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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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      "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_classification": "static",
            
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  "data_collection_organization": "CSIR",
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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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  "email": "vanderLaanM@arc.agric.za",
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  "email": "vanderLaanM@arc.agric.za",
            
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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 
49
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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  "metadata_modified": "2026-04-07T11:15:00.702454",
            
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      "maximum_vertical_extent": "3451",
            
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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 
66
  "notes": "The dataset was generated to support the incorporation of 
            
67
environmental fate and transport modelling into the risk assessment 
67
environmental fate and transport modelling into the risk assessment 
            
68
process for pesticide registration in South Africa. The study aimed to 
68
process for pesticide registration in South Africa. The study aimed to 
            
69
reduce uncertainty in environmental exposure assessments by evaluating 
69
reduce uncertainty in environmental exposure assessments by evaluating 
            
70
deterministic and probabilistic modelling approaches under South 
70
deterministic and probabilistic modelling approaches under South 
            
71
African agro-hydrological conditions. International pesticide 
71
African agro-hydrological conditions. International pesticide 
            
72
registration practices were reviewed and compared with local 
72
registration practices were reviewed and compared with local 
            
73
regulatory frameworks to identify gaps in exposure 
73
regulatory frameworks to identify gaps in exposure 
            
74
assessment.\r\n\r\nField-based lysimeter experiments were conducted in 
74
assessment.\r\n\r\nField-based lysimeter experiments were conducted in 
            
75
maize and wheat cropping systems to quantify pesticide leaching under 
75
maize and wheat cropping systems to quantify pesticide leaching under 
            
76
different soil, climatic, and management conditions. These experiments 
76
different soil, climatic, and management conditions. These experiments 
            
77
measured pesticide concentrations in drainage water and assessed 
77
measured pesticide concentrations in drainage water and assessed 
            
78
leaching potential relative to international groundwater protection 
78
leaching potential relative to international groundwater protection 
            
79
thresholds. In parallel, exposure scenarios representative of major 
79
thresholds. In parallel, exposure scenarios representative of major 
            
80
South African agricultural regions were developed using national 
80
South African agricultural regions were developed using national 
            
81
climate, soil, and land-use datasets.\r\n\r\nDeterministic exposure 
81
climate, soil, and land-use datasets.\r\n\r\nDeterministic exposure 
            
82
models, including the Pesticide Root Zone Model (PRZM) and Pesticide 
82
models, including the Pesticide Root Zone Model (PRZM) and Pesticide 
            
83
Water Calculator (PWC), were parameterised for South African 
83
Water Calculator (PWC), were parameterised for South African 
            
84
conditions and validated using field data. A Bayesian Network model 
84
conditions and validated using field data. A Bayesian Network model 
            
85
was also developed to explicitly account for uncertainty in key 
85
was also developed to explicitly account for uncertainty in key 
            
86
environmental and physicochemical parameters influencing pesticide 
86
environmental and physicochemical parameters influencing pesticide 
            
87
transport. Model outputs were integrated into a tiered risk assessment 
87
transport. Model outputs were integrated into a tiered risk assessment 
            
88
framework linking predicted environmental concentrations to 
88
framework linking predicted environmental concentrations to 
            
89
ecotoxicological endpoints.\r\n\r\nThe dataset supported the 
89
ecotoxicological endpoints.\r\n\r\nThe dataset supported the 
            
90
conclusion that integrating modelling tools into pesticide 
90
conclusion that integrating modelling tools into pesticide 
            
91
registration would strengthen environmental protection, particularly 
91
registration would strengthen environmental protection, particularly 
            
92
for surface and groundwater resources, and enable more transparent, 
92
for surface and groundwater resources, and enable more transparent, 
            
93
science-based regulatory decision-making in South Africa.",
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science-based regulatory decision-making in South Africa.",
            
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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 
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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 
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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 
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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 
103
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 
104
Africa\u2019s most limiting factors in the 21st century. In 1971 when 
            
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the WRC was founded, water research and development (R&D) in South 
105
the WRC was founded, water research and development (R&D) in South 
            
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Africa was limited to a few institutions and the funding level 
106
Africa was limited to a few institutions and the funding level 
            
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inadequate. There was no research co-ordination and an apparent 
107
inadequate. There was no research co-ordination and an apparent 
            
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neglect of some key research fields. In addition, there was little 
108
neglect of some key research fields. In addition, there was little 
            
109
strategic direction or leadership that would provide for the 
109
strategic direction or leadership that would provide for the 
            
110
identification of priority areas or appropriate technology transfer. 
110
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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  "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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}