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A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and random effects

  • Neslihan İyit ORCID logo EMAIL logo and Ferhat Sevim ORCID logo
Published/Copyright: August 30, 2023
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Open Chemistry
From the journal Open Chemistry Volume 21 Issue 1

Abstract

The coronavirus disease 2019 (COVID-19) pandemic was defined by the World Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious disease that threatens public health fatally. In this study, the main aim is to model the impact of various air pollution causes on mortality data due to the COVID-19 pandemic by Generalized Linear Mixed Model (GLMM) approach to make global statistical inferences about 174 WHO member countries as subjects in the six WHO regions. “Total number of deaths by these countries due to the COVID-19 pandemic” until July 27, 2022, is taken as the response variable. The explanatory variables are taken as the WHO regions, the number of deaths from air pollution causes per 100.000 population as “household air pollution from solid fuels,” “ambient particulate matter pollution,” and “ambient ozone pollution.” In this study, Poisson, geometric, and negative binomial (NB) regression models with “country” taken as fixed and random effects, as special cases of GLMM, are fitted to model the response variable in the aspect of the above-mentioned explanatory variables. In the Poisson, geometric, and NB regression models, Iteratively Reweighted Least Squares parameter estimation method with the Fisher-Scoring iterative algorithm under the log-link function as canonical link function is used. In the GLMM approach, Laplace approximation is also used in the prediction of random effects. In this study, six different Poisson, geometric, and NB regression models with fixed and random effects are established for 174 countries all over the world to make global statistical inferences for investigating the relationships between “total number of deaths” by these countries due to the COVID-19 pandemic and “air pollution causes.” As a result of this study, “NB mixed-effects regression model” as the most appropriate GLMM is used to make global statistical inferences about the impact of the various air pollution causes on the mortality data due to the COVID-19 pandemic.

Keywords: COVID-19 pandemic; air pollution; generalized linear mixed model; fixed effect; random effect; Poisson regression; geometric regression; negative binomial regression

1 Introduction

The coronavirus disease 2019 (COVID-19) pandemic caused by “Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)” infection has led to severe acute respiratory diseases all over the world and has fatally affected the whole world since December 2019 [1,2,3,4,5,6,7,8]. Physical, biological, and especially chemical factors cause air pollution, harm the health of humans and other living things, and change the natural structure of the indoor and outdoor environment.

“Household air pollution (HAP) from solid fuels,” “ambient particulate matter pollution,” and “ambient ozone pollution” discussed in this study are the three leading air pollution causes. HAP is an important measure of indoor air pollution with respect to the smoke from traditional household solid fuel combustion during cooking and heating methods [9,10,11]. Particulate matter pollution in the environment, as the annual weighted average mass concentration of aerodynamic particles with a diameter of less than 2.5 µm in one m3 of air, is one of the most important criteria of the outdoor air population [12,13,14,15,16]. Ambient ozone pollution as another important measure of the outdoor air population is seen as the highest seasonal average of eight-hour daily maximum ozone concentrations [17,18,19]. It is known that air pollution causes especially chronic obstructive pulmonary and lung cancer. It is clearly known that the harmful effects of air pollution increase especially respiratory and cardiovascular diseases and also mortality rates in the world. In light of this information, in this study, the global effect of air pollution on the COVID-19 pandemic is examined by applying generalized linear model (GLM) and generalized linear mixed model (GLMM) approaches in terms of indoor and outdoor air pollution indicators.

Ibarra-Espinosa et al. [20] investigated associations between air pollution and daily COVID-19 cases and deaths in Sao Paulo, Brazil, by negative binomial (NB) and quasi-Poisson regression methods. They indicated that even small increases in air pollution cause significant increases in cases and deaths of COVID-19. Odhiambo et al. [21] estimated the relationships between the number of patients infected with COVID-19 in Kenya, the number of people in contact with these patients, and the number of daily air travels to Kenya by the compound Poisson regression model. Oztig and Askin [22] investigated the number of individuals infected with COVID-19 in 144 countries and the mobility of individuals within these countries using the NB regression approach. Similarly, Janković et al. [23] modeled the number of COVID-19 cases and mobility trends in European countries by the same approach. Fitriani and Jaya [24] demonstrated a positive relationship between the population density and the incidence of COVID-19 cases in East Java Province by geographically weighted NB regression model. Coker et al. [25] found a positive relationship between the number of deaths due to the COVID-19 pandemic and the fine particulate matter (PM2.5) in Northern Italy by the NB regression approach. Lee et al. [26] modeled the relationships between the total number of confirmed cases of the COVID-19 pandemic and also the mergers and acquisitions of 145 countries by quasi-Poisson and NB regression approaches. Wu et al. [27] reveal that the increase in the fine particulate matter positively affects the COVID-19 death rates in the United States by NB mixed-effects regression approach. Szyszkowicz [28], Chuang et al. [29], Sun et al. [30], Bülbül et al. [31], Cheng et al. [32], Stieb et al. [33], Chisini et al. [34], Das et al. [35], Karmakar et al. [36], Khalilpourazari et al. [37], Muhsen et al. [38], Tanis and Karakaya [39], Tirkolaee et al. [40], Faruk et al. [41], Gürbüz and Gökçe [42], Özköse and Yavuz [43], Seçilmiş et al. [44], Shamsi et al. [45], and Joshi et al. [46] used various regression models including NB regression, hurdle regression, Poisson regression, zero-inflated regression with fixed and random effects as special cases of GLM and GLMM approaches for modeling the COVID-19 pandemic data.

In light of the studies given in the literature, in this study, an advanced statistical modeling approach based on the GLM and GLMM approaches with Poisson, geometric, and NB distributions is proposed to investigate the relationships between the “mortality data due to the COVID-19 pandemic” and various “causes of air pollution” of 174 World Health Organization (WHO) member countries in the six WHO regions.

2 Materials

In this study, the “total number of deaths by 174 WHO member countries due to the COVID-19 pandemic” until July 27, 2022, is taken as the response variable. “WHO regions,” “the numbers of deaths from air pollution causes per 100.000 population” as “HAP from solid fuels,” “ambient particulate matter pollution,” and “ambient ozone pollution” are taken as the explanatory variables as given in Table 1.

Table 1

All variables taken into the study to model the relationships between the COVID-19 pandemic and air pollution causes

Variables Description
Total deaths due to the COVID-19 pandemic The total number of deaths in each country taken in the study due to the COVID-19 pandemic until July 27, 2022 [47]
Deaths from HAP from solid fuels The number of due deaths from air pollution causes per 100.000 population as “HAP from solid fuels” belonging to 2019 [48]
Deaths from ambient particulate matter pollution The number of due deaths from air pollution causes per 100.000 population as “Ambient particulate matter pollution” belonging to 2019 [48]
Deaths from ambient ozone pollution The numbers of due deaths from air pollution causes per 100.000 population as “Ambient ozone pollution” belonging to 2019 [48]

About 174 countries are taken in the study as subjects: 47 countries from the African Region (AFR), 21 countries from the Eastern Mediterranean Region (EMR), 49 countries from the European Region (EUR), 33 countries from the Americas Region (AMR), 9 countries from the South-East Asian Region (SEAR), and 15 countries from the Western Pacific Region (WPR) according to the WHO given in Table 2.

Table 2

WHO regions and countries taken in the study according to the total number of deaths due to the COVID-19 pandemic in decreasing order [49]

WHO regions Countries
AFR South Africa, Ethiopia, Algeria, Kenya, Zimbabwe, Namibia, Zambia, Uganda, Nigeria, Botswana, Malawi, Mozambique, Senegal, Cameroon, Angola, Rwanda, Ghana, Eswatini, Madagascar, Democratic Republic of Congo, Mauritius, Mauritania, Tanzania, Cote d'Ivoire, Mali, Lesotho, Guinea, Cape Verde, Burkina Faso, Congo, Gambia, Niger, Gabon, Liberia, Togo, Chad, Equatorial Guinea, Guinea-Bissau, Seychelles, Benin, Comoros, South Sudan, Sierra Leone, Central African Republic, Eritrea, Sao Tome and Principe, Burundi
EMR Iran, Pakistan, Tunisia, Iraq, Egypt, Morocco, Jordan, Lebanon, Saudi Arabia, Afghanistan, Libya, Sudan, Oman, Syria, Kuwait, United Arab Emirates, Yemen, Bahrain, Somalia, Qatar, Djibouti
EUR Russia, United Kingdom, Italy, France, Germany, Poland, Ukraine, Spain, Turkey, Romania, Hungary, Czechia, Bulgaria, Belgium, Greece, Portugal, Netherlands, Austria, Slovakia, Sweden, Kazakhstan, Georgia, Croatia, Serbia, Bosnia and Herzegovina, Switzerland, Moldova, Israel, Azerbaijan, North Macedonia, Lithuania, Armenia, Ireland, Belarus, Slovenia, Denmark, Latvia, Finland, Norway, Albania, Kyrgyzstan, Montenegro, Estonia, Uzbekistan, Luxembourg, Cyprus, Malta, Iceland, Tajikistan
AMR United States, Brazil, Mexico, Peru, Colombia, Argentina, Chile, Canada, Ecuador, Bolivia, Paraguay, Guatemala, Honduras, Costa Rica, Cuba, Panama, Uruguay, Venezuela, Dominican Republic, El Salvador, Trinidad and Tobago, Jamaica, Suriname, Guyana, Haiti, Bahamas, Belize, Barbados, Saint Lucia, Nicaragua, Grenada, Antigua and Barbuda, Saint Vincent and the Grenadines
SEAR India, Indonesia, Thailand, Bangladesh, Myanmar, Sri Lanka, Nepal, Maldives, Timor
WPR Philippines, Vietnam, Malaysia, Japan, South Korea, Australia, China, Cambodia, Mongolia, Singapore, New Zealand, Fiji, Laos, Papua New Guinea, Brunei

Descriptive statistics of the response variable as “total deaths due to the COVID-19 pandemic,” and explanatory variables as “deaths from different causes of air pollution” according to the 174 countries are given in Table 3. Countries having the highest total number of deaths due to the COVID-19 pandemic are the United States, Brazil, India, Russia, Mexico, Peru, United Kingdom, Italy, Indonesia, and France with 1.029.359, 677.804, 526.212, 374.523, 327.412, 214.120, 203.987, 171.439, 156.940, and 151.851 deaths until July 27, 2022, respectively.

Table 3

Descriptive statistics of the variables taken in the study to model the impact of air pollution causes on the COVID-19 pandemic

Variables Min. Median Mean ± Sd. Max.
Total deaths due to the COVID-19 pandemic 38 4,792 36686.28 ± 111497.94 1,029,359
Deaths related to the air pollution from household solid fuels 0 9 47.22 ± 64.758 273
Deaths related to the air pollution from ambient particulate matter 3 36 44.89 ± 31.522 177
Deaths related to the air pollution from ambient ozone 0 2 2.26 ± 3.262 35

Descriptive statistics of the response variable as “total deaths due to the COVID-19 pandemic” according to the six WHO regions are given in Table 4. WHO regions with the highest number of total deaths due to the COVID-19 pandemic until July 27, 2022 are AMR, EUR, SEAR, EMR, WPR, and AFR with 2.788.838, 2.066.096, 792.100, 339.131, 224.251, and 172.997, respectively.

Table 4

Descriptive statistics of the total deaths due to the COVID-19 pandemic according to the six WHO regions

WHO Regions Number of Countries Min. Median Mean ± Sd. Max.
AFR 47 38 810 3680.79 ± 14765.171 101,955
EMR 21 189 6,431 16149.10 ± 30406.18 141,795
EUR 49 125 15,851 42165.22 ± 69735.78 374,523
AMR 33 115 7,403 84510.24 ± 215144.47 77,506
SEAR 9 133 19,434 88011.11 ± 171189.42 526,212
WPR 15 225 3,056 14950.07 ± 19491.88 60,694

Histogram of the “total deaths due to the COVID-19 pandemic” according to the 174 WHO member countries used in this study is given in Figure 1. As seen in Figure 1, 33 countries have less than 500 total deaths due to the COVID-19 pandemic; 13, 22, 20, 23, 41, 4, and 13 countries have “total deaths due to the COVID-19 pandemic” between 500 and 1,000; 1,000 and 2,500; 2,500 and 5,000; 5,000 and 10,000; 10,000 and 50000; 50,000 and 100,000; 100,000 and 250000, respectively. Five countries have greater than 250,000 total deaths due to the COVID-19 pandemic until July 27, 2022.

Figure 1 Histogram of the total deaths due to the COVID-19 pandemic according to the 174 WHO member countries taken in the study.
Figure 1

Histogram of the total deaths due to the COVID-19 pandemic according to the 174 WHO member countries taken in the study.

Histogram of the total deaths due to the COVID-19 pandemic for six WHO regions is given in Figure 2. Countries for six WHO regions with the highest number of total deaths due to the COVID-19 pandemic until July 27, 2022, are South Africa from AFR, the United States from AMR, Iran from EMR, Russia from EUR, India from SEAR, and the Philippines from WPR with 101.955, 1.029.359, 141.795, 374.523, 526.212, and 60.694, respectively. Countries for six WHO regions with the lowest number of total deaths due to the COVID-19 pandemic until July 27, 2022, are Burundi from AFR, Saint Vincent and the Grenadines from AMR, Djibouti from EMR, Tajikistan from EUR, Timor from SEAR, and Brunei from WPR with 38, 115, 189, 125, 133, and 225, respectively.

Figure 2 Histogram of the total deaths due to the COVID-19 pandemic according to the six WHO regions taken in the study.
Figure 2

Histogram of the total deaths due to the COVID-19 pandemic according to the six WHO regions taken in the study.

Figures 1 and 2 illustrate that the structures of the response variable for both 174 countries all over the world and the six WHO regions are highly right-skewed distributed.

Histograms of the number of deaths from causes of air pollution per 100.000 population are given in Figure 3. The largest to the smallest numbers of deaths from causes of air pollution per 100.000 population are 8,217, 7,811, and 440 deaths from “household solid fuels,” “ambient particulate matter,” and “ambient ozone,” respectively.

Figure 3 Histograms of the number of deaths from causes of air pollution per 100.000 population.
Figure 3

Histograms of the number of deaths from causes of air pollution per 100.000 population.

As can be seen from Figure 3, the top 10 countries with the highest number of deaths related to air pollution from “household solid fuels” are Somalia from EMR, Central African Republic from AFR, Papua New Guinea from WPR, Niger from AFR, Guinea-Bissau from AFR, Chad from AFR, Burundi from AFR, Mozambique from AFR, Guinea from AFR, and Madagascar from AFR with 273, 252, 230, 200, 199, 196, 187, 186, 185, and 182 deaths, respectively.

The top 10 countries with the highest number of deaths related to air pollution from “ambient particulate matter” are Uzbekistan from EUR, Egypt from EMR, Qatar from EMR, Oman from EMR, Iraq from EMR, Tajikistan from EUR, Saudi Arabia from EMR, Azerbaijan from EUR, Mongolia from WPR, and Bahrain from EMR with 177, 158, 129, 128, 122, 116, 110, 109, 107, and 104 deaths, respectively.

Finally, the top 10 countries with the highest number of deaths related to air pollution from “ambient ozone” are Nepal from SEAR, India from SEAR, Pakistan from EMR, Bangladesh from SEAR, Central African Republic from AFR, Afghanistan from EMR, Bahrain from EMR, China from WPR, Kyrgyzstan from EUR, and Myanmar from SEAR with equally 35, 19, 14, 9, 7, 6, 6, 6, 6, and 6 deaths, respectively.

3 Methods

Traditionally used linear regression models are based on the assumption that the response variable is “normally” distributed. However, in a statistical study, the data of interest for the response variable may not always have a normal distribution. In this case, the GLM approach bringing the advantage that the response variable comes from various distributions due to the exponential family can be used to analyze data [50,51,52,53].

The GLM constitutes of “random component,” “systematic component,” and the “link function.” The random component part of the GLM can be given in the form of the exponential family as follows [50,54,55,56,57,58]:

(1) f ( y ; θ , ϕ ) = exp y θ − b ( θ ) a ( ϕ ) + c ( y , ϕ ) ,

where θ is the location parameter, ϕ is the dispersion parameter, a ( ϕ ) , b ( θ ) , and c ( y , ϕ ) are known functions due to the distribution of the exponential family.

The systematic component also called as “linear predictor” in the form of the linear function can be given as follows [51,54,58,59]:

(2) η = β 0 + β 1 x 1 + … + β p x p .

The link function between the systematic component and the random component of the GLM can be given as follows [52,58,60,61]:

(3) g ( μ ) = η or μ = g − 1 ( η ) .

For an easier understanding of the GLM structure, these components of the GLM are presented visually [58]. Known functions as a ( ϕ ) , b ( θ ) , and c ( y , ϕ ) in the structure of the exponential family given in equation (1), canonical link functions as η = g ( μ ) , and the inverse link functions as μ = g − 1 ( η ) due to the Poisson, geometric, and NB distributions are given in Table 5 [52,60,62,63].

Table 5

Structures of the exponential family, canonical link functions, and the inverse link functions due to the Poisson, geometric, and NB distributions in the GLM approach

Distribution a ( ϕ ) b ( θ ) c ( y , ϕ ) η = g ( μ ) μ = g ‒ 1 ( η )
Poisson 1 e θ ‒ log y ! log ( μ ) exp ( μ )
Geometric 1 ‒ log ( 1 ‒ e θ ) 0 log ( μ ) exp ( μ )
NB 1 ‒ r log ( 1 ‒ e θ ) log r + y ‒ 1 y log ( μ ) exp ( μ )

The GLM is built on the “fixed-effects” terms that have constant effects on the response variable across the subjects taken into the study. On the other hand, it may be necessary to include “random-effects” terms in the model that have varying effects on the response variable across the subjects. In this situation, GLMM is an extended version of the GLM to include “random-effects” terms as well as a linear function of the “fixed-effects” terms included in the model called as the “linear predictor” [55,64,65,66]. For an easier understanding of the GLMM structure, fixed-effects, random-effects, and also the linear predictor part consisting of these effects are presented visually in ref. [58].

The “link function” and the “inverse link function” of the GLMM consisting of the “fixed-effects” and the “random-effects” can be given as follows [55,58,64,67]:

(4) g ( μ ̲ ) = g ( E [ y ̲ ∣ u ̲ ] ) = η ̲ = X β ̲ + Z u ̲ ,

(5) μ ̲ = g − 1 ( η ̲ ) .

In this study, the GLMM approach with Poisson, geometric, and NB distributions are used when the response variable consists of “count data” as non-negative integer values where the canonical link function is in the form of the “log-link function” given as follows [55,64,67]:

(6) η i = log ( λ i ) = x i ′ β ̲ + z i ′ u ̲ .

“Iteratively reweighted least squares (IRLS)” method with “Fisher-Scoring (FS) iterative algorithm” is used for the GLM approach with the Poisson, geometric, and NB distributions. “Maximum likelihood (ML)” method with “Laplace approximation” is also used for the GLMM approach with these distributions [51,55,64,67,68]. The performances of the Poisson, geometric, and NB regression models using the IRLS method with the FS iterative algorithm, and also Poisson, geometric, and NB mixed regression models using the ML method with the Laplace approximation under the log-link function are compared using “information criteria” (IC) as goodness-of-fit test statistics given in the studies by Iyit et al. [58,69,70,71,72,73].

4 Results and discussion

In this study, mortality data due to the COVID-19 pandemic are modeled in the aspect of various causes of air pollution by GLM and GLMM approaches with Poisson, geometric, and NB distributions under the log-link function to make statistical inferences about the 174 WHO member countries in the six WHO regions.

For this aim, the “total number of deaths by the 174 WHO member countries due to the COVID-19 pandemic” until July 27, 2022, is taken as the response variable. WHO regions, the numbers of deaths from causes of air pollution per 100.000 population as “household solid fuels,” “ambient particulate matter,” and “ambient ozone” are taken as the explanatory variables [43,44].

In this study, Poisson, geometric, and NB regression models with “country” taken as fixed and random effects, as special cases of GLM and GLMM, are fitted to model the response variable in the aspect of the above explanatory variables to make global statistical inferences for investigating the relationships between the total number of deaths by these countries due to the COVID-19 pandemic and the mentioned causes of air pollution.

In this study, “RStudio” program [74] is used for the statistical analysis and modeling of the data. In terms of visualization, the “ggplot” function from the “ggplot2” package is used for drawing all the graphs. The “glm” function from the “stats” package, and the “glmer” function from the “lme4” package are used in the parameter estimation of the GLM and GLMM models, respectively.

Poisson, geometric, and NB regression models in the GLM approach, and also Poisson, geometric, and NB mixed regression models in the GLMM approach under the log-link function are fitted to the 174 countries’ COVID-19 pandemic and causes of air pollution data given in Table 1.

First, the results of the Poisson regression model using the IRLS method with the FS iterative algorithm under the log-link function are given in Table 6.

Table 6

The results of the Poisson regression model for the WHO regions and causes of air pollution under the log-link function

Explanatory variables β ˆ s . e ( β ˆ ) P ( Z > ∣ z ∣ ) e x p ( β ˆ ) 95% Confidence interval for e x p ( β )
Lower bound Upper bound
Intercept 10.5194 0.00282 <2 × 10‒16*** 37026.231 36822.4634 37231.1262
EMR 0.3543 0.00328 <2 × 10‒16*** 1.4252 1.4161 1.4344
EUR 0.4066 0.00280 <2 × 10‒16*** 1.5017 1.4935 1.5100
Region of the Americas (AMR) 1.3569 0.00270 <2 × 10‒16*** 3.8843 3.8638 3.9049
SEAR 1.2801 0.00307 <2 × 10‒16*** 3.5968 3.5753 3.6185
WPR -0.2073 0.00340 <2 × 10‒16*** 1.2303 1.2222 1.2386
AFR (Reference Category)
Deaths related to the air pollution from household solid fuels 0.0245 0.00003 <2 × 10‒16*** 1.0249 1.0248 1.0249
Deaths related to the air pollution from ambient particulate matter 0.0150 0.00002 <2 × 10‒16*** 1.0151 1.0151 1.0152
Deaths related to the air pollution from ambient ozone 0.1438 0.00012 <2 × 10‒16*** 1.1547 1.1544 1.1549

By using the IRLS parameter estimates of the Poisson regression model due to the WHO regions and causes of air pollution given in Table 6, the expected value of the COVID-19 mortality is given as follows:

(7) log ( λ ∧ ) = 10 . 5194 + 0 . 3543 ( EMR ) + 0.4066 ( EUR ) + 1.3569 ( AMR ) + 1.2801 ( SEAR ) − 0.2073 ( WPR ) + 0.0245 ( Household solid fuels ) + 0.0150 ( Ambient particulate matter ) + 0.1438 ( Ambient ozone ) ,

or

(8) λ ∧ = e x p 10 . 5194 + 0 . 3543 ( EMR ) + 0.4066 ( EUR ) + 1.3569 ( AMR ) + 1.2801 ( SEAR ) − 0.2073 ( WPR ) + 0.0245 ( Household solid fuels ) + 0.0150 ( Ambient particulate matter ) + 0.1438 ( Ambient ozone ) .

The results of the geometric regression model using the IRLS method with the FS iterative algorithm under the log-link function are given in Table 7.

Table 7

The results of the geometric regression model for the WHO regions and causes of air pollution under the log-link function

Explanatory variables β ˆ s . e ( β ˆ ) P ( Z > ∣ z ∣ ) e x p ( β ˆ ) 95% Confidence interval for e x p ( β )
Lower bound Upper bound
Intercept 10.2251 0.48934 <2 × 10−16*** 27587.8343 10572.7079 71986.16
EMR 1.2668 0.53007 0.01798* 3.5495 1.2559 10.03179
EUR 0.9263 0.46846 0.04968* 2.5250 1.0081 6.32453
Region of the Americas (AMR) 1.3313 0.48583 0.00681** 3.7861 1.4610 9.811602
SEAR 1.8637 0.73336 0.01196* 6.4477 1.5317 27.14267
WPR 0.8422 0.54412 0.12360 2.3214 0.7991 6.743879
AFR (Reference Category)
Deaths related to the air pollution from household solid fuels 0.0195 0.00288 2.28 × 10−10*** 1.0197 1.0139 1.025464
Deaths related to the air pollution from ambient particulate matter 0.0211 0.00528 9.63 × 10−5*** 1.0213 1.0108 1.031959
Deaths related to the air pollution from ambient ozone 0.1740 0.05114 0.00084*** 1.1900 1.0765 1.31547

By using the IRLS parameter estimates of the geometric regression model due to the WHO regions and causes of air pollution given in Table 7, the expected value of the COVID-19 mortality is given as follows:

(9) log ( λ ∧ ) = 10 . 2251 + 1 . 2668 ( EMR ) + 0.9263 ( EUR ) + 1.3313 ( AMR ) + 1.8637 ( SEAR ) + 0.8422 ( WPR ) + 0.0195 ( Household solid fuels ) + 0.0211 ( Ambient particulate matter ) + 0.1740 ( Ambient ozone ) ,

or

(10) λ ∧ = e x p 10 . 2251 + 1 . 2668 ( EMR ) + 0.9263 ( EUR ) + 1.3313 ( AMR ) + 1.8637 ( SEAR ) + 0.8422 ( WPR ) + 0.0195 ( Household solid fuels ) + 0.0211 ( Ambient particulate matter ) + 0.1740 ( Ambient ozone ) .

The results of the NB regression model using the IRLS method with the FS iterative algorithm under the log-link function are given in Table 8.

Table 8

The results of the NB regression model for the WHO regions and causes of air pollution under the log-link function

Explanatory variables β ˆ s . e ( β ˆ ) P ( Z > ∣ z ∣ ) e x p ( β ˆ ) 95% Confidence interval for e x p ( β )
Lower bound Upper bound
Intercept 10.2252 0.41401 <2 × 10−16*** 27590.6351 12255.9361 62112.1992
EMR 1.2670 0.41010 0.00200** 3.5503 1.5892 7.9315
EUR 0.9263 0.35801 0.00968** 2.5250 1.2518 5.0935
Region of the Americas (AMR) 1.3312 0.41033 0.00118** 3.7857 1.6938 8.4612
SEAR 1.8638 0.60591 0.00210** 6.4479 1.9663 21.1436
WPR 0.8422 0.41904 0.04444* 2.3216 1.0211 5.2781
AFR (Reference Category)
Deaths related to the air pollution from household solid fuels 0.0195 0.00232 <2 × 10−16*** 1.0197 1.0151 1.0243
Deaths related to the air pollution from ambient particulate matter 0.0211 0.00477 9.68 × 10−6*** 1.0213 1.0118 1.0309
Deaths related to the air pollution from ambient ozone 0.1740 0.05361 0.00117** 1.1900 1.0713 1.3219
Theta 0.5193 0.0461 — 1.0197 1.0151 1.0243

By using the IRLS parameter estimates of the NB regression model due to the WHO regions and causes of air pollution given in Table 8, the expected value of the COVID-19 mortality is given as follows:

(11) log ( λ ∧ ) = 10 . 2252 + 1 . 2670 ( EMR ) + 0.9263 ( EUR ) + 1.3312 ( AMR ) + 1.8638 ( SEAR ) + 0.8422 ( WPR ) + 0.0195 ( Household solid fuels ) + 0.0211 ( Ambient particulate matter ) + 0.1740 ( Ambient ozone ) ,

or

(12) λ ∧ = e x p 10 . 2252 + 1 . 2670 ( EMR ) + 0.9263 ( EUR ) + 1.3312 ( AMR ) + 1.8638 ( SEAR ) + 0.8422 ( WPR ) + 0.0195 ( Household solid fuels ) + 0.0211 ( Ambient particulate matter ) + 0.1740 ( Ambient ozone ) .

The results of the Poisson mixed regression model under the log-link function with “country” taken as the random effect using the ML with the Laplace approximation method are given in Table 9.

Table 9

The results of the Poisson mixed regression model for the WHO regions and causes of air pollution under the log-link function

Explanatory variables β ˆ s . e ( β ˆ ) P ( Z > ∣ z ∣ ) e x p ( β ˆ ) 95% Confidence interval for e x p ( β )
Lower bound Upper bound
Intercept 8.1613 0.49932 <2 × 10−16*** 3502.8974 1316.4210 9320.9473
EMR 1.5000 0.54082 0.00555** 4.4816 1.5527 12.9357
EUR 1.5958 0.47801 0.00084*** 4.9325 1.9328 12.5879
Region of the Americas (AMR) 1.1174 0.49575 0.02420*** 3.0568 1.1569 8.0773
SEAR 1.4987 0.74641 0.04466* 4.3003 0.9918 18.6448
WPR 1.0163 0.55517 0.06715 2.7631 0.9307 8.2027
AFR (Reference Category)
Deaths related to the air pollution from household solid fuels 0.0117 0.00294 6.76 × 10−5*** 1.0118 1.0060 1.0176
Deaths related to the air pollution from ambient particulate matter 0.0123 0.00539 0.02213*** 1.0124 1.0018 1.0232
Deaths related to the air pollution from ambient ozone 0.1411 0.05218 0.00685*** 1.1515 1.0396 1.2755

By using the ML parameter estimates of the Poisson mixed regression model due to the WHO regions and causes of air pollution given in Table 9, the expected value of the COVID-19 mortality is given as follows:

(13) log ( λ ∧ ) = 8 . 1613 + 1 . 5000 ( EMR ) + 1.5958 ( EUR ) + 1.1174 ( AMR ) + 1.4987 ( SEAR ) + 1.0163 ( WPR ) + 0.0117 ( Household solid fuels ) + 0.0123 ( Ambient particulate matter ) + 0.1411 ( Ambient ozone ) ,

or

(14) λ ∧ = e x p 8 . 1613 + 1 . 5000 ( EMR ) + 1.5958 ( EUR ) + 1.1174 ( AMR ) + 1.4987 ( SEAR ) + 1.0163 ( WPR ) + 0.0117 ( Household solid fuels ) + 0.0123 ( Ambient particulate matter ) + 0.1411 ( Ambient ozone ) .

The results of the geometric mixed regression model under the log-link function with “country” taken as the random effect using the ML with the Laplace approximation method are given in Table 10.

Table 10

The results of the geometric mixed regression model for the WHO regions and causes of air pollution under the log-link function

Explanatory variables β ˆ s . e ( β ˆ ) P ( Z > ∣ z ∣ ) e x p ( β ˆ ) 95% Confidence interval for e x p ( β )
Lower bound Upper bound
Intercept 8.6367 0.43030 <2 × 10−16*** 5634.4754 2424.2490 13095.7314
EMR 1.4856 0.46609 0.00144** 4.4174 1.7718 11.0132
EUR 1.4989 0.41194 0.00027*** 4.4769 1.9968 10.0374
Region of the Americas (AMR) 1.2692 0.42722 0.00297*** 3.5581 1.5401 8.2201
SEAR 1.8713 0.64490 0.00371** 6.4970 1.8355 22.9965
WPR 1.0122 0.47846 0.03438* 2.7517 1.0773 7.0287
AFR (Reference Category)
Deaths related to the air pollution from household solid fuels 0.0135 0.00254 1 × 10−7*** 1.0136 1.0086 1.0186
Deaths related to the air pollution from ambient particulate matter 0.0150 0.00464 0.00124** 1.0151 1.0059 1.0244
Deaths related to the air pollution from ambient ozone 0.1558 0.04497 0.00053*** 1.1685 1.0700 1.2762

By using the ML parameter estimates of the geometric mixed regression model due to the WHO regions and causes of air pollution given in Table 10, the expected value of COVID-19 mortality is given as follows:

(15) log ( λ ∧ ) = 8 . 6367 + 1 . 4856 ( EMR ) + 1.4989 ( EUR ) + 1.2692 ( AMR ) + 1.8713 ( SEAR ) + 1.0122 ( WPR ) + 0.0135 ( household solid fuels ) + 0.0150 ( ambient particulate matter ) + 0.1558 ( ambient ozone ) ,

or

(16) λ ∧ = e x p 8 . 6367 + 1 . 4856 ( EMR ) + 1.4989 ( EUR ) + 1.2692 ( AMR ) + 1.8713 ( SEAR ) + 1.0122 ( WPR ) + 0.0135 ( household solid fuels ) + 0.0150 ( ambient particulate matter ) + 0.1558 ( ambient ozone ) .

The results of the NB mixed regression model under the log-link function with “country” taken as the random effect using the ML with the Laplace approximation method are given in Table 11.

Table 11

The results of the NB mixed regression model for the WHO regions and causes of air pollution under the log-link function

Explanatory variables β ˆ s . e ( β ˆ ) P ( Z > ∣ z ∣ ) e x p ( β ˆ ) 95% Confidence interval for e x p ( β )
Lower bound Upper bound
Intercept 8.9878 0.52340 <2 × 10−16*** 8005.1883 2869.7675 22330.3946
EMR 1.4785 0.51282 0.00394** 4.3864 1.6054 11.9848
EUR 1.4853 0.46450 0.00139* 4.4163 1.7769 10.9763
Region of the Americas (AMR) 1.2771 0.49630 0.01007* 3.5863 1.3558 9.4865
SEAR 1.8683 0.74817 0.01252* 6.4770 1.4946 28.0689
WPR 1.0825 0.52966 0.04097* 2.7373 0.9693 7.7299
AFR (Reference Category)
Deaths related to the air pollution from household solid fuels 0.0137 0.00297 4.24 × 10−6*** 1.0137 1.0079 1.0197
Deaths related to the air pollution from ambient particulate matter 0.0152 0.00542 0.00512** 1.0153 1.0046 1.0261
Deaths related to the air pollution from ambient ozone 0.1570 0.06525 0.01608* 1.1700 1.0296 1.3297
Theta 0.881 0.2561 — — 0.3790 1.3830

By using the ML parameter estimates of the NB mixed regression model due to the WHO regions and causes of air pollution given in Table 11, the expected value of the COVID-19 mortality is given as follows:

(17) log ( λ ∧ ) = 8 . 9878 + 1 . 4785 ( EMR ) + 1.4853 ( EUR ) + 1.2771 ( AMR ) + 1.8683 ( SEAR ) + 1.0825 ( WPR ) + 0.0137 ( household solid fuels ) + 0.0152 ( ambient particulate matter ) + 0.1570 ( ambient ozone ) ,

or

(18) λ ∧ = e x p 8 . 9878 + 1 . 4785 ( EMR ) + 1.4853 ( EUR ) + 1.2771 ( AMR ) + 1.8683 ( SEAR ) + 1.0825 ( WPR ) + 0.0137 ( household solid fuels ) + 0.0152 ( ambient particulate matter ) + 0.1570 ( ambient ozone ) .

As a main statistical result from this study, the performances of the Poisson, geometric, and NB regression models in the GLM approach, and also Poisson, geometric, and NB mixed regression models in the GLMM approach under the log-link function fitted to the 174 countries’ COVID-19 pandemic and causes of air pollution data are compared using the log-likelihood and IC values given in Table 12.

Table 12

Goodness-of-fit test statistics for the GLM and GLMM approaches under the log-link function due to the COVID-19 pandemic and causes of air pollution data

Goodness-of-fit test statistics Poisson regression model Geometric regression model NB regression model Poisson mixed regression model Geometric mixed regression model NB mixed regression model
Log-likelihood −6,657,003 −1850.006 −1817.05 −1810.277 −1816.68 −1807.756*
AIC 13,314,024 3716.012 3652.1 3635.554 3651.36 3633.512*
AICC 13314025.098 3716.885 3653.198 3636.427 3652.458 3634.610*
BIC 13314026.165 3717.936 3654.265 3637.478 3653.525 3635.677*
CAIC 13314035.165 3725.936 3663.265 3645.478 3662.525 3644.677*

As can be seen from Table 12, the best fitted model is determined as the “NB mixed regression model” from the GLMM approach due to the COVID-19 pandemic and causes of air pollution data according to the maximum value of the log-likelihood, and also the smallest values of the AIC, AICC, BIC, and CAIC information criteria with ‒1807.756, 3633.512, 3634.610, 3635.677, and 3644.677, respectively.

Moreover, variance and standard deviation of the random effect due to the NB mixed regression model for the COVID-19 pandemic and causes of air pollution data are determined as the smallest as given in Table 13.

Table 13

Variance and standard deviation of the random effect belonging to the GLMM approach for the COVID-19 pandemic and causes of air pollution data

GLMMs Variance Standard deviation
Poisson mixed regression model 3.08 1.755
Geometric mixed regression model 1.288 1.135
NB mixed regression model 1.278* 1.131*

*Indicates the minimum variance and standard deviation of the random effect belonging to the GLMMs.

The residual graphs for the NB mixed regression model as the best fitted model due to the COVID-19 pandemic and causes of air pollution data are given in Figure 4.

Figure 4 The residual graphs for the NB mixed regression model due to the COVID-19 pandemic and causes of air pollution data.
Figure 4

The residual graphs for the NB mixed regression model due to the COVID-19 pandemic and causes of air pollution data.

In Figure 4, the scatter graph of the Pearson residuals of the NB mixed regression model for the COVID-19 pandemic data of 174 countries illustrates that the Pearson residuals are randomly dispersed around zero and fall within the range (−1, 2). From the histogram of the Pearson residuals of the model, it can be seen that the Pearson residuals are homogeneously located in the range (−1, 0.1) and gradually decrease in the range (0.1, 2). From the Q–Q plot of the Pearson residuals of the model, it can be seen that the Pearson residuals are closer fitted to the q–q line in the red color. From the box-plot of the Pearson residuals of the model, the Pearson residuals are especially located in the range (−0.6, 0.1), and it can be easily seen that there is no outlier.

5 Conclusion

In this study, global “COVID-19 pandemic” data in terms of the “causes of air pollution” are modeled comparatively using six different models in the GLM and GLMM approaches taking “country” as the random effect. When the studies in the literature are investigated, it has been observed that there are very few studies examining the impact of the “causes of air pollution” on the “COVID-19 pandemic” as a whole as in this study. This study provides superiority to the other studies in the literature in the aspect of evaluating the effects of various causes of air pollution on the total deaths due to the COVID-19 pandemic by six powerful statistical modeling techniques in the GLM and GLMM approaches for the 174 countries taken from the six WHO regions. So in addition to the studies in the literature, these extensive features reveal the originality of this study.

In statistical terms, the performances of the Poisson, geometric, and NB regression models in the GLM approach, and also Poisson, geometric, and NB mixed regression models in the GLMM approach for the COVID-19 pandemic and causes of air pollution data have not been broadly compared as given in this study.

As a conclusion of this study, the following global statistical inferences are obtained by using the NB mixed regression model as a powerful statistical modeling method in the GLMM approach when “country” is taken as the random effect.

The main conclusions from this study, by using the NB mixed regression model under the “log-link” function given in equation (18) with “country” taken as the random effect and the ML with the Laplace approximation method, can be given as follows:

The total number of deaths by the 174 WHO member countries due to the COVID-19 pandemic increases e 0.0137 = 1.0138 , e 0.0152 = 1.0046 , and e 0.1570 = 1.0296 times by 1 death per 100.000 population from air pollution caused by “household solid fuels,” “ambient particulate matter,” and “ambient ozone,” respectively.

On the other hand, the total number of deaths of the 174 WHO member countries due to the COVID-19 pandemic according to the six WHO regions as EMR, EUR, Region of the Americas (AMR), SEAR, and WPR are expected to be e 1.4785 = 4.3864 , e 1.4853 = 4.4163 , e 1.2771 = 3.5862 , e 1.8683 = 6.4773 , and e 1.0825 = 2.9521 times higher than the total number of deaths in the AFR taken as the reference category, respectively.

Based on this study, the future outlook for further investigation will be to explore the effects of panel data structures for different indicators of air pollution on worldwide pandemics and natural disasters as a highlighted topic on the world agenda.

Acknowledgments

Neslihan İyit is the main author of this study. This study is a part of Ferhat Sevim’s M.Sc. Thesis entitled “A Statistical Application of GLMMs on COVID-19 Data” supervised by Assoc.Prof.Dr. Neslihan Iyit submitted by Statistics Department, Graduate School of Natural Sciences, Selcuk University, Konya, Türkiye. The earlier version of this study is presented at the 9th International Conference on Computational and Experimental Science and Engineering (ICCESEN 2022) by the same authors. The authors are grateful to the editors and anonymous referees for their valuable comments and contributions to the improvement of this article.

  1. Funding information: The authors state no funding involved.

  2. Author contributions: All authors have read and approved the manuscript. N.I.; supervising, conceptualizing, writing, reviewing, and editing the original draft preparation, F.S.; literature review, data collection, statistical data analysis, and visualizing the original draft.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Ethical approval: The conducted research is not related to either human or animal use.

  5. Data availability statement: All the data used in this manuscript are available in Our World in Data Repository. [https://ourworldindata.org/coronavirus, https://ourworldindata.org/grapher/death-rates-from-air-pollution]

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Received: 2023-01-02
Revised: 2023-06-24
Accepted: 2023-07-07
Published Online: 2023-08-30

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Characteristics, source, and health risk assessment of aerosol polyaromatic hydrocarbons in the rural and urban regions of western Saudi Arabia
  2. Regular Articles
  3. A network-based correlation research between element electronegativity and node importance
  4. Pomegranate attenuates kidney injury in cyclosporine-induced nephrotoxicity in rats by suppressing oxidative stress
  5. Ab initio study of fundamental properties of XInO3 (X = K, Rb, Cs) perovskites
  6. Responses of feldspathic sandstone and sand-reconstituted soil C and N to freeze–thaw cycles
  7. Robust fractional control based on high gain observers design (RNFC) for a Spirulina maxima culture interfaced with an advanced oxidation process
  8. Study on arsenic speciation and redistribution mechanism in Lonicera japonica plants via synchrotron techniques
  9. Optimization of machining Nilo 36 superalloy parameters in turning operation
  10. Vacuum impregnation pre-treatment: A novel method for incorporating mono- and divalent cations into potato strips to reduce the acrylamide formation in French fries
  11. Characterization of effective constituents in Acanthopanax senticosus fruit for blood deficiency syndrome based on the chinmedomics strategy
  12. Comparative analysis of the metabolites in Pinellia ternata from two producing regions using ultra-high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry
  13. The assessment of environmental parameter along the desalination plants in the Kingdom of Saudi Arabia
  14. Effects of harpin and carbendazim on antioxidant accumulation in young jujube leaves
  15. The effects of in ovo injected with sodium borate on hatching performance and small intestine morphology in broiler chicks
  16. Optimization of cutting forces and surface roughness via ANOVA and grey relational analysis in machining of In718
  17. Essential oils of Origanum compactum Benth: Chemical characterization, in vitro, in silico, antioxidant, and antibacterial activities
  18. Translocation of tungsten(vi) oxide/gadolinium(iii) fluoride in tellurite glasses towards improvement of gamma-ray attenuation features in high-density glass shields
  19. Mechanical properties, elastic moduli, and gamma ray attenuation competencies of some TeO2–WO3–GdF3 glasses: Tailoring WO3–GdF3 substitution toward optimum behavioral state range
  20. Comparison between the CIDR or sponge with hormone injection to induce estrus synchronization for twining and sex preselection in Naimi sheep
  21. Exergetic performance analyses of three different cogeneration plants
  22. Psoralea corylifolia (babchi) seeds enhance proliferation of normal human cultured melanocytes: GC–MS profiling and biological investigation
  23. A novel electrochemical micro-titration method for quantitative evaluation of the DPPH free radical scavenging capacity of caffeic acid
  24. Comparative study between supported bimetallic catalysts for nitrate remediation in water
  25. Persicaline, an alkaloid from Salvadora persica, inhibits proliferation and induces apoptosis and cell-cycle arrest in MCF-7 cells
  26. Determination of nicotine content in locally produced smokeless tobacco (Shammah) samples from Jazan region of Saudi Arabia using a convenient HPLC-MS/MS method
  27. Changes in oxidative stress markers in pediatric burn injury over a 1-week period
  28. Integrated geophysical techniques applied for petroleum basins structural characterization in the central part of the Western Desert, Egypt
  29. The impact of chemical modifications on gamma-ray attenuation properties of some WO3-reinforced tellurite glasses
  30. Microwave and Cs+-assisted chemo selective reaction protocol for synthesizing 2-styryl quinoline biorelevant molecules
  31. Structural, physical, and radiation absorption properties of a significant nuclear power plant component: A comparison between REX-734 and 316L SS austenitic stainless steels
  32. Effect of Moringa oleifera on serum YKL-40 level: In vivo rat periodontitis model
  33. Investigating the impact of CO2 emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions
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  36. Performance analyses of detonation engine cogeneration cycles
  37. Sterols from Centaurea pumilio L. with cell proliferative activity: In vitro and in silico studies
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  40. Energy consumption, mechanical and metallographic properties of cryogenically treated tool steels
  41. Optimization of ultra-high pressure-assisted extraction of total phenols from Eucommia ulmoides leaves by response surface methodology
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  43. Physicochemical and biological properties of carvacrol
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https://doi.org/10.1515/chem-2023-0364
Keywords for this article
COVID-19 pandemic; air pollution; generalized linear mixed model; fixed effect; random effect; Poisson regression; geometric regression; negative binomial regression
Creative Commons
BY 4.0

Articles in the same Issue

  1. Characteristics, source, and health risk assessment of aerosol polyaromatic hydrocarbons in the rural and urban regions of western Saudi Arabia
  2. Regular Articles
  3. A network-based correlation research between element electronegativity and node importance
  4. Pomegranate attenuates kidney injury in cyclosporine-induced nephrotoxicity in rats by suppressing oxidative stress
  5. Ab initio study of fundamental properties of XInO3 (X = K, Rb, Cs) perovskites
  6. Responses of feldspathic sandstone and sand-reconstituted soil C and N to freeze–thaw cycles
  7. Robust fractional control based on high gain observers design (RNFC) for a Spirulina maxima culture interfaced with an advanced oxidation process
  8. Study on arsenic speciation and redistribution mechanism in Lonicera japonica plants via synchrotron techniques
  9. Optimization of machining Nilo 36 superalloy parameters in turning operation
  10. Vacuum impregnation pre-treatment: A novel method for incorporating mono- and divalent cations into potato strips to reduce the acrylamide formation in French fries
  11. Characterization of effective constituents in Acanthopanax senticosus fruit for blood deficiency syndrome based on the chinmedomics strategy
  12. Comparative analysis of the metabolites in Pinellia ternata from two producing regions using ultra-high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry
  13. The assessment of environmental parameter along the desalination plants in the Kingdom of Saudi Arabia
  14. Effects of harpin and carbendazim on antioxidant accumulation in young jujube leaves
  15. The effects of in ovo injected with sodium borate on hatching performance and small intestine morphology in broiler chicks
  16. Optimization of cutting forces and surface roughness via ANOVA and grey relational analysis in machining of In718
  17. Essential oils of Origanum compactum Benth: Chemical characterization, in vitro, in silico, antioxidant, and antibacterial activities
  18. Translocation of tungsten(vi) oxide/gadolinium(iii) fluoride in tellurite glasses towards improvement of gamma-ray attenuation features in high-density glass shields
  19. Mechanical properties, elastic moduli, and gamma ray attenuation competencies of some TeO2–WO3–GdF3 glasses: Tailoring WO3–GdF3 substitution toward optimum behavioral state range
  20. Comparison between the CIDR or sponge with hormone injection to induce estrus synchronization for twining and sex preselection in Naimi sheep
  21. Exergetic performance analyses of three different cogeneration plants
  22. Psoralea corylifolia (babchi) seeds enhance proliferation of normal human cultured melanocytes: GC–MS profiling and biological investigation
  23. A novel electrochemical micro-titration method for quantitative evaluation of the DPPH free radical scavenging capacity of caffeic acid
  24. Comparative study between supported bimetallic catalysts for nitrate remediation in water
  25. Persicaline, an alkaloid from Salvadora persica, inhibits proliferation and induces apoptosis and cell-cycle arrest in MCF-7 cells
  26. Determination of nicotine content in locally produced smokeless tobacco (Shammah) samples from Jazan region of Saudi Arabia using a convenient HPLC-MS/MS method
  27. Changes in oxidative stress markers in pediatric burn injury over a 1-week period
  28. Integrated geophysical techniques applied for petroleum basins structural characterization in the central part of the Western Desert, Egypt
  29. The impact of chemical modifications on gamma-ray attenuation properties of some WO3-reinforced tellurite glasses
  30. Microwave and Cs+-assisted chemo selective reaction protocol for synthesizing 2-styryl quinoline biorelevant molecules
  31. Structural, physical, and radiation absorption properties of a significant nuclear power plant component: A comparison between REX-734 and 316L SS austenitic stainless steels
  32. Effect of Moringa oleifera on serum YKL-40 level: In vivo rat periodontitis model
  33. Investigating the impact of CO2 emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions
  34. Influence of WO3 content on gamma rays attenuation characteristics of phosphate glasses at low energy range
  35. Study on CO2 absorption performance of ternary DES formed based on DEA as promoting factor
  36. Performance analyses of detonation engine cogeneration cycles
  37. Sterols from Centaurea pumilio L. with cell proliferative activity: In vitro and in silico studies
  38. Untargeted metabolomics revealing changes in aroma substances in flue-cured tobacco
  39. Effect of pumpkin enriched with calcium lactate on iron status in an animal model of postmenopausal osteoporosis
  40. Energy consumption, mechanical and metallographic properties of cryogenically treated tool steels
  41. Optimization of ultra-high pressure-assisted extraction of total phenols from Eucommia ulmoides leaves by response surface methodology
  42. Harpin enhances antioxidant nutrient accumulation and decreases enzymatic browning in stored soybean sprouts
  43. Physicochemical and biological properties of carvacrol
  44. Radix puerariae in the treatment of diabetic nephropathy: A network pharmacology analysis and experimental validation
  45. Anti-Alzheimer, antioxidants, glucose-6-phosphate dehydrogenase effects of Taverniera glabra mediated ZnO and Fe2O3 nanoparticles in alloxan-induced diabetic rats
  46. Experimental study on photocatalytic CO2 reduction performance of ZnS/CdS-TiO2 nanotube array thin films
  47. Epoxy-reinforced heavy metal oxides for gamma ray shielding purposes
  48. Black mulberry (Morus nigra L.) fruits: As a medicinal plant rich in human health-promoting compounds
  49. Promising antioxidant and antimicrobial effects of essential oils extracted from fruits of Juniperus thurifera: In vitro and in silico investigations
  50. Chloramine-T-induced oxidation of Rizatriptan Benzoate: An integral chemical and spectroscopic study of products, mechanisms and kinetics
  51. Study on antioxidant and antimicrobial potential of chemically profiled essential oils extracted from Juniperus phoenicea (L.) by use of in vitro and in silico approaches
  52. Screening and characterization of fungal taxol-producing endophytic fungi for evaluation of antimicrobial and anticancer activities
  53. Mineral composition, principal polyphenolic components, and evaluation of the anti-inflammatory, analgesic, and antioxidant properties of Cytisus villosus Pourr leaf extracts
  54. In vitro antiproliferative efficacy of Annona muricata seed and fruit extracts on several cancer cell lines
  55. An experimental study for chemical characterization of artificial anterior cruciate ligament with coated chitosan as biomaterial
  56. Prevalence of residual risks of the transfusion-transmitted infections in Riyadh hospitals: A two-year retrospective study
  57. Computational and experimental investigation of antibacterial and antifungal properties of Nicotiana tabacum extracts
  58. Reinforcement of cementitious mortars with hemp fibers and shives
  59. X-ray shielding properties of bismuth-borate glass doped with rare earth ions
  60. Green supported silver nanoparticles over modified reduced graphene oxide: Investigation of its antioxidant and anti-ovarian cancer effects
  61. Orthogonal synthesis of a versatile building block for dual functionalization of targeting vectors
  62. Thymbra spicata leaf extract driven biogenic synthesis of Au/Fe3O4 nanocomposite and its bio-application in the treatment of different types of leukemia
  63. The role of Ag2O incorporation in nuclear radiation shielding behaviors of the Li2O–Pb3O4–SiO2 glass system: A multi-step characterization study
  64. A stimuli-responsive in situ spray hydrogel co-loaded with naringenin and gentamicin for chronic wounds
  65. Assessment of the impact of γ-irradiation on the piperine content and microbial quality of black pepper
  66. Antioxidant, sensory, and functional properties of low-alcoholic IPA beer with Pinus sylvestris L. shoots addition fermented using unconventional yeast
  67. Screening and optimization of extracellular pectinase produced by Bacillus thuringiensis SH7
  68. Determination of polyphenols in Chinese jujube using ultra-performance liquid chromatography–mass spectrometry
  69. Synergistic effects of harpin and NaCl in determining soybean sprout quality under non-sterile conditions
  70. Field evaluation of different eco-friendly alternative control methods against Panonychus citri [Acari: Tetranychidae] spider mite and its predators in citrus orchards
  71. Exploring the antimicrobial potential of biologically synthesized zero valent iron nanoparticles
  72. NaCl regulates goldfish growth and survival at three food supply levels under hypoxia
  73. An exploration of the physical, optical, mechanical, and radiation shielding properties of PbO–MgO–ZnO–B2O3 glasses
  74. A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and random effects
  75. Treatment activity of the injectable hydrogels loaded with dexamethasone In(iii) complex on glioma by inhibiting the VEGF signaling pathway
  76. An alternative approach for the excess lifetime cancer risk and prediction of radiological parameters
  77. Panax ginseng leaf aqueous extract mediated green synthesis of AgNPs under ultrasound condition and investigation of its anti-lung adenocarcinoma effects
  78. Study of hydrolysis and production of instant ginger (Zingiber officinale) tea
  79. Novel green synthesis of zinc oxide nanoparticles using Salvia rosmarinus extract for treatment of human lung cancer
  80. Evaluation of second trimester plasma lipoxin A4, VEGFR-1, IL-6, and TNF-α levels in pregnant women with gestational diabetes mellitus
  81. Antidiabetic, antioxidant and cytotoxicity activities of ortho- and para-substituted Schiff bases derived from metformin hydrochloride: Validation by molecular docking and in silico ADME studies
  82. Antioxidant, antidiabetic, antiglaucoma, and anticholinergic effects of Tayfi grape (Vitis vinifera): A phytochemical screening by LC-MS/MS analysis
  83. Identification of genetic polymorphisms in the stearoyl CoA desaturase gene and its association with milk quality traits in Najdi sheep
  84. Cold-acclimation effect on cadmium absorption and biosynthesis of polyphenolics, and free proline and photosynthetic pigments in Spirogyra aequinoctialis
  85. Analysis of secondary metabolites in Xinjiang Morus nigra leaves using different extraction methods with UPLC-Q/TOF-MS/MS technology
  86. Nanoarchitectonics and performance evaluation of a Fe3O4-stabilized Pickering emulsion-type differential pressure plugging agent
  87. Investigating pyrolysis characteristics of Shengdong coal through Py-GC/MS
  88. Extraction, phytochemical characterization, and antifungal activity of Salvia rosmarinus extract
  89. Introducing a novel and natural antibiotic for the treatment of oral pathogens: Abelmoschus esculentus green-formulated silver nanoparticles
  90. Optimization of gallic acid-enriched ultrasonic-assisted extraction from mango peels
  91. Effect of gamma rays irradiation in the structure, optical, and electrical properties of samarium doped bismuth titanate ceramics
  92. Combinatory in silico investigation for potential inhibitors from Curcuma sahuynhensis Škorničk. & N.S. Lý volatile phytoconstituents against influenza A hemagglutinin, SARS-CoV-2 main protease, and Omicron-variant spike protein
  93. Physical, mechanical, and gamma ray shielding properties of the Bi2O3–BaO–B2O3–ZnO–As2O3–MgO–Na2O glass system
  94. Twofold interpenetrated 3D Cd(ii) complex: Crystal structure and luminescent property
  95. Study on the microstructure and soil quality variation of composite soil with soft rock and sand
  96. Ancient spring waters still emerging and accessible in the Roman Forum area: Chemical–physical and microbiological characterization
  97. Extraction and characterization of type I collagen from scales of Mexican Biajaiba fish
  98. Finding small molecular compounds to decrease trimethylamine oxide levels in atherosclerosis by virtual screening
  99. Prefatory in silico studies and in vitro insecticidal effect of Nigella sativa (L.) essential oil and its active compound (carvacrol) against the Callosobruchus maculatus adults (Fab), a major pest of chickpea
  100. Polymerized methyl imidazole silver bromide (CH3C6H5AgBr)6: Synthesis, crystal structures, and catalytic activity
  101. Using calcined waste fish bones as a green solid catalyst for biodiesel production from date seed oil
  102. Influence of the addition of WO3 on TeO2–Na2O glass systems in view of the feature of mechanical, optical, and photon attenuation
  103. Naringin ameliorates 5-fluorouracil elicited neurotoxicity by curtailing oxidative stress and iNOS/NF-ĸB/caspase-3 pathway
  104. GC-MS profile of extracts of an endophytic fungus Alternaria and evaluation of its anticancer and antibacterial potentialities
  105. Green synthesis, chemical characterization, and antioxidant and anti-colorectal cancer effects of vanadium nanoparticles
  106. Determination of caffeine content in coffee drinks prepared in some coffee shops in the local market in Jeddah City, Saudi Arabia
  107. A new 3D supramolecular Cu(ii) framework: Crystal structure and photocatalytic characteristics
  108. Bordeaux mixture accelerates ripening, delays senescence, and promotes metabolite accumulation in jujube fruit
  109. Important application value of injectable hydrogels loaded with omeprazole Schiff base complex in the treatment of pancreatitis
  110. Color tunable benzothiadiazole-based small molecules for lightening applications
  111. Investigation of structural, dielectric, impedance, and mechanical properties of hydroxyapatite-modified barium titanate composites for biomedical applications
  112. Metal gel particles loaded with epidermal cell growth factor promote skin wound repair mechanism by regulating miRNA
  113. In vitro exploration of Hypsizygus ulmarius (Bull.) mushroom fruiting bodies: Potential antidiabetic and anti-inflammatory agent
  114. Alteration in the molecular structure of the adenine base exposed to gamma irradiation: An ESR study
  115. Comprehensive study of optical, thermal, and gamma-ray shielding properties of Bi2O3–ZnO–PbO–B2O3 glasses
  116. Lewis acids as co-catalysts in Pd-based catalyzed systems of the octene-1 hydroethoxycarbonylation reaction
  117. Synthesis, Hirshfeld surface analysis, thermal, and selective α-glucosidase inhibitory studies of Schiff base transition metal complexes
  118. Protective properties of AgNPs green-synthesized by Abelmoschus esculentus on retinal damage on the virtue of its anti-inflammatory and antioxidant effects in diabetic rat
  119. Effects of green decorated AgNPs on lignin-modified magnetic nanoparticles mediated by Cydonia on cecal ligation and puncture-induced sepsis
  120. Treatment of gastric cancer by green mediated silver nanoparticles using Pistacia atlantica bark aqueous extract
  121. Preparation of newly developed porcelain ceramics containing WO3 nanoparticles for radiation shielding applications
  122. Utilization of computational methods for the identification of new natural inhibitors of human neutrophil elastase in inflammation therapy
  123. Some anticancer agents as effective glutathione S-transferase (GST) inhibitors
  124. Clay-based bricks’ rich illite mineral for gamma-ray shielding applications: An experimental evaluation of the effect of pressure rates on gamma-ray attenuation parameters
  125. Stability kinetics of orevactaene pigments produced by Epicoccum nigrum in solid-state fermentation
  126. Treatment of denture stomatitis using iron nanoparticles green-synthesized by Silybum marianum extract
  127. Characterization and antioxidant potential of white mustard (Brassica hirta) leaf extract and stabilization of sunflower oil
  128. Characteristics of Langmuir monomolecular monolayers formed by the novel oil blends
  129. Strategies for optimizing the single GdSrFeO4 phase synthesis
  130. Oleic acid and linoleic acid nanosomes boost immunity and provoke cell death via the upregulation of beta-defensin-4 at genetic and epigenetic levels
  131. Unraveling the therapeutic potential of Bombax ceiba roots: A comprehensive study of chemical composition, heavy metal content, antibacterial activity, and in silico analysis
  132. Green synthesis of AgNPs using plant extract and investigation of its anti-human colorectal cancer application
  133. The adsorption of naproxen on adsorbents obtained from pepper stalk extract by green synthesis
  134. Treatment of gastric cancer by silver nanoparticles encapsulated by chitosan polymers mediated by Pistacia atlantica extract under ultrasound condition
  135. In vitro protective and anti-inflammatory effects of Capparis spinosa and its flavonoids profile
  136. Wear and corrosion behavior of TiC and WC coatings deposited on high-speed steels by electro-spark deposition
  137. Therapeutic effects of green-formulated gold nanoparticles by Origanum majorana on spinal cord injury in rats
  138. Melanin antibacterial activity of two new strains, SN1 and SN2, of Exophiala phaeomuriformis against five human pathogens
  139. Evaluation of the analgesic and anesthetic properties of silver nanoparticles supported over biodegradable acacia gum-modified magnetic nanoparticles
  140. Review Articles
  141. Role and mechanism of fruit waste polyphenols in diabetes management
  142. A comprehensive review of non-alkaloidal metabolites from the subfamily Amaryllidoideae (Amaryllidaceae)
  143. Discovery of the chemical constituents, structural characteristics, and pharmacological functions of Chinese caterpillar fungus
  144. Eco-friendly green approach of nickel oxide nanoparticles for biomedical applications
  145. Advances in the pharmaceutical research of curcumin for oral administration
  146. Rapid Communication
  147. Determination of the contents of bioactive compounds in St. John’s wort (Hypericum perforatum): Comparison of commercial and wild samples
  148. Retraction
  149. Retraction of “Two mixed-ligand coordination polymers based on 2,5-thiophenedicarboxylic acid and flexible N-donor ligands: The protective effect on periodontitis via reducing the release of IL-1β and TNF-α”
  150. Topical Issue on Phytochemicals, biological and toxicological analysis of aromatic medicinal plants
  151. Anti-plasmodial potential of selected medicinal plants and a compound Atropine isolated from Eucalyptus obliqua
  152. Anthocyanin extract from black rice attenuates chronic inflammation in DSS-induced colitis mouse model by modulating the gut microbiota
  153. Evaluation of antibiofilm and cytotoxicity effect of Rumex vesicarius methanol extract
  154. Chemical compositions of Litsea umbellata and inhibition activities
  155. Green synthesis, characterization of silver nanoparticles using Rhynchosia capitata leaf extract and their biological activities
  156. GC-MS analysis and antibacterial activities of some plants belonging to the genus Euphorbia on selected bacterial isolates
  157. The abrogative effect of propolis on acrylamide-induced toxicity in male albino rats: Histological study
  158. A phytoconstituent 6-aminoflavone ameliorates lipopolysaccharide-induced oxidative stress mediated synapse and memory dysfunction via p-Akt/NF-kB pathway in albino mice
  159. Anti-diabetic potentials of Sorbaria tomentosa Lindl. Rehder: Phytochemistry (GC-MS analysis), α-amylase, α-glucosidase inhibitory, in vivo hypoglycemic, and biochemical analysis
  160. Assessment of cytotoxic and apoptotic activities of the Cassia angustifolia aqueous extract against SW480 colon cancer
  161. Biochemical analysis, antioxidant, and antibacterial efficacy of the bee propolis extract (Hymenoptera: Apis mellifera) against Staphylococcus aureus-induced infection in BALB/c mice: In vitro and in vivo study
  162. Assessment of essential elements and heavy metals in Saudi Arabian rice samples underwent various processing methods
  163. Two new compounds from leaves of Capparis dongvanensis (Sy, B. H. Quang & D. V. Hai) and inhibition activities
  164. Hydroxyquinoline sulfanilamide ameliorates STZ-induced hyperglycemia-mediated amyleoid beta burden and memory impairment in adult mice
  165. An automated reading of semi-quantitative hemagglutination results in microplates: Micro-assay for plant lectins
  166. Inductively coupled plasma mass spectrometry assessment of essential and toxic trace elements in traditional spices consumed by the population of the Middle Eastern region in their recipes
  167. Phytochemical analysis and anticancer activity of the Pithecellobium dulce seed extract in colorectal cancer cells
  168. Impact of climatic disturbances on the chemical compositions and metabolites of Salvia officinalis
  169. Physicochemical characterization, antioxidant and antifungal activities of essential oils of Urginea maritima and Allium sativum
  170. Phytochemical analysis and antifungal efficiency of Origanum majorana extracts against some phytopathogenic fungi causing tomato damping-off diseases
  171. Special Issue on 4th IC3PE
  172. Graphene quantum dots: A comprehensive overview
  173. Studies on the intercalation of calcium–aluminium layered double hydroxide-MCPA and its controlled release mechanism as a potential green herbicide
  174. Synergetic effect of adsorption and photocatalysis by zinc ferrite-anchored graphitic carbon nitride nanosheet for the removal of ciprofloxacin under visible light irradiation
  175. Exploring anticancer activity of the Indonesian guava leaf (Psidium guajava L.) fraction on various human cancer cell lines in an in vitro cell-based approach
  176. The comparison of gold extraction methods from the rock using thiourea and thiosulfate
  177. Special Issue on Marine environmental sciences and significance of the multidisciplinary approaches
  178. Sorption of alkylphenols and estrogens on microplastics in marine conditions
  179. Cytotoxic ketosteroids from the Red Sea soft coral Dendronephthya sp.
  180. Antibacterial and biofilm prevention metabolites from Acanthophora spicifera
  181. Characteristics, source, and health risk assessment of aerosol polyaromatic hydrocarbons in the rural and urban regions of western Saudi Arabia
  182. Special Issue on Advanced Nanomaterials for Energy, Environmental and Biological Applications - Part II
  183. Green synthesis, characterization, and evaluation of antibacterial activities of cobalt nanoparticles produced by marine fungal species Periconia prolifica
  184. Combustion-mediated sol–gel preparation of cobalt-doped ZnO nanohybrids for the degradation of acid red and antibacterial performance
  185. Perinatal supplementation with selenium nanoparticles modified with ascorbic acid improves hepatotoxicity in rat gestational diabetes
  186. Evaluation and chemical characterization of bioactive secondary metabolites from endophytic fungi associated with the ethnomedicinal plant Bergenia ciliata
  187. Enhancing photovoltaic efficiency with SQI-Br and SQI-I sensitizers: A comparative analysis
  188. Nanostructured p-PbS/p-CuO sulfide/oxide bilayer heterojunction as a promising photoelectrode for hydrogen gas generation
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