Incorporating Environmental Fate Models into Risk Assessment for Pesticide Registration in RSA [Draft]

The dataset was generated to support the incorporation of environmental fate and transport modelling into the risk assessment process for pesticide registration in South Africa. The study aimed to reduce uncertainty in environmental exposure assessments by evaluating deterministic and probabilistic modelling approaches under South African agro-hydrological conditions. International pesticide registration practices were reviewed and compared with local regulatory frameworks to identify gaps in exposure assessment.

Field-based lysimeter experiments were conducted in maize and wheat cropping systems to quantify pesticide leaching under different soil, climatic, and management conditions. These experiments measured pesticide concentrations in drainage water and assessed leaching potential relative to international groundwater protection thresholds. In parallel, exposure scenarios representative of major South African agricultural regions were developed using national climate, soil, and land-use datasets.

Deterministic exposure models, including the Pesticide Root Zone Model (PRZM) and Pesticide Water Calculator (PWC), were parameterised for South African conditions and validated using field data. A Bayesian Network model was also developed to explicitly account for uncertainty in key environmental and physicochemical parameters influencing pesticide transport. Model outputs were integrated into a tiered risk assessment framework linking predicted environmental concentrations to ecotoxicological endpoints.

The dataset supported the conclusion that integrating modelling tools into pesticide registration would strengthen environmental protection, particularly for surface and groundwater resources, and enable more transparent, science-based regulatory decision-making in South Africa.