A Temporal Analysis of Cancer Risk Associated with Cadmium and Arsenic Found in PM 2.5 in the University of Ilorin and its Environs; A probabilistic approach

A Temporal Analysis of Cancer Risk Associated with Cadmium and Arsenic Found in PM 2.5 in the University of Ilorin and its Environs; A probabilistic approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Temporal Analysis of Cancer Risk Associated with Cadmium and Arsenic Found in PM 2.5 in the University of Ilorin and its Environs; A probabilistic approach O. A. Falaiye, S. Nwabachili, M. M. Orosun, T. B. Ajibola, O. E. Abiye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5837506/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract As a result of the rapid industrialization of various cities in Nigeria, rural-urban migration, and the rapid increase in population, there has been a spike in the level of pollutants getting into the atmosphere which is majorly a result of various anthropogenic factors such as combustion of fuel, usage of vehicles, indiscriminate burning of refuse, just to mention a few. Air pollution has become a thing of concern due to the health effects associated with it such as Chronic Obstructive Pulmonary Disease (COPD), Lung Cancer, asthma, etc. This study aimed at analyzing cancer risks associated with PTMs (Cd and As) found PM 2.5 , Using a probabilistic approach. The concentrations of the PTMs that were analysed were collected from the Surface Particulate Matter Network (SPARTAN) which is mounted a the Department of Physics, University of Ilorin. The mean Concentration of Cd collected from this site ranged from 0.000377μg/m 3 and 0.00767μg/m 3 with the lowest being recorded in March, and highest in November. For As, the concentration ranged from 8.67e-05μg/m 3 and 0.00329μg/m 3 with the highest being recorded in November, and the lowest in March. Cd recorded concentrations that were higher than the WHO and EU set limits, in July (0.00648 μg/m 3 ), August (0.007487 μg/m 3 ), and November (0.00767 μg/m3). From the Monte Carlo Simulation for Cancer Risk assessment, it was found out that for Cd, the highest level of risk via inhalation was recorded in August with a value of 6.08e-03, and the least was recorded in March with a value of 3.06e-04 these values were a cause for concern. Via dermal contact, the least mean risk was recorded in October with a value of 1.47e-06, and the highest was recorded in August with a value of 2.94e-05 which were all in the safe zone. For As, via Inhalation the highest was recorded in November with a value of 2.36e-03, and the least was recorded in April with a value of 1.56e-04, while via dermal contact, the highest was recorded in November with a value of 3.81e-07, and the least was in April with a value of 1.01e-08. These results therefore indicated that via inhalation, both PTMs showed a great Cancer Risk, but the reverse was the case for dermal contact. Arsenic Cadmium PM2.5 Monte Carlo Simulation (MCS) Air quality Atmosphere Potentially Toxic Metals (PTMs) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1.0. INTRODUCTION As a result of rapid urbanization, air pollution (a heterogeneous mixture of gases, vapor, and particles) has become in recent years, a major concern associated with the world (Soleimani,2018; Ediagbonya and Olabiyi, 2024 ). A major constituent of air pollution is the Particulate Matter (PMs) which are classified into PM 2.5, and PM 10 . These PMs are made up of pollutants that are either organic or inorganic such as Potentially Toxic Metals (PTMs) (Shah et al., 2006 ; Ediagbonya, 2022). These PTMs are classified into 2 groups, the cancer-causing causing which include Nickel, Arsenic, Cadmium, and Chromium, according to the International Agency for Research on Cancer (IARC); and the non-cancer-causing PTMs. Long-term exposure to these PTMs is highly injurious and can cause severe environmental pollution, affect, through the bioaccumulation process, the natural geochemical properties, as well as threaten human well-being and that of animals. These PTMs are highly toxic, have a high level of concealment, are irreversible, persistent (because they are not broken down), and highly biologically accumulative i.e. they accumulate in the environment (Soleimani,2018). PM is introduced into the atmosphere by either Natural methods such as dust storms, or anthropogenic methods such as industrial activities (Soleimani,2018). In Urban areas, the emission of PTMs as a sub of PM 2.5 is directly related to various mobile (Squires et al., 2019; Rajé et al., 2018 ; Comert et al., 2020) and stationary sources which include industrial population, traffic emission (exhaust, and non-exhaust), weathering of building and pavement, combustion of fossil fuels, garbage, and Tobacco, and atmospheric deposition (Gunawardana et al. , 2012; Manasreh, 2010 ). The various pathways by which these PTMs in the atmosphere get into the body include ingestion i.e. eating food that is exposed to these PTMs in the atmosphere such as food gotten from roadside vendors, inhalation, breathing exhaust fumes, and finally dermal contact i.e. absorption route. Inhalation and absorption routes are the most common ways by which these PTMs enter the human body (Goudarzi et al., 2018). A lot of studies to determine the concentration of various PTMs in PM 2.5 have been carried out. In Rio de Janeiro, Ventura et al. ( 2017 ) studied the concentrations of PTMs in PM 2.5 samples, and their result showed three (3) groups of metals including; group 1 (Cu, Cd, Pb), group 2 (Cr, Mn, Ni, V, and Zn), and group 3 (Na, K, Ca, Ti, Al, Mg, Fe) originated from industrial areas, traffic, and natural sources. Li et al. ( 2016 ) investigated the constituents of both PM 2.5 and PM 10 from an industrial layout in China, and found that there was a lifetime lung cancer due to the presence of Cr, Cd, and Co. Hadad et al. ( 2003 ) measured to determine the distribution of suspended PMs associated with some PTMs (Pb, Br, V, Ca, Al, Fe, Cu, Cr, Mn, Sc, and Zn) at four (4) locations in Shiraz, Iran. They found out that the traffic pollution in Shiraz was higher than the WHO, and EPA standards. In the Sina district of Tehran, Kermani et al. (2016) determined the level of PM 2.5 and Pb, Cd, Cr, Ni, Hg, As, and Zn in the ambient air. It was revealed that the concentrations of PM 2.5 and metals such as Cd were higher than the US-EPA standard. Soleimani et al. ( 2018 ) analyzed the content of PTMs in PM 2.5 in the atmospheric monitoring stations in Isfahan City Iran, in different seasons between March 2014, and March 2015. They reported that As, Cd, Ni, Cr, and Cu exceeded the US-EPA standard. In Nigeria, studies to determine the concentration of PTMs in Particulate Matter have been carried out. Abiye et al. ( 2013 ) carried out research to assess the mass concentration and elemental characterization of airborne PM 2.5 and PM 10 in Abuja. The concentration of (V, Co, Ni, Cd, Zn, and Pb) was determined. It was reported that the PM 2.5 /PM 10 mass ratio was well within the WHO-specified range. Falaiye et al. ( 2021 ), carried out a characterization of Atmospheric Particulate matter from urban-traffic sources in Ilorin. The concentrations of the heavy metals in PM 2.5 were excessively high, although not more than the WHO standard, but could impact the health of people living in that environment significantly. Ezeh et al. ( 2018 ) monitored the total atmospheric deposit (TAD) around a smelting plant in Ile Ife, Nigeria, to assess the contribution of the industry to Nigerian air-shed pollution. They detected 23 elements, with Fe having the highest concentration and Na having the least concentration. Fe, Zn, Pb, and Mn, were enriched in that environment. They concluded that the smelting activity pose a great hazard to receptors around the area. Ezeh et al. ( 2019 ) analyzed PM 2.5−10 samples collected from industrial, low-, and high-density areas in Lagos state, Nigeria for 12 months. The analyses were done using the ion beam analyses technique, Particle Induced X-ray Emission (PIXE), and Positive Matrix Factorization (PMF) were used to apportion sources to the PTMs that were found in the PM 2.5 . It was reported that there was a gross violation of both local and international guidelines. They reported that PMF revealed that soil dust, physical construction, and industrial activities were the major emissions of PM 2.5−10 and could lead to negative health implications for the inhabitants of Lagos State, Nigeria. However, these studies, especially the ones carried out in Nigeria have failed to answer the question of “to what extent can the inhaling of these trace elements affect the populace? i.e. what is the Cancer Risk associated with these PTMs”. There is, therefore, a need to assess the level of risk associated with PTMs found in PM 2.5 collected from an area, this is because they can adversely affect the respiratory systems of human beings (Fann and Risley 2013 ; Terrouche et al.,2016; Burns et al., 2020 ; Patel et al., 2020 ) hence, the importance of this work. This work aims not just to assess the concentration levels of these PTMs, but also the level of Cancer Risk associated with Cadmium, and Arsenic found in PM 2.5 . For the year 2019, using the SPARTAN data, situated at the University of Ilorin, Nigeria, using a probabilistic approach. According to the IARC, Cd and As have been classified as PTMs that are carcinogens. This study was able to carry out an assessment on the level of concentration of these PTMs, as well as a comparative analysis with threshold limits placed by governing bodies, WHO, and the EU. A Cancer Risk assessment was carried out by using a probabilistic method (the Monte Carlo Simulation) (Mohanraj et al., 2004 ). 2.0. METHODOLOGY 2.1. Study Area Ilorin, the capital city of Kwara State, Nigeria, is located on latitude 8°29’47” N and longitude 4°32'30"E. The climate of Ilorin is characterized by both wet and dry seasons. The temperature in Ilorin ranges from 33°C to 34°C from November to January, while from February to April; the value ranges between 34°C to 53°C (Ilorin Atlas, 1982). The monthly mean temperatures are very high varying from 25.0°C to 28.9°C. The total amount of annual rainfall in the area is about 1200 mm (1971–2000) (Falaiye et al, 2021 ). The University of Ilorin which is the only federal University in the state is located on Tanke Road, and its map is shown in the Fig. 1. The University has a land mass of 150,000,000 m 2 , and a total of 108 academic departments, according to ebaoxford.co.uk as accessed on the 12th of December, 2022. In the year 2019, which is the year of study, the University of Ilorin had a population of 55,242 students, and 4,218 staff consisting of 2,718 non-academic staff, and 1,504 academic staff (ebaoxford.co.uk). Figure 1 shows a map of the study area. 2.2. Data collection The data (monthly concentration) used was collected from the SPARTAN (Surface Particulate Matter Network) which is available at the SPARTAN website ( www.spartan-network.org ). SPARTAN is installed at the top of the physics department (Block 4), University of Ilorin, Ilorin, Nigeria, with a coordinate of longitude 8.484 o N, latitude 4.674 o E, and elevation 400m. The data was collected from the website. The data was in comma-separated value (CSV) format. It was imported into the Excel sheet and converted into usable data for analysis. The frequency of the data collected was monthly, for 2019. 2.3. Health Risk Assessment The methods recommended by the US-EPA were used to assess the carcinogenic risks of heavy metals in PM 2.5 , which can get into the body through three (3) major pathways which include ingestion, inhalation, and dermal contact, however for this study only two pathways were considered which is the inhalation, and dermal contact, as these are the major ways by which these PTMs in PM 2.5 get into the body (Goudarzi et al., 2018). These methods include; Exposure Concentration (EC) through inhalation, measured in µg/m 3 , and Dermally Absorbed Dose (DAD). These were calculated using equations 1 , and 2 respectively (Xu et al. , 2020). $$\:EC=\frac{C\times\:ET\times\:EF\times\:ED}{{AT}_{n}}$$ 1 $$\:DAD=\frac{C\times\:SA\times\:AF\times\:ABS\times\:EF\times\:ED\times\:CF}{BW\times\:AT}$$ 2 Table 1 shows the summary of the input parameters for the calculation of the various health risks. Tables 2 and 3 show the Summary of the CDI, EC, and DAD. Table 1 Input parameters and abbreviations for cancer and non-cancer exposure assessment (Wang et al., 2018 ) Parameter Notation Unit Value Metal Concentration in PM 2.5 C \(\:\mu\:g/{m}^{3}\) Table 2 Average lifetime AT n hours ED \(\:\times\:365\times\:24\) (for non-carcinogens) \(\:53\times\:365\times\:24\) (for carcinogens) Average lifetime AT days ED \(\:\times\:365\) (for non-carcinogens) \(\:53\times\:365\:\) (for carcinogens) Body Weight BW Kg 70 Conversion Factor CF mg/Kg 10 − 6 Exposure Duration ED year 24 Exposure Frequency EF days/year 365 Exposure time ET h/day 24 Ingestion Rate IR mg/day 100 Skin Surface area adherence that contacts the airborne particles SA Cm 2 5700 Skin adherence factor for airborne particulate AF mg/cm 2 0.07 Dermal Absorption Factor ABS 0.03(As), 0.001(Cd) 2.4. Carcinogenic Risk (CR) The Carcinogenic risks (CRs), which are defined as the probability for an individual to develop any kind of cancer by lifelong exposure to carcinogenic hazards, were calculated using Eq. 5 . Cancer risk greater than \(\:1\times\:{10}^{-4}\) pose a higher concentration risk, due to the high value. Risks less than \(\:1\times\:{10}^{-6}\) are considered not to pose any cancer risk. The acceptable range is \(\:1\times\:{10}^{-4}and\:1\times\:{10}^{-6}\) . Table 2 classifies the various risk values into 7 levels according to the Delphi Method (Orosun et al. , 2021). $$\:CR=\:DAD\times\:\left(\frac{S{F}_{0}}{GIABS}\right)=IUR\times\:EC$$ 5 RfDo is the oral reference dose (mg/kg/day), RfCi is the inhalation reference concentration (mg/m 3 ), GIABS means the gastrointestinal absorption factor, SFo is the oral slope factor (mg/kg/ day) −1 , and IUR meant inhalation unit risk (mg/m 3 ) −1 The values of SF 0 , GIABS, and IUR for the PTMs are stated in Table 3 below. Table 2 levels and values of assessment standard according to Haque et al. ( 2018 ), Li et al. ( 2017 ), and Orosun et al. (2021). Risk level Range of Risk level Acceptability Level 1 (extremely low risk) < 10 − 6 Completely accept Level 2 (Low risk) 10 − 6 , -10 − 5 Not eager to care about the probable risk Level 3 (Low medium risk) 10 − 5 , -5 \(\:\times\:{10}^{-5}\) Not to be mindful of the risk. Level 4 (Medium risk) \(\:5\times\:{10}^{-5},-{10}^{-4}\) Worry about probable risk. Level 5 (Medium High risk) \(\:{10}^{-4},-5\times\:{10}^{-4}\) Care about the risk, and willing to invest. Level 6 (High risk) \(\:5\times\:{10}^{-4},-{10}^{-3}\) Pay attention and take action to solve it. Level 7 (Extremely High Risk) > 10 − 3 Must solve it Table 3 Input Parameters for Cancer Risk Assessment PTMs SF 0 IUR GIABS Cd 0.38 0.0018 0.025 As 1.5 0.004 1 2.5. Monte Carlo Simulation The evaluation of risk assessment becomes, in a way, complicated. This is due to the uncertainties brought about by the following; Ingestion rate of toxic substance by the exposed individual; the body weight of the individual; concentration of the toxic element collected in the sample; and the Carcinogenic slope factor. The uncertainties brought minimizing uncertainty, by these sources can cause either an underestimation or overestimation of cancer risk. The overestimation of cancer risk can lead to a waste of limited resources on unnecessary remediation, while an underestimation of the cancer risk can cause preventable health risks to the populace. Without simulation, estimating health risk becomes difficult, in that, it will be difficult to ascertain the likelihood that the populace would be at risk. A probabilistic approach using the Monte Carlo Simulation (MCS) which has the advantage of handling uncertainties and variabilities was used in this study to obtain a more realistic risk that is associated with the toxic pollutant. The Simulations for monthly risks were carried out using the Python program. The simulation was carried out 1000 times. 3.0. RESULT AND DISCUSSION 3.1. The Seasonal Variation Of The EC, DAD For Cd: Implications For A Monitoring And Mitigation That Is Continuous. Table 4 shows the summary of the EC and DAD for Cd. The following are the results. The highest EC level was recorded in August with a value of 7.49μg/m 3 , while the lowest was recorded in March with a value of 0.38 μg/m 3 followed by April with a value of 0.51 μg/m 3 . In November, Cd was below the detectible limit, which indicated little or no presence. Also, July had a significantly high value, 6.48 μg/m 3 . The highest level of DAD was recorded in August with a value of 4.27E-06 μg/kg/day. March and April recorded the lowest values of 2.15E-07 μg/kg/day and 2.89E-07 μg/kg/day respectively. July also recorded a significantly high value of 3.69E-06 μg/kg/day. EC and DAD recorded their peaks between July and August. This may be attributed to environmental or human activities which may include: Industrial emissions, seasonal weather conditions, as well as atmospheric deposition that may affect the dispersion of Cd. These findings therefore show that there is a need for monitoring that are continuous, as well as continuous assessment which will help in mitigating health associated with Cd. Table 4: Summary of EC (mg/m 3 ) and DAD ( μg/kg/day) for Cd. Month EC DAD Mean SD Mean SD March 0.38 0.096 2.15E-07 5.45E-08 April 0.51 0.14 2.89E-07 8.16E-08 May 1.75 0.56 9.98E-07 3.19E-07 June 1.02 0.27 5.81E-07 1.52E-07 July 6.48 1.69 3.69E-06 9.66E-07 August 7.49 1.51 4.27E-06 8.60E-07 September 1.61 0.33 9.18E-07 1.88E-07 October 3.67 1.32 2.09E-06 7.53E-07 November BDL BDL BDL BDL December 4.4 0.71 2.51E-06 4.06E-07 BDL: Below detectible limit EC: Exposure Concentration DAD: Dermally Absorbed Dose 3.2. The Seasonal Variation Of The EC, DAD For As: Implications For A Monitoring And Mitigation That Is Continuous. Table 5 shows the summary of the EC and DAD for As for the study period. From this table, EC recorded its lowest in April with a value of 0.039 μg/m 3 . The highest was recorded in November with a value of 1.49 μg/m 3 and in December with a value of 1.47 μg/m 3 . August recorded a BDL value meaning no contamination was measurable. For the DAD, the lowest was recorded in April also with a value of 6.71E-09 μg/kg/day, the highest was recorded in November as well with a value of 2.55E-07 μg/kg/day, as well as December with a value of 2.52E-07 μg/kg/day. As seen in the table, there was an increase that was significant from October through to December, and their levels were higher than those in the first half. There was variability in May and July which may be a result of pollution events that may be classified as sporadic which could be from emissions that are industrial. It may be due to meteorological conditions, or other environmental factors. November and December, which are in the dry-cold season, also recorded higher As values, which may be attributed to the stoppage in rainfall which in turn causes an accumulation of pollutants, as well as emission from biomass burning. There was a sharp drop in August, this may be because the heavy rainfall that occurred during that period may have cleared the atmosphere of As. The high concentration that was recorded in November and December indicates an exposure to higher health risks. March and April had lower exposure levels. This means that there was reduced health risk in these months which may be due to the dispersion of pollutants that is favourable. From the above analysis, there should be a focus on reducing the level of exposure to As in Peak months, as this will help in reducing health risks associated with As. Table 5: Summary of EC (mg/m 3 ) and DAD ( μg/kg/day) for As. Month EC DAD Mean SD Mean SD March 0.092 0.046 1.57E-08 7.91E-09 April 0.039 0.010 6.71E-09 1.79E-09 May 0.344 0.416 5.88E-08 7.12E-08 June 0.279 0.057 4.78E-08 9.80E-09 July 0.234 0.288 4.00E-08 4.93E-08 August BDL BDL BDL BDL September 0.266 0.138 4.54E-08 2.36E-08 October 0.59 0.211 1.01E-07 3.62E-08 November 1.49 0.218 2.55E-07 3.72E-08 December 1.471 0.475 2.52E-07 8.12E-08 BDL: Below detectible limit EC: Exposure Concentration DAD: Dermally Absorbed Dose 3.3. Temporal Variations in Cd and As Concentrations: Implications for Air Quality and Public Health Table 6 shows the summary of the monthly concentration for the various PTMs being studied. Also, figure 2 shows the plot of the monthly concentration mean of the various PTMs being studied. For Cd, the highest mean concentration was recorded in November with a value of 0.00767, and its lowest value of mean concentration was recorded in March with a value of 0.000377. As recorded its highest mean concentration in November also with a value of 0.00329, and its lowest concentration was recorded in April with a value of 8.67E-05 (all concentrations are measured in μg/m 3 ). We can attribute the high value of the mean monthly concentration, in November as being a result of the fact that the harmattan season just set in, and also the school just resumed thereby causing much vehicular activities. Similarly, the months of March and April recorded the lowest values of concentrations. This may be due to the setting-in of the wet season, and also the school being going on a short break during this period, thereby causing a drastic drop in the activities going on in and around the school. From Figure 2a which shows the trend of Cd concentration for the year 2019, the concentration had an upward trend which further proves that seasons indeed affect the level of concentration of PTMs in the Atmosphere. Comparing both Tables 6a and 7, none of the monthly mean concentrations for As in Table 6a exceeded the WHO standard nor did any exceed the European Union standard. For Cd, the mean monthly concentration for July, August, and November exceeded the standards set by both the WHO and the European Union. These months had values of 0.00648, 0.007487, and 0.00767 all measured in . This goes to say that the mean monthly concentrations of As did not exceed the safe limit thereby posing no threat to people living and working in the vicinity. However, Cd recorded some high values, which pose a threat to the people living and working in the vicinity, just for the said months. Cd is found as a mineral combined with elements such as oxygen (CdO), chlorine (CdC l2 ), or sulfur (CdSO 4 or CdS). It enters the air from mining, industry, and burning coal and household wastes. Particles of Cd in the air can travel long distances before falling to the ground or water (Godwin et al 2013). The concentration of Cd found in PM 2.5 can be attributed to crustal sources (mineral dust) this is because, during the period of this study, there was heavy construction going on very close to the University. Furthermore, it can be attributed to various legal and illegal mining activities going on in and around the environs of Ilorin. Since Cd can travel for very long distances before falling to the ground (Muhibbu-din et al., 2021). Furthermore, biomass burning is another source of Cd as it exits naturally in biomass. This could be a major course of the concentration being higher than normal in the said months, as the University of Ilorin is home to a lot of agricultural activities, possibly, in a bid to prepare their lands for the farming season, a lot of biomass burning could have happened. Figure 2b shows the trend analysis of the concentration of As for the year 2019. The figure shows an upward trend in the concentration of As, indicating the effect of seasons on the concentration. Table 6b shows the concentration level of Cd and As by other studies in some locations in Nigeria. Comparing the monthly mean concentration values in Table 6a with Table 6b. The mean concentration values for every month obtained from this study were all less than the values obtained in Calabar, Aba, Eket, and Port Harcourt all in Nigeria. This could be because all these cities are where crude oil is being mined. Furthermore, the result from this study was compared with that obtained by Muhibbu-din et al. (2021) which carried out their study in traffic-dense areas in Ilorin, as shown in Table 6b. Their results were quite higher than ours even though the studies were carried out in the same city but in different locations. Their studies were around traffic-dense areas, while ours was not dense with so much traffic. This could be a factor in the reason why their results were far higher than the results in this study. Table 6a: Concentration ( of the PTMs being studied Month Cadmium (Cd) Arsenic (As) M ax M in Mean M ax M in Mean March 0.0006 0.00018 0.000377 0.00032 0.00013 0.000203 April 0.00086 0.00025 0.000507 0.0001 0.00006 8.67E-05 May 0.00175 BDL 0.00175 0.00141 0.00011 0.00076 June 0.00102 BDL 0.00102 0.00073 0.00048 0.000617 July 0.00648 BDL 0.00648 0.00113 BDL 0.000517 August 0.01107 0.00448 0.007487 0.0007 BDL BDL September 0.0024 0.00097 0.00161 0.0009 0.00029 0.000587 October 0.00618 0.00047 0.00367 0.00184 0.00099 0.001303 November 0.00767 0.00767 0.00767 0.00363 0.00295 0.00329 December 0.00619 0.00324 0.0044 0.00438 0.00231 0.00325 BDL: Below Detectible Limit Table 6b: Concentration ( Results from similar work around Nigeria Uno et al. (2013) Muhibbu-din et al. (2021) Location Cd As Location Cd As Calabar 0.44 0.33 Geri Alimi 0.0153 0.0581 Aba 0.42 0.37 Offa Garage 0.0253 0.0389 Eket 0.54 0.58 Post office 0.0357 0.0404 Port Harcourt 0.82 1.17 General 0.0527 0.0396 Table 7: Summary of AQI standards PTM WHO (μg/m 3 ) European Union (μg/m 3 ) USEPA (μg/m 3 ) As 10 0.006 ---- Cd 0.005 0.005 ---- 3.4. Cancer Risk Assessment of Arsenic (As) via Inhalation and Dermal Contact: Seasonal Trends and Health Implications Table 8 shows a summary of the monthly Cancer risk associated with As via inhalation and dermal contact. Figures 3a and 3b, shows the visual illustration of these summaries respectively. Figure 3a which is the bar chart showing the variation of CR associated with As via inhalation showed that there were relatively low risks recorded in March and April. The values of these months from Table 8 were 3.68E-04, and 1.57E-04 respectively. There was a gradual increase from May through July and September, and there was no detectable risk in August. There was a significant rise in October, as well as November, and December as shown in Figure 3a, with values of 2.36E-03, 5.96E-03, and 5.89E-03 respectively, as shown in Table 8. Figure 3b shows the CR associated with As via dermal contact. In August, just like inhalation didn’t record any dermal contact risk associated with Cancer. The highest risks were recorded in November and December with values of 3.82E-07, and 3.77E-07, respectively. The lowest was recorded in April with a value of 1.01E-08. The lowest risk being recorded in March to April could be related to the rainfall pattern i.e. these are the months in which the heavy rain sets in. Furthermore, there could have been a reduction in industrial, and human activities, which may be attributed to the lockdown that was due to the COVID-19 pandemic. The BDL value of August may be attributed to various dispersion factors that cause a clearing in the atmosphere. The highest risks that were recorded in October through to December were associated with various factors such as the season of the year (Dry season), as well as an increase in human activities leading to much more combustion (that is when the students resume fully for another session). Furthermore, the area being one where agricultural activities go on, there must have been an increase in biomass burning. Thermal inversion is a process that traps pollutants closer to ground level, and this is a feature often associated with these months. Comparing Zhang et al (2021), who carried out an analysis on the potential health risk of As in the atmosphere, they had a key finding that stated that As levels peaked in late autumn as well as winter, therefore correlating with the spike that was recorded in October through to December in this study. The CR associated with inhalation of As for November and December both having values of 5.96E-03, and 5.89E-03 respectively, when compared to Table 2 fell in the extremely high-risk region. This therefore means that this problem must be solved. The CR for As via dermal contacts was very low all in the 1E-07 range, which means they fall in the extremely low-risk region. Even though November and December had values that are lower than inhalation risks, 3.77E-07, and 3.82E-07 , it remains a matter of concern when chronic exposure is involved as this can lead to skin lesions and carcinogenesis (Weerasundara et al.,2018). Table 8: Summary of the Cancer Risk of As via Inhalation, and Dermal Contact Month Inhalation Dermal Contact Mean SD Mean SD March 3.68E-04 0.0042 2.36E-08 1.25E-16 April 1.57E-04 0.00041 1.01E-08 1.20E-17 May 1.38E-03 0.143 8.83E-08 4.19E-15 June 1.12E-03 0.016 7.16E-08 4.68E-16 July 9.36E-04 0.068 6E-08 1.97E-15 August BDL BDL BDL BDL September 1.06E-03 0.037 6.81E-08 1.074E-15 October 2.36E-03 0.12 1.51E-07 3.65E-15 November 5.96E-03 0.32 3.82E-07 9.49E-15 December 5.89E-03 0.698 3.77E-07 2.04E-14 BDL: Below Detectable Limit. SD: Standard Deviation. CR: This is unitless Table 9 shows a summary of the CR associated with Cd through Inhalation and Dermal Contact. Via inhalation, there is a significant variation with July recording the highest value with a value of 5.28E-03 and August with a value of 6.10E-03, and the lowest being recorded in April with a value of 4.13E-04. These values, when compared with Table 2, fell in the region of extremely high risk, indicating that there was a severe risk associated with Cd via inhalation in all the months that were studied. Furthermore, from these values, it can be said that there is a potential increase in airborne concentrations of Cd during these months. Figure 4a which is a visual representation of CR associated with Cd via inhalation, shows a trend line that suggests that there is a very slight upward trend, which means that over time there was no significant increase in CR via inhalation. The table also shows the CR associated with Cd via dermal contact. For this exposure, the values, compared to inhalation were much lower with values ranging from the lowest in March with a value of 1.48E-06 to the highest in August with a value of 2.94E-05. July also had a high value of 2.54E-05. Compared with Table 2, all the CR values are associated with Dermal contact in the low-risk region. Figure 4a shows the monthly variation of CR for Cd via dermal contact. There was a trend line that showed the trend of the CR and it indicated that there was a slightly stronger upward trend than that of inhalation. This suggested a more modest increase in dermal exposure risk over time. From this study, the inhalation route is the most dominant route of intake for Cd-related Cancer Risk, this is because the inhalation had values of two orders of magnitude higher than dermal contact. July and August had peak exposure as shown in Figure 4, and this suggests that there were higher levels of Cd in the atmosphere for that period, which could be due to human activities such as the resuspension of dust, and industrial emission. It could also have been because of the dry conditions of the atmosphere during that period. Comparing with studies from other regions such as Nawrot et al. (2015); Beveridge et al. (2010); and Chen et al. (2016) where they concluded that there was an association between exposure to atmospheric Cd and increased risk of lung cancer, their study also highlights the importance of monitoring environmental Cd levels to protect public health. These studies, including this present study, emphasize the fact that inhalation is the primary route of exposure to Cd which in turn leads to an increase in the risk of lung cancer. The consistency between these studies reinforces the need to reduce airborne Cd Concentration for the sake of the health of the public, as this can be achieved by adopting regulatory measures. Table 9: Summary of the Cancer Risk for Cd via, Inhalation, and Dermal contact Month Inhalation Dermal Contact Mean SD Mean SD March 3.07E-03 2.25E-07 1.48E-06 1.03E-11 April 4.13E-04 4.83E-07 1.99E-06 1.45E-11 May 1.43E-03 3.85E-06 6.87E-06 9.63E-11 June 8.31E-04 2.35E-06 4.00E-06 4.58E-11 July 5.28E-03 4.31E-05 2.54E-05 2.91E-10 August 6.102E-03 6.55E-05 2.94E-05 1.75E-10 September 1.31E-03 3.098E-06 6.32E-06 3.79E-11 October 2.99E-03 3.20E-05 1.44E-05 1.35E-10 November BDL BDL BDL BDL December 3.59E-03 1.73E-05 1.73E-05 8.70E-11 BDL: Below Detectable Limit. SD: Standard Deviation. CR: This is unitless 3.5. Cancer Risk Assessment of Cadmium (Cd) via Inhalation: Monte Carlo Simulation Analysis and Comparison with Existing Studies. 3.5.1. Summary of Monte Carlo Simulation Results for Cd Inhalation Risk Figure 5a – I and Table 10 show the Monte Carlo Simulation for cancer risk for Cd via inhalation for March, April, May, June, July, August, September, October, and December. For March, as shown in Table 10 and Figure 5a the 5 th percentile which is also known as the best-case scenario was 2.83E-05 which is a borderline value to the 1E-06 value as stated in Table 2 meaning it was in the extremely low-risk region, making it completely acceptable. The 95 th percentile which is the worst-case scenario has a value of 5.91E-04 in the high-risk region, a region which meant that attention should be paid to it, and actions should be carried out. The 50 th percentile was 3.07E-04 which is used used to know the level of skewness of the mean risk. The value was a bit higher than the mean risk, which meant that the mean value was right-skewed. The mean risk which is the most likely estimation recorded a value of 3.06E-04, stating that the likelihood of anyone contracting cancer in March was 3.06 people in 10000. Comparing the mean value with what is in Table 2, it fell in the high-risk region which means that attention should be paid to it, and actions should be taken to solve it. In April as reported in Table 10, and Figure 5b, the 5 th percentile which is the best-case scenario had a value of -1.23E-05, a value that was far lower than the acceptable range of 10 -6 as shown in Table 2. This just meant that the value was acceptable, and shouldn’t be bothered about. The 95 th percentile had a value of 8.4E-04 which is greater than the receptor’s acceptable range. It fell in the high-risk region according to Figure 4 meaning that attention should be paid to it, as well as actions need to be taken to solve the problem. The 50 th percentile which just shows the skewness of the risk had a value of 4.12E-04 which is a teeny-little bit lower than the mean value of 4.13e-04. This points out the fact that the mean risk value which is the most probable risk was tending towards the low region of the risk. As stated earlier, the mean risk value which is the most probable risk had a value of 4.12E-04 which was in the high-risk region as stated in Table 2. This meant that the risk should be paid attention to and that actions should be taken to mitigate the effect. Furthermore, the mean risk meant that approximately 4 out of 10000 were at risk for April. For May as shown in Table 10, and Figure 5c, the 5 th percentile which denoted the best-case scenario had a value of -2.39E-03 which fell in the extremely low-risk region as shown in Table 2, which meant that there was no feasible problem associated with the inhalation of Cd for this period. The 95 th percentile which denoted the worst-case scenario had a value of 3.11E-03 which fell in the extremely high-risk region as shown in Table 2. This indicated that the situation must be solved. The 50 th percentile which showed the skewness of the risk associated with the PTM had a value of 1.42E-03 which was a little bit lower than the mean risk value meaning it was left skewed. This indicated that the risk tended to the lower region. The mean risk value which indicated the most probable risk had a value of 1.43E-03. As shown in Table 2, this indicated that this value fell in the extremely high-risk region. It also meant that approximately 1.4 persons in 1000 were at risk. Because of this value, this problem should be solved as stated in Table 2. In June, as shown in Table 10, and Figure 5d, the 5 th percentile which was the best-case scenario had a value of 6.56E-05 which fell in the low-risk region as classified in Table 2. This shows that the lower risk probability was still in the safe zone thereby needing no intervention. The worst case scenario which is the 95 th percent had a value of 1.62E-03, which according to Table 2 falls in the high-risk range, a situation that must not be left unsolved. The most likely risk i.e. the mean risk had a value of 8.32E-04 and the 50 th percentile, i.e. the median risk had a value of 8.27E-04. The 50 th percentile was just the skewness of the risks that were simulated by the MCS. Since the 50 th percentile was less than the mean risk, it meant that the risk was left skewed i.e. the mean risk was tending towards the lower region. The mean risk meant that approximately 8 people in 10000 were at risk. From Table 2, it is seen that the mean risk fell in the high-risk region, which indicates that attention should be paid, and actions be taken to help solve the problem. As shown in Table 10, and Figure 5e, July had the following results. The 5 th percentile which was the best-case scenario had a value of 2.34E-04. This was in the high-risk region as shown in Table 2. This meant that the populace was at a high level of risk. The 95 th percentile which was the worst-case scenario had a value of 1.01E-02 which was in the extremely high-risk region. This goes to say that the risk ranged between the 5 th percentile, and the 95 th percentile indicating that the populace was at serious risk. Since the lower percentage was in the high-risk region. The mean risk which is the most probable risk had a value of 5.32E-03, which meant that approximately 5 people in a thousand were at great risk. Compared with Table 2, the value falls in the extremely high-risk region, and there could be only one (1) solution, and that is to solve the problem. The 50 th percentile had a value of 5.18E-03. The 50 th percentile which is the median risk, shows the level of skewness of the risk. Since the 50 th percentile was less than the mean risk, it meant that the risk was left-skewed, i.e. tending to the lower region. For August, as shown in Table 10, and Figure 5f, the 5 th percentile which is the best-case scenario had a value of 6.08E-03 which was greater than the acceptable limit as stated in Table 2, and the worst-case scenario had a value of 1.06E-02 which is higher than the acceptable receptor range of 1E-03. The best-case scenario fell in the extremely high-risk region as shown in Table 2, and attention should be paid to it, as well as appropriate action should be taken. The worst-case scenario was in the extremely high-risk region, and according to Table 2, this problem must be solved. This just shows the range of the risk of the populace and can be seen from the result of the mean risk. The mean risk which is the most probable risk shows this. It had a value of 6.08E-03 which meant that approximately 6 in 1000 were at great risk. Furthermore, looking at Table 2, the mean value was in the extremely high-risk region, and from that same table it states that the problem must be solved. The 50 th percentile was 6.09E-03, which was a little bit higher than the mean risk value, meaning that it was right-skewed. As shown in Table 10, and Figure 5g, September, had a best-case scenario (5 th percentile) with a value of 3.41E-04 which fell according to Table 2 in the region of high-risk. For this kind of situation, the solution that was proffered is the fact that attention should be paid to the situation, not only that, the appropriate actions should be taken. The month had a worst-case scenario of 2.28E-03, which fell, according to the ranking in Table 2, in the extremely high-risk region. The only solution that was proffered was the fact that a solution must be provided for this situation. The 5 th and 95 th percentile just show the range and the level to which the population was at risk. The risk lay in the range of high-risk to extremely high-risk meaning they stood a great risk. This is shown in the mean risk which is the most probable risk, which had a value of 1.31E-03. Compared with Table 2, it fell in the region of extreme high-risk, with approximately 1 person per 1000 being at risk. According to Table 2, the proffered solution is that this situation must be solved. The 50 th percentile, which shows the skewness of the risk had a value of 1.30E-03 which was a little bit less than the mean value. This meant that the risk was right-skewed In October, as shown in Table 10, and Figure 5h, had a 5 th percentile risk, which is the best-case scenario, of 1.43E-03. Compared with Table 2, the best-case scenario fell in the range of extremely high risk. The worst case scenario, which is the 95 th percentile risk had a value of 5.7E-03E-03 which means that according to this, approximately 6 people in 1000 were at risk. Compared with Table 2, the risk fell in the extremely high-risk region meaning that. This range of value was indeed a very dangerous one as the lowest (best-case scenario) was in the extremely high-risk region. This goes to say that no matter what, for October, everyone was at risk. This can be seen from the mean risk, which is the most probable risk had a value of 3.59 E-03, which insinuated that approximately 4 people in 1000 were at risk. Compared with Table 2, the mean risk fell in the extremely high-risk region, and the solution preferred to this was that it must be solved, according to Table 2. For December, as shown in Table 10 and Figure 5i, the 5 th percentile which is the best-case scenario had a value of 1.45E-03 which fell in the extremely high-risk region. The 95 th percentile which was the worst-case scenario had a value of 5.69E-03 which also fell in the extremely high-risk region as shown in Table 2. This just goes to indicate that the risk faced by the populace was high as the range was from extreme highness to another point of extreme highness. This was shown in the mean risk which had a value of 3.61E-03. This meant that approximately 4 people in 1000 were at risk. Compared with Table 2, this value falls in the extremely high-risk region, and according to that same Table 2, the only proffered solution was that the problem must be solved. The 50 th percentile which is the median, and also shows the degree of skewness, had a value of 3.61E-03 as shown in Table 10, and Figure 5i. This just goes to show that there was no skewness in the risk. This study was compared with Nawrot et al. (2015), who found that Cd was associated with a relative risk of 1.22 for total cancer, and a risk of 1.68 for Lung cancer. Ajah et al. (2015), found that 91% of ecological risk was associated with Cd, thereby taking note of the significant presence of this metal in the area. This therefore reinforces the result from this study that great risk is associated with the intake of Cd via inhalation. Table 10: Summary of MCS result for Cd via Inhalation Month Mean Risk 5 th percentile 50 th percentile 95 th percentile March 3.06E-04 2.83E-05 3.07E-04 5.91E-04 April 4.13E-04 -1.23E-05 4.12E-04 8.39E-04 May 1.43E-03 -2.39E-04 1.42E-03 3.11E-03 June 8.32E-04 6.57E-05 8.27E-04 1.62E-03 July 5.23E-03 2.35E-04 5.18E-03 1.01E-02 August 6.08E-03 1.58E-03 6.09E-03 1.06E-02 September 1.31E-03 3.41E-04 1.30E-03 2.28E-03 October 3.59E-03 1.48E-03 3.60E-03 5.70E-03 November NA NA NA NA December 3.61E-03 1.45E-03 3.61E-03 5.69E-03 NA: Not Available 3.5.2. Summary of Monte Carlo Simulation Results for Cd Dermal Contact Risk Figure 6a-I shows the probabilistic health risk assessment of cadmium via dermal contact for March, April, May, June, July, August, September, October, and December. For March, from Table 11, and Figure 6a the 5 th percentile which is the best-case scenario had a value of 1.13e-07 which is less than the expected limit of 1e-06 as shown in Table 2. This means that there is no associated risk. The 95 th percentile which is the worst-case scenario had a value of 2.84e-06 which is below the receptor-acceptable limit. This shows that the mean risk which is the most probable risk will fall in this range. This is shown in the mean risk which had a value of 1.47E-06. Compared with Table 2, it fell in the extremely low-risk range. From the same Table 2, it can be seen that is acceptable, without any associated risk. The 50 th percentile had a value of 1.47E-06 also. The 50 th percentile which is the median score shows the skewness of the risk. However, the mean risk and 50 th percentile had the same value thereby indicating that there was no skewness. Similarly from Table 11, and Figure 6b, April had a value of 6.02E-08 for the 5 th percentile which is the best-case scenario. This fell into the extremely low-risk region, as shown in Table 2. This means that there was no risk associated with Cd at this point. The 95 th percentile which was the worst-case scenario, had a value of 4.05E-06. Compared with Table 2, this fell in the extremely low-risk region, indicating that there was no risk associated with Cd at this point. This range shows that the cancer risk can not be high. This can be seen in the mean risk which had a value of 2.01E-06. The mean risk is the most probable risk associated with Cd via this route. Compared with Table 2, this value fell into the extremely low-risk range, meaning that there was no risk associated with it. The 50 th percentile which is the median score, showed the level of skewness of the mean risk. It had a value of 2.01E-06 which was the same as the value of the mean risk, meaning that the risk was not skewed. As shown in Table 11, and Figure 6c, the 5 th percentile which was the best-case scenario had a value of 1.04E-06. Compared with Table 2, this value fell in the extremely low-risk region, meaning it is completely acceptable. The worst-case scenario which is the 95 th percentile had a value of 1.5E-05. When compared with Table 2, the value fell in the low-risk range which meant that the risk was nothing to be bothered about. The mean risk value was 6.86E-06. Compared with Table 2, this value fell in the range of extremely low risk, which means that the risk is completely acceptable. The mean risk stands for the most probable risk. The 50 th percentile which is the median risk had a value of 6.88E-08. The 50 th percentile also shows the degree of skewness of the mean risk. The value of the 50th percentile was more than the mean risk meaning that the risk was right-skewed i.e. the mean is tending towards the high value region. In June, the 5 th percentile, 95 th percentile, 50 th percentile, and mean risk had values of 1.97E-07 (which was lower than the acceptable limit, indicating no risk to the populace), 7.74E-06 (which was less than the receptor’s acceptable limit of 10 -3 , thereby indicating no risk), 4.02E-06, and 4.02E-06 respectively. The mean values were borderline with the acceptable set limit for Cancer risk assessment as shown in Table 2. This meant that approximately 4 people in 100000 were at the risk of infection, this is a very minimal risk and can be ignored. During July, the mean risk, 5 th percentile (best case scenario), 50 th percentile, and 95 th percentile (worst case scenario) had values of 2.53E-05, 8.01E-07 (which was slightly greater than the acceptable limit of 1e-06, indicating little risk), 2.54E-05(which was less than the receptor’s limit, which indicated little risk to the populace), and 4.94E-05, respectively. The mean value which is the most likely risk value was greater than the acceptable limit of 1E-06 but is still in the negligible range as shown in figure 4. For August, the mean risk (the most probable risk), 5 th percentile (best case scenario), 50 th percentile, and 95 th percentile (worst case scenario) had values of 2.94E-05 (indicating that approximately 3 people out of 100000 were at risk of being infected), 8.40E-06 (was less than the acceptable limit of 1E-06, indicating no risk), 2.94E-05, 5.10E-05 (was less than the receptor’s acceptable limit, indicating no risk) respectively. These values were all borderline values with the acceptable limit of 1E-06 as shown in Table 2. For September, the mean risk, 5 th percentile, 50 th percentile, and 95 th percentile had values of 6.33E-6, 1.59E-06, 6.33E-06, 1.10E-05, respectively. The mean risk which was the most probable risk had a value that was borderline with the limit which has no cause for concern. Furthermore, the mean risk indicates that approximately 6 people out of 1000000 were the risk of infection. The best-case scenario had a value that indicated no risk, the same with the worst-case scenario. These values were all borderline values with the acceptable limit of 1e-06 as shown in Table 2. For October, the mean risk, 5 th percentile (best case scenario), 50 th percentile, and 95 th percentile (worst case scenario) had values of 1.44E-05, -4.46E-06, 1.43E-05, 3.34E-05, respectively. The mean risk which is the most probable risk had a value that was one that according to Table 2, is not a cause for concern. This also applies to the best-case scenario, and worst-case scenario, indicating risk level that does not call for concern. These values were all borderline values with the acceptable limit of 1e-06 as shown in Table 2. For December, the mean risk, 5 th percentile, 50 th percentile, and 95 th percentile had values of 1.71E-05, 6.78E-06, 1.72E-05, 2.75E-05, respectively. These values were all borderline values with the acceptable limit of 1e-06 as shown in Table 2. This goes further to indicate that according to Table 2, there was no risk associated with this route for this month. This study was compared with Eid et al. (2024) which assessed the health risks associated with Heavy metals found in groundwater using a Monte Carlo simulation. Their results indicated that the Hazard quotient for Cd did not exceed the limits that were stated limits. This suggested that there was a minimal health risk from that region. However, this study presents a different one. The mean risks from this study over the months were higher than the results from their study. Also, Ghaderpoori et al. (2020), carried out an assessment of the CR associated with Cd found in cosmetics, via dermal contact. Their study showed the mean hazard quotient for Cd was lower than that of the present study. An assessment of heavy metals found in cosmetics using MCS revealed that the mean hazard quotient (HQ) for Cd was 1.05E-03, which is lower than the mean risks observed in the current study for most months. This suggests that dermal exposure to Cd in the studied environment may pose a higher risk compared to exposure through cosmetic products in Iran. Table 11: Summary of MCS result for Cd via Dermal Contact Month Mean Risk 5 th percentile 50 th percentile 95 th percentile March 1.47E-06 1.13E-07 1.47E-06 2.84E-06 April 2.01E-06 6.02E-08 2.01E-06 4.05E-06 May 6.86E-06 -1.04E-06 6.88E-06 1.50E-05 June 4.02E-06 1.97E-07 4.02E-06 7.74E-06 July 2.53E-05 8.01E-07 2.54E-05 4.94E-05 August 2.94E-05 8.40E-06 2.94E-05 5.10E-05 September 6.33E-06 1.59E-06 6.33E-06 1.10E-05 October 1.44E-05 -4.46E-06 1.43E-05 3.34E-05 November NA NA NA NA December 1.71E-05 6.78E-06 1.72E-05 2.75E-05 NA: Not Available 3.5.3. Summary of Monte Carlo Simulation Results for As Inhalation Risk Table 12 and Fig 7a-i show the summary of the MCS result of Cancer Risk associated with As via Inhalation for March through to December, excluding the month of August. The best fit, 50 th percentile, and worst risk scenario had values of 6.81E-05, 3.69E-04, and 6.78E-04 respectively. The best fit was greater than the acceptable limit, and the worst risk was less than the receptor limit of 10E-03 as shown in Table 2. The mean risk which is the most likely risk had a value of 3.71E-04 and was left-skewed, i.e. it could be lower. The value of the mean risk fell in the range of a growing concern for the quality of air for March. This goes to say that approximately 4 people out of 10000 are at risk of an infection. For April, the best-case scenario, worst-case scenario, mid-score, and mean risk had values of 8.74E-05, 1.57E-04, 2.25E-04, and 1.56E-04 respectively. The value of the best-case scenario was greater than the acceptable limit, and the worst-case scenario was a little less than the receptor’s limit as shown in Table 2. The mean risk which is the most probable risk had a value that was classified in Table 2 as a cause for concern. The mean value showed that approximately 2 people in 10000 were at the risk of being infected. For May, as shown in Table 12, it can be seen that the mean risk, best-case scenario, mid score, and worst-case scenario had values of 1.38E-03, -1.31E-03 (less than the acceptable limit i.e. no risk), 1.39E-03, and 4.12E-03 respectively. The value for the best-case scenario was in the range that calls for great concern according to Table 2. The mean risk which is the most probable risk had a value that showed that approximately 1 out of 1000 people were at the risk of infection. The value of the mean risk showed that there is a cause for alarm. For June, as shown in the table 12. The mean risk, best-case scenario, mid score, and worst-case scenario had values of 1.12E-03, 7.30E-04, 1.12E-03, and 1.50E-03 respectively. Best best-case scenario was greater than the acceptable limit as shown in Table 2, and the worst-case scenario was in the receptor’s risk. This indicates that the populace is at great risk of being infected. The mean risk which is the most probable risk showed that approximately 1 person out of 1000 was at risk of being infected. Furthermore, the value showed that there is a cause for concern. The month of July had for the mean risk, 5 th percentile, 50 th percentile, and 95 th percentile, values of 9.11E-04, -9.87E-04, 8.95E-04, and 2.84E-03 respectively. The best-case scenario was higher than the acceptable limit, while the worst-case scenario was borderline with the receptor limit. All these values were in the range of having a cause for concern according to Table 2. The value of the mean risk which is the most probable risk indicated that approximately 9 people out of 1000 stood a risk of being infected. For September, 1.07E-03, 1.87E-04, 1.06E-03, and 1.96E-03 were the values gotten for the mean risk, 5 th percentile, 50 th percentile, and 95 th percentile as shown in Table 12 above. The best-case scenario was less than the acceptable limit, while the worst-case scenario was borderline with the receptor’s limit. The mean risk value which is the most probable risk showed that approximately 1 person in 1000 stood the risk of being infected. Furthermore, in comparison with Table 2, the mean value shows that there is a cause for concern. October had, for the mean risk, 5 th percentile, 50 th percentile, and 95 th percentile, values of 2.35E-03, 9.62E-04 (which was greater than the acceptable limit as shown in Table 2), 2.35E-03, and 3.75E-03 (which was borderline with the receptor acceptable limit of 10e-03) respectively. The mean risk which is the most probable risk had a value that indicates that approximately 2 people out of 1000 were at the risk of an infection. For November, 2.36E-03, 9.37E-04, 2.34E-03, and 3.82E-03 were the values for the mean risk, 5 th percentile (best case scenario), the 50 th percentile, and the 95 th percentile. The best-case scenario had a value that was greater than the acceptable limit of 1e-06, and the worst-case scenario had a value that was borderline with the receptor’s acceptable limit of 1e-03 indicating that the populace was at risk. The mean risk which is the most probable risk had a value that indicated that approximately 2 people out of 1000 were at the risk of infection. The value of the mean risk showed that there is a great risk as compared to Table 2, and something needs to be done. Finally, for December, 5.87E-03, 2.75E-03, 5.87E-03, and 9.03E-03, were the values recorded for the mean risk, 5 th percentile, 50 th percentile, 95 th percentile, respectively. The best-case scenario was greater than the acceptable limit of 1e-06, and the worst-case scenario was borderline with the receptor limit of 1e-03. This indicated the presence of health risks. The mean value which is the most probable risk had a value that indicated that approximately 6 people in a thousand are at the risk of an infection. This therefore indicates that the populace is at a great risk when the value of mean risk is compared to Table 2. The results from this study for October, November, and December face a greater risk than the results in the study carried out by Nawrot et al. (2015). Similarly, this study was compared with that of Yu et al. (2017). The results obtained in March, and September were comparable to that of their study, however, the extremely high-risk value in December surpasses that which was recorded in the study. Ajah et al. (2015) had results that align with the results of October, November, and December of this study. In conclusion, there were extreme risk values that were observed in some months especially in December exceeding the report from other studies indicating factors that are localized causing a heightening in exposure risk. Further studies may be necessary to ascertain these localized factors. Table 12: Summary of MCS result for As via Inhalation Month Mean Risk 5 th percentile 50 th percentile 95 th percentile March 3.71E-04 6.81E-05 3.69E-04 6.78E-04 April 1.56E-04 8.74E-05 1.57E-04 2.25E-04 May 1.38E-03 -1.31E-03 1.39E-03 4.12E-03 June 1.12E-03 7.30E-04 1.12E-03 1.50E-03 July 9.11E-04 -9.87E-04 8.95E-04 2.84E-03 August NA NA NA NA September 1.07E-03 1.87E-04 1.06E-03 1.96E-03 October 2.35E-03 9.62E-04 2.35E-03 3.75E-03 November 2.36E-03 9.37E-04 2.34E-03 3.82E-03 December 5.87E-03 2.75E-03 5.87E-03 9.03E-03 NA: Not Available 3.5.4. Summary of Monte Carlo Simulation Results for As Dermal Contact Risk Table 13 shows the summary of the MCS results as graphically shown in Figure 8a-i below, for March to December, for As via Dermal contact. For March, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 2.37E-08, 3.91E-09, 2.37E-08, and 4.32E-08 respectively. The best-case scenario had a value that was lower than the acceptable limit of 1e-06, the same as the worst-case scenario. The mean risk value when compared with Table 2 indicated that the populace was not at risk. For April, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 1.01E-08, 5.65E-09, 1.00E-08, and 1.45E-08 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk was lower than the acceptable limit as shown in Table 2. For May, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 8.84E-08, -8.41E-08, 8.80E-08, and 2.64E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2. For June, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 7.15E-08, 4.73E-08, 7.16E-08, and 9.58E-08 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2. For July, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 5.94E-08, -5.99E-08, 5.92E-08, and 1.81E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2. For September, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 6.83E-08, 1.19E-08, 6.76E-08, and 1.26E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2. For October, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 1.52E-07, 6.35E-08, 1.52E-07, and 2.40E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2. For November, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 3.81E-07, 2.90E-07, 3.82E-07, and 4.72E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2. For December, the mean risk, 5 th percentile (best-case scenario), 50 th percentile, and 95 th percentile (worst-case scenario), had values of 3.76E-07, 1.77E-07, 3.75E-07, and 5.76E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2. There has been a consistent association of As exposure with an increase in cancer risk. A review by the American Cancer Society highlighted a relative risk of 2.1 for skin cancer associated with arsenic exposure (www.cancer.org accessed 20/03/2025). Similarly, a retrospective cohort study carried out by the American Cancer Society, in Taiwan reports that there was an odd ratio of 7.58 associated with keratinocyte carcinoma at cumulative As exposure level. This study has not been able to associate its findings with dermal arsenic exposure and cancer risk. Table 13: Summary of MCS result for As via Dermal Contact Month Mean Risk 5 th percentile 50 th percentile 95 th percentile March 2.37E-08 3.91E-09 2.37E-08 4.32E-08 April 1.01E-08 5.65E-09 1.00E-08 1.45E-08 May 8.84E-08 -8.41E-08 8.80E-08 2.64E-07 June 7.15E-08 4.73E-08 7.16E-08 9.58E-08 July 5.94E-08 -5.99E-08 5.92E-08 1.81E-07 August NA NA NA NA September 6.83E-08 1.19E-08 6.76E-08 1.26E-07 October 1.52E-07 6.35E-08 1.52E-07 2.40E-07 November 3.81E-07 2.90E-07 3.82E-07 4.72E-07 December 3.76E-07 1.77E-07 3.75E-07 5.76E-07 NA: Not Available 4.0. CONCLUSION This study tried to evaluate and assess the level of concentration of Cd and As in the study area over the seasons in the study year. Further, cancer risks associated with Cd and As were assessed using a probabilistic method (Monte Carlo Simulation). The results of this study showed that the monthly concentration of both Cd and As didn’t exceed the limit that was set by some regulatory bodies such as the WHO, and EU. Furthermore, there was a greater risk (cancer) associated with these metals via inhalation, than dermal contact indicating that Inhalation was the most prominent pathway for these metals to get into the body from the atmosphere. The variation of the risk associated with these metals showed that seasons have a major effect on them, as the risks were higher in the dry months of the year 2019. The implication of this is that people stand great health risks during the dry seasons of the year, as these particles can stay longer in the atmosphere, and can move over long distances during these periods. This study was limited, making it impossible to achieve the desired robustness for the study. For example, the availability of more data from the study environment could have helped in the analysis of the changes in the properties of PMs in the study environment over time. The lack of moveable equipment that could help in collecting data around various areas in the state, would have helped us in carrying out a spatial analysis of As and Cd. The policy-makers can help by providing funds for an in-depth study of PTMs found in the atmosphere, which in turn will help them in developing standards for this area. They can also work hand-in-hand with international regulatory bodies to help in the mitigation of the effects of these metals. In conclusion, this study will help policy-makers achieve primarily, one of the Millennium Development Goals (MDGs) which is a sustainable environment, as it provides an insight into what is wrong, and where to come in. Also, another MDG that can help to sort out secondarily is infant mortality, as a polluted atmosphere is at the forefront of infant mortality. Declarations Ethics Declaration Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interest The Authors declare no competing interest Consent to publish Not Applicable Consent to Participate Not applicable Authors’ Contribution Statement Conceptualization: O. A. Falaiye, S. Nwabachili, M. Orosun Data curation: T. B. Ajibola Formal analysis: S. Nwabachili, O.E. Abiye Investigation: S. Nwabachili, O. A. Falaiye Methodology S. Nwabachili Project administration: P. O. Ijila Software: S. Nwabachili, M. M. Orosun Supervision: O. A. Falaiye Validation: O. A. Falaiye, M. Orosun Visualization: S. Nwabachili Writing – original draft: S, Nwabachili Writing – review and editing: S. Nwabachili Data Availability The Data used is available on the SPARTAN website References Abiye O. E., Imoh B. O., Godwin C. E., (2013). Elemental characterization of urban particulates at receptor locations in Abuja, north-central Nigeria. Atmospheric Environment. 2013; 81: 695e701 Ajah, K.C., Ademiluyi, J. & Nnaji, C.C. Spatiality, seasonality and ecological risks of heavy metals in the vicinity of a degenerate municipal central dumpsite in Enugu, Nigeria. J Environ Health Sci Engineer . 2015; 13 :15. https://doi.org/10.1186/s40201-015-0168-0 Beveridge R, Pintos J, Parent ME, Asselin J, Siemiatycki J. Lung cancer risk associated with occupational exposure to nickel, chromium VI, and cadmium in two population-based case-control studies in Montreal. Am J Ind Med. 2010;53(5):476-85. doi: 10.1002/ajim.20801. PMID: 20187007. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 16 May, 2025 Reviews received at journal 09 May, 2025 Reviews received at journal 24 Apr, 2025 Reviews received at journal 20 Apr, 2025 Reviewers agreed at journal 20 Apr, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers invited by journal 15 Apr, 2025 Submission checks completed at journal 12 Apr, 2025 First submitted to journal 21 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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5","display":"","copyAsset":false,"role":"figure","size":882757,"visible":true,"origin":"","legend":"\u003cp\u003eMonte Carlo Simulation for Cancer Risk for Cd via Inhalation\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5837506/v1/6725d26cf844fe8bec14b326.png"},{"id":80724039,"identity":"f6930d26-ac24-4e06-8f22-84c84f89f9fc","added_by":"auto","created_at":"2025-04-16 11:33:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":828915,"visible":true,"origin":"","legend":"\u003cp\u003eMonte Carlo Simulation for Cancer Risk for Cd via Dermal Contact\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5837506/v1/3710124a3385fff9ff742b4f.png"},{"id":80726283,"identity":"32e5046a-eba2-4053-ae51-9e0e8b2da40c","added_by":"auto","created_at":"2025-04-16 11:49:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":694564,"visible":true,"origin":"","legend":"\u003cp\u003eMonte Carlo Simulation for Cancer Risk for As via Inhalation\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5837506/v1/943a33999e175f6d422022cd.png"},{"id":80723382,"identity":"5f5ec8c7-3ad1-4f69-a746-3e636ce588ee","added_by":"auto","created_at":"2025-04-16 11:25:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":818069,"visible":true,"origin":"","legend":"\u003cp\u003eMonte Carlo Simulation for Cancer Risk for As via Inhalation\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5837506/v1/c7eaa8c35f38ed4da3e84bcf.png"},{"id":80727164,"identity":"16e3b817-586f-45e3-aafc-44c5cdf7bc84","added_by":"auto","created_at":"2025-04-16 11:57:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5951701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5837506/v1/3596e28c-3256-4ca6-b1e9-f6017029e2a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Temporal Analysis of Cancer Risk Associated with Cadmium and Arsenic Found in PM 2.5 in the University of Ilorin and its Environs; A probabilistic approach","fulltext":[{"header":"1.0. INTRODUCTION","content":"\u003cp\u003eAs a result of rapid urbanization, air pollution (a heterogeneous mixture of gases, vapor, and particles) has become in recent years, a major concern associated with the world (Soleimani,2018; Ediagbonya and Olabiyi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A major constituent of air pollution is the Particulate Matter (PMs) which are classified into PM\u003csub\u003e2.5, and\u003c/sub\u003e PM\u003csub\u003e10\u003c/sub\u003e. These PMs are made up of pollutants that are either organic or inorganic such as Potentially Toxic Metals (PTMs) (Shah et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ediagbonya, 2022). These PTMs are classified into 2 groups, the cancer-causing causing which include Nickel, Arsenic, Cadmium, and Chromium, according to the International Agency for Research on Cancer (IARC); and the non-cancer-causing PTMs. Long-term exposure to these PTMs is highly injurious and can cause severe environmental pollution, affect, through the bioaccumulation process, the natural geochemical properties, as well as threaten human well-being and that of animals. These PTMs are highly toxic, have a high level of concealment, are irreversible, persistent (because they are not broken down), and highly biologically accumulative i.e. they accumulate in the environment (Soleimani,2018). PM is introduced into the atmosphere by either Natural methods such as dust storms, or anthropogenic methods such as industrial activities (Soleimani,2018). In Urban areas, the emission of PTMs as a sub of PM\u003csub\u003e2.5\u003c/sub\u003e is directly related to various mobile (Squires et al., 2019; Raj\u0026eacute; et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Comert et al., 2020) and stationary sources which include industrial population, traffic emission (exhaust, and non-exhaust), weathering of building and pavement, combustion of fossil fuels, garbage, and Tobacco, and atmospheric deposition (Gunawardana \u003cem\u003eet al.\u003c/em\u003e, 2012; Manasreh, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The various pathways by which these PTMs in the atmosphere get into the body include ingestion i.e. eating food that is exposed to these PTMs in the atmosphere such as food gotten from roadside vendors, inhalation, breathing exhaust fumes, and finally dermal contact i.e. absorption route. Inhalation and absorption routes are the most common ways by which these PTMs enter the human body (Goudarzi et al., 2018).\u003c/p\u003e \u003cp\u003eA lot of studies to determine the concentration of various PTMs in PM\u003csub\u003e2.5\u003c/sub\u003e have been carried out. In Rio de Janeiro, Ventura et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) studied the concentrations of PTMs in PM\u003csub\u003e2.5\u003c/sub\u003e samples, and their result showed three (3) groups of metals including; group 1 (Cu, Cd, Pb), group 2 (Cr, Mn, Ni, V, and Zn), and group 3 (Na, K, Ca, Ti, Al, Mg, Fe) originated from industrial areas, traffic, and natural sources. Li et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) investigated the constituents of both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e from an industrial layout in China, and found that there was a lifetime lung cancer due to the presence of Cr, Cd, and Co. Hadad et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) measured to determine the distribution of suspended PMs associated with some PTMs (Pb, Br, V, Ca, Al, Fe, Cu, Cr, Mn, Sc, and Zn) at four (4) locations in Shiraz, Iran. They found out that the traffic pollution in Shiraz was higher than the WHO, and EPA standards. In the Sina district of Tehran, Kermani et al. (2016) determined the level of PM\u003csub\u003e2.5\u003c/sub\u003e and Pb, Cd, Cr, Ni, Hg, As, and Zn in the ambient air. It was revealed that the concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and metals such as Cd were higher than the US-EPA standard. Soleimani et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) analyzed the content of PTMs in PM\u003csub\u003e2.5\u003c/sub\u003e in the atmospheric monitoring stations in Isfahan City Iran, in different seasons between March 2014, and March 2015. They reported that As, Cd, Ni, Cr, and Cu exceeded the US-EPA standard. In Nigeria, studies to determine the concentration of PTMs in Particulate Matter have been carried out. Abiye et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) carried out research to assess the mass concentration and elemental characterization of airborne PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in Abuja. The concentration of (V, Co, Ni, Cd, Zn, and Pb) was determined. It was reported that the PM\u003csub\u003e2.5\u003c/sub\u003e/PM\u003csub\u003e10\u003c/sub\u003e mass ratio was well within the WHO-specified range. Falaiye et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), carried out a characterization of Atmospheric Particulate matter from urban-traffic sources in Ilorin. The concentrations of the heavy metals in PM\u003csub\u003e2.5\u003c/sub\u003e were excessively high, although not more than the WHO standard, but could impact the health of people living in that environment significantly. Ezeh et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) monitored the total atmospheric deposit (TAD) around a smelting plant in Ile Ife, Nigeria, to assess the contribution of the industry to Nigerian air-shed pollution. They detected 23 elements, with Fe having the highest concentration and Na having the least concentration. Fe, Zn, Pb, and Mn, were enriched in that environment. They concluded that the smelting activity pose a great hazard to receptors around the area. Ezeh et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analyzed PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e samples collected from industrial, low-, and high-density areas in Lagos state, Nigeria for 12 months. The analyses were done using the ion beam analyses technique, Particle Induced X-ray Emission (PIXE), and Positive Matrix Factorization (PMF) were used to apportion sources to the PTMs that were found in the PM\u003csub\u003e2.5\u003c/sub\u003e. It was reported that there was a gross violation of both local and international guidelines. They reported that PMF revealed that soil dust, physical construction, and industrial activities were the major emissions of PM\u003csub\u003e2.5\u0026minus;10\u003c/sub\u003e and could lead to negative health implications for the inhabitants of Lagos State, Nigeria.\u003c/p\u003e \u003cp\u003eHowever, these studies, especially the ones carried out in Nigeria have failed to answer the question of \u0026ldquo;to what extent can the inhaling of these trace elements affect the populace? i.e. what is the Cancer Risk associated with these PTMs\u0026rdquo;. There is, therefore, a need to assess the level of risk associated with PTMs found in PM\u003csub\u003e2.5\u003c/sub\u003e collected from an area, this is because they can adversely affect the respiratory systems of human beings (Fann and Risley \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Terrouche et al.,2016; Burns et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) hence, the importance of this work. This work aims not just to assess the concentration levels of these PTMs, but also the level of Cancer Risk associated with Cadmium, and Arsenic found in PM\u003csub\u003e2.5\u003c/sub\u003e. For the year 2019, using the SPARTAN data, situated at the University of Ilorin, Nigeria, using a probabilistic approach.\u003c/p\u003e \u003cp\u003eAccording to the IARC, Cd and As have been classified as PTMs that are carcinogens. This study was able to carry out an assessment on the level of concentration of these PTMs, as well as a comparative analysis with threshold limits placed by governing bodies, WHO, and the EU. A Cancer Risk assessment was carried out by using a probabilistic method (the Monte Carlo Simulation) (Mohanraj et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e"},{"header":"2.0. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study Area\u003c/h2\u003e\n \u003cp\u003eIlorin, the capital city of Kwara State, Nigeria, is located on latitude 8\u0026deg;29\u0026rsquo;47\u0026rdquo; N and longitude 4\u0026deg;32\u0026apos;30\u0026quot;E. The climate of Ilorin is characterized by both wet and dry seasons. The temperature in Ilorin ranges from 33\u0026deg;C to 34\u0026deg;C from November to January, while from February to April; the value ranges between 34\u0026deg;C to 53\u0026deg;C (Ilorin Atlas, 1982). The monthly mean temperatures are very high varying from 25.0\u0026deg;C to 28.9\u0026deg;C. The total amount of annual rainfall in the area is about 1200 mm (1971\u0026ndash;2000) (Falaiye et al, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The University of Ilorin which is the only federal University in the state is located on Tanke Road, and its map is shown in the Fig.\u0026nbsp;1. The University has a land mass of 150,000,000 m\u003csup\u003e2\u003c/sup\u003e, and a total of 108 academic departments, according to ebaoxford.co.uk as accessed on the 12th of December, 2022. In the year 2019, which is the year of study, the University of Ilorin had a population of 55,242 students, and 4,218 staff consisting of 2,718 non-academic staff, and 1,504 academic staff (ebaoxford.co.uk). Figure\u0026nbsp;1 shows a map of the study area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Data collection\u003c/h2\u003e\n \u003cp\u003eThe data (monthly concentration) used was collected from the SPARTAN (Surface Particulate Matter Network) which is available at the SPARTAN website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.spartan-network.org\u003c/span\u003e\u003c/span\u003e). SPARTAN is installed at the top of the physics department (Block 4), University of Ilorin, Ilorin, Nigeria, with a coordinate of longitude 8.484\u003csup\u003eo\u003c/sup\u003eN, latitude 4.674\u003csup\u003eo\u003c/sup\u003eE, and elevation 400m. The data was collected from the website. The data was in comma-separated value (CSV) format. It was imported into the Excel sheet and converted into usable data for analysis. The frequency of the data collected was monthly, for 2019.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Health Risk Assessment\u003c/h2\u003e\n \u003cp\u003eThe methods recommended by the US-EPA were used to assess the carcinogenic risks of heavy metals in PM\u003csub\u003e2.5\u003c/sub\u003e, which can get into the body through three (3) major pathways which include ingestion, inhalation, and dermal contact, however for this study only two pathways were considered which is the inhalation, and dermal contact, as these are the major ways by which these PTMs in PM\u003csub\u003e2.5\u003c/sub\u003e get into the body (Goudarzi et al., 2018). These methods include; Exposure Concentration (EC) through inhalation, measured in \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, and Dermally Absorbed Dose (DAD). These were calculated using equations \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e respectively (Xu \u003cem\u003eet al.\u003c/em\u003e, 2020).\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:EC=\\frac{C\\times\\:ET\\times\\:EF\\times\\:ED}{{AT}_{n}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:DAD=\\frac{C\\times\\:SA\\times\\:AF\\times\\:ABS\\times\\:EF\\times\\:ED\\times\\:CF}{BW\\times\\:AT}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the summary of the input parameters for the calculation of the various health risks. Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e show the Summary of the CDI, EC, and DAD.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInput parameters and abbreviations for cancer and non-cancer exposure assessment (Wang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNotation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetal Concentration in PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:g/{m}^{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage lifetime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAT\u003csub\u003en\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:365\\times\\:24\\)\u003c/span\u003e\u003c/span\u003e (for non-carcinogens)\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:53\\times\\:365\\times\\:24\\)\u003c/span\u003e\u003c/span\u003e (for carcinogens)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage lifetime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:365\\)\u003c/span\u003e\u003c/span\u003e (for non-carcinogens)\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:53\\times\\:365\\:\\)\u003c/span\u003e\u003c/span\u003e(for carcinogens)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConversion Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/Kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edays/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eh/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIngestion Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin Surface area adherence that contacts the airborne particles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin adherence factor for airborne particulate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/cm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDermal Absorption Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03(As), 0.001(Cd)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Carcinogenic Risk (CR)\u003c/h2\u003e\n \u003cp\u003eThe Carcinogenic risks (CRs), which are defined as the probability for an individual to develop any kind of cancer by lifelong exposure to carcinogenic hazards, were calculated using Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Cancer risk greater than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\\times\\:{10}^{-4}\\)\u003c/span\u003e\u003c/span\u003e pose a higher concentration risk, due to the high value. Risks less than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\\times\\:{10}^{-6}\\)\u003c/span\u003e\u003c/span\u003e are considered not to pose any cancer risk. The acceptable range is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\\times\\:{10}^{-4}and\\:1\\times\\:{10}^{-6}\\)\u003c/span\u003e\u003c/span\u003e. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e classifies the various risk values into 7 levels according to the Delphi Method (Orosun \u003cem\u003eet al.\u003c/em\u003e, 2021).\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:CR=\\:DAD\\times\\:\\left(\\frac{S{F}_{0}}{GIABS}\\right)=IUR\\times\\:EC$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eRfDo is the oral reference dose (mg/kg/day), RfCi is the inhalation reference concentration (mg/m\u003csup\u003e3\u003c/sup\u003e), GIABS means the gastrointestinal absorption factor, SFo is the oral slope factor (mg/kg/ day)\u003csup\u003e\u0026minus;1\u003c/sup\u003e, and IUR meant inhalation unit risk (mg/m\u003csup\u003e3\u003c/sup\u003e)\u003csup\u003e\u0026minus;1\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eThe values of SF\u003csub\u003e0\u003c/sub\u003e, GIABS, and IUR for the PTMs are stated in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e below.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003elevels and values of assessment standard according to Haque et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), Li et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Orosun et al. (2021).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange of Risk level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcceptability\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 1 (extremely low risk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompletely accept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 2 (Low risk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, -10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot eager to care about the probable risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 3 (Low medium risk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, -5\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:{10}^{-5}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot to be mindful of the risk.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 4 (Medium risk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:5\\times\\:{10}^{-5},-{10}^{-4}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorry about probable risk.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 5 (Medium High risk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{10}^{-4},-5\\times\\:{10}^{-4}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCare about the risk, and willing to invest.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 6 (High risk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:5\\times\\:{10}^{-4},-{10}^{-3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePay attention and take action to solve it.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 7 (Extremely High Risk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMust solve it\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInput Parameters for Cancer Risk Assessment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePTMs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSF\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIUR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGIABS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Monte Carlo Simulation\u003c/h2\u003e\n \u003cp\u003eThe evaluation of risk assessment becomes, in a way, complicated. This is due to the uncertainties brought about by the following; Ingestion rate of toxic substance by the exposed individual; the body weight of the individual; concentration of the toxic element collected in the sample; and the Carcinogenic slope factor. The uncertainties brought minimizing uncertainty, by these sources can cause either an underestimation or overestimation of cancer risk. The overestimation of cancer risk can lead to a waste of limited resources on unnecessary remediation, while an underestimation of the cancer risk can cause preventable health risks to the populace. Without simulation, estimating health risk becomes difficult, in that, it will be difficult to ascertain the likelihood that the populace would be at risk. A probabilistic approach using the Monte Carlo Simulation (MCS) which has the advantage of handling uncertainties and variabilities was used in this study to obtain a more realistic risk that is associated with the toxic pollutant. The Simulations for monthly risks were carried out using the Python program. The simulation was carried out 1000 times.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3.0. RESULT AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003e3.1. The Seasonal Variation Of The EC, DAD For Cd: Implications For A Monitoring And Mitigation That Is Continuous.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 shows the summary of the EC and DAD for Cd. The following are the results. The highest EC level was recorded in August with a value of 7.49\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e, while the lowest was recorded in March with a value of 0.38 \u0026mu;g/m\u003csup\u003e3\u0026nbsp;\u003c/sup\u003efollowed by April with a value of 0.51 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e. In November, Cd was below the detectible limit, which indicated little or no presence. Also, July had a significantly high value, 6.48 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e. The highest level of DAD was recorded in August with a value of \u0026nbsp;4.27E-06 \u0026mu;g/kg/day. March and April recorded the lowest values of 2.15E-07 \u0026mu;g/kg/day and 2.89E-07 \u0026mu;g/kg/day respectively. July also recorded a significantly high value of 3.69E-06 \u0026mu;g/kg/day. EC and DAD recorded their peaks between July and August. This may be attributed to environmental or human activities which may include: Industrial emissions, seasonal weather conditions, as well as atmospheric deposition that may affect the dispersion of Cd. These findings therefore show that there is a need for monitoring that are continuous, as well as continuous assessment which will help in mitigating health associated with Cd.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 606px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4: Summary of EC (mg/m\u003csup\u003e3\u003c/sup\u003e) and DAD (\u003c/strong\u003e\u003cstrong\u003e\u0026mu;g/kg/day) for Cd.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.15E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5.45E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.89E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8.16E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9.98E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e3.19E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5.81E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1.52E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.69E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e9.66E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4.27E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8.60E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9.18E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1.88E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.09E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7.53E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.51E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e4.06E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 606px;\"\u003e\n \u003cp\u003eBDL: Below detectible limit EC: Exposure Concentration DAD: Dermally Absorbed Dose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. The Seasonal Variation Of The EC, DAD For As: Implications For A Monitoring And Mitigation That Is Continuous.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 shows the summary of the EC and DAD for As for the study period. From this table, EC recorded its lowest in April with a value of 0.039 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e. The highest was recorded in November with a value of 1.49 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e and in December with a value of 1.47 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e. August recorded a BDL value meaning no contamination was measurable. For the DAD, the lowest was recorded in April also with a value of 6.71E-09 \u0026mu;g/kg/day, the highest was recorded in November as well with a value of \u0026nbsp;2.55E-07 \u0026mu;g/kg/day, as well as December with a value of 2.52E-07 \u0026mu;g/kg/day. As seen in the table, there was an increase that was significant from October through to December, and their levels were higher than those in the first half. There was variability in May and July which may be a result of pollution events that may be classified as sporadic which could be from emissions that are industrial. It may be due to meteorological conditions, or other environmental factors. November and December, which are in the dry-cold season, also recorded higher As values, which may be attributed to the stoppage in rainfall which in turn causes an accumulation of pollutants, as well as emission from biomass burning. There was a sharp drop in August, this may be because the heavy rainfall that occurred during that period may have cleared the atmosphere of As. The high concentration that was recorded in November and December indicates an exposure to higher health risks. March and April had lower exposure levels. This means that there was reduced health risk in these months which may be due to the dispersion of pollutants that is favourable. From the above analysis, there should be a focus on reducing the level of exposure to As in Peak months, as this will help in reducing health risks associated with As.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 636px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5: Summary of EC\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and DAD\u003c/strong\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u0026mu;g/kg/day)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for As.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.57E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e7.91E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e6.71E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.79E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e5.88E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e7.12E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e4.78E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e9.80E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e4.00E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e4.93E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e4.54E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.36E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.01E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.62E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.55E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.72E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.52E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e8.12E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 636px;\"\u003e\n \u003cp\u003eBDL: Below detectible limit EC: Exposure Concentration DAD: Dermally Absorbed Dose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTemporal Variations in Cd and As Concentrations: Implications for Air Quality and Public Health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 6 shows the summary of the monthly concentration for the various PTMs being studied. Also, figure 2 shows the plot of the monthly concentration mean of the various PTMs being studied.\u0026nbsp;For Cd, the highest mean concentration was recorded in November with a value of 0.00767, and its lowest value of mean concentration was recorded in March with a value of 0.000377. As recorded its highest mean concentration in November also with a value of 0.00329, and its lowest concentration was recorded in April with a value of 8.67E-05 (all concentrations are measured in\u0026nbsp;\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e). We can attribute the high value of the mean monthly concentration, in November as being a result of the fact that the harmattan season just set in, and also the school just resumed thereby causing much vehicular activities. Similarly, the months of March and April recorded the lowest values of concentrations. This may be due to the setting-in of the wet season, and also the school being going on a short break during this period, thereby causing a drastic drop in the activities going on in and around the school. From Figure 2a which shows the trend of Cd concentration for the year 2019, the concentration had an upward trend which further proves that seasons indeed affect the level of concentration of PTMs in the Atmosphere.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparing both Tables 6a and 7, none of the monthly mean concentrations for As in Table 6a exceeded the WHO standard nor did any exceed the European Union standard. For Cd, the mean monthly concentration for July, August, and November exceeded the standards set by both the WHO and the European Union. These months had values of 0.00648, 0.007487, and 0.00767 all measured in \u003cimg width=\"47\" height=\"20\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e. This goes to say that the mean monthly concentrations of As did not exceed the safe limit thereby posing no threat to people living and working in the vicinity. However, Cd recorded some high values, which pose a threat to the people living and working in the vicinity, just for the said months.\u0026nbsp;Cd is found as a mineral combined with elements such as oxygen (CdO), chlorine (CdC\u003csub\u003el2\u003c/sub\u003e), or sulfur (CdSO\u003csub\u003e4\u003c/sub\u003e or CdS). It enters the air from mining, industry, and burning coal and household wastes. Particles of Cd in the air can travel long distances before falling to the ground or water (Godwin et al 2013). The concentration of Cd found in PM\u003csub\u003e2.5\u003c/sub\u003e can be attributed to crustal sources (mineral dust) this is because, during the period of this study, there was heavy construction going on very close to the University. Furthermore, it can be attributed to various legal and illegal mining activities going on in and around the environs of Ilorin. Since Cd can travel for very long distances before falling to the ground (Muhibbu-din et al., 2021). Furthermore, biomass burning is another source of Cd as it exits naturally in biomass. This could be a major course of the concentration being higher than normal in the said months, as the University of Ilorin is home to a lot of agricultural activities, possibly, in a bid to prepare their lands for the farming season, a lot of biomass burning could have happened. Figure 2b shows the trend analysis of the concentration of As for the year 2019. The figure shows an upward trend in the concentration of As, indicating the effect of seasons on the concentration.\u003c/p\u003e\n\u003cp\u003eTable 6b shows the concentration level of Cd and As by other studies in some locations in Nigeria. Comparing the monthly mean concentration values in Table 6a with Table 6b. The mean concentration values for every month obtained from this study were all less than the values obtained in Calabar, Aba, Eket, and Port Harcourt all in Nigeria. This could be because all these cities are where crude oil is being mined. Furthermore, the result from this study was compared with that obtained by Muhibbu-din et al. (2021) which carried out their study in traffic-dense areas in Ilorin, as shown in Table 6b. Their results were quite higher than ours even though the studies were carried out in the same city but in different locations. Their studies were around traffic-dense areas, while ours was not dense with so much traffic. This could be a factor in the reason why their results were far higher than the results in this study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"489\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 489px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6a: Concentration (\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cimg width=\"61\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003cstrong\u003eof the PTMs being studied\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCadmium (Cd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArsenic (As)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003cstrong\u003eax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003cstrong\u003ein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003cstrong\u003eax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003cstrong\u003ein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e8.67E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.01107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.007487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.001303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.00438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.00231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 489px;\"\u003e\n \u003cp\u003eBDL: Below Detectible Limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 511px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6b: Concentration (\u003c/strong\u003e\u003cimg width=\"61\" height=\"21\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003cstrong\u003eResults from similar work around Nigeria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 261px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUno et al. (2013)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuhibbu-din \u0026nbsp; \u0026nbsp; et al. (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCalabar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eGeri Alimi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.0153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.0581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eAba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eOffa Garage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.0253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.0389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEket\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003ePost office\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.0357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.0404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePort Harcourt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.0527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.0396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 687px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 7: Summary of AQI standards\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHO (\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEuropean Union (\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUSEPA (\u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e----\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e----\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e3.4. Cancer Risk Assessment of Arsenic (As) via Inhalation and Dermal Contact: Seasonal Trends and Health Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 8 shows a summary of the monthly Cancer risk associated with As via inhalation and dermal contact. Figures 3a and 3b, shows the visual illustration of these summaries respectively. Figure 3a which is the bar chart showing the variation of CR associated with As via inhalation showed that there were relatively low risks recorded in March and April. The values of these months from Table 8 were 3.68E-04, and 1.57E-04 respectively. There was a gradual increase from May through July and September, and there was no detectable risk in August. There was a significant rise in October, as well as November, and December as shown in Figure 3a, with values of 2.36E-03, 5.96E-03, and 5.89E-03 respectively, as shown in Table 8. Figure 3b shows the CR associated with As via dermal contact. In August, just like inhalation didn\u0026rsquo;t record any dermal contact risk associated with Cancer. The highest risks were recorded in November and December with values of 3.82E-07, and 3.77E-07, respectively. The lowest was recorded in April with a value of 1.01E-08. The lowest risk being recorded in March to April could be related to the rainfall pattern i.e. these are the months in which the heavy rain sets in. Furthermore, there could have been a reduction in industrial, and human activities, which may be attributed to the lockdown that was due to the COVID-19 pandemic. The BDL value of August may be attributed to various dispersion factors that cause a clearing in the atmosphere. The highest risks that were recorded in October through to December were associated with various factors such as the season of the year (Dry season), as well as an increase in human activities leading to much more combustion (that is when the students resume fully for another session). Furthermore, the area being one where agricultural activities go on, there must have been an increase in biomass burning. Thermal inversion is a process that traps pollutants closer to ground level, and this is a feature often associated with these months. Comparing Zhang et al (2021), who carried out an analysis on the potential health risk of As in the atmosphere, they had a key finding that stated that As levels peaked in late autumn as well as winter, therefore correlating with the spike that was recorded in October through to December in this study. The CR associated with inhalation of As for November and December both having values of 5.96E-03, and 5.89E-03 respectively, when compared to Table 2 fell in the extremely high-risk region. This therefore means that this problem must be solved. The CR for As via dermal contacts was very low all in the 1E-07 range, which means they fall in the extremely low-risk region. Even though November and December had values that are lower than inhalation risks, \u003cstrong\u003e3.77E-07, and 3.82E-07\u003c/strong\u003e, it remains a matter of concern when chronic exposure is involved as this can lead to skin lesions and carcinogenesis (Weerasundara et al.,2018).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 672px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 8: Summary of the Cancer Risk of As via Inhalation, and Dermal Contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 286px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInhalation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 271px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDermal Contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e3.68E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.0042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2.36E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.25E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1.57E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.00041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.01E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.20E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1.38E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e8.83E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e4.19E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1.12E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e7.16E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e4.68E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e9.36E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e6E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.97E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e1.06E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e6.81E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.074E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e2.36E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.51E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e3.65E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e5.96E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3.82E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e9.49E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e5.89E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3.77E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e2.04E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBDL: Below Detectable Limit. \u0026nbsp;SD: Standard Deviation. CR: This is unitless\u003c/p\u003e\n\u003cp\u003eTable 9 shows a summary of the CR associated with Cd through Inhalation and Dermal Contact. Via inhalation, there is a significant variation with July recording the highest value with a value of 5.28E-03 and August with a value of 6.10E-03, and the lowest being recorded in April with a value of 4.13E-04. These values, when compared with Table 2, fell in the region of extremely high risk, indicating that there was a severe risk associated with Cd via inhalation in all the months that were studied. Furthermore, from these values, it can be said that there is a potential increase in airborne concentrations of Cd during these months. Figure 4a which is a visual representation of CR associated with Cd via inhalation, shows a trend line that suggests that there is a very slight upward trend, which means that over time there was no significant increase in CR via inhalation. The table also shows the CR associated with Cd via dermal contact. For this exposure, the values, compared to inhalation were much lower with values ranging from the lowest in March with a value of 1.48E-06 to the highest in August with a value of 2.94E-05. July also had a high value of 2.54E-05. Compared with Table 2, all the CR values are associated with Dermal contact in the low-risk region. Figure 4a shows the monthly variation of CR for Cd via dermal contact. There was a trend line that showed the trend of the CR and it indicated that there was a slightly stronger upward trend than that of inhalation. This suggested a more modest increase in dermal exposure risk over time. From this study, the inhalation route is the most dominant route of intake for Cd-related Cancer Risk, this is because the inhalation had values of two orders of magnitude higher than dermal contact. July and August had peak exposure as shown in Figure 4, and this suggests that there were higher levels of Cd in the atmosphere for that period, which could be due to human activities such as the resuspension of dust, and industrial emission. It could also have been because of the dry conditions of the atmosphere during that period. Comparing with studies from other regions such as Nawrot et al. (2015); Beveridge et al. (2010); and Chen et al. (2016) where they concluded that there was an association between exposure to atmospheric Cd and increased risk of lung cancer, their study also highlights the importance of monitoring environmental Cd levels to protect public health. These studies, including this present study, emphasize the fact that inhalation is the primary route of exposure to Cd which in turn leads to an increase in the risk of lung cancer. The consistency between these studies reinforces the need to reduce airborne Cd Concentration for the sake of the health of the public, as this can be achieved by adopting regulatory measures.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 9: Summary of the Cancer Risk for Cd via, Inhalation, and Dermal contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInhalation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 269px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDermal Contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.07E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2.25E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.48E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.03E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e4.13E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e4.83E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.99E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.45E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.43E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3.85E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e6.87E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e9.63E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e8.31E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2.35E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4.00E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e4.58E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e5.28E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e4.31E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.54E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e2.91E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e6.102E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6.55E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.94E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.75E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.31E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3.098E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e6.32E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e3.79E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e2.99E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3.20E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.44E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.35E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eBDL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 134px;\"\u003e\n \u003cp\u003e3.59E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.73E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.73E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e8.70E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003eBDL: Below Detectable Limit. \u0026nbsp; \u0026nbsp; SD: Standard Deviation. CR: This is unitless\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e3.5. Cancer Risk Assessment of Cadmium (Cd) via Inhalation: Monte Carlo Simulation Analysis and Comparison with Existing Studies.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1. Summary of Monte Carlo Simulation Results for Cd Inhalation Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5a \u0026ndash; I and Table 10 show the Monte Carlo Simulation for cancer risk for Cd via inhalation for March, April, May, June, July, August, September, October, and December. For March, as shown in Table 10 and Figure 5a the 5\u003csup\u003eth\u003c/sup\u003e percentile which is also known as the best-case scenario was 2.83E-05 which is a borderline value to the 1E-06 value as stated in Table 2 meaning it was in the extremely low-risk region, making it completely acceptable. The 95\u003csup\u003eth\u003c/sup\u003e percentile which is the worst-case scenario has a value of 5.91E-04 in the high-risk region, a region which meant that attention should be paid to it, and actions should be carried out. The 50\u003csup\u003eth\u003c/sup\u003e percentile was 3.07E-04 which is used used to know the level of skewness of the mean risk. The value was a bit higher than the mean risk, which meant that the mean value was right-skewed. The mean risk which is the most likely estimation recorded a value of 3.06E-04, stating that the likelihood of anyone contracting cancer in March was 3.06 people in 10000. Comparing the mean value with what is in Table 2, it fell in the high-risk region which means that attention should be paid to it, and actions should be taken to solve it.\u003c/p\u003e\n\u003cp\u003eIn April as reported in Table 10, and Figure 5b, the 5\u003csup\u003eth\u003c/sup\u003e percentile which is the best-case scenario had a value of -1.23E-05, a value that was far lower than the acceptable range of 10\u003csup\u003e-6\u003c/sup\u003e as shown in Table 2. This just meant that the value was acceptable, and shouldn\u0026rsquo;t be bothered about. The 95\u003csup\u003eth\u003c/sup\u003e percentile had a value of 8.4E-04 which is greater than the receptor\u0026rsquo;s acceptable range. It fell in the high-risk region according to Figure 4 meaning that attention should be paid to it, as well as actions need to be taken to solve the problem. The 50\u003csup\u003eth\u003c/sup\u003e percentile which just shows the skewness of the risk had a value of 4.12E-04 which is a teeny-little bit lower than the mean value of 4.13e-04. This points out the fact that the mean risk value which is the most probable risk was tending towards the low region of the risk. As stated earlier, the mean risk value which is the most probable risk had a value of 4.12E-04 which was in the high-risk region as stated in Table 2. This meant that the risk should be paid attention to and that actions should be taken to mitigate the effect. Furthermore, the mean risk meant that approximately 4 out of 10000 were at risk for April.\u003c/p\u003e\n\u003cp\u003eFor May as shown in Table 10, and Figure 5c, the 5\u003csup\u003eth\u003c/sup\u003e percentile which denoted the best-case scenario had a value of -2.39E-03 which fell in the extremely low-risk region as shown in Table 2, which meant that there was no feasible problem associated with the inhalation of Cd for this period. The 95\u003csup\u003eth\u003c/sup\u003e percentile which denoted the worst-case scenario had a value of 3.11E-03 which fell in the extremely high-risk region as shown in Table 2. This indicated that the situation must be solved. The 50\u003csup\u003eth\u003c/sup\u003e percentile which showed the skewness of the risk associated with the PTM had a value of 1.42E-03 which was a little bit lower than the mean risk value meaning it was left skewed. This indicated that the risk tended to the lower region. The mean risk value which indicated the most probable risk had a value of 1.43E-03. As shown in Table 2, this indicated that this value fell in the extremely high-risk region. It also meant that approximately 1.4 persons in 1000 were at risk. Because of this value, this problem should be solved as stated in Table 2.\u003c/p\u003e\n\u003cp\u003eIn June, as shown in Table 10, and Figure 5d, the 5\u003csup\u003eth\u003c/sup\u003e percentile which was the best-case scenario had a value of 6.56E-05 which fell in the low-risk region as classified in Table 2. This shows that the lower risk probability was still in the safe zone thereby needing no intervention. The worst case scenario which is the 95\u003csup\u003eth\u003c/sup\u003e percent had a value of 1.62E-03, which according to Table 2 falls in the high-risk range, a situation that must not be left unsolved. The most likely risk i.e. the mean risk had a value of 8.32E-04 and the 50\u003csup\u003eth\u003c/sup\u003e percentile, i.e. the median risk had a value of 8.27E-04. The 50\u003csup\u003eth\u003c/sup\u003e percentile was just the skewness of the risks that were simulated by the MCS. Since the 50\u003csup\u003eth\u003c/sup\u003e percentile was less than the mean risk, it meant that the risk was left skewed i.e. the mean risk was tending towards the lower region. The mean risk meant that approximately 8 people in 10000 were at risk. From Table 2, it is seen that the mean risk fell in the high-risk region, which indicates that attention should be paid, and actions be taken to help solve the problem.\u003c/p\u003e\n\u003cp\u003eAs shown in Table 10, and Figure 5e, July had the following results. The 5\u003csup\u003eth\u003c/sup\u003e percentile which was the best-case scenario had a value of 2.34E-04. This was in the high-risk region as shown in Table 2. This meant that the populace was at a high level of risk. The 95\u003csup\u003eth\u003c/sup\u003e percentile which was the worst-case scenario had a value of 1.01E-02 which was in the extremely high-risk region. This goes to say that the risk ranged between the 5\u003csup\u003eth\u003c/sup\u003e percentile, and the 95\u003csup\u003eth\u003c/sup\u003e percentile indicating that the populace was at serious risk. Since the lower percentage was in the high-risk region. The mean risk which is the most probable risk had a value of 5.32E-03, which meant that approximately 5 people in a thousand were at great risk. Compared with Table 2, the value falls in the extremely high-risk region, and there could be only one (1) solution, and that is to solve the problem. The 50\u003csup\u003eth\u003c/sup\u003e percentile had a value of 5.18E-03. The 50\u003csup\u003eth\u003c/sup\u003e percentile which is the median risk, shows the level of skewness of the risk. Since the 50\u003csup\u003eth\u003c/sup\u003e percentile was less than the mean risk, it meant that the risk was left-skewed, i.e. tending to the lower region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor August, as shown in Table 10, and Figure 5f, the 5\u003csup\u003eth\u003c/sup\u003e percentile which is the best-case scenario had a value of 6.08E-03 which was greater than the acceptable limit as stated in Table 2, and the worst-case scenario had a value of 1.06E-02 which is higher than the acceptable receptor range of 1E-03. The best-case scenario fell in the extremely high-risk region as shown in Table 2, and attention should be paid to it, as well as appropriate action should be taken. The worst-case scenario was in the extremely high-risk region, and according to Table 2, this problem must be solved. This just shows the range of the risk of the populace and can be seen from the result of the mean risk. The mean risk which is the most probable risk shows this. It had a value of 6.08E-03 which meant that approximately 6 in 1000 were at great risk. Furthermore, looking at Table 2, the mean value was in the extremely high-risk region, and from that same table it states that the problem must be solved. The 50\u003csup\u003eth\u003c/sup\u003e percentile was 6.09E-03, which was a little bit higher than the mean risk value, meaning that it was right-skewed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Table 10, and Figure 5g, September, had a best-case scenario (5\u003csup\u003eth\u003c/sup\u003e percentile) with a value of 3.41E-04 which fell according to Table 2 in the region of high-risk. For this kind of situation, the solution that was proffered is the fact that attention should be paid to the situation, not only that, the appropriate actions should be taken. The month had a worst-case scenario of 2.28E-03, which fell, according to the ranking in Table 2, in the extremely high-risk region. The only solution that was proffered was the fact that a solution must be provided for this situation. The 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentile just show the range and the level to which the population was at risk. The risk lay in the range of high-risk to extremely high-risk meaning they stood a great risk. This is shown in the mean risk which is the most probable risk, which had a value of 1.31E-03. Compared with Table 2, it fell in the region of extreme high-risk, with approximately 1 person per 1000 being at risk. According to Table 2, the proffered solution is that this situation must be solved. The 50\u003csup\u003eth\u003c/sup\u003e percentile, which shows the skewness of the risk had a value of 1.30E-03 which was a little bit less than the mean value. This meant that the risk was right-skewed\u003c/p\u003e\n\u003cp\u003eIn October, as shown in Table 10, and Figure 5h, \u0026nbsp;had a 5\u003csup\u003eth\u003c/sup\u003e percentile risk, which is the best-case scenario, of 1.43E-03. Compared with Table 2, the best-case scenario fell in the range of extremely high risk. The worst case scenario, which is the 95\u003csup\u003eth\u003c/sup\u003e percentile risk had a value of 5.7E-03E-03 which means that according to this, approximately 6 people in 1000 were at risk. Compared with Table 2, the risk fell in the extremely high-risk region meaning that. This range of value was indeed a very dangerous one as the lowest (best-case scenario) was in the extremely high-risk region. This goes to say that no matter what, for October, everyone was at risk. This can be seen from the mean risk, which is the most probable risk had a value of 3.59 E-03, which insinuated that approximately 4 people in 1000 were at risk. Compared with Table 2, the mean risk fell in the extremely high-risk region, and the solution preferred to this was that it must be solved, according to Table 2.\u003c/p\u003e\n\u003cp\u003eFor December, as shown in Table 10 and Figure 5i, the 5\u003csup\u003eth\u003c/sup\u003e percentile which is the best-case scenario had a value of 1.45E-03 which fell in the extremely high-risk region. The 95\u003csup\u003eth\u003c/sup\u003e percentile which was the worst-case scenario had a value of 5.69E-03 which also fell in the extremely high-risk region as shown in Table 2. This just goes to indicate that the risk faced by the populace was high as the range was from extreme highness to another point of extreme highness. This was shown in the mean risk which had a value of 3.61E-03. This meant that approximately 4 people in 1000 were at risk. Compared with Table 2, this value falls in the extremely high-risk region, and according to that same Table 2, the only proffered solution was that the problem must be solved. The 50\u003csup\u003eth\u003c/sup\u003e percentile which is the median, and also shows the degree of skewness, had a value of 3.61E-03 as shown in Table 10, and Figure 5i. This just goes to show that there was no skewness in the risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was compared with Nawrot et al. (2015), who found that Cd was associated with a relative risk of 1.22 for total cancer, and a risk of 1.68 for Lung cancer. Ajah et al. (2015), found that 91% of ecological risk was associated with Cd, thereby taking note of the significant presence of this metal in the area. This therefore reinforces the result from this study that great risk is associated with the intake of Cd via inhalation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 10: Summary of MCS result for Cd via Inhalation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.06E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.83E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.07E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.91E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.13E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-1.23E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.12E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.39E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.43E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-2.39E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.42E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.11E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.32E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.57E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.27E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.62E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.23E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.35E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.18E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.01E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.08E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.58E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.09E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.06E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.31E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.41E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.30E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.28E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.59E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.48E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.60E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.70E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.61E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.45E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.61E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.69E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eNA: Not Available\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2. Summary of Monte Carlo Simulation Results for Cd Dermal Contact Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 6a-I shows the probabilistic health risk assessment of cadmium via dermal contact for March, April, May, June, July, August, September, October, and December.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor March, from Table 11, and Figure 6a the 5\u003csup\u003eth\u003c/sup\u003e percentile which is the best-case scenario had a value of 1.13e-07 which is less than the expected limit of 1e-06 as shown in Table 2. This means that there is no associated risk. \u0026nbsp;The 95\u003csup\u003eth\u003c/sup\u003e percentile which is the worst-case scenario had a value of 2.84e-06 which is below the receptor-acceptable limit. This shows that the mean risk which is the most probable risk will fall in this range. This is shown in the mean risk which had a value of 1.47E-06. Compared with Table 2, it fell in the extremely low-risk range. From the same Table 2, it can be seen that is acceptable, without any associated risk. The 50\u003csup\u003eth\u003c/sup\u003e percentile had a value of 1.47E-06 also. The 50\u003csup\u003eth\u003c/sup\u003e percentile which is the median score shows the skewness of the risk. However, the mean risk and 50\u003csup\u003eth\u003c/sup\u003e percentile had the same value thereby indicating that there was no skewness.\u003c/p\u003e\n\u003cp\u003eSimilarly from Table 11, and Figure 6b, April had a value of 6.02E-08 for the 5\u003csup\u003eth\u003c/sup\u003e percentile which is the best-case scenario. This fell into the extremely low-risk region, as shown in Table 2. This means that there was no risk associated with Cd at this point. The 95\u003csup\u003eth\u003c/sup\u003e percentile which was the worst-case scenario, had a value of 4.05E-06. Compared with Table 2, this fell in the extremely low-risk region, indicating that there was no risk associated with Cd at this point. This range shows that the cancer risk can not be high. This can be seen in the mean risk which had a value of 2.01E-06. The mean risk is the most probable risk associated with Cd via this route. Compared with Table 2, this value fell into the extremely low-risk range, meaning that there was no risk associated with it. The 50\u003csup\u003eth\u003c/sup\u003e percentile which is the median score, showed the level of skewness of the mean risk. It had a value of 2.01E-06 which was the same as the value of the mean risk, meaning that the risk was not skewed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Table 11, and Figure 6c, the 5\u003csup\u003eth\u003c/sup\u003e percentile which was the best-case scenario had a value of 1.04E-06. Compared with Table 2, this value fell in the extremely low-risk region, meaning it is completely acceptable. The worst-case scenario which is the 95\u003csup\u003eth\u003c/sup\u003e percentile had a value of 1.5E-05. When compared with Table 2, the value fell in the low-risk range which meant that the risk was nothing to be bothered about. The mean risk value was 6.86E-06. Compared with Table 2, this value fell in the range of extremely low risk, which means that the risk is completely acceptable. The mean risk stands for the most probable risk. The 50\u003csup\u003eth\u003c/sup\u003e percentile which is the median risk had a value of 6.88E-08. The 50\u003csup\u003eth\u003c/sup\u003e percentile also shows the degree of skewness of the mean risk. The value of the 50th percentile was more than the mean risk meaning that the risk was right-skewed i.e. the mean is tending towards the high value region.\u003c/p\u003e\n\u003cp\u003eIn June, the 5\u003csup\u003eth\u003c/sup\u003e percentile, 95\u003csup\u003eth\u003c/sup\u003e percentile, 50\u003csup\u003eth\u003c/sup\u003e percentile, and mean risk had values of 1.97E-07 (which was lower than the acceptable limit, indicating no risk to the populace), 7.74E-06 (which was less than the receptor\u0026rsquo;s acceptable limit of 10\u003csup\u003e-3\u003c/sup\u003e, thereby indicating no risk), 4.02E-06, and 4.02E-06 respectively. The mean values were borderline with the acceptable set limit for Cancer risk assessment as shown in Table 2. This meant that approximately 4 people in 100000 were at the risk of infection, this is a very minimal risk and can be ignored.\u003c/p\u003e\n\u003cp\u003eDuring July, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst case scenario) had values of 2.53E-05, 8.01E-07 (which was slightly greater than the acceptable limit of 1e-06, indicating little risk), 2.54E-05(which was less than the receptor\u0026rsquo;s limit, which indicated little risk to the populace), and 4.94E-05, respectively. The mean value which is the most likely risk value was greater than the acceptable limit of 1E-06 but is still in the negligible range as shown in figure 4.\u003c/p\u003e\n\u003cp\u003eFor August, the mean risk (the most probable risk), 5\u003csup\u003eth\u003c/sup\u003e percentile (best case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst case scenario) had values of 2.94E-05 (indicating that approximately 3 people out of 100000 were at risk of being infected), 8.40E-06 (was less than the acceptable limit of 1E-06, indicating no risk), 2.94E-05, 5.10E-05 (was less than the receptor\u0026rsquo;s acceptable limit, indicating no risk) respectively. These values were all borderline values with the acceptable limit of 1E-06 as shown in Table 2.\u003c/p\u003e\n\u003cp\u003eFor September, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile, 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile had values of 6.33E-6, 1.59E-06, 6.33E-06, 1.10E-05, respectively. The mean risk which was the most probable risk had a value that was borderline with the limit which has no cause for concern. Furthermore, the mean risk indicates that approximately 6 people out of 1000000 were the risk of infection. The best-case scenario had a value that indicated no risk, the same with the worst-case scenario. These values were all borderline values with the acceptable limit of 1e-06 as shown in Table 2.\u003c/p\u003e\n\u003cp\u003eFor October, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst case scenario) had values of 1.44E-05, -4.46E-06, 1.43E-05, 3.34E-05, respectively. The mean risk which is the most probable risk had a value that was one that according to Table 2, is not a cause for concern. This also applies to the best-case scenario, and worst-case scenario, indicating risk level that does not call for concern. These values were all borderline values with the acceptable limit of 1e-06 as shown in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor December, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile, 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile had values of 1.71E-05, 6.78E-06, 1.72E-05, 2.75E-05, respectively. These values were all borderline values with the acceptable limit of 1e-06 as shown in Table 2. This goes further to indicate that according to Table 2, there was no risk associated with this route for this month.\u003c/p\u003e\n\u003cp\u003eThis study was compared with Eid et al. (2024) which assessed the health risks associated with Heavy metals found in groundwater using a Monte Carlo simulation. Their results indicated that the Hazard quotient for Cd did not exceed the limits that were stated limits. This suggested that there was a minimal health risk from that region. However, this study presents a different one. The mean risks from this study over the months were higher than the results from their study. Also, Ghaderpoori et al. (2020), carried out an assessment of the CR associated with Cd found in cosmetics, via dermal contact. Their study showed the mean hazard quotient for Cd was lower than that of the present study. An assessment of heavy metals found in cosmetics using MCS revealed that the mean hazard quotient (HQ) for Cd was 1.05E-03, which is lower than the mean risks observed in the current study for most months. This suggests that dermal exposure to Cd in the studied environment may pose a higher risk compared to exposure through cosmetic products in Iran.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eTable 11: Summary of MCS result for Cd via Dermal Contact\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.47E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.13E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.47E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.84E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.01E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.02E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.01E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.05E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.86E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-1.04E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.88E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.50E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.02E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.97E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.02E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7.74E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.53E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.01E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.54E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.94E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.94E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.40E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.94E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.10E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.33E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.59E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.33E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.10E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.44E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-4.46E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.43E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.34E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.71E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.78E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.72E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.75E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eNA: Not Available\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.3. Summary of Monte Carlo Simulation Results for As Inhalation Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 12 and Fig 7a-i show the summary of the MCS result of Cancer Risk associated with As via Inhalation for March through to December, excluding the month of August. The best fit, 50\u003csup\u003eth\u003c/sup\u003e percentile, and worst risk scenario had values of 6.81E-05, 3.69E-04, and 6.78E-04 respectively. The best fit was greater than the acceptable limit, and the worst risk was less than the receptor limit of 10E-03 as shown in Table 2. The mean risk which is the most likely risk had a value of 3.71E-04 and was left-skewed, i.e. it could be lower. The value of the mean risk fell in the range of a growing concern for the quality of air for March. This goes to say that approximately 4 people out of 10000 are at risk of an infection.\u003c/p\u003e\n\u003cp\u003eFor April, the best-case scenario, worst-case scenario, mid-score, and mean risk had values of 8.74E-05, 1.57E-04, 2.25E-04, and 1.56E-04 respectively. The value of the best-case scenario was greater than the acceptable limit, and the worst-case scenario was a little less than the receptor\u0026rsquo;s limit as shown in Table 2. The mean risk which is the most probable risk had a value that was classified in Table 2 as a cause for concern. The mean value showed that approximately 2 people in 10000 were at the risk of being infected. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor May, as shown in Table 12, it can be seen that the mean risk, best-case scenario, mid score, and worst-case scenario had values of 1.38E-03, -1.31E-03 (less than the acceptable limit i.e. no risk), 1.39E-03, and 4.12E-03 respectively. The value for the best-case scenario was in the range that calls for great concern according to Table 2. The mean risk which is the most probable risk had a value that showed that approximately 1 out of 1000 people were at the risk of infection. The value of the mean risk showed that there is a cause for alarm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor June, as shown in the table 12. The mean risk, best-case scenario, mid score, and worst-case scenario had values of 1.12E-03, 7.30E-04, 1.12E-03, and 1.50E-03 respectively. Best best-case scenario was greater than the acceptable limit as shown in Table 2, and the worst-case scenario was in the receptor\u0026rsquo;s risk. This indicates that the populace is at great risk of being infected. The mean risk which is the most probable risk showed that approximately 1 person out of 1000 was at risk of being infected. Furthermore, the value showed that there is a cause for concern.\u003c/p\u003e\n\u003cp\u003eThe month of July had for the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile, 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile, values of 9.11E-04, -9.87E-04, 8.95E-04, and 2.84E-03 respectively. The best-case scenario was higher than the acceptable limit, while the worst-case scenario was borderline with the receptor limit. All these values were in the range of having a cause for concern according to Table 2. The value of the mean risk which is the most probable risk indicated that approximately 9 people out of 1000 stood a risk of being infected.\u003c/p\u003e\n\u003cp\u003eFor September, 1.07E-03, 1.87E-04, 1.06E-03, and 1.96E-03 were the values gotten for the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile, 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile as shown in Table 12 above. The best-case scenario was less than the acceptable limit, while the worst-case scenario was borderline with the receptor\u0026rsquo;s limit. The mean risk value which is the most probable risk showed that approximately 1 person in 1000 stood the risk of being infected. Furthermore, in comparison with Table 2, the mean value shows that there is a cause for concern.\u003c/p\u003e\n\u003cp\u003eOctober had, for the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile, 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile, values of 2.35E-03, 9.62E-04 (which was greater than the acceptable limit as shown in Table 2), 2.35E-03, and 3.75E-03 (which was borderline with the receptor acceptable limit of 10e-03) respectively. The mean risk which is the most probable risk had a value that indicates that approximately 2 people out of 1000 were at the risk of an infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor November, 2.36E-03, 9.37E-04, 2.34E-03, and 3.82E-03 were the values for the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best case scenario), the 50\u003csup\u003eth\u003c/sup\u003e percentile, and the 95\u003csup\u003eth\u003c/sup\u003e percentile. The best-case scenario had a value that was greater than the acceptable limit of 1e-06, and the worst-case scenario had a value that was borderline with the receptor\u0026rsquo;s acceptable limit of 1e-03 indicating that the populace was at risk. The mean risk which is the most probable risk had a value that indicated that approximately 2 people out of 1000 were at the risk of infection. The value of the mean risk showed that there is a great risk as compared to Table 2, and something needs to be done.\u003c/p\u003e\n\u003cp\u003eFinally, for December, 5.87E-03, 2.75E-03, 5.87E-03, and 9.03E-03, were the values recorded for the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile, 50\u003csup\u003eth\u003c/sup\u003e percentile, 95\u003csup\u003eth\u003c/sup\u003e percentile, respectively. The best-case scenario was greater than the acceptable limit of 1e-06, and the worst-case scenario was borderline with the receptor limit of 1e-03. This indicated the presence of health risks. The mean value which is the most probable risk had a value that indicated that approximately 6 people in a thousand are at the risk of an infection. This therefore indicates that the populace is at a great risk when the value of mean risk is compared to Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results from this study for October, November, and December face a greater risk than the results in the study carried out by Nawrot et al. (2015). Similarly, this study was compared with that of Yu et al. (2017). The results obtained in March, and September were comparable to that of their study, however, the extremely high-risk value in December surpasses that which was recorded in the study. Ajah et al. (2015) had results that align with the results of October, November, and December of this study.\u003c/p\u003e\n\u003cp\u003eIn conclusion, there were extreme risk values that were observed in some months especially in December exceeding the report from other studies indicating factors that are localized causing a heightening in exposure risk. Further studies may be necessary to ascertain these localized factors.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eTable 12: Summary of MCS result for As via Inhalation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.71E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.81E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.69E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.78E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.56E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.74E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.57E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.25E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.38E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-1.31E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.39E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.12E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.12E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7.30E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.12E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.50E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9.11E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-9.87E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.95E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.84E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.07E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.87E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.06E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.96E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.35E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9.62E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.35E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.75E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.36E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9.37E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.34E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.82E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.87E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.75E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.87E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9.03E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eNA: Not Available\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.4. Summary of Monte Carlo Simulation Results for As Dermal Contact Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 13 shows the summary of the MCS results as graphically shown in Figure 8a-i below, for March to December, for As via Dermal contact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor March, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 2.37E-08, 3.91E-09, 2.37E-08, and 4.32E-08 respectively. The best-case scenario had a value that was lower than the acceptable limit of 1e-06, the same as the worst-case scenario. The mean risk value when compared with Table 2 indicated that the populace was not at risk.\u003c/p\u003e\n\u003cp\u003eFor April, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 1.01E-08, 5.65E-09, 1.00E-08, and 1.45E-08 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk was lower than the acceptable limit as shown in Table 2. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor May, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 8.84E-08, -8.41E-08, 8.80E-08, and 2.64E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2.\u003c/p\u003e\n\u003cp\u003eFor June, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 7.15E-08, 4.73E-08, 7.16E-08, and 9.58E-08 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2.\u003c/p\u003e\n\u003cp\u003eFor July, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 5.94E-08, -5.99E-08, 5.92E-08, and 1.81E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2.\u003c/p\u003e\n\u003cp\u003eFor September, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 6.83E-08, 1.19E-08, 6.76E-08, and 1.26E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2.\u003c/p\u003e\n\u003cp\u003eFor October, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 1.52E-07, 6.35E-08, 1.52E-07, and 2.40E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2.\u003c/p\u003e\n\u003cp\u003eFor November, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 3.81E-07, 2.90E-07, 3.82E-07, and 4.72E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2.\u003c/p\u003e\n\u003cp\u003eFor December, the mean risk, 5\u003csup\u003eth\u003c/sup\u003e percentile (best-case scenario), 50\u003csup\u003eth\u003c/sup\u003e percentile, and 95\u003csup\u003eth\u003c/sup\u003e percentile (worst-case scenario), had values of 3.76E-07, 1.77E-07, 3.75E-07, and 5.76E-07 respectively. The best-case scenario had a value that was less than the acceptable limit, same with the worst-case scenario showing that there was no risk associated with As via this route. The mean risk which is the most probable risk showed that the populace was not at risk, as compared with Table 2.\u003c/p\u003e\n\u003cp\u003eThere has been a consistent association of As exposure with an increase in cancer risk. A review by the American Cancer Society highlighted a relative risk of 2.1 for skin cancer associated with arsenic exposure (www.cancer.org accessed 20/03/2025). Similarly, a retrospective cohort study carried out by the American Cancer Society, in Taiwan reports that there was an odd ratio of 7.58 associated with keratinocyte carcinoma at cumulative As exposure level. This study has not been able to associate its findings with dermal arsenic exposure and cancer risk.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 13: Summary of MCS result for As via Dermal Contact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.37E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.91E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.37E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.32E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.01E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.65E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.45E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.84E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-8.41E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e8.80E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.64E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7.15E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.73E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7.16E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e9.58E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.94E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-5.99E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.92E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.81E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.83E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.19E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.76E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.26E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.52E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.35E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.52E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.40E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.81E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.90E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.82E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.72E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.76E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.77E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.75E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e5.76E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eNA: Not Available\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4.0. CONCLUSION","content":"\u003cp\u003eThis study tried to evaluate and assess the level of concentration of Cd and As in the study area over the seasons in the study year. Further, cancer risks associated with Cd and As were assessed using a probabilistic method (Monte Carlo Simulation). The results of this study showed that the monthly concentration of both Cd and As didn\u0026rsquo;t exceed the limit that was set by some regulatory bodies such as the WHO, and EU. Furthermore, there was a greater risk (cancer) associated with these metals via inhalation, than dermal contact indicating that Inhalation was the most prominent pathway for these metals to get into the body from the atmosphere. The variation of the risk associated with these metals showed that seasons have a major effect on them, as the risks were higher in the dry months of the year 2019. The implication of this is that people stand great health risks during the dry seasons of the year, as these particles can stay longer in the atmosphere, and can move over long distances during these periods. This study was limited, making it impossible to achieve the desired robustness for the study. For example, the availability of more data from the study environment could have helped in the analysis of the changes in the properties of PMs in the study environment over time. The lack of moveable equipment that could help in collecting data around various areas in the state, would have helped us in carrying out a spatial analysis of As and Cd. The policy-makers can help by providing funds for an in-depth study of PTMs found in the atmosphere, which in turn will help them in developing standards for this area. They can also work hand-in-hand with international regulatory bodies to help in the mitigation of the effects of these metals. In conclusion, this study will help policy-makers achieve primarily, one of the Millennium Development Goals (MDGs) which is a sustainable environment, as it provides an insight into what is wrong, and where to come in. Also, another MDG that can help to sort out secondarily is infant mortality, as a polluted atmosphere is at the forefront of infant mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Authors declare no competing interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot Applicable\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthors’ Contribution Statement\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eConceptualization: O. A. Falaiye, S. Nwabachili, M. Orosun\u003c/li\u003e\n \u003cli\u003eData curation: T. B. Ajibola\u003c/li\u003e\n \u003cli\u003eFormal analysis: S. Nwabachili, O.E. Abiye\u003c/li\u003e\n \u003cli\u003eInvestigation: S. Nwabachili, O. A. Falaiye\u003c/li\u003e\n \u003cli\u003eMethodology S. Nwabachili\u003c/li\u003e\n \u003cli\u003eProject administration: P. O. Ijila\u003c/li\u003e\n \u003cli\u003eSoftware: S. Nwabachili, M. M. Orosun\u003c/li\u003e\n \u003cli\u003eSupervision: O. A. Falaiye\u003c/li\u003e\n \u003cli\u003eValidation: O. A. Falaiye, M. Orosun\u003c/li\u003e\n \u003cli\u003eVisualization: S. Nwabachili\u003c/li\u003e\n \u003cli\u003eWriting – original draft: S, Nwabachili\u003c/li\u003e\n \u003cli\u003eWriting – review and editing: S. Nwabachili\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Data used is available on the SPARTAN website\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbiye O. E., Imoh B. O., Godwin C. E., (2013). Elemental characterization of urban particulates at receptor locations in Abuja, north-central Nigeria. Atmospheric Environment. 2013; 81: 695e701\u003c/li\u003e\n \u003cli\u003eAjah, K.C., Ademiluyi, J. \u0026amp; Nnaji, C.C. Spatiality, seasonality and ecological risks of heavy metals in the vicinity of a degenerate municipal central dumpsite in Enugu, Nigeria. J Environ Health Sci Engineer\u003cem\u003e.\u003c/em\u003e 2015;\u003cstrong\u003e13\u003c/strong\u003e:15. https://doi.org/10.1186/s40201-015-0168-0\u003c/li\u003e\n \u003cli\u003eBeveridge R, Pintos J, Parent ME, Asselin J, Siemiatycki J. 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PMID: 31885066.\u003c/li\u003e\n \u003cli\u003eGoudarzi G. Nadali A., Sahar G., Esmaeil I., Hamid R., Ali A., Farzaneh A., Sina D., Majid F., Mohammad J. Health risk assessment on human exposed to heavy metals\u003cbr\u003ein the ambient air PM10 in Ahvaz, southwest Iran. International Journal of Biometeorology. 2013.\u003cbr\u003ehttps://doi.org/10.1007/s00484-018-1510-x\u003c/li\u003e\n \u003cli\u003eGoudarzi G, Idani E, Alavi N, Salmanzadeh S, Babaei AA, Geravandi S, Mohammadi MJ, Mahboubi M, Moradi M. Association of polycyclic aromatic hydrocarbons of the outdoor air in Ahvaz, southwest Iran during warm-cold season. Toxin Rev. 2017;36(4):282-289.\u003c/li\u003e\n \u003cli\u003eGunawardana, C., Goonetilleke, A., Egodawatta, P., Dawes, L., Kokot, S. Source characterisation of road dust based on chemical and mineralogical composition. Chemosphere 2017;87:163\u0026ndash;170.\u003c/li\u003e\n \u003cli\u003eHadad, K., Mehdizadeh, S., Sohrabpour, M. Impact of different pollutant sources on shiraz air pollution using SPM elemental analysis. Environ. Int. 2003;29:39\u0026ndash;43\u003c/li\u003e\n \u003cli\u003eHaque, A. \u003cem\u003eet al\u003c/em\u003e. Carcinogenic and non-carcinogenic human health risk from exposure to heavy metals in surface water of Padma River. Res. J. Environ. Toxicol\u003cem\u003e.\u003c/em\u003e2018;\u003cstrong\u003e12\u003c/strong\u003e(1):18\u0026ndash;23.\u003c/li\u003e\n \u003cli\u003eLi, F. \u003cem\u003eet al\u003c/em\u003e. Spatial distribution and fuzzy health risk assessment of trace elements in surface water from Honghu Lake. IJERPH. 2017;\u003cstrong\u003e14\u003c/strong\u003e:1011. https://doi.org/10.3390/ijerph14091011.\u003c/li\u003e\n \u003cli\u003eLi, Y., Zhang, Z., Liu, H., Zhou, H., Fan, Z., Lin, M., et al. Characteristics, sources and health risk assessment of toxic heavy metals in PM2.5 at a megacity of southwest China. Environ. Geochem. Health. 2016;38:353\u0026ndash;362.\u003c/li\u003e\n \u003cli\u003eManasreh, W.A. Assessment of trace metals in street dust of mutah city, Kurak, Jordan. Carpath. J. Earth Environ. 2010;5:5\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eMohanraj, R., Azeez, P., Priscilla, T. Heavy metals in airborne particulate matter of urban Coimbatore. Arch. Environ. Contam. Toxicol. 2004;47:162\u0026ndash;167.\u003c/li\u003e\n \u003cli\u003eOrosun, M.M., Nwabachili, S., Alshehri, R.F. \u003cem\u003eet al.\u003c/em\u003e Potentially toxic metals in irrigation water, soil, and vegetables and their health risks using Monte Carlo models. Sci Rep. 2023;\u003cstrong\u003e13\u003c/strong\u003e:21220 https://doi.org/10.1038/s41598-023-48489-4\u003c/li\u003e\n \u003cli\u003ePatel, H., Talbot, N., Salmond, J., Dirks, K., Xie, S., \u0026amp; Davy, P. Implications for air quality management of changes in air quality during lockdown in Auckland (New Zealand) in response to the 2020 SARS-CoV-2 epidemic. Science of The Total Environment, 2020; 141129.\u003c/li\u003e\n \u003cli\u003eRaj\u0026eacute;, F., Tight, M., \u0026amp; Pope, F. D. Traffic pollution: A search for solutions for a city like Nairobi. Cities. 2018. doi:10.1016/j.cities.2018.0.5.008\u003c/li\u003e\n \u003cli\u003eSoleimani M., Nasibeh A, Babak S, Dongsheng W, Liping F. (2018). Heavy metals and their source identification in particulate matter (PM2.5) in Isfahan City, Iran. Journal of environmental Science. 2018;72:166-175.\u003c/li\u003e\n \u003cli\u003eShah, M.H., Shaheen, N., Jaffar, M., Khalique, A., Tariq, S.R., Manzoor, S. Spatial variations in selected metal contents and particle size distribution in an urban and rural atmosphere of Islamabad, Pakistan. J. Environ. Manag. 2006;78: 128\u0026ndash;137.\u003c/li\u003e\n \u003cli\u003eSquires, F. A., Nemitz, E., Langford, B., Wild, O., Drysdale, W. S., Acton, W. J. F., \u0026hellip; Zhang, Y. Measurements of traffic-dominated pollutant emissions in a Chinese megacity. Atmospheric Chemistry and Physics. 2020;20(14): 8737\u0026ndash;8761. doi:10.5194/acp-20-8737-2020\u003c/li\u003e\n \u003cli\u003eTerrouche, A., Ali-Khodja, H., Kemmouche, A., Bouziane, M., Derradji, A., Charron, A. Identification of sources of atmospheric particulate matter and trace metals in Constantine, Algeria. Air Qual. Atmos. Health. 2016;9:69\u0026ndash;82\u003c/li\u003e\n \u003cli\u003eVentura, L.M.B., Mateus, V.L., de Almeida, A.C.S.L., Wanderley, K.B., Taira, F.T., Saint\u0026apos;Pierre, T.D., et al. Chemical composition of fine particles (PM2.5): water-soluble organic fraction and trace metals. Air Qual. Atmos. Health. 2017;10:845\u0026ndash;852.\u003c/li\u003e\n \u003cli\u003eWang X, He S, Chen S, Zhang Y, Wang A, Luo J, Ye X, Mo Z, Wu L, Xu P, Cai G, Chen Z, Lou X. Spatiotemporal Characteristics and Health Risk Assessment of Heavy Metals in PM\u003csub\u003e2.5\u003c/sub\u003e in Zhejiang Province. Int J Environ Res Public Health. 2018;15(4):583. doi: 10.3390/ijerph15040583.\u003c/li\u003e\n \u003cli\u003eXu P, Chen Y, He S, Chen W, Wu L, Xu D, Chen Z, Wang X, Lou X. A follow-up study on the characterization and health risk assessment of heavy metals in ambient air particles emitted from a municipal waste incinerator in Zhejiang, China. Chemosphere. 2019;246:125777.\u003c/li\u003e\n \u003cli\u003eZhang, L., Zhou, H., Chen, X., Liu, G., Jiang, C., \u0026amp; Zheng, L. Study of the micromorphology and health risks of arsenic in copper smelting slag tailings for safe resource utilization. Ecotoxicology and environmental safety. 2021;219: 112321.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-atmosphere","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Atmosphere](https://www.springer.com/journal/44292)","snPcode":"44292","submissionUrl":"https://submission.nature.com/new-submission/44292","title":"Discover Atmosphere","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Arsenic, Cadmium, PM2.5, Monte Carlo Simulation (MCS), Air quality, Atmosphere, Potentially Toxic Metals (PTMs)","lastPublishedDoi":"10.21203/rs.3.rs-5837506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5837506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs a result of the rapid industrialization of various cities in Nigeria, rural-urban migration, and the rapid increase in population, there has been a spike in the level of pollutants getting into the atmosphere which is majorly a result of various anthropogenic factors such as combustion of fuel, usage of vehicles, indiscriminate burning of refuse, just to mention a few. Air pollution has become a thing of concern due to the health effects associated with it such as Chronic Obstructive Pulmonary Disease (COPD), Lung Cancer, asthma, etc. This study aimed at analyzing cancer risks associated with PTMs (Cd and As) found PM\u003csub\u003e2.5\u003c/sub\u003e, Using a probabilistic approach. The concentrations of the PTMs that were analysed were collected from the Surface Particulate Matter Network (SPARTAN) which is mounted a the Department of Physics, University of Ilorin. The mean Concentration of Cd collected from this site ranged from 0.000377μg/m\u003csup\u003e3\u003c/sup\u003e and 0.00767μg/m\u003csup\u003e3\u003c/sup\u003e with the lowest being recorded in March, and highest in November. For As, the concentration ranged from 8.67e-05μg/m\u003csup\u003e3 \u003c/sup\u003eand 0.00329μg/m\u003csup\u003e3\u003c/sup\u003e with the highest being recorded in November, and the lowest in March. Cd recorded concentrations that were higher than the WHO and EU set limits, in July (0.00648 μg/m\u003csup\u003e3\u003c/sup\u003e), August (0.007487 μg/m\u003csup\u003e3\u003c/sup\u003e), and November (0.00767 μg/m3). From the Monte Carlo Simulation for Cancer Risk assessment, it was found out that for Cd, the highest level of risk via inhalation was recorded in August with a value of 6.08e-03, and the least was recorded in March with a value of 3.06e-04 these values were a cause for concern. Via dermal contact, the least mean risk was recorded in October with a value of 1.47e-06, and the highest was recorded in August with a value of 2.94e-05 which were all in the safe zone. For As, via Inhalation the highest was recorded in November with a value of 2.36e-03, and the least was recorded in April with a value of 1.56e-04, while via dermal contact, the highest was recorded in November with a value of 3.81e-07, and the least was in April with a value of 1.01e-08. These results therefore indicated that via inhalation, both PTMs showed a great Cancer Risk, but the reverse was the case for dermal contact.\u003c/p\u003e","manuscriptTitle":"A Temporal Analysis of Cancer Risk Associated with Cadmium and Arsenic Found in PM 2.5 in the University of Ilorin and its Environs; A probabilistic approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 11:25:31","doi":"10.21203/rs.3.rs-5837506/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T11:24:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T05:23:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-10T00:29:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-25T03:05:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T01:59:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243831619429681934433109346058761679113","date":"2025-04-21T01:49:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18359767689486224591186767554356255397","date":"2025-04-19T14:53:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-18T23:22:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278209764981663223737833752163116947525","date":"2025-04-17T04:06:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60667827538154021648414315023294364450","date":"2025-04-15T10:11:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-15T09:01:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-12T09:03:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Atmosphere","date":"2025-03-22T00:06:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-atmosphere","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Atmosphere](https://www.springer.com/journal/44292)","snPcode":"44292","submissionUrl":"https://submission.nature.com/new-submission/44292","title":"Discover Atmosphere","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cb56b9b-d47d-4403-b267-eade332b0980","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T05:38:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 11:25:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5837506","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5837506","identity":"rs-5837506","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}