2013 Annual Report
The use of pesticides in modern agriculture has led to large increases in crop production. However, pesticide volatilization is a primary mechanism leading to the dispersion and accumulation of toxic chemicals in the environment. To assess the effects of pesticide emissions upon risks to ecosystems and human health, accurate prediction of volatilization rates is critical. The soil water content influences the partitioning of a pesticide among the soil water, gas, and sorption phases. Studies have shown that pesticide adsorption increases dramatically when the water content decreases below a critical value, which signifies the point where the solid phase is no longer covered by several molecular layers of water. Above this critical value the equilibrium vapor density is generally not affected by soil-water content. To accurately predict the volatilization rate, it is very important to account for the effects of the soil water content. This research shows the impact of soil water content on pesticide vapor adsorption to the soil particles and their effect on the diurnal pattern of pesticide emission. The results demonstrate that the daily peak emission rate depends on the soil water content, the threshold value and the vapor sorption process. Although additional study is needed, this research will one day provide a more accurate and reliable method to predict pesticide emissions and will be of great use to the scientific community, regulators, agricultural consultants and farm advisors.
During FY2012, simulations were performed using a comprehensive non-isothermal model, two water retention functions, and two soil surface resistance functions, resulting in four tested models. Results show that the degree of similarity between volatilization curves simulated using the four models depended on the initial water content, but there were indications that the simulation algorithm was numerical unstable, which brings into question the accuracy of the simulated emission rates.
During FY2013, work began to develop an improved 1-dimensional finite difference simulation algorithm that will provide more stable predictions of fumigant emissions. Compared to the model used in FY2012, the model being developed will also include additional fate and transport processes to enable a more detailed and advanced study of fumigant emissions from agricultural fields.