Abstract
Pesticides are an important tool in modern crop management to ensure high quality and high yield food production. However, pesticide Volatilisation from agricultural fields can have a significant impact on non target, sensitive ecosystems, and can result in a loss of revenue for farmers. I further developed and validated the Pesticide Loss via Volatilisation (PLOVO) model. The model is based on multiphase partitioning, that describes pesticide Volatilisation loss from a planted agricultural field and predicts 24-hour cumulative percentage Volatilisation (CPV24h) losses. The model allows the user to adjust the chemical-physical properties of the pesticide, species-specific plant surface chemistry, plant size, soil type, and climatic conditions. An average equation for the plant-air partition coefficient (Kplant-air) was found and implemented in the model to describe the plant-surface chemistry interactions with pesticides. The model was used to address questions about how the volatilisation of pesticides from plants and soil contribute to the total volatilisation from a field, and how plant species and growth stage affect the amount of pesticide lost to the atmosphere. Results are displayed on chemical space diagrams for sets of hypothetical Kplant-air, soil-air (Ksoil-air) and water-air (Kwater-air) partition coefficient combinations under different environmental conditions and plant species and growth stage. We found that the CPV24h increased with increasing temperature, foliar intercept fraction and wind speed, and with decreasing plant size. Pesticides tended to volatilise more from plants than soil. CPV24h was highly varied between plant growth stage but was less varied between plant species. Our model is therefore a promising new screening assessment tool for pesticide Volatilisation from a planted agricultural field.
Chlorpyrifos is a chlorinated organophosphorus pesticide that has caused concern over recent years due to its widespread global use and its non-specific toxicity. Chlorpyrifos is used as an insecticide to protect a wide variety of crops. Its mode of action targets the nervous system of an organism by inhibiting the enzyme acetylcholinesterase (AChE). Studies have shown that low environmental doses of chlorpyrifos can cause detrimental effects on non-target organisms such as honey bees. In order to understand the risk of chlorpyrifos exposure posed to non-target organisms, we first must understand the fate of the insecticide in the environment. Thus, I conducted a field study where chlorpyrifos was applied to an agricultural field planted with Phacelia tanacetifolia. The concentration of chlorpyrifos was then monitored in soil and Phacelia leaves over the course of three weeks. The concentration of chlorpyrifos was greater in leaves than soil, and chlorpyrifos was lost (whether through volatilisation, degradation or another loss pathway) more rapidly from leaves than soil. The chlorpyrifos field study was also used to further test and validate the PLOVO model. I compared the predicted chlorpyrifos concentrations generated by the model to the measured chlorpyrifos concentrations from the field. I found that the model-generated concentrations did not agree with the measured concentrations in either soil or leaves. This is not to say that the model does not accurately predict the volatilisation of pesticides from a planted field in the first 24 hours, rather more information would be needed to extend the model to predict the total loss of a pesticide from leaves and soil over a longer time period. In order to extract chlorpyrifos from leaves and soils, I developed and optimised two selective pressurised liquid extraction (SPLE) methods. I utilised the fat absorbing properties of Florisil and the pigment absorbing properties of graphitised carbon black (GCB) to produce clean extracts for GC/MS analysis. The recovery of chlorpyrifos from soil and leaves was 92% and 79%, respectively. While these methods were optimised for the extraction of chlorpyrifos from soil and leaf matrices, it has the potential to be extended as a multi-residue method.