Location: Contaminant Fate and Transport ResearchTitle: Effects of soil moisture on the diurnal pattern of pesticide emission: Comparison of simulations with field measurements Author
|Reichman, Rivka - Israel Institute For Biological Research (IIBR)|
|Rolston, Dennis - University Of California|
Submitted to: Atmospheric Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/24/2012
Publication Date: 2/1/2013
Publication URL: http://www.ars.usda.gov/SP2UserFiles/Place/53102000/pdf_pubs/P2385.pdf
Citation: Reichman, R., Yates, S.R., Skaggs, T.H., Rolston, D.E. 2013. Effects of soil moisture on the diurnal pattern of pesticide emission: Comparison of simulations with field measurements. Atmospheric Environment. 66(2013):52-62.
Interpretive Summary: 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 paper describes an experimental study of the impact of soil water content on pesticide vapor adsorption to the soil particles and their effect on the diurnal pattern of diazinon 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 research 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.
Technical Abstract: Pesticide volatilization from agricultural soils is one of the main pathways in which pesticides are dispersed in the environment and affects ecosystems including human welfare. Thus, it is necessary to have accurate knowledge of the various physical and chemical mechanisms that affect volatilization rates from field soils. A verification of the influence of soil moisture modeling on the simulated volatilization rate, soil temperature and soil-water content is presented. Model simulations are compared with data collected in a field study that measured the effect of soil moisture on diazinon volatilization. These data included diurnal changes in volatilization rate, soil-water content, and soil temperature measured at two depths. The 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. Under fairly wet conditions, the simulated curves mainly differ in the magnitude of their deviation from the measured values. However, under intermediate and low moisture conditions, the simulated curves also differed in their pattern (shape). The model prediction accuracy depended on soil moisture. Under normal practices, where initial soil moisture is about field capacity or higher, a combination of Brooks and Corey water retention and the van de Grind and Owe soil surface resistance functions led to the most accurate predictions. However, under extremely dry conditions, when soil-water content in the top 1 cm is below the volumetric threshold value, the use of a full-range water retention function increased prediction accuracy. The different models did not affect the soil temperature predictions, and had a minor effect on the predicted soil-water content of Yolo silty clay soil.