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  "alternative_identifier": "ISBN: 978-0-6392-0135-1",
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  "alternative_identifier": "ISBN: 978-0-6392-0135-1",
            
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    {
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    {
            
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      "author_department": "",
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      "author_department": "",
            
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      "author_email": "marius.claassen@up.ac.za",
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      "author_email": "marius.claassen@up.ac.za",
            
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      "author_name": "M",
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      "author_name": "M",
            
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      "author_organization": "Centre for Scientific and Industrial 
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      "author_organization": "Centre for Scientific and Industrial 
            
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Research",
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Research",
            
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      "author_surname": "Claasen"
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      "author_surname": "Claasen"
            
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    }
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    }
            
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  ],
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  ],
            
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  "citation_title": "CLAASSEN M, DABROWSKI JM, NEPFUMBADA T, VAN DER 
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  "citation_title": "CLAASSEN M, DABROWSKI JM, NEPFUMBADA T, VAN DER 
            
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LAAN M, SHADUNG J and THWALA M (2020) Incorporating environmental fate 
17
LAAN M, SHADUNG J and THWALA M (2020) Incorporating environmental fate 
            
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models into risk assessment for pesticide registration in South 
18
models into risk assessment for pesticide registration in South 
            
19
Africa. WRC Report No. 2524/1/20. Water Research Commission, 
19
Africa. WRC Report No. 2524/1/20. Water Research Commission, 
            
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Pretoria.",
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Pretoria.",
            
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      "contact_email": "marius.claassen@up.ac.za",
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      "contact_email": "marius.claassen@up.ac.za",
            
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      "contact_name": "M Claasen",
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      "contact_name": "M Claasen",
            
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      "contact_orgnization": "Centre for Scientific and Industrial 
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      "contact_orgnization": "Centre for Scientific and Industrial 
            
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Research"
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Research"
            
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  "creator_user_id": "bc28a33f-dc21-4795-a57f-da7cd1aa23ea",
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  "creator_user_id": "bc28a33f-dc21-4795-a57f-da7cd1aa23ea",
            
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  "data_classification": "static",
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  "data_structure_category": "semi-structured",
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  "data_structure_category": "semi-structured",
            
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  "dataset_language": "English",
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  "dataset_language": "English",
            
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  "dataset_license_url": 
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  "dataset_license_url": 
            
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"https://creativecommons.org/licenses/by-sa/4.0/",
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"https://creativecommons.org/licenses/by-sa/4.0/",
            
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  "did_author_or_contact_organization_collect_the_data": "false",
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  "email": "vanderLaanM@arc.agric.za",
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  "email": "vanderLaanM@arc.agric.za",
            
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  "isopen": false,
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  "isopen": false,
            
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  "keywords": "pesticides, environmental fate, risk assessment, 
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  "keywords": "pesticides, environmental fate, risk assessment, 
            
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pesticide registration, exposure modelling, groundwater, surface 
43
pesticide registration, exposure modelling, groundwater, surface 
            
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water, South Africa, PRZM, Bayesian networks",
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water, South Africa, PRZM, Bayesian networks",
            
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  "license": "Open (Creative Commons)",
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  "license": "Open (Creative Commons)",
            
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  "license_id": null,
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  "license_title": null,
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  "metadata_created": "2026-02-20T13:37:00.849569",
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  "metadata_modified": "2026-10-11T15:22:30.328429",
            
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  "minimum_maximum_extent": [
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  "minimum_maximum_extent": [
            
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    {
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    {
            
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      "maximum_vertical_extent": "3451",
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      "maximum_vertical_extent": "3451",
            
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      "minimum_vertical_extent": "0"
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      "minimum_vertical_extent": "0"
            
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    }
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  ],
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  "name": 
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  "name": 
            
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onmental-fate-models-into-risk-assessment-for-pesticide-registration",
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onmental-fate-models-into-risk-assessment-for-pesticide-registration",
            
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  "notes": "The dataset was generated to support the incorporation of 
60
  "notes": "The dataset was generated to support the incorporation of 
            
61
environmental fate and transport modelling into the risk assessment 
61
environmental fate and transport modelling into the risk assessment 
            
62
process for pesticide registration in South Africa. The study aimed to 
62
process for pesticide registration in South Africa. The study aimed to 
            
63
reduce uncertainty in environmental exposure assessments by evaluating 
63
reduce uncertainty in environmental exposure assessments by evaluating 
            
64
deterministic and probabilistic modelling approaches under South 
64
deterministic and probabilistic modelling approaches under South 
            
65
African agro-hydrological conditions. International pesticide 
65
African agro-hydrological conditions. International pesticide 
            
66
registration practices were reviewed and compared with local 
66
registration practices were reviewed and compared with local 
            
67
regulatory frameworks to identify gaps in exposure 
67
regulatory frameworks to identify gaps in exposure 
            
68
assessment.\r\n\r\nField-based lysimeter experiments were conducted in 
68
assessment.\r\n\r\nField-based lysimeter experiments were conducted in 
            
69
maize and wheat cropping systems to quantify pesticide leaching under 
69
maize and wheat cropping systems to quantify pesticide leaching under 
            
70
different soil, climatic, and management conditions. These experiments 
70
different soil, climatic, and management conditions. These experiments 
            
71
measured pesticide concentrations in drainage water and assessed 
71
measured pesticide concentrations in drainage water and assessed 
            
72
leaching potential relative to international groundwater protection 
72
leaching potential relative to international groundwater protection 
            
73
thresholds. In parallel, exposure scenarios representative of major 
73
thresholds. In parallel, exposure scenarios representative of major 
            
74
South African agricultural regions were developed using national 
74
South African agricultural regions were developed using national 
            
75
climate, soil, and land-use datasets.\r\n\r\nDeterministic exposure 
75
climate, soil, and land-use datasets.\r\n\r\nDeterministic exposure 
            
76
models, including the Pesticide Root Zone Model (PRZM) and Pesticide 
76
models, including the Pesticide Root Zone Model (PRZM) and Pesticide 
            
77
Water Calculator (PWC), were parameterised for South African 
77
Water Calculator (PWC), were parameterised for South African 
            
78
conditions and validated using field data. A Bayesian Network model 
78
conditions and validated using field data. A Bayesian Network model 
            
79
was also developed to explicitly account for uncertainty in key 
79
was also developed to explicitly account for uncertainty in key 
            
80
environmental and physicochemical parameters influencing pesticide 
80
environmental and physicochemical parameters influencing pesticide 
            
81
transport. Model outputs were integrated into a tiered risk assessment 
81
transport. Model outputs were integrated into a tiered risk assessment 
            
82
framework linking predicted environmental concentrations to 
82
framework linking predicted environmental concentrations to 
            
83
ecotoxicological endpoints.\r\n\r\nThe dataset supported the 
83
ecotoxicological endpoints.\r\n\r\nThe dataset supported the 
            
84
conclusion that integrating modelling tools into pesticide 
84
conclusion that integrating modelling tools into pesticide 
            
85
registration would strengthen environmental protection, particularly 
85
registration would strengthen environmental protection, particularly 
            
86
for surface and groundwater resources, and enable more transparent, 
86
for surface and groundwater resources, and enable more transparent, 
            
87
science-based regulatory decision-making in South Africa.",
87
science-based regulatory decision-making in South Africa.",
            
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  "organization": {
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2025-11-17T15:41:25.389790",
            
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    "description": "The WRC was established in terms of the Water 
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    "description": "The WRC was established in terms of the Water 
            
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Research Act (Act No 34 of 1971), following a period of serious water 
94
Research Act (Act No 34 of 1971), following a period of serious water 
            
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shortage. It was deemed to be of national importance to generate new 
95
shortage. It was deemed to be of national importance to generate new 
            
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knowledge and to promote the country\u2019s water research 
96
knowledge and to promote the country\u2019s water research 
            
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purposefully, owing to the view held that water would be one of South 
97
purposefully, owing to the view held that water would be one of South 
            
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Africa\u2019s most limiting factors in the 21st century. In 1971 when 
98
Africa\u2019s most limiting factors in the 21st century. In 1971 when 
            
99
the WRC was founded, water research and development (R&D) in South 
99
the WRC was founded, water research and development (R&D) in South 
            
100
Africa was limited to a few institutions and the funding level 
100
Africa was limited to a few institutions and the funding level 
            
101
inadequate. There was no research co-ordination and an apparent 
101
inadequate. There was no research co-ordination and an apparent 
            
102
neglect of some key research fields. In addition, there was little 
102
neglect of some key research fields. In addition, there was little 
            
103
strategic direction or leadership that would provide for the 
103
strategic direction or leadership that would provide for the 
            
104
identification of priority areas or appropriate technology transfer. 
104
identification of priority areas or appropriate technology transfer. 
            
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It was to address these issues, that the WRC was established.",
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It was to address these issues, that the WRC was established.",
            
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    "is_organization": true,
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    "title": "Water Research Commission",
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  "publication_date": "2020-04-01",
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  "publication_date": "2020-04-01",
            
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      "dataset_description": "Model parameters for case study nodes, 
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      "dataset_description": "Model parameters for case study nodes, 
            
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      "name": "Model parameters for selected case study nodes_14 
            
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  "tags": [],
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  "tags": [],
            
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  "title": "Incorporating Environmental Fate Models into Risk 
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  "title": "Incorporating Environmental Fate Models into Risk 
            
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Assessment for Pesticide Registration in RSA",
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Assessment for Pesticide Registration in RSA",
            
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  "type": "dataset",
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  "type": "dataset",
            
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}
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}