Advertisement

Assessment of Heavy Metal Pollution in the Sediments of the River Pra and Its Tributaries

  • Albert Ebo Duncan
  • Nanne de Vries
  • Kwabena Biritwum Nyarko
Open Access
Article
  • 103 Downloads

Abstract

An investigative study was conducted to determine the heavy metal pollution in the sediment in the Pra Basin of Ghana from 27 sampling points during the dry and wet seasons using the geo-accumulation index (Igeo), enrichment factor (EF), and pollution load index (PLI). Sediments were acid digested and analyzed for the following selected metals: arsenic (As), lead (Pb), cadmium (Cd), zinc (Zn), manganese (Mn), total chromium (Cr), nickel (Ni), and iron (Fe) using the dual atomizer and hydride generator atomic absorption spectrophotometer (model ASC-7000 No A309654, Shimadzu, Japan). The metal concentrations (mg kg−1) in the sediments were as follows: As (0.175) < Cd (3.206) < Ni (79.927) < Zn (118.323) < Cr (216.708) < Mn (234.742) < Pb (335.381) < Fe (1354.513) in the dry season and As (0.002) < Cd (7.279) < Ni (72.663) < Zn (35.622) < Pb (135.863) < Cr (167.604) < Mn (183.904) < Fe (1138.551) for the wet season. The EF which is an indication of whether metal concentrations are due to anthropogenic activities shows enrichment at all site for the metals Cr, Pb, and Cd in the wet seasons. However, only 4 out of the 27 sites showed Ni enrichment in the wet season. Contrary to the wet season, only Pb and Cr recorded enrichment at all sites during the dry season. Fifteen out of the 27 sites recorded Cd enrichment and 24 out of the 27 sites recorded Ni enriched during the dry season. None of the sites were enriched with Fe, As, Zn, and Mn in either the dry or wet seasons. For both dry and wet seasons, the pollution load index for all the sites except one was at the background levels which is a sign of non-deterioration of the sites studied. In the wet season, the calculated Igeo reveals that the study area is not contaminated with respect to As, Zn, Fe, and Mn; uncontaminated to moderately contaminated with Cd; moderately contaminated with Cr; uncontaminated to moderately to heavily contaminated with Ni; and moderately to heavily contaminated with Pb. The dry season Igeo results reveal non-contamination of the study area with respect to As, Fe, and Mn; uncontaminated to moderately contaminated with Zn; moderately contaminated with Cr; uncontaminated to heavily contaminated with Cd; uncontaminated to extremely contaminated with Ni; and moderately to extremely contaminated with Pb. The high levels of Cd, Pb, and Cr in all the sites are due to unregulated illegal mining activities occurring in and around the study area. It is hoped that this study will prompt the basin management board to improve their management strategies in controlling unregulated illegal mining in the basin sediments.

Keywords

Pollution Heavy metal Sediments Illegal mining Pra River Ghana 

1 Introduction

Accumulation of heavy metals in the sediments of rivers which are exposed to mining and industrial waste is a common phenomenon in most developing countries (Islam et al. 2015). Such river sediments have become sinks for heavy metals, just like wetlands. The sediments sometimes act as carriers and sources for the heavy metals in the environment (Haiyan et al. 2013). The study of heavy metals in river sediments is very important because sediments serve as habitat for many benthic organisms like the mudfish. Unfortunately most of the time, the rivers are monitored without paying any attention to the sediments which are in constant interaction with the river. Studies have shown that rivers have been severely contaminated with heavy metals due to historic and modern mining and industrial operations (Miller et al. 2004). Heavy metals in river sediments enter through different pathways, either from point or non-point sources (Shazili et al. 2006). Examples of point sources could be the discharges of industrial waste such as metal mine wastes through pipes or drains, into rivers. Non-point sources such as silt-laden runoff from excavated lands and leachate from landfills also contribute to the levels of heavy metals usually discharged into water resources. The fate of heavy metals in an aquatic environment is affected by processes such as precipitation, sorption, and dissolution (Abdel-Ghani and Elchaghaby 2007). These processes are also affected by factors such as pH, temperature, dissolve oxygen concentration, and the disturbance of the water (Atkinson et al. 2007; Simpson et al. 2004). At higher pH, heavy metals precipitate and get adsorbed onto sediment surfaces. Metals are also released more easily into the water at lower pH and higher temperatures. When the dissolved oxygen concentration is low, i.e., less than 7 mg/L, heavy metals especially those bound to organic matter sediments are released into the overlying water and vice versa (Haiyan et al. 2013). A study by Atkinson et al. (2007) shows that physical disturbance of water releases metals more rapidly into water than biological disturbance. The study of heavy metals in sediments can serve as a guide in predicting the extent of pollution of the overlying water under different environmental conditions.

The present study assesses the heavy metal pollution level in the main Pra River and two of its tributaries in the Pra Basin of Ghana. The study area is the largest among the three southwestern river systems in Ghana and occupies an area of 23,000 km2 which is about 9.64% of the area of Ghana. Sediment pollution by heavy metals in the study area is now graduating into a major problem with the increased illegal mining activities in and around the rivers in the basin which are increasing the turbidity and the heavy metal levels, making the rivers physically unstable and chemically and biologically toxic. The present state of the rivers poses serious problems to the environment and the health of those villages which still depend on the rivers for cooking and bathing during water crises. To date, no detailed scientific analysis of the river sediments has been conducted. The aim of this study is to assess the concentrations of lead (Pb), cadmium (Cd), arsenic (As), chromium (Cr), iron (Fe), manganese (Mn), zinc (Zn), and nickel (Ni) using the enrichment factor (EF), pollution load index (PLI), and the geo-accumulation index (Igeo). Geo-accumulation index determines the metal levels of contamination or accumulation with reference to background levels of the same element in the environment. EF which is also an indication of enrichment of a selected metal with reference to a background metal such as iron complements the Igeo by indicating the source of enrichment as either natural or anthropogenic. The pollution load index assesses the cumulative pollution effect of the metals at each site by making reference to the EF of all the metals measured at each site.

2 Materials and Methods

This study was conducted in the Pra Basin of Ghana. The hydrogeology of the Pra Basin is dominated by aquifers of the crystalline basement rocks and the Birimian Province. Sediment texture from the sampling site spans from sand, sandy loam, loamy sand, silty clay loam, and sandy clay loam. The Basin is located between latitudes 5° N and 7° 30′ N, and longitudes 2° 30′ W, and 0° 30′ W, in south-central Ghana. It is the largest among the three southwestern basins in Ghana (Ankobra, Tano, and Pra) and covers an area of 238,540 km2. The basin enjoys sub-equatorial wet climate with two raining seasons (May–June and September–November). The relative humidity in the basin is around 70 to 80% throughout the year. The annual rainfall range is between 1300 and 1900 mm with an annual mean value of 1500 mm. The only natural lake in Ghana, Bosomtwe, which is a major tourist attraction is located in the basin. The land area is largely dominated by agriculture (60%) with the remaining 40% being covered by human settlement (10%) and forest (30%). Towns like Twifo Praso and Kade in the basin are known for their large palm plantations. Gold mining both regulated and unregulated is the most prominent and highly patronized job in the basin. Figure 1 presents the study area map. The sampling order of the sites and their names from upstream to downstream in Fig. 1 are presented in Table 1. All sampling sites were either within or around an illegal mining site. A control site which has no such activities going on was also selected. From a total of 27 sampling points, 108 sediment samples were collected from January to April 2017 for the dry season and 108 from May to August 2017 for the wet season making a total of 216. The sediments were sampled from the riverbank by manual dredging using plastic scoop into polyethylene bags and air dried at room temperature and sieved through a 2-mm sieve for further analysis.
Fig. 1

Map of Pra Basin

Table 1

Mean metal concentrations (mg kg−1) for dry season

Sites

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Ni

Ni

Cr

Cr

Mn

Mn

Fe

Fe

Cd

Cd

Pb

Pb

Zn

Zn

As

As

Lake(LAK)

58.185

85.850

235.055

166.232

44.378

162.550

1665.793

1326.040

0.140

7.945

111.903

147.370

0.000

1.675

0.179

0.000

Oda (OD1)

150.263

95.435

228.178

168.815

214.255

14.993

1711.138

1704.055

8.903

8.204

84.078

133.818

93.825

31.210

0.241

0.003

Oda (OD2)

91.678

89.113

241.080

186.880

37.758

61.230

1557.973

1460.138

0.218

8.775

776.205

148.205

20.755

27.908

0.081

0.003

Oda (OD3)

115.195

69.510

241.940

202.365

96.435

111.345

1635.960

1319.765

1.503

7.803

785.000

150.710

34.423

11.020

0.189

0.001

Oda (OD4)

85.255

68.680

192.903

185.162

107.865

45.355

1272.795

1024.010

3.048

8.923

81.390

157.183

9.645

89.363

0.239

0.003

Praso Town (PT)

54.628

23.608

223.875

135.265

3176.048

1714.375

1508.510

1279.945

0.620

4.928

384.665

111.875

89.865

6.995

0.210

0.002

Praso Subinso (PS)

91.778

52.060

251.403

150.747

356.560

593.503

1643.730

1079.655

0.080

5.270

164.205

113.965

229.058

53.030

0.127

0.001

Twifo Agona (TAG)

60.853

20.740

211.830

146.445

61.085

61.378

1115.605

643.125

3.495

5.613

64.458

115.633

29.078

14.365

0.134

0.002

Twifo Kotokyire (TK)

65.498

29.038

215.270

146.447

80.315

145.343

1474.273

1282.910

2.548

7.773

72.620

116.050

23.463

32.245

0.074

0.002

Assin Awisam (TAW)

51.563

104.918

215.273

174.84

223.750

14.748

1126.935

935.755

2.813

7.878

316.583

117.928

15.348

49.095

0.319

0.002

Assin asaman (AAS)

79.575

73.190

206.670

146.445

84.373

160.345

1307.158

1263.715

1.285

6.895

335.865

119.180

23.040

19.943

0.272

0.002

Assin Nyardom (ANY)

55.715

32.795

214.410

148.165

49.415

10.303

1510.693

1135.858

0.500

7.660

282.328

123.358

52.908

5.755

0.140

0.003

Dunkwa Town (DT)

21.035

17.020

254.845

183.437

7.093

89.653

1252.325

1298.545

3.498

7.040

415.688

127.743

89.730

17.980

0.194

0.003

Dunkwa upstream (DU)

53.540

53.838

242.800

186.022

136.443

53.608

1434.985

734.408

2.835

7.863

152.730

127.743

94.123

11.600

0.181

0.001

Dunkwa Breman (DBR)

87.235

150.365

244.523

178.275

94.130

172.348

1147.093

1232.538

2.348

6.865

394.550

133.173

41.588

118.385

0.061

0.002

Dunkwa downstream (DDO)

104.425

84.073

164.515

175.697

91.923

20.995

1333.933

1345.260

1.490

7.373

64.730

119.865

548.103

25.603

0.125

0.003

Dunkwa Ankaase (DAN)

52.353

123.000

276.353

206.667

161.578

265.855

1673.068

1395.943

2.050

7.305

377.615

135.470

48.550

68.585

0.267

0.002

Dunkwa Kojokrom (DKO)

76.663

58.878

243.660

176.557

7.900

12.138

1480.423

1312.743

2.755

8.188

368.655

138.810

648.300

0.383

0.152

0.002

Appiah Nkwanta (ANK)

73.500

126.950

260.005

206.667

195.320

313.715

1413.263

1445.250

0.635

8.193

406.140

138.393

47.575

95.018

0.139

0.002

Dunkwa Edwuma (DED)

85.058

77.450

243.660

196.345

139.783

117.155

1578.255

1330.470

0.545

8.348

381.775

139.225

16.020

40.028

0.243

0.003

Dunkwa Akropong (DAK)

107.983

97.213

222.153

173.975

135.448

24.075

561.423

621.405

2.803

7.428

258.885

141.105

23.043

1.418

0.136

0.001

Dunkwa Kyekyere (DKY)

24.593

103.765

229.035

178.277

4.153

40.895

1482.793

511.523

0.893

8.558

658.620

142.988

48.428

60.513

0.304

0.002

Anhwia Nkwanta (AAN)

71.333

84.468

252.973

174.837

171.905

139.445

1613.560

1578.720

7.083

4.905

713.723

145.890

108.503

21.068

0.077

0.001

Beposo (BEP)

155.668

72.118

125.923

141.282

179.973

61.525

1085.095

815.428

8.743

6.673

1234.093

374.675

50.665

37.830

0.199

0.001

Daboase (DAB)

39.908

53.740

144.535

136.122

165.708

172.103

1353.083

1030.895

7.815

5.973

34.870

68.185

78.643

17.873

0.136

0.001

Atwereboanda (ATW)

129.630

47.415

188.698

136.985

28.228

48.820

730.488

600.035

9.283

7.078

56.060

43.878

452.268

11.493

0.244

0.001

Shama (SHA)

114.938

66.683

134.130

116.335

286.210

337.623

901.500

1032.758

8.648

7.078

77.865

54.898

277.778

91.418

0.071

0.000

AVG

79.928

72.663

218.729

167.603

234.742

183.904

1354.513

1138.552

3.206

7.279

335.381

132.864

118.323

35.622

0.175

0.002

STD

34.074

33.456

38.537

24.162

594.105

332.209

292.177

315.643

3.038

1.119

289.153

 

169.540

32.440

0.074

0.001

WHO

25

25

50

50

600

600

28,000

28,000

1.1

1.1

23

23

88

88

7

7

Italicized figures are above WHO standard

2.1 Chemicals and Sample Digestion

Deionized water supplied by University of Cape Coast Technology Village was used in all the analyses. All standard solutions used were of the highest purity supplied by MES Equipment Limited, Ghana. The nitric and hydrochloric acids used for the digestion were all of the analytical grades and supplied by MES Equipment. The sieved sediment was further ground with mortar and pestle until fine particles (< 200 μm) were obtained (Ismaeel and Kusag 2015). About 2 g of the ground sediment was taken in a 100-mL beaker and 15 mL of concentrated HNO3 was added. The content was heated at 130 °C for 5 h until 2–3 mL remained in the beaker. The digested sediment was then passed through Whatman no. 41 filter paper and washed with a 0.1 M HNO3 solution and made to 100 mL volume using deionized water (Ali et al. 2016).

2.2 Analytical Technique and Accuracy Check

The heavy metal determination was conducted using a dual atomizer and hydride generator atomic absorption spectrophotometer (model ASC-7000 No A309654, Shimadzu, Japan). All the samples were analyzed for arsenic (As), chromium (Cr), cadmium (Cd), lead (Pb), manganese (Mn), nickel (Ni), zinc (Zn), and iron (Fe). All reagents used were of the analytical grade from MES Equipment, Ghana. Ultrapure metal free deionized water was used for all analyses. All glass and plastic wares were cleaned by soaking them in warm 5% (V/V) aqueous nitric acid for 6–7 h and rinsed with ultrapure deionized water. The standard for the ASS calibration was prepared by diluting standard (1000 ppm) supplied by MES Equipment Limited, Ghana. All measured results were converted from milligram per liter and microgram per liter to milligram per kilogram. Matrix Spike recovery was in the range of 85–100%. The performance of the AAS was checked daily to ensure that the instrument is working according to the specifications.

2.3 Assessment of Heavy Metal Pollution

The choice of background values plays important roles in geochemical data interpretation (Ali et al. 2016). The background value is the natural content of a substance in the soil which is completely dependent on the composition and mineralogical characteristics of the parent/source geological material (Maurizio 2016). The contribution of human activities to the levels of heavy metals in sediments and their pollution can be estimated using Igeo, EF, and PLI.

2.3.1 Geo-Accumulation Index

This index was first proposed for metal concentration determination in 2-μm fraction and later developed to the present form (Müller 1979). The method is used to determine the levels of contamination or accumulation of metals in soil. The formula is mathematically expressed as:
$$ Igeo=\mathit{\log}2\frac{\left[ Cn\right]}{1.5 Bn} $$
(1)
Where Cn is the measured concentration of metal n in the sediment, Bn is the geochemical background value of element n in the background sample (Yu et al. 2011), and 1.5 is the background matrix correction factor due to lithogenic effects. Müller (1979) gave seven classes for interpreting the geo-accumulation index which ranged as follows: Igeo ≤ 0, uncontaminated; 0 < Igeo < 1, uncontaminated to moderately contaminated; 1 < Igeo < 2, moderately contaminated; 2 < Igeo < 3, moderately to heavily contaminated; 3 < Igeo < 4, heavily contaminated; 4 < Igeo < 5, heavily to extremely contaminated; and Igeo ≥ 5, extremely contaminated.

2.3.2 Enrichment Factor and Pollution Load Index

The enrichment factor as proposed by Zoller (1974) is given by:
$$ EF=\frac{\left[ Ai\right]}{\left[ Ao\right]}/\frac{\left[ Bi\right]}{\left[ Bo\right]} $$
(2)
[Ai] and [Bi] are the concentrations of elements A and B at sampling station i; [Ao] and [Bo] are the background concentrations of elements A and B. Values estimated for EF from Eq. (1) provide the pollution state of the sediment. Values of 0.5 ≤ EF ≤ 1.5 are an indication that the metal concentration is a natural weathering process (Zhang and Liu 2002). A value above 1.5 indicates the influence of anthropogenic activity (Klerks and Levinton 1989; Taylor et al. 2010; Zhang and Liu 2002). There are five classes of contamination with reference to EF: EF < 2, depletion to minimal enrichment; EF = 2–5, moderate enrichment; EF = 5–20, significant enrichment; EF = 20–40, very high enrichment; EF > 40, extremely high enrichment. The pollution load index is defined as the nth root of the multiplication of the EF of metals involved
$$ \mathrm{PLI}={\left({\mathrm{EF}}_1\times {\mathrm{EF}}_2\times {\mathrm{EF}}_3\times {\mathrm{EF}}_4\times {\mathrm{EF}}_{\mathrm{n}}\right)}^{1/n} $$

According to Tomilson (1980), a PLI of 0 indicates excellence; a value of 1 indicates baseline levels of the concerned metals, whereas values above 1 are signs of progressive deterioration. Whereas EF gives the individual effects of the metals at a site, the PLI gives the overall effect of all metals studied at a site.

3 Results and Discussion

The mean heavy metal concentrations for sediments in the study sites during the dry and wet seasons are presented in Table 1. Praso Town (PT) recorded the highest average metal concentration during the period under study. Dunkwa Akropong (DAK) and Atweneboanda (ATW) recorded the lowest metal concentrations during the dry and wet seasons respectively (Tables 1). The observed high metal concentrations in PT can be attributed to the uncontrolled and scattered illegal mining activities occurring in and around the area. The lowest metal concentration found in ATW river sediments may be due to dilution in the area as the town is the last point after which the river joins the sea. The river is a major source of water for domestic activities in ATW; the frequent visitation of the river banks and domestic activities such as washing and playing along the banks of the river as compared to other areas sampled may have contributed to the washing away of the top sediments and thereby reduce accumulation of metals. Generally, there is a significant difference in the dry season metal concentration (M = 293.12, SE = 18.31) and wet season metal concentration (M = 217.31, SE = 11.93); the difference in concentration in the dry season may be attributed to the intensification of illegal mining activities which occurred as a result of a government order to halt illegal mining after the dry season of 2017. Excessive washing of the surface soil during the wet season could also account for the lower concentrations in the wet season.

The iron (Fe) and arsenic (As) concentrations in the wet and dry seasons were lower than WHO standards. Regarding manganese (Mn), apart from site PT which recorded concentrations of about 5 and 3 times the background levels for both dry and wet seasons, all other sites recorded values or concentrations below the background levels. The high values of manganese recorded at PT may be due to the sloppy nature of the land which turns to experience high level of siltation from turbid water flowing from nearby illegal mining sites. Zinc (Zn) concentrations in sediments were above the background values for 9 out of 27 of the sites in the dry seasons and only 3 out of 27 of the sites in the wet season. In the case of nickel (Ni), only 2 sites recorded values below the background values (Table 1). Concerning chromium (Cr), all the sites recorded values above the background levels. Cr values as high as 5 and 4 times the background values were recorded for the dry and wet seasons (Table 1). Cadmium (Cd) recorded concentrations as high as 8 times the background values. Unlike the wet season, 8 out of the 27 sites in the dry season recorded Cd values below the background values. Lead (Pb) is the only metal whose concentration is above the background level for all the sites in both dry and wet seasons. The identified metals (Ni, Cr, Cd, Pb) are major components of the soil from which the gold is mined. Furthermore, the metal mercury, which is usually part of the soil sediment because of its use in the gold extraction, was absent. The absence of mercury in the soil is expected because miners now carry out the extraction of the gold far away from the mining location due to the threat posed by arm robbers. The most striking result to emerge from the data is the abnormally high value of Pb concentration at BEP during the dry season. The measured Pb concentration (Table 1) is about 54 times the background value. Metal concentration exceeding the background level is an indication that their presence in the sediments is due to human activities. The BEP environment is highly dominated by illegal mining activities. Exposure to high level of illegal mining activities especially through the use of sophisticated machines recorded the high metal concentrations or values (Table 1). The mean concentration of metals exceeding background level in the wet season is in the order Cr > Pb > Ni > Cd > Zn and in the dry season as Pb > Cr > Ni > Cd.

3.1 Sediment Pollution Assessment

The calculated EF, PLI, and the background concentrations of metals in freshwater ecosystems are presented in Table 2. The EF ranged between 0 and 53.656 during the dry season and 0.003–45 during the wet season which indicates that the measured concentrations of four metals (Mn, Fe, Zn, and As) out of the eight in the studied area in both seasons were due to natural weathering process (0.5 ≤ EF ≤ 1.5), whereas the rest (Pb, Cd, Cr, and Ni) were due to anthropogenic activities (EF > 2). All the sites studied showed depletion to minimal enrichment for the metals Mn, Fe, Zn, and As for the dry and wet seasons. All sites showed moderate enrichment (EF = 2–5) for Cr in both dry and wet seasons. Five sites (TAG, TK, ANY, DT, and ATW) out of the 27 recorded depletion to minimal enrichment for Ni in the wet season with 21 out of the 27 sites recording moderate enrichment and only 1 site (PT) recording extremely high enrichment. Unlike the wet season, only 3 sites (DT, DKY, and DAB) out of the 27 recorded depletion to minimal Ni enrichment for the dry season, the remaining 24 sites recorded values within the range of moderate enrichment to significant enrichment (Table 2). However, there is no significant statistical difference in the dry season nickel enrichment (M = 3.19, SE = 0.26) and wet season nickel enrichment (M = 4.53, SE = 1.57) in the basin. In the case of Pb, there is a significant difference in the dry season enrichment (M = 14.58, SE = 2.41) and wet season enrichment (M = 5.77, SE = 0.66). Four out of the 27 sites recorded moderate Pb enrichment whereas 22 recorded significant enrichment with only 1 site Atweneboanda (ATW) recording depletion to minimum enrichment in the wet season. However, in the dry season, 8 sites recorded moderate Pb enrichment, 13 sites recorded significant enrichment, 4 recorded very high enrichment, and 1 recorded extremely high enrichment. In the dry season, Cd recorded depletion to minimal enrichment in 12 sites, recorded moderate enrichment in 6 sites, and recorded significant enrichment in 9 sites. However, it recorded moderate to significant enrichment for all the sites in the wet season (Table 2). The seasonal influence on Cd enrichment in the sediment is very significant: dry season Cd enrichment (M = 2.76, SE = 0.53) and wet season enrichment (M = 6.61, SE = 0.19) (Table 2). Irrespective of the high enrichment factors recorded for some sites, BEP was the only site polluted (PLI > 1) (Table 2) in both seasons. LAK which is upstream and served as the control site is the only sampling point which recorded excellent value for pollution (PLI = 0) in the dry season (Table 2). Though LAK did not record 0 in the wet season, the value of 0.374 was still within the baseline level. The 0.374 is expected because in the wet season, the lake receives a lot of runoff with high silt content from the surrounding mountains without any means of exiting such inflows. On the contrary, the calculated PLI for the remaining 26 sites though within the baseline level is due to unregulated illegal mining in the area.
Table 2

Enrichment factor (EF) and pollution load index (PLI) for dry and wet season

Sites

Enrichment factor (EF) and pollution load index (PLI) for dry and wet seasons

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Ni

Ni

Cr

Cr

Mn

Mn

Fe

Fe

Cd

Cd

Pb

Pb

Zn

Zn

As

As

PLI

PLI

LAK

2.327

3.434

4.609

3.325

0.074

0.271

0.059

0.047

0.127

7.223

4.865

6.407

0.000

0.019

0.026

0.003

0.000

0.374

OD1

6.011

3.817

4.474

3.376

0.357

0.025

0.061

0.061

8.093

7.458

3.656

5.818

1.066

0.355

0.034

0.044

0.944

0.583

OD2

3.667

3.565

4.727

3.738

0.063

0.102

0.056

0.052

0.198

7.977

33.748

6.444

0.236

0.317

0.012

0.042

0.429

0.685

OD3

4.608

2.78

4.744

4.047

0.161

0.186

0.058

0.047

1.366

7.093

34.130

6.553

0.391

0.125

0.027

0.02

0.750

0.572

OD4

3.410

2.747

3.782

3.703

0.180

0.076

0.045

0.037

2.770

8.111

3.539

6.834

0.110

1.015

0.034

0.041

0.499

0.712

PT

2.185

45

4.390

2.705

5.293

2.857

0.054

0.046

0.564

4.48

16.725

4.864

1.021

0.079

0.030

0.028

0.971

0.968

PS

3.671

2.082

4.929

3.015

0.594

0.989

0.059

0.039

0.073

4.791

7.139

4.955

2.603

0.603

0.018

0.014

0.594

0.685

TAG

2.434

0.83

4.154

2.929

0.102

0.102

0.040

0.023

3.177

5.102

2.803

5.028

0.330

0.163

0.019

0.024

0.468

0.393

TK

2.620

1.162

4.221

2.929

0.134

0.242

0.053

0.046

2.316

7.066

3.157

5.046

0.267

0.366

0.011

0.027

0.450

0.583

TAW

2.063

4.197

4.221

3.497

0.373

0.025

0.040

0.033

2.557

7.161

13.764

5.127

0.174

0.558

0.046

0.023

0.661

0.524

AAS

3.183

2.928

4.052

2.929

0.141

0.267

0.047

0.045

1.168

6.268

14.603

5.182

0.262

0.227

0.039

0.027

0.591

0.615

ANY

2.229

1.312

4.204

2.963

0.082

0.017

0.054

0.041

0.455

6.964

12.275

5.363

0.601

0.065

0.020

0.036

0.479

0.352

DT

0.841

0.681

4.997

3.669

0.012

0.149

0.045

0.046

3.180

6.4

18.073

5.554

1.020

0.204

0.028

0.036

0.497

0.509

DU

2.142

2.154

4.761

3.72

0.227

0.089

0.051

0.026

2.577

7.148

6.640

5.554

1.070

0.132

0.026

0.02

0.698

0.458

DBR

3.489

6.015

4.795

3.566

0.157

0.287

0.041

0.044

2.134

6.241

17.154

5.79

0.473

1.345

0.009

0.023

0.600

0.861

DDO

4.177

3.363

3.226

3.514

0.153

0.035

0.048

0.048

1.355

6.702

2.814

5.212

6.228

0.291

0.018

0.042

0.673

0.551

DAN

2.094

4.92

5.419

4.133

0.269

0.443

0.060

0.05

1.864

6.641

16.418

5.89

0.552

0.779

0.038

0.032

0.765

0.902

DKO

3.067

2.355

4.778

3.531

0.013

0.02

0.053

0.047

2.505

7.443

16.028

6.035

7.367

0.004

0.022

0.031

0.712

0.285

ANK

2.940

5.078

5.098

4.133

0.326

0.523

0.050

0.052

0.577

7.448

17.658

6.017

0.541

1.08

0.020

0.023

0.636

0.945

DED

3.402

3.098

4.778

3.927

0.233

0.195

0.056

0.048

0.495

7.589

16.599

6.053

0.182

0.455

0.035

0.037

0.570

0.738

DAK

4.319

3.889

4.356

3.48

0.226

0.04

0.020

0.022

2.548

6.752

11.256

6.135

0.262

0.016

0.019

0.021

0.576

0.337

DKY

0.984

4.151

4.491

3.566

0.007

0.068

0.053

0.018

0.811

7.78

28.636

6.217

0.550

0.688

0.043

0.024

0.416

0.589

AAN

2.853

3.379

4.960

3.497

0.287

0.232

0.058

0.056

6.439

4.459

31.031

6.343

1.233

0.239

0.011

0.02

0.945

0.616

BEP

6.227

2.885

2.469

2.826

0.300

0.103

0.039

0.029

7.948

6.066

53.656

16.29

0.576

0.43

0.028

0.016

1.027

0.599

DAB

1.596

2.15

2.834

2.722

0.276

0.287

0.048

0.037

7.105

5.43

1.516

2.965

0.894

0.203

0.019

0.016

0.569

0.489

ATW

5.185

1.897

3.700

2.74

0.047

0.081

0.026

0.021

8.439

6.434

2.437

1.908

5.139

0.131

0.035

0.013

0.737

0.341

SHA

4.598

2.667

2.630

2.327

0.477

0.563

0.032

0.037

7.861

6.434

3.385

2.387

3.157

1.039

0.010

0.007

0.792

0.589

Italicized figures are above background values

The calculated geo-accumulation indexes for the four (Pb, Cd, Cr, and Ni) enriching metals during the two seasons are presented in Table 3. In either the dry or wet season, all the non-enriching metals (Mn, Fe, Zn, and As) did not contaminate (Igeo < 0) any of the sites studied except Zn which recorded a value of moderate contamination (1 < Igeo < 2) at a site during the dry season. The result of the geo-accumulation index calculation for both seasons (Table 3) shows that Cr and Cd values for all the 27 sites were within the uncontaminated to the moderately contamination class (0 ≥ Igeo < 2). Only 1 out of the 27 sites was moderately to heavily contaminated (Igeo < 3) with Pb in the wet season whereas the rest recorded values within the uncontaminated to moderately contaminated range (0 < Igeo < 2). Out of the 27 sites, only 2 were moderately to heavily contaminated with Ni, whereas the rest (25) were uncontaminated to moderately contaminated in the wet season (Table 3). The result (Table 3) shows site DAB as a drinking water intake point recording the highest contamination for Ni (11.140) and Pb (64.977) in the dry season. These high values could be attributed to the low flow rate at the time which aided the precipitation of these two metals.
Table 3

Dry and wet season geo-accumulation index (Igeo)

Sites

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Dry

Wet

Ni

Ni

Cr

Cr

Mn

Mn

Fe

Fe

Cd

Cd

Pb

Pb

Zn

Zn

As

As

LAK

1.578

0.837

0.607

0.871

− 0.23

− 0.405

− 0.215

− 0.201

− 0.281

0.441

0.589

0.477

0

− 0.159

− 0.17

− 0.113

OD1

0.499

0.742

0.623

0.854

− 0.483

− 0.169

− 0.217

− 0.216

0.411

0.432

0.778

0.511

− 2.03048

− 0.481

− 0.184

− 0.197

OD2

0.775

0.801

0.594

0.759

− 0.219

− 0.258

− 0.21

− 0.206

− 0.342

0.415

0.223

0.476

− 0.37467

− 0.446

− 0.142

− 0.193

OD3

0.618

1.123

0.592

0.698

− 0.31

− 0.332

− 0.214

− 0.2

− 7.402

0.446

0.222

0.47

− 0.5157

− 0.279

− 0.173

− 0.16

OD4

0.844

1.145

0.734

0.767

− 0.327

− 0.232

− 0.198

− 0.187

1.13

0.411

0.808

0.457

− 0.26493

− 1.777

− 0.183

− 0.192

PT

1.843

− 1.498

0.634

1.175

0.55

1.076

− 0.208

− 0.199

− 0.708

0.634

0.287

0.589

− 1.80275

− 0.236

− 0.177

− 0.174

PS

0.774

2.113

0.573

0.993

− 0.749

− 1.665

− 0.214

− 0.189

− 0.229

0.597

0.444

0.58

1.25759

− 0.76

− 0.157

− 0.149

TAG

1.432

− 1.17

0.668

1.036

− 0.258

− 0.258

− 0.191

− 0.166

0.924

0.566

1.109

0.573

− 0.45818

− 0.313

− 0.159

− 0.168

TK

1.243

− 2.71

0.657

1.036

− 0.287

− 0.38

− 0.207

− 0.199

1.596

0.447

0.931

0.571

− 0.40127

− 0.492

− 0.14

− 0.172

TAW

2.177

0.674

0.657

0.819

− 0.498

− 0.169

− 0.192

− 0.182

1.3

0.443

0.313

0.564

− 0.32212

− 0.701

− 0.198

− 0.166

AAS

0.921

1.037

0.684

1.036

− 0.293

− 0.402

− 0.2

− 0.198

− 2.772

0.485

0.305

0.559

− 0.39709

− 0.367

− 0.19

− 0.172

ANY

1.751

− 5.17

0.66

1.018

− 0.239

− 0.155

− 0.208

− 0.192

− 0.581

0.451

0.33

0.544

− 0.75815

− 0.221

− 0.161

− 0.186

DT

− 1.199

− 0.877

0.567

0.775

− 0.143

− 0.301

− 0.197

− 0.199

0.923

0.478

0.278

0.53

− 1.79573

− 0.348

− 0.174

− 0.186

DU

1.947

1.917

0.59

0.763

− 0.367

− 0.246

− 0.205

− 0.171

1.281

0.444

0.466

0.53

− 2.04949

− 0.285

− 0.171

− 0.16

DBR

0.821

0.499

0.587

0.801

− 0.307

− 0.419

− 0.193

− 0.196

1.966

0.486

0.284

0.513

− 0.60013

− 6.367

− 0.135

− 0.167

DDO

0.677

0.859

0.882

0.814

− 0.304

− 0.184

− 0.201

− 0.201

− 6.796

0.463

1.102

0.557

0.486877

− 0.423

− 0.156

− 0.194

DAN

2.077

0.584

0.531

0.684

− 0.404

− 0.568

− 0.215

− 0.204

3.193

0.466

0.29

0.507

− 0.693

− 1.059

− 0.189

− 0.18

DKO

0.969

1.537

0.588

0.81

− 0.146

− 0.161

− 0.207

− 0.2

1.352

0.433

0.293

0.498

0.435517

− 0.119

− 0.164

− 0.179

ANK

1.03

0.568

0.558

0.684

− 0.454

− 0.658

− 0.204

− 0.206

− 0.726

0.433

0.281

0.499

− 0.67923

− 2.108

− 0.16

− 0.165

DED

0.846

0.956

0.588

0.72

− 0.372

− 0.34

− 0.211

− 0.201

− 0.626

0.428

0.288

0.497

− 0.32867

− 0.581

− 0.184

− 0.188

DAK

0.655

0.728

0.638

0.824

− 0.366

− 0.191

− 0.161

− 0.165

1.308

0.461

0.344

0.492

− 0.39711

− 0.153

− 0.16

− 0.163

DKY

− 1.643

0.681

0.621

0.801

− 0.129

− 0.224

− 0.207

− 0.157

− 1.128

0.421

0.235

0.488

− 0.69126

− 0.889

− 0.196

− 0.168

AAN

1.078

0.854

0.57

0.819

− 0.419

− 0.372

− 0.213

− 0.211

0.476

0.636

0.229

0.481

− 3.53595

− 0.378

− 0.141

− 0.161

BEP

0.487

1.06

1.338

1.095

− 0.431

− 0.258

− 0.19

− 0.176

0.416

0.496

0.194

0.291

− 0.72386

− 0.555

− 0.175

− 0.153

DAB

11.14

1.926

1.057

1.163

− 0.41

− 0.419

− 0.202

− 0.187

0.446

0.539

64.977

1.017

− 1.33841

− 0.347

− 0.159

− 0.152

ATW

0.559

2.955

0.751

1.151

− 0.2

− 0.238

− 0.171

− 0.163

0.401

0.476

1.428

2.883

0.562861

− 0.284

− 0.184

− 0.146

SHA

0.619

1.204

1.192

1.579

− 0.605

− 0.707

− 0.18

− 0.187

0.418

0.476

0.852

1.492

0.931626

− 1.887

− 0.139

− 0.13

Italicized figures are above background values

The reason accounting for the difference in contamination across the seasons may be due to the following: (1) the washing away of the top sediments through the heavy downpour and high runoff in the wet season; (2) the low flow rate during the dry season which aids the process of precipitation and accumulation. The results of the geo-accumulation index shows the need for regular monitoring of the metals Ni and Pb and the illegal mining activities especially during the dry season at the sampling site DAB to avoid further accumulation, contamination, and subsequent pollution of such metals at the intake.

4 Conclusions

The river sediment in the Pra Basin is enriched and contaminated with Ni, Cr, Cd, and Pb, which is an indication of the human activities in the basin. Generally, the mean concentrations of the metals were higher in the dry season than the wet season due to the low flow rate during the dry season which aids the process of precipitation and accumulation. It was only Beposo (BEP) which was found to be polluted (PLI < 1). Extreme contamination (Ni and Pb) occurred at Daboase (DAB) which serves as an intake for the water treatment. This is due to the high illegal mining activities occurring in and around DAB and its environs. The result (Table 3) of the study shows the need for general monitoring of illegal mining activities as well as all four metals (Ni, Cr, Cd, and Pb) especially Ni and Pb at DAB. The monitoring will not only address the problem of further accumulation and pollution of these metals but it will also solve public health concerns which arise from the intake of these metals which are carcinogenic. Crop production on these soils is a potential route for these metals to enter the ecosystem, hence the need for monitoring of activities in and around the river sediments, especially during the dry seasons. Finally, monitoring is required to reduce high-level siltation in the river basins which could lead to the drying of such rivers; a situation which threatens some rivers in some parts of Ghana at the moment.

References

  1. Abdel-Ghani, N. T., & Elchaghaby, G. A. (2007). Influence of operating conditions on the removal of Cu, Zn, Cd and Pb ions from wastewater by adsorption. International journal of Environmental Science and Technology, 4, 451–456.CrossRefGoogle Scholar
  2. Ali, M. M., Ali, M. L., Islam, M. D. S., & Rahman, M. D. Z. (2016). Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh. Environmental Nanotechnology, Monitoring & Management, 5, 27–35.CrossRefGoogle Scholar
  3. Atkinson, C. A., Jolley, D. F., & Simpson, S. L. (2007). Effect of overlying water pH, dissolved oxygen, salinity and sediment disturbances on metal release and sequestration from contaminated marine sediments. Chemosphere, 69(9), 1428–1437.CrossRefGoogle Scholar
  4. Haiyan L., Anbang, S., Mingyi, L., Xiaoran, Z. (2013). Effect of pH, temperature, dissolved oxygen, and flow rate of overlying water on heavy metals release from storm sewer sediments. Journal of Chemistry, 2013, 104316.Google Scholar
  5. Islam, M. S., Ahmed, M. K., Raknuzzaman, M., Habibullah-Al-Mamun, M., & Islam, M. K. (2015). Heavy metal pollution in surface water and sediment: a preliminary assessment of an urban river in a developing country. Ecological Indicators, 48, 282–291.CrossRefGoogle Scholar
  6. Ismaeel, W., & Kusag, A. (2015). Enrichment factor and geo-accumulation index for heavy metals at industrial zone in Iraq. IOSR Journal of Applied Geology and Geophysics, 3, 26–32.Google Scholar
  7. Klerks, P. L., & Levinton, J. S. . (1989). Rapid evolution of metal resistance in a benthic oligochaete inhabiting a metal-polluted site. The Biological Bulletin, 176, 135–141.CrossRefGoogle Scholar
  8. Maurizio, B. (2016). The importance of enrichment factor (EF) and geoaccumulation index (Igeo) to evaluate the soil contamination. Geology and Geophysics, 5(1), 1–4.Google Scholar
  9. Miller, J. R., Hudson-Edwards, K. A., Lechler, P. I., Preston, D., & Macklin, M. G. (2004). Heavy metal contamination of water soil and produce within riverine communities of the Rio Pilcomayo Basin. The Science of the Total Environment, 320, 189–209.CrossRefGoogle Scholar
  10. Müller, G. (1979). Schwermetalle in den sedimenten des Rheinse Veranderrungen seitt 1971. Umschau, 79, 778–783.Google Scholar
  11. Shazili, M. N. A., Yunus, K., Ahmad, A. S., Avdullah, N., & Abd Rashid, M. K. (2006). Heavy metal pollution in the Malaysian aquatic environment. Aquatic Ecosystem Health & Management, 9(2), 137–145.CrossRefGoogle Scholar
  12. Simpson, S. L., Angel, B. M., & Jolley, D. F. . (2004). Metal equilibration in laboratory-contaminated (spiked) sediments used for the development of whole-sediment toxicity tests. Chemosphere, 54(5), 597–609.CrossRefGoogle Scholar
  13. Taylor, M. P., Mackay, A. K., Hudson-Edwards, K. A., & Holz, E. . (2010). Soil Cd, Cu, Pb and Zn contaminant around, around Isa city, Queensland, Australia: Potential sources and risks to human health. Applied Geochemistry, 25, 841–855.CrossRefGoogle Scholar
  14. Tomilson, M.J. (1980). Foundation design and construction. 4th Edn. Pitman Publishing Inc., London.Google Scholar
  15. Yu, G. B., Liu, Y., Yu, S., Wu, S. C., Leug, A. O. W., Luo, X. S., Xu, B., Li, H. B., & Wong, M. H. (2011). Inconsistency and comprehensiveness of risk assessments for heavy metals in urban surface sediments. Chemosphere, 85, 1080–1057.CrossRefGoogle Scholar
  16. Zhang, J., & Liu, C. L. . (2002). Riverine composition and estuarine geochemistry of particulate metals in China-weathering features, anthropogenic impact and chemical fluxes. Estuarine, Coastal and Shelf Science, 54, 1051–1070.CrossRefGoogle Scholar
  17. Zoller, W. H., Gladney, E. S., & Duce, R. A. (1974). Atmospheric concentrations and sources of trace metals at the South Pole. Science, 183(4121), 198–200CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Health Promotion, Faculty of Health Medicine and Life SciencesMaastricht UniversityMaastrichtNetherlands
  2. 2.Department of Civil EngineeringKwame Nkrumah University of Science and TechnologyKumasiGhana

Personalised recommendations