We present theoretical and experimental work on Cryptosporidium parvum oocysts to characterize their transport behavior in saturated, sandy sediments under strictly controlled conditions. Column experiments are implemented with three different sands (effective grain size: 180, 420, and 1400 μm) at two different saturated flow rates (0.7 and 7 m/d). The experiments show that C. parvum oocysts, like other colloids, are subject to velocity enhancement. In medium and coarse sands, the oocysts travel 10−30% faster than a conservative tracer. The classic clean-bed filtration model is found to provide an excellent tool to estimate the degree of C. parvum filtration. Experimentally determined collision efficiencies, α, range from 0.4 to 1.1. The magnitude of α is consistent with the known physical and chemical properties of the oocyst and the transport medium and compares well with, e.g., measured collision efficiencies of similarly sized E. coli bacteria. However, a significant amount of the initial deposition appears to be reversible leading to significant asymmetry and tailing in the oocyst concentration breakthrough curve. We are able to show that the observed late-time oocyst elution can qualitatively be explained by postulating that a significant fraction of the oocyst filtration is reversible and subject to time-dependent detachment.
This review covers, in a comprehensive manner, the approaches available in the literature to upscale soil water processes and hydraulic parameters in the vadose zone. We distinguish two categories of upscaling methods: forward approaches requiring information about the spatial distribution of hydraulic parameters at a small scale, and inverse modeling approaches requiring information about the spatial and temporal variation of state variables at various scales, including so-called "soft data". Geostatistical and scaling approaches are crucial to upscale soil water processes and to derive large-scale effective fluxes and parameters from small-scale information. Upscaling approaches include stochastic perturbation methods, the scaleway approach, the stream-tube approach, the aggregation concept, inverse modeling approaches, and data fusion. With all upscaling methods, the estimated effective parameters depend not only on the properties of the heterogeneous flow field but also on boundary conditions. The use of the Richards equation at the field and watershed scale is based more on pragmatism than on a sound physical basis. There are practically no data sets presently available that provide sufficient information to extensively validate existing upscaling approaches. Use of numerical case studies has therefore been most common. More recently and still under development, hydrogeophysical methods combined with ground-based remote sensing techniques promise significant contributions toward providing high-quality data sets. Finally, most of the upscaling literature in vadose zone research has dealt with bare soils or deep vadose zones. There is a need to develop upscaling methods for real world soils, considering root water uptake mechanisms and other soil-plant-atmosphere interactions.
Understanding soil moisture variability and its relationship with water content at various scales is a key issue in hydrological research. In this paper we predict this relationship by stochastic analysis of the unsaturated Brooks‐Corey flow in heterogeneous soils. Using sensitivity analysis, we show that parameters of the moisture retention characteristic and their spatial variability determine to a large extent the shape of the soil moisture variance‐mean water content function. We demonstrate that soil hydraulic properties and their variability can be inversely estimated from spatially distributed measurements of soil moisture content. Predicting this relationship for eleven textural classes we found that the standard deviation of soil moisture peaked between 0.17 and 0.23 for most textural classes. It was found that the β parameter, which describes the pore‐size distribution of soils, controls the maximum value of the soil moisture standard deviation.
A suite of androgens, estrogens, and progestins were measured in samples from dairy farms, aquaculture facilities, and surface waters with actively spawning fish using gas chromatography-tandem mass spectrometry (GC/MS/ MS) to assess the potential importance of these sources of steroid hormones to surface waters. In a dairywaste lagoon, the endogenous estrogens 17beta-estradiol and estrone and the androgens testosterone and androstenedione were detected at concentrations as high as 650 ng/L. Samples from nearby groundwater monitoring wells demonstrated removal of steroid hormones in the subsurface. Samples from nearby surface waters and tile drains likely impacted by animal wastes demonstrated the sporadic presence of the steroids 17beta-estradiol, estrone, testosterone, and medroxyprogesterone, usually at concentrations near or below 1 ng/L. The endogenous steroids estrone,testosterone, and androstenedione were detected in the raceways and effluents of three fish hatcheries at concentrations near 1 ng/L. Similar concentrations were detected in a river containing spawning adult Chinook salmon. These results indicate that dairy wastewater, aquaculture effluents, and even spawning fish can lead to detectable concentrations of steroid hormones in surface waters and that the concentrations of these compounds exhibit considerable temporal and spatial variation.
Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50ppb and probability of dissolved oxygen concentration to be below 0.5ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971-2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.
Environmental releases of antibiotics from concentrated animal feeding operations (CAFOs) are of increasing regulatory concern. This study investigates the use and occurrence of antibiotics in dairy CAFOs and their potential transport into first-encountered groundwater. On two dairies we conducted four seasonal sampling campaigns, each across 13 animal production and waste management systems and associated environmental pathways: application to animals, excretion to surfaces, manure collection systems, soils, and shallow groundwater. Concentrations of antibiotics were determined using on line solid phase extraction (OLSPE) and liquid chromatography-tandem mass spectrometry (LC/MS/MS) with electrospray ionization (ESI) for water samples, and accelerated solvent extraction (ASE) LC/MS/MS with ESI for solid samples. A variety of antibiotics were applied at both farms leading to antibiotics excretion of several hundred grams per farm per day. Sulfonamides, tetracyclines, and their epimers/isomers, and lincomycin were most frequently detected. Yet, despite decades of use, antibiotic occurrence appeared constrained to within farm boundaries. The most frequent antibiotic detections were associated with lagoons, hospital pens, and calf hutches. When detected below ground, tetracyclines were mainly found in soils, whereas sulfonamides were found in shallow groundwater reflecting key differences in their physicochemical properties. In manure lagoons, 10 compounds were detected including tetracyclines and trimethoprim. Of these 10, sulfadimethoxine, sulfamethazine, and lincomycin were found in shallow groundwater directly downgradient from the lagoons. Antibiotics were sporadically detected in field surface samples on fields with manure applications, but not in underlying sandy soils. Sulfadimethoxine and sulfamethazine were detected in shallow groundwater near field flood irrigation gates, but at highly attenuated levels.
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