Health risk assessments of exposure carbon dioxide among communities and children around Tonasa cement plant, Pangkep Regency, Indonesia. Monte Carlo Simulation (MCS) application

Avaliações de risco à saúde na exposição ao dióxido de carbono entre comunidades e crianças ao redor da fábrica de cimento Tonasa, Pangkep Regency, Indonésia. Aplicação da Simulação de Monte Carlo (MCS)

Abstract

Chronic exposure to carbon dioxide (CO₂) can have a negative influence on one's health and be hazardous to the environment. It could be both directly and indirectly to those communities who are living near the CO₂ point sources. This study aimed to investigate the magnitude of CO₂ level in ambient air and its spatial distribution which then continued to assess the potential health risks posed by communities living surround the site as well as applied the Monte Carlo Simulation (MCS) approach to predict the risks magnitude among adult and children due to CO₂ air pollution from the cement industry activities in Pangkep. This observational analytic study applied health risk assessment due to the CO₂ exposure both to adult and children population. To estimate the non carcinogenic risk, study used the Monte Carlo Simulation model with 10,000 iterations to estimate the risk through the inhalation pathway suffered by communities, as well as analyzing the sensitivity level every single health risk parameter. The highest risks for the adults was in station 7 with 7,641 whereas the lowest risks was in station 3 with 1,194, respectively. Furthermore, the highest risks for child was in station 4 with 498 whereas the lowest one was in station 15 with 32, respectively. Those non carcinogenic HQ were exceed the standard for adult but not at risks for children. The results of the Monte Carlo Simulation that assessed the non risks cancer probability with the 5^th and 95^th percentiles demonstrated that adult population were at value of 0.83 and 1.53 0.83 and 1.53 respectively, that still indicated at low risk for developing adverse health effects among those communities temporarily. However, at the same percentiles children indicated at value of 199 and 388 that indicated at risk for developing adverse health effects among those children. In addition, level of sensitivity analysis result indicated that exposure frequency with (20,9%) for adult and the exposure duration with (25,6%) for children were the most contributing factors to health risks among, respectively. Simulation determines the critical factors with major effects in reducing health risks. The CO₂ magnitude not poses risks to adults, by contrast, children are at risk. Thus, limiting exposure frequency and inhalation of CO₂ levels in the school for children area are highly demanded.

Keywords:
carbon dioxide; health effect; children and adult exposures; Monte Carlo Simulation; health risks

Resumo

A exposição crônica ao dióxido de carbono (CO₂) pode ter uma influência negativa sobre a saúde e ser perigosa para o meio ambiente. Pode ser direta ou indiretamente para as comunidades que vivem perto das fontes pontuais de CO₂. Este estudo teve como objetivo investigar a magnitude do nível de CO₂ no ar ambiente e sua distribuição espacial, que então continuou a avaliar os riscos potenciais à saúde apresentados pelas comunidades que vivem no entorno do local, bem como aplicou a abordagem da Simulação de Monte Carlo (MCS) para prever a magnitude dos riscos entre adultos e crianças devido à poluição do ar por CO₂ das atividades da indústria de cimento em Pangkep. Este estudo analítico observacional aplicou a avaliação de risco à saúde devido à exposição ao CO₂ tanto na população adulta quanto na infantil. Para estimar o risco não cancerígeno, o estudo utilizou o modelo da Simulação de Monte Carlo com 10.000 iterações para estimar o risco pela via inalatória sofrida pelas comunidades, bem como analisar o nível de sensibilidade de cada parâmetro de risco à saúde. O maior risco para os adultos foi na estação 7 com 7.641 enquanto o menor risco foi na estação 3 com 1.194, respectivamente. Além disso, o maior risco para crianças estava na estação 4 com 498 enquanto o menor estava na estação 15 com 32, respectivamente. Aqueles HQ não cancerígenos excederam o padrão para adultos, mas não representam riscos para crianças. Os resultados da Simulação de Monte Carlo que avaliaram a probabilidade de câncer sem riscos com os percentis 5 e 95 demonstraram que a população adulta estava no valor de 0,83 e 1,53 respectivamente, que ainda indicava baixo risco para desenvolver efeitos adversos à saúde entre essas comunidades temporariamente. No entanto, nos mesmos percentis, as crianças indicaram valores de 199 e 388, que mostraram risco de desenvolver efeitos adversos à saúde entre essas crianças. Além disso, o resultado da análise de nível de sensibilidade indicou que a frequência de exposição com 20,9% para adultos e a duração da exposição com 25,6% para crianças foram os fatores que mais contribuíram para riscos à saúde, respectivamente. A simulação determina os fatores críticos com efeitos importantes na redução dos riscos à saúde. A magnitude do CO₂ não oferece riscos aos adultos, ao contrário, as crianças correm risco. Assim, a limitação da frequência de exposição e inalação de níveis de CO₂ na área escolar para crianças é altamente exigida.

Palavras-chave:
dióxido de carbono; efeitos na saúde; exposições de crianças e adultos; Simulação de Monte Carlo; riscos à saúde

1. Introduction

The greenhouse gases reduction emitted both by industry and human activities has bec

ome a great challenging in Indonesia particularly in the area of Industrial site like in Makassar City, Indonesia. It become of a buffer of the climate change mitigation in this century. Particularly carbon dioxide (CO₂) emissions as at the top on the list of the threats to the environment degradation and threat to human health (Lin et al., 2014).

Serious impact of climate change globally due to mainly CO₂ on environment, human society, social economy and human health has highly require a focused and smart effective management regarding the emission of greenhouse gas on every single country (Mi et al., 2017). As a result, intellectual controlling, management in order to reducing CO₂ gas emissions has become a together necessary common goal for all researchers, academia, industries, government, NGO, and the global community (National Development and Reform Commission of China, 2022).

Only by togetherness of planners that vision and commitment the initiated program will be achieved in advance successfully. Furthermore, all countries including Indonesia has also announced a target of reducing its carbon intensity by 50-60% from the 2015 level of reaching the peak of its CO₂ emissions around 2030 and of neutralizing its emissions by 2060 (Rose and Casler, 1996; Zhou and Ang, 2008; Liao et al., 2022). The present studies on the energy consumption and the carbon gas emissions mostly focuses on traditional factors such as emission factors, energy structure, energy intensity, industrial structure and economic activities and there is a consensus that economic activity is the most important factor leading to increased carbon emissions (The State Council the People's Republic of China, 2016). In fact, it is obviously, that in reducing of carbon gas emission need energy intensity which is an necessary factor to be implemented immediately (Lin and Ouyang, 2014).

The very fast rapid progress of industrial manufacture generate great amount of carbon dioxide emissions which then trans boundary to all countries in the world particularly cement industry as the highly demanded production materials (Lin and Ouyang, 2014). Therefore, it is of great research, theoretical and practical significance to explore the factors that influence CO₂ emissions in manufacturing industry and the driving factors of CO₂ emissions in different sectors of the manufacturing industry to reduce CO₂ emissions, develop a low-carbon economy and the realize green sustainable development of manufacturing as soon as possible (Shao et al., 2017).

Recently research results indicated that the industrial activity effect is the main factor that promotes the increase of CO₂ emissions and the energy continuously used effect is the great factor that inhibits an increase in CO₂ emissions, which is consistent with most previous studies (Liu et al., 2019; Ren et al., 2014). Economic development is the core of USA and China’s reform and opening-up in the past 40 years. The manufacturing industry is one of the most important sectors of economic development (Ma et al., 2016; Xu et al., 2011), which have been generate huge income as well as great CO₂ pollutants.

In Asia and cement producers countries including Indonesia, if the cement industry were a country, it would be the third largest polluters in the world - behind China and the US. It produces more CO₂ than aircraft fuel (2.5%) and is not far behind the global agriculture industry (12%) (Rodgers, 2018). Cement industry leaders were met in Poland for the United Nations climate change conference - COP24 - to discuss ways to meet the terms of the Paris Agreement on climate change. To do this, annual emissions from cement need to fall by at least 16% by 2030 (Kompas.com, 2018).

Prospectively, The Intergovernmental Panel on Climate Change - the leading international body on global warming - several last month argued that global average temperature rise should be kept below 1.5 Celsius - not the 2 Celsius as stipulated in the Paris Agreement. This means CO₂ emissions should fall by 45% from 2010 levels by 2030 (Republik Indonesia, 2023). Carbon dioxide in the Earth's atmosphere is considered a trace gas with a concentration of approximately 385 ppm by volume and 582 ppm by mass. The mass of Earth's atmosphere is 5.14×10¹⁸ kg, so the total mass of atmospheric carbon dioxide is 3.0×10¹⁵ kg (3,000). The concentration of carbon dioxide varies seasonally. In urban areas, CO₂ concentrations are generally higher, whereas in closed spaces, carbon dioxide can be up to 10 times greater than the concentration in the open atmosphere. Many of these concentrations result from a number of large operating industries (Mitchell et al., 2018).

The emissions of CO₂ produced form cement industry are resulted through 2 processes. First, CO₂ is produced from the limestone decarbonation when the burned of raw materials occurs stated of CaCO₃ ⇔ CaO + CO₂ . The second results from heavy fuel burning at temperatures above 2000^oC. The cement industry facility contributed a significant quantity of CO₂ pollution through the use of electric fuel and from the by-products of the production process due to the high energy consumption during production.

The concentration of CO₂ in greenhouse gas emissions is the most critical, it has increased from 280 ppm in per-industrial times to over 380 ppm is increasing by more than 2ppm per year driven by global CO₂ emissions which are currently increasing at more than 3.3% per year (Barker et al., 2009; Hegerland et al., 2006; Blamey et al., 2010; Gardarsdottir et al., 2019).

According to the meeting report for the United Nations climatic Change Secretariat, changes in climatic indicators such heat, rainfall, temperature rise, and ocean acidification are what cause the elevated CO2 concentration levels (United Nation Frame of Convention on Climate Change, 2019). Particularly on the pollution spot close to the point source, those changes have the ability to affect both the environment and the health of populations (Rauf et al., 2021).

Calcination is the term for the chemical process that results in the direct emissions of cement. When calcium carbonate, the main component of limestone, is heated, calcium oxide and carbon dioxide are produced. About 50% of all pollution emissions from cement manufacture occur at this step. Whereas the indirect pollutant emissions are resulted by the burning of fossil fuels to heat the kiln that heated by coal, natural gas, or oil, and where the combustion of these fuels generates additional carbon dioxide emissions when it starting to produce electricity which represents around 40% of cement emissions (Boden et al., 2011; Mun et al., 2017).

However, in the near future, due to environmental threats and the phenomenon of global warming, cement plants were allocated to devote budget and time to mitigate the strategies of pollution. Initially, studies were conducted to increase energy efficiency, minimize fuel use, and optimize cost (Benhelal et al., 2012).

Similarly, according to Bakhtyar et al. (2017) almost all emissions burning CO₂ is produced by four different sources fuel is related to production cement, with an estimated 8% of global CO₂ is produced by four different sources emissions. During cement production, clinker is charred at around 1450°C. As a result, ecological contamination and global warming continue growing and natural resources, and shrinking energy from day to day. Clinker production is the most production process results in an increase in energy, of which about 80% energy used in cement manufacturing.

In Tonasa cement industry, Pangkep Regency in Indonesia where this study conducted. The largest source of CO₂ is produced by four main different sources emissions such as the burning of fossil fuels coal, oil and gas in power plants, cars, industrial facilities and emission from sources. Fossil fuels are burned to produce energy from the carbon stored in them. Huge Emission of CO₂ is produced during the extraction and maintenance processes in the Pangkep cement industry.

Therefore, it is of great and significance research to explore the multi health risks assessment factors due to exposure to CO₂ emissions from cement manufacturing industry in Indonesia and the driving factors of CO₂ emissions due to sectors involved in the process to reduce pollutant to communities and school children surround the plant.

The research applied both health risks assessment and the Monte Carlo Simulation approach that is expected to improve public health, environmental air quality and to overcome CO₂ pollution due to air pollution from the Pangkep cement industry activities.

2. Materials and Methods

2.1. Study area

This observational analytic research was conducted at 18 sampling points within the 6 residential areas where 3 points samples each around the Pangkep cement industry. occupies a land area of 715 ha in Biring Ere Village, Bungoro Subdistrict, Pangkep Regency, Indonesia. This Industry was established in 1968 and has capacity of 59,80000 tons of cement per year. The locations are; Bontoa district (three stations), Taraweang district (three stations), district of Taqwa mosque (three stations), Biringere district (three stations), Kampung Sela dist (three stations), and Mangilu dist (three stations), in total of 18 stations. Data collection is carried out on a scale of outside, in community housing areas and in schools around the cement factory.

2.2. Meteorological data

Meteorological data such as the wind speed, wind direction , temperature, and solar radiation was collected from Maros Climatology Station website. The meteorological data were analyzed for estimating the concentrations of CO₂ from the cement industry stacks. The monthly average air temperature, solar radiation, wind speed and direction are applied to trends in meteorological parameters.

2.3. Acceptable Daily Intake (ADI)

The adverse effects posed by pollutant in environmental media can be predicted using the health risk assessment formulation. Air that is contaminated with CO₂ and other pollution may pose a health risk due to human exposure to CO₂ through inhalation and skin (Barker et al., 2009; Hegerland et al., 2006; Blamey et al., 2010; Gardarsdottir et al., 2019), by using the given Equations 1 and 2.

$ADDinh=\frac{Chm\times IRinh\times EF\times ED}{PEF\times BW\times AT}\times {10}^{-6}$ (1)

$HQ=\frac{\sum ADD}{RfC}$ (2)

2.4. Monte Carlo Simulation (MCS)

The simulation of Monte Carlo is applied in order to calculate the potential risks occur on the population subject using a mathematical approach. In the past however, communities’ health risks due exposure of chemical substances were determined merely by calculating the available manual data which frequently culminate only for a single point of risk which may contain ambiguity results.

By using the MCS, all inputted uncertainty factors correlated to risk estimation able to be identified in the calculation results which then attributed as a probability distribution to predict risk or exposure correctly (Astuti et al.., 2021; Rauf et al., 2021). Here on this study, the Monte Carlo Simulation design was performed to forecast the total cancer risk variability and determine the influential factors that contribute to cancer risk in the Pangkep Regency's community. This method used the random parameter of interest (Y), with the Equation 3 (Millard, 2002).

$Y=h\left(X\right)=h\left(X_1,X_{\mathrm{2,}}{X}_{3\dots \dots \dots \dots }{X}_{k}0\right)$ (3)

On this study, X represents the input parameters or the distribution of variables that characterize dose-response and exposure, h represents the elements of random vector X, and Y is the probability distribution. This study used 10,000 repetitions of each health risk variable using Crystal Ball software (11.1.2 ver inc USA) in Microsoft Excel. The final results were presented in the 5th percentile, mean, 95th percentile.

3. Results

3.1. Meteorological data

Meteorology is an important factor in the distribution and accumulation of gas and particulates pollutants. It is the main component in determining the dilution effect of the atmosphere because the released substances are carried by the wind (Goudarzi et al., 2018). Temperature, wind speed, direction, and humidity were compiled from the Indonesian Meteorological, Climatological and Geophysical Agency (BMKG, 2023) online database. During the sampling days, temperatures ranged between 25.6 and 31.08°C, with an average value of 29.02°C, relative humidity ranged between 71.11% and 87%, average 81.6%, and wind speed ranged between 4.0 m/s, average 2.8 m/s. Figure 1 indicates that the wind was heading Southeast and East. These locations are the main residential area in Tukamasea and Bungaeja Villages, with a resultant vector of this visualization at 109°. For six years, wind patterns were fairly consistent despite daily changes in direction (2016- 2021). The wind rose plot was processed with Microsoft Excel software and WRPLOT 4.0.1 from Lakes Environmental Software. It was confirmed that the particulate concentration near cement plant areas in Southeast and East was higher than in others. Therefore, the residents will continuously be exposed to more particulates due to the wind direction.

Figure 1
Annual wind rose around the Pangkep cement plant complex in 2016-2021.

Below is the definitions, units, values as well as the sources used to determine the health risk assessment are presented in Table 1.

Table 1
Definitions, units, and values for the health risk assessment.

No	Definitions	Units	Symbol	Value	Sources
1	CO2 concentration	mg/m3 for ADDinhalation, and mg.kg−1 for ADDoral and ADDdermal	C	Results of Laboratory Analysis	Ernyasih et al. (2023)
2	Inhalation rate	M3.day-1	inhrate	7.6 children; 20 adult	Qadeer et al. (2019)
3	Skin rate	Cm2	SA	-	Mallongi et al., 2022
4	Exposure frequency	Days/year	EF	350	Mallongi et al. (2022)
5	Exposure duration	Year	ED	Site data	United States Environmental Protection Agency (2002)
6	Conversion factor	Kg.mg-1	CF	1x10-6	United States Environmental Protection Agency (2002)
7	Body weight	Kg	BW	Site data	Mallongi et al. (2022)
8	Exposure even	Per day	EV	2	Mallongi et al. (2022)
9	Average time	Days	AT	ED x 365 days	Ernyasih et al. (2023)
10	Dermal absorption	Unit less	ABS	0.001	Mallongi et al. (2022)
11	Skin surface area	cm2	SA	Site data	Mallongi et al. (2022)
12	Skin adherence factor	mg.cm−2 h	SL	0.07, adults 0.2, children	United States Environmental Protection Agency (2002)
13	Reference Concentration	-	RFC	EPA IRIS	Mallongi et al. (2022)

• SA = skin area; EF = Exposure Frequency; ED = Exposure duration; CF = Conversion factor; BW = body weight; EV = exposure even; AT = average time; ABS = absorption factor (unitless); SA = skin area; SL = Skin adherence factor; RFC = reference concentration.

3.2. Carbon dioxide emissions by the cement sector

Currently, cement production is considered as responsible for approximately 7.4% of the global carbon dioxide emission (2.9 Gtons in 2016). Thus, the cement industry will play a key role in reducing carbon dioxide emissions to minimize climate change. The cement industry has hugely reduced the carbon dioxide emissions per ton of cement from the last century (Figure 2) (Bakhtyar et al., 2017), but the cement production is growing worldwide due to the economic growth, notably in developing countries.

Figure 2
Cement production in some countries.

The following is the concentration and risk assessment at 18 stations in 6 sub-districts of Pangkep Regency, Indonesia, presented in Table 2

Table 2
Concentration of CO_{2 ,} Risks Assessment and the Location for 18 stations in Six Districts in Pangkep Regency, Indonesia

No	Study Areas	CO2 Concentration µg/m3	HQ Inhalation Risks Assessment (CO2)		Locations
			Adult	Child	Southern	Eastern
1	Bontoa dist.1	143.23	5.232	0.225	04 ° 48'07.9 "	119 ° 35'44.0"
2	Bontoa dist.2	152.92	7.063	0.350	04 ° 48'04.9 "	119 ° 35'23.0"
3	Bontoa dist.3	141.18	1.194	0.343	04 ° 48'11.9 "	119 ° 35'17.0"
4	Taraweang dist.1	108.31	4.345	0.498	04 ° 46'37.9 "	119 ° 36'08.5"
5	Taraweang dist.2	120.22	3.043	0.122	04 ° 46'32.9 "	119 ° 36'07.5"
6	Taraweang dist.3	110.15	6.878	0.321	04 ° 46'28.9 "	119 ° 36'09.5"
7	Taqwa mosq dist.1	123.22	7.642	0.234	04 ° 47'08.0 "	119 ° 36'29.5"
8	Taqwa mosq dist.2	129.67	6.456	0.497	04 ° 47'06.0 "	119 ° 36'26.5"
9	Taqwa mosq dist.3	122.17	7.548	0.434	04 ° 47'02.0 "	119 ° 36'18.5"
10	Biringere dist.1	110.19	5.251	0.164	04 ° 46'43.6 "	119 ° 36'50.6"
11	Biringere distr.2	99.77	4.432	0.267	04 ° 46'39.6 "	119 ° 36'43.6"
12	Biringere dist.3	89.67	5.263	0.782	04 ° 46'35.6 "	119 ° 36'37.6"
13	Kampung sela dist.1	91.23	3.261	0.413	04 ° 46'47.0 "	119 ° 37'40.7"
14	Kampung sela dist.2	96.85	1.416	0.345	04 ° 46'43.0 "	119 ° 37'37.7"
15	Kampung sela dist.3	99.68	2.564	0.032	04 ° 46'39.0 "	119 ° 37'29.7"
16	Mangilu dist.1	102.15	2.678	0.242	04 ° 47'48.3 "	119 ° 38'22.3"
17	Mangilu dist.2	99.28	1.759	0.412	04 ° 47'45.3 "	119 ° 38'17.3"
18	Mangilu dist.3	87.73	2.181	0.346	04 ° 47'40.3 "	119 ° 38'13.3"

• HQ = Hazard Quotient.

The higher production growth of cement, the bigger CO₂emissions are increasing too. According to An et al. (2019) the new technology of Carbon capture and storage is the most feasible option to reduce the acidification and acid rain in water resources, as its known that turbidity, heat increase and other health impacts are critical concerns that children and adults have experience.

3.3. Monte Carlo Simulation

The Monte Carlo Simulation (MCS) model is an advanced, a sophisticated and have a high validity method that result exact precise number or risk point forecast. The association between the exposure frequency and health or ecological impact determine the probability risk of health or ecological effect according to the observed levels of exposure and characterizes the uncertainty factor associated with the estimated risk.

The application of MCS on this study assuring the assessment of factors uncertainty related to the estimated risk is well identified as a probability distribution to estimate risk or the exposure. It evolving huge numbers of random from some distribution of specified theoretical probability and can investigate the variables that most sensitive leading to health risk. The simulation study is operated by ORACLE inc software, (Crystal ball version 11.1.2) in Microsoft Excel 2019, USA (Mallongi et al., 2023; Ratna et al., 2023; Goudarzi et al., 2018).

3.4. HQ adult and children

Based on the Monte Carlo Simulation (MCS) results, in Figure 3, the probability of cancer risk occurrence at the 5th and 95th percentiles among adult communities were 0.83 and 1.53 that indicated low cancer risks. The percentiles for children had 388, respectively, that show a potential of cancer risks occurrence. This due to the children exposure duration for children more frequently as they play more out their home. Those condition indicating that CO₂ exposure in the Pangkep regency population has a low risk of cancer for adults which was contrast to children that have higher possibility to be risks. This also show that the greater of CO₂ value, the greater potential health risk to be suffered to communities both adults and children

Figure 3
The Predicted probability of CO₂ risks for adult (a) and children (b) population in the area of Cement Plant, Pangkep Regency, Indonesia.

According to the sensitivity chart in Figure 4a, exposure frequency (EF) of CO (20.4%) and inhalation rate (IR) were the two most important factors for escalating health issues in adults. In contrast, in children the exposure duration (ED) of CO (25.6%) and EF (25.1%) made the largest contributions. This indicates that exposure to CO pollution and their concentration had the greatest influence on people's ability to acquire negative health impacts.

Figure 4
(a) Adult and (b) child HQ Sensitivity.

Therefore, the best-suited scenarios concentrate on minimizing inhalation rates and exposure times on the site, as well as on routinely monitoring industrial emissions close to residential areas. This shows that the likelihood that inhabitants may be at risk increases as the frequency of discrete exposure occurrences increases. As a result, both kids and adults should limit their activities and use protective gear to reduce their interaction with air CO pollution that may include hazardous compounds. Body weight (BW) made a negative contribution, indicating that it had little impact and may be disregarded.

4. Conclusion

Adults are not at risk from the CO2 magnitude, but children are. Therefore, it is imperative to reduce exposure frequency and CO2 levels in the vicinity of schools for youngsters. Children and adults should reduce their activities and use protection to decrease their contact with the air CO pollution that possibly contains harmful materials.

4.1. Paper context

This participatory research project conducted jointly with the local communities leader and school in assisting the data collection and method of sampling collection. Research aimed investigate the magnitude of CO2 concentration out side the cement Plant such as in front of communities’ house and in the near by school children. Then this study also assessed the potential risks suffered from CO inhalation both to adults and children, here we develop health risks assessment risk calculation model that prepared by EPA. At the end this research applied Monte Carlo Simulation (MCS) to estimate the various risks factors with its numeric value and set the sensitivity level of each parameter to indicated which parameter gave the highest contribution the Risk occurrence due to CO emission inhalation. Data gathered for this manuscript can be retrieved by contacting the corresponding authors.

Ethics and consent

Ethical approval for the study was granted by the Faculty of Public Health, University Muhammadiyah Jakarta (No: 10.712.B/KEPK-FKMUMJ/X/2022) .

Acknowledgement

The authors would like to thank to M. Faizal and Mr. Wildan, M.Kes for their assistance and support during the site visit and data collection. We all also would like to appreciate our high respect to all anonymous reviewers whose constructive criticism and suggestions have greatly improved the quality of this paper manuscript.

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Publication Dates

• Publication in this collection
  25 Sept 2023
• Date of issue
  2023

History

• Received
  24 Jan 2023
• Accepted
  03 July 2023