Table 1.
The detail key finding of 19 reviewed studies on the relation between atmospheric particulate matter and SARS-CoV-2 prevalence.
| Study ID | The main objective | Study Design | Type of PM (μg/m3) |
Meteorological parameters |
Key finding |
||
|---|---|---|---|---|---|---|---|
| Tem (°C) | RH (%) | ||||||
| PM 10 | PM2.5 | ||||||
| (Chia, Coleman et al., 2020), Singapore | Hospital air and surface contamination | Experimental | ✓ | 23 | 53–59 | Air samples from two of 3 airborne infection isolation rooms tested positive. | |
| (Kumar 2020), India | meteorological parameters and spread of virus | Experimental | ✓ | ✓ | – | 30–55 | Increase level of PM2.5, may increase the incidences and deaths of disease in India. |
| (Yao, Pan et al. 2020a, 2020b), Chinese | Association of PM and case fatality rate | Experimental | 80.2 | 49.1 | – | – | By 10 μg/m3 increase in concentration of PM2.5 and PM10, the case fatality rate enhanced about 0.24–0.26% |
| (Sasidharan, Singh et al., 2020), London | human-mobility reduction for countering the virus transmission | Experimental | 88 | – | – | – | A strong correlation between increment in PM2.5 levels and an increased risk of virus transmission |
| (Chennakesavulu and Reddy 2020), tropical and Temperate zone countries | The effect of PM2.5 and latitude on spreading of the virus | Experimental | 0–150 | – | Tropical: 20–45 Temperate zone: 3–13 |
In temperate zone countries, PM2.5 concentration below 20 mg/m3 increases SARS-CoV-2 spreading rate. | |
| (Cartenì, Di Francesco et al., 2020), Italy | mobility habits on the spread of the virus | case study | PM | – | – | PM pollutant has a direct association with the virus infection. | |
| (Zoran, Savastru et al., 2020), Italy | Surface concentration of PM10 and PM2.5 and with SARS-CoV-2 | Experimental | ✓ | ✓ | 0–18 | 23–92 | Ambient airborne aerosols might be possible diffusion routes of SARS-CoV-2. Dry weather is favorable for SARS-CoV-2 viral infection spreading, but humid weather has the opposite effect. |
| (Yao, Pan et al. 2020a, 2020b), China | PM pollution and the disease case fatality rate | Experimental | 52.77 | 41.77 | 7.18 | 81.37 | A positive association between PM10 and PM2.5 and the case fatality rate of SARS-CoV-2 in Wuhan |
| (Hendryx and Luo 2020), USA | SARS-CoV-2 infection and pollution concentrations | Experimental | – | ✓ | – | – | Higher SARS-CoV-2 prevalence was observed in association with PM2.5 |
| (Zhu, Xie et al. 2020a, 2020b), China | air quality and SARS-CoV-2 infection | Experimental | 62.97 | 46.43 | 2.82 | 67.25 | Limiting the movements could reduce SARS-CoV-2 cases by improving air quality. |
| (Setti, Passarini et al., 2020), Italy | Finding the virus RNA on PM | Experimental | 25.1 to 52.1 | – | 6.8–8.5 | 61–69 | Detection of the virus RNA on ambient PM |
| (Fattorini and Regoli 2020), Italy | the Covid-19 outbreak risk and the chronic air pollution levels | – | 16.9 to 37.7 | 5.7 to 31.5 | – | – | Long term exposure to atmospheric pollution may act as a favorable route to the spread of Covid-19. |
| (Coccia 2020), Italy | Determined factors in diffusion of COVID-19 | Experimental | ✓ | Hinterland cities: 9.11 Coastal cities: 10.61 |
Hinterland cities:68.31 Coastal cities:74.40 |
Cities with more than 100 days of air pollution have a very high average number of infected individuals | |
| (Li, Xu et al., 2020), China | Association of Air pollution with increased COVID-19 incidence | Experimental | Wuhan: 51.88 XiaoGan: 59.65 |
Wuhan: 44.16 XiaoGan: 50.39 |
Wuhan: 7.19 XiaoGan: 7.26 |
All pollutants on the outdoor air has a positive relationship with daily SARS-CoV-2 incidence (PM2.5 exhibited statistical significance). Daily temperature and daily lowest temperatures were predominantly correlated with SARS-CoV-2 incidence (inversely). COVID-19 incidence inversely correlated with the daily sunshine duration and temperature. |
|
| (Zhu, Xie et al. 2020a, 2020b), China | short-term exposure to air pollution and SARS-CoV-2 | Experimental | 62.97 | 46.43 | 2.82 | 67.25 | Positive associations of PM2.5, PM10 with SARS-CoV-2 confirmed cases |
| (Bontempi 2020), Italy | possible virus airborne transmission due to air PM | Computational | 3 to 87 | – | – | – | It is not possible to conclude that COVID-19 diffusion mechanism also occurs through the air, by using PM10 as a carrier. |
| (Adhikari and Yin 2020), USA | Short-Term effects of PM2.5 on confirmed cases and deaths of COVID-19 | Experimental | – | 4.733 | 8 | 62.90 | A one-unit increase in the moving average of PM2.5 (μg/m3) was related with a 33.11% decrease in the daily new disease cases. |
| (Fronza, Lusic et al., 2020), European nations, Spain, Germany, France, and Italy | Effects of spatial-temporal variations in atmospheric factors on COVID-19 outbreak | Computational | 40 | 30 | – | – | SARS-CoV-2 infection frequency positively correlates with PM2.5 |
| (Gupta, Bherwani et al., 2020), Asian | Air pollution and possible aggravating of COVID-19 lethality | statistical models | 59 to 292 | 45 to 173 | – | – | Percentage mortality per reported SARS-CoV-2 cases is correlated significantly with PM2.5 than with PM10 |