particulate matter concentration causes more COVID-19 cases and mortality. Gaseous pollutant and COVID-19 cases are positively correlated. Local meteorology plays crucial role in the spread of corona virus and thus mortality. Decline in number of cases with rising temperature observed. Few studies also find that lowest and highest temperatures were related to lesser number of cases. Similarly humidity shows negative or no relationship with COVID-19 cases. Rainfall was not related whilst wind-speed plays positive role in spread of COVID-19. Solar radiation threats survival of virus, areas with lower solar radiation showed high exposure rate. Air quality tremendously improved during lockdown. A significant reduction in PM10, PM2.5, BC, NOx, SO2, CO and VOCs concentration were observed. Lockdown had a healing effect on ozone; significant increase in its concentration was observed. Aerosols Optical Depths were found to decrease up to 50%.Entities:
Keywords: COVID-19; Lockdown; Meteorology; Pollutants; SARS-CoV-2
Year: 2020 PMID: 33297239 PMCID: PMC7487522 DOI: 10.1016/j.chemosphere.2020.128297
Source DB: PubMed Journal: Chemosphere ISSN: 0045-6535 Impact factor: 7.086
Effect of various pollutants on number of COVID19 cases and mortality.
| S. No. | Parameter | Country | Variation in pollution parameter | Effect |
|---|---|---|---|---|
| 1 | PM2.5 | USA (3000 counties) | 1 μg/m3 increase in PM2.5 | 8% increase in the COVID-19 death rate ( |
| China (120 cities) | 10 μg/m3 increase in PM2.5 and PM10 | 2.24% and 1.76% increase in the daily counts of confirmed cases respectively ( | ||
| Italy (71 provinces) | Chronic exposure to atmospheric PM2.5 and PM10 | Favorable for the spread of virulence of the SARS-CoV-2 ( | ||
| Middle Eastern countries | Elevated indoor PM2.5 and PM10 concentration | Facilitate transmission of SARS-CoV-2 virus droplets and particles in indoor environments ( | ||
| Italy (northern) | PM10 daily limit value exceedances | Significant increase in the number of cases ( | ||
| USA (California) | PM10 | Significant Correlation ( | ||
| 2 | NO2 | 66 regions of Germany, Italy France and Spain | highest NO2 concentrations combined with downwards airflow | 4443 total fatality cases, 3487 (78%) in north Italy and central Spain ( |
| China (120 cities) | 10 μg/m3 increase in NO2 | 6.94% increase in the daily counts of confirmed cases ( | ||
| USA (California) | NO2 | Significant Correlation ( | ||
| 3 | SO2 | China (120 cities) | 10 μg/m3 increase in SO2 | 7.79% decrease in the daily counts of confirmed cases ( |
| USA (California) | SO2 | Significant Correlation ( | ||
| 4 | CO | China (120 cities) | 1 μg/m3 increase in CO | 15.11% increase in the daily counts of confirmed cases ( |
| USA (California) | CO | Significant Correlation ( | ||
| 5 | O3 | China (120 cities) | 10 μg/m3 increase in O3 | 4.76% increase in the daily counts of confirmed cases ( |
Fig. 1Relationship between various pollution parameters with number of COVID19 cases.
Effect of various meteorological parameters on number of COVID19 cases and mortality.
| S. No. | Parameter | Country | Relationship and Result |
|---|---|---|---|
| 1 | Temperature | China (10 affected provinces) | Asymmetric Nexus Between Temperature and COVID-19, few show positive, few negative and some mixed trend ( |
| USA (New York) | Increase in average and minimum temperature significantly lower number of COVID19 cases ( | ||
| China (Wuhan) | No significance of an increase in temperature to contain or slow down the COVID-19 infections ( | ||
| Italy | Increase in the average daily temperature by 1 oF reduced the number of cases by approximately 6.4 per day ( | ||
| Iran | No significant relationship between temperature and COVID19 ( | ||
| China (17 different cities) | 1 °C increase in ambient temperature was related to the decline of daily confirmed case counts ( | ||
| Turkey | Lower the temperature on a day, the higher is the number of COVID-19 cases on that day ( | ||
| Indonesia (Jakarta) | Temperature is significantly related to number of COVID19 cases ( | ||
| China | Lower and higher temperatures might be positive to decrease the COVID-19 incidence ( | ||
| 2 | Humidity | USA (New York) | Average Humidity doesn’t play much significant role in number of cases or total number of cases ( |
| Iran | Humidity has a reverse relationship within the virus outbreak speed ( | ||
| China (all provincial capitals) | Absolute humidity was significantly related, 1 g/m3 increase in AH was significantly associated with reduced confirmed case ( | ||
| Turkey | An increase in humidity results in a decrease in number of cases ( | ||
| China | No significant association between COVID-19 incidence and absolute humidity was observed (Shu et al., 2020) | ||
| General | Air humidity is negatively correlated with COVID19 morbidity and mortality ( | ||
| 3 | Rain Fall | USA | Rainfall is negatively and weakly correlated with spread of COVID19 ( |
| Italy | Rainfall showed an increase in disease transmission. For each average inch/day, there was an increase of 56.01 cases/day ( | ||
| Iran | No correlation between rainfall and number of COVID19 cases ( | ||
| Indonesia (Jakarta) | Rainfall was not significantly correlated with COVID-19 ( | ||
| 3 | Wind speed | USA | Wind speed insignificantly play some role in the spread of the virus ( |
| Iran | Outbreak at low speed of the wind is significant ( | ||
| Turkey | Higher the wind speed is, more the number of cases is ( | ||
| 5 | Solar Radiation | Iran | Solar radiation threats the virus’s survival. Areas with low values of solar radiation showed high rate of exposure to infection ( |
Fig. 2Relationship between various meteorological parameters with number of COVID19 cases.
Effect of lockdown due to COVID19 on various atmospheric pollutants.
| S. No. | Parameter | Country | Effect |
|---|---|---|---|
| 1 | PM2.5 | China | 26–48% reduction ( |
| Italy (Milan) | 37.1–44.4% reduction ( | ||
| Brazil (Sao Paulo) | Up to 29.8% reduction ( | ||
| Major city of World | 11–58% reduction ( | ||
| India (22 cities) | 43% reduction ( | ||
| Southeast Asia | 23–32% reduction ( | ||
| 2 | PM10 | China | 29–34% reduction ( |
| Italy (Milan) | 13.1–18.9% reduction ( | ||
| Brazil (Sao Paulo) | Up to 22.8% reduction ( | ||
| Estern India | 73–78% ( | ||
| India (22 cities) | 31% reduction ( | ||
| Southeast Asia | 26–31% reduction ( | ||
| 3 | Black Carbon | Italy (Milan) | 57.5–71% reduction ( |
| 2 | NOx | China | 29–47% reduction ( |
| Italy (Milan) | 47 ± 15% reduction in tropospheric NOx ( | ||
| Brazil (Sao Paulo) | 77.3% decrease in NO and 54.3% in NO2 ( | ||
| India (22 cities) | 18% reduction ( | ||
| Southeast Asia | 63–64% reduction ( | ||
| 3 | SO2 | China | 16–26% reduction ( |
| Italy (Milan) | 20–27% reduction ( | ||
| Brazil (Sao Paulo) | 18–33% reduction ( | ||
| India (22 cities) | No change ( | ||
| Southeast Asia | 9–20% reduction ( | ||
| 4 | CO | China | 21–26% reduction ( |
| Italy (Milan) | 55–75% reduction ( | ||
| Brazil (Sao Paulo) | 36–65% reduction ( | ||
| India (22 cities) | 10% reduction ( | ||
| Southeast Asia | 25–31% reduction ( | ||
| 5 | VOC | China | 37–57% reduction ( |
| Italy (Milan) | 48–68% reduction in benzene ( | ||
| 6 | O3 | China | 20.5% increase ( |
| Italy (Milan) | 50% increase ( | ||
| Brazil (Sao Paulo) | 30% increase ( | ||
| India (22 cities) | 17% increase ( | ||
| 7 | AOD | Southeast Asia | Notable decrease ( |
| India | Significant reduction | ||
| Indo-Gangetic Basin, India | 20–60% reduction ( |
Fig. 3Effect on various pollution parameters due to lockdown.