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The Texas Stream Team (TST) is one of an increasing number of citizen science water monitoring programs throughout the US which have been continuously collecting surface water quality data under quality assurance protocols for decades. Volunteer monitoring efforts have generated monitoring datasets that are long-term, continuous, and cover a large geographic area - characteristics shown to be valuable for scientists and professional agencies. However, citizen science data has been of limited use to researchers due to concerns about the accuracy of data collected by volunteers, and the decades of water quality monitoring data collected by TST volunteers is not widely used, if at all. A growing body of studies have attempted to address accuracy concerns by comparing volunteer data to professional data, but this has rarely been done with large-scale, existing datasets like those collected by TST. This study assesses the accuracy of the volunteer water quality data collected across the state of Texas by the TST citizen science program between 1992-2017 by comparing it to professional data from corresponding stations during the same time period, as well as comparing existing and experimental data from a local TST partner agency. The results indicate that even large-scale, existing volunteer and professional data with unpaired samples that may have been taken months apart can show statewide agreement of 80% for all parameters (DO = 77%, pH = 79%, conductivity = 85%) over the 38 years of sampling included in the analyses, across all locations. The local case study using paired datasets for which a greater number of factors were controlled for show an even higher agreement between volunteers and professionals (DO = 91%, pH = 87%, conductivity = 100%) and show no significant difference between experimental and existing sampling data. The results from this study indicate that TST has been collecting water sampling data for decades that is comparable to professional data and provides compelling evidence for researchers and managers to better utilize TST's and other large-scale monitoring datasets already in existence from similar citizen science water monitoring programs across the country.
The first book dedicated specifically to automated sample preparation and analytical measurements, this timely and systematic overview not only covers biological applications, but also environmental measuring technology, drug discovery, and quality assurance. Following a critical review of realized automation solutions in biological sciences, the book goes on to discuss special requirements for comparable systems for analytical applications, taking different concepts into consideration and with examples chosen to illustrate the scope and limitations of each technique.
Guide and compilation of relevant Canadian and United States information on community-based water quality monitoring, including the chemistry of surface waters; organization of a monitoring program; water sampling; and biological monitoring. The document also includes five regional case studies, a list of vendors of monitoring equipment, and a list of funding agencies. A glossary is also included.
In 1997, New York City adopted a mammoth watershed agreement to protect its drinking water and avoid filtration of its large upstate surface water supply. Shortly thereafter, the NRC began an analysis of the agreement's scientific validity. The resulting book finds New York City's watershed agreement to be a good template for proactive watershed management that, if properly implemented, will maintain high water quality. However, it cautions that the agreement is not a guarantee of permanent filtration avoidance because of changing regulations, uncertainties regarding pollution sources, advances in treatment technologies, and natural variations in watershed conditions. The book recommends that New York City place its highest priority on pathogenic microorganisms in the watershed and direct its resources toward improving methods for detecting pathogens, understanding pathogen transport and fate, and demonstrating that best management practices will remove pathogens. Other recommendations, which are broadly applicable to surface water supplies across the country, target buffer zones, stormwater management, water quality monitoring, and effluent trading.
Ecological Informatics is defined as the design and application of computational techniques for ecological analysis, synthesis, forecasting and management. The book provides an introduction to the scope, concepts and techniques of this newly emerging discipline. It illustrates numerous applications of Ecological Informatics for stream systems, river systems, freshwater lakes and marine systems as well as image recognition at micro and macro scale. Case studies focus on applications of artificial neural networks, genetic algorithms, fuzzy logic and adaptive agents to current ecological management issues such as toxic algal blooms, eutrophication, habitat degradation, conservation of biodiversity and sustainable fishery.