The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen.
Author (Year) | Objective | Location | Finding | ||||||||||||||||||||
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Zhu et al. (2020) | Investigate the link between ambient air pollution and the new coronavirus infection. | One-hundred and twenty cities in China | PM | 2.5 | , PM | 10 | , NO | 2 | , and O | 3 | levels were significantly higher in the previous two weeks in areas with newly confirmed COVID-19 cases. | ||||||||||||
Gupta et al. (2021) | Calculate the elevated incidence of coronavirus disease (COVID-19) induced by severe acute respiratory syndrome coronavirus 2 by demonstrating a correlation between the death rate of infected individuals and air pollution, especially Particulate Matters (PM). | Nine cities in Asia, Delhi India, Nagpur India, Kanpur India, Islamabad Pakistan, Lahore Pakistan, Jakarta Indonesia, Tianjin China, Guilin China, Hebei China | There is a positive association between a region’s degree of air pollution and the mortality associated with COVID-19, demonstrating that air pollution is a significant and hidden factor exacerbating the worldwide burden of COVID-19-related mortality. | ||||||||||||||||||||
Lolli et al. (2020) | The correlation between meteorological and air quality indicators and COVID-19 transmission was quantified. | Northern Italy, Milan, and Florence | Although elements such as temperature and humidity are inversely connected with viral transmission, air pollution (PM | 2.5 | ) is positively correlated (to a lesser degree). | ||||||||||||||||||
Bashir et al. (2020) | The connection between COVID-19 and climatic factors was analyzed. | New York City, USA | The COVID-19 pandemic was substantially related with average temperature, lowest temperature, and air quality. | ||||||||||||||||||||
Suhaimi et al. (2020) | Establish connections between air quality, climatic variables, and COVID-19 instances. | Kuala Lumpur, Malaysia | Spearman’s correlation analysis revealed a positive connection between COVID-19 cases and PM | 10 | (r = 0.131, | p | < 0.001), PM | 2.5 | (r = 0.151, | p | < 0.001), SO | 2 | (r = 0.091, | p | = 0.003), NO | 2 | (r = 0.228, | p | < 0.001), CO (r = 0.269, | p | = 0.001), and relative humidity (RH) (r = 0.106, | p | = 0.001). |
Mehmood et al. (2021) | Using geospatial approaches to examine the connection between COVID-19 instances, air pollution, meteorological, and socioeconomic characteristics. | Three out of four provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa) | The findings reveal that daily COVID-19 is positively linked with PM | 2.5 | and other meteorological variables, implying that climate has a significant role in determining the COVID-19 incidence rate in Pakistan. | ||||||||||||||||||
Hoang and Tran | The generalized additive model was used to evaluate the temporal connection between ambient air pollution, weather, and COVID-19 infection. | Seven metropolitan cities and nine provinces across Korea | Daily temperature had a substantial nonlinear relationship with verified COVID-19 cases. | ||||||||||||||||||||
Travaglio et al. (2021) | Evaluated recent SARS-CoV-2 cases and fatalities from public databases to regional and subregional air pollution data collected at several locations. | England | There is a positive correlation between COVID-19 mortality and infectivity and air pollution concentrations, notably nitrogen oxides. | ||||||||||||||||||||
Lorenzo et al. (2021) | Determine the relationship between core air pollutant concentrations, climatic factors, and daily verified COVID-19 cases. | Singapore | There is a statistically significant positive correlation between NO | 2 | , PSI, PM | 2.5 | , and temperature and COVID-19 case numbers. | ||||||||||||||||
Mandalapu et al. (2022) | The link between air pollution and COVID-19 severity has been studied at the regional and metropolitan levels, but it is uncertain if this link holds true at the neighborhood level. | Los Angeles County, California | Eighteen of the twenty-three significant comparisons for the COVID-19 weekly death rate confirmed that NO | 2 | levels were higher in neighborhoods with higher COVID-19 weekly death rates. Similarly, 12 of the 19 comparisons confirmed the same relationship with CO levels, as 14 of the 23 comparisons confirmed the same relationship with ozone levels, and 6 of the 6 comparisons confirmed the same relationship with PM | 10 | . | ||||||||||||||||
Sidell et al. (2022) | To examine at both long-term and short-term air pollution exposure, as well as COVID-19 occurrence, from 1 March 2020 to 28 February 2021. | Southern California | In all case peaks before February 2021, long-term PM | 2.5 | and NO | 2 | exposures were linked to an elevated probability of COVID-19 occurrence. Short-term exposures to PM | 2.5 | and NO | 2 | were also linked. Air pollution may have a role in raising the likelihood of COVID-19 infection. | ||||||||||||
Luo et al. (2022) | This study assessed the relationship between population movement and air quality in 332 Chinese cities from January to March (2019–2021), and the influence of three city factors (pollution level, city scale, and lockdown status) in this impact. | Three-hundred and thirty-two Chinese cities | Lower migration was linked to lower pollution levels (other than O | 3 | ). Susceptibility to pollution changes is more probable as NO | 2 | decreases and O | 3 | increases, whereas insusceptibility to pollution is more likely for CO and SO | 2 | , and in cities with low migration. Cities with less air pollution and dense populations may benefit the most from lowering PM | 10 | and PM | 2.5 | . Those with rigorous traffic limits have higher links with population movement and air pollution than cities without limitations. The impacts of inter-city migration (ICM) and within city migration (WCM) on air pollution were found to be minor when city characteristics were considered. | ||||||||
Abdullah et al. (2022) | The connection between the Air Pollution Index (API) and COVID-19 infections is the objective of this research. | Malaysia | Each area has a positive connection between API and COVID-19: North 0.4% (R | 2 | = 0.004), Central 2.1% (R | 2 | = 0.021), South 0.04% (R | 2 | = 0.0004), East 1.6% (R | 2 | = 0.016), Sarawak 0.2% (R | 2 | = 0.002), whereas Sabah has a negative correlation of 4.3% (R | 2 | = 0.043). | ||||||||
Huang et al. (2022) | Data on air pollution and verified COVID-19 cases were collected from five severely affected cities in three South American nations. COVID-19’s spread was measured using daily real-time population regeneration (Rt). The influence of environmental contaminants on the pandemic was investigated using two commonly used models: generalized additive models (GAM) and multiple linear regression. | South America | (1) In all five locations, Rt, which potentially represents COVID-19 dissemination, exhibited a progressive drop. (2) Rt had a substantial effect on PM | 10 | and SO | 2 | in all of the locations studied. These two contaminants should be better monitored by regulators. (3) In cities with varying levels of air pollution, the link between air pollution and the spread of COVID-19 varied. The results indicate that there is a significant relationship between air pollution and COVID-19 infection. |