The Application of Microbial Source Tracking to aid in Site Prioritization for Remediation in Lower Michigan

Non-point source fecal pollution is a threat to both the environment and public health. Climate change, aging infrastructure, and intensified agricultural practices are predicted to accentuate this issue. In Michigan, due to the high instance of aging infrastructure and intensified agriculture, non-point source fecal pollution has caused many waterbodies to exceed the state standards posing a risk to recreational activities and source water. Due to this threat, there is an increased effort to identify and remediate these sources. My study focused on improving the identification of non-point source fecal pollution through a combination of culture-based and molecular fecal indicator bacteria (FIB) identification, combined with geospatial and statistical modeling approaches. In Chapter 2, I assessed associations between measured FIB and key watershed characteristics in two watersheds located in Ottawa County, Michigan: Bass River and Deer Creek. Results indicated several associations between watershed characteristics and monitored FIB, which should be considered in future non-point source monitoring efforts. In Chapter 3, I created a new tool to aid stakeholders in interpreting FIB monitoring results. This tool was applied to FIB data from the previous chapter as well as FIB data from five public beaches in Macomb County, Michigan. Results indicated that the framework could improve the interpretation of monitored data. Using this tool, stakeholders can better identify and remediate the most impaired areas first, maximizing their impact and minimizing costs. In Chapter 4, I assessed potential improvements to components of a commonly used geospatial model, the Agricultural Conservation Planning Framework (ACPF), and looked at the model’s ability to assess non-point source fecal pollution from runoff derived events. To determine this, I first updated the sediment delivery ratio (SDR) in runoff risk and compared the updated outputs to measured FIB to identify ACPF’s ability to assess FIB concentrations. Results indicated a significant difference between model outputs, but limitations in experimental design precluded an adequate assessment of my objective for this chapter. Recommendations on future studies to properly assess these objectives were offered

Integrating Knowledge for River Basin Management: Progress in Thailand

Resource /Energy Economics and Policy,

Decision-Support Tool for Residential Pesticides in the South Carolina Coastal Zone

The Environmental Protection Agency (EPA) is charged with ensuring pesticides do not pose unreasonable adverse risks to the public and to the environment. This is a daunting task with over one billion pounds of pesticides used across the nation each year. The U.S. EPA estimates approximately 75% of all pesticide usage in the U.S. are agricultural while 25% is for home, garden, industrial, commercial, and government applications. One area of application of concern to public health and the environment regarding misuse of pesticides is in residential settings. In these instances, individuals may not have any knowledge of identifying whether they have a pest problem (i.e., pests have reached intolerable levels), the proper steps to take in determining the best solution to solve the pest problem, and measures needed to protect themselves and the surrounding area from pesticide exposure if chemical application occurs. As the nation\u27s population continues to grow, it is imperative to learn which pesticides - as well as uses - should be accounted for in residential scenarios. Using a three county study area in coastal South Carolina, we developed a pesticide knowledgebase, a hazard-based relative cumulative ranking system for one hundred of the most commonly used pesticides, and geospatial models allowing for more informed choices regarding pesticide use and application. Implemented as an easy-to-use dynamic system of tools for residential pesticides - sccoastalpesticides.org acts an educational platform - allowing users to quickly make decisions regarding pesticides, and allowing us to educate more of the target by using a website, acting as a cost effective strategy to maximize efficiency in reaching multiple stakeholder groups

A Framework For Assessing Water Quality, Prioritizing Recovery Potential, And Analyzing Placement Of Best Management Practices

Motivated by the U.S. EPA goals, this research developed a framework to support identification and restoration of nutrient-impaired water bodies. The study objectives were developing total nitrogen (TN) and total phosphorus (TP) prediction models, evaluating the impact of social indicators on assessing recovery potential, and developing a spatial decision support system for choice and placement of best management practices (BMPS). An artificial neural network was used to develop TN and TP predictive regional models for U.S. lakes using easily measurable and cost-effective variables. The performance of models was superior for regions trained with larger datasets and/or regions with lower temperature and precipitation variability. The use of datasets larger than existing records and obtained from homogeneous climatic region was suggested to achieve the desired performance. The impact of social indicators on assessing a recovery potential was studied by comparing four watersheds using ecological, stressor, and social indicators. Social indicators were grouped into socio-economic, organizational, and information and planning subcategories. The existing U.S. EPA recovery potential screening tool prioritizes restoration for a water body with the most favorable ecological and social condition as well as the least stressing factors. In the present study, water bodies ranked lowest were observed with lower social scores associated with lower socio-economic conditions. This could mean a manager would take a water body with lower socio-economic condition as the lowest priority for restoration. It is suggested that such prioritization plan should carefully incorporate community goals in a prioritization effort because restoration supports an improvement of quality of life. A spatial decision support system was developed with the necessary information to assess nitrogen (n) pollution and methods to estimate an annual exported n load into Beasley Lake, Mississippi. A decision analysis of choice and placement of BMPS was performed based on performance, site suitability, and establishment cost criteria. From this analysis, a BMP scenario that reduces 25% of the exported load at an establishment and an annual opportunity cost-to-performance ratios of 148 $/kg and 29$/kg, respectively, was developed. The presented approach supports similar efforts when the use of existing watershed models is limited by data availability

The Analysis of Open Source Software and Data for Establishment of GIS Services Throughout the Network in a Mapping Organization at National or International Level

Federal agencies and their partners collect and manage large amounts of geospatial data but it is often not easily found when needed, and sometimes data is collected or purchased multiple times. In short, the best government data is not always organized and managed efficiently to support decision making in a timely and cost effective manner. National mapping agencies, various Departments responsible for collection of different types of Geospatial data and their authorities cannot, for very long, continue to operate, as they did a few years ago like people living in an island. Leaders need to look at what is now possible that was not possible before, considering capabilities such as cloud computing, crowd sourced data collection, available Open source remotely sensed data and multi source information vital in decision-making as well as new Web-accessible services that provide, sometimes at no cost. Many of these services previously could be obtained only from local GIS experts. These authorities need to consider the available solution and gather information about new capabilities, reconsider agency missions and goals, review and revise policies, make budget and human resource for decisions, and evaluate new products, cloud services, and cloud service providers. To do so, we need, choosing the right tools to rich the above-mentioned goals. As we know, Data collection is the most cost effective part of the mapping and establishment of a Geographic Information system. However, it is not only because of the cost for the data collection task but also because of the damages caused by the delay and the time that takes to provide the user with proper information necessary for making decision from the field up to the user’s hand. In fact, the time consumption of a project for data collection, processing, and presentation of geospatial information has more effect on the cost of a bigger project such as disaster management, construction, city planning, environment, etc. Of course, with such a pre-assumption that we provide all the necessary information from the existing sources directed to user’s computer. The best description for a good GIS project optimization or improvement is finding a methodology to reduce the time and cost, and increase data and service quality (meaning; Accuracy, updateness, completeness, consistency, suitability, information content, integrity, integration capability, and fitness for use as well as user’s specific needs and conditions that must be addressed with a special attention). Every one of the above-mentioned issues must be addressed individually and at the same time, the whole solution must be provided in a global manner considering all the criteria. In this thesis at first, we will discuss about the problem we are facing and what is needed to be done as establishment of National Spatial Data Infra-Structure (NSDI), the definition and related components. Then after, we will be looking for available Open Source Software solutions to cover the whole process to manage; Data collection, Data base management system, data processing and finally data services and presentation. The first distinction among Software is whether they are, Open source and free or commercial and proprietary. It is important to note that in order to make distinction among softwares it is necessary to define a clear specification for this categorization. It is somehow very difficult to distinguish what software belongs to which class from legal point of view and therefore, makes it necessary to clarify what is meant by various terms. With reference to this concept there are 2 global distinctions then, inside each group, we distinguish another classification regarding their functionalities and applications they are made for in GIScience. According to the outcome of the second chapter, which is the technical process for selection of suitable and reliable software according to the characteristics of the users need and required components, we will come to next chapter. In chapter 3, we elaborate in to the details of the GeoNode software as our best candidate tools to take responsibilities of those issues stated before. In Chapter 4, we will discuss the existing Open Source Data globally available with the predefined data quality criteria (Such as theme, data content, scale, licensing, and coverage) according to the metadata statement inside the datasets by mean of bibliographic review, technical documentation and web search engines. We will discuss in chapter 5 further data quality concepts and consequently define sets of protocol for evaluation of all datasets according to the tasks that a mapping organization in general, needed to be responsible to the probable users in different disciplines such as; Reconnaissance, City Planning, Topographic mapping, Transportation, Environment control, disaster management and etc… In Chapter 6, all the data quality assessment and protocols will be implemented into the pre-filtered, proposed datasets. In the final scores and ranking result, each datasets will have a value corresponding to their quality according to the sets of rules that are defined in previous chapter. In last steps, there will be a vector of weight that is derived from the questions that has to be answered by user with reference to the project in hand in order to finalize the most appropriate selection of Free and Open Source Data. This Data quality preference has to be defined by identifying a set of weight vector, and then they have to be applied to the quality matrix in order to get a final quality scores and ranking. At the end of this chapter there will be a section presenting data sets utilization in various projects such as “ Early Impact Analysis” as well as “Extreme Rainfall Detection System (ERDS)- version 2” performed by ITHACA. Finally, in conclusion, the important criteria, as well as future trend in GIS software are discussed and at the end recommendations will be presented

The Effects of Impervious Surfaces and Forests on Water Quality in a Southern Appalachian Headwater Catchment: A Geospatial Modeling Approach

The water quality of streams is impacted by the land cover types that occur within their watersheds and stream corridors. Research has indicated that impervious surfaces (roads, roofs, and parking lots) exert significant stress on stream system health by increasing storm runoff and transporting pollutants into streams. Forests, on the other hand, serve to protect water quality by slowing runoff, which allows rainfall to percolate into the ground, and absorbing pollutants. This thesis research examined the effects of impervious surfaces and forests on water quality in the headwaters of the New River in Watauga County. Results demonstrated that these effects are clearly identifiable and statistically significant. Limiting the amount of impervious surfaces that occur within 100 meters of streams and establishing 50 meter forested stream buffer zones could improve water quality and help preserve stream system health

Have genetic targets for faecal pollution diagnostics and source tracking revolutionised water quality analysis yet?

The impacts on faecal pollution analysis using nucleic acid-based methods, such as PCR and sequencing, in health-related water quality research were assessed by rigorous literature analysis. A wide range of application areas and study designs has been identified since the first application more than 30 years ago (>1,100 publications). Given the consistency of methods and assessment types, we suggest defining this emerging part of science as a new discipline: genetic faecal pollution diagnostics (GFPD) in health-related microbial water quality analysis. Undoubtedly, GFPD has already revolutionised faecal pollution detection and microbial source tracking, the current core applications. GFPD is also expanding to many other research areas, including infection and health risk assessment, evaluation of microbial water treatment, and support of wastewater surveillance. In addition, storage of DNA extracts allows for biobanking, which opens up new perspectives. The tools of GFPD can be combined with cultivation-based standardised faecal indicator enumeration, pathogen detection, and various environmental data types, in an integrated data analysis approach. This comprehensive meta-analysis provides the scientific status quo of this field, including trend analyses and literature statistics, outlining identified application areas, and discussing the benefits and challenges of nucleic acid-based analysis in GFPD

Agricultural nutrient budgets in Europe: data, methods, and indicators

Agricultural production systems feed humanity but also cause a range of adverse environmental effects, including climate change, loss of biodiversity, and pollution of air and water. A main cause of these effects is the emissions of nitrogen (N) and phosphorus (P) that occur as a side effect of nutrient cycling in agriculture. One of the things that is needed to mitigate N and P pollution is a quantitative understanding of N and P flows in agricultural systems. A common tool for this is the nutrient budget. A nutrient budget quantifies inputs and outputs of nutrients in a system and can be used to understand how the system functions as well as to calculate quantitative environmental indicators for farms, regions, or products.This thesis aims to explore and expand the limits of how agricultural N and P budgets can be used to support environmental research and decision-making, focusing on European agriculture. To this end, the thesis looks into two broad research questions: (1) What are the limits to the accuracy and level of detail that can be attained in N and P budgets of European agricultural systems? (2) How are present and proposed uses of agricultural N and P budgets and derived indicators limited by (a) the inherent property that agricultural nutrient budgets do not account for environmental impacts, and (b) by uncertainties and lack of data in the estimation of nutrient budgets?This thesis builds on five appended research papers that explore various aspects of data sources, uncertainties, and possible uses of N and P budgets in Europe. International and national data sources are scrutinized and used to estimate N and P budgets. Novel ways to combine existing data sources are explored. The use of nutrient budgets with various system boundaries, with different degrees of spatial resolution, and in different time periods is discussed, emphasizing that the best approach is not only a question of data supply but also of intended audience and purpose

An experiment on the parameter uncertainty of hydrological models with different levels of complexity in a climate change context

La possibilité d’estimer l’impact du changement climatique en cours sur le comportement hydrologique des hydro-systèmes est une nécessité pour anticiper les adaptations inévitables et nécessaires que doivent envisager nos sociétés. Dans ce contexte, ce projet doctoral présente une étude sur l'évaluation de la sensibilité des projections hydrologiques futures à : (i) La non-robustesse de l’identification des paramètres des modèles hydrologiques, (ii) l’utilisation de plusieurs jeux de paramètres équifinaux et (iii) l’utilisation de différentes structures de modèles hydrologiques. Pour quantifier l’impact de la première source d’incertitude sur les sorties des modèles, quatre sous-périodes climatiquement contrastées sont tout d’abord identifiées au sein des chroniques observées. Les modèles sont calés sur chacune de ces quatre périodes et les sorties engendrées sont analysées en calage et en validation en suivant les quatre configurations du Different Splitsample Tests (Klemeš, 1986; Wilby, 2005; Seiller et al. (2012); Refsgaard et al. (2014)). Afin d’étudier la seconde source d’incertitude liée à la structure du modèle, l’équifinalité des jeux de paramètres est ensuite prise en compte en considérant pour chaque type de calage les sorties associées à des jeux de paramètres équifinaux. Enfin, pour évaluer la troisième source d'incertitude, cinq modèles hydrologiques de différents niveaux de complexité sont appliqués (GR4J, MORDOR, HSAMI, SWAT et HYDROTEL) sur le bassin versant québécois de la rivière Au Saumon. Les trois sources d'incertitude sont évaluées à la fois dans conditions climatiques observées passées et dans les conditions climatiques futures. Les résultats montrent que, en tenant compte de la méthode d'évaluation suivie dans ce doctorat, l'utilisation de différents niveaux de complexité des modèles hydrologiques est la principale source de variabilité dans les projections de débits dans des conditions climatiques futures. Ceci est suivi par le manque de robustesse de l'identification des paramètres. Les projections hydrologiques générées par un ensemble de jeux de paramètres équifinaux sont proches de celles associées au jeu de paramètres optimal. Par conséquent, plus d'efforts devraient être investis dans l'amélioration de la robustesse des modèles pour les études d'impact sur le changement climatique, notamment en développant les structures des modèles plus appropriés et en proposant des procédures de calage qui augmentent leur robustesse. Ces travaux permettent d’apporter une réponse détaillée sur notre capacité à réaliser un diagnostic des impacts des changements climatiques sur les ressources hydriques du bassin Au Saumon et de proposer une démarche méthodologique originale d’analyse pouvant être directement appliquée ou adaptée à d’autres contextes hydro-climatiques.The possibility to estimate the impact of climate change on the hydrological behavior of hydrosystems, the hydrological risks, and the associated resources is a necessity in order to anticipate the inevitable and necessary adaptations that must consider our societies. In this context, the doctoral project presents a study on the evaluation of the uncertainty of hydrological projections for the future climate when considering: (i) The non-robustness of hydrological model parameter identification, (ii) the use of several ensembles of equifinal parameter sets over a given calibration period and (iii) the use of different model structures for the hydrological model. To quantify the impact of the first source of uncertainty on the model outputs, four climatically contrasted sub-periods are first identified within the observed time series. The models are calibrated on each of these four periods, then generated outputs are analyzed on calibration and validation data. The calibration and validation tests were performed according to the configurations of four Different Split-sample Tests (Klemeš, 1986; Wilby, 2005; Seiller et al., 2012; Refsgaard et al., 2014). In order to study the second source of uncertainty related to the model structure, the equifinality of the parameter sets is taken into account by considering an ensemble of equifinal parameter sets for each sub-period calibration. Finally, to assess the third source of uncertainty, five hydrological models of different levels of complexity are applied (GR4J, MORDOR, HSAMI, SWAT, and HYDROTEL) on the watershed of the Au Saumon River (Québec, Canada).The three sources of uncertainty are assessed in the past observed period and in future climate conditions. Results show that, given the evaluation approach followed in this Ph.D. research, the use of different levels of complexity of hydrological models is the major source of variability in streamflow projections in future climate conditions for the five models tested. This is followed by the lack of robustness of parameter identification. The hydrological projections generated by an ensemble of equifinal parameter sets are close to those associated with the optimal set. Therefore, it seems that greater effort should be invested in improving the robustness of models for climate change impact studies, especially by developing more suitable model structures and proposing calibration procedures that increase their robustness. This work serves to provide a detailed response on our ability to make a diagnosis of the impacts of climate change on water resources of the Au Saumon watershed and proposes a novel methodological approach that can be directly applied or adapted to other hydro-climatic contexts