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.
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