Browsing by Author "Shukla, Sanjay"

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    Assessment of groundwater vulnerability to pesticide contamination in Albemarle and Louisa counties, Virginia
    Shukla, Sanjay (Virginia Tech, 1995)
    Groundwater contamination potential by pesticide was evaluated in Albemarle and Louisa counties of the Thomas Jefferson Planning District in Virginia. A qualitative method was developed to perform an assessment of pesticide contamination potential, using an existing pesticide screening model. The Attenuation Factor (AF), was selected for assessment of groundwater vulnerability to pesticide contamination in Albemarle and Louisa counties. Input data availability, consideration of the major transport processes, and ease of its linkage with a suitable geographic information system (GIS) were the main factors considered for selection of the AF model. The input data requirement of the AF model includes soil, hydrogeologic, and pesticide chemical characteristics. An extensive database was developed to perform AF model simulations within a GIS. The database developed for this study included map databases (resolution = 1/9 ha) for landuse, soils, groundwater recharge, and groundwater depth, and non-spatial (relational tables) databases for pesticide chemical characteristics, SCS curve number, and soil properties. A total of 12 landuse categories were identified for Albemarle and Louisa counties. Groundwater recharge, an input to the AF model, was estimated using a water balance model. Runoff and evapotranspiration components of the water balance model were estimated using SCS curve number (CN), and Thornthwaite’s methods, respectively. Forty years of climatological data records were used for estimating groundwater recharge. Two types of groundwater depths, spatially varying and a constant depth of 2 m, were used for computing AF, The groundwater depth was mapped using the information available in groundwater well completion reports. All the data layers were overlaid within a GIS for spatial computation of AF for actual and 2m groundwater depth. This spatial (map) database was categorized into five categories of pollution potential namely, high, medium, low, very low, and unlikely, based on the numerical values of the AF. For evaluating the contamination potential of pesticides, three pesticide leaching potential scenarios were considered in order to facilitate the evaluation of pesticide leaching under maximum, average and minimum cases of degradation and sorption in the soil. A combination of high, average, and low values of half life and sorption coefficient were selected for three leaching scenarios. A total of six simulations were performed (two groundwater depths and three leaching scenarios) for each pesticide. Toxicity of the pesticides was not considered in the contamination potential assessment in this study. A total of 11 relatively mobile pesticides were identified in Albemarle and Louisa counties, based on the results for various leaching scenarios. Groundwater contamination potential maps were produced for mobile pesticides and the results were discussed. Picloram was identified to be the most mobile pesticide in the two counties. Atrazine, carbofuran, metolachlor, simazine and triclopyr were found to have considerable potential to move to the groundwater. Contamination potential of three herbicides, atrazine, simazine and metolachlor, was predicted to be higher than other pesticides in light of the fact that they are often used in combination (tank mixed) for a wide variety of weed control. Dicamba was found to be the most heavily-used pesticide with regard to its area of application. In light of dicamba's moderate contamination potential and its higher usage amount this herbicide was identified to have a considerable potential to move to the groundwater, especially, in Albemarle County. Other pesticides such as fenarimol, lindane, metalaxyl, and metsulfuron methyl were shown to be relatively more mobile than some other pesticides. However, in light of small application areas for these pesticides, the contamination potential of these pesticides was predicted to be relatively small. In addition to 11 mobile pesticides, a few low mobility pesticides were also identified. Among the low mobility pesticides, diazinon was found to have relatively high contamination potential. Comparison between the contamination potential maps and the groundwater recharge map revealed that most of the high contamination potential areas coincided with the higher groundwater recharge regions in the two counties. Soil characteristics such as organic matter and percent sand and clay were also observed to affect the contamination potential of pesticides. The performance of the AF model was evaluated by using six years of groundwater monitoring data from the Nomini Creek watershed study. Two types of rankings were made, one using the AF model simulation results, while the other ranking was based on the frequency of detection of pesticides in the Nomini Creek watershed. A comparison of the rankings revealed that the AF model performed fairly well in identifying the top few mobile pesticides. Sensitivity analysis was performed to identify the important parameters affecting the contamination potential of pesticides. Results of the sensitivity analysis revealed that considerable uncertainty in the model prediction can be invoked due to the variability in the soil and chemical data. To make a better use of results of this study, it was recommended that groundwater monitoring be performed in the two counties to verify the so that results of this study. The results of this study will provide information about the potential threat to groundwater by pesticides to the citizens and policymakers in the two counties.
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    Impacts of Best Management Practices on Nitrogen Discharge From a Virginia Coastal Plain Watershed
    Shukla, Sanjay (Virginia Tech, 2000-10-30)
    Long-term watershed and field nitrogen (N) balances were used in this study to quantify the surface (baseflow) and ground water lag times and effects of BMPs on N discharge from a Virginia Coastal Plain watershed. Ten-year water quantity/quality data (1986-1996) collected at the Nomini Creek (NC) watershed were used. Field (Field-N) and watershed (Watershed-N) scale N models were developed for computing the N balances. BMPs evaluated in this study included no-till corn and split N application. The role of atmospheric N (atm-N) deposition (dry+wet) in masking the effects of BMPs on watershed N loading was also investigated. Nitrogen retention and discharge from the forest areas in the NC watershed were simulated using the 5-year water and N input and output data from forested subwatersheds. Field and watershed N balances (WNBAL) were used to evaluate the effects of BMPs on measured surface and ground water N in the NC watershed. A 6-month laboratory study was conducted to develop N mineralization (Nmin) models for agricultural, forest, and fallow soils in the NC watershed. Mineralization potential (N0) and rate constants (k) for surface and subsurface soils from agricultural, forest, and fallow soils were estimated by fitting the laboratory measured data to a first-order model, using the nonlinear regression procedure. A large variability (300%, 163 - 471 kg/ha) in N0 of agricultural surface soils was observed. On average, forest soils had much higher potentially mineralizable N than agricultural soils. The first-order model was incorporated into the Field-N model to predict daily Nmin using the measured N0 and k and daily values of soil water and temperature. Atmospheric deposition was a major source of N in the NC watershed, accounting for 23% of the total N input. Variation in atm-N deposition during the 10-year period was from 10 to 42 kg/ha (average = 25 kg/ha); much larger than the variation in fertilizer N (37 to 51 kg/ha). Atm-N deposition was found to be a controlling factor affecting surface water DIN (dissolved inorganic N) and TDN (total dissolved N) loading in the NC watershed; an indication that atm-N deposition is a masking factor in the BMP impact evaluation. Large uncertainty in atm-N deposition existed due to uncertainty involved in quantifying dry N deposition. Forested areas of the NC watershed retained 77% of the atm-N deposition. Forest area N discharge was simulated using the 77% retention and annual atmospheric deposition. Comparison of Field-N predicted N balance and leaching (steady-state and transient conditions) with observed ground water NO3 concentration revealed that the ground water lag time ranged from 2 to 8 months. Unusually rapid transport of solute in the watershed was facilitated by the network of discontinuous clay lenses. Based on the lag time, the pre-BMP (1986-1990) and post-BMP (1991-1995) periods were defined. Results from Field-N indicated that implementation of split fertilizer N on corn reduced the post-BMP ground water NO3 concentration by 10-12% at two of the four ground water monitoring sites. The split N application reduced the frequency of detection of high NO3 (> 9 mg/l) concentration by 44% during the post-BMP period. Considerably large uncertainty existed in evaluating the effects of BMPs on ground water NO3 due to N contributions from neighboring agricultural and forest areas. Effects of no-till corn could not be evaluated since this BMP was already implemented at the sites prior to the beginning of the study. Results of statistical trend analysis of the ground water N supported the modeling results. Watershed-N model was able to accurately predict the effects of land use activities on watershed N balances (WNBAL) and baseflow and ground water N. A one-to-one relationship between the WNBAL and observed N loading and concentration time series was observed. Comparison of WNBAL and measured baseflow N revealed that the baseflow lag time or residence time was between 4-11 months. Multivariate regression models were developed to predict baseflow N using Watershed-N results. The multivariate model predicted the N loading and concentration exceptionally well (R2 > 90%). Corn N input and output and acreage was an important predictor of ground water N and baseflow N loading and concentration. Post-BMP WNBAL was considerably less than the WNBAL for the pre-BMP period. However, these reductions were mainly due to the 43% reductions in atm-N deposition and 31% increase in the plant uptake during the post-BMP period. Reductions in WNBAL caused by BMPs were only 5%. Reductions in N loading caused by BMPs were 10%. Statistical trend analysis of monitoring and modeling results indicated significant post-BMP reductions in WNBAL and DIN and TDN loading. However, poor to moderate evidence was available to suggest that BMPs caused a significant reductions in WNBAL and N loading. Marginal effects of BMPs could mainly be attributed to insufficient BMP implementation. Watershed-N was used to evaluate N reduction scenarios and to design BMPs. Irrigating corn was one of the best BMPs, as it could reduce N loading from NC watershed by 50%. Quantification of lag time and long-term watershed N balances from this study provide crucial information for understanding N cycling and factors controlling N discharges which is essential for designing programs for controlling N discharges from Mid-Atlantic Coastal Plain watersheds.