https://ir.unisa.ac.za/handle/10500/30873 2026-06-19T20:41:41Z https://ir.unisa.ac.za/handle/10500/32647 Outsourcing in the forestry industry in South Africa: a study of the indicators for long-term sustainability Van Loggerenberg, Carl The business environment is changing globally more rapidly than most individuals and organisations are willing to accept or are prepared to anticipate. The forestry industry itself is faced with various changes in the political, economical and social environment. Change is marked by the globalisation of timber markets, new technology, reformed political regulations and changing values in rural societies. Initially outsourcing was all about costs - finding someone who could do the same job, better, faster, cheaper or all three. Outsourcing then became strategic - the focus encompassing the even larger opportunity costs savings that come when an organisation reinvests the freed resources back into even higher value producing processing capabilities. Management is currently looking at their outsourcing partners to do all these things and to become sources of innovation - helping them to create new ways of doing business. However, companies often do not choose outsourcing partners with any degree of science or structure and then they fail to appreciate that business is dynamic. The deal negotiated today will probably be obsolete before pen is put to paper. Outsourcing initiatives require governance and ongoing management to ensure success. Governance ensures that the partners understand the what, when and how of the outsourcing agreement, as well as the roles and responsibilities of each partner. Ongoing management ensures cost savings don't just come from labour arbitrage but from improved productivity as well. Outsourcing does not remove the principle companies need to manage the process. A comprehensive outsourcing arrangement requires monitoring and redefining as well as strategic management and other retained functions. Every time an organisation outsources successfully, it lowers its costs, improves its balance sheet, reduces its business risks, and expands its capabilities. If outsourcing is to continue to grow over the next ten years and to be sustainable then organisations will have to produce better results with greater regularity and at lower costs. Doing this will take a collaborative effort from all the stakeholders. This research document will explore the presence of indicators for sustainable outsourcing. The approach will be to deal with both the 'hard' management tools and processes which should be uitilised and the 'softer' issues of what should be being communicated and the personal styles and attributes required to achieve outstanding sustainable results 2006-01-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32563 Determining factors affecting tax evasion in the Mercosur region Ansari, Fahad Nuzair Theoretically, fiscal revenue maximisation remains the administrative goal of any country. Undoubtedly, tax evasion has reduced the government’s tax revenues and its ability to offer public goods. Moreover, tax evasion has adverse effects on a country's economic development. It garbles the overall tax system of the country and reduces the government's tax revenues through its expenses on law enforcement. Therefore, tax evasion is an important phenomenon to study, particularly in developing countries. Variables included in tax noncompliance studies are complex tax systems, tax information, penalties, reporting requirements, the probability of audit, tax rates, age, level of education, gender, income levels, and others. After reviewing the literature, it appears that existing studies yield mixed findings, which necessitate further empirical investigations, particularly in developing countries. The present study empirically investigates the sociodemographic, economic, and cultural factors as determinants of tax evasion in developing countries, particularly in the MERCOSUR region. The study employs the Panel Autoregressive Distributed Lags (ARDL) technique for the period 1990-2022. By comparing the findings of the MG, PMG, and DFE estimators, the Hausman test results indicate that the PMG is the most consistent and efficient estimator for this dataset. Consequently, the long-run analysis is based on the PMG estimator, which reveals that the age of the taxpayer, economic development, culture and democracy have statistically significant relationships with tax evasion. While the source of income from agriculture and education has a positive relationship, this relationship is statistically weak in the MERCOSUR region. A limitation of the study is that data is not easily available for developing countries, unlike those in OECD countries. Socio-political determinants of personal income tax evasion should be further explored.; Ngokwemfundiso, ukukhulisa imali engenayo yezimali kusalokhu kuyinjongo yokuphatha yanoma yiliphi izwe. Akungabazeki ukuthi ukugwema intela kuye kwehlisa imali engenayo yentela kahulumeni kanye nekhono lakhe lokunikeza izimpahla zomphakathi. Ngaphezu kwalokho, ukugwema intela kunemiphumela emibi ekuthuthukisweni komnotho kwanoma yiliphi izwe. Kuphazamisa uhlelo lwentela oluphelele lwezwe. Kwehlisa imali engenayo yentela kahulumeni ngezindleko zakhe zomthetho. Ngakho-ke, ukugwema intela kuyinto ebaluleke kakhulu okufanele ifundwe, ikakhulukazi emazweni asathuthuka. Izinto ezifakiwe ezifundweni zokungalandeli imithetho yentela izinhlelo zentela eziyinkimbinkimbi, ulwazi lwentela, izinhlawulo, ukubika, amathuba okucwaningwa kwamabhuku, amazinga entela, ubudala, izinga lemfundo, ubulili, amazinga emali engenayo, njll. Ngemva kokubuyekeza izincwadi, kubonakala sengathi izincwadi ezikhona zinikeza imiphumela exubile, okuholela ophenyweni olwengeziwe olusekelwe ebufakazini, ikakhulukazi emazweni asathuthuka. Lolu cwaningo luhlola ngobuciko izici zezenhlalo kanye nezomnotho, kanye namasiko, njengezinto ezibangela ukugwema intela emazweni asathuthuka, ikakhulukazi esifundeni sase-MERCOSUR. Lolu Cwaningo Lusebenzisa Indlela ye-Panel Autoregressive Distributed Lags (ARDL) yesikhathi sika-1990-2022. Ngokuqhathanisa okutholakele kwabalinganisi be-MG, PMG kanye ne-DFE, imiphumela yokuhlolwa kwe-Hausman ikhombisa ukuthi i-PMG iyisilinganiso esivumelana kakhulu nesisebenza kahle salolu lwazi. Ngenxa yalokho, ukuhlaziywa kwesikhathi eside kusekelwe kusilinganiso se-PMG, esiveza ukuthi iminyaka yomkhokhi wentela, intuthuko yezomnotho, isiko kanye nentando yeningi kunobudlelwano obubalulekile ngokwezibalo nokugwema intela. Ngenkathi umthombo wemali engenayo evela kwezolimo kanye nemfundo unobudlelwano obuhle, lobu budlelwano buwubudlelwano obubuthakathaka ngokwezibalo esifundeni sase-MERCOSUR. Umkhawulo wocwaningo ukuthi idatha ayitholakali kalula emazweni asathuthuka njengalawo asemazweni e-OECD. Izinto ezibangela ukugwema intela yengeniso yomuntu siqu ngokwezenhlalo kufanele zihlolwe kabanzi. Amagama angukhiye; Teoreties bly die verhoging van fiskale inkomste die administratiewe doelwit van enige land. Daar is geen twyfel dat belastingontduiking die regering se belastinginkomste en sy vermoë om openbare goedere te verskaf, verminder het nie. Boonop het belastingontduiking negatiewe uitwerking op die ekonomiese ontwikkeling van enige land. Dit ontwrig die hele belastingstelsel van die land. Dit verminder die regering se belastinginkomste vir sy regskoste. Daarom is belastingontduiking 'n baie belangrike ding om te bestudeer, veral in ontwikkelende lande. Onderwerpe wat in belasting-nie-nakomingskursusse gedek word, is komplekse belastingstelsels, belastinginligting, boetes, verslagdoening, ouditgeleenthede, belastingkoerse, ouderdom, opvoedingsvlak, geslag, inkomstevlakke, ens. Nadat die literatuur nagegaan is, blyk dit dat die bestaande literatuur gemengde resultate verskaf, wat lei tot meer bewysgebaseerde ondersoeke, veral in ontwikkelende lande. Hierdie studie ondersoek sosiale en ekonomiese faktore, sowel as kultuur, krities as faktore agter belastingontduiking in ontwikkelende lande, veral in die MERCOSUR-streek. Hierdie studie gebruik die Panel Autoregressive Distributed Lags (ARDL) metode vir die tydperk 1990-2022. Deur die bevindinge van die MG-, PMG- en DFE-beramers te vergelyk, toon die resultate van die Hausman-toets dat die PMG die mees konsekwente en doeltreffendste beramer van hierdie inligting is. Gevolglik is die langtermyn-ontleding gebaseer op die PMG-skatting, wat aan die lig bring dat die belastingbetaler se ouderdom, ekonomiese ontwikkeling, kultuur en demokrasie 'n statisties betekenisvolle verband met belastingontduiking het. Terwyl die bron van inkomste uit landbou en onderwys 'n positiewe verband het, is hierdie verhouding statisties swak in die MERCOSUR-streek. 'n Beperking van die studie is dat data nie so geredelik beskikbaar is in ontwikkelende lande soos in OESO-lande nie. Die sosiale determinante van persoonlike inkomstebelastingontduiking moet verder ondersoek word. Text in English with abstract and keywords in Zulu and Afrikaans 2026-02-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32560 Potential value of community-based water monitoring in water resource protection decision-making in a developing country context: South Africa Nyamande, Tovhowani Brenda Over the past two decades, most developing countries, especially in Africa, have been transitioning towards water sector reform. While new water legislation in many African countries – as in South Africa – has included the environmental protection and the Polluter Pays Principle as priorities, it has given little attention to community involvement. South Africa faces many water-related challenges, the most significant being water quality deterioration, which requires constant monitoring. Water resource monitoring is essential for protecting water resources but presents challenges that constitute research gaps this study aimed to address. Key gaps include: the lack of policy support for community stakeholder participation in water resource monitoring; the exclusion of community data from water resource protection decision-making; poor integration of monitoring programmes and information sharing among different institutions; and uncertainty about the effectiveness of stakeholder engagement in reaching the intended objectives. As a result of the identified gaps, a main research question was formulated: What is the potential value of community-based water monitoring (CBWM) in water resource protection decision-making in a developing country context such as South Africa?. The main research question was informed by the five sub- questions: 1. What are the benefits, values, pitfalls, and challenges of CBWM programmes? 2. What criteria should be considered in CBWM in developing countries? 3. Based on the opinions of governance structures and the real-life experiences of communities, to what extent do the selected catchments in South Africa, as a developing country, benefit from CBWM? 4. What are the views of governance structures on the extent to which CBWM operates in South African communities? 5. How might a conceptual framework work by enhancing the use of CBWM data in decision making for developing countries? A case study research design utilising a mixed-methods research approach, incorporating both qualitative and quantitative methods, was followed to answer the research questions. The study integrated a literature review with primary data collection through three case studies. Data were gathered through interviews and a survey questionnaire. The study’s findings indicate that sustainable CBWM programmes promote community involvement in water monitoring while encouraging public ownership and trusteeship of water resources. However, the absence of appropriate guiding policy and CBWM-related challenges have hampered progress and prevented community data from contributing to decision-making. This study demonstrates the importance of a standardised protocol as a baseline for monitoring, managing data, ensuring value for money, and securing funding. The study highlighted the extent of catchments benefiting from CBWM initiatives as an early warning system and emphasised the importance of collaboration between different community governance structures for effective operation. A conceptual framework was developed for integrating CBWM data into the decision-making process to enhance the use of CBWM data for decision-making and to enhance sustainability and establish consistent information-sharing channels among relevant institutions. Engaging with the main research question yielded important insights into the role of CBWM data in decision-making. CBWM improves the capacity for monitoring water impacts, enabling more informed decisions through increased data availability. This helps bridge the gap between scientific knowledge and local community knowledge that traditional methods involving only professional scientists have overlooked. The integration of community data in water resource protection fosters a proactive management framework, allowing community members to identify potential water issues early and optimise resource allocation and protection. A key insight gained is that CBWM participation can guide both data collection and implementation of CBWM initiatives, ultimately promoting sustainable practices. This exploration underscores that CBWM not only enhances data collection, but also supports policy development and stakeholder engagement, thus filling critical gaps identified in both knowledge and practical applications within the field of sustainable water management; Kha mahumi mavhili a miṅwaha yo fhiraho, mashango a no khou bvelela, zwihulusa a dzhango ḽa Afurika, a khou shandukela kha tshanduko ya zwa maḓi. Musi milayo miswa kha mashango manzhi a dzhango ḽa Afurika, sa Afurika Tshipembe, yo katela na u tsireledza mupo na tshikafhadzo yawo i vhidzwaho “Polluter Pays Principle”, ya dovha hafhu ya katela zwiṱukuṱuku na u dzhenelela ha vhadzulapo. Afurika Tshipembe ḽi khou livhana na khaedu nnzhi dza maḓi, zwihulusa u kuna hao zwine zwa tea u dzula zwi tshi khou poswa iṱo. U ṱola zwiko zwa maḓi ndi zwa ndeme kha u tsireledza zwiko, fhedzi zwi na khaedu kana magake ane a ṱoḓa tsedzuluso ya zwine ngudo iyi ya khou ṱoḓa u bvisela khagala. Magake aya a ndeme a katela, u shaeya ha thikhedzo ya milayo kana mbekanyamaitele ya vhafaramikovhe vha tshitshavha kha u ṱhogomela zwiko zwa maḓi; u sa dzhielwa nṱha ha mawanwa a tshitshavha musi hu tshi dzhiiwa tsheo; u shaeya ha u ṱanganya mbekanyamushumo dza u ṱhogomela na u ṋekana zwidodombedzwa uya nga u fhambana ha madzangano na u sa shumisana ha vhafaramikovhe kha u swikelela zwipikwa. Nga nṱhani ha magake o waniwaho, ho bveledzwa mbudziso nthihi khulwane ya ṱhoḓuluso ‘Ndeme khulwane ya community-based water monitoring (CBWM) kha tsheo dza u tsireledza zwiko zwa maḓi kha shango li no khou bvelela sa shango la Afurika Tshipembe?’ Mbudziso khulwane nga ha thoduluso yo tutuwedzwa nga mbudziso thanu ṱhukhu dzine dza vha: 1. Ndi mbuelo, ndeme, khombo na khaedu dzifhio dza mbekanyamushumo dza CBWM? 2. Ndi nḓila dzifhio dzine dza fanela u tevhelwa kha CBWM kha mashango a no khou bvelela? 3. Zwo disendeka kha kuhumbulele kwa zwiko zwa mavhusele, na kuhumbulele kwa tshitshavha, ndi lini hune khuvhanganyo ya maḓi Afurika Tshipembe, sa shango ļi no khou bvelela ļa bindula u bva kha CBWM? 4. Ndi mbonalele ifhio uya nga ha zwiko zwa muvhuso zwine CBWM ya khou bveledzisa? 5. CBWM data I nga shumiswa hani kha u dzhiya tsheo kha mashango ane a khou bvelela? Ngudo ya tsumbo ya tsedzuluso ye ya shumisa tsedzuluso yo ṱanganelaho, yo katela tsedzuluso ya khwaļithethivi nay a khwanthithethivi ya tevhelwa nga u fhindula mbudziso dza tsedzuluso. Ngudo iyi yo shumisa nḓiļa ya ṱhoḓuluso yo ṱanganelanaho, yo katela tsedzuluso ya maṅwalwa and mawanwa o kuvhanganyiwaho kha ṱhoḓuluso tharu dza ngudo Mawanwa o wanala nga u ita nyambedzano na vhathu vho tou nangiwaho ha dovha hafhu ha netshedzwa dzinwe mbudziso. Mawanwa a tsedzuluso a sumbedza uri mbekanyamushumo dza CBWM dzi thusa tshitshavha kha u vha tshipiḓa na kha u lavhelesa kana tsireledzo ya maḓi, na u vha na vhuḓifhinduleli. Sa zwenezwo, u sa vha na milayo na khaedu dza CBWM zwi ita u ri hu sa vhe na mvelaphanḓa. Ngudo iyi I sumbedza ndeme ya u vha na ndaulo I ṱanganedzwaho nga nnyi na nnyi sa mutheo, u ri hu kone u vha na u londola kana u ṱhogomela, u ṱhogomela mawanwa (data), na u kona u vi wana ndambedzo ya masheleni. Ngudo yo bvisela khagala u ri CBWM I khou thusa tshitshavha u swika ngafhi ya dovha ya tsivhudza vhuṱhogwa ha u shumisana kha zwitshavha u ya nga u fhambana. Ho bveledzwa nḓila ya u dzhenisa mawanwa ya CBWM musi hu tshi dzhiiwa tsheo na u khwaṱhisa u ņekana nḓivho nga zwiimiswa uya nga u fhambana. Kha mbudziso khulwane ya ṱhoḓuluso, zwo bveledza u khwiņisea kha tshipida tsha u tsireledza zwiko zwa maḓi na u shumisa mawanwa a re hone. Mbuelo idzi dzi thusa u fhungudza magake vhukati ha nḓivho ya saintsi na ya vhadzulapo ya sialala. U ṱanganyisa mawanwa a vhadzulapo kha tsireledzo ya zwiko zwa maḓi zwi ṱuṱuwedza u shuma kana u langula, zwine zwa tendela vhadzulapo u kona u vhona zwi sa khou tshimbilaho zwavhuḓi na u kona u ņetshedza na u tsireledza maḓi. Mbuelo dzo wanwaho kha u shumisana na CBWM ndi u kona u sumba nḓila kha u kuvhanganya mawanwa na u sumbedza vhurangaphanḓa, zwine zwa ṱuṱuwedza tshumisano ya tshifhinga tshilapfu. Ṱhoḓulusa iyi I sumbedza vhuṱhogwa ha CBWM kha u ṱandavhudza ṱhoḓuluso na u kuvhanganya mawanwa na u tikedza kha u bveledza mbekanyamaitele na u katela vhafaramikovhe, zwine zwa vala gake vhukati ha nḓivho na nyito. Maipfi a ndeme: Tsedzuluso ya maḓi nga vhadzulapo, tsireledzo ya zwiko zwa maḓi. muangarane wa u dzhia tsheo, tshipirioni na vhupfiwa ha vhafaramikovhe, vhuṱhogwa ha u ṱola, u bindula na dzikhaudu kha vhadzulapo, ṱhanganyo ya mawanwa, Afurika Tshipembe.; Eka makume mambirhi ya malembe lama hundzeke, matiko yo tala lama hluvukaka, ngopfungopfu eAfrika, ya vile ya hundzukela eka mpfuxeto wa sekithara ya mati. Loko milawu leyintshwa ya mati ematikweni yo tala ya Afrika – tanihi le Afrika-Dzonga – yi katsa nsirhelelo wa mbango na Nsinya wa Nawu wa Nthyakiso wa Ntshikelelo tanihi swilo leswi rhangisaka emahlweni, wu nyikile nyingiso wutsongo eka ku nghenelela ka vaaki. Afrika-Dzonga ri langutane na mintlhontlho yo tala leyi fambelanaka na mati, leyi nga ya nkoka swinene i ku hohloka ka khwalithi ya mati, leswi lavaka ku vekiwa tihlo nkarhi hinkwawo. Ku vekiwa tihlo ka switirhisiwa swa mati i swa nkoka eka ku sirhelela switirhisiwa swa mati kambe swi tisa mintlhontlho leyi vumbeke swivandla swa ndzavisiso leswi ndzavisiso lowu a wu kongomisiwile ku swi lulamisa. Swivandla swa nkoka swi katsa: ku pfumaleka ka nseketelo wa pholisi eka ku nghenelela ka vakhomaxiave va vaaki eka ku vekiwa tihlo ka switirhisiwa swa mati; ku hlongoriwa ka datha ya vaaki eka ku tekiwa ka swiboho swa nsirhelelo wa switirhisiwa swa mati; ku hlanganisiwa loku nga riki kahle ka minongonoko yo veka tihlo na ku avelana mahungu exikarhi ka swivandla swo hambana; na ku nga tiyiseki mayelana na ku humelela ka ku nghenelela ka vakhomaxiave eku fikeleleni ka swikongomelo leswi kunguhatiweke. Hikwalaho ka swivandla leswi hlawuriweke, xivutiso lexikulu xa ndzavisiso xi vumbiwile ‘Hi wihi nkoka lowu nga vaka kona wa community-based water monitoring (CBWM) eka ku tekiwa ka swiboho swa nsirhelelo wa switirhisiwa swa mati eka xiyimo xa tiko leri hluvukaka ku fana na Afrika-Dzonga?’. Xivutisonkulu xa ndzavisiso xi tivisiwile hi swivutiso leswitsongo swa ntlhanu: 1. Hi yihi mimpfuno, mimpimanyeto, mintlhamu, na mintlhontlho ya minongonoko ya CBWM? 2. Hi swihi swipimelo leswi faneleke ku tekeriwa enhlokweni eka CBWM ematikweni lama hluvukaka? 3. Hi ku ya hi mavonelo ya swivumbeko swa vulawuri na mintokoto ya xiviri ya miganga, xana swihlovo leswi hlawuriweke eAfrika-Dzonga, tanihi tiko leri hluvukaka, swi vuyeriwa ku fikela kwihi eka CBWM? 4. Hi wahi mavonelo ya swivumbeko swa vulawuri eka mpimo lowu CBWM yi tirhaka ha wona eka miganga ya Afrika-Dzonga? 5. Xana rimba ra miehleketo ri nga tirha njhani hi ku ndlandlamuxa matirhiselo ya datha ya CBWM eka ku teka swiboho eka matiko lama hluvukaka? Dizayini ya ndzavisiso wa dyondzo ya xiyimo leyi tirhisaka endlelo ra ndzavisiso wa maendlelo yo hlangana, leyi katsaka maendlelo ya xiyimo na ya nhlayo, yi landzeriwile ku hlamula swivutiso swa ndzavisiso. Dyondzo yi hlanganisa nkambisiso wa matsalwa na nhlengeleto wa datha ya masungulo hi ku tirhisa tidyondzo tinharhu ta timhaka. Data yi hlengeletiwile hi ku tirhisa mimbulavurisano na ndzavisiso. Swikumiwa swa ndzavisiso swi kombisa leswaku minongonoko ya CBWM leyi nga ta tshama nkarhi wo leha yi tlakusa ku nghenelela ka vaaki eka ku vekiwa tihlo ka mati loko yi ri karhi yi hlohlotela vun’wini bya mfumo na vutshembeki bya switirhisiwa swa mati. Hambiswiritano, ku ix pfumaleka ka pholisi yo kongomisa leyi faneleke na mintlhontlho leyi fambelanaka na CBWM swi kavanyetile nhluvuko na ku sivela datha ya vaaki ku hoxa xandla eka ku teka swiboho. Dyondzo leyi yi kombisa nkoka wa phurotokholo leyi ringaniseriweke tanihi masungulo yo veka tihlo, ku lawula datha, ku tiyisisa nkoka wa mali, na ku kuma mali. Dyondzo yi kombisile mpimo wa swihlovo leswi vuyeriwaka eka migingiriko ya CBWM tanihi sisiteme ya xitsundzuxo xa le mahlweni naswona yi kandziyisile nkoka wa ntirhisano exikarhi ka swivumbeko swo hambana swa vulawuri bya vaaki ku endlela matirhelo lama humelelaka. Rimba ra miehleketo ri tumbuluxiwile ro hlanganisa datha ya CBWM eka phurosese yo teka swiboho ku ndlandlamuxa matirhiselo ya datha ya CBWM ku teka swiboho na ku ndlandlamuxa ku kondletela na ku simeka tindlela leti nga cincekiki to avelana mahungu exikarhi ka swivandla leswi faneleke. Ku nghenelela na xivutiso xa ndzavisiso, swi humesile vutivi bya nkoka lebyi ndlandlamuxaka ku twisisa ntirho wa datha ya CBWM eka ku teka swiboho. CBWM yi antswisa vuswikoti byo langutisisa ku khumbeka ka mati, ku endla leswaku ku va na swiboho leswi nga na vutivi byo tala hi ku engetela ku kumeka ka datha. Leswi swi pfuneta ku hlanganisa xivandla exikarhi ka vutivi bya sayense na vutivi bya vaaki va ndhawu lebyi tindlela ta ndhavuko to katsa vativi va sayense va xiphurofexinali ntsena ti honiseke. Ku hlanganisiwa ka datha ya vaaki eka nsirhelelo wa switirhisiwa swa mati swi kurisa rimba ra vulawuri lebyi nga na vuxiyaxiya, ku pfumelela swirho swa vaaki ku kuma timhaka leti nga vaka kona ta mati ka ha ri na nkarhi na ku antswisa avelo na nsirhelelo wa switirhisiwa. Vutivi bya nkoka lebyi kumiweke hileswaku ku nghenelela ka CBWM ku nga kongomisa havumbirhi bya nhlengeleto wa datha na ku tirhisiwa ka migingiriko ya CBWM, eku heteleleni ku tlakusa maendlelo lama nga ta tshama nkarhi wo leha. Ku lavisisa loku ku kandziyisa leswaku CBWM a yi ndlandlamuxi ntsena nhlengeleto wa datha, kambe yi tlhela yi seketela nhluvukiso wa pholisi na ku nghenelela ka vakhomaxiave, xisweswo yi tata swivandla swa nkoka leswi hlawuriweke eka vutivi na matirhiselo lama tirhaka endzeni ka xiyenge xa vulawuri bya mati lebyi nga heriki. Marito ya nkoka: Ku vekiwa tihlo ka mati loku simekiweke eka vaaki, nsirhelelo wa switirhisiwa swa mati, ku teka swiboho, rimba ra miehleketo, mavonelo na ntokoto wa vakhomaxiave, not yak u hlawuleka, mabindza na mintlhontlho ya nhlayo, hlanganiso, Afrika-Dzonga Abstract in English with Venda and Tsonga translations 2026-03-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32555 Assessing the degree of pollution of groundwater by heavy metals in and around Kolomela Mine, Northern Cape Province Tsele, Goitseone Precious This study has assessed the extent of heavy metal contamination in groundwater around the Kolomela iron ore mine in the Northern Cape Province of South Africa. A case study research design was employed, focusing on four existing groundwater monitoring boreholes (BH1–BH4) located in and around the mine. Groundwater samples were collected repeatedly over an eight-month period, thus covering different seasonal conditions to capture temporal variability or similarity in metal concentrations. Sampling followed standardised groundwater collection procedures, and laboratory analyses were conducted to determine concentrations of selected heavy metals, including Lead (Pb), Calcium (Ca), Copper (Cu), Chromium (Cr); Manganese (Mn); Nickel (Ni), Thallium (Tl); and Zinc (Zn). The data were analysed in the laboratory by means of Inductively Coupled Plasma–Optical Emission Spectroscopy and their patterns and trends were summarised by the use of descriptive statistical methods. The results were evaluated against South African drinking water standards (SANS 241) and World Health Organization (WHO) guideline values to assess groundwater quality. The results revealed a spatially and seasonally heterogeneous distribution of heavy metals across the monitored boreholes. While several metal concentrations remained within acceptable limits, elevated levels of certain metals were observed at specific boreholes and sampling periods, thus exceeding recommended guideline values and indicating potential risks to groundwater quality. These findings demonstrate that groundwater quality in mining environments may vary significantly over space and time, even within a single mining operation. The study highlights the need for improved groundwater management and regulatory oversight at Kolomela Mine, particularly in semi-arid regions where communities rely heavily on groundwater as a primary water source. The research indicates the importance of routine groundwater monitoring, adherence to national water quality standards, and transparent reporting of water quality data to regulatory authorities and affected communities. Thus, the findings provide an empirical basis to support evidence-based mine water management and policy implementation aimed at protecting groundwater resources and safeguarding environmental and public health in South Africa’s mining sector. 2026-05-01T00:00:00Z