Determination of local background and baseline values of elements within the soils of the Birimian Terrain of the Wassa Area of Southwest Ghana

ABSTRACT

This study establishes the local background and baseline values of elements in surface soils in Wassa area underlain by Birimian rocks using probability Q–Q plots. In this study, a total of 2868 soil samples were collected. The chemical elements in these samples were analysed by XRF technique. The results from the analysis were processed following Papastergios and Karim methods that normalized and transformed the data after which a best of fit is estimated to determine the background and baseline values. The method excludes the outliers at the extremities from the transformed data until the distribution follows a straight line. The method yielded the following background values in this study: 6.41 mg/kg for As, 52.55 mg.kg for Cr, 9.89 mg/kg for Pb, 10.21 mg/kg for Co and 17.18 mg/kg for Ni. The calculated baseline values were also 13.2 mg/kg, 80.7 mg/kg, 6.7 mg/kg, 6.4 mg/kg and 8.7 mg/kg for As, Cr, Pb, Co and Ni, respectively. The departure between the locally calculated background and baseline values of elements relative to worldwide accepted background and baseline values suggests the relevance to conduct such studies for the various geological provinces in the country as a vital step to properly and effectively manage and sustain the environment against environmental health diseases.

KEYWORDS:

• Geochemistry
• Background
• Baseline
• Environment
• Surface soil
• Ghana

Introduction

The elements in soils have been identified as vital components of the environment because they can be hazardous or essential to human health. Translocation of these elements in soils to food chains may impact on human health. Monitoring and managing the impacts of the natural environment on public health require the establishment of environmental background and baseline values as this will guide in outlining the environmental risk areas. The background values provide a guide to ascertain the presence of anthropogenic effects that could lead to hazardous or beneficial consequences. In environmental geochemistry and health, the background is considered as the natural concentration in environmental material devoid of human influences. It is distinguished from environmental baseline values that refer to the natural variations in the concentration of an element in the surface environment, at a determined place and time. This concept includes natural geographic concentrations (background level) and the diffuse anthropogenic contribution in soils (Kobierski & Dabkowska-Naskret, 2012). The Impact of this may be deleterious or beneficial.

It is unclear if Ghana has its own background and baseline values to monitor and manage the impact of the natural environment on public health issues. The available testimonies in this discipline suggest otherwise because earth science and public health researchers either use World Health Organization (WHO) values or worldwide acceptable values instead of locally established background and baseline values. Background values are determined by the composition of the underlying rock as well as the weathering processes prevalent in the area and are considered as the normal values of potentially polluting substances present in natural soils without human influence (Matschullat, Ottenstein, & Reimann, 2000). It has been argued that rocks have a large influence on element contents in soils, with concentrations sometimes lower and above critical values (Salonen & Korkka-Niemi, 2007). However, it is almost impossible to establish natural background levels, i.e., the geochemical composition of virgin soils, since atmospheric deposition can contaminate soils with certain trace elements (Cicchella et al., 2005, Albanese, 2014). But from Riemann and de Caritat (2005) human activity is a key determinant of the level of element concentrations in soils. Ordinarily, places such as industrial and residential areas are often considered as environments at higher risk of anthropogenic influences on elemental soil concentrations. Relatively, Alloway (2013) recognized less modified and undisturbed areas such as forests and subsistent farmlands are mainly considered as close to pristine zones with minimal pollution.

Based on Alloway’s assertion, the establishment of local background and baseline values for different geological domains is critical due to the variations in the modal composition of rocks from place to place and the numerous environmental activities. The detection of the human-induced contamination in an environment is properly assessed using the baseline value, which is uncommon in Ghana. The most commonly used method of determining human influences on soils in works done in Ghana relies on the use of continental crustal averages and worldwide accepted values, which have been proven to be problematic (Arhin, Mouri, & Kazapoe, 2017). The present spate of illegal mining and infrastructural developments coupled with population growth in Ghana makes it challenging to identify undisturbed soils or virgin soils. Example, illegal mining and logging, conversion of farmlands to residential areas tend to suggest the destruction of precinct lands hence calculated local background values if not expressed as a range may be problematic. The variable nature of element concentrations due to underlying geology, geological processes and anthropogenic chemical contribution due to human activities results in variable element contents. The present research sees the inappropriateness of just using the natural concentrations to establish the pollution status at a place because of the numerous processes influencing the landscape modifications. Rather, both background and baseline values calculated should be determined separately for each element in geologically different regions; otherwise, the limit values for contaminated soils may be lower than the natural concentrations (background levels) calculated for an extensive area (Gałuszka, 2007). The factors that influence the soil trace element content are very variable. This thus makes the use of normative values of environmental legislation of other countries or regions or global values inappropriate, as they must be determined locally. Currently, published studies that have determined the concentrations and patterns of the spatial distribution of heavy metals in soils for virgin soils appear insufficient or patchy. It was also unclear whether the reported work done in this discipline considered geology as the source of the elements in the soils. Geological sciences reveal that different geological domains present different arrays of rock units and hence local backgrounds and baseline values computed for different geological domains will differ in values. Therefore, to ensure effective environmental management policies based on background and baseline values, local background and baseline values must be determined for the different geological domains. Although a lot of studies have determined the concentration of metals and metalloids in similar areas, there are no categorical studies which determine baseline and background concentrations for a specific geological province in Ghana. This study, therefore, attempts to establish the local background and baseline concentrations of elements within the soils found in the Birimian terrain of the Wassa area using probability plots (Q–Q plots) as an important step towards in environmental studies and management in Ghana.

Location, demography and population of study area

The study area forms part of geological field sheet 0503B of Ghana and covers Asankragwa, Akropong, Samreboi and Bogosu, within the Wassa traditional area (Figure 1). The area has an estimated population of 282 000 according to the 2000 National population census (Ghana Statistical Service, 2002). The main occupation of the population is farming; primarily the cultivation of food and cash crops like plantain cassava, cocoa, coffee and rubber. Small-scale artisanal gold mining and the illegal artisanal gold mining, popularly known as “Galamsey” are not uncommon in the area. Aside from the large-scale Wassa Mine operated by Golden Star gold at Bogoso, there are medium scale mining activities at Jukwa and Akropong. The area lacks any impactful industries despite the presence of a few exploration companies whose activities are at the various stages of exploratory work. The study area was selected on the basis of the diverse rocks present in the area. It represents a typical example of the Birimian terrain which covers a good portion of the West of Ghana. Soil samples used as part of this study were taken from relatively undisturbed areas within the study area with minimal mining activities and from a deep horizon so as to be representative of the local background and baseline values of the area.

Figure 1. Location of the study area

Physiographic and geological settings

The area is predominantly undulating with the highest points averaging about 150 m. Draining the area are the Tano and Ankobra Rivers. The climate is tropical humid with two rainfall patterns. The major rainfall occurs from March to July and the minor season occurs from September to early December and Average annual rainfall is 173 mm. The vegetation in the area has been influenced by the rainfall pattern. The temperature ranges from 29°C to 24°C between March and August, respectively.

The area is underlain by rocks of Birimian System and lies within the southern portion of the Ashanti Greenstone Belt, along the eastern margin within a volcano-sedimentary assemblage located close to the Tarkwaian formation contact. A fault zone to the east separates the Tarkwaian Formation of rock units from the Volcano-sedimentary rocks of the Sefwi group (Parra-Avila et al., 2015). The lithological sequence of the study area is characterized by a sequence of meta-volcanic (basaltic to andesitic protoliths) flows intercalated with metasedimentary rocks such as greywacke, magnetite-rich argillite and rare black shale layers, micaschists and felsic porphyry. The area is also intruded by granitoids and some mafic intrusives. Most of the granitoids correspond to typical tonalite-trondhjemite-granodiorite (TTG) suites (Perrouty et al., 2016). Rocks are aggregates of minerals, implying that after weathering different types of elements will be released into the soils and depending on their concentrations may impact on the health of the exposed populations.

Materials and methods

A total of 2868 soil samples were collected, dried, and sieved to <106 µm fraction for elemental analysis. The soil samples were conducted on a 1 km² grid using GARMIN E-TREX 30 GPS device to navigate to the predetermined sampling points. The top 20 cm depth of the soil was collected as a sample from a 30 cm nominal diameter hole. The samples collected were put in labelled Plastic bags. Sample weights of 2 kg were collected at all sample stations. The collected samples were sent to a commercial laboratory for chemical analysis using XRF technique on pressed pellets except for gold that was analysed using Fire Assay. The elements analysed included As, K, Co, Cr, Mn, Ni, Pb, Mg, Fe and Au.

Results obtained from the XRF analysis were interpreted using descriptive statistical analysis. Information such as standard deviation, standard error, and mean for the various elements were calculated. All the variables were transformed using normal score transformation prior to the principal component analysis (PCA), followed by the cluster analysis using a furthest neighbourhood method. The relationships of elements in soil samples were presented in a component matrix and a Dendrogram.

The data normalities for As, Co, Pb, Cr, Pb, Ni, Mg, Fe, K and Mn were examined using one-sample Kolmogorov–Smirnov test (the K–S test of normality) as well as the Shapiro–Wilk tests. The normalised data was then log-transformed to achieve near straight line distribution using Q–Q plot. Outliers that were noticed in the upper and lower extreme corners for the Q–Q plots of the various elements were excluded until the distribution followed a straight line (extreme high and low values, respectively). This was repeated until the best fitness to the straight line was achieved following the methodology by Papastergios, Fernandez-Turiel, Georgakopoulos, and Gimeno (2010) and Karim, Qureshi, and Mumtaz (2015).

The Q–Q plot supported the delimiting three classes that included geochemical background, baseline, and geochemical anomalies on the basis of their geometric mean (g) and geometric deviations (d) following the computation as demonstrated by Papastergios, Fernandez-Turiel, Filippidis, and Gimeno (2011) and Papastergios et al. (2010) (Table 2). The analytical quality was assessed by inserting quality control and assurance samples SARM 1, SARM 2 and AMIS 17 for accuracy analysis.

Results and discussions

Quality control and assurance analysis performed on the three certified reference materials SARM 1, SARM 2 and AMIS 17 shown in Table 1 displayed no apparent signs of contamination with precision and bias recorded primarily below 9.3%. The recovery rates for the certified reference material (known also as standard sample) varied between 87.5% and 120% (Table 1). This is considered satisfactory for the study relating the calculated recovery rates in a similar study in Rio Doce Basin in Brazil (Guevara et al., 2018).

Table 1. Summary statistics of major and trace elements in soil samples in the study area (Unit measurements, mg/kg).

Elements	Min	Mean	Median	Standard Deviation	Skewness	Kurtosis	Geometric Mean	Geometric Standard Deviation	Max
As	2	17.36	14	15.54	5.09	48.72	4.31	1.26	246
Cr	12	89.52	81	55.03	12.68	288.12	80.74	1.54	1500
Pb	5	7.43	6	4.93	10.82	250.07	6.70	1.48	148
Co	2.64	4.84	3.96	2.75	3.53	33.02	6.44	1.58	43.28
Fe	4	3.22	2.9	1.7	1.57	4.8	2.82	1.69	0.44
K	0.05	0.77	0.76	0.35	0.56	0.86	0.68	1.72	2.92
Mg	0.01	0.19	0.18	0.1	2.92	22.16	0.1	1.68	1.52
Mn	69	203.84	159	152.84	5.96	62.4	176.78	1.68	2600
Ni	2	10.97	9	8.62	4.68	57.02	1.98	8.67	174

The summary statistics developed from the 2868 sample results processed to establish the local background and baseline values are presented in Table 2. From Table 2, As averages at 17.36 14 mg/kg with a standard deviation of 15.54. This is not very high suggesting clustered As spread. Meanwhile, the range is between 2 and 246 mg/kg meaning toxic and deficient As concentrations coexist contemporaneously. Toxic concentration of 246 mg/kg will pose a debilitating effect to the exposed population and thus required environmental monitoring to locate and define such terrains. However, comparing the measured average values with local background and baseline values that factor element contributions from the local activities, chemical and surface process may be useful. Co values ranged from a maximum of 43.28 mg/kg to a minimum of 2.64 mg/kg with an average value of 4.84 mg/kg (Table 2). Cr recorded a maximum value of 1500 mg/kg and a minimum value of 12 mg/kg, the mean Cr value measured in this study seems lower than the allowable global limit set for Cr worldwide (i.e., 200 mg/kg).

Table 2. The five classes determined after Papastergios et al. (2011)

Class	Concentration
Negative Anomaly	<g/d
Background	g/d-gd
Baseline	g
Low Anomaly	gd-gd^2
Intermediate Anomaly	gd^2-gd^3
High Anomaly	>gd^3

g = geometric mean, d = geometric deviation. Determined after Papastergios et al. (2011)

The essential element Fe component in the oxide ranges from 14.94 wt. % – 0.44 wt. % with a mean value of 3.22 wt. % (Table 2). This average is lower than expected in river sediments measured as 4.8% (McLennan & Murray, 1999). Considering the measured average to be consistent for the entire area, the Fe uptake as a dietary supplement may be deficient hence cases of anaemia may be predominant. Rose, Hawkes, and Webb (1979) also identified the median value for Fe as 2.1% in soils. In this study, 2.9% was obtained for the median with a very low standard deviation of 1.7. The data for Fe mean that certain areas will be deficient in Fe whilst some areas may have enough Fe in soils to support the dietary supplementation through cultivated food in some areas. Another essential element that supports over 300 enzymic reactions Mg had maximum values of 1.52 wt. % and a mean of 0.19 wt. %. The upper continental crust had an average of 50.1% (Wedepohl, 1995). This is several percentages higher than the maximum measured in this study; suggesting the entire area is deficient in Mg. The prevalence of Mg-deficiency diseases will be common and require immediate public health action. Mn concentration in the area ranges from 69.00 to 2600 mg/kg, with a median value of 159 mg/kg. The average from Table 2 is 203.84 mg/kg but the mean Mn content of 8354 world soils was reported as 761 mg/kg by Ure and Berrow (1982) with a range of <1–18,300 mg/kg. It is clear from Table 2 that the measured average from this study is much lower than the accepted average of 761 mg/kg in soils. Mn again is deficient and may, therefore, be a problem for those who require Mn as a dietary supplement from food.

The deficiencies and toxicities of essential metals in major oxides and trace elements make it an important priority to establish local backgrounds and baseline for some toxic and essential elements due to the variability in local environments. Also, from Figure 2, the rescaled cluster Dendrogram defined four clusters that showed the various elemental associations. SiO₂ in Cluster 1 is detached from all the other chemicals (Figure 2). This means that soils with more quartz may contain less content of the other minerals implying the SiO₂ may be from the hydrothermal quartz veins reported by Kesse (1985), Adjimah et al. (1993) and Griffis et al. (2000) that are associated with the Birimian System of Ghana. Most of these veins host gold mineralization and hence become the target for the artisanal small-scale miners. In their quest to mine the auriferous quartz veins facilitate the spread of the elements released from the underlying rocks through weathering and erosion. Cluster 2 contains Th, Ti, U, and Ba, which occur as oxides, often considered as resistant minerals, unsusceptible to weathering and tend to enrich the secondary environment (Cornu et al., 1999; Scheinost, 2005). The MgO, CaO, Zn, Mn, Cr, Ni, V, Fe, and Al are assigned Cluster 3, which was ascribed to the underlying geology. Lead (Pb) and Cu occurred with the sulphide minerals and these are linked to the auriferous quartz veins deposited as part of the hydrothermal deposit in this area (Kesse, 1985, Adjimah et al., 1993, Griffis et al., 2000). Similarly, the Sr, Na₂O, Rb, and K₂O assigned in one group (cluster 20) may mainly occur in the mineral lattices. The dissociation of the metalloid As (in a single cluster) from other elements suggests its special feature in the environment. It is possible that As has a weak relation to the underlying rocks, but may be found in hydrothermal veins common in the metasedimentary and volcanoclastic rocks similar to Pb and Cu in this area (Kesse, 1985). The deep weathering resulting in thick regolith profiles at the study area is controlled predominantly by chemical weathering. This weathering type will decompose the rocks and the sulphide minerals (including arsenopyrite) associated with the auriferous quartz veins possibly explaining the adsorption of As by various minerals, such as iron and aluminium oxides, and clay minerals common in tropical soils. The high As averages obtained for Bogoso Akropong and Asankragwa confirms As occurrence as an accessory mineral in gold ore deposits particularly hosted in the metasedimentary and volcanoclastic rocks of Ghana (Kesse, 1985). The geochemical variations across the landscape as shown in Table 3 suggest the need to calculate local background and baseline values not just for geochemical anomaly detection but for environmental health management towards the attainment of sustainable development goal on wellbeing and good health for all. The knowledge of the local background and baseline concentrations of elements in specific areas are essential for environmental studies on health (Salminen & Tarvainen, 1997).

Figure 2. Dendrogram using a complete linkage to define element associations in soils

Background and baseline calculations and their usefulness in environmental health monitoring

Papastergios et al. (2011) method (Table 3) was used to calculate the background and baseline values and other variables useful in assessing element impacts in the environments is presented in Table 3. The background values expressed as a range in this study (Table 5) appear to have values different from continental crustal values calculated by Taylor (1964) and Rudnick et al. (2003). The disparities in local background values and the continental crustal values may lead to different predictions and hence a promulgation of unreliable environmental policies Example the application of using Table 3 in calculating the background value; placed As baseline value at 13.29 in this study compared with the global As values ranging between 10 mg/kg and 15 mg/kg. The calculated As value though falls within worldwide As range but show great contrast between Webb et al. (1979), Zhu, Williams, and Meharg (2008) and Italian reference values for soils for Arsenic. The contrast in background/baseline values from different geographic locations from the established local background/baseline values could lead to several detrimental outcomes to expose people in the affected areas. For instance, if As background is set at 20 mg/kg (Table 6) it will lead to an outbreak of As-related diseases because the people eat what they grow. The authors anticipate the contrast between the calculated local baseline values with the existing globally accepted values to bring some distortions in environmental policies. This thus may influence strategies to remediate health issues linked to excess toxic elements or depleted essential elements. From Table 6 no single calculated baseline value is the same for different geographic locations for the elements. It meant the adaptation of worldwide accepted values or WHO accepted values may be tricky because the controls on element concentrations depend on the underlying geology, local environmental activities, soil-forming processes, geochemical, chemical and physical processes. Similarly, if we compare the baseline values with crustal averages (Table 5) the likely conclusion is As and Cr contaminations with depletions of Pb, Co and Ni in surface soils. It is therefore imperative that using upper continental crustal (UCC) averages as a baseline to monitor environmental health issues may not be accurate for the study area and will result in speculative conclusions that may be detrimental to human health particularly for the toxic elements.

Table 3. Recovery rates for the various elements

	SARM 1			SARM 2			NCSDC 73,334			NCSDC 73,310			BLANK			AMIS 17
ELEMENTS	AVG	CRM	RR	AVG	CRM	RR	AVG	CRM	RR	AVG	CRM	RR	AVG	CRM	RR	AVG	CRM	RR
As	2.8	2.6	107	19.2	18.4	104	496.9	492.1	101	110	109.6	100	2.7	2.87	94	4.3	3.9	109
Cr	21	18.5	112	23	22.8	101							31	29.5	105
Fe							2.72	2.6	104	3.31	3.1	106	1.36	1.28	106	6.54	7.1	91
MgO	0.4	0.45	88	0.07	0.04	143	1.32	1.05	120	0.52	0.7	65	0.51	0.31	139	19.51	20	97
Pb	8.8	9	98	39.3	37.2	105	500.4	499.8	100	296	298	99	25.7	26.1	98	42.3	43	98

*AVG = Average

*CRM = Certified Reference Material

*RR = Recovery Rate

No certified values are available for the areas left blank

Table 4. Geochemical background and anomaly threshold values (mg Kg⁻¹/wt%)

	Min	Negative Anomaly	Background	Baseline	Low Anomaly	Intermediate Anomaly	High Anomaly	Max
As	2.00	6.41	6.41–27.56	13.29	27.56–57.14	57.14–118.47	176.67	246
Cr	12.00	52.55	52.55–124.06	80.74	124.06–190.62	190.62–292.88	6519.23	1500
Pb	5.00	4.54	4.54–9.89	6.70	9.89–14.59	14.59–21.54	44.86	148
Co	4.00	4.07	4.07–10.21	6.44	10.21–16.16	16.16–25.60	41.52	65
Fe	0.44	1.67	1.67–4.76	2.82	4.76–8.02	8.02–13.53	7.95	14.94
K	0.05	0.39	0.39–1.16	0.68	1.16–2.00	2-3.44	0.46	2.92
Mg	0.01	0.06	0.06–0.17	0.10	0.17–0.29	0.29–0.49	0.01	0.92
Mn	70.00	110.00	110.00–281.60	176.00	280-450	450-730	31,249	2600
Ni	2.00	4.38	4.38–17.18	8.67	17.18–34.04	34.04–67.43	75.21	174

Table 5. Comparison of estimated background values (milligrams per kg of dry weight) with the continental crustal averages

Element	This Study	Continental crustal average
As	6.41–27.56	1.8
Cr	52.55–124.06	100
Pb	4.54–9.89	12.5
Co	4.07–10.21	25
Ni	4.38–17.18	75

Table 6. Comparison of estimated baseline values (milligrams per kg of dry weight) with the values published in the literature

Element	1	2	3	4	5
As	13.29	14	9.92	9.4	20
Cr	80.74	36	100.56	79.3	150
Pb	6.69	-	33.05	19.5	100
Co	6.44	-	22.62	9	20
Ni	8.67	-	31.97	19.45	120

1 = This study

2 = Jiangxi Geochemical Survey (1987)

3 = Webb et al. 1978

4 = Soil background in Taihu, China (Zhu et al., 2008)

5 = Italian reference values for soils, D. LGS 152/2006

The safest approach is to use the calculated baseline values in the study because it incorporates element concentrations in both precinct and contaminated soils. The calculated baseline values for As and Cr in the study shown in Table 6 appear to be in a similar As in column 2 and Cr in column 4.All others are different because of the different environmental activities and variations in the underlying geology forming the soils. From Table 4 and using Sinclair's Experiential model (1991), the background limits for As, Cr, Pb, Co and Ni in soils for the study area were 6.41 mg/kg, 52.55 mg/kg, 9.89 mg/kg, 10.21 mg/kg and 17.18 mg/kg, respectively, for the Birimian of Southwest Ghana. The choice of these background values incorporated the global accepted values for these elements and the consequences thereof when toxic elements particularly exceeded these accepted global values.

A background value ranging between 4.1 and 10.2 mg/kg and a baseline value of 6.4 mg/kg was determined for Co. This indicates that 1437 samples (50.1% of the 2868 samples) belong to the background class. These values are below the normal Co range of 100 mg/kg for mafic rocks and 1 mg/kg −15 mg/kg for acidic rocks (Kabata-Pendias, 2010). Weathering mechanisms that transform and distribute Co in soils are very complex due to their differing oxidation states and also the prevalent microbial activities (Ma & Hooda, 2010). The baseline value for Co is 8.0 mg/kg. This, however, is higher than the measured baseline value of 6.44 mg/kg in the study. Cobalt acts as a bio-essential trace element for bacteria, plants, and humans in the natural environment. The low baseline value calculated implies inadequate Co supplies to plants and humans. This thus makes environmental monitoring of Co still important because Co concentration levels can reach hazardous levels in humans as its bioaccumulation may damage vital organs such as the lever, kidney, pancreas, and heart (Czarnek, Terpiłowska, & Siwicki, 2015; Lange et al., 2017). Magnesium recorded a background value ranging between 0.062 wt. % – 0.173 wt. % with a baseline of 0.103 wt. %. From Mg data set, 2094 samples (i.e., 73% of the 2868 samples collected) have values in the background class. The source of Mg measured in the samples may be from the ferromagnesian-rich rocks reported by Kesse (1985) to underlie the area. Magnesium notably is essential for human development. Its toxic effects to humans are yet to be defined. However, its deficiency has an association with numerous diseases (Rude, Singer, & Gruber, 2009; Whang, 1987). The calculated average-Mg value is low. In such circumstances, dietary Mg from food will be a challenge from a dietary source. The net effect may be an outbreak of Mg-related diseases. Similarly, background values established for Fe ranged from 1.67 wt. %-4.76 wt. % and a baseline of 2.82wt%. The data on Fe show that 1989 samples (i.e., 69.4% of the 2868 samples collected) belong to the background class. Iron is an essential element that controls the oxygen-carrying haemoglobin and myoglobin at a suitable dosage. Anaemia is associated with Fe-deficiency, while the Fe-excesses are related to Fe-overload, which could cause hemochromatosis, liver, heart, and pancreatic damages, and diabetes (Siah, Ombiga, Adams, Trinder, & Olynyk, 2006).

In addition to the discussed elements, the background values recorded for Mn ranged from 110 mg/kg- 281.6 mg/kg with a corresponding baseline value of 176 mg/kg. This shows that 2117 samples, representing 73.8% of the total 2868 samples collected, fall within the background class. Other studies revealed Mn to be among the most abundant trace elements in the environment (Caballero, Trugo, & Finglas, 2003). The human body has been identified to contain 12–20 mg of Mn, of which most of it is found in the liver, bones and kidneys. It is thus needed for normal skeletal growth and development. It is also essential for glucose utilization, lipid synthesis and lipid metabolism, cholesterol metabolism, pancreatic function and development, as well as prevention of sterility. Manganese is again important for protein and nucleic acid metabolism; it thus activates enzyme functions and also involved the thyroid hormone synthesis. Deficiency of Mn results in; fatness, ataxia, blood clotting, skin problems, lowered cholesterol levels, skeleton disorders, birth defects, reduced immune function, impaired glucose metabolism, changes of hair colour and neurological symptoms. Obtaining the correct baseline values for these elements for the local area may help in developing a workable environmental policy that may reduce the incessant environmental health issues in the study area.

Other trace elements considered in this study included K and Cr. Background value for K was determined as 0.39 wt.% −1.16 wt.% with the corresponding baseline of 0.68 wt.% (6800 ppm). The analyses with respect to the background value showed about 73.2% samples out of the total collected samples to be within the background class. Since it is an essential mineral micronutrient that acts as an intracellular ion for all types of cells; the low concentrations in soils may impact on the population if their source of dietary supplement is via food. It also plays a role in the maintenance of fluid and electrolyte balance. A dietary survey in the US indicated the average dietary K intake is about 2,300 mg/day for adult women and 3,100 mg/day for adult men. Diseases related to K-deficiency may not affect the population and there will not be the need for K-supplemented pills to make up for K-deficiency in the US. But the same might not be said about the study area where most areas have K-concentrations in the background class. Correspondingly, Cr had a background value between 52.5 mg/kg- 124.1 mg/kg with a baseline value of 80.7 mg/kg. From the chromium data 2037 samples (71% of the 2868 samples) belong to the background class. Cr contents similar to these values are associated with acidic and argillaceous rocks (Kabata-Pendias, 2010). Most soil Cr exists as Cr³⁺ and is marginally mobile in very limited acidic media. Cr⁶⁺ has been reported to be toxic for plants and animals whiles Cr³⁺ in certain doses has a detrimental effect for the biochemical activity of soils (Shrivastava, Upreti, Seth, & Chaturvedi, 2002). There are Cr-related diseases which can be evaluated for geographic area relative to the calculated baseline value of Cr.

Ni calculated background value in the study ranged from 4.4 mg/kg- 17.2 mg/kg and a calculated baseline value of 8.7 mg/kg. A total of 1992 samples (i.e., representing 69.5% of the 2868 samples collected) belong to the Ni background class. As seen in the study, the distribution of Ni is similar to Fe and Co. Ni occurrence in the area may be associated with the underlying sulphide-associated rocks (Figure 2).

Another known toxic element Pb reported a background value ranging from 4.5 to 9.9 mg/kg and a baseline of 6.7 mg/kg. The data set shows that 82.7% of the samples fit the background class of Pb. The low Pb values obtained in the study could be the primary Pb in the underlying rocks hence source is geogenic. Whilst the high Pb assays recorded in the area may be associated with the chalcophile element corridors in the underlying rocks or from human-induced activities. Some contributions might come from the Light Industries where vehicle fumes, oil spillages and vehicle batteries can introduce Pb into the environment. Elsewhere, Pb concentration levels exceeding 100 mg/kg was linked to industrial pollution in countries such as Japan and Great Britain (Bellis, Satake, Noda, Nishimura, & McLeod, 2002; Markus & McBratney, 2001)

A comparison of the baseline values calculated in this study with the average crustal values and similar works conducted shows that the baseline value of Co is relatively low (Table 5 and Table 6) . The baseline value of Cr is comparable with values calculated for Taihu in China and slightly lower than the average crustal value for Cr. The calculated baseline for Ni in this study is relatively low compared with the average crustal value and other values calculated for Ni as shown in Table 4. As baseline 131.2 mg/kg falls in the range estimated by global accepted value in soils (10 mg/kg – 15 mg/kg) but outside the Italian reference value for As in soils (20 mg/kg). Food and most drinking water are exported so this baseline for As may not lead to any environmental health problems. Adapting this value will result in As-related diseases such as heart diseases (hypertension-related cardiovascular diseases) cancer, stroke (cerebrovascular diseases), chronic lower respiratory diseases, and diabetes Järup (2003), Tchounwou, Yedjou, Patlolla, & Sutton, 2012). The baseline value calculated for Pb (6.70 mg/kg) in this study was lower than the average crustal value of 12.5 mg/kg and much lower than the Italian reference value for Pb in soils peg at 100 mg/kg.

Conclusion

Background and baseline values have been calculated for As, Cr, Pb, Co and Ni. The background values were 6.41 mg/kg for As, 52.55 mg/kg for Cr, 9.89 mg/kg for Pb, 10.21 mg/kg for Co and 17.18 mg/kg for Ni. The calculated baseline values for As, Cr, Pb, Co and Ni were 13.2 mg/kg, 80.7 mg/kg, 6.7 mg/kg, 6.4 mg/kg and 8.7 mg/kg, respectively. The inconsistencies in calculated background and baseline values relative to the worldwide values further underscore the need to conduct environmentally health-related studies. The authors conclude that local background and baseline value application to establish hotspots and cold-spots of disease-causing elements as a vital step in environmental health management.

Acknowledgments

The appraised soil geochemical data were obtained from European Development Fund (EDF) Project 8-ACP-GH-027 carried out in Ghana between the government of Ghana and the European Union. The authors greatly appreciate the funds released for the project. The support in kind from members of the International Medical Geology Association (IMGA), Ghana Chapter served as a source of motivation to the authors to complete this investigation. We humbly appreciate your kind contributions and hope you will encourage others who want to address environmental health issues via medical geology research. Our indebted thanks are extended to all the reviewers for their helpful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.