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2024 Volume 4
Article Contents
Abstract
Introduction
Materials and methods
Description of the study area
Soil sample collection and soil fertility evaluation
Nodules collection
Statistical data analysis
Results
Various physico-chemical parameters of the study areas
Soil pH and exchangeable cations and nodule number
Relationships among different soil parameters with nodulation
Nodulation as influenced by different chemical parameters of the soils
Nodulation as influenced by exchangeable potassium, soil texture and micronutrients
Discussion
Suitability of studied soils in production of soybean crop and rhizobia inoculants
Soil pH, exchangeable cations, extractable phosphorus and nodule number
Organic carbon, total nitrogen, CN ratio, micronutrients and soil texture
Soil quality index using principle component analysis
Relationships between nodulation and different physico-chemical parameters of the soil
The effect of different physical and chemical parameters of soil in soybean nodulation
Conclusions
Author contributions
Data availability
Acknowledgments
Conflict of interest
Rights and permissions
References
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ARTICLE   Open Access    

Health effects of oil and waste pollutants on Delta Nigeria inhabitants' well being and its mitigation

  • 1.

    Department of Civil Engineering, University of Benin, Edo - State 300213, Nigeria

  • 2.

    Current address: Civil Engineering Department, Federal University of Oye-Ekiti, Ekiti State, Nigeria

  • 3.

    Department of Civil Engineering, Ambrose Alli University, Ekpoma, Edo State 310006, Nigeria

  • 4.

    Department of Civil Engineering, Federal University of Technology, Akure, Ondo State 340110, Nigeria

More Information
  • Water quality, trace gas (SO 2, CO 2, and NO 2), particulate matter (PM 10 as well as PM2.5), and effluent emissions were quantified near cement and oil factories and nearby suburban areas within Delta Nigeria. Results display that ambient air particulate matter PM2.5 varies between 2.1 to 7.9 μg/m 3 and VOC (0.013−8.53 μg/m 3), while CO and CO 2 were 100% and 30% respectively not within regulatory limits, consequently leading to asthma, coughing, and difficulty breathing. Four out of nine sites investigated for noise effects were above WHO stipulated limits. While some parameters such as BOD and COD display critical levels for effluent scrutiny and conductivity, calcium, TDS, total hardness, DO (Dissolved oxygen) and total alkalinity were also above clean water specifications. Wastewater consists of spills and other water effects which produce pollutants such as soluble organic chemicals that deplete dissolved oxygen, anions, volatile materials, and other heavy metals. Based on age, the greatest impact (52%) was seen in ages varying from 0 to 16 while that of the age set 16 to 60 was 45%. Curbing of oil and cement particulate pollutants and requesting a buffer region between the cement and oil depots and neighborhoods, complemented with regulatory enforcement and persistent monitoring, should be a top precedence to the regulatory authority.

  • Introduction

    Global human population growth threatens current agricultural systems to sustain the rising demand for food and uncertainty in securing safe and nutritious food for society[1]. This highlights the significance of cultivating high-value crops, particularly those with high-quality nutritional components[2] and economical production with application of only essential fertilizers which are necessary for enhancing the nutrients in the soil and maintaining the sustainability for production[3, 4]. Soybean (Glycine max) is among the world's most important crops due to its high-quality plant-based protein and oil content[2−5]. Worldwide, the crop is grown on about 6% of available arable land[2] and 50% of the legume growing areas[6]. Globally, the major soybean-producing countries include the United States of America (USA), Brazil, Argentina, China, and India[7]. In Africa, the soybean crop is among the most common legumes grown in Sub-Saharan Africa (SSA)[1,8,9]. Despite the production of the crop, the yield obtained by smallholder farmers in SSA is not promising as compared with the major soybean growing areas in the world[9]. This needs alternate efforts to increase the productivity of crops while lowering the cost of production, which can be achieved with the judicious use of fertilizers with supplementation of biofertilizers, made from effective rhizobia, in which their effectiveness in increasing productivity lies in soil fertility evaluation for proper decision in type and quantity of fertilizers to be used[10].

    Sub-Saharan Africa is among the regions with a fast-growing population but decreasing crop productivity[11]. The decrease in productivity of crops is contributed by different factors such as poor soil health especially soil fertility degradation due to continuous growing and harvesting of crops, unbalanced soil ecology and poor nutrient cycling. Other factors include pests and diseases, climate change, poor crop and nutrient management, use of low yielding varieties as well as unappropriated timing of planting[12−14]. Of all the factors, poor soil health, which includes soil fertility degradation, unbalanced soil ecology and nutrient cycling, is the major factor, which contributes to stagnating or decreasing crop productivity[15] in SSA hence unsustainability in agricultural systems.

    Soil health, specifically soil fertility is a very important aspect in the productivity of soil and crops. Nutrient cycling and their availability for uptake by plants are among the key factors, which make the fertile soil productive and hence crop productivity. In the soil, nutrients are harvested through the growing and harvesting of crops. Moreover, when the crop residues are not recycled into the soil as manures, it is a complete harvesting of nutrients, which are taken by plants for their growth and development[16]. Leaving plant residues in the field after harvesting the edible and economic parts of plants is important for return of the nutrients into the soil. The harvesting of nutrients through crops results in serious degradation of soil fertility leading to inadequate nutrients for meeting the crop's requirements in the following season. To ensure sustainable availability of nutrients and good soil health, soil supplementation with fertilizers and manure is a key aspect[17]. For economical production of safe and nutritious food as well as environmental resilience, soil fertility evaluation is the most important aspect for decision-making on the type and amount of fertilizers and, manures to be supplemented for optimum growth and development of plants. This is because, excessive use of fertilizers and manures are linked to environmental pollution especially agricultural soils, water and air as well as higher levels of toxic metals in both the edible and non-edible parts of plants[18] calling for a shift in a use of alternative approaches such as biofertilizers made from beneficial soil microorganisms.

    Biofertilizers like other fertilizers and manures supply different nutrients in soil for uptake by plants. Microorganisms as other living creatures are affected by different edaphic factors including[19]: stress environments such as moisture, temperature, acidity, alkalinity, salinity and nutrient composition. The evidence from research shows that the rhizobial effectiveness and populations in nitrogen fixation are interconnected with the fertility of soil whereby extreme soil conditions have been observed to negatively affect the effectiveness of rhizobial strains[20].

    In SSA, smallholder farmers dominate the agricultural systems for food crops[21,22]. Synthetic fertilizers are most widely available and accessible ones to many farmers moreso than organic manure, because not all of them keep livestock. In addition, in order to meet crop requirements through application of organic manure, it needs larger quantities per hectare to meet crop requirements. Although, organic manure contain nutrients in very small quantities, they are well known to possess the characteristics of improving the soil conditions[23] making unreplaceable advantages of using organic manure along with other types of fertilizers. Despite their availability, smallholder farmers rarely use synthetic fertilizers due to their cost, hence less amount is used. Biofertilizers are observed to be cheaper, effective and environmentally friendly with more advantages in balancing the soil ecosystem as well as nutrient cycling[24]. For sustainability in agricultural systems the use of biofertilizers together with soil fertility evaluation is crucial in crop production. However, the evaluation of soil fertility for decision making in type (including biofertilizers) and quantity of fertilizers and the importance of biofertilizers have been ignored in smallholder farming systems. This calls for the creation of awareness of importance of soil fertility evaluation and the use of biofertilizers as an alternative approach to industrial fertilizers in meeting crop's requirements to improve productivity for optimum yield. Therefore, this study aimed at evaluating soil fertility between the soybean growing areas and the non soybean growing areas of Tanzania and their suitability for the use of rhizobia inoculants. The findings of this study will contribute to enriching the knowledge which the researchers could tap into, for the benefit of further studies on the management of soil nutrients as well as the importance of evaluating the fertility status of soil in improving the productivity of different crops in meeting the food demand.

    Materials and methods

    Description of the study area

    Soils and nodules for this study were collected from the major soybean growing regions in the eastern, northern, and southern highlands, of Tanzania. In the southern highlands, the regions included were: Iringa, Njombe, Ruvuma, Songwe, Rukwa, and Mbeya. This zone is located between latitudes 7° and 11.5° S and longitudes 30° and 38° E with an elevation ranging from 302 to 2,925 meters above sea level (m.a.s.l.). Rainfall is unimodal falling in November to May with annual rainfall of 1,650 mm and dry periods ranging from June to September[25]. The mean annual temperature ranges from 7 to 32.2 °C. The eastern zone included Morogoro region which is located between latitudes 5° and 9° S and longitudes 35° and 38° E. The mean annual temperature ranges from 15 to 32 °C and the average annual rainfall is around 740 mm[26]. In the northern zone, Arusha and Kilimanjaro regions were included in the present study. The Arusha region lies between latitudes 1° and 4° S and longitudes 34° and 37° E with an average annual rainfall of 873 mm while the temperature ranges from 12.1 to 28.8 °C. The Kilimanjaro region lies between the latitudes 2° and 4° S and longitudes 36° and 38° E. The average annual rainfall in the Kilimanjaro region ranges from 700 mm to 2,000 mm and the temperature ranges from 12.5 to 27 °C. The site description of the sampled areas are presented in Fig. 1.

    Figure 1.  Map showing maximum and minimum elevations of the regions in the study sites.
    DownLoad: Full-Size Img PowerPoint

    Soil sample collection and soil fertility evaluation

    In each region, three representative districts were selected, three villages in each district, and one field with a history of using only organic manure, for at least three seasons, consecutively, were selected. The study sites and sampling location map was generated using QGIS 3.14.0 software (Fig. 2). Soil samples were taken from three spotted locations per field depending on the color of the soil because the area was too small (< one acre). For each spotted location, about 1 kg of soil sample was collected from a depth of 0−30 cm. One composite soil sample was prepared by mixing three soil samples, removing the roots and crumps. Prior to laboratory analysis, the soil was air-dried and sieved through 2 mm mesh.

    Figure 2.  Map showing soil and nodules sampling fields in different regions of Tanzania (SHZ-Southern Highland Zone, EZ-Eastern Zone and NZ-Northern Zone).
    DownLoad: Full-Size Img PowerPoint

    The soil texture was determined using the hydrometer method. Total nitrogen was determined by the micro-Kjeldahl digestion-distillation method. Cation exchange capacity was measured at pH 7 with 1 M Ammonium Acetate (NH4OAc) and exchangeable cations K+ and Na+ were determined by flame photometer while, Ca2+ and Mg2+ as well as micronutrients Iron (Fe), Copper (Cu), Zinc (Zn) and Manganese (Mn) were determined by an atomic absorption spectrophotometer[27]. The soil organic carbon was characterized by the wet digestion (oxidation) method of Walkley-Black[28]. The soil pH was measured electrochemically in 1:2.5 (weight/volume) soil using the water suspension potentiometric method[29]. The availability of P in soils is influenced by soil pH, hence P analysis of soils was done by two methods, for soils with pH ≥ 6.5, extractable P was determined by the Olsen method and for soils with pH ≤ 6.5, Bray 1 method was used[30].

    Nodules collection

    The nodule samples were collected during cropping seasons from the same sites where the soils were sampled. The collection of nodule samples focused on the farmers' fields where rhizobia inoculants have never been used before for the purpose of obtaining the indigenous rhizobia which can effectively form nodules with soybean in the soils of Tanzania. At 50% flowering, from each farmer field, three healthy plants (treated as replicates) with intense green leaves were randomly collected by uprooting to obtain nodules in each field, making a total of 243 plants which were treated as separate samples[31,32]. The intense greening of leaves was considered as sufficiency of nitrogen in plants. The nodules for each plant were counted and the data was recorded.

    Statistical data analysis

    Different statistical methods were applied to analyze the collected data in terms of its distribution and correlation among the studied parameters. The Principle Component Analysis (PCA) for Soil Quality Indices was plotted by using XLSTAT software Version 2023.5.1. All the collected nodule data were statistically analyzed by Jamovi version 2.3.2.0, GenStat 15th Edition and the graphs were plotted using Excel 2016 in Windows 10. The mean and standard errors within the sites for nutrients in soil as well as correlation matrix between nodules number and soil nutrients were calculated by using Jamovi version 2.3.2.0. The mean separation within and between sites for nodule number were determined by one-way analysis of variance (ANOVA) following the factor effect model as shown in Eqn 1. Tukey's-HSD multiple comparison test at a threshold of 5% in GenStat 15th Edition was conducted to separate mean values among replications of the nodule number. Therefore, only one factor – the sampling site (i.e., 81 sites) with different soil characteristics was considered as the fixed main effect whereas sample replicates were treated as random effect.

    Yi=μ+αi+εi (1)

    Where Yi is the observed response variable in the ith factor; µ is the overall (grand) mean; αi is the main effect of the factor sampling site; εi is the random error associated with the observation of response variable in the ith factor.

    Results

    Various physico-chemical parameters of the study areas

    Soil pH and exchangeable cations and nodule number

    The data for soil physico-chemical properties of the studied sites are summarized in Table 1 with details in Supplemental Tables S1 & S2. The soil pH in all 81 sites was extremely acidic to moderately alkaline with an average of 6.222 ± 0.655 and total acidity low to very high with an average of 0.292 ± 0.589 cmol(+)Kg−1. The CEC of the soils was very low to medium with an average of 7.899 ± 4.582 cmol(+)Kg−1. In the case of exchangeable bases, Ca was low to high with an average of 5.099 ± 3.698 cmol(+)Kg−1), Mg been very low to high with an average of 1.257 ± 0.906 cmol(+)Kg−1), while K was low to very high with an average of 0.277 ± 0.397 cmol(+)Kg−1) and Na was very low to low with an average value of 0.026 ± 0.034 cmol(+)Kg−1). The concentration of extractable P in the soils was low to very high with an average value of 33.909 ± 37.264 mg·kg−1. The OC in the soils ranged from very low to high with an average of 1.663% ± 0.893 and total N varied from very low to medium with an average of 0.153% ± 0.074% while the ratio of carbon and nitrogen (CN ratio) was of low quality which was less than 8 and moderate quality which was greater than 13 with an average value of 11.385 ± 2.591. On the other hand, there was variation in the levels of micronutrients whereby Cu and Zn ranged from very low to very high with their averages been 3.312 ± 7.984 and 4.410 ± 5.859 mg·kg−1 while Mn varied from medium to very high with an average of 80.462 ± 43.892 mg·kg−1 and Fe from high to very high with an average of 66.553 ± 63.671 mg·kg−1.

    Table 1.  Soil chemical parameters of the study sites.
    ParameterNumberMinimumMaximumMeanStd. deviation
    Soil pH (1:2.5) (H2O)814.4807.9236.2220.655
    Cu (mg·kg−1)810.04849.1853.3127.984
    Zn (mg·kg−1)810.06240.7784.4105.859
    Mn (mg·kg−1)811.597172.53580.46243.892
    Fe (mg·kg−1)817.840526.72666.55363.671
    TN-Kjeld (%)810.0660.3780.1530.074
    OC-BlkW (%)810.4274.9931.6630.893
    C/N ratio814.67019.77811.3852.591
    Ext. P (mg·kg−1)812.352166.17933.90937.264
    CEC (cmol(+)Kg−1)811.91320.8017.8994.582
    Ca2+ (cmol(+)Kg−1)810.20214.3755.0993.698
    Mg2+ (cmol(+)Kg−1)810.1773.4791.2570.906
    Na+ (cmol(+)Kg−1)810.0000.2550.0260.034
    K+ (cmol(+)Kg−1)810.0342.6920.2770.397
    Total acidity
    (cmol(+)Kg−1)    		810.0855.1530.2920.589
    Exch. Al
    (cmol(+)Kg−1)    		810.0000.8470.0450.125
    Exch. H
    (cmol(+)Kg−1)    		810.0004.7430.2470.543
     | Show Table
    DownLoad: CSV

    Principal Component Analysis (PCA) shows that the first two PCs explain around 43.7% of the variance and the first five PCs explain 70.1% of the variance (Fig. 3; Supplemental Tables S3 & S4). These PCs were selected according to the method described by other researchers[33−35]. Twelve other PCs were excluded from the present study. The eigenvectors in the context of the PCA (Table 2; Fig. 4) revealed the relationships between the original variables (soil pH, Cu, Zn, Mn, Fe, TN-Kjeldahl, OC-BlkW, C/N ratio, P, CEC, Ca2+, Mg2+, Na+, K+, Total acidity, Al3+, and H+) and the extracted PCs (F1 to F5). Results further indicated that the first PC (F1) reflect relatively higher positive contributions from variables soil pH, Cu, Zn, Ext. P, CEC, Ca2+, Mg2+, K+, Total Acidity, Al3+, and H+. The PC (F2) has notable positive contributions from variables Fe, Na, K, Total acidity, Al3+, and H+. The third PC (F3) is negatively influenced by the variable soil pH, Cu, Zn, TN-Kjeld, OC-BlkW, C/N ratio, Total acidity, Al3+, and H+ while positively correlated with extractable P, Ca2+, Mg2+, Na+, and K+. The fourth PC has strong positive contributions from variables Cu, Zn, Mn, Fe, P, and C/N ratio and the last component F5 is negatively influenced by Fe, Na+, K+, Total acidity, Al3+, and H+.

    Figure 3.  Eigen values and cumulative variability of the Principal Component Analysis.
    DownLoad: Full-Size Img PowerPoint
    Table 2.  Summarization of the Principal Component Analysis.
    VariablesF1F2F3F4F5
    Soil pH (1:2.5) (H2O)0.209−0.1430.5200.0310.033
    Cu (mg·kg−1)0.077−0.002−0.1130.332−0.515
    Zn (mg·kg−1)0.2120.045−0.0080.5210.045
    Mn (mg·kg−1)−0.1150.034−0.0660.406−0.399
    Fe (mg·kg−1)0.0150.318−0.1280.169−0.008
    TN-Kjeld (%)0.2910.104−0.473−0.071−0.136
    OC-BlkW (%)0.2840.137−0.4930.0580.185
    C/N ratio0.0630.115−0.1140.2090.654
    Ext. P (mg·kg−1)0.2850.0500.3040.190−0.025
    CEC (cmol(+)Kg−1)0.4060.0890.1570.0210.010
    Ca2+ (cmol(+)Kg−1)0.3960.0080.1710.0930.025
    Mg2+( cmol(+)Kg−1)0.3640.0140.069−0.0140.006
    Na+ (cmol(+)Kg−1)0.2510.035−0.044−0.431−0.287
    K+ (cmol(+)Kg−1)0.3170.058−0.042−0.345−0.074
    Total acidity (cmol(+)Kg−1)−0.1030.6060.171−0.047−0.040
    Exch. Al (cmol(+)Kg−1)−0.0610.3350.044−0.139−0.026
    Exch. H (cmol(+)Kg−1)−0.1020.5810.174−0.021−0.037
     | Show Table
    DownLoad: CSV
    Figure 4.  Principal component plot of soil physicochemical properties (81 samples).
    DownLoad: Full-Size Img PowerPoint

    Relationships among different soil parameters with nodulation

    A correlation analysis (Table 3) performed across nodules number, chemical and physical parameters of the studied soils, showed variation across the parameters ranging from negative non-significant to strong positive correlations. A total of 13 out of 17 physico-chemical parameters were negatively correlated with nodules number. A positive significant (p < 0.05) correlations for nodules number was observed with soil pH (r = 0.14) and a negative significant (p < 0.05) correlations with total N (r = −0.22), OC (r = −0.27) and Mg2+ (r = −0.24). Soil pH had positive significant (p < 0.001) correlation with P (r = 0.48), CEC (r = 0.46), Ca2+ (r = 0.52), Mg2+ (r = 0.39) and p < 0.05 with K+ (r = 0.23). Total N had positive significant (p < 0.001) correlation with OC (r = 0.88), CEC (r = 0.47), Ca2+ (r = 0.43), Mg2+ (r = 0.50), Na+ (r = 0.44) and p < 0.05 with P (r = 0.22) and Zn (r = 0.25). There was positive significant (p < 0.001) correlation between OC and CN ratio (r = 0.37), CEC (r = 0.47), Ca2+ (r = 0.44), Mg2+ (r = 0.47) and K+ (r = 0.41) and at p < 0.01 with Zn (r = 0.33 while at p < 0.05 with P and Na+ both with r = 0.24.

    Table 3.  The correlation matrix among different soil chemical parameters and nodule number.
    ParametersNodulesSoil pH%C/N
    ratio    		Mg/kgcmol(+)kg-1Mg/kg
    TN-KjeldOC-BlkWExt. PCECCa2+Mg2+Na+K+Tot. acidityAl3+H+CuZnMnFe
    Nodules−
    Soil pH0.14*−
    TN-Kjeld−0.22*−0.056ns−
    OC-BlkW−0.27*−0.09ns0.88***−
    C/N ratio−0.13ns−0.03ns−0.03ns0.37***−
    Ext. P−0.11ns0.48***0.22*0.24*0.07ns−
    CEC−0.19ns0.46***0.47***0.47***0.12ns0.61***−
    Ca2+−0.15ns0.52***0.43***0.44***0.11ns0.61***0.97***−
    Mg2+−0.24*0.39***0.50***0.47***0.07ns0.54***0.79***0.70***−
    Na+−0.07ns0.21ns0.44***0.24*−0.12ns0.16ns0.47***0.42***0.38***−
    K+−0.03ns0.23*0.45***0.41***0.07ns0.34**0.62***0.52***0.51***0.68ns***−
    Tot. acidity−0.07ns−0.17ns−0.11ns−0.10ns0.05ns−0.03ns−0.04ns−0.16ns−0.15ns−0.05ns−0.08−
    Al3+0.09ns−0.09ns0.02ns0.04ns−0.03ns0.05ns−0.11ns−0.17ns−0.06ns−0.09ns0.0040.46***−
    H+−0.10ns−0.17ns−0.13ns−0.12ns0.07ns−0.05ns−0.02ns−0.14ns−0.16ns−0.03ns−0.100.96***0.28*−
    Cu−0.15ns−0.01ns0.18ns0.09ns−0.16ns0.19ns0.11ns0.12ns0.07ns0.09ns0.07−0.07−0.09−0.05−
    Zn−0.05ns0.23*0.25*0.33**0.22ns0.30**0.42***0.46***0.31ns**0.06ns0.19−0.08−0.09−0.070.17−
    Mn0.15ns−0.19ns−0.07ns−0.15ns−0.15ns−0.11ns−0.22ns−0.20ns−0.17ns−0.13ns−0.27*0.100.060.090.170.14−
    Fe−0.19ns−0.13ns0.11ns0.15ns0.06ns−0.001ns0.06ns0.04ns0.00ns−0.023ns0.0010.29**0.0040.32**0.140.10−0.08−
    Correlation coefficients (r) in individual cells represent each correlation between variables. Values with asterisk (*) are statistically significant different at * < 0.05, ** p < 0.01 and *** p < 0.001. ns-non significant. The charges in initials of nutrient names represents exchangeable
     | Show Table
    DownLoad: CSV

    Furthermore, extractable P had positive significant (p < 0.001) correlation with CEC and Ca2+ (r = 0.61), Mg2+ (0.54), and at p < 0.01 with K+ (r = 0.34) and Zn (r = 0.30). Cation Exchange Capacity had positive significant (p < 0.001) correlation with Ca2+ (r = 0.97), Mg2+ (r = 0.79), Na+ (r = 97), K+ (r = 0.62) and Zn (r = 0.42). Calcium had positive significant (p < 0.001) correlation with Mg2+ (r = 0.70), Na+ (r = 0.42), K+ (r = 0.52) and Zn (r = 0.46). Magnesium had positive significant (p < 0.001) with Na+ (0.38), K+ (0.51) and at p < 0.01 with Zn (r = 0.31). Other parameters which were significantly (p < 0.001) correlated include Na+ with K+ (r = 0.64), total acidity with Al3+ (r = 0.46) and H+ (r = 0.98). On the other hand, there was significant (p < 0.01) correlation between total acidity and H+ with Fe (r = 0.29) and (r = 0.32), respectively, while K+ significantly (p = 0.05) correlated with Mn (r = −0.27) and Al3+ with H+ (r = 0.28).

    Nodulation as influenced by different chemical parameters of the soils

    Two hundred and forty-three plants were sampled from 81 farmers' fields and the number of nodules were counted per plant by treating one plant as a replicate. The distribution of the number of nodules was evaluated basing on the different physico-chemical characteristics of soils in study areas. Soil pH, total N, OC, extractable P, exchangeable Ca2+ and Mg2+ were observed to influence the formation of nodules in different areas (Fig. 5). In the case of soil pH, the higher average number of nodules (8.82) was observed in neutral pH soils which was closely followed by (8.67) in slightly acidic soils while the lowest (3.3) was in very strongly acidic soils. Nodules number were observed to be higher (10.86) in the soils with very low total N, closely followed by (6.98) in the soils with low total N while the lowest 6.19 was in soils with medium N levels. The soils with higher OC had the highest average nodules number (15.55) closely followed by (8.79) in soils with very high OC whilst the lowest (2.95) was in soils with very low OC.

    Figure 5.  The influence of total nitrogen, organic carbon and extractable phosphorus on nodulation (VL = very low, L = low, M = medium, H = high and VH = very high), soil pH ratings as per Msanya[36] (VSA = very strongly acidic, StA = strongly acidic, MeA = medium acidic, SlA = slightly acidic, N = neutral, MiA = mildly alkaline and MoA = moderate alkaline.
    DownLoad: Full-Size Img PowerPoint

    The highest average nodules number (10.81) was observed in soils with higher P (> 10, by Olsen method of determination) and (9.3) (extractable p > 10, by Bray method) while the lowest (3.62) was in soils with low extractable P (p < 7, by Bray method). For the case of exchangeable Ca, the highest number of nodules (9.7) was observed in clayey soils with high Ca levels, followed by sandy soils with very high Ca levels (8.10) and loamy soils with medium Ca levels (8.1) whereas the lowest number (3.66) was in loamy soils with very high Ca levels. Highest exchangeable Mg levels in sandy soils favored nodules formation by exhibiting the highest (9.7) nodules number, followed by low Mg loamy soils (8.8) and low Mg clayey soils (5.5) while, the lowest (3.2) was in medium Mg clayey soil.

    Nodulation as influenced by exchangeable potassium, soil texture and micronutrients

    Exchangeable potassium, soil texture and micronutrients (Cu, Zn, Mn, and Fe) were observed to influence nodule formation (Fig. 6). In this study, sandy soils with high levels of K were observed to possess the highest number of nodules (11.17), followed by clayey soils with medium levels of K (10.00) and clayey soils with low levels of K while loamy soils with very low levels of K had the lowest (6.2) number of nodules. Furthermore, the highest number of nodules (9.50) was observed in sandy soil closely followed by sandy loam soil (9.36) while the lowest (4.51) was in clay loamy soil. The soils with low levels of Cu had the highest nodules (11.4) which was closely followed by (9.0) in medium and (8.8) in high Cu levels whilst the lowest (5.6) was in soils with very low levels of Cu. For the case of Zn, the soils with very high levels had the highest (11.1) number of nodules, this was followed by (7.5) and (7.2) in medium and high Cu levels, respectively. Conversely, the soils with very low levels of Zn had the lowest (1.3) number of nodules. The soils with very high levels of Mn had the highest (7.8) number of nodules while those with medium levels had the lowest (5.3) nodules. For the case of Fe, the soils with very high levels possessed the highest (12.0) nodules while those with high Fe levels had the lowest (7) nodules. However, for the case of Mn and Fe, it is difficult to exactly determine the influence of the nutrients basing on the distribution of nodules as the soils were categorized only in two groups.

    Figure 6.  The influence of exchangeable potassium, soil texture and micronutrients on nodulation (VL = very low, L = low, M = medium, H = high and VH = very high), soil texture (C = clay, CL = clay loam, LS = loamy sand, S = sandy, SC = sandy clay, SCL = sandy clay loam and SL = sandy loam).
    DownLoad: Full-Size Img PowerPoint

    Discussion

    Suitability of studied soils in production of soybean crop and rhizobia inoculants

    Soil pH, exchangeable cations, extractable phosphorus and nodule number

    Soils in the study area can be characterized as extremely to strongly acidic (13%), medium acidic to neutral (85%) and mildly to moderately alkaline (2%) (Table 1; Supplemental Table S1). This implies that, the fields with extremely to strongly acidic and alkaline soils are most likely to be associated with the deficiencies of phosphorus. The deficiency of phosphorus in acidic soils is caused by its fixation on the oxides and hydroxides of iron and aluminium while in alkaline soils it is fixed on the oxides and hydroxide of calcium and magnesium, hence, unavailable for uptake by plants[37]. Nevertheless, the two fields (Supplemental Table S1), one at the Ikovano site and the other at the Igomaa site were characterized as alkaline soils (pH > 7.5) yet, they are observed to have medium P levels which may be associated with addition of P fertilizers by farmers through organic manure. The 69 (85%) soils of the total surveyed fields with pH ranging from > 5.5 to 7.5 are within the favorable pH range for most crops[38]. Furthermore, the soil pH is observed to influence some chemical parameters[39] such as low CEC in soils with pH < 5.5 may be attributed to less basic cations in the exchange sites. Although, in some soils the total exchangeable acidity was higher, especially for those with detectable Al3+, but, their individual levels of Al3+ are below the critical concentration of 1 cmol(+)kg−1[38]. Despite the inhibition of nutrients availability and limitations to plant growth, which results in low productivity, extreme soil pH, has an influence on the activity and diversity of rhizobia[40]. This calls for the need to isolate rhizobia which are tolerant to extremely acidic and alkaline soils, as a starting point for site specific biofertilizer formulations. Furthermore, rhizobia which are capable of fixing N together with solubilization of nutrients such as P, K, and Zn have the added advantages as biofertilizers in particular soil pH conditions[41]. For successful improvement in the productivity of soybean along with the use of rhizobia biofertilizers, it is important to consider the suitability or amendment of soil pH.

    The findings of this study revealed that, soils in most of the surveyed fields (81%) had low CEC (Supplemental Table S1). Such low CEC values are typical of weathered soils with limited capacity to supply essential plant nutrients[42]. For instance, under excessive rainfall or irrigation, such strongly weathered soils are prone to leaching of nutrients like Ca, K+, and Mg2+ leading to inefficient and more costly fertilization program[39,42]. The low CEC in acidic soils, is an indication of less exchange sites for exchangeable bases such as K+, Ca2+ and Mg2+ in colloidal surfaces, suggesting the need for some management practices such as addition of more organic matter to buffer the pH of soil, increase nutrients retention and exchange of nutrients[42] to improve rhizobia activities and hence improved productivity of soybean.

    Different soils have different capacities of holding the basic cations, based on the exchange sites in colloidal surfaces (Supplemental Table S1). This is categorized based on the texture of soils which are clay rich in 2:1 clay minerals, loamy and sandy soils. In this study, the soil textures with regards to Ca, fell into three categories which are clayey, loamy and sandy. The findings of this study demonstrated that only 7% of the sites had low levels of exchangeable Ca, suggesting the deficiency of this nutrient. On the other hand, soils with high Ca signifies the dominance of cation in the exchange sites. Under highly weathered conditions, soils with high Ca levels tends to have low organic carbon and nitrogen, with limited availability of P, Fe, B and Zn, as well as imbalance of K and Mg[36,38,43]. To correct K and Mg imbalance as well as P, Fe, B and Zn, it needs addition of organic matter through compost or farmyard manure and addition of liming in Ca deficient soils, to buffer pH and increase the availability of essential plant nutrients as suggested in earlier studies[43]. The solubilization of limited P and Zn as well as Fe chelation by rhizobia is an added advantage to Ca deficient acidic soils[44].

    Despite the importance in plant growth and development, Mg plays a key role in defense mechanisms in abiotic stress situations[45]. Low Mg concentrations in soils of most of the fields, in all textural classes, may be attributed to leaching losses due to its high mobility, which is linked to low affinity on the soil colloidal surfaces. Conversely, higher levels of Mg in some of the fields, may be attributed to natural soil fertility variations in the study site[45,46]. The amendment of high Mg levels in the soils needs an integrated approach such as, application of chemicals including CaCl2 and Ca(H2PO4) 2H2O. Furthermore, addition of OM is important for stabilizing the soil pH as it is hampered high Mg levels[45]. Correction of Mg levels for balanced soil nutrients and availability, is essential for improving crop productivity as well as, the use of rhizobia biofertilizers in legume production, including soybean.

    Potassium is among the major essential nutrients for plant growth and development as well as rhizobia activities. The observed small amounts of K below the critical recommendations, suggests inadequacy for meeting crop nutrition requirements. Potassium in soil is lost through various ways such as, nutrient export by crop harvesting and leaching especially in acidic sandy, water logged or saline soils[38,47,48]. Optimum levels of K and other essential plant nutrients as well as, suitable soil pH is necessary for the better performance of biofertilizers as the nutrient is involved in regulation of water in plants, enhancement of root growth and thus, high chances of nodulation[49]. Nevertheless, K natural fertility may be high in the soil, but not necessarily available soil solution due to their slow release from the secondary minerals such as mica. Therefore, K solubilizing bacteria[50] including N2-fixing rhizobia has added advantages in effective utilization of the nutrient by crops. Furthermore, low levels of potassium in the soil, can be increased by co-addition of organic matter and biochar[51].

    Conversant to varying potassium levels, the concentration of exchangeable Na in all soils except for the NM-AIST site was very low. Very low to low levels of Na are desirable for plant growth as this indicates low exchangeable Na percent, non-sodic soils and hence low electrical conductivity with no yield reduction impact[27,36,52], the desirability of soils in production of soybean. Sodium is less required in the soil for the growth of plants as well as rhizobia activities and its roles can be replaced by potassium which is mostly required for plant growth and development and, formation of symbiotic nodules[19,52].

    In this study, 63% of the soils had Ca/Mg in the desired range of between 2 and 4, indicating the balance of these nutrients for suitability in the growth and development of the wide range of crops. On the other hand, only 23 % had favorable Mg/K, which ranged between 1 and 4. The shift of this ratio indicates that one cation is in excess and has to be amended to increase the availability of inhibited counterparts[36,46]. Therefore, very high levels of soil K in this case, may be attributed to the large quantities of mica minerals that can lead to imbalance of other nutrients including Mg, N, P, Zn and B[53].

    Most of the investigated soil samples, had sufficient amounts of P, regardless of the methods used in the analysis (Supplemental Table S1). On the other hand, low P levels which are observed in some of the soils in this study, indicates the deficiency of the nutrient to support plant growth and development, as well as, rhizobial activities[54,55]. Phosphorus is a dynamic nutrient in soil which is highly affected by soil pH. In acidic soils, P is fixed in the oxides or hydroxides of aluminium/iron, while in alkaline soils the nutrient is fixed in the oxides and hydroxides of calcium/magnesium hence, becomes unavailable for plant uptake. Its deficiency leads to 30%−40% yield reduction, necessitating excessive application of P fertilizers to meet crop requirements. However, only 15%−20% is available for plant uptake[56] while the rest ends up contaminating surface and underground water[57]. Nevertheless, P solubilizing rhizobia have added advantages in the soils with extreme pH for effective utilization of the fixed P by plants[58].

    Organic carbon, total nitrogen, CN ratio, micronutrients and soil texture

    Organic carbon is a very important component of soil fertility as it is involved in the supply and balance of many nutrients as well as improvement of soil structure which allows the exchange of nutrients and water retention[59]. The low and very low OC (Table 1; Supplemental Table S2) in some soils for this study is attributed to less organic matter (OM) whereby the particular soils are in the risk of unbalanced exchange of many nutrients including exchangeable bases. The 58% of the studied soils which had high to very high levels of OC have the advantages of good nutrients exchange, improved water retention and enough substrate for symbiotic rhizobia[51,60]. Moreover, the observed good quality (8−13) of CN ratio for most of the soils in this study, is an indication of their desirability for soybean productivity along the use of rhizobia biofertilizers.

    Micronutrients play different essential roles for the growth and development of plants as well as SNF[61]. The availability of micronutrients in different soils, apart from application of fertilizers and pesticides is influenced by parent rock materials, soil type, pH, quality and quantity of OM, redox potentials, soluble salts, macro and micro nutrients interactions and vegetation. The observed higher levels of micronutrients (Supplemental Table S2) in this study indicates their sufficiency for crop requirements while the lower levels suggests the need for their supplementation from various sources[62]. However, these nutrients are required in very small quantities, hence excess levels result in their toxicities in soils to plants. Nevertheless, rhizobia require more micronutrients in SNF than their host plants.

    In this study, different textural classes which included clay, clay loam, sandy clay loam, sandy loam, sandy clay and loamy sand were determined. These results are in agreement with the previously explained textural characteristics of the soils in tropics. Soil texture is also a determinant of other factors such as nutrients availability, soil pH, organic matter and CEC as well as aeration and water movements[63−66]. In this study, soil OC and CEC were among the factors that were clearly influenced by soil texture. The observed decreasing trend of CEC with decrease in clay content may be attributed to the less exchange sites in colloidal particles[67]. Furthermore, soil texture has an influence in SNF especially on nodulation whereby medium textured soils are observed to favor more nodulation followed by light textured and then heavy textured soils. Medium texture soils such as sandy clay loam allows the penetration of roots than heavy textured clay soils while light textured soils are linked to low availability of nutrients and soil acidity which inhibits the growth of roots[68].

    Soil quality index using principle component analysis

    The results of PCA are summarized in Table 2, Supplemental Table S3 and S4. The contribution of variables to each principal component provides insights into which variables play the most significant role in forming the patterns captured by each component. Higher contribution percentages indicate that a variable strongly influences a particular principal component's variation[33,35,69]. The results show that the first PC explain CEC (16.464%), Ca2+ (15.675%), Mg2+ (13.241%). These variables representing overall soil nutrient contents and cation exchange capacity. The second principal component show highest contribution to H+ (33.748%), Al3+ (11.243%), Cu (0.594%) that representing soil acidity-related factors and likely indicating a relationship with soil acidity. The third component has highest contribution to TN-Kjeldahl (22.340%), OC-BlkW (24.302%), P (9.242%) representing nutrient availability and organic content[69]. The forth component has highest contribution: Cu (11.045%), Fe (10.112%), Mn (16.483%) indicating a relationship with heavy metals and nutrient concentrations and the last component has highest contribution to C/N ratio (42.735%), Na+ (18.574%), K+ (11.907%) indicating distinctions in these variables[34,69].

    The factor scores represent the projected values of that observation onto each principal component. These scores highlight which aspects of soil properties are prominent for each location with respect to the identified principal components how much an observation contributes to each component. Results indicated that Songwe-Mbozi_Mbimba show strong negative score for F1 and F3 suggesting lower values related to CEC, Ca2+, Mg2+, nutrient availability, and organic content. The positive score for F2 suggests higher values associated with H+, Al3+, and Fe and negative score for F4 and F5 Suggests lower values for Cu, Mn, K+, and compositional differences[33,69] Mbeya-Chunya_Kibaoni has positive scores for F1, F2, and F5: indicates higher values for CEC, H+, Al3+, Fe, and K+ and negative score for F3: suggests lower values for TN-Kjeld, P, and OC-BlkW while positive score for F4 indicates higher values for Cu, Mn, and Fe. Njombe-Wanging'ombe_Mngate has egative score for F1, F2, and F4: suggests lower values for CEC, H+, Al3+, Fe, Cu, and Mn and positive score for F5 indicates higher values for K+ while near-zero score for F3 suggests average values for TN-Kjeldahl, P, and OC-BlkW[69].

    Relationships between nodulation and different physico-chemical parameters of the soil

    Pearson's correlations among different soil characteristics and the number of nodules showed a clear pattern of influence over each other, suggesting the influence of different factors over each other as well as in formation of nodules[39,45,46,53,70]. There was notable positive significant correlation between soil pH and the number of nodules indicating the influence of pH on nodulation[40]. On the other hand, the negative correlation of nodules number with OC and total N is an indication that, of low substrate (carbon source) SNF, influenced by the sites with low OC while that with total N signifies the shift of plants in utilizing mineral N which is influenced by the sites with sufficient N level[70]. Interestingly, there was positive significant correlation between the soil pH and the basic cations except for Na+, indicating less cation leaching in particular soils[56,70−73]. The observed positive and significant correlation of nitrogen and OC with extractable P and all basic cations indicates that, N enhances the uptake of the particular nutrients by plants and OM helps in retention and balance of nutrients[10,56,70−74].

    The positive significant correlation between extractable P with CEC and basic cations except Na+ may be linked to availability of OM for cations retention and less P fixation due to stable soil pH[61,70,74]. The strong positive and significant (p < 0.001) correlation between CEC and basic cations, Ca2+, Mg2+ and K+ indicates that these cations are available in soil solution due to their abundance on soil colloidal surfaces, which also contributes to soil CEC optimization. The observed positive correlation between total acidity with Al3+, H+ and Fe, clearly suggest their substantial contribution in the acidity of soils[19,56,61,63,64,74]. The positive significant correlations were noted between Zn with soil pH, total N, OC, P, CEC, Ca and Mg, suggesting the availability of particular nutrients without being inhibited by the availability of Zn and this may further be attributed to availability of OM in most the studied soils[19,43,61,63,64,70].

    The positive correlation between the Fe and H+ may be attributed to the fixation of Fe by 3-layer silicate clays and OM whereby Fe3+ is fixed in the hydroxides of exchangeable H. The fixation results to formation of Fe(OH)3 as explained by higher CEC and pH at the slow and extended release of hydrogen in soil[19,61,63,64]. However, the cause of the significant negative correlation between K+ and Zn in this study is not clear. The observed significant relationships among different soils though their characterization indicates their suitability for the use of rhizobia inoculants.

    The effect of different physical and chemical parameters of soil in soybean nodulation

    With regards to soil pH, the highest number of nodules (8.82) was observed in the soils with neutral pH. With the exception of extremely acidic soils, there was a distinct pattern showing an increase in nodules from very strongly acidic to neutral and a steady decrease in nodules from mildly alkaline to moderately alkaline soils. This is the clear indication that soil pH affects rhizobia population and nodules development[40,75]. The lower number of nodules in extremely and very strong acidic soils, suggest that the limited rhizobia population, recolonization and the chances of nodulation in particular soils. These results conform with other previous studies which demonstrated the effects of soil acidity in endangering the survival of microorganisms and injury of plant roots as well as impairing the nutrients availability to plants[19,75].

    Interestingly, the nodulation was higher in the areas with low levels of nitrogen with decline towards the medium levels of nitrogen as a sign of the shifting from mineral N utilization to N2-fixation[70,75]. Conversely, the higher levels of N in the soil, especially from synthetic fertilizers affects the SNF as the plants uses lesser energy to utilize mineral N than fixing N, hence, shifting from symbiotic to inorganic N utilization. The shifting from N2-fixation to inorganic N utilization in the soils with higher levels of N, is attributed to reduced nitrogenase activity and infection threads, hence, limited N2 fixation. In the case of OC, the highest number of nodules were observed in the soils with high levels of percentage OC followed by very high and then medium levels. This signifies that rhizobia are carbon limited in nature. Furthermore, for better activity, rhizobia prefer the organic carbon which is naturally available in soil or through rhizodeposition[60,70,75]. Nevertheless, application of biofertilizer which is supplement with organic sources such as compost and farmyard manure are observed to yield many healthy nodules with higher dry weight and effective in fixing nitrogen.

    Phosphorus plays a vital role of energy acquisition and storage in plants as well as utilization for the SNF process[54,76]. Adequate levels of extractable P in the soils, contributes in increasing the number, size and weight of nodules as compared with those with low levels of extractable P[55,76]. Therefore, higher levels of extractable P are crucial for effectiveness of rhizobia in fixing nitrogen. Similarly, in this study, higher number of nodules were observed in the soils with higher levels of extractable P. Also, increase in the number of nodules followed the increasing trend of extractable P, suggesting the essential influence of P in nodule development[37,55,76].

    Nevertheless, exchangeable calcium plays several essential roles including an increase in rhizobia abundance, enhancement of rhizobia attachment to root hairs, infection of host plant roots and formation of nodules[37,77] Regardless of Ca variations basing on the clayey, loamy and sandy dominance, higher numbers of nodules were observed in medium, high and very high levels of calcium, respectively. This is a clear indication that, Ca is essential in the infection of plant roots leading to formation of nodules. Furthermore, higher numbers of nodules in Ca rich soils is linked to its involvement in plant-bacteria signaling and recognition of nod factors from rhizobia which increases the activity of Nod genes[19,77].

    Despite the fact that the highest number of nodules were found in soils with low levels of Mg than in medium levels, the nodule formation in clayey and loamy soils did not exhibit a clear pattern. Nevertheless, in sandy soils, the pattern is evident as the highest number of nodules were found in soils with medium and higher Mg levels, indicating that the sufficient levels of Mg triggered the development of nodules[19,78]. Furthermore, it was stated earlier that, Mg is essential in the metabolism of rhizobia by facilitating the alteration of carbohydrate partitioning and transport into nodules[46, 78]. Therefore, it is important to explore more, on the direct and clear roles of Mg in the infection of host plant by rhizobia and formation of nodules.

    Potassium is among the important essential nutrients in symbiotic nitrogen fixation and symbiotic rhizobia are observed to be more sensitive to lower levels of potassium than in higher levels, as compared with their host plants. This calls for special considerations in the levels of potassium in soils for effective symbiosis[19,52,78]. The observed higher levels of potassium in sandy soils, with the highest number of nodules (11.17), suggests availability of the nutrient in the favor of the light textured (sandy) soils to allow effective root penetration and nodulation. However, medium textured (loamy) soils, were observed to possess lowest (6.2) average number of nodules, which may be attributed to the lower levels of potassium[49,52,75,78].

    Soil texture has substantial influence in the formation of nodules. Heavy textured soils are well known for having many exchange sites due to their large surface area for holding many cations and other nutrients. However, the textural class is observed to have limitations in the formation of nodules due to poor aeration and hindrance of plant root penetration[68,76,79]. Likewise, in this study, light textured (sandy) soils, possessed the highest (9.50) number of nodules which is closely followed (9.36) in medium textured (sandy loam) soils and lastly, clay loam soil which possessed the lowest (4.51) nodules.

    Micronutrients play different roles in SNF, and rhizobia needs more micronutrients in their activities than their host plants[61,63,79]. Zinc is an important micronutrient, for the expression of superoxide dismutase required by plants and rhizobia during the development of nodules. Copper is required in promotion of N2-fixation per nodule as well as raising the levels of N in plant tissues[61,64,79]. The results of this study for Zn showed the clear trend that, the number of nodules were higher in soils with the higher levels of zinc. Although, the levels of Cu were low to very low in the soils, even the slight increase in the level of Cu was associated with increased nodule numbers similar to report by Kafeel et al.[79] and Rubio et al.[64]. Manganese is required in the initial colonization of root, formation of nodule and N2-fixation while Fe is the component of nitrogenase enzyme and co-factor for proteins such as cytochrome and leghemoglobin which are inside the nodules and bacteroides, crucial for N2-fixation. The observed higher number of nodules in Mn and Fe rich soils, signifies the influence of the particular nutrients in the development of nodules[19,61,79]. Therefore, it is crucial to observe the availability and sufficient levels of micronutrients for successful BNF process.

    Conclusions

    Basing on the results of this study, the positive significant correlation between the number of nodules and soil pH is an evidence that, the particular soil parameter had the greater influence in the development of nodules. The positive significant correlations among different chemical parameters is an evidence that, there are common influencing factors across the studied soils. Soil pH was observed as the best indicator which favors the development of nodules with up to 8.82 in neutral pH soils, however, the number may be smaller than those obtained in inoculated seeds. More interestingly, the number of nodules were observed to be influenced by different individual physical and chemical parameters as evidenced by the distribution of nodules number following their different concentrations in the soils. The results of this study suggests the suitability of the soils for production of soybean and the use of rhizobia inoculants. However, the site specific inoculants will have added advantages for the very strongly acidic soils which had much fewer nodules.

    Author contributions

    The authors confirm contribution to the paper as follows: conceptualization and methodology: Nakei MD, Ndakidemi PA, and Venkataramana PB; original draft preparation: Nakei MD; review and editing: Ndakidemi PA and Venkataramana PB. All authors have read and agreed to the published version of the manuscript.

    Data availability

    All data generated or analyzed during this study are included in this published article and its supplementary information files.

    Acknowledgments

    The authors thank all staff and technical experts from Nelson Mandela African Institution of Science and Technology (NMAIST), Arusha-Tanzania for their guidance and support during sampling, laboratory and screen house experiments.

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Igibah CE, Ilaboya IR, Iyeke SD, Ufuah E, . 2024. Health effects of oil and waste pollutants on Delta Nigeria inhabitants' well being and its mitigation. Emergency Management Science and Technology 4: e017 doi: 10.48130/emst-0024-0015
    Igibah CE, Ilaboya IR, Iyeke SD, Ufuah E, . 2024. Health effects of oil and waste pollutants on Delta Nigeria inhabitants' well being and its mitigation. Emergency Management Science and Technology 4: e017 doi: 10.48130/emst-0024-0015
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Health effects of oil and waste pollutants on Delta Nigeria inhabitants' well being and its mitigation

  • 1.

    Department of Civil Engineering, University of Benin, Edo - State 300213, Nigeria

  • 2.

    Current address: Civil Engineering Department, Federal University of Oye-Ekiti, Ekiti State, Nigeria

  • 3.

    Department of Civil Engineering, Ambrose Alli University, Ekpoma, Edo State 310006, Nigeria

  • 4.

    Department of Civil Engineering, Federal University of Technology, Akure, Ondo State 340110, Nigeria

  • Received: 10 August 2023
  • Revised: 20 April 2024
  • Accepted: 17 June 2024
  • Published online: 03 September 2024
Emergency Management Science and Technology  4,  Article number: e017  (2024)  |  Cite this article

Abstract: Water quality, trace gas (SO 2, CO 2, and NO 2), particulate matter (PM 10 as well as PM2.5), and effluent emissions were quantified near cement and oil factories and nearby suburban areas within Delta Nigeria. Results display that ambient air particulate matter PM2.5 varies between 2.1 to 7.9 μg/m 3 and VOC (0.013−8.53 μg/m 3), while CO and CO 2 were 100% and 30% respectively not within regulatory limits, consequently leading to asthma, coughing, and difficulty breathing. Four out of nine sites investigated for noise effects were above WHO stipulated limits. While some parameters such as BOD and COD display critical levels for effluent scrutiny and conductivity, calcium, TDS, total hardness, DO (Dissolved oxygen) and total alkalinity were also above clean water specifications. Wastewater consists of spills and other water effects which produce pollutants such as soluble organic chemicals that deplete dissolved oxygen, anions, volatile materials, and other heavy metals. Based on age, the greatest impact (52%) was seen in ages varying from 0 to 16 while that of the age set 16 to 60 was 45%. Curbing of oil and cement particulate pollutants and requesting a buffer region between the cement and oil depots and neighborhoods, complemented with regulatory enforcement and persistent monitoring, should be a top precedence to the regulatory authority.

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    Introduction

    • Emissions of effluents, anions, metals, cations, and particulate pollutants from industrial establishments are one of the key sources of environmental contagion [ 1, 2] . Anions, gaseous emission and other metals such as SO 2, particulate matter (PM), combustible gas (CHN), carbon monoxide (CO), hazardous materials in contagion soils, can be transported by wind, water, and other human activity with their resultant health impacts and effects on the environment [ 3− 5] . Cement and oil factories have been reported to be a main source of various emissions and air pollutants to the environs with numerous reports displaying higher concentrations of contagions around oil and cement factories.

      Air quality is an appraiser of the appropriateness of air for breathing by animals, the populace, and plants in terms of potential fitness impacts. Good outdoor air quality is fundamental to human well-being [ 6− 8] . Averagely, A person breathes, on average, about 14,000 litres of air every day, and the presence of contagions in this air can harmfully affect public health. Poor air quality has been ascertained to have somber effects on human fitness and well-being as well as the environment [ 8− 10] . Air pollution is implicated as the major cause of many environmental problems including the ozone layer destruction, acerbic rain, mercury contagion, and global warming. Hygienic air is indispensable for sustaining the insubstantial balance of existence on the globe — not only human populace, but also flora and fauna, water, foliage, shrubbery, and soil [ 11− 13] . Air contamination isn't just an al fresco matter, but enclosed spaces, for instance workplaces, habitats, or schools can also be contaminated via contagions that have been emitted through outdoor and indoor sources. Furthermore, some categories of air contagion may be worse for indoors than outdoors, for instance chemicals discharged from synthetic textiles, cigarettse, fittings, and home-use products [ 14− 16] . Contamination of the environment by particulates, effluents, metals, and other pollutants is of major concern due to their toxicity and risk to the environment and human life. Particulate and emission toxicity seems to be dependent on exposure route, duration, dose, and exposure frequency. For instance, when oil is explored in water sparse zones the water resources become strained. Off-shore oil exploration creates threats to oceanic life whereas oil circulation and haulage will create extreme hazards for the ecosystem in case of seepage or accidents [ 17, 18] . When oil spill-outs occur, or when there is waste matter expulsion, it percolates into the soil and blends with the underground water system. It has been ascertained that contaminated underground water takes many years to recover [ 19− 21] . Nonetheless this underground water moves into brooks, streams, rivulets, and boreholes or wells which are the key sources of local water supplies in the neighborhood whose consequences are the upsurge of water-borne sicknesses [ 5, 22] . This has disturbed the traditional rapport of the inhabitants within the oil-bearing enclaves. There is a discernible trepidation that instead of being the life-giver, these water scenarios have become sources of death, misery, and sickness [ 23− 26] .

    Materials and techniques

      Regions of study

    • Apapa, one of the Local Government Area in Lagos State and home to two of Nigeria's busiest seaports that have numerous tank farms and several big companies was chosen, because it is a significant business zone and a huge component of Nigeria's economy. Similarly, although it is a very significant area, the deplorable state of Apapa instigated over time because of the Federal Governments failure in expanding infrastructure development in line with the rising population has been horrendous to port operators, travelers, chauffeurs, and the general public.

      Apapa falls within the tropical savannah climate based on the Köppen climate categorization, as there is a momentous precipitation discrepancy between the humid and the arid seasons. The average temperature in January is 27 °C ( Fig. 1a− c). Temperatures in Lagos rarely get colder than 20 °C, and rarely get hotter than 30 °C. Temperature values are high throughout the year over the project environment. The temperature within the study location was between 32 °C and 35 °C. Because the project area has a tropical savannah climate, there are two different rainy seasons. The heavy rain season is between April and July, and the milder rain season occurs from October to November. A very brief dry season occurs in August and September usually called the 'August break' and a long dry spell occurs from December to March. The rainfall between May and July averages over 300 mm, while the average rainfall in August and September is only 75 mm. In January, the average rainfall is only about 35 mm. Relative humidity is normally in excess of 80%, the peak of the wet period is shown in Fig. 1c. The standard yearly relative humidity is 84.7% and mean monthly relative humidity varies from 80.0% in March to 88.1% in June. This is understandable given the geographical location of Lagos and the fact that rain falls almost all year round. The micro-climatic condition of the study area falls within the historical humidity of 66.2%–90.9% peculiar to the Lagos area. The climate is influenced by two key trade winds – the warm, humid Southern-west trade winds during the rainy period and the Northern-east trade wind during the arid and grimy harmattan. The wind speed directions are synchronous to the periodic attitude of the Inter-Tropical Convergence Region. During the wet season, the Southwest winds prevail, and during the dry season, the Northeast winds prevail as they sweep in the arid season. The wet season begins around April and ends around October. August is the coolest and also the windiest with a mean wind speed of 13 km/h. A weather tracker, a sophisticated multi-function environment monitoring instrument, was utilized to measure key environmental conditions such as altitude, temperature, wind speed, relative humidity, barometer pressure, density, and wind chill. It can also measure wet bulb, and heat index.

      Figure 1. 

      (a) Apapa port, (b) study region, and (c) climate and activities around its environ.

      Figure 2. 

      Airborne particulate (left), noise level monitor counter (middle), and multigas monitor (right).

      Figure 3. 

      Outcome of noise level scrutiny.

      Techniques

    • A pilot origin categorization and separation were carried out at the company and its environment in Lagos metropolis of Nigeria from July 2021 to October 2022 for gathering data on water attributes as well as composition, generation level, and compliance rate of waste management. Techniques used for measurement with its water attributes physiognomies are presented in Table 1, while most of the techniques and health effects of contagions are highlighted in Table 2.

      Table 1.  Measurement techniques with water attributes physiognomies.

      Parameters Units Acronyms Techniques and instruments
      Total suspended particles TSP Temptop PMD 351Handheld air borne aerosoil meter
      (HPPC6+) sum efficiency –50.0% @0.30 μm.
      Temperature, dew point, relative humidity and
      wind speed       		T (°C), DP
      RH and WS       		Kestrel 5500 weather meter was utilized for the measurements
      Heavy metals
      Manganese, lead, copper, nickel, cadmium, cobalt, zinc Mg, Pb, Cu, Ni, Cd, Co, Zn APHA 3111B, UNICAM 969 AAS
      Microbiology (cfu/mi)
      Coliform, E. coli, Staphylococcu,Vibrio, total coliforms,
      Aerobic plant count, yeast & mould       		APHA 9215C
      Anions
      Nitrate, nitrite mg/L NO 3 −, NO 2 − APHA 4500, UN spectrophotometer
      Sulphate, phosphate mg/L SO 4, PO 4 3− APHA 4500B, UN spectrophotometer
      Calcium mg/L Ca APHA 4500A, UN spectrophotometer
      Physico-chemical
      Hydrogen ion concentration pH pH meter/ in situ
      Colour Pt.Co APHA 2120A
      Dissolved oxygen mg/L APHA 5220A, DO meter
      Conductivity μs/cm Cond APHA 2510A, multi- parameter
      Chloride mg/L Cl − APHA 4500 B, titration
      Total dissolve solids mg/L TDS APHA 2540A, gravimetric
      Total suspended solids mg/L TSS APHA 2540D
      Appearance APHA 2110
      Acidity mg/L APHA 2310B
      Ammonial nitrogen mg/L NH3C APHA 4500
      Turbidity mg/L Turb APHA 2130B, turbidity meter
      Bacterial oxygen demand mg/L BOD APHA 5210B, Incubator/winkler
      Chemical oxygen demand mg/L COD APHA 5220D, K2C-207 reflux
      Alkalinity mg/L Alk APHA 5220D, K2C-207 reflux
      Detergent mg/L APHA 5540C
      Oil and grease mg/L APHA 5520B

      Table 2.  Scrutiny techniques and some health impacts of contagions.

      Parameters Instrument Techniques Above Permissible Limit Health Impact
      Sulphur IV oxide (SO 2) SO 2 Gas alert test meter Direct reading Causes irritation of the respiratory tract
      Particulate matter (PM) Gas alert test meter Gravimetric Causes catarrh, cough, lung infections and other respiratory diseases
      Combustible gas (CHN) Gas alert test meter Direct reading Induces a despondent and depressed state
      Carbon monoxide (CO) CO test meter Direct reading Lessens the oxygen-carrying blood capability to damage of the central nervous system
      Oxides of nitrogen (NO X) NOx test meter Direct reading Causes inflammation of the lungs but less toxic
      Noise Noise meter, Rion sound level
      meter NA model       		Direct reading
      Air emissions

    • Air emissions associated with the operations of the study zones are gaseous emissions and particulate matter. The sampling frequency was quarterly, during the dry and rainy seasons. Measurements were carried out during the day, which is the busiest period and night. The sources of particulate emissions include the general depot operations, loading and offloading activities, and truck movements while sources of gaseous emissions within zones include working losses, fugitive losses, and generator operations, as well as operations of trucks. Therefore, Suspended Particulate Matter (SPM), Volatile Organic Compounds (VOCs), Carbon monoxide (CO), Sulphur dioxide (SO 2), Hydrogen Sulphide (H 2S), Oxides of Nitrogen (NOx), in the ambient environment were measured during the study. The in-situ ambient air quality measurements were carried out in line with the regulatory requirements to ascertain outdoor air conditions of the facility's environment.

      Particulate, gaseous emissions, and environmental condition level measurements

    • Airborne particulates were collected from several sampling locations using a Bosean airborne particulate counter. The dust monitor was zeroed and set at the run mode 15 s. This is to directly measure the exact concentration of the airborne particulates in 1 L of air. The concentrations of the airborne particulates were then read off the screen of the monitor. Sampling and measurement for temperature and humidity were carried out using the same Bosean equipment ( Fig. 2). Noise level measurement was carried out using the Bosean noise level monitor capable of measuring wind speed and wind flow, equipped with a back-lit LCD dual display and a variety of choice of readings in kM/hour, knots, or meters/second. These instruments are powered by a battery cell and are hand held.

    Results and discussion

      Air quality outcome

      Sulphur (iv) oxide (SO 2), nitrogen oxide (NO), and carbon monoxide (CO)

    • Inhalation of air with extreme concentrations of NO 2may irritate airways in the human respiratory system, and within a short time exacerbate respiratory sicknesses, for instance asthma, with signs such as difficulty in breathing, coughing, or wheezing [ 11, 27] . While longer exposures to huge quantities of NO 2 might increase vulnerability to respiratory infections [ 5, 22] . The result of sampling at Apapa for the presence and concentration of nitrogen (iv) oxide (NO 2) were less than 0.01 ppm thus within the NAAQS (FEPA 1991) stipulated limit of 0.113 ppm, while all CO and 30% of CO 2 measured were above specified limits ( Table 3). The most important oxides of sulphur are sulphur (iv) oxide (SO 2) and sulphur trioxide (SO 3), when SO 2softens inside water vapor in the atmosphere/sky to create acids as well as interrelate with other gas plus particles also to generate particles identified as sulphates, which in turn have unsympathetic impacts on human health and the ecosystem. The impacts of sulphur (iv) oxide (SO 2) are felt very swiftly, and most of the populace would experience the most awful signs within 10−15 min, after breathing it in. People with asthma or any similar health issue or health condition are at high risk of developing problems after exposure to sulphur (iv) oxide [ 1, 5] .

      Table 3.  Outcome of ambient air quality at Apapa Lagos.

      Site/parameter PM10
      (μg/m 3)       		PM25
      (μg/m 3)       		CO
      (ppm)       		NO 2
      (ppm)       		H 2S
      (ppm)       		CO 2
      (ppm)       		VOC
      (ppm)       		SO 2
      (ppm)       		O 2
      (%)       		Comb
      (ppm)       		HM
      (%)       		Temp
      (°C)
      FMEnv limits 250 NS 0.03 0.313 0.008 425 8.53 0.01 NS NS NS NS
      1 (Near gate) 10.9 5.2 < 0.10 < 0.01 < 0.1 417.1 0.014 < 0.01 21.1 < 0.01 62.3 35.1
      2 (Generator) 8.1 4.3 < 0.10 < 0.01 < 0.1 398.2 0.013 < 0.01 20.8 < 0.01 61.1 33.9
      3 (Sitting room) 5.3 2.1 < 0.10 < 0.01 < 0.1 401.9 0.019 < 0.01 20.9 < 0.01 60.9 29.0
      4 (Water pump area) 12.1 6.9 < 0.10 < 0.01 < 0.1 692.8 0.357 < 0.01 21 < 0.01 63.2 34.1
      5 (Laboratory zone) 9.2 6.3 < 0.10 < 0.01 < 0.1 460.1 0.162 < 0.01 21.1 < 0.01 67.2 31.9
      6 (Tank Farm region) 10.8 7.9 < 0.10 < 0.01 < 0.1 397.9 0.000 < 0.01 21 < 0.01 68.9 32.0
      7 (Water treatment) 8.9 4.1 < 0.10 < 0.01 < 0.1 468.2 0.194 < 0.01 21 < 0.01 69.0 31.8
      8 (Water separator) 8.2 4 < 0.10 < 0.01 < 0.1 527.8 0.036 < 0.01 20.9 < 0.01 68.4 33.2
      9 (Loading zone) 9.3 2 < 0.10 <0.01 < 0.1 575.4 0.194 < 0.01 20.8 < 0.01 61.9 35.3
      10 (Jetty region) 7.2 3 < 0.10 < 0.01 < 0.1 397.9 0.056 < 0.01 21.0 < 0.1 65.3 35.0
      Within limits (%) 100 0 100 100 70 100 100 − − − −
      Not within limits (%) 0 100 0 0 30 0 0 − − − −
      NS, Not Seen; −, NIL.

      Measurement for the presence of SO 2 at all sampled locations was less than 0.01 ppm which is less than the NAAQS (FEPA 1991) statutory limit of 0.3 ppm. The presence and amount of carbon monoxide (CO) and CO 2 in the environment might be allied from sources such as; reprehensively vented appliances with hydrocarbon fuel sources, outdoor vehicular hydrocarbon emissions, boilers, heating systems, or other industrial sources [ 4, 18] . CO might thus louden or accumulate within buildings where there is derisory aeration. Measurement for the presence of CO at all sampled locations were less than 0.1 ppm which is greater than the NAAQS (FEPA 1991) statutory limit of 0.03 ppm.

      Hydrogen sulphide and suspended particulate matter (SPM)

    • Hydrogen sulphide is a colorless, monochrome, combustible, tremendously hazardous gas with a 'putrid egg' scent. It can be created by the cessation of animal/or human waste, for instance, sewage as well as organic matter. When exposed to small quantities it triggers eyes irritation, upper respiratory system issues, and its effects can be delayed. At moderate concentrations it causes more irritation of the eyes and respiratory effects, headache, dizziness, coughing, and vomiting. In contrast, at higher concentrations it causes shock, convulsion, coma, and potentially death. People with asthma may be at greater risk as a result of difficulty in breathing. Detection of hydrogen sulphide at all sampling points was less than 0.1 ppm. Particulates are tiny solid or liquid particles suspended in air. Particulates such as dust, smoke, diesel soot, and products resulting from burning can be emitted directly into the air. It might also be created via photochemical reactions, like polluting gases (nitrogen plus sulphur oxides). Particulates may have short as well as long-term effects on human health and the environment [ 5, 7, 22, 27] . These effects range from eye and throat irritation to chronic respiratory diseases to cancer. Fine particulates which are microscopic and less than 10 microns (PM10) are of greatest concern because of their ability to bypass the body's natural filtering system, thus posing a threat to the respiratory system. The concentration of particulate matter (PM10) at all the sampled locations ranged from 5 μg/m 3 at the Reception and Pump house to 12 μg/m 3 at the Water booster pump which is within the NAAQS (FEPA 1991) permissible limit of 250 μg/m 3. Particulate matter (dust) is generated from general cleaning operations and movement of tanker trucks. Although the particulate levels measured show they are within permissible limits, however, depending on the duration of exposure, particulate emissions can impact negatively on workers' health and exacerbate existing health conditions, e.g. asthma.

      Noise scrutiny outcome

    • Noise is mostly sound that is anti-information intensity, which usually fluctuates erratically with time. Noise is the term frequently used to describe unwanted sound, which impedes with the sensitivity of wanted sound and is likely to be physiologically damaging. Environmental noise is the accretion of all noise available in a precise environment. Exposure to noise is correlated with numerous pessimistic health outcomes, based on the level of exposure, and extent [ 3, 8] . Noise might prop up cardiovascular sicknesses, hearing loss, sleep turbulence, high blood pressure, as well as birth defects. The ambient noise levels within the facility were measured in some selected locations with the use of a Mastech multi-environment survey meter as shown in Tables 4 & 5 and Fig. 3. The highest noise value recorded was 89.9d BA at site 2 (Generator house) while the lowest was 57.2 dBA at the Pump house. The noise level at the four sampled site locations was found to be above the WHO 2007 and FMEnv regulatory limit. High noise levels could be harmful to employees' hearing ability if exposed for prolonged periods without adequate hearing protection. Exposure to high noise levels is implicated in increased stress levels and high blood pressure, insomnia, partial hearing loss etc. Noise levels were measured at a range no more than 3 m from the source [ 2, 26] .

      Table 4.  Guideline limits.

      WHO limits 2007 Biern et al. 2015
      Receptor Noise level (dBA) Environ Health effect Noise level (dBA)
      Day
      (7.00 am−22.00 pm)       		Night
      (22.00 pm−7.00 am)
      Built-up, institutional, and education 55 45 Indoors bedroom Speech intelligibility, and moderate annoyance (daytime) 35
      Business, shopping, marketable, indoors, outdoors and traffic region 70 70 Sleep fracas (Nocturnal) 30
      Outside bedrooms Sleep fracas window open 45
      Outdoor, living room Serious annoyance (night time) 55
      Moderate annoyance
      (day/evening time)       		50

      Table 5.  Outcome of ambient noise in Apapa Lagos during the day time.

      Site GPS coordinate Noise level (dBA) Limit remarks
      1 (Near gate) N 6°26'23.27649",
      E 3°20'1.36792"       		61.42 Not
      2 (Generator) N 6°26'20.63513",
      E 3°20ʼ0.899"       		89.88 Not
      3 (Sitting room) N 6°26'19.01821",
      E 3°20'1.1585"       		59.48 Not
      4 (Water pump area) N 6°26'18.85029",
      E 3°20'1.72881"       		61.79 Within
      5 (Laboratory zone) N 6°26'17.65008",
      E 3°20'0.24541"       		67.12 Within
      6 (Tank farm region) N 6°26'18.24981",
      E 3°19'57.87729"       		63.42 Within
      7 (Water treatment) N 6°26'16.64496",
      E 3°19'57.845"       		62.79 Within
      8 (Water separator) N 6°26'16.93323",
      E 3°20'0.22097"       		66.4 Within
      9 (Loading zone) N 6°26'17.69233",
      E 3°20'0.54988"       		70.39 Not
      World Bank %: Within – 44.4%; % Not within – 55.6%.

      Outcome of effluent and hazardous materials

    • The analysis of effluent samples from the Oil Water Separator (OWS) showed that parameters such as Oil and Grease (0.04 mg/L), Total Dissolved Solids (78.0 mg/L) and Total Suspended Solids (0.11 mg/L) were found to be within their stipulated regulated limits. However, parameters such as COD (48.2, 47.9, and 48.1 mg/L), BOD 5 (11.4, 11.2, and 11.1 mg/L), ammonia (0.24, 0.23, and 0.23 mg/L), turbidity (22.1, 22.3, and 22.4 mg/L) and manganese (0.52, 0.51, and 0.53 mg/L) were above their prescribed limits as stated in Guidelines for Noise, Industrial Effluent, and Gaseous Emissions Limitations (FEPA 1991). The pH of 6.8 indicates that the sample is slightly acidic and also within the stipulated regulatory limit of 6.5−8.5 ( Tables 6− 8 ). Microbial analysis was also carried out on the raw water sample and the result for Salmonella was negative. Wastewater (effluent) from the region encompasses depot operations which arise from a mixture of minor spills and water as well as wastewater from the laboratory, kitchen, equipment maintenance etc. and can impact negatively on the environment if discharged directly into public drains without treatment. The contagions in such wastewater might encompass soluble macrobiotic chemicals creating exhaustion of heavy metals, DO, suspended solids plus volatile materials, hydrocarbons, bases, or acids (displayed as small or high pH). Though some depot's installed OWS works to skim out the oil from the water and ensure it is tested and treated before discharge. Hazardous materials within the study zone involve the storage of bulk quantities of petroleum products and in smaller quantities, chemicals such as foam, caustic, solvents, lubricants, etc. Hazardous materials carrying, storage, and management cause spills or other kinds of release with potentially negative effects on water, atmosphere, and soil resources. Besides, their combustibility and other impending hazardous physiognomies likewise present a threat of fire and blasts. Used/spent oil at some depots within the study zone is derived basically from maintenance operations of equipment and generators. The improper collection and disposal of used/spent oil will adversely impact the environment. Spilled oils on the floor can also cause slips and falls resulting in injuries if not properly cleaned and floors kept dry at all times.

      Table 6.  Outcome of effluent from oil water separator.

      Variables Unit Sites Limits Compliant status (%)
      1 2 3 EGASPIN 2018 FEPA 1991 Compliant Non-within
      Calcium mg/L 26.7 26.8 26.5 NS NS 100 0
      Sulphate mg/L 15.7 15.6 15.8 NS 100 − −
      Dissolved oxygen mg/L 4.88 4.90 4.87 NS NS − −
      COD mg/L 48.2 47.9 48.1 NS 40 0 100
      BOD mg/L 11.4 11.2 11.1 NS 10 0 100
      Ammonia mg/L 0.24 0.23 0.23 NS 0.2 0 100
      Total hardness mg/L 66.3 66.1 66.4 NS 200 100 0
      Total nitrogen mg/L 0.53 0.54 0.53 NS 10 100 0
      Total suspended solida mg/L 0.12 0.14 0.10 30 10 100 0
      Total suspended matter mg/L 124.9 124.8 125.1 NS NS − −
      Conductivity μS/cm 155.9 156.1 156.0 NS 1,000 100 0
      Total dissolved solids 78.2 78.0 79.8 < 2,000 500 100 0
      Turbidity NTU 22.1 22.3 22.4 10 5 0 100
      True colour/ Lovibond Hz 0.17 0.15 0.16 NS 7 100 0
      pH 6.7 6.8 6.9 6.5−8.5 6.5−8.5 100 0
      Temperature °C 24.8 24.9 25.1 Ambient ± 2 < 40 100 0
      Total alkalinity mg/L 51.8 51.9 52.0 NS NS − −
      Total phosphorus mg/L 0.34 0.32 0.33 NS NS − −
      Salinity % 0.00 0.01 0.00 NS NS − −
      Sodium mg/L 5.82 5.84 5.83 NS NS − −
      Nitrate mg/L <0.01 <0.01 <0.01 NS NS −
      Hydroxide alkalinity mg/L 0.01 0.02 0.00 NS NS −
      Carbonate alkalinity mg/L 0.01 0.02 0.00 NS NS −
      Magnesium mg/L 9.67 9.68 9.69 NS NS −
      Manganese mg/L 0.52 0.51 0.53 NS 0.05 0 100
      Chloride mg/L 14.7 14.8 14.9 NS 250 100 0
      Silica (SiO 2) mg/L 1.12 1.13 1.11 NS NS −
      Acidity mg/L 1.12 1.13 1.11 NS NS −
      Nitrite mg/L 0.32 0.33 0.31 NS NS −
      Total iron mg/L 1.14 0.12 1.13 1.0 1.0 0 100
      Total oil and grease mg/L 0.03 0.04 0.03 NS 10 100 0
      Detergent mg/L ND ND ND NS NS −
      ND, Not Detected; NS, Not Seen; −, NIL.

      Table 7.  Outcome of effluent (microbiological and others) from oil water separator.

      Variables Unit Sites Limits Compliant status (%)
      1 2 3 EGASPIN 2018 FEPA 1991 Compliant Non-within
      Salmonella in 25 mL Absent Absent Absent NS NS − −
      Shigella in 25 mL Present Present Present NS NS − −
      E. coli CFU/mL 7.1 7.0 7.1 NS NS − −
      Yeast and mould CFU/mL 27.9 28.0 28.1 NS NS − −
      Staphylococcus CFU/mL 9.2 9.0 9.1 NS NS − −
      Vibrio CFU/mL 12.0 12.2 12.1 NS NS − −
      Total coliforms CFU/mL 11.2 11.1 11.0 NS NS − −
      Aerobic plant count CFU/mL 9 8.9 9.0 NS NS − −
      Others
      BTEX mg/L < 0.001 < 0.001 < 0.001 NS NS − −
      PAH mg/L < 0.001 < 0.001 < 0.001 10 NS 100 0
      Odour Objectionable NS NS
      Appearance Light brown with particles Colourless
      ND, Not Detected; NS, Not Seen; −, NIL.

      Table 8.  Some contagions and effects.

      Contagions Health impacts
      Short term Long term
      Ethylbenzene Dizziness, eye and throat irritation Blood disorders
      Benzene Skin blister and irritation, upper respiratory tract Developmental and reproductive disorders
      n-Hexane Headache, giddiness and nausea Blurred vision, fatigue, extremities and headaches
      Toluene Sleep difficulty, dizziness, skin and eyes irritation Birth defects
      Xylenes Nose, gastric and throat irritation, neurological, vomiting and nausea Nervous system disorders

      Outcome of water Attribute analysis

    • The outcomes of physicochemical variables obtained from groundwater within the project region ( Tables 9− 11 and Figs 4 & 5a− h) indicated that the conductivity (1094.0, 1695.0, and 1695.1 mg/L), calcium (109.1, 108.9, and 109.2 mg/L), ammonia (0.48, 0.47, and 0.49 mg/L), DO (2.07, 2.06, and 2.07 mg/L) and total dissolved solid (847.2, 847.4, and 847.3 mg/L) values were high, indicating freshwater aquifer. The turbidity values (1.00−1.13 NTU) reveal that all the sampled boreholes was well within the standard for hygienic water criteon of 5 NTU. The pH values measuring neutral were because of the bicarbonate quantity with values within 30.0−500.0 mg/L CaCO 3 WHO limit. The carbonate alkalinity perceived in the sampling was very low. TD values of the groundwater samplings vary between 51.8 mg/L CaCO 3 as well as 48.6 mg/L CaCO 3. Discrepancy between the TD values for the boreholes was low, signifying recharge from the equivalent origin. Hardness in water encompasses calcium plus magnesium as the key constituents. For instance, magnesium ions quantified in the groundwater varied from 39.7−39.8 mg/L respectively, small phosphate, chloride, and sulphate amounts were seen in groundwater samplings compared to the WHO limits. Likewise, small anions quantities substantiate the airiness of groundwater from the research site. Sulphide and nitrate ions were not perceived in the groundwater sampling. Oil and grease (O&G) were not detected within the facility area, two of the sample points were below the instrument detection limits. Total hydrocarbon was not detected in the ground water samplings of the project influence zone. The origin can, however, be ascribed to biogenic instead of anthropogenic involvement since petroleum hydrocarbon correlated deeds were not seen in the region at the period of this research. Heavy metals ascertained in the samplings displayed varied quantities with Cd having values of 0.02 mg/L in one of the points and not detected in the two other BH water samples. The concentration span of other metals is in the order of Cu < Pb < Zn < Ni < Mn < Fe. Zinc as well as copper displayed concentrations beneath their maximum tolerable limits in hygienic water, whereas Pb, Cr, Ni, Fe, Cd, Hg plus Mn were available at extreme concentrations than intervention and target WHO criterion level. The happening and concentrations of these metals in the samplings imitate the general physiognomies of groundwater systems in nearly all parts of Nigeria. The microbial characteristic of groundwater samplings from the research region is presented that the total coliform, fecal coliform fecal streptococci, and enterococci determined ranged from 1.9 × 102 to 2.0 × 102 CFU/100 mL, 0.5 × 102 to 1.2 × 102 CFU/100 mL, 0.29 × 102 to 0.82 × 102 CFU/100 mL, and 0.43 × 102 to 0.57 × 102 CFU/100 mL respectively, while no detection was recorded at GW2. Fungi are, however, not recorded in the sample, showing no growth. Fecal coliforms were not detected. For PAHs, it is branded that PAHs are comparatively intractable in soils, and some PAHs have been recognized as teratogens, carcinogens, or mutagens [ 4, 16] .

      Table 9.  Outcome of water attributes.

      Variables Unit Sites Limits Compliant status (%)
      1 2 3 FEPA WHO USEPA NIS EU Compliant Non-within
      pH 7.08 7.09 7.07 6.5−8.5 6.5−8.5 100 0
      Conductivity μs/cm 1,694.9 1,695 1,695.1 1,000 − 1,000 1,000 2,500 0 100
      Calcium mg/L 109.1 108.9 109.2 75 0 100
      Phosphate mg/L 1.01 1.00 1 < 5.0 − − NS − 100 0
      Ammonia mg/L 0.48 0.47 0.49 0.05 0 100
      TSS mg/L 0.09 0.08 0.10 < 10 − − NS − 100 0
      Temperature °C 25.4 25.5 25.3 Ambient 100 0
      Turbidity NTU 0.01 0.02 0.01 5 5 5 5 − 100 0
      Total acidity mg/L 61.4 61.3 61.4 NS −
      TDS mg/L 847.2 847.4 847.3 500 − 500 500 − 0 100
      Total hardness mg/L 272.4 272.3 273.3 200 80−100 − 150 − 0 100
      Total alkalinity mg/L 408.4 408.2 408.3 − − − 200 − 0 100
      Total solids mg/L 1,039.9 1,040.2 1,040.3 NS −
      Magnesium mg/L 39.8 39.7 39.8 20 0 100
      Sodium mg/L 10.7 10.8 10.6 200
      Chloride mg/L 26.8 26.9 26.7 250 250 250 250 250
      DO mg/L 2.07 2.06 2.07 2
      Nitrite mg/L 0.13 0.14 0.12 0.2
      Manganese mg/L 0.23 0.22 0.21 0.2
      Total iron mg/L < 0.01 < 0.01 < 0.01 0.3
      Sulphate mg/L 50.2 50.3 50.1 500 250 250 250 250 100 0
      Silica mg/L 3.93 3.94 3.92 40 100 0
      Salinity % 0.09 0.08 0.07 NS −
      Nitrate mg/L < 0.01 < 0.01 < 0.01 50 50 50 50 50 100 0
      Total oil/grease mg/L 0.08 0.09 0.10 10 100 0
      Colour TCU 0.17 0.16 0.18 15 100 0
      Appearance Colourless Colourless
      Odour Objectionable Unobjectionable
      Taste Objectionable Unobjectionable
      ND, Not Detected; NS, Not Seen; −, NIL.

      Table 10.  Outcome of water attributes (microbiological).

      Variables Unit Sites Limits Compliant status (%)
      1 2 3 FEPA WHO EU USEPA NIS Compliant Non-within
      Salmonella in 25 mL Absent Absent Absent NS − − − NS − −
      Shigella in 25 mL Absent Absent Absent − − − − NS − −
      E. coli CFU/mL 0.00 0.00 0.00 0 − − − 0 100 0
      Yeast/mould CFU/mL 7 7.1 7 − − − − 0 100 0
      Staphylococcu CFU/mL 0 0 0 NS − − − NS − −
      Vibrio CFU/mL 0 0 0 NS − − − NS − −
      Aerobic plate count CFU/mL 26 26.1 26.2 NS − − − 100 100 0
      Nigerian Industrial Standards for Drinking Water (NIS 554:2015). NS, Not Seen; −, NIL.

      Table 11.  Cluster scrutiny outcome.

      Paramenter Initial Final
      C1 C2 C2
      Cl 26.5 26.8 26.75
      SO 2 15.8 15.6 15.65
      DO 4.87 4.9 4.89
      COD 48.1 47.9 48.05
      BOD 11.1 11.2 11.3
      AMMO 0.23 0.22 0.23
      TH 66.4 66.1 66.2
      Total Nitr 0.53 0.54 0.54
      TSS 0.1 0.14 0.13
      TSM 125.1 124.8 124.85
      Cond 156 156.1 156
      TDS 79.8 78 78.1
      Tur 22.4 22.3 22.2
      True Color 0.16 0.15 0.16
      pH 6.9 6.8 6.75
      T (°C) 25.1 24.9 24.85
      TA 52 51.9 51.85
      TPhos 0.33 0.32 0.33
      Mg 9.69 9.68 9.68
      Salinity 0 0.01 0.01
      Na 5.83 5.84 5.83
      HydrAlk 0 0.02 0.02
      NO 2 0 0 0
      Mn 0.53 0.51 0.52
      Cl 14.9 14.8 14.75
      SiO 2 1.13 1.11 1.12
      CaCO 3 1.11 1.13 1.13
      CarAlk 0.02 0 0.01

      Figure 4. 

      Compliant and noncompliant levels of water attribute parameters.

      Figure 5. 

      Water attribute scrutiny outcome for some parameters.

      Outcome of clustering scrutiny

    • Cluster analysis or clustering is the technique that groups unlabeled examples based on a similarity measure. It is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is the main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and machine learning. Further scrutiny through algorithm cluster produces two steps with 28 inputs, cluster quality between 0.5−1.0 was classified as good and 0−0.5 as fair water ( Fig. 6a− c). Cluster A is heavily loaded with parameters like DO, conductivity, salinity, and ammonia, while Cluster B parameters are Turbidity, TDS, and TSS. Cluster quality displays that conductivity initial and final scrutiny was the highest (156 mg/L), followed by TSM with a value of 125.1 mg/L at initial and slightly reduced to 124.85 mg/L at final scrutiny. Most of these parameters are greatly influenced by the expulsion of produced effluent water that creates serious environmental threats to humans and the ecosystems. The use of technologies like highly proficient halophile oil-degrading microbes in genetic treatment must coalesced with membranes (SBR) genetic treatment systems for efficient management of generated water, since the Lagos (South-west Nigeria) crude oil is tart and contains heavy metals, the pipelines are creating serious problems via leaking, then causing substantial oil spills along the haulage line to the sea terminal at haven Nigeria necessitate appropriate monitoring plus maintenance. At the moment, most oil manufacturers re-inject created water or reclaim it for onshore wells at 98.0%. Conversely, 91.0% of created water from offshore wells is disposed of into the deep sea.

      Figure 6. 

      Cluster investigation outcome of some parameters scrutinized.

      Health and well-being in the Apapa communities

    • Table 11 and Fig. 7 display that the common health problems identified among the adult population were malaria (23%), upper respiratory infections (i.e., cough, catarrh, sore throats etc., 27%), typhoid (21%), dysentery/diarrhea (22%), and sores/injuries (7%). Among the children the most common health problems are malaria (27%), dysentery/diarrhoea (25%), worm infestation (6%), and typhoid (23%), while upper respiratory infections (cough, catarrh, sore throats, etc. accounted for 19%). Most of the diseases recorded are those that have been endemic in the communities since the inception of the communities apart from upper respiratory tract infection which is associated with mining activities, cement production, and heavy vehicular movement in the area.

      Figure 7. 

      Common health problems among the (a) adult and (b) children population.

    Air, effluent, wastewater, and domestic water quality management

    • Secretion and air attribute monitoring programs afford information that might be utilized for assessing the efficacy of emissions management stratagems. The systematic planning of air attribute monitoring is vital to ensuring that the data gathered are ample for their wished-for-rationales. The effluent and water attribute monitoring program that have tolerable resources and management oversight must be created. Also elements such as: monitoring parameters (that indicate contagions apprehension should encompass variables that are synchronized under the Standards for Noise, Industrial Effluent, and Gaseous Emissions Limitations and Environmental Guidelines for the Petroleum Industry in Nigeria's acquiescence requirements), monitoring kind and rate of recurrence (monthly/quarterly effluent monitoring that will take into consideration the discharge physiognomies from the operation of the depots and any other area with more potential for spills), monitoring locations (effluent sampling positions should be located at the final discharge point from the Oil Water Separator, also process expulsion must not be diluted preceding or after treatment), data quality (application of nationally approved techniques for sampling gathering, preservation, and analysis). Besides, samples must be carried out under or by trained individuals and tests to be performed by accredited laboratories or by the entities allowed or expert for this rationale. Although opportunities for water savings in industrial processes are highly industry-specific, there are opportunities for water-use monitoring in the Apapa Lagos State. Thus the indispensable elements of a water management program should encompass: an upgrade of treatment processes to meet needs for domestic purposes e.g., hand washing etc., classification, recurring measurement, and demo of principal flow within the region. This can be achieved through the installation of flow meters. Also constant similarity of water flows with functioning targets to ascertain where action must be taken to lessen water usage and water metering should accentuate zones with peak water usage [ 2, 13] .

    Conclusions

    • In the last decades, spill out from aging, ailing maintained or incapacitated pipelines have upsurge and no clear inclination with respect to nosh-up, that is the number of happenings or total of spilled-out oil. This scrutiny displays the secretion of effluents, particulates contagions, and anions from Apapa, Lagos Nigeria. The outcomes flaunt that the presence and concentration of nitrogen (iv) oxide (NO 2) were less than 0.01 ppm thus within the stipulated limit of 0.113 ppm, whereas 100% CO and 30% of CO 2 measured was above the regulatory limits. Similarly, the highest noise value recorded was 89.9 dBA at the Generator house while the lowest was 57.2 dBA at the Pump house. The noise level at four sites of the sampled locations was found to be above the WHO 2007 and FMEnv regulatory limit. The concentration span of other metals arrangements is in the order of Cu < Pb < Zn < Ni < Mn < Fe. Zinc as well as copper displays concentrations beneath their maximum tolerable limits in hygienic water, whereas Pb, Cr, Ni, Fe, Cd, Hg plus Mn were available at extreme concentrations than intervention and target WHO criterion levels. Furthermore, the inhabitants of the shoreline regions disturbed by the spill-out are also indirectly or diametrically exposed to the oil chemicals and are facing economic together with social consequences of the spill-out. For this reason, epidemiologic research that centered on threat appraisal must consider all the variables ascribed to exposure to oil spills-out, no matter what the source, as these are indispensable for thorough appraisal of the possible impacts on human healthiness at various levels and also launch proper precautionary programs.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Igibah CE, Agashua OJL; data collection: Ilaboya IR, Iyeke SD; analysis and interpretation of results: Igibah CE, Ufuah E, Agashua OJL; draft manuscript preparation: Igibah CE, Ufuah E, Agashua OJL, Ilaboya IR, Iyeke SD. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • All data generated or analyzed during this study are included in this published article.

    Acknowledgements

    • The authors appreciate the authorities of Apapa Local Government Area Lagos State, for creating the environment for this study and Bosean Equipment Company for providing the equipment for this research.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press on behalf of Nanjing Tech University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (7)  Table (11) References (27)
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    Cite this article
    Igibah CE, Ilaboya IR, Iyeke SD, Ufuah E, . 2024. Health effects of oil and waste pollutants on Delta Nigeria inhabitants' well being and its mitigation. Emergency Management Science and Technology 4: e017 doi: 10.48130/emst-0024-0015
    Igibah CE, Ilaboya IR, Iyeke SD, Ufuah E, . 2024. Health effects of oil and waste pollutants on Delta Nigeria inhabitants' well being and its mitigation. Emergency Management Science and Technology 4: e017 doi: 10.48130/emst-0024-0015
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  • Introduction
  • Materials and techniques

  • Regions of study

  • Techniques

  • Air emissions
  • Particulate, gaseous emissions, and environmental condition level measurements
  • Results and discussion

  • Air quality outcome

  • Sulphur (iv) oxide (SO 2), nitrogen oxide (NO), and carbon monoxide (CO)
  • Hydrogen sulphide and suspended particulate matter (SPM)

  • Noise scrutiny outcome

  • Outcome of effluent and hazardous materials

  • Outcome of water Attribute analysis

  • Outcome of clustering scrutiny

  • Health and well-being in the Apapa communities

  • Air, effluent, wastewater, and domestic water quality management
  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgements
  • Conflict of interest
  • Rights and permissions
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