Saied Mostaghimi

Die-off of E. coli and enterococci in dairy cowpats

E. coli and enterococci re-growth and decay patterns in cowpats applied to pasturelands were monitored during the spring, summer, fall, and winter. First-order approximations were used to determine die-off rate coefficients and decimal reduction times (D-values). Higher-order approximations and weather parameters were evaluated by multiple regression analysis to identify environmental parameters impacting in-field E. coli and enterococci decay. First-order kinetics approximated E. coli and enterococci decay rates with regression coefficients ranging from 0.70 to 0.90. Die-off rate constants were greatest in cowpats applied to pasture during late winter and monitored into summer months for E. coli (k = 0.0995 d-1) and applied to the field during the summer and monitored until December for enterococci (k = 0.0978 d-1). Decay rates were lowest in cowpats applied to the pasture during the fall and monitored over the winter (k = 0.0581 d-1 for E. coli, and k = 0.0557 d-1 for enterococci). Higher-order approximations and the addition of weather variables improved regression coefficients to values ranging from 0.82 to 0.96. Statistically significant variables used in the models for predicting bacterial decay included temperature, solar radiation, rainfall, and relative humidity. Die-off rate coefficients previously reported in the literature are usually the result of laboratory-based studies and are generally higher than the field-based seasonal die-off rate coefficients presented here. To improve predictions of in-field E. coli and enterococci concentrations, this study recommends that higher-order approximations and additional parameters such as weather variables are necessary to better capture re-growth and die-off trends over extended periods of time.

Work in progress - spiral curriculum approach to reformulate engineering curriculum

A theme-based spiral curriculum approach is being adopted to initiate the department-level reform (DLR) of the freshman engineering and the bioprocess engineering curricula at Virginia Tech. A large number of engineering faculty members are collaborating with experts in educational psychology and academic assessment to accomplish the objectives of this 3-year NSF supported project that began in September 2004. Successful implementation of the spiral approach will be used as a model for incorporating similar reforms in other engineering departments and elsewhere

Sensitivity to grid and time resolution of hydrology components of DANSAT.

A sensitivity analysis of the Dynamic Agricultural Nonpoint Source Assessment Tool (DANSAT) model to different grid sizes and time steps was conducted to investigate scale effects on hydrology and to provide users with a guideline for selecting an appropriate grid size and time step in order to enhance computational time efficiency. Response of the hydrology components to different grid sizes was analyzed by considering: (1) changes in input parameter values due to GIS manipulation processes, and (2) comprehensive response of the model through applications to a small agricultural subwatershed (QN2) in the Nomini Creek watershed, Virginia. In addition, model response to different time steps was analyzed in QN2 by changing the storm event time step (SET) of the model (1, 5, 10, 15, 30, 45, and 60 min) against a fixed grid size. A maximum acceptable grid size (MAG) of 90 m was selected for QN2, considering changes in the accuracy of spatial data for different grid sizes. Only the overall response of the hydrology components to down-scaled (60 m) grid size in QN2 was acceptable without any further calibration. Daily streamflow for storm events decreased with increases in time step from 1 to 60 min, while total runoff for the simulation period increased slightly by 8%. Use of MAG (90 m) with an acceptable larger time step (10 min) based on monthly runoff criteria exponentially reduced computational time compared to an application using the smallest grid size and time step. Site-specific sensitivity analysis is recommended due to the possible differences in response of the hydrology components to watersheds with different hydrologic characteristics.

A Stochastic Characterization of Palmer Drought Severity Index

The Palmer Drought Severity Index (PDSI) combines a set of key meteorologic and hydrologic variables to assign a numerical value for drought severity which can be used as a unified drought scale for comparison among different geographical regions. The index values are further put in several drought severity classes with highest class being the severest drought. This class assignment is taken advantage of, to formulate a time varying (non-homogeneous) Markov chain approach for characterizing the underlying drought stochastic process. The approach identifies drought prone geographical regions, persisting drought classes, periods of return to a particular drought class, and yields short term predictions for future droughts. A time homogenous Markov chain is also formulated. The average of the statistics taken over all months from the non-homogeneous Markov chain agrees quite well with that of the time homogeneous chain. Monthly statistics from the non-homogeneous chain also compare very well with the empirical results obtained with the aid of 1152 months of data for the years 1895–1990 belonging to two climatic divisions in Virginia, USA, namely the Tidewater Region the Southwest Mountains Regions. The Tidewater Region is short of water as opposed to the Southwest Mountains Regions which is water rich.