Browsing by Subject "water quality"

Now showing 1 - 8 of 8
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    Combining Multivariate Statistical Methods and Spatial Analysis to Characterize Water Quality Conditions in the White River Basin, Indiana, U.S.A.
    (2011-02-25) Gamble, Andrew Stephan; Babbar-Sebens, Meghna; Tedesco, Lenore P.; Peng, Hanxiang
    This research performs a comparative study of techniques for combining spatial data and multivariate statistical methods for characterizing water quality conditions in a river basin. The study has been performed on the White River basin in central Indiana, and uses sixteen physical and chemical water quality parameters collected from 44 different monitoring sites, along with various spatial data related to land use – land cover, soil characteristics, terrain characteristics, eco-regions, etc. Various parameters related to the spatial data were analyzed using ArcHydro tools and were included in the multivariate analysis methods for the purpose of creating classification equations that relate spatial and spatio-temporal attributes of the watershed to water quality data at monitoring stations. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. The linear statistical multivariate method uses a combination of principal component analysis, cluster analysis, and discriminant analysis, whereas the non-linear multivariate method uses a combination of Kohonen Self-Organizing Maps, Cluster Analysis, and Support Vector Machines. The final models were tested with recent and independent data collected from stations in the Eagle Creek watershed, within the White River basin. In 6 out of 20 models the Support Vector Machine more accurately classified the Eagle Creek stations, and in 2 out of 20 models the Linear Discriminant Analysis model achieved better results. Neither the linear or non-linear models had an apparent advantage for the remaining 12 models. This research provides an insight into the variability and uncertainty in the interpretation of the various statistical estimates and statistical models, when water quality monitoring data is combined with spatial data for characterizing general spatial and spatio-temporal trends.
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    A Multi-User Interactive Optimization Tool (WRESTORE)
    (Office of the Vice Chancellor for Research, 2013-04-05) Singh, Vidya B.; Mukhopadhyay, Snehasis; Babbar-Sebens, Meghna
    This is NSF funded joint project between Earth Science and Computer Science. It’s one of the objective is to provide best farming practices to the people of Eagle Creek, Indiana, so as to minimize the soil erosion, fertilizer loss and maintain water quality of the region while maximizing profit of farmers. The most important benefit to general public will be increase in quality of drinking water and decrease in flooding of the region. The tool we have built is a distributed system which uses high performance computing techniques to run model simulations in an efficient manner. The tool has various components which run on multiple computers. The user login via a web based interface, the design parameters are specified which are being used to generate different possible designs. The design evaluations are done using powerful cluster of computers (having 768 or 224 CPUs), which uses concept of virtual agents in doing the design evaluation. The user provides their feedback to different designs which are again considered to generate another set of better designs. Various optimization and machine learning techniques are used to model the user’s preferences and provide best possible designs based on given scenario. It is like human computer collaborative search, where human and computer both work together to achieve the goal in a better way. The project is still ongoing, till now we have run simulated user model only, but sooner we will be running the tests for the real human users. This will help the farmers, govt. agencies like USDA and environmentalists in doing environmental planning in an efficient manner. Our collaborators are Empower Results, Eagle Creek Watershed Alliance, Indiana NRCS, Center for Earth and Environmental Sciences, Upper White River Watershed Alliance.
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    Pilot Tap Water Sampling Project to Study Urban Drinking Water Quality in Indianapolis for Community Exposure Assessment
    (Office of the Vice Chancellor for Research, 2014-04-11) Sundararajan, Madhura; Parvez, Shahid
    supply exposure data. This data is collected at treatment sites (water stations) and is not representative of true exposure concentrations to humans because of several known and unknown factors. These include temporal-spatial changes, source water type characteristics, retention time in the distribution systems, byproducts formation, poor condition of pipes, and water contamination. By the time water reaches its destination i.e., residential areas, its quality can deteriorate. Also, the water utilities do not test for un-regulated water contaminants which can be more potent. Due to the urban location of White River, Indianapolis drinking water supply has a higher risk of contamination with emerging contaminants such as pesticides, personal care-products, and pharmaceuticals. Hence, a direct method of water sampling is needed for true exposure assessment. Study Plan: We are designing a pilot tap water sampling project to study drinking water quality for pesticides and other urban contaminants in the Indianapolis Community Water System (IndyCWS). Seven residential sites are identified to capture sufficient parts of IndyCWS. The samples will be collected weekly, biweekly, and monthly during the April-June period. The samples will be analyzed in a certified laboratory using EPA recommended methods. The data from this study will be compared with utilities data, used to identify the presence of new contaminants, evaluate cumulative mixture exposure, and assess potential health risks. This work is currently in progress and the results from the study will be discussed in future meetings.
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    Slope algorithm to map algal blooms in inland waters for Landsat 8/ Operational Land Imager images
    (SPIE, 2016-12) Ogashawara, Igor; Li, Lin; Moreno-Madriñán, Max Jacobo; Department of Environmental Health Science, School of Public Health
    Monitoring algal blooms using traditional methods is expensive and labor intensive. The use of satellite technology can attenuate such limitations. A common problem associated with the application of such technology is the need to eliminate the effects of atmosphere, which can be, at least, a time-consuming task. Thus, a remote sensed algal bloom monitoring system needs a simple algorithm which is nonsensitive to atmospheric correction and that could be applied to small aquatic systems. A slope algorithm (SAred−NIR) was developed to detect and map the extension of algal blooms using the Landsat 8/Operational Land Imager. SAred−NIR was shown to have advantages over other commonly used indices to monitor algal blooms, such as normalized difference vegetation index (NDVI), normalized difference water index, and floating algae index. SAred−NIR was shown to be less sensitive to different atmospheric corrections, less sensitive to thin clouds, and less susceptible to confusion when classifying water and moderate bloom conditions. Based on ground truth data from Eagle Creek Reservoir, Indiana, SAred−NIR showed an accuracy of 88.46% while NDVI only showed a 46.15% accuracy. Finally, based on qualitative and quantitative results, SAred−NIR can be used as a tool to improve the governance of small size water resources.
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    Spatio-Temporal Variability in a Turbid and Dynamic Tidal Estuarine Environment (Tasmania, Australia): An Assessment of MODIS Band 1 Reflectance
    (MDPI, 2017-10-15) Fischer, Andrew M.; Pang, Daniel; Kidd, Ian M.; Moreno-Madriñán, Max J.; Environmental Health Sciences, School of Public Health
    Patterns of turbidity in estuarine environments are linked to hydrodynamic processes. However, the linkage between patterns and processes remains poorly resolved due to the scarcity of data needed to resolve fine scale highly dynamic processes in tidal estuaries. The application of remote sensing technology to monitor dynamic coastal areas such as estuaries offers important advantages in this regard, by providing synoptic maps of larger, constantly changing regions over consistent periods. In situ turbidity measurements were correlated against the Moderate Resolution Imaging Spectrometer Terra sensor 250 m surface reflectance product, in order to assess this product for examining the complex estuarine waters of the Tamar estuary (Australia). Satellite images were averaged to examine spatial, seasonal and annual patterns of turbidity. Relationships between in situ measurements of turbidity and reflectance is positively correlated and improves with increased tidal height, a decreased overpass-in situ gap, and one day after a rainfall event. Spatial and seasonal patterns that appear in seasonal and annual MODIS averages, highlighting the usefulness of satellite imagery for resource managers to manage sedimentation issues in a degraded estuary.
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    Spatio-Temporal Variability in a Turbid and Dynamic Tidal Estuarine Environment (Tasmania, Australia): An Assessment of MODIS Band 1 Reflectance
    (MDPI, 2017-10-25) Fischer, Andrew M.; Pang, Daniel; Kidd, Ian M.; Moreno-Madrinan, Max J.; Environmental Health Sciences, School of Public Health
    Patterns of turbidity in estuarine environments are linked to hydrodynamic processes. However, the linkage between patterns and processes remains poorly resolved due to the scarcity of data needed to resolve fine scale highly dynamic processes in tidal estuaries. The application of remote sensing technology to monitor dynamic coastal areas such as estuaries offers important advantages in this regard, by providing synoptic maps of larger, constantly changing regions over consistent periods. In situ turbidity measurements were correlated against the Moderate Resolution Imaging Spectrometer Terra sensor 250 m surface reflectance product, in order to assess this product for examining the complex estuarine waters of the Tamar estuary (Australia). Satellite images were averaged to examine spatial, seasonal and annual patterns of turbidity. Relationships between in situ measurements of turbidity and reflectance is positively correlated and improves with increased tidal height, a decreased overpass-in situ gap, and one day after a rainfall event. Spatial and seasonal patterns that appear in seasonal and annual MODIS averages, highlighting the usefulness of satellite imagery for resource managers to manage sedimentation issues in a degraded estuary.
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    The Use of Sentinel-3 Imagery to Monitor Cyanobacterial Blooms
    (MDPI, 2019-06) Ogashawara, Igor; Earth Sciences, School of Science
    Cyanobacterial harmful algal blooms (CHABs) have been a concern for aquatic systems, especially those used for water supply and recreation. Thus, the monitoring of CHABs is essential for the establishment of water governance policies. Recently, remote sensing has been used as a tool to monitor CHABs worldwide. Remote monitoring of CHABs relies on the optical properties of pigments, especially the phycocyanin (PC) and chlorophyll-a (chl-a). The goal of this study is to evaluate the potential of recent launch the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 satellite to identify PC and chl-a. To do this, OLCI images were collected over the Western part of Lake Erie (U.S.A.) during the summer of 2016, 2017, and 2018. When comparing the use of traditional remote sensing algorithms to estimate PC and chl-a, none was able to accurately estimate both pigments. However, when single and band ratios were used to estimate these pigments, stronger correlations were found. These results indicate that spectral band selection should be re-evaluated for the development of new algorithms for OLCI images. Overall, Sentinel 3/OLCI has the potential to be used to identify PC and chl-a. However, algorithm development is needed.
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    Using Band Ratio, Semi-Empirical, Curve Fitting, and Partial Least Squares (PLS) Models to Estimate Cyanobacterial Pigment Concentration from Hyperspectral Reflectance
    (2009-09-03T15:01:53Z) Robertson, Anthony Lawrence; Li, Lin; Tedesco, Lenore P.; Wilson, Jeffrey S. (Jeffrey Scott), 1967-
    This thesis applies several different remote sensing techniques to data collected from 2005 to 2007 on central Indiana reservoirs to determine the best performing algorithms in estimating the cyanobacterial pigments chlorophyll a and phycocyanin. This thesis is a set of three scientific papers either in press or review at the time this thesis is published. The first paper describes using a curve fitting model as a novel approach to estimating cyanobacterial pigments from field spectra. The second paper compares the previous method with additional methods, band ratio and semi-empirical algorithms, commonly used in remote sensing. The third paper describes using a partial least squares (PLS) method as a novel approach to estimate cyanobacterial pigments from field spectra. While the three papers had different methodologies and cannot be directly compared, the results from all three studies suggest that no type of algorithm greatly outperformed another in estimating chlorophyll a on central Indiana reservoirs. However, algorithms that account for increased complexity, such as the stepwise regression band ratio (also known as 3-band tuning), curve fitting, and PLS, were able to predict phycocyanin with greater confidence.