Volatilization rate constant

Volatilization volatilization rate constant Iq = 0.03 d-1, microcosm exptl. (Wakeham et al. 1986). 

A volatilization rate constant of 1.1 x lO Vsec was determined when pyrene on a glass surface was subjected to an air flow rate of 3 L/min at 24 °C (Cope and Kalkwarf, 1987). 

In the present work, it was desired to 1) verify the prediction that the Henry s law constant controlled variations in the experimental volatilization rate constants under constant environmental conditions, and 2) compare experimental volatilization rate constants to predicted constants using the two methods for estimating kg for water and the molecular weight adjustment procedure of Liss and Slater as discussed above. 

Samples were prepared and analyzed as reported previously (18). Because of slow concentration decreases with time, low volatilization rates relative to hydrolysis rates in some cases, and small artificial losses of pesticide due to repeated water sampling, the most accurate method of determining volatilization rate constants was to divide the average pesticide concentration for that day into the average volatilization rate over the same period (Equation 1). Rate constants for the seven days were averaged. The entire experiment was performed in triplicate. 

Experimental and predicted volatilization rate constants for the five pesticides are listed in Table II. It should be noted that, despite low H values for the pesticides, experimental volatilization rates for diazlnon and parathlon are fairly rapid from water under the conditions of our tests (t> of 4.2 and 9.6 days, respectively). When compared to their hydrolysis rate constants (Table I), volatilization can be seen to be a more important route of loss than hydrolysis for diazlnon, parathlon, and methyl parathlon. The relative volatilization rates reported here for diazlnon and parathlon are in good agreement with those reported by Lichtenstein (14). 

Except for mevinphos, agreement between experimental volatilization rate constants and rate constants predicted using k ... 

In the present study, EXAMS was used to calculate volatilization rate constants from water, wet soil, and a water-soil mixture. EXAMS uses the two-film theory to calculate volatilization rates from the 10 cm wind speed as discussed above. EXAMS requires as a minimum environment at least one littoral (water) and one benthic (sediment) compartment. A very small benthic compartment for the water system and a very small littoral compartment for the wet soil system (7.09 x 10 m3 volume and 1 x 10 8 m depth in both cases) was used, so that these compartments and their input parameters had a negligible effect on the calculated rates. For the water-soil system, the same proportions were used as in the laboratory experiment. Transfer rates between soil and water were assumed to be rapid relative to volatilization rates, and were set as recommended in the EXAMS manual (24). The input data needed by EXAMS in order to calculate volatilization rates from a water-soil system, using parathlon as an example, are shown in Table IV. 

Percents volatilized in one day for the various media were calculated using initial pesticide amounts and the overall volatilization rate constants, obtained from the half life for volatilization as output by EXAMS. Mevlnphos results are not included here, for as discussed previously, methods for calculation used in EXAMS are not appropriate for water miscible compounds. The experimental and computer predicted percents volatilized in one day are qualitatively similar (Figure 2). Quantitatively, experimental and predicted percents volatilized agreed within a factor of three for diazlnon, methyl parathion, and malathion, and within a factor of five for parathion. Considering the fact that EXAMS was not intended for use with wet soil systems, these results are encouraging. 

Table IV. EXAMS Volatilization Rate Constant Calculation for a Water-Soil System Input Data for Parathlon ...

The estimated volatilization rate constant K c, is related to the measured concentration and time by the following regression equation ... 

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