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Developing Risk Models of Cryptosporidium Transport in Soils from Vegetated, Tilted Soilbox Experiments

^a Dep. of Land, Air, and Water, Univ. of California—Davis, Davis, California 95616-8628
^b Veterinary Medicine Extension, School of Veterinary Medicine, Univ. of California—Davis, Davis, California 95616
^c Veterinary Medicine Teaching and Research Center, School of Veterinary Medicine, Univ. of California—Davis, Tulare, California 93274
^d Dep. of Plant Sciences, Univ. of California—Davis, Davis, California 95616

View larger version (48K): [in this window] [in a new window]   Fig. 1. Results from the numerical modeling analysis of matrix flow and transport in the soilbox experiments: Distribution of equipotential lines, velocity field, and tracer concentration (gray-scale) for three different hydraulic conductivities at 5% slope (top left) and 20% slope (top right). Resulting breakthrough curves at the lower right hand outflow lip over a 4-h period (bottom). Due to the steady-state conditions, the equipotential lines are assumed identical for the matrix and macropore domain. Hence, the orientation of the velocity field is representative of both domains.

View larger version (19K): [in this window] [in a new window]   Fig. 2. Normal probability plot (top) and quantiles (bottom) of measured subsurface outflow rates for each of the two rainstorms (15 and 40 mm h–1, respectively). Qtot: total outflow volume (mL).

View larger version (25K): [in this window] [in a new window]   Fig. 3. Bromide breakthrough curves (BrBTC) (top panel) and Cryptosporidium BTC (CpBTC) (bottom panel) in surface runoff (first row of each of the two panels) and in subsurface outflow (second row of each of the two panels). Left: Mean, minimum, maximum, and 95% confidence interval of the mean concentration for experiments with BrBTC = 0 (bromide BTCs) or CpBTC = 0. Right: As on the left side, but for BrBTC = 1 and CpBTC = 1, respectively.

View larger version (24K): [in this window] [in a new window]   Fig. 4. Linear mixed effects (LME) model predictions (left side) for (a) macropore water content, o (cm3 cm–3), (c) macropore hydraulic conductivity, Ko (cm d–1), and (e) oocyst attenuation, eff20 (dimensionless), as a function of bulk density. The LME model predicted values are compared with measured (observed) values (right side). Comparing observed and predicted values of the three variables, the SEs are 0.67, 0.59, and 0.34, respectively. Further statistical evaluation of the LME model is given in Table 3.

View larger version (17K): [in this window] [in a new window]   Fig. 5. Linear mixed effects (LME) model predicted oocyst attenuation, eff20 (dimensionless), as a function of macropore water content, o (cm3 cm–3), and macropore hydraulic conductivity, Ko (cm d–1). Higher Ko leads to less attenuation (top). Predicted values are compared with measured values (bottom), yielding a SE of 0.32. Further statistical evaluation of the LME model is given in Table 5.

View larger version (23K): [in this window] [in a new window]   Fig. 6. Logistic regression model showing the risk for oocyst contamination at the 20-cm depth as a function of the ratio of actual subsurface outflow to maximum matrix domain outflow rate.

View larger version (19K): [in this window] [in a new window]   Fig. 7. Soil thickness needed to achieve at least eight orders of magnitude oocyst attenuation. The necessary thickness increases with soil bulk density. At the experimental settings, more soil thickness is needed for protection in low intensity precipitation than in high intensity precipitation.

View larger version (49K): [in this window] [in a new window]   Fig. 8. Spatial extent of soil thickness in California derived from the USDA-NRCS State Soil Geographic Database (STATSGO). Regions identified in black depict areas where the depth to bedrock of dominant soil components of map units is within 1.5 m of the soil surface. Regions in white represent dominant soil components of map units where the depth to bedrock is >1.5 m. (Courtesy of Dr. Anthony T. O'Geen, Dep. Land, Air, and Water Resources, University of California, Davis, CA.)