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Article

Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador

by
Andrea Damaris Hernández-Allauca
1,*,
Carlos Gabriel Pérez Castillo
2,
Juan Federico Villacis Uvidia
3,
Paula Abdo-Peralta
1,
Catherine Frey
2,
Guicela Margoth Ati-Cutiupala
1,
Juan Ureña-Moreno
2 and
Theofilos Toulkeridis
4
1
Faculty of Natural Resources, Escuela Superior Politecnica de Chimborazo, Panamericana Sur, km 1 ½, Riobamba EC-060155, Ecuador
2
Independent Researcher, Riobamba EC-060155, Ecuador
3
Faculty of Accounting and Auditing, Technical University of Ambato, Ambato EC-180207, Ecuador
4
School of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2024, 21(10), 1336; https://doi.org/10.3390/ijerph21101336
Submission received: 29 August 2024 / Revised: 29 September 2024 / Accepted: 1 October 2024 / Published: 9 October 2024
(This article belongs to the Special Issue 2nd Edition: Public Health during and after the COVID-19 Pandemic)
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Abstract

:
The relationship between COVID-19 infections and environmental contaminants provides insight into how environmental factors can influence the spread of infectious diseases. By integrating epidemiological and environmental variables into a mathematical framework, the interaction between virus spread and the environment can be determined. The aim of this study was to evaluate the impact of atmospheric contaminants on the increase in COVID-19 infections in the city of Quito through the application of statistical tests. The data on infections and deaths allowed to identify the periods of greatest contagion and their relationship with the contaminants O3, SO2, CO, PM2.5, and PM10. A validated database was used, and statistical analysis was applied through five models based on simple linear regression. The models showed a significant relationship between SO2 and the increase in infections. In addition, a moderate correlation was shown with PM2.5, O3, and CO, and a low relationship was shown for PM10. These findings highlight the importance of having policies that guarantee air quality as a key factor in maintaining people’s health and preventing the proliferation of viral and infectious diseases.

1. Introduction

The coronavirus (COVID-19) pandemic has marked a turning point in contemporary history. Over time, various outbreaks of viral epidemic diseases have emerged [1]. However, COVID-19 stands out as one of the most tragic challenges in a century, after World War II [2]. This disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [3,4], which presents symptoms such as fever, dry cough, and difficulty breathing [5]. Since the first case was identified in Wuhan, China, in December 2019 [6], the virus has spread rapidly throughout the world [7,8], triggering an unprecedented impact at a social, economic, and environmental level [9,10].
Initially, several studies reported that COVID-19 had a low mortality rate (2.3%) [11,12] compared to MERS (34.4%) and SARS (9.2%) [13]. However, a research study found that the number of patients with COVID-19 doubled every 6.4 days, indicating that it was a more dangerous and infectious disease than expected [14]. This spread generated a global crisis [9] that challenged health systems [15] and highlighted the vulnerability of populations to emerging diseases.
The scientific community has been interested in understanding the spread of the virus and its determining factors [16,17]. According to several studies, environmental factors can affect the epidemiological dynamics of many infectious diseases [11,18]. Among these factors, special attention has been paid to the possible influence of air quality on the incidence of respiratory diseases such as COVID-19 [19], considering that there is evidence indicating that air pollution can facilitate the transmission and pathogenesis of various bacterial and viral infections [20]. This pollution is the result of the presence of harmful substances in the atmosphere known as air contaminants [21], which come from various anthropogenic and natural sources [22]. Among the main contaminants are sulfur dioxide (SO2) [23,24], nitrogen dioxide (NO2), carbon monoxide (CO) [25], and ozone (O3) [26], in addition to fine particles (PM10) and ultrafine particles (PM2.5) [27]. This type of contamination exerts an adverse effect on human health [28] because it increases the host’s susceptibility to respiratory viral infections [29].
Several studies have suggested a correlation between exposure to air contaminants and the increase in COVID-19 cases [16,30,31,32], as well as its impact on the severity of the health of people infected by the virus [33,34]. Air contaminants such as NO2, O3, PM2.5, and PM10 can weaken the respiratory system [35]. People exposed to high levels of these contaminants may have inadequate lung function and a reduced immune response [36], especially in densely populated urban areas [37]. These areas have a higher number of viral infections and deaths due to high levels of suspended particles or ozone [38,39]. These findings are supported by research studies in countries such as China [25,31,33], the United States of America [25,40,41], and some European countries [42,43], where significant associations have been found between air contaminants and transmission or mortality from COVID-19. Furthermore, in the Latin American context, emissions of PM10, NO2, O3, and CO are the main determining factors of the increase in virus infections [44]. This can be attributed to relentless population growth and urbanization, which have worsened air quality conditions in many cities in developing countries [37].
Ecuador has not been the exception. For instance, cities such as Quito, Guayaquil, and Cuenca with a high number of inhabitants face challenges in terms of air pollution [37,45,46,47,48]. According to the last official census carried out by the National Institute of Statistics and Censuses (INEC), Quito and Guayaquil are the cantons with the highest number of inhabitants in 2022, with 2,679,722 and 2,746,403 inhabitants, respectively [49]. Therefore, there are stations to measure the presence and incidence of contaminants [50]. These measurements are essential to evaluate air quality and its possible impact on the health of the population [51]. Therefore, this type of contamination may be an important factor to consider in the transmission and severity of COVID-19 in urban areas [52]. In this context, mathematical modeling emerges as a tool to understand the spread of infectious diseases [53]. Mathematical models are based on differential equations that describe the dynamics of the disease in a population over time [54], considering variables such as the transmission rate, the recovery rate, and the susceptibility of the population [55]. Additionally, they allow us to analyze the relationship between COVID-19 infections and atmospheric contaminants with greater precision [56] by integrating epidemiological and environmental data with sophisticated mathematical methods [57]. This may involve the use of linear regression techniques, nonlinear regression, or time series models, depending on the nature of the data and the expected relationship [58].
The aim of this research was to evaluate the impact of air contaminants on the increase in COVID-19 infections in the city of Quito through the application of statistical tests. For this purpose, the existence of a significant relationship between air pollution and the number of COVID-19 cases was identified. Mathematical models made it possible to establish this link through the estimates of coefficients, standard errors, and p values for each variable. This provided a quantitative understanding of the relationship between air quality and the spread of the virus, in which an adverse influence of atmospheric contaminants is denoted, depending on the infection cases caused by the virus.

2. Materials and Methods

2.1. Study Area

This study was carried out in Quito city, province of Pichincha, Ecuador (Figure 1). Quito canton is one of the 221 cantons and is in the north center of the Andean natural region. It has an area of 372.4 km2 and a total of 52 parishes, 32 urban and 33 rural [59]. It has 2,679,722 inhabitants in total, 1,763,275 in the urban area and 916,447 in the rural area [49].

2.2. Methodology

The approach of this research was cross-sectional. The methodology applied in this study considered a quantitative research approach with an exploratory, analytical, and descriptive design. Three stages were considered: (i) COVID-19 cases; (ii) atmospheric contaminants vs. COVID-19 infections; and (iii) development of statistical tests. This was conducted through data collection, statistical data analysis, and the application of mathematical modeling.

2.2.1. COVID-19 Infections

A bibliographic review was carried out to understand previous scientific evidence and obtain data necessary for the analysis of the variables involved (COVID-19 infections and air contaminants). Data on deaths for the years 2019, 2020, and 2021 were collected from the database of the National Institute of Statistics and Censuses (INEC) [60]. In addition, the data on COVID-19 infections were obtained from the database of the Ministry of Public Health of Ecuador for 2020 and 2021 [61].
Microsoft Excel software (version 2020) was used for data analysis and the generation of tables and figures at the national level. Total deaths in Ecuador during the year 2019 were compared with deaths during 2020 and 2021. This was intended to obtain data that corroborates the increase in deaths in the years that COVID-19 appeared. Subsequently, at the provincial level, COVID-19 deaths were compared between 2020 and 2021 in one of the four regions of the country: the Andean natural region. This region has eleven provinces: Azuay, Bolívar, Cañar, Carchi, Cotopaxi, Chimborazo, Imbabura, Loja, Pichincha, Tungurahua, and Santo Domingo of Tsáchilas [24]. These data allowed us to corroborate the presence of a greater number of infections in the province of Pichincha. Based on these data, the COVID-19 cases were analyzed in the eight cantons of this province: Cayambe, Mejía, Pedro Moncayo, Pedro Vicente Maldonado, Puerto Quito, Rumiñahui, San Miguel de los Bancos, and Quito. These data revealed a higher number of infections in the canton of Quito. Consequently, the number of COVID-19 infections from March 2020 to March 2021 in Quito city was analyzed, along with the total deaths attributed to the virus during those two years. In this context, it was evident that national, provincial, and cantonal data were considered when selecting the study area, in order to corroborate the impact of atmospheric contaminants with the increase in COVID-19 cases in the city of Quito. This was one of the most affected cities nationwide, and it has a high concentration of air contaminants [37,62,63]. The descriptive statistics made it possible to summarize the results of the variables involved and recognize the possible patterns to which their causes were attributed.

2.2.2. Atmospheric Contaminants vs. Covid-19 Infections in Quito City

A database of the atmospheric contaminants present in Quito city during March 2020 to March 2021 was built, such as CO, O3, SO2, PM2.5, and PM10. Based on the values obtained from two measurement stations in the city: the Historic Center and Belisario. The atmospheric contaminants with the greatest presence in the city were analyzed. The contaminants were measured based on the Air Quality Index (AQI) scale established by the United States Environmental Protection Agency (EPA) [64] (Figure 2). This scale has been adapted globally and may have local variations, but the basic principles are generally consistent.
These standards allowed air quality to be categorized into six levels, from “Good” to “Hazardous.” For this, atmospheric contaminants were valued based on the scale proposed by the Ministry of Environment, Water and Ecological Transition of Ecuador (MAATE) (Table 1). These values are presented in µg/m³ or mg/m³, depending on the type of contaminant. The data obtained were compared with the number of infections recorded in the same period to determine a graphical relationship. The Microsoft Excel statistical program was used for data analysis and the generation of tables and figures.

2.2.3. Statistical Tests

A normality analysis was applied to the data obtained on COVID-19 infections, using the Statistical Package for the Social Sciences (SPSS 27.0®) software. In addition, the average values of each contaminant (O3, SO2, CO, PM2.5, PM10) were determined according to the Shapiro–Wilks and Kolmogorov–Smirnov tests [65,66]. A supervised learning approach was applied, using machine learning algorithms to model the influence of predictors on responses through regression analysis [67]. In a simple linear regression, each air contaminant was analyzed independently for its relationship with COVID-19 infections. In a mathematical model, a simple linear regression analyzes how a dependent variable, such as the number of COVID-19 infections, relates to an independent variable, such as the concentration of a contaminant in the air [68]. Five simple regression models were generated in RStudio 9.4.0.813654®. The statistical analysis allowed each model to be compared, with the purpose of correcting, debugging, and obtaining a final, solid model that demonstrates the relationship between atmospheric contaminants and COVID-19 cases. The five models analyzed the number of COVID-19 infections in relation to each atmospheric contaminant: O3, SO2, CO, PM2.5, and PM10 (one matrix for each). These models allowed us to verify those contaminants that were significantly related to the increase in COVID-19 cases. In addition, confounding factors were considered for the development of models, including various variables that had not been considered in the analysis, but that may have influencee the results. The confounding factors for the development of the models were temperature and precipitation.

3. Results

3.1. COVID-19 Infections

Figure 3a,b show the deaths in Ecuador from 2019 to 2021. In 2019, 74,439 deaths were registered, a figure that increased by 57.4% for the year 2020, with a total of 117,200 deaths. A growth is shown in the number of deaths during the year 2020 compared to the base year. This increase can be attributed to the spread of the COVID-19 virus worldwide. However, for the year 2021, this figure decreased by 9.4%, with a total of 106,211 deaths. This can be attributed to the control measures taken to control the spread of the virus, such as social distancing, the mandatory use of masks, and vaccination campaigns. Furthermore, another factor that could have been influenced was the natural immunity that the population acquired after having contracted the virus and recovered.
Figure 3c shows the deaths caused by COVID-19 in the country during 2020 and 2021. It shows a negative impact of the disease in mortality, with 20.4% of deaths being caused in 2020. That is, 23,921 people died from infection with the virus. This can be attributed to factors such as: (1) the saturation of health systems in the initial stage of the pandemic; (2) the lack of an effective cure; (3) the influence of psychological elements on the ability of the immune system to combat this disease; (4) exposure to atmospheric contaminants. On the other hand, in 2021, deaths from COVID-19 represented 19.7%, that is, 20,900 deaths from the virus. This 12.7% decrease in deaths compared to 2020 can be attributed to factors such as mass vaccination, social distancing, the body’s resilience to the virus, and the improvement of air quality during confinement due to the temporary reduction in levels of atmospheric contaminants.
The COVID-19 contagion in the Andean natural region has presented different dynamics over time. Figure 4 shows the deaths caused by this virus in 2020 and 2021 in relation to the provinces of this region. In general, it is evident that the highest percentage of deaths due to contagion is in the province of Pichincha with 48%, which represents 10,789 deceased people. In addition, it was identified that the provinces of Bolívar and Carchi presented a lower number of deaths due to infection with the virus, with 2% (455) and 2.1% (474), respectively. A significant difference is noted in the province of Pichincha in relation to the other provinces of the Andean natural region, possibly marked by the demographic composition, considering that this province has a higher population density. This factor, combined with the constant mobility of people inside and outside the province, caused the rapid spread of the virus.
Due to the negative impact of COVID-19 in the province of Pichincha, it was considered important to analyze infections due to this virus in the cantons of the province from March 2020 to December 2021 (Figure 5). During this period, it was determined that Quito had been one of the cantons most affected by the pandemic. It reached the highest percentage of 92.5% in relation to the other cantons, where the percentages remained consistently low, oscillating between 0.2% and 3.3%.
It is important to consider that COVID-19 infections in the Quito canton were not distributed homogeneously, with a greater presence of contagion in places with a high concentration of people, such as the urban area. Figure 6 shows the monthly infections in Quito city from March 2020 to March 2021. In this period, there were a total of 109,359 infections. In the first months of the pandemic, significant peaks in contagion cases were observed due to the rapid spread of the virus and the lack of effective control measures, with an increasing trend from March to October 2020. 193 infected people were registered in March, while in October, there was an increase of 90.5% in seven months, reaching a total of 17,662 infected people. Subsequently, in December 2020, a reduction in the number of infections was recorded, ending the year with 5449 cases. This can be attributed to the implementation of protective measures, social distancing, and reduced mobility in the city. However, by 2021, infections had increased again, reaching 12,981 cases by March of that year. This fact can be attributed to the appearance of new variants of the virus, such as Delta and Omicron, and the reduction in compliance with preventive and control measures [69,70].
COVID-19 directly and indirectly influenced the increase in population mortality. Figure 7 shows the total deaths and those caused by the virus in Quito city during 2020 and 2021. In 2020, a total of 12,325 deaths were recorded, of which 33% died due to the contagion. In 2021, a total of 11,940 deaths were reported, with 30.1% of them related to the virus. The analysis of factors that contributed to increasing the magnitude of the impact of COVID-19 on human health in Quito became an important aspect, considering that the virus was the cause of a third of the deaths registered in 2020 and 2021.
In this context, the increase in infections and deaths can be associated with various factors. Quito, as the country’s capital and the second largest city nationwide, has a high population density and a continuous flow of people, which allowed the spread of the virus and the saturation of the health systems. Furthermore, being a point of economic, commercial, and tourist interest at the national level, it became a focal point for the rapid spread of the virus. Besides, at an environmental level, it is important to consider the quality of the city’s air, particularly the presence of atmospheric contaminants that influence people’s health, mainly those who suffer from respiratory conditions.

3.2. Atmospheric Contaminants vs. COVID-19 Cases in Quito City

Figure 8 indicates the concentration of atmospheric contaminants Quito city recorded by the Historic Center station from March 2020 to March 2021. CO levels range from 4.1 mg/m³ to 8.1 mg/m³ (Figure 8a). Most of these values are within the “Moderate” range (4.5–9.0 mg/m³), except for the month of April, which, at 4.1 mg/m³, is below the threshold of 4.4 mg/m³ for the “Good” category. PM2.5 levels range from 35.1 to 63.8 µg/m³ (Figure 8b). Most values are in the “Unhealthy for Sensitive Individuals” category, except for those in November (63.8 µg/m³), December (61.4 µg/m³) and January (59.9 µg/m³), which are in the “Unhealthy” category. O3 concentrations, on the other hand, ranged between 27.9 and 54.7 µg/m³ (Figure 8c). All values are in the “Good” category. SO2 concentrations also ranged between 2.0 and 6.6 µg/m³ (Figure 8d), which are below the 20 µg/m³ threshold and are therefore in the “Good” category.
Figure 9 indicates the concentration of atmospheric contaminants in Quito city recorded by the Belisario station from March 2020 to March 2021. CO values remained relatively stable, ranging between 2.6 mg/m³ and 9.1 mg/m³ (Figure 9a). A gradual increase was observed from May 2020 until reaching the maximum in March 2021. CO levels remained mostly within the “Moderate” category with values ranging between 4.8 and 7.3 mg/m³. On the other hand, PM2.5 levels were mostly classified as “Unhealthy for Sensitive Individuals”, with values between 36.5 and 63.8 µg/m³ (Figure 9b). However, in November and December 2020, concentrations of 67.5 and 59.6 µg/m³ were recorded respectively, which corresponds to the “Unhealthy” category. The concentrations of coarse particulate matter (PM10) were also analyzed (Figure 9c). These values fluctuated between 20.8 and 45 µg/m³, which are within the “Good” category. As with PM2.5, a peak was recorded in November 2020, followed by a reduction in the following months. Regarding O3 levels (Figure 9d), there is a variation between 27.9 and 52.5 µg/m³. All values are within the “Good” category. Finally, regarding SO2 values (Figure 9e), low levels of this contaminant were observed. These values remained in the “Good” category throughout the period, with 1.2 and 5.9 µg/m³.
Figure 10 shows the relationship between the concentrations of atmospheric contaminants (CO, O3, PM2.5, SO2, and PM10) with the number of COVID-19 cases. In March 2020, most contaminants were found at moderate levels, except for PM2.5 with 51.6 µg/m³, which is at an “Unhealthy for sensitive individuals” level. COVID-19 infections were low, with 193 in total. On the other hand, from June to September, an increase is observed for all contaminants, with levels fluctuating between “Moderate”, “Unhealthy for sensitive individuals”, and “Unhealthy”. During this period, infections increased significantly, reaching a peak in September. For example, the data indicate that in October 2020, when there was the highest number of infections, with 17,662 infected people, there were also high levels of CO and PM2.5, with 5.3 mg/m3 and 47.3 µg/m3, respectively. Besides, in this month, ozone levels (O3), despite being within the appropriate ranges, also show an increase, reaching their maximum point at 53.6 µg/m³. On the other hand, a significant increase in PM2.5 is also indicated in November 2020 (65.7 µg/m³), coinciding with a high number of infections (9843).
These findings suggest a possible relationship between the concentration of air contaminants and the incidence of COVID-19 infections in Quito. The months with the highest concentrations of these contaminants coincided with the increase in infections, which could indicate that poor air quality contributes to the spread of the virus or aggravates the disease in infected individuals. It is important to consider that the presence of air contaminants in the city of Quito may be influenced by factors such as: (1) location, (2) development of industrial activities, (3) greater number of vehicles, and (4) increase in the vehicle fleet. The flow of water is very fluid. Its location in a valley surrounded by mountains facilitates the accumulation of air contaminants. In addition, the presence of industrial and commercial activities in factories and production plants also contributes to the emission of gases and particles. The high number of vehicles is one of the main sources of air pollution, mainly due to emissions from mobile sources and the generation of greenhouse gases such as CO2, NOx, and suspended particles [71,72].

3.3. Statistical Tests

Table 2 and Figure 11 show a normality analysis of daily COVID-19 infections and the average of the values of each contaminant (PM2.5, PM10, O3, SO2, and CO). The results of these tests provided important information about the way the data is distributed.
There are significance values less than 0.05 in the case of the Y variables, as for X3_SO2 (Figure 11e), which shows that the data do not follow a normal distribution. On the other hand, in the case of the variables X1_PM2.5 (Figure 11b), X2_PM10 (Figure 11c), X3_O3 (Figure 11d), and X5_CO (Figure 11f), a significance level greater than 0.05 was obtained, showing a normal distribution.
Five mathematical models were developed, one for each atmospheric contaminant (PM2.5, PM10, O3, SO2zo, and CO) that reflected the incidence of these with the increase in cases due to COVID-19 infection from March 2020 to March 2021 (Table 3).
The first model was based on COVID-19 infections vs. the PM2.5 contaminant (Figure 12). The linear regression coefficients R2 and adjusted R2 obtained in SPSS are 0.214 and 0.201, respectively. This shows a moderate correlation between PM2.5 concentrations and cases of contagion. Therefore, model 1 is defined with the following equation:
y = −1667.48x − 69.54
The second model was based on COVID-19 infections versus the PM10 contaminant (Figure 13). The linear regression coefficients R2 and adjusted R2 obtained in SPSS are 0.083 and 0.068, respectively. This correlation is considered scarce. Therefore, model 2 is defined with the following equation:
y = 308.36x − 45.96
The third model was based on COVID-19 infections vs. the O3 contaminant (Figure 14). The linear regression coefficients R2 and adjusted R2 obtained in SPSS are 0.070 and 0.055, respectively. This shows a moderate correlation between the atmospheric contaminant SO2 and COVID-19 infections. Therefore, model 3 is defined as:
y = 25.590x − 42.80
The fourth model was based on COVID-19 infections vs. the SO2 contaminant (Figure 15). The linear regression coefficients R2 and adjusted R2 obtained in SPSS are 0.250 and 0.238, respectively. This shows a moderate correlation in model 4, defined by the following equation:
y = −30.75 – 544.95
Finally, the fifth model was based on COVID-19 infections vs. the CO contaminant (Figure 16). The linear regression coefficients R2 and adjusted R2 obtained in SPSS are 0.143 and 0.129, respectively. This shows a moderate correlation in model 5, defined by the following equation:
y = −535.292x − 382.915
The models developed in this study show different levels of correlation between contaminants and the increase in COVID-19 cases. The fourth model, associated with sulfur dioxide (SO2), shows the significant correlation between the analyzed contaminants and COVID-19 cases, with a coefficient of determination (R²) of 0.250. This finding shows that SO2 influenced the dynamics of the pandemic. Exposure to SO2 has been associated with adverse effects on respiratory health, which could increase vulnerability to viral infections such as COVID-19. On the other hand, the first PM2.5 model presents an R² of 0.214, indicating a moderate correlation. This suggests that as PM2.5 levels increase, so do COVID-19 cases. Scientific literature has documented that exposure to PM2.5 can weaken the immune system and increase susceptibility to respiratory infections, which could explain this correlation. Ozone (O3) also shows a moderate correlation (R² = 0.070), indicating that elevated ozone levels may be associated with an increase in COVID-19 cases. Ozone exposure has been linked to lung inflammation and exacerbation of respiratory diseases, which could contribute to greater severity of COVID-19 in exposed populations. Carbon monoxide (CO) is another contaminant that shows a moderate correlation (R² = 0.148), indicating that there is a relationship between CO levels and the increase in COVID-19 cases, although this relationship is less pronounced than that observed with other contaminants. Finally, the model analyzing the relationship between PM10 and COVID-19 cases shows a low correlation (R² = 0.083). This suggests that although PM10 is a relevant contaminant, its direct impact on the increase in COVID-19 cases may be less significant compared to other contaminants. However, this does not diminish the importance of monitoring PM10 levels, as it remains a risk factor for respiratory health.
In this context, it is evident that the model with the highest incidence in COVID-19 infections is the SO2 model 4. On the other hand, the PM2.5, O3, and CO models show moderate correlations, suggesting that these contaminants may have some effect on infections, although they are not as significant as SO2. The PM10 model 2 shows very low correlations, indicating that this contaminant has little impact on COVID-19 infections.
Figure 17 shows the correlation of COVID-19 infections with atmospheric contaminants and environmental variables (temperature and precipitation). Moderate positive correlations are evident with PM2.5 (0.46), SO2 (0.50), and CO (0.38), suggesting that increases in these contaminants are associated with a rise in infections. In terms of environmental variables, temperature and precipitation show low (negative) correlations with COVID-19 cases, indicating that they do not have a significant direct relationship with the increase in infections. Environmental variables, such as temperature and precipitation, could modify the impact of contaminants on infections; however, since they do not have significant correlations with COVID-19 cases, their effect in this relationship is minimal. Because they are not correlated with COVID-19 cases, environmental variables may act as confounding factors, meaning they can influence the relationship between contaminants and infections without being direct determinants of the number of cases.
Figure 18 shows the statistical analyses of COVID-19 infections with contaminants and environmental variables (temperature and precipitation). Three clusters are displayed: Cluster 1 (green), Cluster 2 (red), and Cluster 3 (blue) (Figure 18a). Clusters 1 and 2 group the weeks of the months in which contamination and infection levels were moderate to high, such as September, October, November, and December 2020. Weeks with high contamination levels tend to cluster with higher numbers of COVID-19 infections, reinforcing the positive correlation observed between some pollutants (PM2.5, SO2, and CO). Meanwhile, Cluster 3 shows weeks with atypical or significantly different behavior compared to the others, possibly due to a low correlation with precipitation, temperature, and contamination variables. Additionally, the angle of the temperature vector is small relative to the infection vector, indicating a negative relationship between temperature and infections, although it is not as strong as the relationship observed with contamination (such as PM2.5 or PM10).
On the other hand, Figure 18b shows a biclustering analysis through a heatmap that integrates COVID-19 infection data and their relationship with atmospheric contaminants and environmental factors (Figure 18b). In several weeks of October 2020, high levels of PM2.5 coincide with high rates of COVID-19 infections, indicating a correlation between the levels of certain air contaminants. Similarly, in several weeks where carbon monoxide (CO) and sulfur dioxide (SO2) levels are high (red), COVID-19 infections also show an increase. This behavior reinforces the idea that air pollution is related to an increase in COVID-19 cases, possibly by increasing the vulnerability of the respiratory system. In contrast, during weeks when temperatures are lower (green colors in the temperature column), infection levels tend to be high (red colors), indicating a negative correlation between temperature and infections. Additionally, precipitation does not show a correlation with the increase in infections, as it does not have a significant direct relationship with COVID-19 cases. The weeks that cluster in nearby branches in the dendrogram tend to share similar values in contamination variables and infection cases. This may indicate seasonal trends or specific time periods where air conditions and infections evolved together.

4. Discussion

The relationship between air quality and public health has become critically important in the context of the COVID-19 pandemic. The analysis of COVID-19 cases has revealed a dynamic evolution in time and space [73]. In the findings of this study, periods of acceleration in the transmission of the virus are observed, followed by stabilization or a decrease in the number of cases. Since its appearance, several scientific studies have focused on studying the behavior of these infections [11,16,17], the transmission patterns [74] and the factors that contribute to the spread of the virus [75]. Among these factors, air quality and the presence of atmospheric contaminants have emerged as areas of scientific interest [29,43,55,56]. The consequences of air quality degradation are manifested in a significant percentage of global mortality each year [76]. Therefore, in recent years, the impact on people’s health and its consequences over time has been the subject of several studies [77,78]. The evolution of infections has not been uniform, but has depended on various factors. The authors believe that this highlights the importance of analyzing air quality as a factor that affects the health of people infected by the virus.
Globally, it was found that contaminant emissions such as particulate matter (PM) [27], carbon monoxide (CO), carbon dioxide (CO2) [25], sulfur oxides (SOx), and nitrogen oxides (NOx) [79] are generated in greater quantities [80], particularly in urban areas [81]. This contrasts with the data taken at the stations in the study area for this research, which evidences the accumulation of these contaminants in Quito, particularly CO, O3, SO2, PM2.5, and PM10. The concentration of these contaminants can be influenced by industrial activity, vehicular traffic, and climatic conditions [29], which act as vectors of virus transmission, facilitating its spread in densely populated areas [46]. Previous research shows that atmospheric contaminants compromise the immune system, causing various health problems [82,83,84]. For instance, they have influenced the increase in cases of hospitalization for respiratory diseases [29,62,63], the development of cardiovascular diseases [85], cancer [86], and complications during pregnancy [87]. Furthermore, in the context of the COVID-19 pandemic, a possible relationship has been found between exposure to air contaminants and the increase in cases or the severity of the disease [30,31,73]. This may be because air contaminants can damage the lungs and respiratory system, making people more vulnerable to complications caused by the virus [16]. We believe that people who live or work in urban areas are exposed to higher levels of air pollution, which increases their risk of developing respiratory-related health problems, such as COVID-19.
It is important to understand the impact of air contaminants on the spread of this disease in urban environments. Understanding this relationship is complex and multifaceted [46]. According to some studies, in urban environments with high levels of air pollution, COVID-19 can be transmitted faster than in rural areas [44,67,73]. Based on this, it is important to highlight that these studies support the findings in the study area, where peaks of contagion were identified during the months in which there was a greater presence of atmospheric contaminants (October and September 2020). Furthermore, this relationship was analyzed through mathematical modeling. This type of model can be used to investigate the association between the incidence of atmospheric contaminant concentrations and various diseases [88,89]. Mathematical models can be used to quantify the strength and direction of this relationship [57]. In this study, the mathematical modeling developed provided a powerful framework to explore and quantify the relationships between complex and dynamic variables. These variables provided information to better understand the dynamics of the pandemic. Quantitative variables, such as the number of COVID-19 infections and the concentration of atmospheric contaminants, offered specific data on the incidence of contaminants in the virus [90]. We believe that, while models are certainly useful tools for understanding the dynamics of the pandemic, they should be used with caution and in combination with other types of evidence and expert knowledge.
Furthermore, the final mathematical model showed a higher incidence of COVID-19 infections with contaminants such as CO2, O3, and SO2. This can be associated with studies suggesting that exposure to air contaminants influences the increase in COVID-19 cases [26,27]. For instance, in China, an incidence of contaminants such as CO, NO2, O3, PM2.5, and PM10 has been revealed [91]. In Europe, prolonged exposure to high concentrations of NO2 and SO2 may contribute to COVID-19 mortality rates [92]. In this regard, the main finding of this research is the validity of the mathematical model, supported by the use of linear regression techniques [93] to identify significant correlations, for analyzing the relationship between a dependent variable [94], in this case COVID-19 infections, and one or more independent variables [90], in this case air contaminants. We believe that mathematical models can contribute to obtaining data that can help create policies and measures to mitigate the effects of the pandemic and improve air quality. However, it is important to keep in mind that these models are simplifications of reality and are subject to uncertainty, so additional research is needed to fully understand this relationship.
However, it is important to highlight the limiting factors when analyzing the relationship between air contaminants and COVID-19. One of these was the limitation and reliability of data. In several studies, infectious disease models depend on accurate and reliable data for validation [95]. Data on COVID-19 and air quality may be incomplete or biased [96], especially in areas with less developed epidemiological and environmental surveillance systems [97], as is the case with the Ecuadorian health system. This can introduce uncertainty into the model’s results and limit its usefulness for decision-making [98,99]. Another factor to consider is geographic and temporal variability. Mathematical models may have difficulty capturing this variability adequately, which limits their ability to generalize the results to different geographical and temporal contexts [100]. A third factor is associated with limitations in causality. Association models between COVID-19 and air quality can identify statistically significant correlations, but establishing a strong causal relationship requires additional evidence, such as longitudinal studies, controlled experiments, or analysis of underlying biological mechanisms [101]. Finally, another limitation was the sensitivity to assumptions and parameters [102]. In this case, several models were developed to obtain the final model, with the changes that were considered necessary. However, changes in model structure or parameter values can lead to significantly different results [103]. We believe it is important to take these limitations into account when interpreting the results of COVID-19 studies analyzed through mathematical models and using them to inform public health policies and measures.
Globally, mathematical models are increasingly used to forecast the future of epidemics, such as COVID-19 [83]. These predictions have far-reaching consequences, as they provide relevant information so that governments can act quickly to stop or mitigate an epidemic [86,87]. Mathematical models can be useful tools for making public health decisions and ensuring the optimal use of resources [57]. This is intended to reduce morbidity and mortality associated with the COVID-19 pandemic, but only if the models are rigorously evaluated, and their projections are robust and reliable.

5. Conclusions

The significant increase in deaths between 2019 and 2020, followed by a decrease in 2021, reflects the direct impact of COVID-19 on national mortality rates. In the Andean natural region, the province of Pichincha, particularly Quito, experienced a disproportionate burden of COVID-19 deaths, with significant spikes in the first months due to the rapid spread of the virus associated with its high population density and subsequent declines attributed to the protection measures followed at the national level.
Predictions resulting from the analysis of the relationship between COVID-19 and air contaminants using statistical tests and mathematical models can help inform public health policies and mitigation measures to reduce the impact of the pandemic. In this sense, this study provides a comprehensive assessment of the relationship between COVID-19 infections and environmental contaminants in Quito, revealing significant findings on how air pollution levels can influence the spread and severity of infections. Our results indicate that air contaminants, such as SO2, O3, CO, and PM2.5, are associated with an increase in the incidence and severity of COVID-19 cases in the city. These findings underscore the need for close monitoring of air quality and public policies aimed at reducing pollution to mitigate adverse impacts on respiratory health, especially in the context of emerging pandemics.
The research therefore contributes to the understanding of how specific environmental factors can interact with pathogens, providing a scientific basis for the implementation of more effective public health strategies tailored to local conditions. In addition, these findings underscore the need to address both the pandemic and air pollution comprehensively. Furthermore, improving air quality could benefit public health in both the short and long term, reducing respiratory disease vulnerability and enhancing readiness for future pandemics.

Author Contributions

Conceptualization, A.D.H.-A. and C.G.P.C.; methodology, A.D.H.-A., C.G.P.C., C.F., P.A.-P., J.U.-M., J.F.V.U., G.M.A.-C. and T.T.; software, C.G.P.C., A.D.H.-A. and T.T.; validation, C.G.P.C., A.D.H.-A. and P.A.-P.; formal analysis, C.F., C.G.P.C. and A.D.H.-A.; investigation, C.G.P.C., A.D.H.-A., C.F. and J.F.V.U.; resources, C.G.P.C., A.D.H.-A., G.M.A.-C., J.U.-M. and P.A.-P.; data curation, C.G.P.C. and A.D.H.-A.; writing—original draft preparation, C.G.P.C., A.D.H.-A., C.F., P.A.-P., J.F.V.U. and T.T.; writing—review and editing, T.T., C.F., G.M.A.-C., J.U.-M. and A.D.H.-A.; visualization, C.G.P.C., A.D.H.-A., G.M.A.-C., J.U.-M. and T.T.; supervision, C.G.P.C. and A.D.H.-A.; project administration, A.D.H.-A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable

Informed Consent Statement

Not applicable

Data Availability Statement

All the data generated and analyzed during this study are included in this published article.

Acknowledgments

The authors of this article thank the Escuela Superior Politecnica de Chimborazo (ESPOCH) for creating research spaces and opportunities for academic and professional growth.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Gautam, S.; Hens, L. COVID-19: Impact by and on the Environment, Health and Economy. Environ. Dev. Sustain. 2020, 22, 4953–4954. [Google Scholar] [CrossRef] [PubMed]
  2. Khan, M.; Adil, S.F.; Alkhathlan, H.Z.; Tahir, M.N.; Saif, S.; Khan, M.; Khan, S.T. COVID-19: A Global Challenge with Old History, Epidemiology and Progress So Far. Molecules 2021, 26, 39. [Google Scholar] [CrossRef]
  3. Jin, Y.; Yang, H.; Ji, W.; Wu, W.; Chen, S.; Zhang, W.; Duan, G. Virology, Epidemiology, Pathogenesis, and Control of COVID-19. Viruses 2020, 12, 372. [Google Scholar] [CrossRef] [PubMed]
  4. Chow, E.J.; Uyeki, T.M.; Chu, H.Y. The Effects of the COVID-19 Pandemic on Community Respiratory Virus Activity. Nat. Rev. Microbiol. 2023, 21, 195–210. [Google Scholar] [CrossRef] [PubMed]
  5. Lima-Martínez, M.M.; Boada, C.C.; Madera-Silva, M.D.; Marín, W.; Contreras, M. COVID-19 and Diabetes: A Bidirectional Relationship. Clínica E Investig. En Arterioscler. (Engl. Ed.) 2021, 33, 151–157. [Google Scholar] [CrossRef]
  6. Zhao, J.; Kang, J.; Cao, X.; Bian, R.; Liu, G.; Hu, S.; Wu, X.; Li, C.; Wang, D.; Qi, W.; et al. Impacts of Comorbidity and Mental Shock on Organic Microcontaminants in Surface Water during and after the COVID-19 Pandemic in Wuhan (2019–2021), China. Engineering 2024, 37, 40–48. [Google Scholar] [CrossRef]
  7. Lai, A.; Chang, M.L.; O’Donnell, R.P.; Zhou, C.; Sumner, J.A.; Hsiai, T.K. Association of COVID-19 Transmission with High Levels of Ambient Contaminants: Initiation and Impact of the Inflammatory Response on Cardiopulmonary Disease. Sci. Total Environ. 2021, 779, 146464. [Google Scholar] [CrossRef]
  8. Espejo, W.; Celis, J.E.; Chiang, G.; Bahamonde, P. Environment and COVID-19: Contaminants, Impacts, Dissemination, Management and Recommendations for Facing Future Epidemic Threats. Sci. Total Environ. 2020, 747, 141314. [Google Scholar] [CrossRef]
  9. Padhan, R.; Prabheesh, K.P. The Economics of COVID-19 Pandemic: A Survey. Econ. Anal. Policy 2021, 70, 220–237. [Google Scholar] [CrossRef]
  10. Ismail, I.H.M.; Khatib, S.F.A.; Abbas, A.F.; Khan, M.N.A.A.; Sulimany, H.G.H.; Bazhair, A.H. Crisis and Environmental Governance Decisions amidst the COVID-19 Pandemic: Lessons from European Countries. Heliyon 2024, 10, e25673. [Google Scholar] [CrossRef]
  11. Shi, P.; Dong, Y.; Yan, H.; Zhao, C.; Li, X.; Liu, W.; He, M.; Tang, S.; Xi, S. Impact of Temperature on the Dynamics of the COVID-19 Outbreak in China. Sci. Total Environ. 2020, 728, 138890. [Google Scholar] [CrossRef] [PubMed]
  12. She, J.; Jiang, J.; Ye, L.; Hu, L.; Bai, C.; Song, Y. 2019 Novel Coronavirus of Pneumonia in Wuhan, China: Emerging Attack and Management Strategies. Clin. Transl. Med. 2020, 9, 19. [Google Scholar] [CrossRef] [PubMed]
  13. Ceccarelli, M.; Berretta, M.; Venanzi Rullo, E.; Nunnari, G.; Cacopardo, B. Differences and Similarities between Severe Acute Respiratory Syndrome (SARS)-CoronaVirus (CoV) and SARS-CoV-2. Would a Rose by Another Name Smell as Sweet? Eur. Rev. Med. Pharmacol. Sci. 2020, 24, 2781–2783. [Google Scholar] [CrossRef]
  14. Wu, J.T.; Leung, K.; Leung, G.M. Nowcasting and Forecasting the Potential Domestic and International Spread of the 2019-nCoV Outbreak Originating in Wuhan, China: A Modelling Study. Lancet 2020, 395, 689–697. [Google Scholar] [CrossRef] [PubMed]
  15. Domingo, J.L.; Marquès, M.; Rovira, J. Influence of Airborne Transmission of SARS-CoV-2 on COVID-19 Pandemic. A Review. Environ. Res. 2020, 188, 109861. [Google Scholar] [CrossRef] [PubMed]
  16. Sharma, G.D.; Tiwari, A.K.; Jain, M.; Yadav, A.; Srivastava, M. COVID-19 and Environmental Concerns: A Rapid Review. Renew. Sustain. Energy Rev. 2021, 148, 111239. [Google Scholar] [CrossRef] [PubMed]
  17. Barouki, R.; Kogevinas, M.; Audouze, K.; Belesova, K.; Bergman, A.; Birnbaum, L.; Boekhold, S.; Denys, S.; Desseille, C.; Drakvik, E.; et al. The COVID-19 Pandemic and Global Environmental Change: Emerging Research Needs. Environ. Int. 2021, 146, 106272. [Google Scholar] [CrossRef]
  18. Zhong, J.; Zhang, X.; Dong, Y.; Wang, Y.; Liu, C.; Wang, J.; Zhang, Y.; Che, H. Feedback Effects of Boundary-Layer Meteorological Factors on Cumulative Explosive Growth of PM2.5 during Winter Heavy Pollution Episodes in Beijing from 2013 to 2016. Atmos. Chem. Phys. 2018, 18, 247–258. [Google Scholar] [CrossRef]
  19. Karan, A.; Ali, K.; Teelucksingh, S.; Sakhamuri, S. The Impact of Air Pollution on the Incidence and Mortality of COVID-19. Glob. Health Res. Policy 2020, 5, 39. [Google Scholar] [CrossRef]
  20. Woodby, B.; Arnold, M.M.; Valacchi, G. SARS-CoV-2 Infection, COVID-19 Pathogenesis, and Exposure to Air Pollution: What Is the Connection? Ann. N. Y. Acad. Sci. 2021, 1486, 15–38. [Google Scholar] [CrossRef]
  21. O’Reilly, K.M.; Auzenbergs, M.; Jafari, Y.; Liu, Y.; Flasche, S.; Lowe, R. Effective Transmission across the Globe: The Role of Climate in COVID-19 Mitigation Strategies. Lancet Planet Health 2020, 4, e172. [Google Scholar] [CrossRef] [PubMed]
  22. Ren, S.; Du, M.; Bu, W.; Lin, T. Assessing the Impact of Economic Growth Target Constraints on Environmental Pollution: Does Environmental Decentralization Matter? J. Environ. Manag. 2023, 336, 117618. [Google Scholar] [CrossRef] [PubMed]
  23. Yan, C.; Li, H.; Li, Z. Environmental Pollution and Economic Growth: Evidence of SO2 Emissions and GDP in China. Front. Public Health 2022, 10, 930780. [Google Scholar] [CrossRef] [PubMed]
  24. Wan, Q.; Shi, D. Smarter and Cleaner: The Digital Economy and Environmental Pollution. China World Econ. 2022, 30, 59–85. [Google Scholar] [CrossRef]
  25. Shakoor, A.; Chen, X.; Farooq, T.H.; Shahzad, U.; Ashraf, F.; Rehman, A.; Sahar, N.E.; Yan, W. Fluctuations in Environmental Contaminants and Air Quality during the Lockdown in the USA and China: Two Sides of COVID-19 Pandemic. Air Qual. Atmos. Health 2020, 13, 1335–1342. [Google Scholar] [CrossRef]
  26. Li, A.; Zhou, Q.; Xu, Q. Prospects for Ozone Pollution Control in China: An Epidemiological Perspective. Environ. Pollut. 2021, 285, 117670. [Google Scholar] [CrossRef]
  27. Meo, S.A.; Almutairi, F.J.; Abukhalaf, A.A.; Usmani, A.M. Effect of Green Space Environment on Air Contaminants PM2.5, PM10, CO, O3, and Incidence and Mortality of SARS-CoV-2 in Highly Green and Less-Green Countries. Int. J. Environ. Res. Public Health 2021, 18, 13151. [Google Scholar] [CrossRef]
  28. Suzuki, T.; Hidaka, T.; Kumagai, Y.; Yamamoto, M. Environmental Contaminants and the Immune Response. Nat. Immunol. 2020, 21, 1486–1495. [Google Scholar] [CrossRef]
  29. Faustini, A.; Stafoggia, M.; Colais, P.; Berti, G.; Bisanti, L.; Cadum, E.; Cernigliaro, A.; Mallone, S.; Scarnato, C.; Forastiere, F. Air Pollution and Multiple Acute Respiratory Outcomes. Eur. Respir. J. 2013, 42, 304. [Google Scholar] [CrossRef]
  30. Filippini, T.; Rothman, K.J.; Cocchio, S.; Narne, E.; Mantoan, D.; Saia, M.; Goffi, A.; Ferrari, F.; Maffeis, G.; Orsini, N.; et al. Associations between Mortality from COVID-19 in Two Italian Regions and Outdoor Air Pollution as Assessed through Tropospheric Nitrogen Dioxide. Sci. Total Environ. 2021, 760, 143355. [Google Scholar] [CrossRef]
  31. Wang, B.; Liu, J.; Fu, S.; Xu, X.; Li, L.; Ma, Y.; Zhou, J.; Yaoc, J.; Liu, X.; Zhang, X.; et al. An Effect Assessment of Airborne Particulate Matter Pollution on COVID-19: A Multi-City Study in China. medRxiv 2020. [Google Scholar] [CrossRef]
  32. Toulkeridis, T.; Seqqat, R.; Torres, A.M.; Ortiz-Prado, E.; Debut, A. COVID-19: Pandemic in Ecuador: A Health Disparities Perspective. Rev. De Salud Pública 2023, 22, 304–308. [Google Scholar] [CrossRef] [PubMed]
  33. Pansini, R.; Fornacca, D. COVID-19 Higher Mortality in Chinese Regions with Chronic Exposure to Lower Air Quality. Front. Public Health 2021, 8, 597753. [Google Scholar] [CrossRef] [PubMed]
  34. Soiza, R.L.; Niven, M. The COVID-19 Vaccine Gamble—By Luck They Worked in the Oldest, Frailest People. Rev. Esp. De Geriatr. Y Gerontol. 2024, 59, 101427. [Google Scholar] [CrossRef] [PubMed]
  35. Feng, B.; Wang, W.; Zhou, B.; Zhou, Y.; Wang, J.; Liao, F. Mapping the Long-Term Associations between Air Contaminants and COVID-19 Risks and the Attributable Burdens in the Continental United States. Environ. Pollut. 2023, 324, 121418. [Google Scholar] [CrossRef]
  36. Chen, Z.; Sidell, M.A.; Huang, B.Z.; Chow, T.; Eckel, S.P.; Martinez, M.P.; Gheissari, R.; Lurmann, F.; Thomas, D.C.; Gilliland, F.D. Ambient Air Contaminant Exposures and COVID-19 Severity and Mortality in a Cohort of Patients with COVID-19 in Southern California. Am. J. Respir. Crit. Care Med. 2022, 206, 440–448. [Google Scholar] [CrossRef]
  37. Zalakeviciute, R.; Vasquez, R.; Bayas, D.; Buenano, A.; Mejia, D.; Zegarra, R.; Diaz, V.; Lamb, B. Drastic Improvements in Air Quality in Ecuador during the COVID-19 Outbreak. Aerosol Air Qual. Res. 2020, 20, 1783–1792. [Google Scholar] [CrossRef]
  38. Coccia, M. An Index to Quantify Environmental Risk of Exposure to Future Epidemics of the COVID-19 and Similar Viral Agents: Theory and Practice. Environ. Res. 2020, 191, 110155. [Google Scholar] [CrossRef]
  39. Bashir, M.F.; Ma, B.; Bilal; Komal, B.; Bashir, M.A.; Tan, D.; Bashir, M. Correlation between Climate Indicators and COVID-19 Pandemic in New York, USA. Sci. Total Environ. 2020, 728, 138835. [Google Scholar] [CrossRef]
  40. Bashir, M.F.; MA, B.J.; Bilal; Komal, B.; Bashir, M.A.; Farooq, T.H.; Iqbal, N.; Bashir, M. Correlation between Environmental Pollution Indicators and COVID-19 Pandemic: A Brief Study in Californian Context. Environ. Res. 2020, 187, 109652. [Google Scholar] [CrossRef]
  41. Meo, S.A.; Abukhalaf, A.A.; Alomar, A.A.; Alessa, O.M.; Sami, W.; Klonoff, D.C. Effect of Environmental Contaminants PM-2.5, Carbon Monoxide, and Ozone on the Incidence and Mortality of SARS-COV-2 Infection in Ten Wildfire Affected Counties in California. Sci. Total Environ. 2021, 757, 143948. [Google Scholar] [CrossRef] [PubMed]
  42. Bilal; Bashir, M.F.; Benghoul, M.; Numan, U.; Shakoor, A.; Komal, B.; Bashir, M.A.; Bashir, M.; Tan, D. Environmental Pollution and COVID-19 Outbreak: Insights from Germany. Air Qual. Atmos. Health 2020, 13, 1385–1394. [Google Scholar] [CrossRef]
  43. Lembo, R.; Landoni, G.; Cianfanelli, L.; Frontera, A. Air Contaminants and SARS-CoV-2 in 33 European Countries. Acta Biomed. 2021, 92, e2021166. [Google Scholar] [CrossRef]
  44. Bilal; Bashir, M.F.; Komal, B.; Benghoul, M.; Bashir, M.A.; Tan, D. Nexus Between the COVID-19 Dynamics and Environmental Pollution Indicators in South America. Risk Manag. Healthc. Policy 2021, 14, 67–74. [Google Scholar] [CrossRef]
  45. Morantes, G.; Rincon, G.; Chanaba, A.; Jones, B. Addressing Air Quality Challenges: Comparative Analysis of Barcelona, Venezuela, and Guayaquil, Ecuador. Heliyon 2024, 10, e29211. [Google Scholar] [CrossRef] [PubMed]
  46. Parra, R.; Espinoza, C. Insights for Air Quality Management from Modeling and Record Studies in Cuenca, Ecuador. Atmosphere 2020, 11, 998. [Google Scholar] [CrossRef]
  47. Borja-Urbano, S.; Rodríguez-Espinosa, F.; Luna-Ludeña, M.; Toulkeridis, T. Valuing the Impact of Air Pollution in Urban Residence Using Hedonic Pricing and Geospatial Analysis, Evidence from Quito, Ecuador. Air Soil Water Res. 2021, 14, 11786221211053277. [Google Scholar] [CrossRef]
  48. Cornejo-Vásconez, D.; Rodríguez-Espinosa, F.; Guasumba, A.; Toulkeridis, T. Contrasting Effects of Air Pollution Assessment in Two Areas of the Quito Metropolitan District, Ecuador. La Granja 2022, 36, 98–112. [Google Scholar] [CrossRef]
  49. Instituto Nacional de Estadísticas y Censos (INEC) Información Demográfica. Available online: https://www.ecuadorencifras.gob.ec/institucional/home/ (accessed on 12 April 2022).
  50. Valencia, V.H.; Hertel, O.; Ketzel, M.; Levin, G. Modeling Urban Background Air Pollution in Quito, Ecuador. Atmos. Pollut. Res. 2020, 11, 646–666. [Google Scholar] [CrossRef]
  51. Laña, I.; Del Ser, J.; Padró, A.; Vélez, M.; Casanova-Mateo, C. The Role of Local Urban Traffic and Meteorological Conditions in Air Pollution: A Data-Based Case Study in Madrid, Spain. Atmos. Environ. 2016, 145, 424–438. [Google Scholar] [CrossRef]
  52. Zambrano-Monserrate, M.A.; Ruano, M.A. Has Air Quality Improved in Ecuador during the COVID-19 Pandemic? A Parametric Analysis. Air Qual. Atmos. Health 2020, 13, 929–938. [Google Scholar] [CrossRef] [PubMed]
  53. Montoya, O.L.Q.; Niño-Ruiz, E.D.; Pinel, N. On the Mathematical Modelling and Data Assimilation for Air Pollution Assessment in the Tropical Andes. Environ. Sci. Pollut. Res. 2020, 27, 35993–36012. [Google Scholar] [CrossRef] [PubMed]
  54. Martínez Burgos, W.J.; Lima Serra, J.; Parody Muñoz, A.E.; Wedderhoff Herrmann, L.; Gallego Cartagena, E.; Paternina-Arboleda, C.; Thomas Soccol, V.; Santiago Martínez, V.J.; Soccol, C.R. Analysis on Air Pollution in South America during the Propagation of COVID-19. Rev. Técnica De La Fac. De Ingeniería. Univ. Del Zulia 2023, 46, e234612. [Google Scholar] [CrossRef]
  55. Gómez Manotoa, O.X. The Effect of Air Pollution, Meteorology and HumanMobility on the Mortality of COVID-19 in Ecuador. Master’s Thesis, Dalarna University, Dalarna County, Sweden, 2022. [Google Scholar]
  56. Gupta, A.; Bherwani, H.; Gautam, S.; Anjum, S.; Musugu, K.; Kumar, N.; Anshul, A.; Kumar, R. Air Pollution Aggravating COVID-19 Lethality? Exploration in Asian Cities Using Statistical Models. Environ. Dev. Sustain. 2021, 23, 6408–6417. [Google Scholar] [CrossRef] [PubMed]
  57. Naik, P.A.; Zu, J.; Ghori, M.B.; Naik, M. Modeling the Effects of the Contaminated Environments on COVID-19 Transmission in India. Results Phys. 2021, 29, 104774. [Google Scholar] [CrossRef]
  58. Simarda, L.; Lestari, D.; Novkaniza, F.; Haqqi, A.; Devila, S. Modeling the Number of Toddler Pneumonia Sufferers in DKI Jakarta Using Negative Binomial Regression. Asian J. Manag. Entrep. Soc. Sci. 2024, 4, 622–643. [Google Scholar]
  59. Gobierno Autónomo Descentralizado del Distrito Metropolitano de Quito. Plan Metropolitano de Ordenamiento Territorial 2012–2022; Municipio del Distrito Metropolitano de Quito: Quito, Ecuador, 2022; pp. 1–400. [Google Scholar]
  60. Instituto Nacional de Estadísticas y Censos (INEC). Estadísticas de Defunciones Generales en Ecuador; Instituto Nacional de Estadísticas y Censos: Quito, Ecuador, 2020. [Google Scholar]
  61. Ministerio de Salud Pública del Ecuador Información COVID-19 Con Fines de Análisis Estadísticos e Investigación Científica. Available online: https://app.powerbi.com/view?r=eyJrIjoiZDBmZGQwNTEtMWE2NS00NDM4LTk5MDEtNGJkNDZiNTM3MjU1IiwidCI6IjcwNjIyMGRiLTliMjktNGU5MS1hODI1LTI1NmIwNmQyNjlmMyJ9&pageName=ReportSection042264456352cb264131 (accessed on 4 April 2023).
  62. Toulkeridis, T.; Ortiz-Prado, E.; Chunga-Moran, J.; Heredia-R, M.; Debut, A. Excess Mortality Data Analysis of COVID-19 Infections and Fatalities in Ecuador. Uniciencia 2022, 36, 280–289. [Google Scholar] [CrossRef]
  63. Quiroz, D.; Guanochanga, B.; Fuertes, W.; Benítez, D.; Torres, J.; Tapia, F.; Toulkkeridis, T. Visual Analytics for the Reduction of Air Pollution on Real-Time Data Derived from WSN; Springer: Berlin/Heidelberg, Germany, 2020; pp. 109–119. [Google Scholar]
  64. Environmental Protection Agency (EPA). Health-Based Categories of Air Quality. Available online: https://www.epa.gov/wildfire-smoke-course/wildfire-smoke-and-your-patients-health-air-quality-index (accessed on 4 April 2023).
  65. Wijekularathna, D.K.; Manage, A.B.W.; Scariano, S.M. Power Analysis of Several Normality Tests: A Monte Carlo Simulation Study. Commun. Stat.—Simul. Comput. 2020, 51, 757–773. [Google Scholar] [CrossRef]
  66. Souza, R.R.d.; Toebe, M.; Mello, A.C.; Bittencourt, K.C. Sample Size and Shapiro-Wilk Test: An Analysis for Soybean Grain Yield. Eur. J. Agron. 2023, 142, 126666. [Google Scholar] [CrossRef]
  67. Alsaqr, A.M. Remarks on the Use of Pearson’s and Spearman’s Correlation Coefficients in Assessing Relationships in Ophthalmic Data. Afr. Vis. Eye Health 2021, 80, a612. [Google Scholar] [CrossRef]
  68. Arreguín, M.; Hernández, A.; Sampayo, S.; Taco, P. Fundamentos Matemáticos de Regresión Lineal Parte III; Casa Editora del Polo: Manta, Ecuador, 2024; ISBN 978-9942-621-66-5. [Google Scholar]
  69. Ortiz-Prado, E.; Simbaña-Rivera, K.; Gómez-Barreno, L.; Rubio-Neira, M.; Guaman, L.P.; Kyriakidis, N.C.; Muslin, C.; Jaramillo, A.M.G.; Barba-Ostria, C.; Cevallos-Robalino, D. Clinical, Molecular, and Epidemiological Characterization of the SARS-CoV-2 Virus and the Coronavirus Disease 2019 (COVID-19), a Comprehensive Literature Review. Diagn. Microbiol. Infect. Dis. 2020, 98, 115094. [Google Scholar] [CrossRef] [PubMed]
  70. Ortiz-Prado, E.; Izquierdo-Condoy, J.S.; Fernandez-Naranjo, R.; Vasconez, J.; Dávila Rosero, M.G.; Revelo-Bastidas, D.; Herrería-Quiñonez, D.; Rubio-Neira, M. The Deadly Impact of COVID-19 among Children from Latin America: The Case of Ecuador. Front. Pediatr. 2023, 11, 1060311. [Google Scholar] [CrossRef] [PubMed]
  71. Fuertes, W.; Carrera, D.; Villacís, C.; Toulkeridis, T.; Galárraga, F.; Torres, E.; Aules, H. Distributed System as Internet of Things for a New Low-Cost, Air Pollution Wireless Monitoring on Real Time. In Proceedings of the 2015 IEEE/ACM 19th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), Chengdu, China, 14–16 October 2015; pp. 58–67. [Google Scholar]
  72. Guanochanga, B.; Cachipuendo, R.; Fuertes, W.; Benítez, D.S.; Toulkeridis, T.; Torres, J.; Villacís, C.; Tapia, F.; Meneses, F. Towards a Real-Time Air Pollution Monitoring Systems Implemented Using Wireless Sensor Networks: Preliminary Results. In Proceedings of the 2018 IEEE Colombian Conference on Communications and Computing (COLCOM), Medellin, Colombia, 16–18 May 2018; pp. 1–4. [Google Scholar]
  73. Nakada, L.Y.K.; Urban, R.C. COVID-19 Pandemic: Impacts on the Air Quality during the Partial Lockdown in São Paulo State, Brazil. Sci. Total Environ. 2020, 730, 139087. [Google Scholar] [CrossRef] [PubMed]
  74. Zambrano-Monserrate, M.A.; Ruano, M.A.; Sanchez-Alcalde, L. Indirect Effects of COVID-19 on the Environment. Sci. Total Environ. 2020, 728, 138813. [Google Scholar] [CrossRef]
  75. Jalloul, H.; Pinto, A.D.; Choi, J. Investigating the Variance in the Adaptation Pathways of U.S. Municipal Solid Waste Management Systems to the COVID-19 Pandemic. Sustain. Cities Soc. 2024, 101, 105080. [Google Scholar] [CrossRef]
  76. Zhang, Q.; Jiang, X.; Tong, D.; Davis, S.J.; Zhao, H.; Geng, G.; Feng, T.; Zheng, B.; Lu, Z.; Streets, D.G.; et al. Transboundary Health Impacts of Transported Global Air Pollution and International Trade. Nature 2017, 543, 705–709. [Google Scholar] [CrossRef]
  77. Rodríguez-Urrego, D.; Rodríguez-Urrego, L. Air Quality during the COVID-19: PM2.5 Analysis in the 50 Most Polluted Capital Cities in the World. Environ. Pollut. 2020, 266, 115042. [Google Scholar] [CrossRef]
  78. Molina, M.J.; Molina, L.T. Megacities and Atmospheric Pollution. J. Air Waste Manag. Assoc. 2004, 54, 644–680. [Google Scholar] [CrossRef]
  79. Paucar, N.E.; Kiggins, P.; Blad, B.; De Jesus, K.; Afrin, F.; Pashikanti, S.; Sharma, K. Ionic Liquids for the Removal of Sulfur and Nitrogen Compounds in Fuels: A Review. Environ. Chem. Lett. 2021, 19, 1205–1228. [Google Scholar] [CrossRef]
  80. Cohen, A.J.; Brauer, M.; Burnett, R.; Anderson, H.R.; Frostad, J.; Estep, K.; Balakrishnan, K.; Brunekreef, B.; Dandona, L.; Dandona, R.; et al. Estimates and 25-Year Trends of the Global Burden of Disease Attributable to Ambient Air Pollution: An Analysis of Data from the Global Burden of Diseases Study 2015. Lancet 2017, 389, 1907–1918. [Google Scholar] [CrossRef]
  81. Fan, Y.; Ding, X.; Hang, J.; Ge, J. Characteristics of Urban Air Pollution in Different Regions of China between 2015 and 2019. Build. Environ. 2020, 180, 107048. [Google Scholar] [CrossRef]
  82. Conticini, E.; Frediani, B.; Caro, D. Can Atmospheric Pollution Be Considered a Co-Factor in Extremely High Level of SARS-CoV-2 Lethality in Northern Italy? Environ. Pollut. 2020, 261, 114465. [Google Scholar] [CrossRef] [PubMed]
  83. Carugno, M.; Dentali, F.; Mathieu, G.; Fontanella, A.; Mariani, J.; Bordini, L.; Milani, G.P.; Consonni, D.; Bonzini, M.; Bollati, V.; et al. PM10 Exposure Is Associated with Increased Hospitalizations for Respiratory Syncytial Virus Bronchiolitis among Infants in Lombardy, Italy. Environ. Res. 2018, 166, 452–457. [Google Scholar] [CrossRef] [PubMed]
  84. Glencross, D.A.; Ho, T.-R.; Camiña, N.; Hawrylowicz, C.M.; Pfeffer, P.E. Air Pollution and Its Effects on the Immune System. Free Radic. Biol. Med. 2020, 151, 56–68. [Google Scholar] [CrossRef] [PubMed]
  85. Lee, B.-J.; Kim, B.; Lee, K. Air Pollution Exposure and Cardiovascular Disease. Toxicol. Res. 2014, 30, 71–75. [Google Scholar] [CrossRef]
  86. Jenwitheesuk, K.; Peansukwech, U.; Jenwitheesuk, K. Accumulated Ambient Air Pollution and Colon Cancer Incidence in Thailand. Sci. Rep. 2020, 10, 17765. [Google Scholar] [CrossRef]
  87. Abraham, E.; Rousseaux, S.; Agier, L.; Giorgis-Allemand, L.; Tost, J.; Galineau, J.; Hulin, A.; Siroux, V.; Vaiman, D.; Charles, M.-A.; et al. Pregnancy Exposure to Atmospheric Pollution and Meteorological Conditions and Placental DNA Methylation. Environ. Int. 2018, 118, 334–347. [Google Scholar] [CrossRef]
  88. Chen, C.; Wang, J.; Kwong, J.; Kim, J.; van Donkelaar, A.; Martin, R.V.; Hystad, P.; Su, Y.; Lavigne, E.; Kirby-McGregor, M.; et al. Association between Long-Term Exposure to Ambient Air Pollution and COVID-19 Severity: A Prospective Cohort Study. CMAJ 2022, 194, E693. [Google Scholar] [CrossRef]
  89. Mejía, C.D.; Faican, G.; Zalakeviciute, R.; Matovelle, C.; Bonilla, S.; Sobrino, J.A. Spatio-Temporal Evaluation of Air Pollution Using Ground-Based and Satellite Data during COVID-19 in Ecuador. Heliyon 2024, 10, e28152. [Google Scholar] [CrossRef]
  90. Notari, A.; Torrieri, G. COVID-19 Transmission Risk Factors. Pathog. Glob. Health 2022, 116, 146–177. [Google Scholar] [CrossRef]
  91. Zhu, Y.; Xie, J.; Huang, F.; Cao, L. Association between Short-Term Exposure to Air Pollution and COVID-19 Infection: Evidence from China. Sci. Total Environ. 2020, 727, 138704. [Google Scholar] [CrossRef] [PubMed]
  92. Ogen, Y. Assessing Nitrogen Dioxide (NO2) Levels as a Contributing Factor to Coronavirus (COVID-19) Fatality. Sci. Total Environ. 2020, 726, 138605. [Google Scholar] [CrossRef] [PubMed]
  93. Shafiev, T. Development of a Mathematical Model and an Efficient Computational Algorithm for Predicting Atmospheric Pollution in Industrial Regions. AIP Conf. Proc. 2024, 3004, 060005. [Google Scholar] [CrossRef]
  94. Becchetti, L.; Beccari, G.; Conzo, G.; Conzo, P.; De Santis, D.; Salustri, F. Air Quality and COVID-19 Adverse Outcomes: Divergent Views and Experimental Findings. Environ. Res. 2021, 193, 110556. [Google Scholar] [CrossRef]
  95. Hernandez-Vargas, E.A.; Velasco-Hernandez, J.X. In-Host Mathematical Modelling of COVID-19 in Humans. Annu. Rev. Control 2020, 50, 448–456. [Google Scholar] [CrossRef]
  96. Bouchnita, A.; Jebrane, A. A Multi-Scale Model Quantifies the Impact of Limited Movement of the Population and Mandatory Wearing of Face Masks in Containing the COVID-19 Epidemic in Morocco. Math. Model. Nat. Phenom. 2020, 15, 31. [Google Scholar] [CrossRef]
  97. Chaharborj, S.S.; Asl, J.H.; Mohammadi, B. Optimal Control Strategy to Control Pandemic COVID-19 Using MSILIHR_V Model. Math. Model. Nat. Phenom. 2022, 17, 23. [Google Scholar] [CrossRef]
  98. Neiderud, C.-J. How Urbanization Affects the Epidemiology of Emerging Infectious Diseases. Infect. Ecol. Epidemiol. 2015, 5, 27060. [Google Scholar] [CrossRef]
  99. Medina-Gómez, O.S. Impact of the COVID-19 Pandemic on Cardiovascular Disease Mortality Trends in Mexico, 2000–2022. Semergen 2024, 50, 102170. [Google Scholar] [CrossRef]
  100. Jensen, B.C.S.; Engsig-Karup, A.P.; Knudsen, K. Efficient Uncertainty Quantification and Variance-Based Sensitivity Analysis in Epidemic Modelling Using Polynomial Chaos. Math. Model. Nat. Phenom. 2022, 17, 8. [Google Scholar] [CrossRef]
  101. Samui, P.; Mondal, J.; Khajanchi, S. A Mathematical Model for COVID-19 Transmission Dynamics with a Case Study of India. Chaos Solitons Fractals 2020, 140, 110173. [Google Scholar] [CrossRef] [PubMed]
  102. Gumel, A.B.; Iboi, E.A.; Ngonghala, C.N.; Elbasha, E.H. A Primer on Using Mathematics to Understand COVID-19 Dynamics: Modeling, Analysis and Simulations. Infect. Dis. Model. 2021, 6, 148–168. [Google Scholar] [CrossRef] [PubMed]
  103. Kucharski, A.J.; Russell, T.W.; Diamond, C.; Liu, Y.; Edmunds, J.; Funk, S.; Eggo, R.M.; Sun, F.; Jit, M.; Munday, J.D.; et al. Early Dynamics of Transmission and Control of COVID-19: A Mathematical Modelling Study. Lancet Infect. Dis. 2020, 20, 553–558. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Map of study area.
Figure 1. Map of study area.
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Figure 2. The Air Quality Index Scale. Adapted from Environmental Protection Agency, 2023.
Figure 2. The Air Quality Index Scale. Adapted from Environmental Protection Agency, 2023.
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Figure 3. (a) Deaths from COVID-19 in Ecuador 2020–2021 per 100,000 inhabitants, (b) deaths in Ecuador from 2019 to 2021, (c) deaths caused by COVID-19 in 2020 and 2021.
Figure 3. (a) Deaths from COVID-19 in Ecuador 2020–2021 per 100,000 inhabitants, (b) deaths in Ecuador from 2019 to 2021, (c) deaths caused by COVID-19 in 2020 and 2021.
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Figure 4. Deaths in the Andean natural region provinces caused by COVID-19 in 2020 and 2021.
Figure 4. Deaths in the Andean natural region provinces caused by COVID-19 in 2020 and 2021.
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Figure 5. COVID-19 infections in the province of Pichincha cantons from March 2020 to December 2021.
Figure 5. COVID-19 infections in the province of Pichincha cantons from March 2020 to December 2021.
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Figure 6. Monthly COVID-19 infections in Quito city during March 2020 to March 2021.
Figure 6. Monthly COVID-19 infections in Quito city during March 2020 to March 2021.
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Figure 7. Total deaths and deaths caused by COVID-19 in the city of Quito during the year 2020 and 2021.
Figure 7. Total deaths and deaths caused by COVID-19 in the city of Quito during the year 2020 and 2021.
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Figure 8. Concentration of atmospheric contaminants in Quito city during the period March 2020 to March 2021 obtained from the Historic Center station: (a) CO; (b) PM2.5; (c) O3; (d) SO2.
Figure 8. Concentration of atmospheric contaminants in Quito city during the period March 2020 to March 2021 obtained from the Historic Center station: (a) CO; (b) PM2.5; (c) O3; (d) SO2.
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Figure 9. Concentration of atmospheric contaminants in Quito city during the period March 2020 to March 2021 obtained from the Belisario station: (a) CO; (b) PM2.5; (c) PM10; (d) O3; (e) SO2.
Figure 9. Concentration of atmospheric contaminants in Quito city during the period March 2020 to March 2021 obtained from the Belisario station: (a) CO; (b) PM2.5; (c) PM10; (d) O3; (e) SO2.
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Figure 10. Concentration of atmospheric contaminants and COVID-19 infections in Quito city during the period March 2020 to March 2021.
Figure 10. Concentration of atmospheric contaminants and COVID-19 infections in Quito city during the period March 2020 to March 2021.
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Figure 11. Normality tests. (a) Y_Covid_cases; (b) X1_PM2.5; (c) X2_PM10; (d) X3_O3; (e) X4_SO2; (f) X5_CO.
Figure 11. Normality tests. (a) Y_Covid_cases; (b) X1_PM2.5; (c) X2_PM10; (d) X3_O3; (e) X4_SO2; (f) X5_CO.
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Figure 12. Model 1: COVID-19 vs. PM2.5.
Figure 12. Model 1: COVID-19 vs. PM2.5.
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Figure 13. Model 2: COVID-19 vs. PM10.
Figure 13. Model 2: COVID-19 vs. PM10.
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Figure 14. Model 3: COVID-19 vs. O3.
Figure 14. Model 3: COVID-19 vs. O3.
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Figure 15. Model 4: COVID-19 vs. SO2.
Figure 15. Model 4: COVID-19 vs. SO2.
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Figure 16. Model 5: COVID-19 vs. CO.
Figure 16. Model 5: COVID-19 vs. CO.
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Figure 17. Correlation analysis.
Figure 17. Correlation analysis.
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Figure 18. Statistical analyses: (a) multivariate analysis Hj-biplot; (b) biclustering analysis.
Figure 18. Statistical analyses: (a) multivariate analysis Hj-biplot; (b) biclustering analysis.
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Table 1. Scale for assessing air contaminants.
Table 1. Scale for assessing air contaminants.
CategoryAQIAir Contaminants
PM10 (µg/m³)PM2.5 (µg/m³)O3 (µg/m³)SO2 (µg/m³)CO (mg/m³)
Good0–50≤54≤12.0≤180≤20≤4.4
Moderate51–10055–15412.1–35.4181–24021–504.5–9.0
Unhealthy for Sensitive individuals101–150155–25435.5–55.4241–30051–1509.1–15.0
Unhealthy 151–200255–35455.5–150.4301–400151–200>15.0
Very Unhealthy 201–300355–424150.5–250.4400–500201–300-
Hazardous>300>424>250.4>500>300-
Table 2. Data normality analysis.
Table 2. Data normality analysis.
Kolmogorov–Smirnov aShapiro–Wilk
StatisticglSig.StatisticGlSig.
Y_Covid_Cases0.107640.1010.9412640.1854
X1_PM2.50.064640.0570.9734640.0789
X2_PM100.065640.6050.9784640.0893
X3_O30.073640.4010.9862640.0623
X4_SO20.1351640.0050.9471640.0002
X5_CO0.0732640.4460.9804640.1086
a Lilliefors significance correction.
Table 3. Simple linear regression models based on COVID-19 infections in relation to air contaminants.
Table 3. Simple linear regression models based on COVID-19 infections in relation to air contaminants.
SPSS
ModelRR SquaredR Squared AdjustedError
X1_PM2.50.463 a0.2140.2011235.42
X2_PM100.288 a0.0830.0681334.69
X3_O30.264 a0.0700.0551343.99
X4_SO20.500 a0.2500.2381207.10
X5_CO0.378 a0.1480.1291290.06
a Predictors (constant).
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Hernández-Allauca, A.D.; Pérez Castillo, C.G.; Villacis Uvidia, J.F.; Abdo-Peralta, P.; Frey, C.; Ati-Cutiupala, G.M.; Ureña-Moreno, J.; Toulkeridis, T. Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador. Int. J. Environ. Res. Public Health 2024, 21, 1336. https://doi.org/10.3390/ijerph21101336

AMA Style

Hernández-Allauca AD, Pérez Castillo CG, Villacis Uvidia JF, Abdo-Peralta P, Frey C, Ati-Cutiupala GM, Ureña-Moreno J, Toulkeridis T. Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador. International Journal of Environmental Research and Public Health. 2024; 21(10):1336. https://doi.org/10.3390/ijerph21101336

Chicago/Turabian Style

Hernández-Allauca, Andrea Damaris, Carlos Gabriel Pérez Castillo, Juan Federico Villacis Uvidia, Paula Abdo-Peralta, Catherine Frey, Guicela Margoth Ati-Cutiupala, Juan Ureña-Moreno, and Theofilos Toulkeridis. 2024. "Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador" International Journal of Environmental Research and Public Health 21, no. 10: 1336. https://doi.org/10.3390/ijerph21101336

APA Style

Hernández-Allauca, A. D., Pérez Castillo, C. G., Villacis Uvidia, J. F., Abdo-Peralta, P., Frey, C., Ati-Cutiupala, G. M., Ureña-Moreno, J., & Toulkeridis, T. (2024). Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador. International Journal of Environmental Research and Public Health, 21(10), 1336. https://doi.org/10.3390/ijerph21101336

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