Quantifying Pathogen Sources in Streams by Hydrograph Separation

A new technique for quantifying pathogen sources to streams is proposed and demonstrated. Hydrograph separation is used to partition measured streamflow into surface runoff and base flow, and characteristic pathogen concentrations are assigned to each flow component along with a background source flux. The maximum-likelihood characteristic concentrations and background flux are determined from measured instream pathogen concentrations. This approach is shown to yield comparable to superior performance in predicting instream pathogen concentrations compared with much more complex terrestrial fate and transport models. Application of the proposed approach to six catchments yields Nash-Sutcliffe efficiencies of the log-transformed fecal-coliform concentrations in the range of 0.21 to 0.48. The characteristic fecal-coliform concentrations in surface runoff are in the range of 200–700??cfu/dL and the base-flow characteristic concentrations are in the range of 20–100??cfu/dL. It is shown that the frequency distribution of bacteria concentrations derived from sample measurements can sometimes differ significantly from their long-term frequency distribution.     

A Strategy for Atherosclerosis Image Segmentation by Using Robust Markers

The watersheds method is a powerful segmentation tool developed in mathematical morphology, which has the drawback of producing over-segmentation. In this paper, in order to prevent its over-segmentation, we present a strategy to obtain robust markers for image segmentation of atherosclerotic lesions of the thoracic aorta. In such sense, we introduced an algorithm, which was very useful in order to obtain the markers of the atherosclerotic lesions. Images were pre-processed using the Gauss filter and a contrast enhancement. The obtained results by using our strategy were validated calculating the false negatives (FN) and false positives (FP) according to criterion of physicians, where 0% for FN and less than 11% for FP were obtained. Extensive experimentation showed that, using real image data, the proposed strategy was very suitable for our application. These images will be subject to an additional morphometrical analysis in order to study automatically the atherosclerosis and its organic-consequences.     

A multiscale gradient algorithm for image segmentation using watershelds

Watershed transformation is a powerful tool for image segmentation. However, the effectiveness of the image segmentation methods based on watershed transformation is limited by the quality of the gradient image used in the methods. In this paper we present a multiscale algorithm for computing gradient images, with effective handling of both step and blurred edges. We also present an algorithm for eliminating irrelevant minima in the resulting gradient images. Experimental results indicate that watershed transformation with the algorithms proposed in this paper produces meaningful segmentations, even without a region merging step. The proposed algorithms can efficiently improve segmentation accuracy and significantly reduce the computational cost of watershed-based image segmentation methods.     

Evaluating Temporal Variability in Bacterial Indicator Samples for an Urban Watershed

Temporal variability in water quality sampling can lead to under or overestimation of pollution. More frequent sampling during times of expected high variability can reduce temporal bias. However, bacterial indicator measurements require manual sampling and time-intensive and expensive laboratory analysis. Thus, methods to assess the effect of temporal variability would be useful. In this work, fecal coliform and E. coli samples were taken weekly during the recreational season at 25 sites in an urban watershed. The data were categorized into wet versus dry days, and upper versus lower watershed, and the geometric means and geometric standard deviations of various five-sample data sets were analyzed to determine if sample data selection would result in different stream classifications. Results indicate if the bacterial indicator samples are near the regulated limits for water contact recreational use, temporal bias could sway impairment classification decisions. To reduce the temporal bias, sampling data submitted for stream classification should include several sampling groups within the recreational season, particularly for sites near point sources of pollution or with low fecal indicator contamination.     

Development of an Empirical Lag Time Equation

A regression analysis was performed on measured lag times from gauged watersheds to develop a lag time equation. The watersheds are part of the Agricultural Research Service’s database. They are located in several states and are comprised of varying terrain. The goal of the analysis was to develop a lag time equation that is useful in hydrologic modeling. The study included measurements from approximately 10,000 direct runoff events from 52 watersheds to determine which watershed parameters are best for predicting lag time. The lag time was found to correlate strongly with the longest hydraulic length of the watershed. Therefore an equation was developed that used only this parameter. The inclusion of any other watershed characteristics in the equation did not improve its ability to predict the lag time. Finally, the National Resource Conservation Service procedures for calculating watershed lag time were used to determine the lag times of the watersheds. These estimated lag times were then compared with the measured lag time of the watershed. It was found that the use of these methods generally underpredicted the true lag time of a watershed.     

Integrated Storm-Water Management for Watershed Sustainability

One aspect of integrated watershed management evaluates the impact of development on the local hydrologic cycle and, in particular, drinking water, wastewater, and storm-water infrastructure. Sustainable storm-water management focuses on selecting storm-water controls based on an understanding of the problems in local receiving waters that result from runoff discharges. For example, long-term problems associated with accumulations of pollutants in water bodies include sedimentation in conveyance systems and receiving waters, nuisance algal growths, inedible fish, undrinkable water, and shifts to less sensitive aquatic organisms. Short-term problems associated with high pollutant concentrations or frequent high flows (event-related) include swimming beach closures, water quality violations, property damage from increased flooding, and habitat destruction. A wide variety of individual storm-water controls usually must be combined to form a comprehensive wet weather management strategy. Unfortunately, combinations of controls are difficult to analyze. This will require new modeling techniques that can effectively evaluate a wide variety of control practices and land uses, while at the same time ensure that the flood-control objectives also are met. The results of these new models and novel techniques used for storm-water control then can be incorporated into an evaluation of the urban water cycle for a specific service area to determine whether storm-water controls can provide additional benefits such as reduction of potable water use and reduction of sanitary sewer overflow events.     

Application of Watershed-Based Tank System Model for Rainwater Harvesting and Irrigation in India

The SOFTANK model optimally designs the watershed-based tank system by simulating field, tank, and groundwater balances. We applied this model to a small watershed consisting of six tanks (small reservoirs) in the semiarid region of India. We evaluated the existing tank system in this watershed and compared it to a one-tank system. Results showed that one tank at the outlet of the watershed would have been more beneficial [with benefit-cost (BC) ratio of 1.80] than the existing six-tank system (with BC ratio of 1.71). Finally, we performed the analysis for obtaining the optimal tank system for the watershed, and we found that the tanks for irrigation purposes are not economical for the small watershed. The groundwater source was enough for irrigation, so any additional investment in the tanks would be uneconomical. The results demonstrate the importance of the watershed-based tank system approach to design.     

Long-term sodium and chloride surface water exports from the Dallas/Fort Worth region

Sodium and chloride in surface water are typically related to urbanization and population density and can have a significant impact on drinking water sources and the subsequent salinity of aquatic ecosystems. While the majority of research has focused on the impact of deicing salts on urban surface waters in colder climates, the effect of urbanization on sodium and chloride concentrations has been found to occur in warmer climates. This study investigated long-term exports of sodium and chloride from watersheds with increasing urbanization in the humid subtropical Dallas-Fort Worth region. We compared exports to characteristics of urbanization: urban land cover, impervious surface area, and calculated contributions from wastewater discharges. Long-term data (1980-2008) were obtained from five USGS gages located in and around the cities. Exports were calculated by regression analysis between concentrations and discharge and normalized for time and the watershed area. Grab samples were collected from June 2009 to May 2010 and sodium and chloride concentrations quantified. Our results show a strong positive relationship between the mean annual sodium and chloride exports from each watershed and the percent urban land cover and impervious surface area. Long-term increases in sodium and chloride fluxes were found for the three watersheds with the highest percentage of urban land cover. The single largest contributor was wastewater effluent that was estimated to contribute approximately half of the total loads in the three urbanized watersheds. Atmospheric deposition and deicing salts accounted for small amounts of the total export for urbanized watersheds. The source of the remaining salt load is still unknown and may be a combination of non-point sources. Estimates of urban salt exports were similar to estimates from northern watersheds affected by deicing salts.     

Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images

In this work, we present an automated method for the detection and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The detection of the candidate nuclei areas is based on a morphological image reconstruction process and the segmentation of the nuclei boundaries is accomplished with the application of the watershed transform in the morphological color gradient image, using the nuclei markers extracted in the detection step. For the elimination of false positive findings, salient features characterizing the shape, the texture and the image intensity are extracted from the candidate nuclei regions and a classification step is performed to determine the true nuclei. We have examined the performance of two unsupervised (K-means, spectral clustering) and a supervised (Support Vector Machines, SVM) classification technique, employing discriminative features which were selected with a feature selection scheme based on the minimal-Redundancy-Maximal-Relevance criterion. The proposed method was evaluated on a data set of 90 Pap smear images containing 10,248 recognized cell nuclei. Comparisons with the segmentation results of a gradient vector flow deformable (GVF) model and a region based active contour model (ACM) are performed, which indicate that the proposed method produces more accurate nuclei boundaries that are closer to the ground truth.     

Artificial Intelligence-Based Inductive Models for Prediction and Classification of Fecal Coliform in Surface Waters

This paper describes the use of inductive models developed using two artificial intelligence (AI)-based techniques for fecal coliform prediction and classification in surface waters. The two AI techniques used include artificial neural networks (ANNs) and a fixed functional set genetic algorithm (FFSGA) approach for function approximation. While ANNs have previously been used successfully for modeling water quality constituents, FFSGA is a relatively new technique of inductive model development. This paper will evaluate the efficacy of this technique for modeling indicator organism concentrations. In scenarios where process-based models cannot be developed and/or are not feasible, efficient and effective inductive models may be more suitable to provide quick and reasonably accurate predictions of indicator organism concentrations and associated water quality violations. The relative performance of AI-based inductive models is compared with conventional regression models. When raw data are used in the development of the inductive models described in this paper, the AI models slightly outperform the traditional regression models. However, when log transformed data are used, all inductive models show comparable performance. While the work validates the strength of simple regression models, it also validated FFSGA to be an effective technique that competes well with other state-of-the-art and complex techniques such as ANNs. FFSGA comes with the added advantage of resulting in a simple, easy to use, and compact functional form of the model sought. This work adds to the limited amount of research on the use of data-driven modeling methods for indicator organisms.