Seasonal variation of PH2.5 and PM10 concentrations and potential human health risk in 5 urban and 1 rural residential communities of Rivers State Boma Abiye Fubara1, *, Amalo Ndu Dibofori-Orji1,2 1Department of Chemistry/Faculty of Pure and Applied Sciences, Ignatius Ajuru University of Education, Port Harcourt, Rivers State, Nigeria. 2Department of Chemistry/Faculty of Pure and Applied Sciences, Ignatius Ajuru University of Education, Port Harcourt, Rivers State, Nigeria. Email address: Bomafubara@gmail.com (Boma Abiye Fubara), amaloorji@gmail.com (Amalo Ndu Dibofori-Orji) To cite this article: Author’s Name. Paper Title. International Journal of XXXXXX. Vol. x, No. x, 2021, pp. x-x. doi: 10.11648/j.xxx.xxxxxxxx.xx. Received: MM DD, 2021; Accepted: MM DD, 2021; Published: MM DD, 2021 Abstract: This study investigated the spatial and temporal variations and health risk associated with particulate matter (PM) (10µm and 2.5µm), total volatile organic compounds (TVOC), methanal (HCHO), relative humidity and temperature of six (6) communities (Mgbosimini, GRA Phase II, Rumueprikom, Ozuoba, Aluu and Isiokpo) of two (2) Local Government Areas in Rivers State during the months of November 2020 through April 2021. Air samples from the communities were measured for mass concentration for PM using a high-volume air sampler and weighing scale while a hand-held air quality device for TVOC, HCHO, relative humidity and temperature during the sampling period. Three (3) locations each in the sampling area where analysed and the average computed as the average for the month. Results obtained showed significant difference within the period of sampling (temporal) and across the communities (spatial) for PM, TVOC and HCHO. Also, the results showed a decrease in concentration for PM but an increase for TVCO and HCHO over the period of sampling except for Aluu that had a decrease in TVOC. Health risk assessment for adults revealed that except for Mgbosimini in November 2020, all other stations in November/December 2020 and January 2021 had health index greater than one (HI ˃1) indicating risk to adults due to inhalation of PM2.5 in the air. In the months of February, March and April 2021, Rumueprikom, Aluu and Isiokpo had HI ˃1, indicating health risk at these locations while Mgbosimini and GRA Phase II had HI <1 indicating no risk. Thus, the quality of air is not necessarily determined by urban-rural distribution but on the specific activities that may contribute to the quality of air and if the activities are persistent or consistent irrespective of the month, then there would be no significant reduction in air pollution. Keywords: particulate matter, spatial, temporal, health risk, Rivers state  1. Introduction The increase in industrial and vehicular activities in major cities of Nigeria leading to the release of particulate into the atmosphere is a cause for serious concern for the health of its citizens. The composition of gases in the atmosphere is 78% of nitrogen (N2) and 21% of oxygen (O2), with some trace gases such as: argon (Ar) – 0.93%; carbon dioxide (CO2) – 0.03%; neon (Ne) – 0.0018%; helium (He) – 0.0005%; methane (CH4) – 0.04% and krypton (Kr) – 0.0001% [1]. These gaseous components in the atmosphere support the balance in existence between the abiotic and biotic components of the environment. [2] defines air pollution as a matter of concentration rather than a mere presence in the atmosphere of particular elements or compounds. Therefore, for an element released in the environment to rise to the level of being considered a pollutant, the concentration at which it is released must cause environmental alteration. Therefore, air pollution is the release of gases into the atmosphere which will cause an imbalance in the natural air composition resulting in harm to the biotic or (and) abiotic components of the environment. Any substance released in the air which will, due to its concentration, cause a harm to health, animals, properties of plants is termed a pollutant. Due to anthropogenic activities in Nigeria, the most common air pollutants released into the atmosphere are carbon monoxide, nitrogen dioxides, hydrocarbons, sulphur dioxide, and solid particles such as dust and soot [3]. When some of these pollutants get into the food chain they could lead to the contamination of fish, fowl and livestock. The dissolution of some of these gases in the water could also lead to acidification of surface water bodies and hence increase the toxicity of metals such as lead, mercury, aluminium, cadmium and copper [4]–[6]. In this study, particulate matter (PM) is the air pollutant of focus due to its ability to be inhaled by residents of the city and cause detrimental health effects. Particulates are released into the air during the combustion of fossil fuels and biomass, both of these are done in the City of Port Harcourt; during the process, soot, ash and dust is ejected into the atmosphere [7]. PM pollution consists of solid particles and liquid droplets in air, and may include mixtures of organics, acids, metals, minerals, and elemental carbon. PM in the atmosphere, through chemical reactions, can be formed naturally from gases or are emitted through anthropogenic sources. Anthropogenic emission sources may include vehicle emissions, forest fires, and industrial, domestic, and agricultural pollutants [8]. Windblown soil dust, marine and biogenic aerosols, road traffic and off-road vehicles, stationary combustion processes, industrial and construction processes, and combustion of agricultural waste are some natural and anthropogenic emission sources of PM. PM with diameter <10 mm (PM10) or <2.5 m (PM2.5) are particularly of public health concern; nano particulates are considered the most hazardous [9]. PM10 are inhaled by human beings and this may lead to serious health effects; PM2.5 are usually referred to as fine particulates [10]. Fine particles are particularly of concern because they remain suspended in the air for longer periods of time because of their smaller diameters [11]. PM can be viewed either as: primary PM (those emitted directly into the atmosphere by the sources such as industry, electric power plants, diesel buses, and automobiles) and secondary PM (those formed as a by-product of the primary PM such as sulphur dioxide and nitrogen oxide gases) [12]. Biomass burning leads to the release of black carbon (soot) particles into the atmosphere. These particles are made up of carbon, oxygen and hydrogen bound into layered, hexagonal structure similar to graphite [13]. Black carbon is particularly dangerous because it is a major constituent of PM2.5 [14], hence it has the ability to linger in the atmosphere for a longer period of time and cause serious human health damage. Soot is a product of vaporized organic matter, usually polycyclic aromatic hydrocarbons. Formation of soot involves two steps: the production of benzene and acetylene and their transformation into phenyl [15]. According to [13], in the year 2015 household sources made up 58% of global emission of carbon black to the atmosphere, followed by transportation (24%). In Africa, Asia and the Pacific regions, biomass cookstoves, biomass heating stoves, coal stoves and other residential combustion including kerosene lamps contributed over 50% of black carbon to the atmosphere [13]. Exposure to acute concentrations of PM in the atmosphere leads to airway irritation and small reduction in lung volume [16]. Lung epithelial cells and alveolar macrophages have been affected by proinflammation; exposure to particulate pollution has been seen to cause localised (in lungs) and distant inflammatory responses [17], [18]. In addition to cardiovascular respiratory and respiratory diseases associated with chronic and acute exposure to PM [19], other health issues have also been document. [20] review household air pollution and associated health effect in low- and middle-income countries. The study documented a link between PM exposure with alterations in both localized and systemic, immunologic and inflammatory responses. Exposure to smoke from wood burning has also been linked to epithelial inflammation with compromises in the integrity of epithelial barrier leading to increased risk of bacterial invasion; the release of the antioxidant Glutathione has also been associated with exposure to wood smoke [21]. The exposure of rats eye lens to biomass smoke containing metal ions has revealed an association with protein aggregation and oxidative changes resulting in cataract [20]. WHO attributes and estimated 400- 610 death/million to indoor smoke from solid fuels in Sub-Saharan Africa (Fig. 1.3) [22]. [3] performed a review of particulate matter and elemental composition of aerosols at selected locations in Nigeria from 1985–2015. Results of the study revealed that PM2.5 concentration ranged from 5–248 μg/m3, while PM10 concentration ranged from 18–926 μg/m3, revealing that about 50% of the particulate matter loads in Nigeria exceeded both the WHO (25 μg/m3, 50 μg/m3) and NAAQS (35 μg/m3, 150 μg/m3) guideline limits for PM2.5 and PM10 respectively. the results also revealed seasonal variation in Pm concentration; higher concentrations were recorded during the dry season than during the rainy season. In rural areas, PM was lower compared to urban areas. A study aimed at exploring the influence of meteorological parameters such as wind direction, wind speed, rainfall, air temperature and relative humidity on PM2.5 and PM10 concentration was also carried out in Woji town, Port Harcourt City, Nigeria. this study involved the measurement of PM2.5 and PM10 for 236 days with the use of photo laser-based PM monitor while meteorological parameters were collected using Misol weather station. Results of this study revealed that PM concentrations were below USEPA 24-hr standard for all months studied except December with PM2.5 = 58.8 μg/m³ and PM10 = 164.5 μg/m³. Concentration of PM also a weak negative but significant correlation with rainfall and air temperature. PM2.5 concentration showed a weak negative but not significant correlation with relative humidity measured. However, PM10 centration exhibited weak but significant correlation with relative humidity [23]. In Ibadan Metropolis, Nigeria, sixteen grain milling shops were randomly selected from two major markets and monitored for PM10 and PM2.5. The PM2.5 concentrations for both market locations ranged between 1,269.3 and 651.7 μg/m3, while PM10 concentrations were between 1,048.2 and 818.1 μg/m3. These concentrations far exceeded the World Health Organization guideline limit of 50 μg/m3 and 25 μg/m3 for PM2.5 and PM10 respectively [24]. The increasing population in Rivers State, in addition to the anthropogenic activities taking place, will affect the air quality. This will also lead to health risks for those resident, students and workers. It is, therefore, necessary to monitor the air quality in the universities where students from around the country come together to study. There has been less study carried out in the City of Port Harcourt assessing the levels of PM in the atmosphere and particularly indoors air pollution. Although the industrial activities, increased population and other anthropogenic activities taking place therein is a cause for human health concern, there is little encouragement to the scientific community, in terms of research funding, to carry out these studies. Therefore, this study aims to investigate the concentration of PM2.5 and PM10 in five urban communities (Mgbuosimini, GRA Phase 2, Rumueprikom, Ozuoba, Aluu) and one rural community (Isiokpo) as control. This study will also assess the Air Quality Index (AQI) in these communities. Objectives of the study are to identify three (3) stations in the six (6) communities, to collect air quality data from these sample stations, to sample these stations for six (6) months to cover three months each of the wet and dry seasons, and to compare the differences in air quality parameters along different communities and across different seasons. 2. Material and Method 2.1. Study Area 2.1.1 Rivers State The study area is the city of Port Harcourt in Rivers State, Nigeria (Fig. 3.1). The city has a tropical climate with significant rainfall most months of the year and short dry season. Port Harcourt has an average total annual rainfall of 119.6 mm with most rainfall recorded in June, July, August, September and October and the least rainfalls in November, December, January and February [25]. Figure SEQ Figure \* ARABIC \s 1 1. The city of Port Harcourt in Rivers State, Nigeria. 2.1.2. Sample Stations Study locations, stations and coordinates are presented in Table 1 and figures 2– 7 are the map of the locations of the sample stations. Table SEQ Table \* ARABIC \s 1 1. Study locations, stations and coordinate Location Station Coordinate Mgbosimini Station 1 4°48'47.03"N 6°58'15.80"E Station 2 4°48'29.46"N 6°58'22.51"E   Station 3 4°48'22.88"N 6°58'27.11"E GRA Station 1 4°49'34.95"N 6°59'43.36"E Station 2 4°49'31.93"N 6°59'52.96"E   Station 3 4°49'21.69"N 6°59'56.48"E Rumueprikom Station 1 4°49'45.80"N 6°58'35.15"E Station 2 4°49'49.90"N 6°58'54.98"E   Station 3 4°50'1.94"N 6°59'10.66"E Ozuoba Station 1 4°52'14.89"N 6°55'45.24"E Station 2 4°52'5.87"N 6°55'37.46"E   Station 3 4°51'54.92"N 6°55'36.89"E Aluu Station 1 4°56'1.77"N 6°56'37.44"E Station 2 4°56'1.16"N 6°56'52.26"E   Station 3 4°56'2.88"N 6°57'1.00"E Isiokpo Station 1 4°58'42.56"N 6°52'45.49"E Station 2 4°58'0.50"N 6°52'42.11"E   Station 3 4°57'12.62"N 6°52'48.07"E Figure SEQ Figure \* ARABIC \s 1 2. The three stations of Mgbosimini Figure 3. New GRA stations Figure SEQ Figure \* ARABIC \s 1 4. Rumueprikom stations Figure SEQ Figure \* ARABIC \s 1 5. Ozuoba stations Figure SEQ Figure \* ARABIC \s 1 6. Aluu stations Figure SEQ Figure \* ARABIC \s 1 7. Isiokpo stations Details of equipment used are: model: HV-500RD, power supply: 100VAC, 10A, accuracy: +/- 5%, ambient temperature range: 0 - 40℃, maximum suction flow: 400L/min (when loading Φ110mm QR-100 1 sheet, 2 urethane forms), specification: Instantaneous flow rate, integrating flow is 25℃, automatically corrected to a value of 1atm. (20℃ + 1 atm or you can also correct in the real flow rate) the temperature protection device is operated when the suction air temperature in the equipment becomes high and the blower motor is stopped, accessories. TVOC, HCOH, temperature and percentage humidity were measured in-situ using a DI ZENE Air Quality Detector, Model: DZ8600. TVOC, HCHO, temperature and humidity measurement solution uses laser scattering particle acceleration, faster and more accurate 3D convection air holes. 2.2 Data analysis To test for statistically significant difference between PM concentration, Analysis of Variance (ANOVA) was employed using SigmaPlot for Windows [26]. To compare data from all stations, principal component analysis plot (PCA) and hierarchical clustering analysis using Euclidean distance were used to show similarity in data with PAST Statistics (PAST 4.06) [27]. 2.2.1. Human health risk assessment Human health risk assessment was performed to examine to potential human health risk due to exposure to PM in the air. Exposure assessment was done based on the lifetime average daily dose (LADD) and was calculated with the following equation: (1) where: LADD = exposure dose (mg/kg/day) C = contaminant concentration (mg/m3) IR = intake rate (m3)/day) 10m3/day for a child 6–8 years old, 15.2m3/day male, 19–65 years old EF = exposure factor (250 days/year) BW = body weight (kg) adult = 70 kg, child 1–6 years = 16 kg ED = exposure duration (25 years) AT = averaging time (non- carcinogenic exposure: ED x 365 days/year) [28]–[30]. Risk characterisation was determined using the following equation Non- carcinogenic risk (Hazard Index: HI) = LAAD/ RfC The inhalation Reference Concentration (RfC) used was the RfC of diesel engine exhaust (5 µg/m3) [31]. When HI < 1, it indicates a condition of no risk, however, when HI ≥ 1, it indicates risk. 2.3. Hypothesis There is a statistically significant difference in air quality parameters between urban and rural communities. 2.3.1 Null Hypothesis There is NO statistically significant difference in air quality parameters between urban and rural communities. Aim of the Study This study aims to investigate the concentration of PM2.5 and PM10 in five urban communities (Mgbuosimini, GRA Phase 2, Rumueprikom, Ozuoba, Aluu) and one rural community (Isiokpo) as control This study will also assess the Air Quality Index (AQI) in these communities. 3.0 Results and Discussion 3.1 Spatial variation of air quality parameters 3.1.1 Particulate matter 3.1.1.1 PM2.5 concentration Range of PM2.5 concentration in the locations were as follows: Mgbosimini: 20 – 44 μg/m3, GRA: 18 – 51 μg/m3, Rumueprikom: 17 – 98 μg/m3, Ozuoba: 15 – 144 μg/m3, Aluu: 24 – 63 μg/m3, Isiokpo: 64 – 69 μg/m3. The highest mean concentration of PM2.5 concentration in the six locations were 32.28 ± 7.14 μg/m3, 33.00 ± 7.54 μg/m3, 40.94 ± 16.35 μg/m3, 43.83 ± 26.75 μg/m3, 44.78 ± 12.23 μg/m3, 67.00 ± 1.61 μg/m3 at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo respectively. Geometric means were 31.50 μg/m3, 32.16 μg/m3, 38.59 μg/m3, 39.40 μg/m3, 43.01 μg/m3 and 66.98 μg/m3 while coefficients of variance were 22.13, 22.84, 39.93, 61.03, 27.31 and 2.40 at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo respectively (Table 2). ANOVA revealed statistically significant difference at each station and among all stations (p <0.05). (Table 2). 3.1.1.2 Concentration of PM10 Mean concentration of PM10 across all communities were as follows: 34.83 ± 1.83 μg/m3, 36.50 ± 1.80 μg/m3, 44.11 ± 2.38 μg/m3, 45.06 ± 4.60 μg/m3, 51.11 ± 3.85 μg/m3, 78.72 ± 0.69 μg/m3 at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo respectively. Medians were 32 μg/m3, 37 μg/m3, 43 μg/m3, 36 μg/m3, 60 μg/m3 and 78 μg/m3 and variance were 60.26, 58.03, 101.99, 380.88, 267.16 and 8.57 at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo respectively. ANOVA for PM10 at each station revealed statistically significant difference (p < 0.05), ANOVA also revealed statistically significant difference when all stations were compared (p < 0.05) (Table 3). Table 2. Statistical summary of PM2.5 concentrations at all stations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo p-value N 18 18 18 18 18 18 <0.05 Min 20 18 17 15 24 64 Max 44 51 98 144 63 69 Sum 581 594 737 789 806 1206 Mean 32.28 33.00 40.94 43.83 44.78 67.00 Std. error 1.68 1.78 3.85 6.31 2.88 0.38 Variance 51.04 56.82 267.23 715.56 149.59 2.59 Stand. dev 7.14 7.54 16.35 26.75 12.23 1.61 Median 31 35 39 38.5 51 67 25 prcntil 27.75 27.75 34.25 32 33.5 66 75 prcntil 40 36.25 44.5 45.75 54 68.25 Skewness -0.02 0.26 2.52 3.35 -0.39 -0.48 Kurtosis -0.80 1.14 9.32 13.02 -1.29 -0.51 Geom. mean 31.50 32.16 38.59 39.40 43.01 66.98 Coeff. var 22.13 22.84 39.93 61.03 27.31 2.40 p- value <0.05 <0.05 <0.05 <0.05 <0.05 <0.05   Table SEQ Table \* ARABIC 3. Statistical summary of PM10 concentrations at all stations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo p-value N 18 18 18 18 18 18 <0.05 Min 27 23 30 29 30 76 Max 55 48 59 111 70 89 Sum 627 657 794 811 920 1417 Mean 34.83 36.50 44.11 45.06 51.11 78.72 Std. error 1.83 1.80 2.38 4.60 3.85 0.69 Variance 60.26 58.03 101.99 380.88 267.16 8.57 Stand. dev 7.76 7.62 10.10 19.52 16.35 2.93 Median 32 37 43 36 60 78 25 prcntil 30 31 3.70E+01 3.50E+01 3.50E+01 7.75E+01 75 prcntil 37.75 42.25 52.75 52 65.75 79 Skewness 1.49 -0.25 0.08 2.57 -0.20 2.68 Kurtosis 1.65 -0.86 -1.16 7.70 -2.00 9.42 Geom. mean 34.13 35.70 42.99 42.35 48.45 78.67 Coeff. var 22.29 20.87 22.89 43.32 31.98 3.72 p- value <0.05 <0.05 <0.05 <0.05 <0.05 <0.05   About 43% of the world's population relies on solid fuels for heating and cooking [32]. Use of improved biomass cookstoves (ICS) has the potential to reduce household air pollution (HAP). As part of an evaluation to identify ICS for use in Kenya, a study carried out by [33] collected indoor air and personal air samples to assess differences between traditional cookstoves (TCS) and ICS. They conducted a cross-over study in 2012 in two Kenyan villages; up to six different ICS were installed in 45 households during six two-week periods. Forty-eight-hour kitchen measurements of fine particulate matter (PM2.5) and carbon monoxide (CO) were collected for the TCS and ICS. Concurrent personal CO measurements were conducted on the mother and one child in each household. They performed descriptive analysis and compared paired measurements between baseline (TCS only) and each ICS. Results from their study revealed that the geometric mean of 48-hour baseline PM2.5 and CO concentrations in the kitchen was 586 μg/m3 (95% CI: 460, 747) and 4.9 ppm (95% CI: 4.3, 5.5), respectively. For each ICS, the geometric mean kitchen air pollutant concentration was lower than the TCS: median reductions were 38.8% (95% CI: 29.5, 45.2) for PM2.5 and 27.1% (95% CI: 17.4, 40.3) for CO, with statistically significant relationships for four ICS. A reduction in personal exposures to CO with ICS use was observed. They observed a reduction in mean 48-hour PM2.5 and CO concentrations compared to the TCS; however, concentrations for both pollutants were still consistently higher than WHO air quality guidelines. Many studies probing the link between air quality and health have pointed towards associations between particulate matter (PM) exposure and decreased lung function, aggravation of respiratory diseases like asthma, premature death and increased hospitalisation admissions for the elderly and individuals with cardiopulmonary diseases. Of recent, it is believed that the chemical composition and physical properties of PM may contribute significantly to these adverse health effects. As part of a Belgian Science Policy project (“Health effects of particulate matter in relation to physical-chemical characteristics and meteorology”), the chemical composition (elemental and ionic compositions) and physical properties (PM mass concentrations) of PM were investigated, indoors and outdoors of old age homes in Antwerp, Belgium. The case reported here specifically relates to high versus normal/low pollution event periods. PM mass concentrations for PM1 and PM2.5 fractions were determined gravimetrically after collection via impaction. These same samples were hence analysed by EDXRF spectrometry and IC for their elemental and ionic compositions, respectively. During high pollution event days, PM mass concentrations inside the old age home reached 53μgm−3 and 32μgm−3 whilst outside concentrations were 101μgm−3 and 46μgm−3 for PM2.5 and PM1, respectively. The sum of to -sulphate, nitrate and ammonium, dominates the composition of PM, and contribute the most towards an increase in the PM during the episode days constituting 64% of ambient PM2.5 (52μgm−3) compared to 39% on non-episode days (10μgm−3). Other PM components, such as mineral dust, sea salt or heavy metals were found to be considerably higher during PM episodes but relatively less important. Amongst heavy metals, Zn and Pb were found at the highest concentrations in both PM2.5 and PM1. Acid-base ionic balance equations were calculated and point to acidic aerosols during event days and acidic to alkaline aerosols during non-event days. No significant sources of indoor pollutants could be identified inside the old-age home as high correlations were found between outdoor and indoor PM, confirming mainly the outdoor origin of indoor air [34]. Emissions of fine particulate matter (PM2.5) from on-road traffic and their influence on air quality and human health are of major concern in urban areas. Exposure to traffic-related PM2.5 indoors has received considerable attention as people spend about 80% of their time in indoor environments, but little information is currently available on the assessment and mitigation of this exposure. A systematic field study was conducted with the key objective to assess and mitigate indoor human exposure to traffic-related PM2.5 in a typical naturally ventilated residential apartment. Results indicated that traffic-related PM2.5 levels indoors exceeded the air quality guidelines (12 μg/m3), and the PM2.5 levels decreased significantly (74%) while using an indoor air cleaner. The human health risk assessment based on the bioavailable fraction of toxic trace elements revealed a substantial reduction in potential health risk while using the air cleaner. Overall, the major outcomes of this study would help develop effective air pollution control strategies to reduce indoor human exposure to PM2.5 and potential human health risk caused by vehicular pollution in urban areas in Singapore [35]. 3.1.2 Spatial variation of TVOC TVOC was highest at Isiokpo (1.79 ± 1.66 mg/m3), followed by Aluu (1.56 ± 1.28 mg/m3), Rumueprikom (0.39 ± 0.56 mg/m3), Ozuoba (0.33 ± 0.22 mg/m3) and Mgbosimini (0.33 ± 0.35 mg/m3) and the least concentration was measured at GRA (0.16 ± 0.13 mg/m3). Median of TVOC at each community was 0.24 mg/m3, 0.11 mg/m3, 0.26 mg/m3, 0.31 mg/m3, 1.53 mg/m3, 1.68 mg/m3 at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo respectively. Geometric mean at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo were 0.094 mg/m3, 0.055 mg/m3, 0.082 mg/m3, 0.111 mg/m3, 0.301 mg/m3 and 0.296 mg/m3 respectively (Table 4) Table SEQ Table \* ARABIC 4. Statistical summary of TVOC across all stations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo p-value N 18 18 18 18 18 18 <0.05 Min 0.07 0.03 0.03 0.04 0.12 0.12 Max 1.53 0.61 2.32 0.87 3.91 4.58 Sum 5.88 2.79 6.97 5.86 28.12 32.22 Mean 0.33 0.16 0.39 0.33 1.56 1.79 Std. error 0.08 0.03 0.13 0.05 0.30 0.39 Variance 0.12 0.02 0.31 0.05 1.63 2.74 Stand. dev 0.35 0.13 0.56 0.22 1.28 1.66 Median 0.24 0.11 0.26 0.31 1.53 1.68 25 prcntil 0.15 0.07 0.07 0.13 0.26 0.22 75 prcntil 0.32 0.21 0.34 0.47 2.14 3.13 Skewness 2.94 2.57 2.79 0.94 0.65 0.19 Kurtosis 9.38 8.44 8.56 0.79 -0.51 -1.83 Geom. mean 0.24 0.12 0.19 0.25 0.95 0.83 Coeff. var 105.59 84.53 144.42 66.61 81.75 92.46 p- value <0.05 <0.05 <0.05 <0.05 <0.05 <0.05   Spatial variation of HCHO The highest concentration of formaldehyde was measured at Isiokpo (0.703 0.703 mg/m3). Median and standard error at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo were median - 0.118 mg/m3, 0.064 mg/m3, 0.094 mg/m3, 0.117 mg/m3, 0.325 mg/m3 and 0.297 mg/m3, and standard error - 0.018, 0.009, 0.016, 0.020, 0.159 and 0.260 respectively. The statistical skewness at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo was 0.601, 1.141, 1.374, 1.076, 3.147 and 1.938 respectively, while the statistical kurtosis was -0.378, 1.788, 2.952, 1.124, 10.356 and 2.121 respectively (Table 5). Table SEQ Table \* ARABIC 5. Statistical summary of formaldehyde (HCHO) concentration Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo p-value N 18 18 18 18 18 18 <0.05 Min 0.005 0.008 0.02 0.024 0.068 0.049 Max 0.268 0.173 0.293 0.352 2.885 3.345 Sum 2.197 1.209 1.823 2.433 8.684 12.656 Mean 0.122 0.067 0.101 0.135 0.482 0.703 Std. error 0.018 0.009 0.016 0.020 0.159 0.260 Variance 0.006 0.002 0.004 0.007 0.455 1.220 Stand. dev 0.074 0.040 0.066 0.084 0.674 1.104 Median 0.118 0.064 0.094 0.117 0.325 0.297 25 prcntil 0.062 0.042 0.046 0.062 0.164 0.098 75 prcntil 0.180 0.086 0.136 0.186 0.396 0.371 Skewness 0.601 1.141 1.374 1.076 3.147 1.938 Kurtosis -0.378 1.788 2.952 1.124 10.356 2.121 Geom. mean 0.094 0.055 0.082 0.111 0.301 0.296 Coeff. var 60.835 59.833 65.375 62.307 139.755 157.073 p- value <0.05 <0.05 <0.05 <0.05 <0.05 <0.05   Spatial variation of temperature and relative humidity ANOVA of temperature across all stations revealed that there is no statistically significant difference (p > 0.05), however, temperature varied from sampling time to sampling time at each station significantly (p < 0.05). Minimum temperatures recorded at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo were 24 ºC, 24 ºC, 24 ºC, 25 ºC, 23 ºC and 23 ºC respectively, and maximum temperatures were 33 ºC, 35 ºC, 35 ºC, 36 ºC, 33 ºC and 33 ºC respectively. Variance at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo were 8.30, 9.24, 10.71, 8.96, 6.49 and 9.95 respectively, and median temperatures ranged from 31 ºC – 32 ºC (Table 6). Table SEQ Table \* ARABIC 6. Statistical summary of atmospheric temperature across all communities Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo p-value N 18 18 18 18 18 18 > 0.05 Min 24 24 24 25 23 23 Max 33 35 35 36 33 33 Sum 536 544 552 569 547 536 Mean 29.78 30.22 30.67 31.61 30.39 29.78 Std. error 0.68 0.72 0.77 0.71 0.60 0.74 Variance 8.30 9.24 10.71 8.96 6.49 9.95 Stand. dev 2.88 3.04 3.27 2.99 2.55 3.15 Median 31 31 31.5 32 31 31 25 prcntil 28 29 28.75 31.75 30 28 75 prcntil 32 32.25 33 33 32 32 Skewness -1.11 -0.80 -1.00 -1.22 -1.93 -1.24 Kurtosis 0.08 0.04 0.13 1.03 3.50 0.36 Geom. mean 29.64 30.07 30.49 31.47 30.28 29.61 Coeff. var 9.68 10.06 10.67 9.47 8.38 10.59 p- value <0.05 <0.05 <0.05 <0.05 <0.05 <0.05   Percentage humidity ranged from 48 – 62 %, 44 – 60 %, 44 – 60 %, 42 – 61 %, 47 – 58 % and 47 – 57 % at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo respectively. Variances of percentage humidity at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo communities were 16.54, 28.61, 32.84, 34.62, 13.63 and 13.91 respectively. Highest percentage humidity was measured Mgbosimini – 55.78 %, followed by Isiokpo – 55.17 %. The lowest percentage humidity was measured at Ozuoba – 49.83 %, the second lowest was measured at Rumueprikom – 50.61 % (Table 7). Table SEQ Table \* ARABIC 7. Statistical summary of percentage humidity at each community Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo p-value N 18 18 18 18 18 18 > 0.05 Min 48 44 44 42 47 47 Max 62 60 60 61 58 59 Sum 1004 926 911 897 970 993 Mean 55.78 51.44 50.61 49.83 53.89 55.17 Std. error 0.96 1.26 1.35 1.39 0.87 0.88 Variance 16.54 28.61 32.84 34.62 13.63 13.91 Stand. dev 4.07 5.35 5.73 5.88 3.69 3.73 Median 55.5 51 48.5 48.5 55.5 56.5 25 prcntil 52 46.75 45.75 45 51 53.75 75 prcntil 59.25 57 57.25 54.25 57 58 Skewness -0.19 0.16 0.51 0.56 -0.66 -1.20 Kurtosis -1.04 -1.53 -1.44 -0.68 -0.91 0.41 Geom. mean 55.64 51.18 50.31 49.51 53.77 55.04 Coeff. var 7.29 10.40 11.32 11.81 6.85 6.76 p- value <0.05 <0.05 <0.05 <0.05 <0.05 <0.05   WHO guideline stipulates that PM2.5 not exceed 10 μg/m3 annual mean, or 25 μg/m3 24-hour mean; and that PM10 not exceed 20 μg/m3 annual mean, or 50 μg/m3 24-hour mean [36]. In the present study, all stations had mean PM2.5 concentrations that exceeded the annual mean and 24-hour mean. Although mean concentrations of PM10 exceeded the WHO 24-hour mean, only the mean concentrations at Aluu and Isiokpo exceeded WHO annual mean. PM2.5 and PM10 showed similar trend in concentration. Results showed a pattern of increasing particulate matter (PM2.5 and PM10) concentration moving from the more densely populated areas (Mgbosimini, GRA, Rumueprikom, Ozuoba) to the less populated area; this is contrary to expectations. The spatial trend observed is also contrary to those observed by [37] who observed that population density and secondary industry held the keys to PM2.5 pollution control. It was observed in a study carried out by [37] that vegetation reduces particulate matter concentration in the air. This was contrary to results obtained in the present study; Aluu and Isiokpo had more surrounding vegetation but had higher concentrations of PM in the atmosphere. However, another study carried out by [38] to assess the difference in PM variations between urban and rural areas over eastern China from 2001 to 2015 showed that PM concentrations were higher in rural areas compared to the urban area. This study showed result with trends similar to those obtained in the present study. [39] carried out a study which revealed that land use has a significant effect on particulate concertation in the atmosphere. This was also observed in an earlier study carried out by [40]. This could account for the variation observed moving from the urban areas into the rural area. The rural settlements are usually predominantly farming settlements; these processes may involve burning of woods and forest wastes. [41] also revealed that burning contributes to PM in the atmosphere, this, therefore, could explain the results obtained in the present study. Paved roads also help to reduce suspended particle released from the loose soil; there are more paved roads in the urban roads compared to the rural area and this could also account for the higher PM in the urban areas when compared to the rural areas. This is supported by a study carried out by [42]. TVOC level less than 0.3 mg/m3 is considered to be a low level of concern, when concentration is between 0.3 – 0.5 mg/m3 it is acceptable. A range concentration of 0.5 – 1 mg/m3 is considered marginal while a range concentration of 1 – 3 mg/m3 is considered as high [43]. In the present study, Mgbosimini, GRA, Rumueprikom and Ozuoba had TVOC levels which fell within the marginal range, while Aluu and Isiokpo can be considered high. Mean TVOC measured at all stations in the present study was lower than those measured outdoor of retail stores in the Greater Memphis Area, Tennessee, U.S.A., in summer 2019 [44]. Outdoor VOC sources may include: gasoline, diesel emissions, wood burning, oil and gas extraction and processing and industrial emissions [45]. Although HCHO is a VOC, it is particularly important because it is a carcinogen; formaldehyde (HCHO) is the most important carcinogen in outdoor air among the 187 hazardous air pollutants (HAPs) identified by the U.S. Environmental Protection Agency (EPA), not including ozone and particulate matter [46]. Hence, due to possible burning from farm activities at Aluu and Isiokpo, higher concentrations of HCHO may be to related to burning when compared to the urban areas. Temporal variation of air quality parameters Particulate matter Across all communities, PM2.5 concentration was higher than in the dry season when compared to the wet season. Geometric mean of PM2.5 concentrations at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo in the dry season were 37.24 μg/m3, 36.48 μg/m3, 41.23 μg/m3, 49.12 μg/m3, 49.83 μg/m3 and 68.22 μg/m3 respectively, and 26.64 μg/m3, 28.34 μg/m3, 36.12 μg/m3, 31.61 μg/m3, 37.13 μg/m3 and 65.77 μg/m3 respectively in the wet season (Table 8). PM10 concentrations were also were also higher in the dry season (mean ± standard deviation: Mgbosimini – 39.0 ± 9.18, GRA – 40.78 ± 7.48, Rumueprikom – 48.67 ± 8.25, Ozuoba – 53.22 ± 24.61, Aluu – 56.89 ± 15.07 and Isiokpo – 80.00 ± 3.64) when compared to the wet season (mean ± standard deviation: Mgbosimini – 30.67 ± 2.18, GRA – 32.22 ± 5.12, Rumueprikom – 39.56 ± 10.09, Ozuoba – 36.89 ± 7.35, Aluu – 45.33 ± 16.29 and Isiokpo – 77.44 ± 1.13) (Table 9). Table 8. Statistical summary of seasons variation for PM2.5 concentrations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo Dry season N 9 9 9 9 9 9 Min 29 35 36 32 40 67 Max 44 43 50 144 55 69 Sum 338 329 373 492 451 614 Mean 37.56 36.56 41.44 54.67 50.11 68.22 Std. error 1.68 0.85 1.51 11.41 1.79 0.28 Variance 25.53 6.53 20.53 1170.75 28.86 0.69 Stand. dev 5.05 2.55 4.53 34.22 5.37 0.83 Median 40 36 40 44 52 68 25 prcntil 32.5 35 38 38.5 46.5 67.5 75 prcntil 41 37 45 53 54 69 Skewness -0.61 2.43 0.95 2.76 -1.37 -0.50 Kurtosis -0.89 6.42 0.19 7.96 0.52 -1.28 Geom. mean 37.24 36.48 41.23 49.12 49.83 68.22 Coeff. var 13.45 6.99 10.93 62.59 10.72 1.22 Wet season N 9 9 9 9 9 9 Min 20 18 17 15 24 64 Max 34 51 98 48 63 67 Sum 243 265 364 297 355 592 Mean 27.00 29.44 40.44 33.00 39.44 65.78 Std. error 1.50 3.09 7.79 3.09 5.00 0.40 Variance 20.25 85.78 546.78 85.75 225.03 1.44 Stand. dev 4.50 9.26 23.38 9.26 15.00 1.20 Median 28 28 35 32 35 66 25 prcntil 23.5 24 27 28.5 28 64.5 75 prcntil 29.5 32.5 45 38.5 56.5 67 Skewness -0.54 1.66 2.17 -0.53 0.74 -0.57 Kurtosis 0.20 4.00 5.60 1.32 -1.37 -1.10 Geom. mean 26.64 28.34 36.12 31.61 37.13 65.77 Coeff. var 16.67 31.45 57.82 28.06 38.03 1.83 Table 9. Statistical summary of seasons variation for PM10 concentrations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo Dry season N 9 9 9 9 9 9 Min 27 23 39 34 30 76 Max 55 48 59 111 70 89 Sum 351 367 438 479 512 720 Mean 39.00 40.78 48.67 53.22 56.89 80.00 Std. error 3.06 2.49 2.75 8.20 5.02 1.21 Variance 84.25 55.94 68.25 605.44 227.11 13.25 Stand. dev 9.18 7.48 8.26 24.61 15.07 3.64 Median 37 42 48 45 62 79 25 prcntil 31.5 38.5 40 35 45.5 78.5 75 prcntil 47 46 58 61.5 66.5 80.5 Skewness 0.53 -1.91 0.12 1.92 -1.41 2.18 Kurtosis -0.58 4.39 -1.83 3.91 0.42 5.86 Geom. mean 38.07 40.00 48.04 49.42 54.59 79.93 Coeff. var 23.54 18.34 16.98 46.23 26.49 4.55 Wet season N 9 9 9 9 9 9 Min 27 23 30 29 30 76 Max 35 42 58 52 69 79 Sum 276 290 356 332 408 697 Mean 30.67 32.22 39.56 36.89 45.33 77.44 Std. error 0.73 1.71 3.36 2.45 5.43 0.38 Variance 4.75 26.19 101.78 53.86 265.50 1.28 Stand. dev 2.18 5.12 10.09 7.34 16.29 1.13 Median 30 31 39 35 37 78 25 prcntil 30 31 30.5 32 34 76 75 prcntil 32 34.5 48 40.5 65.5 78 Skewness 0.55 0.30 0.70 1.21 0.83 -0.49 Kurtosis 1.85 2.03 -0.61 1.36 -1.47 -1.39 Geom. mean 30.60 31.86 38.48 36.30 43.00 77.44 Coeff. var 7.11 15.88 25.50 19.89 35.94 1.46 Temporal variation TVOC Concentration of total volatile organic carbons measured at Mgbosimini, GRA, Rumueprikom and Ozuoba were higher in the wet season (mean standard deviation: 0.46 ± 0.44 mg/m3, 0.23 ± 0.15 mg/m3, 0.67 ± 0.69 mg/m3, 0.46 ± 0.21 mg/m3 respectively) compared to dry season (mean standard deviation: 0.19 ± 0.12 mg/m3, 0.08 ± 0.04 mg/m3, 0.11 ± 0.10 mg/m3, 0.19 ± 0.12 mg/m3 respectively). However, Ozuoba and Aluu showed a reverse in trend, i.e., TVOC concentrations were higher in the dry season (Ozuoba – 1.82 ± 0.81 mg/m3, Aluu – 3.35 ± 0.56 mg/m3), and lower in the wet season (Ozuoba – 1.30 ± 1.63 mg/m3, Aluu – 0.23 ± 0.09 mg/m3) (Table 10). Table 10. Statistical summary of seasons variation for TVOC concentrations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo Dry season N 9 9 9 9 9 9 Min 0.069 0.032 0.033 0.04 0.629 2.984 Max 0.455 0.155 0.341 0.402 3.607 4.584 Sum 1.733 0.741 0.975 1.706 16.401 30.185 Mean 0.19 0.08 0.11 0.19 1.82 3.35 Std. error 0.04 0.01 0.03 0.04 0.27 0.19 Variance 0.01 0.00 0.01 0.01 0.65 0.31 Stand. dev 0.12 0.04 0.10 0.12 0.81 0.56 Median 0.155 0.074 0.084 0.142 1.825 3.112 25 prcntil 0.112 0.0525 0.036 0.109 1.3275 2.9905 75 prcntil 0.2555 0.111 0.1535 0.3085 2.014 3.667 Skewness 1.52 0.87 1.77 0.72 1.15 1.74 Kurtosis 2.06 0.20 2.79 -0.76 3.21 2.37 Geom. mean 0.17 0.07 0.08 0.15 1.67 3.32 Coeff. var 62.81 47.59 95.40 63.96 44.26 16.59 Wet season N 9 9 9 9 9 9 Min 0.228 0.093 0.255 0.22 0.118 0.115 Max 1.534 0.607 2.315 0.871 3.914 0.369 Sum 4.149 2.05 5.991 4.153 11.719 2.036 Mean 0.46 0.23 0.67 0.46 1.30 0.23 Std. error 0.15 0.05 0.23 0.07 0.54 0.03 Variance 0.20 0.02 0.48 0.04 2.66 0.01 Stand. dev 0.44 0.15 0.69 0.21 1.63 0.09 Median 0.255 0.209 0.301 0.461 0.271 0.223 25 prcntil 0.237 0.13 0.263 0.2845 0.1665 0.152 75 prcntil 0.5785 0.2245 0.994 0.601 3.1095 0.3005 Skewness 2.22 2.27 2.05 0.76 1.09 0.62 Kurtosis 4.71 6.17 4.28 0.50 -0.72 -0.30 Geom. mean 0.35 0.20 0.47 0.42 0.54 0.21 Coeff. var 96.48 66.69 103.90 45.26 125.33 40.63 Temporal variation HCHO Formaldehyde in the atmosphere was higher in the rainy season compared to the dry season. Geometric mean at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo in the dry season were 0.06 mg/m3, 0.05 mg/m3, 0.05 mg/m3, 0.07 mg/m3, 0.34 mg/m3 and 0.32 mg/m3 respectively, while in wet season HCHO concentrations were 0.158 mg/m3, 0.061 mg/m3, 0.140 mg/m3, 0.173 mg/m3, 0.263 mg/m3 and 0.275 mg/m3 respectively (Table 11). Table 11. Statistical summary of seasons variation for formaldehyde (HCHO)(mg/m3) concentrations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo Dry season N 9 9 9 9 9 9 Min 0.005 0.019 0.02 0.024 0.288 0.288 Max 0.123 0.134 0.096 0.143 0.471 0.385 Sum 0.647 0.519 0.487 0.737 3.137 2.885 Mean 0.07 0.06 0.05 0.08 0.35 0.32 Std. error 0.01 0.01 0.01 0.01 0.02 0.01 Variance 0.00 0.00 0.00 0.00 0.00 0.00 Stand. dev 0.04 0.03 0.03 0.04 0.06 0.04 Median 0.069 0.048 0.046 0.063 0.339 0.301 25 prcntil 0.048 0.036 0.0345 0.0505 0.3045 0.2945 75 prcntil 0.1035 0.069 0.08 0.119 0.3765 0.3575 Skewness -0.40 1.58 0.52 0.17 1.37 1.00 Kurtosis -0.04 3.47 -0.72 -1.57 2.17 -0.72 Geom. mean 0.06 0.05 0.05 0.07 0.34 0.32 Coeff. var 51.31 57.74 48.93 50.30 16.15 11.29 Wet season N 9 9 9 9 9 9 Min 0.060 0.008 0.091 0.096 0.068 0.049 Max 0.268 0.173 0.293 0.352 2.885 3.345 Sum 1.550 0.690 1.336 1.696 5.547 9.771 Mean 0.172 0.077 0.148 0.188 0.616 1.086 Std. error 0.023 0.015 0.020 0.028 0.320 0.501 Variance 0.005 0.002 0.004 0.007 0.923 2.261 Stand. dev 0.069 0.046 0.060 0.084 0.960 1.504 Median 0.177 0.073 0.131 0.173 0.168 0.099 25 prcntil 0.126 0.047 0.109 0.110 0.107 0.074 75 prcntil 0.239 0.098 0.162 0.241 0.941 2.953 Skewness -0.083 0.864 1.983 0.791 2.108 0.883 Kurtosis -0.868 2.035 4.792 0.278 4.057 -1.592 Geom. mean 0.158 0.061 0.140 0.173 0.263 0.275 Coeff. var 39.777 60.060 40.470 44.363 155.838 138.509 Temporal variation of temperature and relative humidity Atmospheric temperature was higher in the dry season (Mgbosimini – 30.67 ± 1.87 ºC, GRA – 31.00 ± 1.66 ºC, Rumueprikom – 31.00 ± 1.87 ºC, Ozuoba – 32.78 ± 1.48 ºC, Aluu – 31.78 ± 0.67 ºC, Isiokpo – 31.56 ± 0.73 ºC) compared to the wet season Mgbosimini – 28.89 ± 3.52 ºC, GRA – 29.44 ± 3.94 ºC, Rumueprikom – 30.33 ± 4.36 ºC, Ozuoba 30.44 ± 3.71 ºC, Aluu – 29.00 ± 1.00 ºC, Isiokpo – 28.00 ± 3.67 ºC (Table 12). Percentage humidity was higher in the dry season compared to the wet season. In the dry season, the lowest percentage humidity measured at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo was 56 %, 47 %, 47 %, 46 % 56% and 55% respectively, and the highest at these locations were 62 %, 60 %, 60 %, 61 % 58 % and 59% respectively. Variance of percentage humidity at Mgbosimini, GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo was 3.44, 22.44, 23.61, 26.50, 0.86 and 1.50 respectively in the dry season, and 5.00, 13.36, 5.00, 8.00, 7.86 and 20.50 respectively in the wet season (Table 13). Table 12. Statistical summary of seasonal variation for atmospheric temperature(ºC) concentrations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo Dry season N 9 9 9 9 9 9 Min 28 29 28 31 31 31 Max 33 33 34 36 33 33 Sum 276 279 279 295 286 284 Mean 30.67 31.00 31.00 32.78 31.78 31.56 Std. error 0.62 0.55 0.62 0.49 0.22 0.24 Variance 3.50 2.75 3.50 2.19 0.44 0.53 Stand. dev 1.87 1.66 1.87 1.48 0.67 0.73 Median 31 31 31 32 32 31 25 prcntil 28.5 29 29.5 32 31 31 75 prcntil 32 32.5 32.5 33.5 32 32 Skewness -0.56 -0.21 0.00 1.37 0.25 1.01 Kurtosis -1.34 -1.67 -0.29 2.19 -0.04 0.19 Geom. mean 30.61 30.96 30.95 32.75 31.77 31.55 Coeff. var 6.10 5.35 6.03 4.52 2.10 2.30 Wet season N 9 9 9 9 9 9 Min 24 24 24 25 23 23 Max 32 35 35 35 32 33 Sum 260 265 273 274 261 252 Mean 28.89 29.44 30.33 30.44 29.00 28.00 Std. error 1.17 1.31 1.45 1.24 1.00 1.22 Variance 12.36 15.53 19.00 13.78 9.00 13.50 Stand. dev 3.52 3.94 4.36 3.71 3.00 3.67 Median 30 31 33 32 30 28 25 prcntil 24.5 25 25 26 26.5 24 75 prcntil 32 32.5 33 33 31 31.5 Skewness -0.70 -0.25 -0.79 -0.62 -1.18 -0.12 Kurtosis -1.63 -1.43 -1.41 -1.48 0.47 -1.49 Geom. mean 28.69 29.20 30.04 30.23 28.85 27.78 Coeff. var 12.17 13.38 14.37 12.19 10.34 13.12 Table 13. Statistical summary of seasonal variation for atmospheric humidity concentrations Statistics Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo Dry season N 9 9 9 9 9 9 Min 56 47 47 46 56 55 Max 62 60 60 61 58 59 Sum 533 493 494 486 512 513 Mean 59.22 54.78 54.89 54.00 56.89 57.00 Std. error 0.62 1.58 1.62 1.72 0.31 0.41 Variance 3.44 22.44 23.61 26.50 0.86 1.50 Stand. dev 1.86 4.74 4.86 5.15 0.93 1.22 Median 59 57 57 54 57 57 25 prcntil 58 49.5 49.5 49.5 56 56 75 prcntil 60.5 58 58.5 59 58 58 Skewness -0.41 -0.80 -0.75 -0.16 0.26 0.00 Kurtosis 0.00 -1.21 -1.29 -0.98 -2.02 -0.29 Geom. mean 59.20 54.59 54.69 53.78 56.88 56.99 Coeff. var 3.13 8.65 8.85 9.53 1.63 2.15 Wet season N 9 9 9 9 9 9 Min 48 44 44 42 47 47 Max 55 54 51 50 55 59 Sum 471 433 417 411 458 480 Mean 52.33 48.11 46.33 45.67 50.89 53.33 Std. error 0.75 1.22 0.75 0.94 0.93 1.51 Variance 5.00 13.36 5.00 8.00 7.86 20.50 Stand. dev 2.24 3.66 2.24 2.83 2.80 4.53 Median 52 47 46 45 51 54 25 prcntil 51 45 44.5 43 48 48.5 75 prcntil 54.5 52 47.5 48 53.5 58 Skewness -0.62 0.60 1.10 0.14 0.01 -0.18 Kurtosis 0.44 -1.34 1.30 -1.01 -1.25 -1.54 Geom. mean 52.29 47.99 46.29 45.59 50.82 53.16 Coeff. var 4.27 7.60 4.83 6.19 5.51 8.49 PM in the present study showed seasonal variation, this was similar to a study designed to assess seasonal variation of fine particulate matter in residential micro–environments of Lahore, Pakistan [47]. In the wet season, there is an increase in precipitation and wind speed. Studies have shown that wind speed has a negative correlation with PM concentration in atmosphere [48], [49]. This may account for the lower PM concentrations observed in all locations in the wet season when compared to the dry season. Similar to PM in the atmosphere, TVOC also showed seasonal variation. Indoor/outdoor ratios of PM2.5, PM10, and TVOC increased during the warm season in a study carried out by [50]; these results were similar to those observed in the present study. As earlier stated, PM have the ability to linger in the atmosphere for long periods of time and the ability to diffuse. This is due to their small diameter and hence very light weight compared to larger particles in the atmosphere. The characteristics and source of PM, therefore, plays a major role in its concentrations. This is because, the characteristics and source determine the diameter of PM in the atmosphere and how long it will linger in the atmosphere. Whereas coarse particles (2.5 and 10 µm diameter) are typically deposited to the Earth within a short period of time (minutes to hours) and travel within short distances (tens of kilometres), fine particles (2.5 µm in diameter) on the other hand remain in the atmosphere for longer periods of time (days to weeks) and can travel much longer distances (thousands of kilometres) [12]. The concentration of particles in the air varies across space and time and is related to the source of the particles and the pollutant transformations that occur in the atmosphere. Also, as mentioned earlier, anthropogenic activities play a major role in the concentration of PM in the atmosphere. Activities that see to the continuous release of atmospheric contaminants, such as gas flaring, have shown to have a positive correlation to PM concentration in the atmosphere [51], [52]. The concentration of PM in the atmosphere, as other atmospheric pollutants, is dependent on the meteorological conditions; meteorology affects dispersion, transformation and removal, hence spatial and temporal characteristics of atmospheric pollutants [53], [54]. In the study area, is a tropical rain forest with a mean annual rainfall of 11.9.6 [25]. Wind speed and direction, humidity, rainfall, ambient temperature, surface pressure and sunlight are metrological factors [55]–[57]. [58] found a positive correlation between PM2.5 concentration in the atmosphere with desert dust, industrialization and agricultural fires in Africa. Human health risk assessment Fine particles are about 30 times smaller than human hair. although there are not present record in the study area, they have been identified to be of greatest risk to human health if inhaled and have been associated to increased risk of cardiovascular disease in human beings [59]–[61]. When PM enters into the body, it could also lead to immune system damage and compromise the immune capacity of the human body increases the risk of a range of diseases [62]. The immune-compromising ability of PM involves the oxidative damage mechanism, apoptosis mechanism and calcium homeostasis disequilibrium mechanism. The health effects of PM can also be related to the particle makeup; PM mainly comprises of ions, reactive gases, organic compounds, metals, and particle carbon core [19]. Thus, when fine particles of this makeup make their way into the human body, they can cause early death patients suffering from cardiovascular diseases [19], [59]. Assessment of human health risk related to PM2.5 due to the inhalation of air revealed hazard for children in the dry season (November, December and January) at all locations (HI > 1). In March, except for Mgbosimini, HI was greater than 1 at GRA, Rumueprikom, Ozuoba, Aluu and Isiokpo. In February and April, HI < 1 at Mgbosimini and GRA, and HI > 1 at all other locations (Table 14). Human health risk assessment for adults revealed that except for Mgbosimini in November, all stations in dry season (November, December and January) had HI > 1 indicating risk to adults due to inhalation of PM2.5 in air. In wet season (February, march and April), Rumueprikom, Aluu and Isiokpo all has HI > 1 indication health risk at these locations, however, Mgbosimini and GRA indicated no risks in the wet season (Table 14). Vehicular activities can lead to a corresponding increase in PM concentrations [63]–[66]. However, it can be observed that the PM concentrations in the areas with higher vehicles had lower PM when compared to Aluu and Isiokpo with less vehicles. The mean HQ related to PM2.5 estimated for Tehran, the most populous city in Iran and Western Asia and the capital of Iran, was 6.1 and 6.4 in 2016 and 2017 respectively [67]; these values are higher than those measured in at all stations. [68] identified human health risk hazard due to exposure of metals bound to PM2.5 to adults and children in transport pathways of Abuja, Nigeria, similar to some stations sampled in the present study. Table SEQ Table \* ARABIC 14. Hazard index (HI) for human health risk assessment for the inhalation of PM2.5 Month Mgbosimini GRA Rumueprikom Ozuoba Aluu Isiokpo Children November 1.0 1.2 1.3 1.2 1.4 2.2 December 1.3 1.2 1.4 2.6 1.7 2.2 January 1.3 1.1 1.3 1.5 1.7 2.2 February 0.9 0.9 1.2 1.2 1.1 2.2 March 0.8 1.0 1.6 1.0 1.3 2.1 April 0.9 0.9 1.1 1.0 1.4 2.1 Adult November 0.9 1.1 1.2 1.1 1.3 2.0 December 1.2 1.1 1.3 2.4 1.6 2.0 January 1.2 1.0 1.2 1.4 1.6 2.0 February 0.8 0.9 1.1 1.2 1.0 2.0 March 0.7 0.9 1.5 0.9 1.2 1.9 April 0.8 0.8 1.0 0.9 1.3 2.0 PM is composed of inorganic materials such as: calcium, potassium, silica, sodium, aluminium, iron and magnesium, unconverted char and bed material (in case of fluidized bed gasifier) [69]. Arsenic, selenium, zinc and lead also make up minor constituent of PM. [70] predicted that based on the rate of emission, atmospheric concentration and trends in deposition, Ag, Cd, Cu, Pb, Sb, Se and Zn can be expected to show great increases in the atmosphere as a result of anthropogenic activities; they found that Hg and Pb were being deposited in some areas at levels toxic to humans and Cd, Cu, Hg, Pb and Zn at levels toxic to other organisms. Although trace metals are released into the atmosphere by natural processes such as volcanic eruption, seas-salt spray, forest fires, rock weathering, biogenic sources and wind-borne soil particles [71], anthropogenic activities have caused an increase in their concentration in the atmosphere [72]. Anthropogenic sources such as: industries, agriculture, wastewater, mining and metallurgical processes have been noted to cause an exceedance of the natural of metals in the atmosphere [71], [73]. Metals such as Cu, Cd and Pb are commonly associated with industrial areas, Cr, Mn, Ni, V and Zn are associated with traffic, while No, K, Ca, Ti, Mg and Fe are associated with natural sources [74]–[76]. The life expectancy in Africa is lowest compared to other regions of the world; Africa´s life expectancy is 61.2 years while global life expectancy is 72.0 years [77]. However, the life expectancy in Nigeria, 55.2, is lower than the mean life expectancy of the continent of African [78]. Several factors have been attributed to the low life expectancy in Nigeria, one of which is poor air quality which has been attributed to several premature deaths in the country [79]. In Nigeria, air pollution is estimated to contribute to the risk of a death rate of 55.37 deaths per 100, 000; respiratory infections cause an estimated 119.97 deaths per 100, 000 [80]. In 2013, ambient PM pollution in Nigeria caused an estimated 39, 825 deaths and household air pollution caused an estimated 67, 148 deaths [79]. These deaths were estimated to cost the country a staggering 41, 796 and 70, 471 million USD for ambient PM pollution and household air pollution respectively [79]. Hence, managing air pollution is not only a health benefit but also an economic benefit. Electricity generation through thermal (oil and gas) power plants in most of southern Nigeria which generates massive gaseous emissions are a contributor to PM in the environment [81]. Petroleum exploration in the Niger Delta region of Nigeria over the years has led to the release of gases in the atmosphere through gas flaring; gas flaring, consequently, increases the input of PM into the atmosphere [51]. For a period of 49 years (1965 – 2013), 55% of gases explored in the Niger Delta regions was being flared an enormous amount of 4.56 × 105 tons (4.11 × 108 tons CO2 equivalent) of black carbon into the environment [82]. 4. Conclusion Therefore, this study realised that the quality of air is not necessarily dictated by urban-rural distribution, but on the specific activities that may contribute to the quality of air. Also, if the activities that contribute to the availability of contaminants in the atmosphere are consistent, there would be no significant reduction or change. Therefore, we should be more concerned about the nature of the natural or anthropogenic activities of wherever we find ourselves and Attention should be placed on the air quality in rural settlement and not only urban areas. Acknowledgements My utmost and sincere gratitude goes to God Almighty for having shown His unending grace upon me to once again attain another academic fit. I sincerely appreciate my wonderful supervisor - Prof. Dibofori-Orji Amalo Ndu, for her motherly words of encouragement and systematic culture of breaking down complex tasks to much simpler executable tasks; which has helped me in completing this research in good time. I also wish to extend my profound gratitude to Dr. Owhonda Ovunda of the Niger Delta Aqua Research Group, who I exposed me to research writing at its best. I would not fail to appreciate Prof. Akens Hamilton-Amachree of Federal University, Otuoke for her words of encouragement from my first-degree years till date; encouraging me to further my studies in a time when I felt I had lost it all. References [1] M. de A. D’Agosto, “Air pollutant and greenhouse gas emissions (GHG),” in Transportation, Energy Use and Environmental Impacts, M. de A. B. T.-T. D’Agosto Energy Use and Environmental Impacts, Ed. Elsevier, 2019, pp. 227–257. doi: https://doi.org/10.1016/B978-0-12-813454-2.00006-4. [2] V. Smil, “Air Pollution,” in International Encyclopedia of the Social & Behavioral Sciences, N. J. Smelser and P. B. B. T.-I. E. of the S. & B. S. Baltes, Eds. Oxford: Pergamon, 2001, pp. 355–359. doi: https://doi.org/10.1016/B0-08-043076-7/04187-5. [3] I. F. Offor, G. U. Adie, and G. R. Ana, “Review of Particulate Matter and Elemental Composition of Aerosols at Selected Locations in Nigeria from 1985–2015,” Journal of Health and Pollution, vol. 6, no. 10, pp. 1–18, 2016, doi: 10.5696/2156-9614-6-10.1. [4] B. P. Degens, R. Krassoi, L. Galvin, B. Reynolds, and T. Micevska, “Net acidity indicates the whole effluent toxicity of pH and dissolved metals in metalliferous saline waters,” Chemosphere, vol. 198, pp. 492–500, 2018, doi: https://doi.org/10.1016/j.chemosphere.2018.01.129. [5] R. Fornaroli et al., “Disentangling the effects of low pH and metal mixture toxicity on macroinvertebrate diversity,” Environmental Pollution, vol. 235, pp. 889–898, 2018, doi: https://doi.org/10.1016/j.envpol.2017.12.097. [6] Z. Wang, J. P. Meador, and K. M. Y. Leung, “Metal toxicity to freshwater organisms as a function of pH: A meta-analysis,” Chemosphere, vol. 144, pp. 1544–1552, 2016, doi: https://doi.org/10.1016/j.chemosphere.2015.10.032. [7] A. Betts, “BOUNDARY LAYER (ATMOSPHERIC) AND AIR POLLUTION | Diurnal Cycle,” in Encyclopedia of Atmospheric Sciences, 2nd ed., G. R. North, J. Pyle, and F. B. T.-E. of A. S. (Second E. Zhang, Eds. Oxford: Academic Press, 2015, pp. 319–323. doi: https://doi.org/10.1016/B978-0-12-382225-3.00135-3. [8] P. K. Rai, “Particulate Matter and Its Size Fractionation,” in Biomagnetic Monitoring of Particulate Matter, P. K. B. T.-B. M. of P. M. Rai, Ed. Elsevier, 2016, pp. 1–13. doi: https://doi.org/10.1016/B978-0-12-805135-1.00001-9. [9] L. Samek et al., “Chemical composition of submicron and fine particulate matter collected in Krakow, Poland. Consequences for the APARIC project,” Chemosphere, vol. 187, pp. 430–439, 2017, doi: https://doi.org/10.1016/j.chemosphere.2017.08.090. [10] E. P. Olaguer, “Particulate Matter and Surface Deposition,” in Atmospheric Impacts of the Oil and Gas Industry, E. P. B. T.-A. I. of the O. and G. I. Olaguer, Ed. Boston: Elsevier, 2017, pp. 47–53. doi: https://doi.org/10.1016/B978-0-12-801883-5.00005-X. [11] M. El-Fadel and M. Massoud, “Particulate matter in urban areas: health-based economic assessment,” Science of The Total Environment, vol. 257, no. 2, pp. 133–146, 2000, doi: https://doi.org/10.1016/S0048-9697(00)00503-9. [12] G. D. Thurston, Outdoor Air Pollution: Sources, Atmospheric Transport, and Human Health Effects, Second Edi., vol. 5, no. 69. Elsevier, 2016. doi: 10.1016/B978-0-12-803678-5.00320-9. [13] CCAC, “2018 Annual Science Update - Black Carbon Briefing Report,” Toronto, Canada, 2018. [14] USEPA, “Black Carbon Report to Congress,” Washington, DC, 2011. [15] P. Chylek, S. G. Jennings, and R. Pinnick, “AEROSOLS | Soot,” in Encyclopedia of Atmospheric Sciences, 2nd ed., G. R. North, J. Pyle, and F. B. T.-E. of A. S. (Second E. Zhang, Eds. Oxford: Academic Press, 2015, pp. 86–91. doi: https://doi.org/10.1016/B978-0-12-382225-3.00375-3. [16] M. Lippmann, “Health Effects of Airborne Particulate Matter,” New England Journal of Medicine, no. December 6, pp. 2395–2397, 2007. [17] R. W. Atkinson, S. Kang, H. R. Anderson, I. C. Mills, and H. A. Walton, “Epidemiological time series studies of PM2.5 and daily mortality and hospital admissions: a systematic review and meta-analysis,” Thorax, vol. 69, no. 7, pp. 660 LP – 665, Jul. 2014, doi: 10.1136/thoraxjnl-2013-204492. [18] A. B. Lumb, “Smoking and Air Pollution,” in Nunn’s Applied Respiratory Physiology, 8th ed., Elsevier Inc., 2017, pp. 281-290.e1. doi: 10.1016/b978-0-7020-6294-0.00019-8. [19] R. B. T.-R. M. in E. S. and E. S. El Morabet, “Effects of Outdoor Air Pollution on Human Health,” in Earth Systems and Environmental Sciences, Elsevier, 2018, pp. 1–9. doi: https://doi.org/10.1016/B978-0-12-409548-9.11012-7. [20] K. Newell, R. P. Cusack, C. Kartsonaki, N. Chaudhary, and O. P. Kurmi, Household Air Pollution and Associated Health Effects in Low and Middle Income Countries, 2nd ed. Elsevier Inc., 2019. doi: 10.1016/b978-0-12-801238-3.11494-1. [21] M. Sehlstedt et al., “Antioxidant airway responses following experimental exposure to wood smoke in man,” Particle and Fibre Toxicology, vol. 7, pp. 1–11, 2010, doi: 10.1186/1743-8977-7-21. [22] WHO, “Deaths attributable to household air pollution,” 2018 [23] C. U. Onuorah, T. G. Leton, and O. L. Y. Momoh, “Analysis of Particle Pollution in Residential Urban Area of Port Harcourt, Nigeria,” Journal of Scientific Research and Reports, no. October, pp. 1–9, 2019, doi: 10.9734/jsrr/2019/v25i230187. [24] K. Iyogun, S. A. Lateef, and G. R. E. E. Ana, “Lung Function of Grain Millers Exposed to Grain Dust and Diesel Exhaust in Two Food Markets in Ibadan Metropolis, Nigeria,” Safety and Health at Work, vol. 10, no. 1, pp. 47–53, 2019, doi: https://doi.org/10.1016/j.shaw.2018.01.002. [25] NiMet, “Port Harcourt,” Nigerian Meteorological Service of Nigeria, 2020. https://www.nimet.gov.ng/ [26] Systat Software. Inc., “SigmaPlot for Windows version 12.5,” 2011 [27] Ø. Hammer, PAST: PAleontological STatistics, vol. 1, no. November. Oslo: Natural History Museum, University of Oslo, 2020. doi: 10.1093/cid/ciq238. [28] USEPA, “Integrated Risk Information System (IRIS),” Washington, DC 20460, 2009. [29] USEPA, “Provisional Guidance for Quantitative Risk Assessment of Polycyclic Aromatic Hydrocarbons (PAH),” Washington, DC 20460, EPA/600/R-93/089 (NTIS PB94116571), 1992. [30] B. Bress, Risk Assessment, 1st ed. Elsevier Inc., 2009. doi: 10.1016/B978-0-12-369521-5.00014-2. [31] USEPA, “Diesel engine exhaust; CASRN N . A .,” Washington, DC, 2003. [32] OECD/ IEA, “WEO-2017 Special Report: Energy Access Outlook,” 2017. [33] F. Yip et al., “Assessment of traditional and improved stove use on household air pollution and personal exposures in rural western Kenya,” Environment International, vol. 99, pp. 185–191, 2017, doi: https://doi.org/10.1016/j.envint.2016.11.015. [34] A. J. Buczyńska et al., “Composition of PM2.5 and PM1 on high and low pollution event days and its relation to indoor air quality in a home for the elderly,” Science of The Total Environment, vol. 490, pp. 134–143, 2014, doi: https://doi.org/10.1016/j.scitotenv.2014.04.102. [35] R. Sharma and R. Balasubramanian, “Assessment and mitigation of indoor human exposure to fine particulate matter (PM2.5) of outdoor origin in naturally ventilated residential apartments: A case study,” Atmospheric Environment, vol. 212, pp. 163–171, 2019, doi: https://doi.org/10.1016/j.atmosenv.2019.05.040. [36] WHO, “Ambient air pollution: a global assessment of exposure and burden of disease,” Geneva, Switzerland, 2016. [37] X. Zhao, W. Zhou, L. Han, and D. Locke, “Spatiotemporal variation in PM2.5 concentrations and their relationship with socioeconomic factors in China’s major cities,” Environment International, vol. 133, p. 105145, 2019, doi: https://doi.org/10.1016/j.envint.2019.105145. [38] C. Lin et al., “Difference in PM2.5 variations between urban and rural areas over eastern China from 2001 to 2015,” Atmosphere (Basel), vol. 9, no. 8, 2018, doi: 10.3390/atmos9080312. [39] C. A. Ku, “Exploring the spatial and temporal relationship between air quality and urban land-use patterns based on an integrated method,” Sustainability (Switzerland), vol. 12, no. 7, 2020, doi: 10.3390/su12072964. [40] S. Superczynski and S. Christopher, “Exploring Land Use and Land Cover Effects on Air Quality in Central Alabama Using GIS and Remote Sensing,” Remote Sensing, vol. 3, pp. 2552–2567, Dec. 2011, doi: 10.3390/rs3122552. [41] A. N. Dibofori-Orji and O. S. Edori, “Suspended Particulate Matter (SPM) and Trace Metals Emission from the Combustion of Tyres in A Nigeria Abattoir,” Chemistry and Materials Research, vol. 3, no. 13, pp. 21–27, 2013. [42] G. Kalaiarasan, R. M. Balakrishnan, N. A. Sethunath, and S. Manoharan, “Source apportionment of PM2.5 particles: Influence of outdoor particles on indoor environment of schools using chemical mass balance,” Aerosol and Air Quality Research, vol. 17, no. 2, pp. 616–625, 2017, doi: 10.4209/aaqr.2016.07.0297. [43] TECAM, “What are acceptable VOC levels in the air?,” Blog, 2019. https://www.tecamgroup.com/acceptable-voc-levels/ (accessed May 03, 2021). [44] C. Jia, K. Cao, R. Valaulikar, X. Fu, and A. B. Sorin, “Variability of Total Volatile Organic Compounds (TVOC) in the Indoor Air of Retail Stores,” Int J Environ Res Public Health, vol. 16, no. 23, p. 4622, Nov. 2019, doi: 10.3390/ijerph16234622. [45] American Lung Association, “Volatile Organic Compounds,” Clean air, 2020. https://www.lung.org/clean-air/at-home/indoor-air-pollutants/volatile-organic-compounds (accessed May 03, 2021). [46] L. Zhu et al., “Formaldehyde (HCHO) As a Hazardous Air Pollutant: Mapping Surface Air Concentrations from Satellite and Inferring Cancer Risks in the United States,” Environmental Science & Technology, vol. 51, no. 10, pp. 5650–5657, May 2017, doi: 10.1021/acs.est.7b01356. [47] S. Sidra, Z. Ali, Z. Ahmad Nasir, and I. Colbeck, “Seasonal variation of fine particulate matter in residential micro–environments of Lahore, Pakistan,” Atmospheric Pollution Research, vol. 6, no. 5, pp. 797–804, 2015, doi: https://doi.org/10.5094/APR.2015.088. [48] J. Liu and S. Cui, “Meteorological Influences on Seasonal Variation of Fine Particulate Matter in Cities over Southern Ontario, Canada,” Advances in Meteorology, vol. 2014, p. 169476, 2014, doi: 10.1155/2014/169476. [49] Z. Bodor, K. Bodor, Á. Keresztesi, and R. Szép, “Major air pollutants seasonal variation analysis and long-range transport of PM10 in an urban environment with specific climate condition in Transylvania (Romania),” Environmental Science and Pollution Research, vol. 27, no. 30, pp. 38181–38199, 2020, doi: 10.1007/s11356-020-09838-2. [50] A. Chamseddine, I. Alameddine, M. Hatzopoulou, and M. El-Fadel, “Seasonal variation of air quality in hospitals with indoor–outdoor correlations,” Building and Environment, vol. 148, pp. 689–700, 2019, doi: https://doi.org/10.1016/j.buildenv.2018.11.034. [51] S. O. Giwa, C. N. Nwaokocha, S. I. Kuye, and K. O. Adama, “Gas flaring attendant impacts of criteria and particulate pollutants: A case of Niger Delta region of Nigeria,” Journal of King Saud University - Engineering Sciences, vol. 31, no. 3, pp. 209–217, 2019, doi: https://doi.org/10.1016/j.jksues.2017.04.003. [52] O. G. Fawole, X. M. Cai, and A. R. Mackenzie, “Gas flaring and resultant air pollution: A review focusing on black carbon,” Environmental Pollution, vol. 216, pp. 182–197, 2016, doi: 10.1016/j.envpol.2016.05.075. [53] G. Tian, Z. Qiao, and X. Xu, “Characteristics of particulate matter (PM10) and its relationship with meteorological factors during 2001–2012 in Beijing,” Environmental Pollution, vol. 192, pp. 266–274, 2014, doi: https://doi.org/10.1016/j.envpol.2014.04.036. [54] N. T. Kim Oanh, P. Chutimon, W. Ekbordin, and W. Supat, “Meteorological pattern classification and application for forecasting air pollution episode potential in a mountain-valley area,” Atmospheric Environment, vol. 39, no. 7, pp. 1211–1225, 2005, doi: 10.1016/j.atmosenv.2004.10.015. [55] C. Song, T. Pei, and L. Yao, “Analysis of the characteristics and evolution modes of PM2.5 pollution episodes in Beijing, China during 2013,” Int J Environ Res Public Health, vol. 12, no. 2, pp. 1099–1111, Jan. 2015, doi: 10.3390/ijerph120201099. [56] Q. Zhang et al., “Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China,” Sci Total Environ, vol. 502, pp. 578–584, Jan. 2015, doi: 10.1016/j.scitotenv.2014.09.079. [57] X. Li et al., “Particulate matter pollution in Chinese cities: Areal-temporal variations and their relationships with meteorological conditions (2015–2017),” Environmental Pollution, vol. 246, pp. 11–18, 2019, doi: https://doi.org/10.1016/j.envpol.2018.11.103. [58] S. E. Bauer, U. Im, K. Mezuman, and C. Y. Gao, “Desert Dust, Industrialization, and Agricultural Fires: Health Impacts of Outdoor Air Pollution in Africa,” Journal of Geophysical Research: Atmospheres, vol. 124, no. 7, pp. 4104–4120, 2019, doi: 10.1029/2018JD029336. [59] J. O. Anderson, J. G. Thundiyil, and A. Stolbach, “Clearing the Air: A Review of the Effects of Particulate Matter Air Pollution on Human Health,” Journal of Medical Toxicology, vol. 8, no. 2, pp. 166–175, 2012, doi: 10.1007/s13181-011-0203-1. [60] C. E. Reid, E. M. Considine, G. L. Watson, D. Telesca, G. G. Pfister, and M. Jerrett, “Associations between respiratory health and ozone and fine particulate matter during a wildfire event,” Environment International, vol. 129, pp. 291–298, 2019, doi: https://doi.org/10.1016/j.envint.2019.04.033. [61] H. Barbara et al., “Chronic Residential Exposure to Particulate Matter Air Pollution and Systemic Inflammatory Markers,” Environmental Health Perspectives, vol. 117, no. 8, pp. 1302–1308, Aug. 2009, doi: 10.1289/ehp.0800362. [62] J. Huang, Q. Liu, and X. Guo, “Short-Term Effects of Particulate Air Pollution on Human Health,” in Encyclopedia of Environmental Health, 2nd ed., J. B. T.-E. of E. H. (Second E. Nriagu, Ed. Oxford: Elsevier, 2019, pp. 655–662. doi: https://doi.org/10.1016/B978-0-12-409548-9.10991-1. [63] T. Zhao et al., “PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and nitrated-PAHs (NPAHs) emitted by gasoline vehicles: Characterization and health risk assessment,” Science of The Total Environment, vol. 727, p. 138631, 2020, doi: https://doi.org/10.1016/j.scitotenv.2020.138631. [64] R. Tong, J. Liu, W. Wang, and Y. Fang, “Health effects of PM2.5 emissions from on-road vehicles during weekdays and weekends in Beijing, China,” Atmospheric Environment, vol. 223, p. 117258, 2020, doi: https://doi.org/10.1016/j.atmosenv.2019.117258. [65] Y.-C. Lin, Y.-C. Li, K. T. T. Amesho, S. Shangdiar, F.-C. Chou, and P.-C. Cheng, “Chemical characterization of PM2.5 emissions and atmospheric metallic element concentrations in PM2.5 emitted from mobile source gasoline-fueled vehicles,” Science of The Total Environment, p. 139942, 2020, doi: https://doi.org/10.1016/j.scitotenv.2020.139942. [66] Y. Hao et al., “Chemical characterisation of PM2.5 emitted from motor vehicles powered by diesel, gasoline, natural gas and methanol fuel,” Science of The Total Environment, vol. 674, pp. 128–139, 2019, doi: https://doi.org/10.1016/j.scitotenv.2019.03.410. [67] M. Yunesian, R. Rostami, A. Zarei, M. Fazlzadeh, and H. Janjani, “Exposure to high levels of PM2.5 and PM10 in the metropolis of Tehran and the associated health risks during 2016–2017,” Microchemical Journal, vol. 150, p. 104174, 2019, doi: https://doi.org/10.1016/j.microc.2019.104174. [68] I. D. Sulaymon et al., “PM2.5 in Abuja, Nigeria: Chemical characterization, source apportionment, temporal variations, transport pathways and the health risks assessment,” Atmospheric Research, vol. 237, p. 104833, 2020, doi: https://doi.org/10.1016/j.atmosres.2019.104833. [69] B. Acharya, “Cleaning of Product Gas of Gasification,” in Biomass Gasification, Pyrolysis and Torrefaction, 3rd ed., P. B. T.-B. G. Basu Pyrolysis and Torrefaction (Third Edition), Ed. Academic Press, 2018, pp. 373–391. doi: https://doi.org/10.1016/B978-0-12-812992-0.00010-8. [70] J. N. Galloway, J. D. Thornton, S. A. Norton, H. L. Volchok, and R. A. N. McLean, “Trace metals in atmospheric deposition: A review and assessment,” Atmospheric Environment (1967), vol. 16, no. 7, pp. 1677–1700, 1982, doi: https://doi.org/10.1016/0004-6981(82)90262-1. [71] V. Masindi and K. L. Muedi, “Environmental Contamination by Heavy Metals,” in Heavy Metals, H. E.-D. M. Saleh and Refaat F. Aglan, Eds. London, UK: IntechOpen, 2017, pp. 115–132. doi: dx.doi.org/10.5772/intechopen.76082. [72] W. Salomons and U. Forstner, Metals in the Hydrocycle. 1984. doi: 10.1017/CBO9781107415324.004. [73] K. Slezakova, M. da Conceição Alvim-Ferraz, and M. do Carmo Pereira, “Elemental Characterization Of Indoor Breathable Particles at a Portuguese Urban Hospital,” Journal of Toxicology and Environmental Health, Part A, vol. 75, no. 13–15, pp. 909–919, Jul. 2012, doi: 10.1080/15287394.2012.690707. [74] A. H. De La Cruz, Y. B. Roca, L. Suarez-Salas, J. Pomalaya, D. A. Tolentino, and A. Gioda, “Chemical characterization of PM 2.5 at rural and urban sites around the metropolitan area of Huancayo (Central Andes of Peru),” Atmosphere (Basel), vol. 10, no. 1, 2019, doi: 10.3390/atmos10010021. [75] P. Liu, Y. Zhang, T. Wu, Z. Shen, and H. Xu, “Acid-extractable heavy metals in PM2.5 over Xi’an, China: seasonal distribution and meteorological influence,” Environmental Science and Pollution Research, vol. 26, no. 33, pp. 34357–34367, 2019, doi: 10.1007/s11356-019-06366-6. [76] M. Soleimani, N. Amini, B. Sadeghian, D. Wang, and L. Fang, “Heavy metals and their source identification in particulate matter (PM2.5) in Isfahan City, Iran,” Journal of Environmental Sciences, vol. 72, pp. 166–175, 2018, doi: https://doi.org/10.1016/j.jes.2018.01.002. [77] WHO, “Life expectancy and Healthy life expectancy Data by WHO region,” Global Health Observatory data repository, 2018. https://apps.who.int/gho/data/view.main.SDG2016LEXREGv? lang=en (accessed Jul. 28, 2020). [78] WHO, “Life expectancy and Healthy life expectancy Data by country,” Global Health Observatory data repository, 2018. https://apps.who.int/gho/data/view.main.SDG2016LEXv?lang=en (accessed Jul. 28, 2020). [79] R. Roy, “The cost of air pollution in Africa,” 333, 2016. doi: https://doi.org/10.1787/5jlqzq77x6f8-en. [80] IHME, “Air pollution,” GBD Compare | Viz Hub, 2017. [81] J. A. Sonibare, “Air pollution implications of Nigeria’s present strategy on improved electricity generation,” Energy Policy, vol. 38, no. 10, pp. 5783–5789, 2010, doi: 10.1016/j.enpol.2010.05.029. [82] S. O. Giwa, O. O. Adama, and O. O. Akinyemi, “Baseline black carbon emissions for gas flaring in the Niger Delta region of Nigeria,” Journal of Natural Gas Science and Engineering, vol. 20, pp. 373–379, 2014, doi: https://doi.org/10.1016/j.jngse.2014.07.026. ? Author Name et al.: Paper title The issue name 2021; X(X): XX-XX ? Journal Title 2021; X(X): XX-XX doi: 10.11648/j.XXXX.2021XXXX.XX ISSN: XXXX - XXXX (Print); ISSN: XXXX - XXXX (Online)