Potentially Harmful Trace Elements (Phtes) in the Groundwater of Greater Giyani, Limpopo Province, South Africa: Possible Health Implications

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How to cite this thesis

Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/ M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved from: https://ujdigispace.uj.ac.za (Accessed: Date). Potentially Harmful Trace Elements (PHTEs) in the groundwater of Greater Giyani, Limpopo Province, South Africa: possible health implications

LIVHUWANI PORTIA MUNYANGANE A dissertation submitted in fulfilment of the requirements for the Degree of Master in Geology (MSc Geology) in Medical Geology Department of Geology

Faculty of Science UNIVERSITY OF JOHANNESBURG

Supervisor: Prof. Hassina Mouri Co-Supervisor: Prof. Jan Kramers

2015

ABSTRACT

Most rural communities in developing countries rely on borehole water as their only source of water. Since borehole water comes from underground, it is often considered pure and clean, but this is frequently not the case. Groundwater contains certain amounts of trace elements that may become deleterious to human health.

The objectives of this investigation were to assess the concentration levels of Potential Harmful Trace Elements (PHTEs) and their spatial distribution patterns in borehole water in the Greater Giyani area of Limpopo, South Africa, and the potential human health risks associated with this.

The method of research comprised two phases:

(I) In the first phase, I assessed the occurrence and distribution patterns of PHTEs in the boreholes of the Giyani area. A total of 29 water samples were collected from boreholes (including 15 community boreholes and 14 primary school boreholes) in the Greater Giyani area during the dry season (July/August 2012), and for comparison another 27 samples (including 15 community boreholes and 12 schools boreholes) from the same localities during the wet season (March 2013). The samples were analysed for the trace elements arsenic (As), cadmium (Cd), chromium (Cr), selenium (Se) and lead (Pb) using the Inductively Coupled Plasma Mass-Spectrometry (ICPMS) technique. In order to assess the groundwater quality, PHTEs concentrations were compared with the South African National Standard of Drinking water (SANS 241-1:2011).

(II) In the second phase, I evaluated the geographic variation between PHTEs and associated human health effects. This involved acquisition of data on a total of 100 cancer cases recorded during the period 2011-2014 at Nkhensani Hospital. ArcGIS Spatial analyst tool was used to create thematic maps illustrating spatial distribution of clinical data and arsenic concentrations in boreholes.

The concentrations of As, Cd, Cr, Se and Pb varied from 4.0 to 112.3, 0.2 to 0.9, 10.5 to 69.5, 0.4 to 18.8 and 6.0 to 19.0 µg/l respectively. Cadmium displayed a low concentration in all sampled boreholes, whereas lead was found to be present at limits of detection in 96.6 % of sampled boreholes. Concentrations of As, Cr, and Se, however, exceed the SANS i

permissible limits for drinking water in more than one borehole. Nearly 13 % of boreholes in the area had an arsenic concentration of more than two times the SANS permissible limit for drinking water with two samples containing five times more arsenic than the SANS acceptable limit. Two significant “hotspots” of cancer incidence rates were identified in the north-central part of the study area however; none of the As- contaminated boreholes coincide with the hotspots with highest cancer incidence rates.

I dedicate this work to Mpho, the apple of my eye.

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ACKNOWLEDGEMENT

I am indebted to my supervisor Prof Hassina Mouri and co-supervisor Prof Jan Kramers for their support and guidance, without them this dissertation would not have been possible.

I cannot find words to express my deepest gratitude to Prof Davies who walk with me during the initial stages of this research. Many thanks.

I wish to thank my Manager Mr Nick Baglow for his continuous support.

I owe my deepest gratitude to my lovely sister for her endless love, support and encouragement. You are my rock.

Tebogo Maja, Mr Philemon Mashao and Ponani Mthembi I greatly appreciate your excellent assistance during field work; you were amazing to work with.

Hilde Cronwright, thank you for your technical assistance in the lab and assisting in writing up the laboratory analysis section, many sincere thanks.

I would also want to express my gratitude and deepest appreciation to all who helped me to complete this work but not mentioned here.

I am thankful to the Council for Geoscience for the financial support.

Above all I would like to thank God the almighty for granting me the endurance to successfully complete this work.

Table of Contents page no ABSTRACT ...... i ACKNOWLEDGEMENT ...... iv LIST OF FIGURES ...... viii LIST OF TABLES ...... x CHAPTER 1: INTRODUCTION ...... 1 1.1. Aims and objectives ...... 2 1.2. Research methodology ...... 3 1.2.1. Desktop study ...... 3 1.2.2. Field reconnaissance survey and findings ...... 4 1.2.3. Borehole water sampling ...... 7 1.2.4. Laboratory analysis ...... 9 1.2.5. Clinical health data acquisition ...... 12 1.2.6. Ethical consideration ...... 13 1.2.7. Clinical data analysis ...... 14 CHAPTER 2: LITERATURE REVIEW ...... 18 2.1. Introduction ...... 18 2.2. Trace elements ...... 18 2.2.1. Arsenic (As) ...... 18 2.2.1.1. Mechanism of control of arsenic concentrations and mobility in groundwater ...... 20 2.2.1.2. Arsenic health effects ...... 22 2.2.2. Cadmium (Cd) ...... 23 2.2.2.1. Cadmium health effects ...... 25 2.2.3. Chromium (Cr) ...... 25 2.2.3.1. Chromium health effects ...... 27 2.2.4. Lead (Pb)...... 27 2.2.4.1. Lead health effects ...... 28 2.2.5. Selenium (Se) ...... 28 2.2.5.1. Selenium health effects ...... 30 2.3. Previous studies on groundwater quality in Limpopo province ...... 31 v

2.4. Conclusion ...... 33 CHAPTER 3: CASE STUDY: PHTES IN THE GREATER GIYANI AREA, POSSIBLE RELATION BETWEEN GEOLOGY AND HEALTH ...... 34 3.1. Introduction ...... 34 3.1.1. Climate ...... 35 3.1.2. Population and water supply in the Greater Giyani area ...... 35 3.2. The geology of the Greater Giyani area ...... 37 3.2.1. Goudplaats Gneiss ...... 37 3.2.2. Giyani Greenstone Belt...... 38 3.2.3. Schiel Alkaline Complex...... 39 3.2.4. Shamariri granite ...... 39 3.3. The economic geology of the Greater Giyani area ...... 39 3.4. The hydrogeology of the Greater Giyani area ...... 40 3.4.1. Goudplaats Gneiss ...... 41 3.4.2. Giyani Greenstone Belt...... 42 3.4.3. Shamariri Granite ...... 42 3.4.4. Schiel Alkaline Complex...... 42 3.5. Borehole water results ...... 43 3.5.1. Seasonal variation of trace elements concentrations ...... 46 3.5.2. Arsenic (As) concentrations in Greater Giyani borehole water ...... 48 3.5.3. Selenium (Se) concentrations in Greater Giyani borehole water ...... 51 3.5.4. Chromium (Cr) concentrations in Greater Giyani borehole water ...... 53 3.5.5. Lead (Pb) and Cadmium (Cd) concentrations in Greater Giyani borehole water ...... 55 3.5.6. Discussion of the borehole water results ...... 59 3.5.6.1. Trace elements occurring at elevated concentrations ...... 59 3.5.6.2. Arsenic mobilisation ...... 61 3.5.6.3. Other trace elements in borehole of Greater Giyani area...... 62 3.5.7. Conclusion ...... 63 3.6. Clinical health data Results...... 64 3.6.1. Limpopo province general health profile ...... 64 3.6.2. Cancer incidence counts from Nkhensani Hospital ...... 66 3.6.3. Age standardised cancer incidence rates results ...... 68 3.6.4. Discussion of the clinical health data ...... 70 vi

CHAPTER4: CONCLUDING REMARKS AND RECOMMENDATIONS ...... 72 REFERENCES ...... 73 Appendix A: Borehole water locations, Greater Giyani area, Limpopo Province, South Africa...... 86 Appendix A: continued ...... 87 Appendix B: Trace element concentrations (µg/l) in boreholes of Greater Giyani area...... 88 Appendix B: Continued ...... 89 Appendix C: Field parameters...... 90 Appendix C: Continued ...... 91 Appendix D: South African National Standard of drinking water (SANS 241-1- 2011)...... 92 Appendix E: Ten leading causes of death, Limpopo Province, 2010 (Statistics South Africa, 2013)...... 93 Appendix F: Department of Health approval letter to conduct the study...... 94 Appendix G: Informed consent form...... 95 Appendix H: University of Johannesburg Ethics clearance letter...... 97 Appendix I: Cancer incidence cases, Greater Giyani area, 2011-2014...... 98 Appendix I: Continued...... 99 Appendix I: Continued...... 100 Appendix I: Continued...... 101 Appendix I: Continued...... 102 Appendix J: Mortality rates due to cancer, Greater Giyani area, 2011-2013...... 103 Appendix J: Continued ...... 104 Appendix J: Continued ...... 105 Appendix J: Continued ...... 106 Appendix K: Abstract presented at the International Medical Geology Conference in USA in August 2013 ...... 107

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LIST OF FIGURES

Figure 1. A photograph showing borehole water storage reservoir supplying communal taps at Ha-Nkomo Village and children in the process of collecting water...... 6 Figure 2. A photograph showing a woman collecting water from a pipe attached to a borehole at Maswanganyi Village, a common practice in most rural villages in Giyani...... 6 Figure 3. A photograph showing WTW digital Multimeter 3430 with pH and ORP probes connected...... 8 Figure 4. Eh-pH Diagram of aqueous arsenic species in the water system at 25°C and 1 bar pressure (Smedley and Kinniburgh, 2005)...... 21 Figure 5. A topographical map showing the location of the study area (Modified from: Council for Geoscience GIS database, 2009)...... 34 Figure 6. Average rainfall measured in Giyani weather station. Average rainfall data were calculated over period of 14 years. (South African Weather Service, 2012)...... 35 Figure 7. Bar chart illustrating the main sources of water in the Greater Giyani Municipality. Private borehole water constitutes a staggering 22 % of the total water supply in the area (Statistics South Africa, 2011)...... 36 Figure 8. Simplified geological map of the Greater Giyani area, (Modified from: Council for Geoscience GIS database, 2009)...... 37 Figure 9. Mineral deposits in the Greater Giyani area (modified from: Council for Geoscience GIS database, 2009)...... 40 Figure 10. Hydrology of the Greater Giyani area; blue box represents the study area. http://www.dwaf.gov.za/Projects/Luvuvhu/images/FIGURE%202%20under%20WRSA.jpg . 41 Figure 11. pH and trace elements plots for Greater Giyani groundwater...... 45 Figure 12. Eh and trace elements plots for Greater Giyani groundwater...... 46 Figure 13. Seasonal variation of selenium in Greater Giyani borehole water. The gap represents the missing samples at BH-12 and BH-27 during the wet season...... 46 Figure 14. Seasonal variation of cadmium in Greater Giyani borehole water. The gap represents the missing samples at BH-12 and BH-27 during the wet season...... 47 Figure 15. Seasonal variation of chromium in Greater Giyani borehole water. The gap represents the missing samples at BH-12 and BH-27 during the wet season...... 47 Figure 16. Seasonal variation of lead in Greater Giyani water. The gap represents the missing sample at BH-12 and BH-27 during the wet season...... 47 Figure 17. Seasonal variation of arsenic in Greater Giyani borehole water. The gap represents the missing samples at BH-12 and BH-27 during the wet season...... 48 Figure 18. Bar chart illustration of arsenic concentration (µg/l) in borehole of Greater Giyani area...... 49 Figure 19. Arsenic spatial distribution pattern in the Greater Giyani area for dry season and wet season in relation to geology...... 50 Figure 20. Bar chart illustration of selenium concentration in the dry and wet season...... 51 Figure 21. Selenium spatial distribution variability in dry season...... 52

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Figure 22. Bar chart representation of chromium concentration in borehole water samples of Greater Giyani area (dry and wet season)...... 53 Figure 23. Chromium spatial distribution pattern in the Greater Giyani area for dry season in relation to geology...... 54 Figure 24. Bar chart illustration of lead concentrations (µg/l) in the dry and wet seasons. 56 Figure 25. Bar chart illustration of cadmium concentration in the dry and wet seasons. .... 56 Figure 26. Lead spatial distribution pattern in the Greater Giyani area for dry season in relation to geology...... 57 Figure 27. Cadmium spatial distribution patterns in the Greater Giyani area for dry season in relation to geology...... 58 Figure 28. pH versus arsenic concentrations plots for ground water from Greater Giyani. 59 Figure 29. Eh versus As concentration plots for ground waters from Greater Giyani...... 60 Figure 30. The top 10 causes of death in Limpopo. (Statistics South Africa, 2013)...... 65 Figure 31. Total number of cancer cases by age in Females...... 67 Figure 32. Total number of cancer cases by age in Males...... 67 Figure 33. A thematic map showing the spatial distribution of age standardised incidence rates of cancer by wards from Greater Giyani area...... 69 Figure 34. Incidence rates of cancer and boreholes with arsenic concentrations of greater than SANS drinking water standard of 10µg/l surface patterns interpolated by IDW technique...... 70

LIST OF TABLES

Table 1. Description of the instrument...... 9 Table 2. Description of the instrument operating conditions...... 10 Table 3. Recovery (in %) for QC STD and QC MIX reference solutions...... 12 Table 4. Within-batch repeatability (precision) expressed as RSD (%). n = 8 replicate measurements of QC MIX...... 12 Table 5. Ward cluster, Greater Giyani Municipality rates (Greater Giyani Municipality, 2008/2011)...... 15 Table 6. Computation of age-standardised incidence rates...... 17 Table 7. Arsenic concentrations in crustal materials...... 18 Table 8. Arsenic concentrations in common rock forming minerals...... 19 Table 9. Health problems related to arsenic...... 23 Table 10. Cadmium concentration in crustal material...... 24 Table 11. Chromium concentrations in crustal material...... 26 Table 12. Lead concentrations in crustal material...... 27 Table 13. Selenium rock forming minerals...... 29 Table 14. Selenium recommended daily intake...... 31 Table 15. Potential geological units inferred to contain arsenic-bearing minerals...... 32 Table 16. Summary statistics for As, Cd, Cr, Pb, Se and Cr in the dry and wet seasons...... 43 Table 17. Trace element concentrations (ug/l), pH, Temp ( °C) and Eh (Mv) in borehole water of rural Greater Giyani, 2012-2013...... 44 Table 18. Summary statistics for groundwater physico-chemical properties...... 45 Table 19. Summary statistics for arsenic concentrations during the dry and the wet seasons...... 48 Table 20. Summary statistics for selenium in the dry and wet seasons...... 51 Table 21. Summary statistics of chromium in the dry and wet seasons...... 53 Table 22. Summary statistics of Pb concentrations in the dry and wet season...... 55 Table 23. Summary statistics of cadmium concentration in the dry and wet seasons...... 55 Table 24. Total number of cancer cases by type and gender in Greater Giyani local Municipality, Limpopo, 2011-2014...... 66 Table 25. Age-Standardised incidence rates for all cancer site by wards for males and females, Greater Giyani, 2011-2014...... 68

CHAPTER 1: INTRODUCTION

Groundwater (borehole water) is a primary source of potable water supply in many parts of rural South Africa (DWAF, 2004). In fact, according to DWAF (2000), groundwater accounts for about two-thirds of the total water supply in the Limpopo Province. Since borehole water comes from underground, it is often considered pure and clean, but this is often not the case (Fawel and Nieuwenhuijsen, 2003). According to these authors, all natural water contains some contaminants i.e. inorganic and organic constituents. Inorganic constituents are subdivided into major- and microelements. The latter occur naturally in minute concentrations and are usually referred to as “trace elements” (Edmunds and Smedley, 1996). Potentially Harmful Trace Elements (PHTEs) that are deleterious to human health include arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb) and selenium (Se). These trace elements may end up in the groundwater resource through dissolution of soils and rock particles as water travels along cavities, fissures and pores of an aquifer (Fawel and Nieuwenhuijsen, 2003). Other sources of trace elements include anthropogenic sources such as effluents from mining activities, industrial waste water, agricultural waste and fossil fuel (Shaw, 2006).

Exposure to PHTEs, either at relatively low concentrations or in excessive amounts, may produce undesirable health conditions (Mehra and Juneja, 2003; Afridi et al., 2006). Health problems related to the occurrence of these elements in drinking water include (i) acute problems, which normally manifest immediately after a large dose of contaminant and (ii) chronic health effects, which are characterised by prolonged exposure to a contaminant (IPCS, 1978). Globally, a large body of work provides evidence of health effects resulting from human consumption of contaminated water (Smith et al., 1999; Duker et al., 2005; Ferreccio and Sancha, 2006; Kapaj et al., 2006; Celik et al., 2008; Bernard, 2008; McCarty et al., 2011; Chen, 2012). These include cancer of the lung, skin, liver and bladder, as well as other non-carcinogenic health effects such as skin tumours, hyperkeratosis, and hyperpigmentation, respiratory and cardiovascular illness.

In South Africa, only a few incidences where local and international water quality guidelines are exceeded have been reported. According to the Water Research Commission, (2009) arsenic was found to occur at potentially harmful levels in borehole samples from

parts of Beaufort West. Groundwater in Leeupoort and Gravelotte is reported to have been contaminated with Hg, whereas As and Pb are reported to be at their highest concentration levels at Rooiberg (Ali, 2010). Similarly, Meyer and Casey (2004) indicate that in the northern region of South Africa, As, Br, Cd, Pb, Hg, Mo and Se are found in the drinking water of indigenous goats occurring as localised anomalies at concentration levels exceeding local and international drinking water guideline values. One study in the Northern region of Limpopo province assessed the relationship between arsenic concentration and haematological abnormalities (leukaemia-related health condition) and concluded that a statistically significant correlation exists between the two (Sami and Druzynski, 2003). These researchers identified geological units in South Africa that may contain substantial amounts of Potentially Harmful Trace Elements. The study further indicated that most of the borehole systems supplying drinking water to the communities in South Africa are underlain by these geological units.

Though these studies shed some light on the issue and clearly show that groundwater resources might locally contain PHTEs that may be detrimental to human health, there is still a dire need for detailed studies on occurrences of these elements in South African groundwater resources, specifically those utilised for rural domestic purposes.

1.1. Aims and objectives

The main objective of this study was to assess concentration levels of PHTEs (As, Cd, Cr, Se and Pb) and their spatial distribution patterns in borehole water of rural communities in Greater Giyani local Municipality Limpopo and the potential human health risks associated with this. The results obtained and conclusions drawn from the investigation will assist in delineating environmentally sensitive areas. It will furthermore have important implications for human health and the formulation of mitigation measures for groundwater management and health consequences arising from domestic use of contaminated water supplies.

To address the research problem, the following research objectives were formulated:

- To investigate and quantify PHTEs concentrations and distribution patterns in borehole water of the rural communities of Greater Giyani local Municipality.

- To assess the extent of potentially harmful trace elements contamination and the implications this has for human health.

These objectives were achieved by the realisation of the following activities:

- Collection of borehole water samples from rural communities of Greater Giyani local Municipality. Sampled boreholes included community and school boreholes. - Laboratory analysis of borehole water samples. - Comparison of PHTEs concentrations with the South African National Standard of Drinking Water (SANS 241-1:2011). - Acquisition of health data records of chronic diseases associated with exposure to PHTEs from local health care facilities. - Production of an arsenic spatial distribution map of potentially harmful trace elements concentration and clinical data.

1.2. Research methodology

This section describes the methods followed in this study and provides a full summary of the research instruments and materials used to collect the data. The methods implemented to maintain validity and reliability of the instruments and data are also described.

The method of research comprised two phases. (i) In the first phase, I assessed the occurrence and distribution patterns of the PHTEs in the boreholes of the Greater Giyani area. This entailed the collection of borehole water. (ii) In the second phase, I evaluated the relationship between PHTEs and associated human health effects. This involved the acquisition of clinical data records of chronic diseases which could be associated with exposure to these elements. The latter phase was initiated after phase one had been completed because a significant water quality problem was found to exist in the study area.

1.2.1. Desktop study

The first phase of the research consisted of a thorough review of materials relevant to this study and problem identification. As part of this phase, geochemical groundwater datasets of Limpopo were obtained from Groundwater Resource Information Project (GRIP)

Limpopo and Groundwater Project Management (GPM) consultants. GRIP is a project aimed at gathering and verifying groundwater data to improve management and development of rural groundwater resources (DWAF, n.d.). The data were studied to assist in the selection of a project area, elements to analyse and specific boreholes to be sampled. Greater Giyani was selected as a study area, since it admirably typifies rural South Africa. For most of people in the rural Greater Giyani and surrounding areas, groundwater is often the only available supply of water. However, knowledge of the quality of the water, including the concentration levels of both nutritional and toxic trace elements, is still limited. The Limpopo Province Groundwater Resource Information Project (GRIP) includes borehole data in the Giyani area with major elements and trace elements. However, close inspection of the GRIP database indicated that trace elements were not routinely analysed for. As such five PHTEs As, Cd, Cr, Se and Pb were selected to be studied. These elements were selected for their known potential effects on human and animal health and because there is not enough information available on them.

1.2.2. Field reconnaissance survey and findings

A field reconnaissance survey was conducted to check for the availability of working boreholes in villages of Greater Giyani, to assess the general conditions of the boreholes, to conduct general observations of the area and to estimate the time required to collect the samples.

A field reconnaissance survey was conducted during July 2012. Prior to site visits, topographical maps obtained from the Council for Geoscience were studied to assess the accessibility of the study area. During sites visit the Indunas (village chief) of the villages were visited (more than 80 % of Greater Giyani villages were visited) to seek permission and to notify them of the intentions of the research. A copy of the project notice letter was handed out to the Indunas, who then conveyed the message to the community during their community meetings. This has proven essential in obtaining cooperation from communities during water sampling sessions. In all the villages visited, permission to carry out the research work was granted, and the Indunas further assisted in finding borehole operator(s) in their respective villages. The contact details of the borehole operator(s) in each village were

obtained and recorded. The location of the borehole site within each village was determined by the global positioning system (GPS).

Field reconnaissance survey materials used:

• Note book for note taking

• Maps, to locate villages and to determine a route

• GPS to record borehole locality

• Digital camera

A total of 45 villages were visited, and 88 % of these had a working community borehole and/or a school borehole used as primary source of drinking water supply. The remaining villages either had no working boreholes, or depended on other sources of water supply.

The draw-off points for borehole water in most villages are communal taps, with a smaller percentage of villagers collecting their water directly from taps installed in their households. Water is drawn from the boreholes and then pumped into a storage reservoir (Figure 1). Women and children are often seen as the ones responsible for collecting water (Figure 2).

Among those villages with working boreholes, only 6 % were insignificant to this study as surface water (water from rivers and dams) mixes with groundwater in the reservoirs, which hindered collection of representative groundwater samples. Almost all schools in the villages visited had a working borehole.

Figure 1. A photograph showing borehole water storage reservoir supplying communal taps at Ha- Nkomo Village and children in the process of collecting water.

Figure 2. A photograph showing a woman collecting water from a pipe attached to a borehole at Maswanganyi Village, a common practice in most rural villages in Giyani. 6

1.2.3. Borehole water sampling

Twenty-nine (29) boreholes were selected for sampling, comprising 15 community boreholes and 14 primary school boreholes. The boreholes were selected based on the following criteria:

 Each borehole had to represent a single village and serve at least 10 individuals.  Additionally, borehole water had to be used primarily for drinking purposes.

All boreholes selected satisfied these criteria.

The sampling task was divided in two phases. Phase one was undertaken in winter (July/August 2012), during which a total of twenty-nine (n=29) boreholes were sampled. The second phase was carried out in summer (March 2013), during which a total of twenty-seven (n=27) water samples were collected from the same localities. Two boreholes were not sampled as they were not working at the time of the second phase of sampling.

a. Sampling procedure

The sampling procedures followed here were based on those detailed in DWAF, (2000); Weaver et al., (2007 and Van Tonder, (2011), with some adjustments and adaptation.

Prior to collecting water samples medical gloves were used to prevent cross- contamination. The sample bottles, caps and probes were thoroughly rinsed using a wash bottle filled with distilled water. All sample bottles were adequately marked on two sides and on the bottle cap using a waterproof marker for easy identification. Sampling beakers, buckets and syringes were thoroughly rinsed with the sample water before taking a sample. The pH, T and Eh meter were calibrated daily according to the manufacturer’s specifications prior to use (WTW, 2011). To ensure validity, the instrument was used prior to the actual sampling session to take the pH, T and Eh readings of tap water in the office to ensure that it measured all parameters it was intended to measure. Reliability was ensured by frequently taking repetitive readings of the pH, T and Eh at one sampling point and the instrument yielded similar readings. Purging of the borehole is a most important and necessary practice during groundwater sampling. This technique removes stagnant water that can alter the chemistry of the water and result in unrepresentative samples (Weaver et al., 2007). All

boreholes sampled in the study area were in use at the time of sampling; as such purging of borehole was not necessary. However, as a precautionary measure all boreholes were purged for at least 2 to 3 minutes.

A 5l bucket was used to collect water from the boreholes and a syringe was used to transfer water from a bucket to a 100 ml sample bottle. To prevent suspended solids from being collected in the sample bottles, a 0.45µ membrane filter was used to filter the sample water and the bottles were filled to the brim to exclude oxygen as described for example by Van Tonder, (2011). The water sample pH, temperature and Eh were taken immediately at the site using a portable WTW multimeter shown in (Figure 3). To minimise water quality changes between sampling and laboratory analysis, samples were stored in an insulated cooler for transport to the laboratory and then stored at 4°C until analysis.

Figure 3. A photograph showing WTW digital Multimeter 3430 with pH and ORP probes connected.

1.2.4. Laboratory analysis a. Instrumentation

The trace element (As, Cr, Cd, Se and Pb) concentrations in the groundwater samples of Greater Giyani were determined at the Council for Geoscience (Pretoria) using a Perkin- Elmer ELAN 6000 Inductively Coupled Plasma Quadrupole Mass Spectrometer (ICP-MS) (Perkin-Elmer SCIEX Instruments, Corncord, Ontario, Canada) equipped with a cross-flow nebuliser and Scott-type Ryton double pass spray chamber. Samples were introduced via Tygon ® peristaltic pump tubing on a Perkin Elmer AS91 autosampler, using a Type F autosampler tray capable of holding 150 sample vials of 16 ml each (CGS, 2009). Details on the instrumentation are summarised in (Table 1) below.

Table 1. Description of the instrument.

Component Description System: Perkin Elmer SCIEX ELAN ® 6000 ICP-MS. Detector: ETP ® model AF 210 electron multiplier. Nebuliser: Standard cross-flow nebuliser. Spray chamber: Scott-type Ryton ® double-pass spray chamber. Pump: Standard three channel peristaltic pump. Pump tubing: Tygon ® tubing. Autosampler: Perkin Elmer AS 91 autosampler. Tray: Type F autosampler tray. Cones: Nickel sampler & skimmer cones. Argon supply: Taylor-Wharton ® XL 45 liquid argon mini-tanks (Ar 5.0)

Table 2 lists the operating conditions used for the analyses. These conditions were optimised to allow the fastest possible sample throughput while still maintaining proper rinse times.

Table 2. Description of the instrument operating conditions.

Component Description Plasma gas flow rate: 15 l/min. Auxiliary gas flow rate: According to daily optimisation Method: Quantitative analysis Scan mode: Peak hopping Autolens: On Detector mode: Dual Dwell time: Per element Sweeps/reading: 20 Readings/replicate: 1 Replicates: 3 Sample uptake rate: +/- 0.75 ml/min. Sample flush: 30 sec at 30 rpm pump speed Read delay: 40 sec at 20 rpm pump speed Analysis speed: 20 rpm pump speed Wash time: 70 sec at 30 rpm pump speed Wash solution: 5 % HNO3 Internal standard: 20 ppb Indium & 30 ppb Iridium

The ICP-MS was regularly optimised according to the manufacturer’s recommendations (i.e. nebuliser gas flow, mass calibration, lens voltages, dual detector and Autolens TM optimisation).

b. Sample preparation and analysis

An aliquot of sample was diluted in 16 ml polypropylene autosampler tubes using ultra-pure 18.2 mega ohm (MΩ) water and acidified with 2% guaranteed reagent (GR) grade nitric acid

(HNO3). 20ppb indium and 30ppb iridium were added as internal standards. Samples were diluted 10 times to add internal standard for analysis. One sample (BH 13) was repeated at 50 times dilution because the sodium content exceeded detector range.

The calibration standards were prepared (diluted to specification) using the Merck VI multi- element standard (containing 30 elements) (CGS, 2009). The standards were prepared in 2%

(v/v) HNO3 to match the sample matrix, with 20ppb indium and 30 ppb iridium added as internal standards.

c. Quality control

Verification of the calibration was done by analysing two reference standard solutions, one a separately prepared solution of the Merck VI-standard (referred to as “QC STD”) and another a custom mixture of major and trace elements, purchased from a separate supplier, with a certificate of analysis tracing concentration of elements to NIST quantities, referred to as “QC MIX”.

Total method blanks were analysed at regular intervals during the analytical batch to monitor possible contamination in the laboratory environment and consumables used.

Lastly, at least 10% of the samples were analysed in duplicate and the results evaluated by plotting linear correlation charts, to test repeatability of analysis of randomly selected samples.

d. Accuracy and precision of data

The limit of detection for the method applied was less than 4 µg/l, 0.2 µg/l, 1 µg/l and 6µg/l for As, Cd, Cr and Pb respectively. Accuracy of the method was determined from the analysis of the two reference standard solutions “QC STD” and “QC MIX” and the results expressed as recovery (%). The recovery results, shown in (Table 3), were calculated as the average measured value for the reference standard, divided by the certified or reference values, expressed as %.

Recovery (in %) = measured value / reference value * 100%

The certified values of the QC STD and QC MIX reference standards, as provided by certificate of analysis, were used as the reference values.

Table 3. Recovery (in %) for QC STD and QC MIX reference solutions.

Element QC STD QC STD Recover QC MIX QC MIX Recove (Isotope) certified measured y (%) certified value measure ry (%) value (ppb) (n = 4) (ppb) d (n = 8)

As (75) 200 197.6 98.8 10 9.9 99.2

Cr (52) 20.2 20.9 103.5 10 10.4 103.7

Cd (114) 20.2 20.3 100.7 10 10.5 104.6

Se (82) 206 203.4 98.7 100 104.5 104.5

Pb (208) 20 20.2 100.8 10 10.5 104.6

Precision (or repeatability) was calculated from repeated measurements of the QC MIX- solution (n=8 replicate measurements). Within-batch repeatability, expressed as relative standard deviation, RSD (%) was found to be as indicated in (Table 4) below.

Table 4. Within-batch repeatability (precision) expressed as RSD (%). n = 8 replicate measurements of QC MIX.

Element QC MIX RSD (%) (Isotope)

As (75) 1.13

Cr (52) 1.07

Cd (114) 1.44

Se (82) 2.96

Pb (208) 3.04

1.2.5. Clinical health data acquisition

Following identification of elevated arsenic levels in some of the sampled boreholes in the study area (see subsection 3.5.7.1), an investigation was carried out to establish whether any geographic variation exists between occurrence of this element in groundwater in the study

area and human health. Cancer was selected as one of the possible consequences of arsenic exposure due to its known relationship with this element.

As this study entails the use of existing clinical data, permission to collect this data was requested from the Limpopo Department of Health on 28 November 2013 (Appendix F). Despite some hurdles along the way, permission to collect the data was finally granted on 29 April 2014. Upon receiving permission Nkhensani Hospital was visited to make further arrangements with the management and on 22 April 2014 meeting with the Chief Executive Officer (CEO) was held. During this meeting the CEO was provided with full information about the research and was also informed that confidentiality and anonymity of the information collected will be assured (Appendix G). The CEO was further informed of the right to withdraw to participate in the research at any time if needs be. He freely gave consent to take part in the research and was not coerced or threatened in order to participate.

One of the challenges faced during this phase of the study, was that the Nkhensani Hospital still largely relies on a traditional paper-based record keeping system and lacks a proper electronic record system to store and manage its health information. As such the admission books were used and information recorded in these books was perused for the time period 2011-2014, which was said to be almost completely recorded.

Furthermore, two clinics namely Khakhala-Hlomela and Mhlava-Willem clinic were also visited in the study area with the intention of obtaining more data on cancer incidence. The clinics were selected on the basis that they are located in villages known to have boreholes containing elevated Arsenic concentrations. However, information related to cancer was not obtained from the clinics’ admission books. This was due to the fact that diagnostic services for cancer are only provided by the District Hospital (Nkhensani Hospital) on referral from the clinics.

1.2.6. Ethical consideration

The permission to conduct the study was granted by the University of Johannesburg’s Ethics Committee (Appendix H ) and the Limpopo Department of Health. In order to maintain confidentiality of the information collected and to protect the privacy of the subject (patient) Patients’ identifying information such as names, phone numbers and Identity

Numbers (ID) were not recorded. Anonymity of the data was achieved by assigning a code to each patient name.

1.2.7. Clinical data analysis

About 110 incidents of cancer counts were obtained from the Nkhensani Hospital admission books for the time period 2011-2014. The following information regarding the participants were captured in an excel spreadsheet, diagnosis, admission date, age, gender and place of residence. Of these, 10 were duplicate data entries which were identified by using the excel filter function. Critical features for filtering were diagnosis, place of residence and age. If two or more data entries were found to have the same diagnosis, place of residence and age, these were considered duplicates, which were discarded and only one copy was kept. To spatially examine the cancer incidence rates with GIS, the cancer data needed to be geocoded. Manual geocoding method was utilised; this involved the use of google maps to search the village names and determine the geo-coordinates for each village. In order to allow for unbiased comparison of cancer incidence rates with arsenic concentrations age-standardised incidence rates were computed based on the calculation recommended by National Cancer Institute, (2015). Age-standardisation is a technique used to account for the difference in population age structure when comparing rates for different periods, geographic areas and or different population (Cancer Research UK, 2014). There are two methods of standardisation, the direct and indirect method (Cancer Research UK, 2014). The direct method was used for the incidence rates reported here. The age standardised incidence rates were calculated at the ward administrative area unit. Table 5 shows villages grouped by wards to allow for computation of the age-standardised rates (Greater Giyani Municipality, 2008/2011).

Table 5. Ward cluster, Greater Giyani Municipality rates (Greater Giyani Municipality, 2008/2011).

Ward No Villages

1 Ximausa Blinkwater Noblehoek 2 Rivala Ndengeza Phikela Mavhuza 3 Babangu Tiyani 4 Basani Ghandlanani Mashavele Dingamazi 5 Sifasonke Nkuri Tomu 6 Hlaneki Mapuve 7 Maswanganyi Magonya Dzingidzingi Bode Kremetart 8 Botshabelo Nwamakena 9 Homu Mapayeni 10 Nkomo A Nkomo B 11 Giyani E Giyani D1 12 Giyani A Homu 14C 13 Giyani D2 Giyani F 14 Xikukwani Makoxa 15 Dumazi Mbato Shivulani 16 Mininginisi 17 Thomo Mhlava- Willem 18 Khakhala Gaula Muyexe 19 Mahlathi Ndindani Vuhehli Mapayeni Nwakhuwani 20 Mavalani Shiviti Thomo 21 Sikhunyani Ngove 22 Xawela Xikumba Hlomela 23 Mbedhle Guwela Kheyi Mushiyani Nsavulani 24 Mbabani Rhangani 25 Dzumeri Daniel 26 Mughonghoma Nkomo 27 Khaxani Xitlakati Mzilela Mayephu Matsotsoela 28 Mphakani Makhwivirini 29 Makhuva Mbawula Phalakubeni 30 Siyandani

The Segi standard world population was used as reference population (Segi, 1960). To calculate the age-standardised rate for each ward the crude incidence rate (age-specific rate) for each age group was calculated first. The Crude incidence rate was calculated using the following formula:

Crude rate = a/b *100 000 (1) 15

Where a is the number of cancer counts for each of the age groups and b is the number of persons in age group of the study population. Since age-standardised incidence rate is defined as the weighted average of the age-specific rates where the weights are the proportions of persons in the corresponding age group of a standard population (National Cancer Institute, 2015); the age distribution of the standard population was computed as follows: c/d (2)

Where c is the standard population for each age group and d is the total standard population. Multiplying the age distribution of the standard population by the crude rate gives the expected cases in the standard population. Summing the expected cases in standard population gives the age-standardised incidence rate for that ward. The 95% confidence interval (CI) for the age standardised rates were calculated using the following formula: RATE ± 1.96 * √푣푎푟 (3)

푝푖 푡푖^2 Where variance is the express as follows: Ʃ ( )^2 * (4) Ʃ푝푖 푘푖

Where pi is the number of persons in age group in the standard population, ti crude rate and ki case counts. Table 6 shows an example of calculating of age-standardised incidence rates for ward 2. After calculating age-standardised rates the next step was to create spatial distribution maps of incidence rates of cancer and arsenic concentrations in borehole water. Inverse distance weighting (IDW) interpolation technique from ArcGIS was used. IDW is a surface interpolation method that uses measured values to predict the values for any unmeasured locations.

Table 6. Computation of age-standardised incidence rates.

Age Count Population a Crude World Age Expected Variance rate standard distribution cases in population standard population <1 0 6676 0.00 24000 0.024 0.00 0.00 1 - 4 0 25466 0.00 96000 0.096 0.00 0.00 5 - 14 0 57626 0.00 190000 0.19 0.00 0.00 15 - 24 0 57471 0.00 170000 0.17 0.00 0.10 25 - 34 1 31794 3.15 140000 0.14 0.44 0.00 35 - 44 0 22327 0.00 120000 0.12 0.00 0.00 45 - 54 1 17724 5.64 110000 0.11 0.62 0.00 55 - 64 0 10909 0.00 80000 0.08 0.00 0.00 65+ 1 14222 7.03 70000 0.07 0.49 0.24

Total 3 244215 15.82 1000000 1 1.6 0.34 95% confidence interval Lower Upper Limit limit 0.41 2.7 a population data were obtained from Statistics South Africa, 2011.

CHAPTER 2: LITERATURE REVIEW

2.1. Introduction This chapter consists of a review of the trace element constituents (arsenic, cadmium, selenium, lead and chromium) of interest in this study in terms of chemistry, geology, and their health effects and significance to humans. It will also consider groundwater quality studies in the type locality.

2.2. Trace elements

2.2.1. Arsenic (As) Arsenic is one of the most toxic chemical elements to occur naturally in the earth crust (Sami and Druzynski, 2003; Duker et al., 2005; Smedley and Kinniburgh, 2002). It is a common constituent in more than 200 minerals (Table 7), particularly sulphide minerals such as arsenopyrite (AsFeS2), enargite (Cu3AsS4), pyrite (FeS2), orpiment (As2S3) and realgar (AsS) (Edmunds and Smedley, 1996; Duker et al., 2005). Pyrite is cited as the most abundant arsenic-containing mineral and is common in sulphide mineralised zones (Sami and Druzynski, 2003; Smedley and Kinniburgh, 2002). Table 8 shows arsenic concentrations in various rock materials. The arsenic content in most igneous rocks is generally low and can vary between < 1-113 mg/kg-1, while sedimentary materials such as shales and clay have relatively higher arsenic levels. Arsenic concentrations in metamorphic rocks are reported to reflect that of their sedimentary and igneous counterparts (Smedley and Kinniburgh, 2002).

Table 7. Arsenic concentrations in crustal materials.

Material As concentration (mg/kg) Igneous material Basalt <1–113 Ultrabasics <1–16 Granites <1–15 Sedimentary material Shales and clays <1–500 Sandstones <1–120 Limestones <1–20 Phosphorites 3–100

(NAS, 1977 in Ratnaike, 2006).

Table 8. Arsenic concentrations in common rock forming minerals.

Mineral As concentration (mg/kg-1) Sulphide minerals Pyrite 100-77,000 Pyrrhotite 5- I00 Marcasite 20- 126,000 Galena 5- 10,000 Sphalerite 5- 17,000 Chalcopyrite 10-5000

Oxide minerals Hematite up to 160 Fe( I I I) oxyhydroxide up to 76,000 Magnetite 2.7-41 Ilmenite

Silicate minerals Quartz 0.4- 1.3 Feldspar <0. 1-2.1 Biotite 1.4 Amphibole 1.1-2.3 Olivine 0.08-0. I7 Pyroxene 0.05-0.8

Carbonate minerals Calcite 1-8 Dolomite <3 Siderite <3

Sulphate minerals Gypsum/anhydrite

Other minerals Apatite <1 - 1000 Halite <3-30 Fluorite <2

(Smedley and Kinniburgh, 2002).

In the hydrogeologic environment, arsenic can be found in dissolved organic and inorganic form in a variety of different species depending on the pH and oxidation potential of the water (Thirunavukkarasu et al., 2002). The major inorganic species found in natural - water are trivalent arsenite (As2O3 AsIII) and a pentavalent arsenate (As2O5AsV) (Smedley and Kinniburgh, 2002; Ratnaike, 2006). 19

Organic arsenic species commonly reported are monomethylarsenate (MMA) and dimethylarsenite (DMA) (Edmunds and Smedley, 1996; Thirunavukkarasu, et al., 2002). These species are less common in the natural waters compared to the inorganic species (Edmunds and Smedley, 1996).

According to Thirunavukkarasu et al. (2002) and reference therein, a few cases where DMA was found at high levels and could be potential harmful were reported in various places. For example, most of the water samples from the wells in northeast and northwest Taiwan had high concentrations of 7µg/l. Furthermore, MDA comprised an average of 24% of the total dissolved arsenic in surface water of various lakes investigated in California, USA.

Concentration of arsenic in most groundwater is usually lower than 10 µg/l, but significantly higher where there are sulphide mineral deposits and sedimentary deposits derived from volcanic rocks (Smedley and Kinniburg, 2005; WHO, 2011).

2.2.1.1. Mechanism of control of arsenic concentrations and mobility in groundwater

Arsenic availability and mobility in groundwater is largely controlled by a variety of factors: arsenic valence state, physical properties of the water i.e. pH and oxidation-reduction conditions, presence and concentrations of competing ions, organic carbon, amount of hydroxides present and microorganism activities (Welch et al., 2000; Smedley and Kinniburgh 2002; Smedley and Kinniburgh, 2005; Ratnaike, 2006). Under oxidising - 2- conditions, As (V) is generally present as H2AsO4 at low pH, while at higher pH HAsO4 is 0 dominant. Under mildly reducing conditions at a pH less than 9.2 H3AsO3 predominates (Figure 4) (Smedley and Kinniburgh, 2002).

Figure 4. Eh-pH Diagram of aqueous arsenic species in the water system at 25°C and 1 bar pressure (Smedley and Kinniburgh, 2005).

a. Oxidation of sulphide minerals

This process involves oxidation of arsenic-bearing sulphides (e.g. pyrite and arsenopyrite) with resulting dissolution of the sulphide minerals into hydroxides, and release of arsenic and sulphates. This reaction may be written as follows (Smedley and Kinniburgh, 2005; Welch et al., 2000).

FeAsS + 7/2 O2 + 4 H2O → Fe (OH)3 + H3AsO4 + H2SO4 (5)

Arsenopyrite iron oxide arsenate sulphate

b. Adsorption and desorption processes

Adsorption and/or desorption (dissolution) reaction between arsenic and oxides (e.g. Fe- oxides and Al-oxides), as well as clay minerals have been cited as an important concentration- and mobility-limiting process in the groundwater (Smedley and Kinniburgh, 2005; Xie et al., 2009). Arsenic co-precipitation and dissolution are influenced by various factors, including changes in the pH conditions of the water, the presence of competing ions and the occurrence of redox reaction (Smedley and Kinniburgh, 2005; Xie et al., 2009).

Arsenate in water shows higher adsorption affinity than arsenite (Smedley and Kinniburgh, 2005, Xie et al., 2009). However, the reduction of arsenate- As(V) to arsenite- As(III) results in the desorption of weakly arsenite-adsorbing complex and hence promotes arsenite mobility (Xie et al., 2009 and reference therein). Phosphate usually competes with arsenic for sorption sites, and this interaction between arsenic and phosphate may limit arsenic concentration and mobility in water (Manning and Goldberg, 1996).

2.2.1.2. Arsenic health effects

Arsenic is one of the highly toxic elements in our natural environment, ranked as the third most toxic substance, after lead and mercury, in the US Toxic Substances and Disease Registry (Ratnaike, 2006). Pathways of human exposure to arsenic toxicity include ingestion, inhalation and dermal contact (Ratnaike, 2006; Fowler et al., 2007). The major route of human exposure to arsenic is by ingesting As-contaminated water and foods (Ratnaike, 2006). The amount and toxicity of arsenic depends largely on the form of arsenic ingested (Smedley and Kinniburgh, 1996; Ratnaike, 2006; Fowler et al., 2007). Table 9 presents the summary of health problems associated with arsenic.

Table 9. Health problems related to arsenic.

Arsenic toxicity Symptoms and signs

Acute As toxicity Gastrointestinal disorders associated with vomiting, nausea, colicky abdominal pain, profuse watery diarrhoea and excessive salivation. Chronic As toxicity Skin - skin disorders such as hyperpigmentation, depigmentation, palmar and keratosis. Cardiovascular effects – peripheral vascular diseases (e.g. black foot diseases), ischemic heart disease, stroke, and hypertension. Neurological system disorder - manifests as polyneuropathy (an illness where sensory and motor nerves of the peripheral nervous system are dysfunctional http://medical- dictionary.thefreedictionary.com/polyneuropathy, Respiratory system effects – respiratory effects include rhinitis, pharyngitis, laryngitis, tracheobronchitis, chronic cough, crepitation, shortness of breath and chronic lung disease. Carcinogenic effects – skin cancer, lung cancer, urothelial cancer and liver cancer.

(Source: Ratnaike, 2006; Fowler et al., 2007; Mazumder et al., 2008.

2.2.2. Cadmium (Cd)

Cadmium is a relatively rare trace element that occurs in the environment, usually in association with sulphide ore of copper, zinc and lead (WHO, 2000; Umemura and Wako 2006). The reported average concentrations in the earth crust vary between 0.1 – 0.2 mg/kg (WHO, 2000). High concentrations of cadmium are reported to exist in sedimentary rocks, particularly in phosphorites and marine black shales (Alloway, 1995). In general, igneous and metamorphic rocks contain low concentrations of cadmium. Table 10 shows typical cadmium concentrations in crustal materials. Cadmium exists in the environment in two oxidation states, the divalent (Cd2+) and the neutral (Cd0) state. The former is soluble in water (UNEP, 2010). Cadmium hardly occurs as pure metal, but as complexes of oxides, sulphides, chloride and carbonate (UNEP, 2010). 23

Table 10. Cadmium concentration in crustal material.

Igneous rocks Rhyolites 0.03-0.57 Granite 0.01-1.60 Basalt 0.01-1.60

Metamorphic rocks Gneisses 0.007-0.26 Schists 0.005-0.87

Sedimentary rocks Shales and clay 0.017-11 Black shales 0.30-219.0 Sandstones and conglomerates 0.019-0.4 carbonate 0.007-12 phosphorites <10-980 coal 0.01-300

Sulphide ore minerals Sphalerite (ZnS) 0.2-0.4 (< 5%) Galena (PbS) 0.5 %

(Alloway, 1995).

In the soil, the occurrence of sorption material such as clay minerals, carbonates or hydroxide of iron and manganese, pH conditions and organic matter are reported to be the contributing factors to the availability and mobility of cadmium. Acidic soil with pH between 4.5 and 5.5 promotes cadmium mobility, while alkaline pH inhibits cadmium mobility (Lalor et al., 1998).

2.2.2.1. Cadmium health effects

Exposure to cadmium can lead to adverse health effects and the main targets in the human body are the kidneys and the skeleton (UNEP, 2010). Chronic cadmium intoxication, the so- called “Itai-Itai disease” (IID) is characterised by renal dysfunction and osteopenic osteomalacia (Umemura and Wako, 2006). Osteopenic osteomalacia is a skeletal condition in which unmineralised bone matrix is in excess due to inadequate mineralisation of newly formed bone, while kidney dysfunction due to cadmium intoxication is mainly characterised by increased excretion of proteins in urine as a result of tubula cell damage (Hutton, 1987).

A link between cadmium exposure and cancer has been suggested in some studies (Waalkes et al., 1999 and Nawrot et al., 2006).

2.2.3. Chromium (Cr)

Chromium is one of the most common, but widely variable elements in the earth crust (Motzer and Engineers, 2004; William et al., 2004). High chromium concentrations can be found in igneous rocks, particularly ultramafic rocks, as compared to felsic rocks (Table 11). Sedimentary and volcanic rocks generally contain low chromium levels (Motzer and

Engineer, 2004). The major chromium containing ore mineral is chromite FeCr2O4 (Oze et al., 2007). Chromium concentrations in the soil reflect the underlying bedrock chromium content released during weathering; reported values range between 1 000 and 60 000 mg/kg (Oze et al., 2007).

Table 11. Chromium concentrations in crustal material.

Material Average concentrations (mg/kg) Igneous granite Diorite, granodiorite, diorite & quartz 20 Gabbro, dunite, peridotite & pyroxenite 2000

Volcanic Basalt 220

Sedimentary Limestone 10 Shale 35 Sandstone 100 (Motzer and Engineer, 2004)

Chromium commonly exists in the environment in two oxidation states, Cr (III) and Cr (VI) (Fendorf et al., 2002). Chromium (VI) is highly toxic in humans, whereas chromium (III) is non- toxic and reported to be an essential nutrient for humans (Motzer and Engineer, 2004; William et al., 2004).

Chromium (VI) is more stable under oxidising conditions, while Chromium (III) is more stable under reducing conditions (IPCS, 2013). In hydrogeologic environments Cr (III) 2+ + exists as soluble hydroxide cations, CrOH and Cr(OH)2 , insoluble chromium oxides

(Cr2O3) and chromium hydroxide [Cr(OH)3] also occur (Motzer and Engineer, 2004). The 2- 2- relatively more soluble and mobile dichromate (Cr2O7 ) and chromate (CrO4 ) are the most dominant form of Cr (VI) species (Motzer and Engineer, 2004).

Most groundwater contains very low chromium content <1.0 µg/l (WHO, 2000). Chromium enrichment and mobility in this environment are largely controlled by the redox reaction and pH of the aqueous system (Richard and Bourg, 1991). Cr (VI) displays low anion sorption affinity with increasing pH, while Cr (III) shows high cation sorption affinity with increasing pH when competing ions are present (Richard and Bourg, 1991).

2.2.3.1. Chromium health effects

The following studies (Langard, 1990 Huvinen et al., 2002; Motzer and Engineer, 2004; Linos et al., 2011) suggest that exposure to chromium may cause respiratory diseases, cancer, ulceration and perforation of the nasal membranes.

2.2.4. Lead (Pb)

Lead is a non-essential element for humans that is evenly distributed in the natural environment, comprising about 0.0013 % of the earth crust (Nagpal, 1987; UNEP, 2010). Concentrations of lead in various rock types is shown in (Table 12). Lead exists in nature in three oxidation states, namely: Pb (0), Pb (II) and Pb (IV) (UNEP, 2010). Metallic Pb (0) is insoluble in water and its occurrence is extremely rare (UNEP, 2010). Galena (PbS) is the primary ore mineral of lead, other common varieties reported include cerussite (PbCO3), plattnerite (PbO2) and anglesite (PbSO4) (UNEP, 2010).

Table 12. Lead concentrations in crustal material.

Material Lead (Pb) concentration in mg/kg Igneous rocks Basalt and gabbro 8 Granite and rhyolite 20 Sedimentary rocks & limestone 20

(Aubert and Pinta, Demayo as cited in Nagpal, 1987)

The lead content in aqueous environments is generally very low. High lead concentrations in the aqueous environment may result from natural sources, i.e. erosion of naturally occurring lead material, and anthropogenic sources, such as burning of fossil fuel and lead use containing lead products (UNEP, 2010). In acidic environments lead primary persists as Pb(II) and forms PbCO3 and Pb(OH)2 under alkaline conditions, while at a basic pH, lead may be oxidised to relatively soluble PbO and Pb3O4. The presence of CO2 can readily convert these oxides to less soluble Pb(II) carbonate (UNEP, 2010). 27

2.2.4.1. Lead health effects

Lead toxicity primarily affects the central nervous system, the haematological system and the renal system (Hutton, 1987). In children, low-level long-term exposure to lead is associated with neurobehavioural problems and cognitive difficulties (Finkelstein et al., 1998). Somewhat similar central nervous effects have been observed in rats, where high levels of lead exposure caused disturbance on myelinated axons (Winder et al., 1983). Disturbance of the functioning of the myelinated axon may disrupt the proper functioning of the nervous system (Winder et al., 1983). Lead exposure has been linked to cancer in some studies (Baker et al., 1980; Alatise and Schrauzer, 2010). However, a recent investigation that examined cancer risk among lead-exposed workers for an average of ten years in New Jersey failed to show any association between lead exposure and cancer, citing a small sample number and lack of adequate information (Lam et al., 2007).

2.2.5. Selenium (Se)

Selenium is a naturally occurring trace element that is unevenly distributed in the earth crust (Tan et al., 2002). It is present in the natural environment in association with sulphur, most abundantly in sulphide minerals (WHO, 2011). Elevated Se concentrations are reported to be present in volcanic rocks, sedimentary and carbonate rocks (Edmunds and Smedley 1996, WHO 2011). In sedimentary rocks, selenium is mainly found in shales in concentrations ranging from 0.6-103 µg g-1, while concentrations in igneous rocks may vary from 0.004-1.5 µg g-1 (Martens, 2003). Table 13 lists some of the common selenium rock- forming minerals.

Table 13. Selenium rock forming minerals.

Selenium minerals Chemical formula

Berzelianite Cu2Se Clausthalite PbSe

Crookesite (Cu,Ti, Ag) 2Se Elemental selenium Se Tiemannite HgSe

Sulphide host minerals

Chalcopyrite CuFeS2 Sphalerite ZnS

Pyrite FeS2

Pyrrhotite CuFeS2

(Fleming; Neal, and Reimann and Caritat as cited in Fordyce, 2005).

Selenium exists naturally in four species: selenides (2- ), elemental selenium (0), selenite (4+), selenate (6+) and organic Se-Selenomethionine and selenocysteine (Edmunds and Smedley 1996; Fordyce, 2005; Martens, 2003). In its 2-valence state, selenium occurs as - hydrogen selenide H2Se , a highly toxic gas at room temperature and thermodynamically unstable in aqueous solutions (Martens, 2003). The elemental selenium form Se0 is insoluble and non-toxic (Martens, 2003; Hogberg and Alexander, 2007). The geochemistry of selenium closely resembles that of sulphur (S) and they both have a garlicky odour (Persico and Brookins, 1988). However, the selenium species e.g. selenites and selenates found in drinking water are odourless (WHO, 2011).

The levels of selenium in groundwater and surface water are generally low and most drinking water contains selenium concentrations much lower than 10 ug/l (WHO, 2011).

The availability and mobility of selenium in groundwater is governed by several physico-chemical properties: the presence of competing ions, its strong adsorption affinity on hydroxides and clay, the selenium valence state, the pH and the redox conditions of the water. 2- The toxic inorganic selenite (SeO3 ) under reducing and acidic conditions is readily reduced to insoluble non-toxic elemental selenides (Edmunds and Smedley 1996). Alkaline and 29

2- oxidation conditions favour the presence of selenate (SeO4 ), a highly soluble Se species in aqueous environment (Edmunds and Smedley, 1996). Selenite may be released to water through oxidation or dissolution of selenium-bearing minerals such as pyrite (FeSe2 ) (Bailey et al., 2009). This process can be illustrated as follows:

- + 2+ 2- 5FeSe2 + 14NO3 + 4H → 5Fe + 10SeO4 + 7N2 + 2H2O (4)

This process may increase selenium concentration in the hydrogeologic environment. However, the high affinity of selenium to hydroxides of Al, Fe and Mn, organic matter and clay may retain and inhibit selenium mobility in water (Persico and Brookins, 1988, Martens, 2003).

2.2.5.1. Selenium health effects

Selenium is an essential trace element for humans in required dietary trace amounts. However, selenium deficiency (<40 µg/ day) and toxicity or selenosis (>400 µg/day) may occur and give rise to adverse health effects (Fordyce, 2005; WHO 2011). Known symptoms of selenium deficiency include anaemia, impeded growth, fertility disorders, muscular degeneration (Edmunds and Smedley 1996; Hogberg and Alexander, 2007). Very low concentrations of selenium have been associated with Keshan disease, a chronic cardiomyopathy that was first discovered in parts of China during the 1940s (Chen et al., 1980; Edmunds and Smedley 1996; WHO 2011). Although Keshan disease has been difficult to understand, the available evidence suggests that the administration of selenium to a selenium-deficient individual can prevent it (WHO, 2011). Selenosis (selenium poisoning) in humans manifests in various ways. Early indicators of excess intake may include nausea, vomiting, diarrhoea, fatigue and skin lesions (Nuttall, 2006). Other health effects include tooth decay, neuropathy and dermatological effects such as nail and hair loss (Yang et al., 1983, Vinceti et al., 2001). Selenium has also been shown to have inhibitory effects on HIV (Bradfield and Foster, 2006; Melse-Boonstra et al., 2007). The possibility of selenium having anti-carcinogenic activity is cited in Ip and Ganther (1992), Combs et al. (2001) and Dennert et al. (2011). Table 14 depicts the permissible selenium daily intake as recommended by the FAO/WHO (2001).

Table 14. Selenium recommended daily intake.

Age Group RNI μg/day Infants and children 0–6 months 6 7–12 months 10 1–3 years 17 4–6 years 22 7–9 years 21 Adolescent Female 10-18 years 26 Male 10-18 32 Adults Female 19-65 26 male 34 Female 65+ 25 Male 65+ 33 Pregnancy 2ndtrimester 28 3rd trimester 30 lactation 0-6 months post-partum 35 7-12 months post-partum 42

Recommended nutrient intake (RNI).

2.3. Previous studies on groundwater quality in Limpopo province

Several trace elements were reported at high concentration levels in a number of boreholes with values exceeding both human and livestock guideline values of maximum permissible intake (Ali, 2010; WRC, 2009; Meyer and Casey 2004). For example, a risk assessment for drinking water in the northern region of South Africa demonstrates that several trace elements, As, Br, Cd, Pb, Hg, Mo and Se may occur at concentrations exceeding local and international permissible levels for drinking water (Meyer and Casey, 2004). The results of the study further suggest that the anomalously elevated concentrations of these harmful elements are closely associated with the surrounding geology, and it is believed that this may pose a significant environmental and health hazard in the area. These observations are in accordance with those reported by Siegel (1984), who indicated that geographic distributions of certain diseases have a strong correlation with the presence of harmful elements in the geological environment. Thus, bedrock geochemistry and several

anthropogenic and industrial inputs play important roles in the distribution of harmful elements in the environment (Bowie and Thornton, 1984).

About thirty one percent (31 %) of borehole water samples within the Limpopo Plateau and fifty four percent (54 %) of borehole water samples within the Letaba Lowveld contain major ion concentrations far above the recommended guidelines for drinking water (Holland, 2010). Elements found to be of particular concern for human consumption includes nitrate, fluoride, Mg, Na and Cl (Holland, 2010). Nitrate in particular is derived from anthropogenic sources while fluoride is largely controlled by the bedrock geology (Holland, 2010). In another study high concentrations of chloride and sulphate were found in water sources of healthcare facilities in the Limpopo Province (Van Heerden, 2011). These elements are thought to be derived from the anthropogenic sources and not related to the bedrock geology (Van Heerden, 2011).

Geological target maps compiled based on an understanding of the geological environment of South Africa have delineated areas where trace elements such as selenium, arsenic and uranium may be abundant (Sami and Druzynski, 2003). The list of identified potential geological settings defining targets of arsenic and selenium in the study area is shown in (Table 15).

Table 15. Potential geological units inferred to contain arsenic-bearing minerals.

Potential geological target for arsenic based on sulphide mineralisation and gold association

Schiel Alkaline Complex

 Sulphide mineralisation occurs within carbonatite - these minerals are potential sources of arsenic.

 This complex contains disseminated apatite - arsenic occurs in trace amounts in apatite

Giyani Group (Giyani greenstone belt)

 Minor sulphides occur in mafic and ultramafic rocks, banded iron formations and quartz veins as arsenopyrite, chalcopyrite and pyrite- these are a potential source of arsenic.

(Adapted from: Sami and Druzynski, 2003).

2.4. Conclusion

Trace elements occur naturally in the environment, usually in low concentrations. The concentrations of these elements in groundwater generally reflect the compositions of their parent rock material which the water interacted with. The behaviour of these elements in groundwater depends largely on its oxidation state, the availability of competing ions, the nature of the substrate available and aquifer conditions. Although these elements occur in nature at relatively low concentrations, numerous studies around the world have demonstrated that they can result in adverse effects in humans. In the South African context, despite a few documented studies on PHTEs toxicity, it is still unknown in what quantity they occur in groundwater and what are their potential health effects on human.

CHAPTER 3: CASE STUDY: PHTES IN THE GREATER GIYANI AREA, POSSIBLE RELATION BETWEEN GEOLOGY AND HEALTH

3.1. Introduction

The study area is located in the Greater Giyani local Municipality (Figure 5), which falls within the Mopani District in Limpopo province, South Africa. The Municipality covers an area of approximately 25 344 13 km2, including part of the Kruger National Park (Mopani District Municipality, 2006/2011). It is bounded to the east and west by the Kruger National Park and the Greater Letaba local municipality respectively.

Figure 5. A topographical map showing the location of the study area (Modified from: Council for Geoscience GIS database, 2009).

3.1.1. Climate

The area is characterised by a warm, dry and subtropical climate with summer rainfall (Figure 6). High rainfall occurs mainly between November and February (South African Weather Service, 2012). Though the region receives high rainfall in summer, it often experiences severe drought conditions that result in serious water shortage (Mopani District Municipality, 2007/2008). The temperature ranges from moderately warm to hot in the mountainous region, to hot and extremely hot in the plains region (DWAF, 2006).

Average rainfall (mm) 140 120 100 80 60 Rain (mm) 40 20 0

Figure 6. Average rainfall measured in Giyani weather station. Average rainfall data were calculated over period of 14 years. (South African Weather Service, 2012).

3.1.2. Population and water supply in the Greater Giyani area

According to Statistics South Africa, (2011) the total estimated population of the Greater Giyani Local Municipality for the 2011 census is 244 217, of which 85 % resides in the rural areas. Approximately 57 % of the population is aged between 15 to 64 years of age, while 36 % is aged under 15 and the remainder is aged 65 or over. The ratio of the non- economically active to the economically active person(s) in the population of Greater Giyani area sits at approximately 74 %. Total number of households in the region with an average household size of 3.80 is 63 548. Female-headed households make up 57 % of all households.

The region is predominately rural, with a supply of water estimated to be below the Reconstruction and Development Programme (RDP) level of 25l per person per day, (Mopani District Municipality, 2006/11). Figure 7 shows the main sources of water in the area. Private boreholes account for 22% of the total volume of water consumed in the area, while the larger section of the population (~61 %) gets their water from the local water schemes operated by the municipality, which are also mainly boreholes. Thus, close to 83% of the population relies on groundwater.

Greater Giyani main source of water

Regional/local water scheme (operated by municipality or other water services provider) 1% Borehole 2% 4% 0% 1% 6% 3% Spring

22% 61% Rain water tank

Dam/pool/stagnant water

River/stream

Water vendor

Figure 7. Bar chart illustrating the main sources of water in the Greater Giyani Municipality. Private borehole water constitutes a staggering 22 % of the total water supply in the area (Statistics South Africa, 2011).

3.2. The geology of the Greater Giyani area

The study area is predominately underlain by Goudplaats Gneiss, with the central area occupied by the Giyani Greenstone Belt, formerly known as Surtherland Greenstone Belt. Small outcrops of Schiel Alkaline Complex and Shamiriri Granite occur as isolated intrusions in the study area (Figure 8).

Figure 8. Simplified geological map of the Greater Giyani area, (Modified from: Council for Geoscience GIS database, 2009).

3.2.1. Goudplaats Gneiss

According to Robb et al., (2006) Goudplaats Gneiss is the oldest preserved gneissic terrain in the area which forms the basement of all other existing lithologies. It generally forms flat ground and has overall poor exposure. The gneiss is believed to form the basement to the Bandelierkop Complex and the Pietersburg Group (Brandl, 1986; Kröner et al., 2000; 37

Robb et al., 2006). The latest isotopic ages (207Pb/206Pb) for the granitoid gneisses obtained by single-grain zircon evaporation provided emplacement ages of between 2.8-3.3 Ma (Brandl and Kröner, 1993; Kröner et al., 2000). According to previous work in the area the gneiss is generally characterised by alternating bands of leucocratic and melanocratic material of several centimetres in width and includes an assemblage of orthogneisses,tonalitic, trondhjemitic and granodioritic (TTG) composition (e.g. Brandl, 1986; Brandl, 1987; Anhaeusseur, 1992; Baglow, 2005 and Robb et al., 2006). To the north of the Giyani Greenstone Belt, the metamorphic grade of the Goudplaats Gneiss is patchily granulite, as it forms part of the Southern Marginal Zone of the Limpopo Belt. The area to south of the Belt is also known as Klein Letaba or Baviaanskloof Gneiss and is characterised by amphibolite facies metamorphism and forms part of the Kaapvaal Craton proper (Robb et al., 2006).

3.2.2. Giyani Greenstone Belt

The north-east trending Giyani Greenstone Belt is a roughly sinusoidal-shaped supracrustal complex with a widened central area and a 30 km extension towards the west, which bifurcates into two arms, a southern Lwaji and north-western Khavagari arm (Brandl, 1987; Kröner et al., 2000). In the southern Lwaji arm, the base succession consists of the ultramafic schists, which passes upward into a thick sequence of metasedimentary rocks including phyllite, quartz-sericite-chlorite schists and quartzite (Kröner et al., 2000). The base succession of the northern Khavagari arm consists of ultramafic schists, probably metamorphosed komatiites, associated with cherts and banded iron formation (Kröner et al., 2000). In the central part, mafic rocks (metagabbros, as well as volcanics) are associated with metapelites. Generally, the belt lacks sufficient exposure, which prohibits lithostratigraphic subdivision. However, according to Brandl (1987), the succession can be divided into the following rocks types: ultramafic schists and amphibolite with subordinate metasedimentary rocks, iron formation and acid metalava.

3.2.3. Schiel Alkaline Complex

The north-western part of the study area contains the mushroom-shaped Schiel Alkaline Complex, which is considered to have been emplaced at 2059 ±35 Ma. According to Walraven et al., (1992) this complex is subdivided into eastern Schiel Complex, which comprises syenogabbro, syenite, quartz syenite and granite and western Schiel Complex, which comprises mainly quartz syenite bodies (Robb et al., 2006).

3.2.4. Shamariri granite

The Shamiriri Granite forms a stock-like intrusion and is situated south of the eastern part of the Giyani Greenstone Belt. The granite is described as a greyish, medium-grained rock, which grades into a coarse to porphyritic phase composed of megacrysts of microcline- microperthite embedded in a groundmass of quartz, oligoclase, microcline and biotite (Robb et al., 2006).

3.3. The economic geology of the Greater Giyani area

The important mineral province in the Greater Giyani area is the Giyani Greenstone Belt. The belt was first discovered in the 1870s by Button and Sutherland soon after the discovery of the Murchison Greenstone Belt further south (Bullen et al., 1995). This belt is known to host gold, tungsten, nickel, magnesite and other mineral deposits and once sustained numerous gold mines and produced substantial amounts of gold (Bullen et al., 1995). The estimated combined gold production from all the mines once operative on this belt is reported at 320 000 ounces (Bullen et al., 1995). Figure 9 gives a graphical representation of known gold occurrences and other minerals in the greenstone belt. Gold mineralisation in the Giyani Greenstone Belt occurs as structurally controlled lode deposits (e.g. McCourt and van Reenen, 1992; Gan and van Reenen, 1995). In shearzone-hosted gold mineralisation, gold is typically associated with arsenopyrite to such an extent that this mineral is considered to be a pathfinder mineral for gold (e.g. Ward and Wilson, 1998, for the case of the Barberton Greenstone Belt).

Figure 9. Mineral deposits in the Greater Giyani area (modified from: Council for Geoscience GIS database, 2009).

3.4. The hydrogeology of the Greater Giyani area

The hydrology of the Greater Giyani area is generally characterised by fracture-bound aquifers formed mainly within the rocks of the Goudplaats Gneiss, the Giyani Greenstone Belt and to a smaller extent the Shamariri Granite and Schiel Alkaline Complex (Haupt and Sami, 2006; Du Toit and Lelyveld, 2010 unpubl). The major rivers that transect the area are the Nsami, Klein Letaba, Shingwedzi, Middle Letaba and Groot Letaba Rivers, which flow in the easterly direction towards the Indian Ocean (Figure 10). These rivers form part of the secondary drainage region B8 and B9, which falls within the Letaba/Luvuvhu Water Management Area (WMA).

Figure 10. Hydrology of the Greater Giyani area; blue box represents the study area. http://www.dwaf.gov.za/Projects/Luvuvhu/images/FIGURE%202%20under%20WRSA.jpg

3.4.1. Goudplaats Gneiss

Based on more than 1000 boreholes analysed in the area, the Goudplaats Gneiss rocks have a moderate to good groundwater potential. The data reveals that 35% of these boreholes yield varies from 0.5 to 0.2l/s. The median yield was 2.0l/s and highest yield was 30.0l/s (Du Toit and Lelyveld, 2010 unpubl). The high yield groundwater in these rocks is associated with fractured zones, pegmatites, transitional zone between weathered and solid gneiss (deep basin of weathering), faults and shear zones, as well as along dyke contacts (Du Toit and Lelyveld, 2010 unpubl).

The groundwater quality in these aquifers, evaluated in terms of electrical conductivity (EC), using 611 analyses is highly variable and generally poor (Du Toit and Lelyveld, 2010 unpubl). About 63% of boreholes have EC values above the recommended limit of 70 mS/m for drinking water, whereas 5% have values higher than the maximum allowable limit of 300 mS/m (Du Toit and Lelyveld, 2010 unpubl). 41

3.4.2. Giyani Greenstone Belt

The highly deformed and metamorphosed Giyani Greenstone Belt is dominated by local fracture-bound aquifers formed as a result of intense folding and associated fracturing (Haupt and Sami, 2006). Drilling results in this region show that fractures are frequently intercepted at the depths of up to 80 m in the Giyani Group (Du Toit and Lelyveld, 2010 unpubl). According to the data obtained by (Du Toit and Lelyveld, 2010 unpubl) out of 80 boreholes analysed in this region, more than 30% had a yield frequency of 0.5 to 2.0l/s. The data further reveals that the highest recorded borehole yield is 21.0l/s, with a median value of 2.5l/s (Du Toit and Lelyveld, 2010 unpubl).

Based on this data the groundwater is of good quality, although some elevated nitrate and magnesium values were recorded, but their averages are below the recommended upper limit for domestic use. In terms of EC, only one point was recorded above the maximum allowable limit of 300 mS/m. The point is believed to be an anomaly and might be due to local pollution (Du Toit and Lelyveld, 2010 unpubl).

3.4.3. Shamariri Granite

In this geological region, groundwater is associated with secondary features such as fractures, quartz veins, pegmatites and a contact zone with surrounding host rock. No information is available for groundwater quality in this area (Du Toit and Lelyveld, 2010 unpubl).

3.4.4. Schiel Alkaline Complex

The ground water is confined to basins of weathering, the fracture zone between the weathered rocks and to a smaller extent in minor fractures and fissures associated with dyke intrusions (Du Toit and Lelyveld, 2010 unpubl). A highest yield of 8.0l/s and median yield of 1.2 l/s have been recorded from boreholes in this Complex (Du Toit and Lelyveld, 2010 unpubl). The groundwater quality is controlled largely by geology. It is typically alkaline and displays magnesium-sodium-calcium-bicarbonate character (Du Toit and Lelyveld, 2010 unpubl). Nitrate concentrations appear to be low, with only one sample quoted values higher than maximum allowable limit of 10mg/l. 42

3.5. Borehole water results

The borehole water samples were analysed for trace elements and physico-chemical properties (pH, Temperature and Eh) and the results are shown in (Table 17). The mean trace element concentrations decrease in the order of: Cr > As > Se > Pb > Cd and Cr >Se > As > Pb > Cd, in the dry and wet seasons respectively (Table 16).

Table 16. Summary statistics for As, Cd, Cr, Pb, Se and Cr in the dry and wet seasons.

Arsenic (As) Selenium (Se) Chromium Cadmium Lead (Pb) (Cr) (Cd) Dry Wet Dry Wet Dry Wet Dry Wet dry Wet season season season season season season season season season season Mean 11.26 10.96 7.11 28.33 33.07 28.33 0.28 0.28 6.02 6.59 (µg/l) Median 4.00 4.00 6.08 26.00 34.42 26.00 0.20 0.20 6.00 6.00 (µg/l) Standard 4.16 4.64 0.73 1.62 2.27 1.62 0.04 0.05 0.02 0.49 error Standard 22.40 24.09 3.85 8.44 12.21 8.44 0.19 0.26 0.13 2.55 deviation Minimum 1.00 4.00 1.73 17.00 10.51 17.00 0.20 0.00 6.00 6.00 (µg/l) Maximum 112.25 110.00 18.80 57.00 69.52 57.00 8.11 1.00 6.69 19.00 (µg/l) Count 29 27 29 27 29 27 29 27 29 27

The summary statistics of physico-chemical characteristics of the borehole water samples collected are shown in (Table 18). The pH of water samples during the dry season varies between 6.6 and 7.6, while during the wet season it varies between 6.8 and 7.9. The highest pH value of 7.9 was recorded at borehole BH-05, located at the Xikukwani Primary school. The lowest pH value of 6.6 was registered at borehole BH-02 from Muyexe village. The pH values for all the samples are within the South African National Standard (SANS) prescribed limits of 5-9.7 for safe drinking water (SANS, 2011). The average temperature recorded was 24.61°C and 27.93°C for the dry and wet seasons respectively. The redox potential (Eh) for the dry season lies between 154.40 Mv and 252.40 Mv, while in the wet season it lies between 179.50 Mv and 270.40 Mv (Figure 11 and 12).

Table 17. Trace element concentrations (ug/l), pH, Temp ( °C) and Eh (Mv) in borehole water of rural Greater Giyani, 2012-2013.

As (ug/l) Cd (ug/l) Cr (ug/l) Pb (ug/l) Se (ug/l) pH Temp (° C) Eh (Mv) Sample Lat Long dry season wet season dry season wet season dry season wet season dry season wet season dry season wet seasondry season wet season dry season wet season dry season wet season BH-01 -23.21533 30.85582 14.34 15.00 <0.2 <0.2 34.51 30.00 <6 <6 2.47 0.40 7.36 7.49 23.80 30.60 225.80 229.00 BH-02 -23.19422 30.92773 4.00 <4 <0.2 <0.2 41.22 29.00 <6 <6 9.23 1.00 6.64 7.30 25.50 27.80 200.60 224.30 BH-03 -23.18640 30.84905 24.20 <4 <0.2 <0.2 34.42 29.00 <6 <6 6.08 3.00 7.08 6.83 27.20 27.90 242.40 179.50 BH-04 -23.14918 30.71390 <4 <4 <0.2 <0.2 32.40 22.00 <6 <6 3.91 1.00 7.27 7.48 24.10 27.40 223.10 208.80 BH-05 -23.23069 30.71076 <4 <4 <0.2 <0.2 30.47 25.00 <6 <6 7.24 0.40 7.58 7.51 26.40 27.90 203.50 221.60 BH-06 -23.30047 30.66045 <4 <4 <0.2 <0.2 33.36 19.00 <6 <6 8.95 5.00 7.02 6.94 27.50 26.60 197.80 209.00 BH-07 -23.26763 30.85361 112.25 110.00 <0.2 <0.2 43.72 33.00 <6 <6 5.76 4.00 7.50 7.50 24.20 26.60 167.30 205.30 BH-08 -23.31408 30.89226 4.97 <4 <0.2 1.00 36.82 35.00 <6 19.00 10.38 5.00 7.54 7.92 22.30 25.90 208.30 201.50 BH-09 -23.33615 30.94734 4.00 <4 <0.2 1.00 69.52 57.00 <6 <6 2.31 0.40 7.51 7.32 25.00 28.80 173.40 201.30 BH-10 -23.44059 31.10174 4.94 <4 <0.2 0.00 39.54 36.00 <6 <6 8.39 4.00 7.16 7.11 25.40 29.30 154.40 244.80 BH-11 -23.55846 31.05078 4.00 <4 <0.2 <0.2 38.54 29.00 <6 <6 5.43 0.40 7.03 6.95 23.90 29.80 169.30 226.40 BH-12 -23.6175831.03363 4.29 - 0.24 - 32.74 - <6 - 11.76 - 6.91 - 24.50 - 201.60 - BH-13 -23.58564 30.98515 8.91 <4 0.28 <0.2 36.08 33.00 <6 <6 18.80 6.00 7.53 7.36 22.10 28.80 199.20 202.50 BH-14 -23.36381 30.72653 5.84 <4 0.56 <0.2 47.51 26.00 <6 <6 4.08 12.00 6.86 6.76 24.80 27.50 188.60 262.90 BH-15 -23.48502 30.86018 6.40 <4 0.64 1.00 30.42 25.00 <6 9.00 13.52 12.00 7.08 7.21 22.90 26.20 200.10 258.50 BH-16 -23.51504 30.95659 <4 <4 <0.2 <0.2 41.26 32.00 <6 <6 4.90 6.00 7.06 7.69 25.20 31.60 242.70 195.80 BH-17 -23.38352 30.96291 5.50 <4 <0.2 <0.2 21.79 23.00 <6 <6 10.25 9.00 6.99 7.15 27.30 27.70 186.50 270.40 BH-18 -23.54833 30.86166 <4 <4 <0.2 <0.2 31.02 24.00 <6 <6 8.67 3.00 7.21 7.12 23.90 27.90 191.00 242.30 BH-19 -23.55485 30.83848 4.14 <4 <0.2 <0.2 44.66 26.00 <6 <6 10.60 11.00 7.32 7.18 25.50 29.40 187.20 216.70 BH-20 -23.55501 30.80944 <4 <4 <0.2 <0.2 24.81 17.00 <6 <6 8.00 0.40 7.30 6.99 25.10 29.30 236.60 214.90 BH-21 -23.57311 30.86919 4.66 <4 <0.2 <0.2 41.62 42.00 <6 <6 6.05 7.00 7.21 7.00 25.80 27.00 183.30 244.20 BH-22 -23.59760 30.89654 <4 <4 <0.2 <0.2 38.93 36.00 <6 <6 4.89 1.00 7.33 7.16 25.60 27.10 209.40 233.60 BH-23 -23.42160 30.77808 <4 <4 <0.2 <0.2 24.49 23.00 <6 <6 3.11 0.40 7.42 7.30 21.10 25.40 215.40 269.30 BH-24 -23.46149 30.65719 <4 <4 0.86 <0.2 15.24 19.00 <6 <6 6.76 7.00 7.00 7.05 21.60 27.00 224.70 201.80 BH-25 -23.61014 30.69219 <4 <4 <0.2 <0.2 36.36 31.00 <6 <6 4.73 2.00 7.15 6.93 23.10 30.00 252.40 212.60 BH-26 -23.51138 30.76202 4.69 <4 <0.2 <0.2 18.95 21.00 <6 <6 10.58 7.00 7.24 7.33 22.70 27.90 211.00 213.20 BH-27 -23.40495 30.34528 <4 - 0.27 - 14.98 - <6 - 2.20 - 7.43 - 24.70 - 207.70 - BH-28 -23.31307 30.60695 61.48 75.00 0.87 <0.2 13.13 24.00 6.69 <6 1.73 0.40 7.19 7.06 26.70 26.90 211.90 206.40 BH-29 -23.25389 30.53895 <4 <4 <0.2 <0.2 10.51 19.00 <6 <6 5.37 5.00 7.06 7.06 25.80 25.80 227.90 227.10

(-) represents missing data, there were two boreholes which were not working during the second sampling campaign (wet season).

Table 18. Summary statistics for groundwater physico-chemical properties.

pH Temperature Eh (Mv) Dry Wet Dry Wet season Dry Wet season season season season season

Mean (µg/l) 7.2 7.2 24.61 27.93 204.93 223.10 Median 7.2 7.2 24.80 2780 203.50 216.70 Standard error 0.04 0.05 0.32 0.30 4.43 4.54 Standard 0.23 0.27 1.70 1.54 23.87 23.58 deviation Minimum (µg/l) 6.6 6.8 21.10 25.40 154.40 179.50 Maximum (µg/l) 7.6 7.9 27.50 31.60 252.40 270.40

120.00

80.00 Cr Pb

Con ( Conµg/l) Se 40.00 As Cd

0.00 5.00 6.00 7.00 8.00 pH

Figure 11. pH and trace elements plots for Greater Giyani groundwater.

120.00

80.00 Cr Cd

Con (µg/l) Con Se 40.00 Pb As

0.00 100.00 120.00 140.00 160.00 180.00 200.00 220.00 240.00 260.00 280.00 Eh (Mv)

Figure 12. Eh and trace elements plots for Greater Giyani groundwater.

3.5.1. Seasonal variation of trace elements concentrations

All analysed trace elements i.e. selenium, chromium, cadmium and lead (Figure 13, 14, 15 and 16), with the exception of arsenic (Figure 17) show significant seasonal variability. Peak values of chromium and selenium are recorded in the dry season, while those for lead and cadmium are higher in the wet season.

20.00 18.00 16.00 14.00 12.00 g/l)

μ 10.00 8.00 Se ( Se 6.00 4.00 2.00 0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

Se dry season Se wet season

Figure 13. Seasonal variation of selenium in Greater Giyani borehole water. The gap represents the missing samples at BH-12 and BH-27 during the wet season.

1.20 1.00 0.80

μg/l) 0.60

Cd ( 0.40 0.20 0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

Cd dry season Cd wet season

Figure 14. Seasonal variation of cadmium in Greater Giyani borehole water. The gap represents the missing samples at BH-12 and BH-27 during the wet season.

80.00 70.00 60.00 50.00

μg/l) 40.00

Cr ( 30.00 20.00 10.00 0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

Cr dry season Cr wet season

Figure 15. Seasonal variation of chromium in Greater Giyani borehole water. The gap represents the missing samples at BH-12 and BH-27 during the wet season.

20.00

15.00

μg/l) 10.00 Pb ( 5.00

0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

Pb dry season Pb wet season

Figure 16. Seasonal variation of lead in Greater Giyani water. The gap represents the missing sample at BH-12 and BH-27 during the wet season. 47

120.00 100.00 80.00 g/l)

μ 60.00

As ( As 40.00 20.00 0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

As dry season As wet season

Figure 17. Seasonal variation of arsenic in Greater Giyani borehole water. The gap represents the missing samples at BH-12 and BH-27 during the wet season.

3.5.2. Arsenic (As) concentrations in Greater Giyani borehole water

The summary statistics for arsenic concentrations in the borehole water samples from rural Greater Giyani area for both dry and wet seasons is presented in (Table 19). The arsenic concentrations during the sampling period varied widely. They ranged from below detection limit of 4.00 to 112.25 µg/l, with a mean concentration of 11.26 and 10.96 µg/l in the dry and wet seasons respectively. In general, there is no discernible difference between arsenic levels in the dry season as compared to the wet season, except for one borehole BH-28-in the Maswanganyi area that produced arsenic levels of 61 and 104 µg/l in the dry and wet seasons respectively.

Table 19. Summary statistics for arsenic concentrations during the dry and the wet seasons.

Arsenic dry season Arsenic wet season Mean (µg/l) 11.26 10.96 Standard Deviation 22.40 24.09 Minimum (µg/l) 4.00 4.00 Maximum (µg/l) 112.25 110.00 Count 29.00 27.00

Approximately 13 % of sampled boreholes have arsenic concentration in excess of the 10µg/l SANS recommended limit for safe drinking water (Figure 18). Two of these boreholes, BH- 07 at Khakhala Primary school and BH-28 at Maswanganyi village gave arsenic levels of more than five times higher than the SANS permissible limit for safe drinking water. The remaining boreholes recorded low to moderate arsenic concentrations.

Arsenic 120.00

100.00

80.00

60.00

40.00

20.00

0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

As dry season As wet season SANS limit

Figure 18. Bar chart illustration of arsenic concentration (µg/l) in borehole of Greater Giyani area.

Figure 19 shows a spatial distribution pattern of arsenic in the dry and wet seasons respectively. Anomalously high arsenic concentrations are observed in the northern part of the Giyani Greenstone Belt and generally low arsenic levels in the adjacent gneisses.

Figure 19. Arsenic spatial distribution pattern in the Greater Giyani area for dry season and wet season in relation to geology.

3.5.3. Selenium (Se) concentrations in Greater Giyani borehole water

The summary statistics for selenium concentrations is shown in (Table 20). The total selenium content varies from 1.73 to 18.80 µg/l and from 0.40 to 12.00 µg/l in the dry season and wet season respectively.

Table 20. Summary statistics for selenium in the dry and wet seasons.

Selenium dry season Selenium wet season Mean (µg/l) 7.11 4.21 Standard Deviation 3.85 3.73 Minimum (µg/l) 1.73 0.40 Maximum (µg/l) 18.80 12.00 Count 29.00 27.00

In approximately 24 % of the sampled boreholes, Se content slightly exceeded the SANS prescribed maximum permissible level for safe drinking water. The highest and lowest values were recorded at borehole BH-13 at Makhuva and BH-10 at Mhlava village respectively (Figure 20).

Selenium 20.00

15.00

10.00

5.00

0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

Se dry season Se wet season SANS limit

Figure 20. Bar chart illustration of selenium concentration in the dry and wet season.

Figure 21 shows that slightly higher values are restricted almost entirely to the gneisses, rather than in any other rock type in the area.

Figure 21. Selenium spatial distribution pattern in the Greater Giyani area for dry season in relation to geology.

3.5.4. Chromium (Cr) concentrations in Greater Giyani borehole water

The descriptive statistic values of chromium during sampling period are shown in (Table 21).

Table 21. Summary statistics of chromium in the dry and wet seasons.

Chromium dry Chromium wet season season Mean (µg/l) 33.02 28.33 Standard Deviation 12.21 8.44 Minimum (µg/l) 10.51 17.00 Maximum (µg/l) 69.52 57.00 Count 29.00 27.00

The total chromium concentrations in the study area are generally low, but relatively higher in the dry season compared to the wet season (Figure 22). Only one borehole exceeded the SANS allowable limit for safe drinking water. Though the remaining samples fall below the SANS recommended upper limit for drinking water, a large proportion of borehole water samples gave Cr levels of more than 30 µg/l in both seasons. These values are moderately high.

Chromium 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28

Cr dry season Cr wet season SANS limit

Figure 22. Bar chart representation of chromium concentration in borehole water samples of Greater Giyani area (dry and wet season).

No apparent spatial variation of Cr concentrations or correlation with different rock types could be observed in the study area (Figure 23).

Figure 23. Chromium spatial distribution pattern in the Greater Giyani area for dry season in relation to geology.

3.5.5. Lead (Pb) and Cadmium (Cd) concentrations in Greater Giyani borehole water

The summary statistics of lead and cadmium for the dry and wet season are presented in (Tables 22 and 23). The results show that in most water samples, the levels of lead and cadmium are generally low, mostly below the detection limits and they fall far below the SANS guideline standard for drinking water (Figures 24 and 25). Only one sample (BH-08) in the wet season contained unusually high Pb (19 µg/l) concentrations. This sample also had a higher than average Cd concentration.

Table 22. Summary statistics of Pb concentrations in the dry and wet season.

Lead dry season Lead wet season Mean (µg/l) 6.02 6.59 Standard Deviation 0.13 2.55 Minimum (µg/l) 6.00 6.00 Maximum (µg/l) 6.69 19.00 Count 29.00 27.00

Table 23. Summary statistics of cadmium concentration in the dry and wet seasons.

Cadmium dry season Cadmium wet season Mean (µg/l) 0.28 0.28 Standard Deviation 0.19 0.26 Minimum (µg/l) 0.20 0.00 Maximum(µg/l) 0.87 1.00 Count 29.00 27.00

Lead 20.00 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

Pb dry season Pb wet season SANS limit

Figure 24. Bar chart illustration of lead concentrations (µg/l) in the dry and wet seasons.

Cadmium 3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00 BH-01 BH-02 BH-03 BH-04 BH-05 BH-06 BH-07 BH-08 BH-09 BH-10 BH-11 BH-12 BH-13 BH-14 BH-15 BH-16 BH-17 BH-18 BH-19 BH-20 BH-21 BH-22 BH-23 BH-24 BH-25 BH-26 BH-27 BH-28 BH-29

Cd dry season Cd wet season SANS limit

Figure 25. Bar chart illustration of cadmium concentration in the dry and wet seasons.

No apparent spatial variation or correlation with rock types was observed for lead and cadmium concentrations (Figure 26 and 27).

Figure 26. Lead spatial distribution pattern in the Greater Giyani area for dry season in relation to geology.

Figure 27. Cadmium spatial distribution patterns in the Greater Giyani area for dry season in relation to geology.

3.5.6. Discussion of the borehole water results

3.5.6.1. Trace elements occurring at elevated concentrations a. Arsenic

The findings of this investigation show that out of five trace elements analysed, arsenic showed elevated concentrations as compared to other trace elements. Elevated arsenic concentrations were observed at pH values greater than 7.08 and progressively increased with an increase in pH, although most water samples with high pH have low As contents (Figure 28). Similar trends have been observed in groundwater of eastern New England (Ayotte et al., 2003) and several areas in the USA (Welch et al., 2000). Given that Fe-oxides are known to occur in the study area, the observed increase in arsenic concentrations at a higher pH suggest that desorption of iron oxides is favoured in this condition (Dzombak and Morel, 1990; Welch et al., 2000). Desorption of arsenic from iron oxides is pH dependent (Masscheleyn et al., 1991). At an elevated pH, ferric oxides generate a negative charge, which results in electrostatic repulsion of arsenic to the ferric oxides subsequent release of arsenic in the water (Welch et al., 2000).

Arsenic vs. pH 120.00

100.00

80.00 µg/l) 60.00

dry season 40.00 Arsenic Arsenic ( 20.00

0.00 6.40 6.60 6.80 7.00 7.20 7.40 7.60 7.80 pH

Figure 28. pH versus arsenic concentrations plots for ground water from Greater Giyani.

Eh negatively correlates with arsenic concentrations, thus high arsenic groundwater are associated with low Eh, although most low Eh samples have low As contents (Figure. 29). 59

This is in agreement with the high arsenic ground water found associated with low Eh from the Cu-W-As belt in SW Finland (Bodenan et al., 2004). Speciation of arsenic was not determined. However, under this condition arsenate is expected to be the dominant arsenic species (Masscheleyn et al., 1991).

Arsenic vs. Eh 120.00

100.00

80.00

60.00

40.00

dry season dry 20.00 Arsenic (µg/l) Arsenic 0.00 0.00 50.00 100.00 150.00 200.00 250.00 300.00 Eh (Mv)

Figure 29. Eh versus As concentration plots for ground waters from Greater Giyani.

Considering the spatial distribution map (Figures 25) of arsenic concentrations, it appears that arsenic occurrence is highly influenced by the surface geology and mineralisation. The boreholes with the highest arsenic concentrations are found in the Giyani Greenstone Belt, mostly along its northern margins (where gold mineralisation is also concentrated), while regional low arsenic concentrations occur in the gneisses and the southern section of the belt. These observations are consistent with that of Smedley et al., (2007), who found that high arsenic in the groundwater of Burkina Faso are spatially variable and arsenic hot spots occur close to sites of known gold mineralisation, while low arsenic groundwater are reported away from the Au-mineralisation. Similarly, arsenic-rich waters related to mineralised zones are reported in Thailand (As concentrations of up to 5000 µg/l, Brazil As levels of up to 350µg/l) and various areas in the USA (As levels of up to 48 000 µg/l) (Smedley and Kinniburgh, 2002). Notably, low concentrations of arsenic in the southern section of the belt are likely due to the lack of sulphide minerals.

3.5.6.2. Arsenic mobilisation

Arsenic concentrations in groundwater are generally low, usually below 10 µg/l, unless a specific source is present (Welch et al., 2000; Smedley and Kinniburgh, 2002). Inorganic arsenic may be attributed to either geogenic or anthropogenic origin (USGS, 1999). The observed association of arsenic-enriched ground water with an Au-mineralised zone in this study suggests that arsenic is of geogenic origin and the sulphide minerals associated with Au-ore are the primary source of arsenic (Welch et al., 2000; Smedley et al., 2007). This is corroborated by the fact that sulphide minerals such as arsenopyrite, pyrrhotite, chalcopyrite, and pyrite minerals are known to occur in the mineralised section of the GGB (Ward and Wilson, 1998) and arsenic constitutes a major component for these minerals (Welch et al., 2000; Smedley and Kinniburgh, 2002). Historic Au-ore exploitation that spans several years in Giyani area is assumed have contributed to the arsenic dispersion sources (Razo et al., 2004). The results of the current study support this assumption as high arsenic groundwater is mainly common in the vicinity of abandoned mines and arsenic concentrations decrease as the distance from the mines increases. Mining activity exposes the arsenic-bearing ore to the atmosphere and water, leading to rapid decomposition of sulphide minerals and subsequent release of arsenic (Yunmei et al., 2004). This is evident from the widespread mining dumps and tailings dams that are still present in the study area on the abandoned mining sites (Steenkamp et al., nd). These tailings are rich in metal concentrations such as Cr- 269.3, Cu- 64.2 and Zn 96.4 ppm (Ogola, 2010). Arsenic concentrations of up to 7.824ppm have also been reported at the Fumani tailings dam (Ogola, 2010).

Based on the findings presented above, arsenic mobilisation in the study area is likely controlled by oxidation of arsenic-bearing minerals (Welch et al., 2000). The oxidation of sulphide minerals generates Fe oxides, liberates As, S and leads to an acidic pH (Bodenan et al., 204; Yunmei et al., 2004). Changes in pH play a significant role in arsenic mobilisation during this process (USGS, 1999). At a low pH, arsenic as arsenate ion may be sorbed onto Fe oxide, thereby limiting arsenic availability in water (USGS, 1999). As the reaction progresses the generated low pH may be neutralised by the presence of acid buffering minerals such as calcite and aluminisilicates, resulting in alkaline pH, such as encountered in Giyani borehole waters (Bodenan et al., 2004). Alkaline pH induces desorption of arsenic from iron oxide surfaces, thus increasing arsenic concentrations in the groundwater (Masscheleyn et al., 1991). 61

3.5.6.3. Other trace elements in borehole of Greater Giyani area

a. Selenium, chromium, cadmium and lead

The results of this study show that selenium distribution in the study area is spatially highly variable, but has a narrow range of 0.4 to 29.0 µg/l. Significantly lower concentrations of selenium are found in the Greenstone Belt, which suggests the presence of low selenium containing minerals in these rocks. Slightly higher selenium concentrations occur sporadically in the gneisses. These anomalies may be attributed to the presence of selenium- bearing minerals such as selenopyrite (FeS2) (Bailey et al., 2009). Other possible sources of selenium include applications of selenium and nitrate fertilisers to agricultural soil (Hudak, 2010; Bailey et al., 2009). Nitrate has been proven to play an important role in mobilising selenium in agricultural lands through oxidation reactions (Wright, 1999). Selenium can be oxidised by dissolved nitrate in soil, releasing selenate (SeO4) in the groundwater (Bailey et al., 2009). For example, high selenium groundwaters are found to be associated with irrigated lands were nitrate is abundant (Bailey et al., 2009). Similarly, a survey of 112 wells in irrigated agricultural regions of South Texas indicates that about 5% of sampled wells exceeded the 50µg/l limit for drinking water (Hudak, 2010).

In terms of Chromium, only one borehole had concentrations above the SANS allowable limit of 10µg/l for safe drinking water. The spatial distribution map of chromium does not reveal any apparent spatial or geology-related pattern. The localised anomaly in borehole BH-09 in Mahlati village may be attributed to the presence of chromium minerals such as chromite, as suggested by Oze et al. (2007).

Cadmium was detected in only a few samples, but most of the analyses were found to be below the detection limit of 0.2 µg/l. The maximum acceptable limit for drinking water was not exceeded in any borehole, suggesting that there is no immediate health concern in the groundwater with regard to this element.

Lead was found below the detection limit of 6 µg/l in almost all boreholes, except in one borehole (BH-08) from Gaula Village during the wet season. This is to be expected, as

the Pb content in the aqueous environment is generally very low (UNEP, 2010). For instance, in the groundwater of Nigeria, lead concentrations were detected below the detection limit except for three boreholes with lead values ranging from 0.00-0.001 mg/l (Agbalagba et al., 2011). The reason for the punctual unusual lead concentrations in the study area is not certain, but it is believed that the source of lead is not geogenic. According to Katz et al. (1999) borehole casing material can affect the amount of Pb in the groundwater. In their study they found that median concentrations of lead in waters from black iron and steel cased boreholes were significantly higher than those from PVC-cased boreholes (Katz et al., 1999). Similarly, a laboratory experiment designed to compare the leaching of metal pollutants from PVC and stainless steel well casings by exposing them to groundwater for four periods ranging from 1 to 40 days showed that stainless steel released the greatest amount of lead into groundwater (Hewit, 1989).

It is therefore highly likely that borehole casing materials are the source of the unusual lead levels in borehole BH-08. This is further supported by the fact that most of the boreholes in Gaula have been reported to have stainless steel casings (GPM Consultants, 2014).

3.5.7. Conclusion

One of the current study objectives was to assess the concentration levels of PHTEs and their spatial distribution patterns in the borehole water of rural communities in the Greater Giyani area. This investigation found that out of five PHTEs considered in this study, arsenic is the only element that raises human health concerns. Elevated arsenic levels have been detected in four boreholes; with two boreholes measuring more than five times higher than the recommended SANS limit of 10µg/l for drinking water. Most of the affected boreholes are predominately underlain by the Giyani Greenstone Belt, which is known to host arsenic-containing sulphide minerals. The results of this investigation suggest that there is a strong correlation between arsenic occurrence and geology. The health implications of this result could be enormous. Chronic arsenic exposure may cause a wide range of adverse health effects, including cancer of the bladder, kidneys, lungs and skin, as well as other non- cancerous health effects such as skin tumours, hyperkeratosis, hyperpigmentation, respiratory and cardiovascular illness (Ferreccio and Sancha, 2006; Celik et al., 2008).

Lead and chromium concentrations were found to be the highest in only one borehole respectively, located in different villages. Several studies (e.g. Langard, 1990; Huvinen et al., 2002; Motzer and Engineer, 2004; Linos et al., 2011) have shown that exposure to chromium may increase risk of cancer, respiratory diseases, ulceration and perforation of the nasal tracts; while anomalous lead levels in water used for human consumption is linked to the disturbance of the functioning of the central nervous system, the haematological system and the renal system (Hutton, 1987).

In the case of the present study, is it possible that the population consuming water from the boreholes with elevated arsenic levels are at risk of arsenic poisoning? The section that follows will attempt to answer that question by evaluating the spatial relationship between arsenic occurrences in the study area and associated human health effects.

3.6. Clinical health data Results

This section explores the possible geographic relationship between cancer incidence rates and PHTEs concentrations in groundwater of the Greater Giyani area. As stated in section 1.2.5 cancer was selected for its known relationship with arsenic and because its occurrence tends to be recorded much better relatively to most common health issues especially in rural areas such as the one under investigation. The section will begin by giving a brief general health profile of the Limpopo province and then cancer counts obtained from Nkhensani Hospital will be presented and discussed.

3.6.1. Limpopo Province general health profile

In order to understand the overall health of the population in the Limpopo province, the mortality statistics were evaluated. These data were obtained from the Statistics South Africa report published in 2013. The death statistics were based on administrative records from death notification forms from the Department of Home Affairs registered between 2010 and 2012 (Statistics South Africa, 2013). The top ten causes of mortality in Limpopo province are shown in (Figure 30).

A total of 543 856 deaths (Appendix E) due to ten leading underlying causes were recorded in Limpopo in the year 2010. Of these 48% were males and 51% were females. Tuberculosis was the number one leading cause of death and accounted for 17.8% of the total number of registered deaths. Influenza and Pneumonia together accounted for 16.7% of the total lives lost, while cerebrovascular diseases and other forms of heart diseases were responsible for 11.4% and 10.4% deaths respectively. Collectively, respiratory and cardiovascular disorders, hypertensive diseases, disorders of the immune system, lower respiratory diseases and diabetes mellitus accounted for 30.9% of the total deaths. Females showed a higher incidence of deaths due to intestinal infectious diseases and various forms of heart diseases, while males showed a higher incidence of death due to tuberculosis. According to the United Nations Development Programme, (2010), Limpopo has a mortality rate for children under the age of five of 110 per thousand live births. This rate is about five times higher than the Millennium Development Goal target (MDG) of 20 per thousand live births. Furthermore, the life expectancy at birth is lower in males than in females.

10 leading causes of death: Limpopo province

Tuberculosis (A15-A19) Influenza and pneumonia (J09-J18) Intestinal infectious diseases (A00-A09) Other forms of heart disease (I30-I52) Cerebrovascular diseases (I60-I69) Diabetes mellitus (E10-E14) Human immunodeficiency virus (HIV)… Hypertensive diseases (I10-I15) Chronic lower respiratory diseases (J40-J47) Other viral diseases (B25-B34)

0 5 10 15 20 25 30 % female males both sexes

Figure 30. The top 10 causes of death in Limpopo. (Statistics South Africa, 2013).

3.6.2. Cancer incidence counts from Nkhensani Hospital

Based on the data obtained from the Nkhensani Hospital admission books, a total of 100 cancer counts were identified during the study period. Table 24 shows the cancer cases counts by type and gender recorded for the years 2011-2014. Using purely cancer counts the majority of people affected by cancer were female. The distribution of cancer in females and males by age is shown in (Figure 31 and 32) respectively.

Table 24. Total number of cancer cases by type and gender in Greater Giyani local Municipality, Limpopo, 2011-2014.

Cancer type Cancer incidence Female Male (2011-2013)

Cancer related to female 37 37 reproductive organs (Cervix, Ovary & Vulva) Breast Cancer 23 23 Prostate Cancer 18 18 Oesophagus Cancer 7 1 6 Lung Cancer 5 1 4 Bladder Cancer 2 2 Liver Cancer 2 1 1 Rectal Cancer 2 2 Pancreatic Cancer 1 1 Carcinoma (type unspecified) 2 2 Carcinoma on face 1 1 Total number of cancer incidents 100 65 35

The most common cancer types were cancer related to female reproductive organs (37 cases), breast cancer (23 cases) in females and Prostate cancer (17 cases) in males. The other most commonly diagnosed cancer types for the population of the study area were oesophageal and lung cancer; these were more prevalent among males.

Female Cancer incidence by age 16 14 12 10 8 6

Frequency 4 2 0 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76-80 81-85 86-90 91-95 96-100 101-105 Age

Figure 31. Total number of cancer cases by age in Females.

Male Cancer incidence by age 8 7 6 5 4 3

Frequency 2 1 0 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76-80 81-85 86-90 91-95 Age

Figure 32. Total number of cancer cases by age in Males.

3.6.3. Age standardised cancer incidence rates results

Table 25 illustrates age-standardised incidence rates for all cancer sites grouped by wards for males and females in the study area. The age-adjusted incidence rates ranged from 0.0 to 7.0 with a mean rate of 1.5.

Table 25. Age-Standardised incidence rates for all cancer site by wards for males and females, Greater Giyani, 2011-2014.

Ward Case counts Age adjusted (no=83) incidence rates 1 1 0.5 2 3 1.6 3 - - 4 2 0.1 5 - - 6 6 3.3 7 11 6.2 8 - - 9 1 0.6 10 3 1.7 11 - - 12 1 0.6 13 - - 14 13 7.0 15 - - 16 1 0.7 17 1 0.7 18 3 1.7 19 7 3.5 20 6 3.2 21 5 2.6 22 4 2.3 23 4 2.2 24 1 0.5 25 - - 26 - - 27 - - 28 1 0.7 29 5 2.6 30 4 2.3 Average 1.5 Maximum 7.0 Minimum 0.0 - no cancer case counts, village names did not correspond to the names in the ward area unit.

The spatial distribution map of age-standardised incidence rates of cancer by ward area unit is shown in (Figure 33). Each block on the map represents a ward area unit. Areas showed in red represents high incidence rates of cancer while areas of lighter shade of pink represent low incidence rates of cancer.

Figure 33. A thematic map showing the spatial distribution of age standardised incidence rates of cancer by wards from Greater Giyani area.

The IDW interpolation map of age-standardised incidence of cancer rates in relation to boreholes with elevated arsenic levels is shown in (Figure 34). The map reveals pronounced “hotspots” of cancer incidence rates in the north-central part of the study area.

Figure 34. Incidence rates of cancer and boreholes with arsenic concentrations of greater than SANS drinking water standard of 10µg/l surface patterns interpolated by IDW technique.

3.6.4. Discussion of the clinical health data

Numerous studies (Hopenhayn-Rich et al., 1998 Celik et al., 2008; Sohel et al., 2009; Nath et al., et al., 2013) have established a link between cancer incidence and exposure to elevated arsenic concentrations in drinking water. For example, Nath et al., (2013) assessed concentrations of arsenic in drinking water and blood samples of residents of the banks of the river Ganges in Patna, India. This study found an association between elevated arsenic concentrations in groundwater and high incidence of breast cancer, liver cancer and gall bladder cancer. Recent evidence from a Chilean study which evaluated 152 lung cancer 70

cases and 419 controls cases has also found an association of arsenic with cancer (Ferreccio, et al., 2000). Further evidence of link between cancer and arsenic exposure was found in a mortality study in Cordoba, Argentina during the study period 1986 to 1991 (Hopenhayn- Rich et al., 1996). Another study from United States involving arsenic concentrations of stream sediments and soil with estimates of smoking prevalence has found arsenic-rich sediments were significantly associated with increased risks of lung cancer (Putila and Guo, 2011).

The current analysis indicates that the most common cancer types in the study area are cancer related to female reproductive organs followed by prostate, lung and oesophageal cancer. The difference in cancer cases between the sexes is most likely attributed to the fact that women have a greater tendency to seek professional help if they feel unwell. Visual inspection of IDW interpolation map of cancer incidence rates and arsenic levels in borehole water shows that out of four As-contaminated boreholes none of the borehole occurs in the area of relatively high cancer incidence rates. Furthermore, only three young females and one male aged under 35 years have cancer, this observation provides further confirmation for a lack of association between cancer incidence rates and arsenic concentrations in drinking borehole water. This is because naturally cancer is less common in children and young adults unless carcinogenic chemicals or elements are present in their environment and or in their diet. The findings of this analysis are consistent with other previous studies which failed to find a clear link between arsenic concentrations in drinking water and cancer incidence (Michaud et al., 2004; Wheeler et al., 2013). However, the results of this analysis must be interpreted with caution as they are limited in several ways. Although, the cancer data were adjusted for age, other factors that may cause cancer such as genetics risk factors, lifestyle- related risk factor and other environmental factors other than arsenic were not considered. The small sample size with a limited number of cancer cases is another major limitation of this analysis. Other limitations pertain to the use of admission books, which may lack accuracy and completeness.

CHAPTER4: CONCLUDING REMARKS AND RECOMMENDATIONS

This study has shown that water quality problems exist in the Greater Giyani area with respect to arsenic. Markedly increased arsenic levels are found in several boreholes in the northern section of the study area. Some of these boreholes contain arsenic levels as much as five times the SANS recommended limit for drinking water. Overall, no spatial association was found between arsenic levels in borehole water and cancer incidence rates in the study area. As some contaminated boreholes are being used extensively for drinking purposes in schools and communities, it is recommended that to prevent further exposure to arsenic, affected communities and schools should be provided with an alternative safe water supply. If no other source of safe drinking water is available, immediate steps on removal of arsenic from contaminated boreholes should be undertaken. Several studies (e.g. Sancha, 2006; Van Halem, 2010) have reported on various methods that can be effectively used for arsenic removal. One such example is the Chilean study where arsenic concentrations were successfully reduced from 400µg/l to 10µg/l, the WHO recommended limit for safe drinking water (Sancha, 2006). To confirm the findings of this study better designed studies are required based on larger cancer datasets if possible. As there are multiple other factors that may also cause cancer i.e. genetic or environmental factors or an interaction of both, it is recommended that such studies should control for the effects of these factors. In addition, individual health risk assessment for people consuming water from boreholes identified to contain elevated arsenic should be conducted.

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APPENDICES

Appendix A: Borehole water locations, Greater Giyani area, Limpopo Province, South Africa. Samples Latitude Longitude Village School BH-01 -23.21533 30.85582 Mhlava BH-02 -23.19422 30.92773 Muyexe BH-03 -23.18640 30.84905 Mininginisi BH-04 -23.14918 30.71390 Dumazi BH-05 -23.23069 30.71076 Xikukwani Xikukwani ps BH-06 -23.30047 30.66045 Siyadhani Sindhani ps BH-07 -23.26763 30.85361 Khakhala Khakhala ps BH-08 -23.31408 30.89226 Gaula Solani ps BH-09 -23.33615 30.94734 Mahlathi Vusizi ps BH-10 -23.44059 31.10174 Hlomela BH-11 -23.55846 31.05078 Phalaubeni BH-12 -23.61758 31.03363 Mbawula BH-13 -23.58564 30.98515 Makhuva Albert mabe ps BH-14 -23.36381 30.72653 Ngove BH-15 -23.48502 30.86018 Xawela Baleni ps BH-16 -23.51504 30.95659 Nsavulani BH-17 -23.38352 30.96291 Ndindani Magome BH-18 -23.54833 30.86166 Guwela Nghilazi ps BH-19 -23.55485 30.83848 Mbhedle Vuyani ps BH-20 -23.55501 30.80944 Loloka-Gidja Bvuma ps BH-21 -23.57311 30.8691944 Kheyi BH-22 -23.59760 30.89654 Mushiyane BH-23 -23.42160 30.77808 Nkomo BH-24 -23.46149 30.65719 Bambeni Shamariri ps BH-25 -23.61014 30.69219 Mpagane BH-26 -23.51138 30.76202 Maphata Muswanama ps BH-27 -23.40495 30.34528 Msengi BH-28 -23.31307 30.60695 Maswanganyi BH-29 -23.25389 30.53895 Nkuri Zamani ps

Appendix A: continued Samples Latitude Longitude Village School BH-25 -23.61014 30.69219 Mpagane BH-26 -23.51138 30.76202 Maphata Muswanama ps BH-27 -23.40495 30.34528 Msengi BH-28 -23.31307 30.60695 Maswanganyi BH-29 -23.25389 30.53895 Nkuri Zamani ps

Appendix B: Trace element concentrations (µg/l) in boreholes of Greater Giyani area.

As dry As wet Cd dry Cd wet Cr dry Cr wet Pb dry Pb wet Se dry Se wet Samples Latitude Longitude season season season season season season season season season season BH-01 -23.21533 30.85582 14.34 15.00 0.20 0.20 34.51 30.00 6.00 6.00 2.47 0.40 BH-02 -23.19422 30.92773 4.00 4.00 0.20 0.20 41.22 29.00 6.00 6.00 9.23 1.00 BH-03 -23.18640 30.84905 24.20 4.00 0.20 0.20 34.42 29.00 6.00 6.00 6.08 3.00 BH-04 -23.14918 30.71390 4.00 4.00 0.20 0.20 32.40 22.00 6.00 6.00 3.91 1.00 BH-05 -23.23069 30.71076 4.00 4.00 0.20 0.20 30.47 25.00 6.00 6.00 7.24 0.40 BH-06 -23.30047 30.66045 4.00 4.00 0.20 0.20 33.36 19.00 6.00 6.00 8.95 5.00 BH-07 -23.26763 30.85361 112.25 110.00 0.20 0.20 43.72 33.00 6.00 6.00 5.76 4.00 BH-08 -23.31408 30.89226 4.97 4.00 0.20 1.00 36.82 35.00 6.00 19.00 10.38 5.00 BH-09 -23.33615 30.94734 4.00 4.00 0.20 1.00 69.52 57.00 6.00 6.00 2.31 0.40 BH-10 -23.44059 31.10174 4.94 4.00 0.20 0.00 39.54 36.00 6.00 6.00 8.39 4.00 BH-11 -23.55846 31.05078 4.00 4.00 0.20 0.20 38.54 29.00 6.00 6.00 5.43 0.40 BH-12 -23.61758 31.03363 4.29 0.24 32.74 6.00 11.76 BH-13 -23.58564 30.98515 8.91 4.00 0.28 0.20 36.08 33.00 6.00 6.00 18.80 6.00 BH-14 -23.36381 30.72653 5.84 4.00 0.56 0.20 47.51 26.00 6.00 6.00 4.08 12.00 BH-15 -23.48502 30.86018 6.40 4.00 0.64 1.00 30.42 25.00 6.00 9.00 13.52 12.00 BH-16 -23.51504 30.95659 4.00 4.00 0.20 0.20 41.26 32.00 6.00 6.00 4.90 6.00 BH-17 -23.38352 30.96291 5.50 4.00 0.20 0.20 21.79 23.00 6.00 6.00 10.25 9.00 BH-18 -23.54833 30.86166 4.00 4.00 0.20 0.20 31.02 24.00 6.00 6.00 8.67 3.00 BH-19 -23.55485 30.83848 4.14 4.00 0.20 0.20 44.66 26.00 6.00 6.00 10.60 11.00 BH-20 -23.55501 30.80944 4.00 4.00 0.20 0.20 24.81 17.00 6.00 6.00 8.00 0.40 BH-21 -23.57311 30.869194 4.66 4.00 0.20 0.20 41.62 42.00 6.00 6.00 6.05 7.00 BH-22 -23.59760 30.89654 4.00 4.00 0.20 0.20 38.93 36.00 6.00 6.00 4.89 1.00 BH-23 -23.42160 30.77808 4.00 4.00 0.20 0.20 24.49 23.00 6.00 6.00 3.11 0.40 BH-24 -23.46149 30.65719 4.00 4.00 0.86 0.20 15.24 19.00 6.00 6.00 6.76 7.00 88

Appendix B: Continued As dry As wet Cd dry Cd wet Cr dry Cr wet Pb dry Pb wet Se dry Se wet Samples Latitude Longitude season season season season season season season season season season BH-25 -23.61014 30.69219 4.00 4.00 0.20 0.20 36.36 31.00 6.00 6.00 4.73 2.00 BH-26 -23.51138 30.76202 4.69 4.00 0.20 0.20 18.95 21.00 6.00 6.00 10.58 7.00 BH-27 -23.40495 30.34528 4.00 0.27 14.98 6.00 2.20 BH-28 -23.31307 30.60695 61.48 75.00 0.87 0.20 13.13 24.00 6.69 6.00 1.73 0.40 BH-29 -23.25389 30.53895 4.00 4.00 0.20 0.20 10.51 19.00 6.00 6.00 5.37 5.00

Appendix C: Field parameters. pH dry pH wet Temp dry Temp wet Eh dry Eh wet Samples Latitude Longitude season season season season season season BH-01 -23.21533 30.85582 7.36 7.49 23.80 30.60 225.80 229.00 BH-02 -23.19422 30.92773 6.64 7.30 25.50 27.80 200.60 224.30 BH-03 -23.18640 30.84905 7.08 6.83 27.20 27.90 242.40 179.50 BH-04 -23.14918 30.71390 7.27 7.48 24.10 27.40 223.10 208.80 BH-05 -23.23069 30.71076 7.58 7.51 26.40 27.90 203.50 221.60 BH-06 -23.30047 30.66045 7.02 6.94 27.50 26.60 197.80 209.00 BH-07 -23.26763 30.85361 7.50 7.50 24.20 26.60 167.30 205.30 BH-08 -23.31408 30.89226 7.54 7.92 22.30 25.90 208.30 201.50 BH-09 -23.33615 30.94734 7.51 7.32 25.00 28.80 173.40 201.30 BH-10 -23.44059 31.10174 7.16 7.11 25.40 29.30 154.40 244.80 BH-11 -23.55846 31.05078 7.03 6.95 23.90 29.80 169.30 226.40 BH-12 -23.61758 31.03363 6.91 24.50 201.60 BH-13 -23.58564 30.98515 7.53 7.36 22.10 28.80 199.20 202.50 BH-14 -23.36381 30.72653 6.86 6.76 24.80 27.50 188.60 262.90 BH-15 -23.48502 30.86018 7.08 7.21 22.90 26.20 200.10 258.50 BH-16 -23.51504 30.95659 7.06 7.69 25.20 31.60 242.70 195.80 BH-17 -23.38352 30.96291 6.99 7.15 27.30 27.70 186.50 270.40 BH-18 -23.54833 30.86166 7.21 7.12 23.90 27.90 191.00 242.30 BH-19 -23.55485 30.83848 7.32 7.18 25.50 29.40 187.20 216.70 BH-20 -23.55501 30.80944 7.30 6.99 25.10 29.30 236.60 214.90 BH-21 -23.57311 30.869194 7.21 7.00 25.80 27.00 183.30 244.20 BH-22 -23.59760 30.89654 7.33 7.16 25.60 27.10 209.40 233.60 BH-23 -23.42160 30.77808 7.42 7.30 21.10 25.40 215.40 269.30 BH-24 -23.46149 30.65719 7.00 7.05 21.60 27.00 224.70 201.80

Appendix C: Continued pH dry pH wet Temp dry Temp wet Eh dry Eh wet Samples Latitude Longitude season season season season season season BH-25 -23.61014 30.69219 7.15 6.93 23.10 30.00 252.40 212.60 BH-26 -23.51138 30.76202 7.24 7.33 22.70 27.90 211.00 213.20 BH-27 -23.40495 30.34528 7.43 24.70 207.70 BH-28 -23.31307 30.60695 7.19 7.06 26.70 26.90 211.90 206.40 BH-29 -23.25389 30.53895 7.06 7.06 25.80 25.80 227.90 227.10 Units of measurement: Eh- = Mv and Temperature =°C

Appendix D: South African National Standard of drinking water (SANS 241-1-2011). Determinant Risk Unit Standard limit pH at 25°C Operational pH Units ≥ 5 ≤ 9.7

Arsenic (As) Chronic health µg/l ≤ 10

Cadmium (Cd) Chronic health µg/l ≤ 3

Chromium (Cr) Chronic health µg/l ≤ 50

Lead (Pb) Chronic health µg/l ≤ 10

Selenium (Se) Chronic health µg/l ≤ 10

Appendix E: Ten leading causes of death, Limpopo Province, 2010 (Statistics South Africa, 2013). 10 Leading causes of death: Limpopo Province, 2011 Both sexes Males Females Tuberculosis (A15-A19) 62 827 35 255 27 483 Influenza and pneumonia (J09-J18) 39 027 19 230 19 700 Intestinal infectious diseases (A00-A09) 27 383 12 999 14 446 Other forms of heart disease (I30-I52) 25 827 11 742 14 310 Cerebrovascular diseases (I60-I69) 24 664 10 200 14 066 Diabetes mellitus (E10-E14) 21 475 8 830 13 055 Human immunodeficiency virus (HIV) disease (B20-B24) 18 325 8 415 9 468 Hypertensive diseases (I10-I15) 14 890 7 864 9 178 Chronic lower respiratory diseases (J40-J47) 13 099 7 060 6 860 Other viral diseases (B25-B34) 12 332 5 708 5 538 Other natural causes 235 630 115 536 117 604 Non-natural causes 48 377 36 843 11 395 All causes 543 856 279 682 263 103

Appendix F: Department of Health approval letter to conduct the study.

Appendix G: Informed consent form.

Appendix H: University of Johannesburg Ethics clearance letter.

Appendix I: Cancer incidence cases, Greater Giyani area, 2011-2014. Patient_ID Diagnosis Village Latitude Longitude Gender Year_born Age Admission Comments months Adm_year P4 Prostate Cancer Gaula -23.31408 30.89226 M 1951 63 23.04.13 April 2013 P6 Prostate Cancer Guwela -23.550525 30.859943 M 1950 64 31.05.13 May 2013 P7 Prostate Cancer Ngove -23.36381 30.72653 M 1939 75 15.06.13 June 2013 P8 Prostate Cancer Nwadzekudzeku -23.117176 30.720716 M 1969 45 15.06.13 June 2013 P12 Prostate Cancer Ngove -23.364416 30.726374 M 1937 77 22.07.13 July 2013 P15 Prostate Cancer Jokong -23.427973 30.605605 M 1938 76 30.07.13 July 2013 P16 Prostate Cancer Mapayeni -23.355185 30.818308 M 1940 74 20.08.13 August 2013 P17 Prostate Cancer Mapayeni -23.355185 30.818308 M 1939 75 26.08.13 August 2013 P20 Prostate Cancer Mbledle -23.55485 30.83848 M 1936 78 18.10.13 October 2013 P23 Prostate Cancer Basani -23.348715 30.532227 M 1930 84 09.12.13 died December 2013 P25 Prostate Cancer Ngove -23.364399 30.726373 M 1939 75 05.12.11 December 2011 P27 Prostate Cancer Xikukwani -23.23069 30.71076 M 1948 65 27.01.12 January 2012 P28 Prostate Cancer Mapuve -23.280978 30.573958 M 1947 67 11.02.12 February 2012 P29 Prostate Cancer Mavalane -23.213003 30.709128 M 1948 66 14.02.12 February 2012 P33 Prostate Cancer Phalakubeni -23.558763 31.059303 M 1920 94 13.5.12 May 2012 P38 Prostate Cancer Xikukwani -23.23069 30.71076 M 1948 66 26.11.12 November 2012 P39 Prostate Cancer Makosha -23.40372 30.747926 M 1923 91 30.11.12 November 2012 P41 Prostate Cancer Mphagane -23.61014 30.69219 M 1950 64 06.03.13 March 2013 P3 Bladder Cancer Bambeni -23.48833 30.69763 M 1949 65 19.04.13 April 2013 P18 Bladder Cancer Thomo -23.254485 30.789585 M 1986 28 30.09.13 September 2013

Appendix I: Continued Patient ID Diagnosis Village Latitude Longitude Gender Year_born Age Admission Comments months Adm_year P2 Breast Cancer Bode -23.335715 30.630057 M 1945 69 P9 Breast Cancer Bode -23.335715 30.630057 F 1931 83 23.06.13 Advanced June 2013 P36 Breast Cancer Ngove -23.36381 30.72653 F 1970 44 21.10.12 June 2012 P42 Breast Cancer Shawela -23.48502 30.86018 F 12.07.12 July 2012 P47 Breast Cancer Siyandhane -23.30047 30.66045 F 1953 61 27.09.12 September 2012 P52 Breast Cancer Shawela -23.48502 30.86018 F 1975 39 2.11.12 November 2012 P53 Breast Cancer Hlaneki -23.284212 30.502004 F 1946 68 19.11.12 November 2012 P55 Breast Cancer Shawela -23.48502 30.86018 F 1960 54 26.11.12 November 2012 P56 Breast Cancer Gandhlanani -23.353859 30.502 F 1950 64 11.12.12 December 2012 P57 Breast Cancer Maswanganyi -23.31307 30.60695 F 1956 58 12.12.12 December 2012 P62 Breast Cancer Shawela -23.48502 30.86018 F 1960 54 22.03.13 March 2013 P64 Breast Cancer Siyandhane -23.30047 30.66045 F 1982 32 11.04.13 April 2013 P70 Breast Cancer Xikukwani -23.23069 30.71076 F 1963 51 28.03.13 March 2013 P77 Breast Cancer Ndindani -23.38352 30.96291 F 1988 26 25.07.13 July 2013 P81 Breast Cancer Xikukwani -23.23069 30.71076 F 1963 51 26.08.13 August 2013 P84 Breast Cancer Xikukwani -23.23069 30.71076 F 1965 49 23.09.13 September 2013 P86 Breast Cancer Hlaneki -23.284212 30.502004 F 1966 48 03.10.13 October 2013 P87 Breast Cancer Makhuva -23.583324 30.966651 F 1968 46 16.10.13 October 2013 P93 Breast Cancer Shivulani -23.148801 30.709128 F 1944 70 15.05.13 May 2013 P97 Breast Cancer Ndindani -23.38352 30.96291 F 1987 27 26.07.13 July 2013 P98 Breast Cancer Xikukwani -23.23069 30.71076 F 1963 51 26.08.13 August 2013 P100 Breast Cancer Makhuva -23.583324 30.966651 F 1978 36 16.10.13 October 2013

Appendix I: Continued Patient ID Diagnosis Village Latitude Longitude Gender Year_born Age Admission Comments months Adm_year P101 Breast Cancer Maphata -23.51138 30.76202 F 1957 57 30.12.13 December 2013 P14 Pancreatic Cancer Skimming village M 1963 51 29.07.13 July P1 Carcinoma right face Mininginisi -23.1864 30.84905 M 1956 58 died Carcinoma P46 (Unspecified) Shikhumba F 1967 47 17.07.12 July 2012 Carcinoma P92 (Unspecified) Nsavulani -23.51316 30.97288 F 1978 36 06.05.13 May 2013 P21 Cervix Cancer F 1962 52 27.11.13 November 2013 - P43 Cervix Cancer Makosha 23.256798 30.747926 F 10.08.12 August 2012 - P44 Cervix Cancer Dzumeri 23.565789 30.718212 F 14.08.12 August 2012 P45 Cervix Cancer Gaula -23.31408 30.89226 F 1935 79 2.08.12 August 2012 - P48 Cervix Cancer Ndengeza 23.262421 30.396869 F 1966 48 15.10.12 October 2012 - P49 Cervix Cancer Makhuva 23.583324 30.966651 F 1974 40 17.1.12 January 2012 - P50 Cervix Cancer Mageva 23.556332 30.731582 F 1957 57 23.10.12 October 2012 - P51 Cervix Cancer Ngove 23.364399 30.726373 F 1974 40 27.10.12 October 2012 P54 Cervix Cancer Mhlava -23.21533 30.85582 F 1948 66 22.11.12 November 2012 P59 Cervix Cancer Ndhambi -23.59843 30.82077 F 1936 78 09.02.13 February 2013 P60 Cervix Cancer Ndindani -23.38352 30.96291 F 1975 39 12.02.13 February 2013 P63 Cervix Cancer Makosha -23.40372 30.747926 F 1942 72 01.04.13 April 2013 - P65 Cervix Cancer Hlaneki 23.284212 30.502004 F 1961 53 16.04.13 April 2013 - P67 Cervix Cancer Hlaneki 23.284212 30.502004 F 1961 53 16.05.13 May 2013 - P68 Cervix Cancer Shivulani 23.148801 30.709128 F 1963 51 16.05.13 May 2013 - P69 Cervix Cancer Makosha 23.256798 30.747926 F 1944 70 14.06.13 June 2013 100

Appendix I: Continued Patient ID Diagnosis Village Latitude Longitude Gender Year_born Age Admission Comments months Adm_year P71 Cervix Cancer Maswanganyi -23.31307 30.60695 F 1977 37 04.07.13 July 2013 P72 Cervix Cancer Maswanganyi -23.31307 30.60695 F 1945 69 11.07.13 July 2013 P74 Cervix Cancer Ndindani -23.38352 30.96291 F 1975 39 23.07.13 July 2013 - P75 Cervix Cancer Homu 23.312055 30.795331 F 1963 51 25.07.13 July 2013 P76 Cervix Cancer Xikukwani -23.23069 30.71076 F 1930 84 25.07.13 July 2013 - P79 Cervix Cancer Mageva 23.556332 30.731582 F 1982 32 06.08.13 August 2013 - P80 Cervix Cancer Makhuva 23.583324 30.966651 F 1982 32 20.08.13 August 2013 P82 Cervix Cancer Xikukwani -23.23069 30.71076 F 1930 84 04.09.13 September 2013 P83 Cervix Cancer Ndindani -23.38352 30.96291 F 1975 39 20.09.13 September 2013 - P85 Cervix Cancer Hlaneki 23.284212 30.502004 F 1913 101 01.10.13 October 2013 P88 Cervix Cancer Xikukwani -23.23069 30.71076 F 1966 48 18.03.13 March 2013 - P89 Cervix Cancer Nwadzekudzeku 23.117141 30.720716 F 1948 66 20.10.13 October 2013 P90 Cervix Cancer Maswanganyi -23.31307 30.60695 F 1963 51 24.10.13 October 2013 P91 Cervix Cancer Khakhala -23.26763 30.85361 F 1947 67 28.10.13 October 2013 P94 Cervix Cancer Maswanganyi -23.31307 30.60695 F 1976 38 04.07.13 July 2013 P95 Cervix Cancer Dzingidzingi F 1945 69 11.07.13 July 2013 - P96 Cervix Cancer Homu 23.312055 30.795331 F 1963 51 25.07.13 July 2013 P99 Cervix Cancer Dzingidzingi F 1945 69 29.08.13 August 2013 P102 Cervix Cancer Maswanganyi -23.31307 30.60695 F 1970 44 17.03.14 March 2014 - P78 Ovarian Cancer Ndengeza 23.262421 30.396869 F 1930 84 01.08.13 August 2013 - P58 Vulvar Cancer Thomo 23.254485 30.789585 F 1961 53 04.02.13 February 2013

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Appendix I: Continued Patient ID Diagnosis Village Latitude Longitude Gender Year_born Age Admission Comments months Adm_year P22 Rectal Cancer Maswanganyi -23.31307 30.60695 M 57 2013 P40 Rectal Cancer Thomo -23.254485 30.789585 M 50 2013 P10 Oesophagus Cancer Mabiligwe M 1942 72 24.06.13 June 2013 P11 Oesophagus Cancer Siyandhane -23.30047 30.66045 M 1956 58 11.07.13 July 2013 P13 Oesophagus Cancer Noblehoek -23.342405 30.373815 M 1942 72 22.0713 July 2013 P19 Oesophagus Cancer Nwadzekudzeku -23.117141 30.720716 M 1960 54 17.10.13 October 2013 P26 Oesophagus Cancer Hlomela -23.38233 30.96548 M 1943 71 22.12.11 December 2011 P32 Oesophagus Cancer Ndengeza -23.262421 30.396869 M 1988 26 12.05.12 May 2012 P61 Oesophagus Cancer Nkomo -23.40372 30.78393 F 1935 79 21.02.13 February 2013 P30 Lung Cancer Thomo -23.254485 30.789585 M 1964 50 25.03.12 March 2013 P34 Lung Cancer Makoxa -23.256798 30.747926 M 1938 76 13.05.12 May 2012 P35 Lung Cancer Thomo -23.254485 30.789585 M 1964 50 19.06.12 June 2012 P37 Lung Cancer Nkomo -23.40372 30.78393 M 1964 50 26.10.12 October 2012 P79 Lung Cancer Nkomo -23.40372 30.78393 F 1960 54 18.07.13 July 2013 P31 Liver Cancer Siyandhane -23.30047 30.66045 M 1965 49 12.04.12 April 2012 P72 Liver Cancer Siyandhane -23.30047 30.66045 F 06.05.13 May 2013

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Appendix J: Mortality rates due to cancer, Greater Giyani area, 2011-2013. No Causes of death Residence Latitude Longitude Gender 0 to 4 05 to 14 15 to 25 26 to 44 45 to 59 60+ Year 1 Cervix Cancer 1 2011 2 Prostate Cancer 1 2011 3 Cancer (Unspecified) Nkomo A Female 1 2011 4 Cancer (Unspecified) Giyan sect F -23.289308 30.688906 Female 1 2011 5 Cancer (Unspecified) Mhlava Willem Female 1 2011 6 Cancer (Unspecified) Homu -23.312055 30.795331 Female 1 2011 7 Cancer (Unspecified) Sefasonke Female 1 2011 8 Cancer (Unspecified) Gawula -23.304956 30.89116 Female 1 2011 9 Cancer (Unspecified) Nsavulani -23.51316 30.97288 Female 1 2011 10 Cancer (Unspecified) Homu -23.312055 30.795331 Female 1 2011 11 Cancer (Unspecified) Mamaila Male 1 2011 12 Cancer (Unspecified) Siyandhani -23.30032 30.66083 Female 1 2011 13 Cancer (Unspecified) Mininginisi -23.150417 30.801075 Female 1 2011 14 Cancer (Unspecified) Xikukwani -23.234404 30.709128 Female 1 2011 15 Cancer (Unspecified) Matsotsesela Female 1 2011 16 Cancer (Unspecified) Giyani Sect F -23.289308 30.688906 Female 1 2011 17 Cancer (Unspecified) Bode -23.335715 30.630056 Male 1 2011 18 Cancer (Unspecified) Bode -23.335715 30.630056 Female 1 2011 19 Cancer (Unspecified) Nkomo A Female 1 2011 20 Cancer (Unspecified) Giyani Sect F -23.289308 30.688906 Female 1 2011 21 Oesophagus Cancer Mavalani -23.213003 30.709128 Female 1 2011

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Appendix J: Continued No Causes of death Residence Latitude Longitude Gender 0 to 4 05 to 14 15 to 25 26 to 44 45 to 59 60+ Year 22 Prostate Cancer Mavalani -23.213003 30.709128 Male 1 2011 23 Oesophagus Cancer Makoxa Female 1 2011 24 Oesophagus Cancer Ndengeza -23.262421 30.396869 Male 1 2012 25 Cervix Cancer Nkuri -23.253742 30.537984 Male 1 2012 26 Oesophagus Cancer Nthaveni Female 1 2012 27 Liver Cancer Siyandhani -23.30032 30.66083 Female 1 2012 28 Endometrium Cancer Giyani Sect E Female 1 2012 29 Rectal Cancer Maphata -23.505619 30.751877 Female 1 2012 30 Liver Cancer Jokong -23.427974 30.605609 Female 1 2012 31 Cervix Cancer Daniel Female 1 2012 32 Cervix Cancer Gawula -23.304956 30.89116 Female 1 2012 33 Cancer (unspecified) Dingamazi -23.373012 30.581152 Female 1 2012 34 Oesophagus Cancer Ntlhaveni -22.896102 30.908738 Female 1 2012 35 Liver Cancer Siyandhani -23.262421 30.396869 Female 1 2012 36 Oesophagus Cancer Ntlhaveni -22.896102 30.908738 Female 1 2012 37 Liver Cancer Siyandhani -23.262421 30.396869 Female 1 2012 38 Endometrium Cancer Giyani Sect E Female 1 2012 39 Oesophagus Cancer Sikhunyani -23.399171 30.673912 Male 1 2012 40 Cervix Cancer Ndengeza -23.262421 30.396869 Male 1 2012 41 Prostate Cancer Ngove -23.364418 30.726373 Female 1 2012

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Appendix J: Continued No Causes of death Residence Latitude Longitude Gender 0 to 4 05 to 14 15 to 25 26 to 44 45 to 59 60+ Year 42 Oesophagus Cancer Sikhunyani -23.399171 30.673912 Male 1 2012 43 Oesophagus Cancer Sikhunyani -23.399171 30.673912 Male 1 2012 44 Uteris Cancer Ndengeza -23.262421 30.396869 Male 1 2012 45 Uteris Cancer Mageva -23.563167 30.716314 1 2012 46 Prostate Cancer Ngove -23.364418 30.726373 Male 1 2012 47 Oesophagus Cancer Sikhunyani -23.399171 30.673912 Male 1 2012 48 Breast Cancer Nwamankena -23.375704 30.525031 Female 1 2013 49 Cervix Cancer Makoxa -23.256798 30.747926 Female 1 2013 50 Liver Cancer Siyandhani -23.262421 30.396869 Female 1 2013 51 Cancer (unspecified) Sikhunyani -23.399171 30.673912 Male 1 2013 52 Cancer (unspecified) Ndengeza -23.262421 30.396869 Male 1 2013 53 Cancer (unspecified) Mageva -23.563167 30.716314 Male 1 213 54 Cancer (unspecified) Ngove -23.36442 30.726371 Female 1 2013 55 Cancer (unspecified) Sikhunyani -23.399171 30.673912 Male 1 2013 56 Cancer (unspecified) Bode -23.335716 30.630066 Female 1 2013 57 Cancer (unspecified) Xivulani Female 1 2013 58 Cancer (unspecified) Hlomela -23.38233 30.96548 Female 1 2013 59 Cancer (unspecified) Xitlakati -23.658947 30.881779 Male 1 2013 60 Cancer (unspecified) Giyani A -23.316277 30.726373 Male 1 2013

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Appendix J: Continued No Causes of death Residence Latitude Longitude Gender 0 to 4 05 to 14 15 to 25 26 to 44 45 to 59 60+ Year 61 Cancer (unspecified) Nkuri Female 1 2013 62 Cancer (unspecified) Khaxani -23.673038 30.80682 Female 1 2013 63 Cancer (unspecified) Homu -23.312055 30.795331 Female 1 2013 64 Cancer (unspecified) Mzilela -23.587097 30.811846 Female 1 2013 65 Cancer (unspecified) Mininginisi -23.150417 30.801075 Male 1 2013 66 Cancer (unspecified) Lekgwareng Female 1 2013 67 Cancer (unspecified) Ngove -23.364418 30.726368 Female 1 2013

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Appendix K: Abstract presented at the International Medical Geology Conference in USA in August 2013

107