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Watershed-Based Survey Designs 总被引:2,自引:0,他引:2
Detenbeck NE Cincotta D Denver JM Greenlee SK Olsen AR Pitchford AM 《Environmental monitoring and assessment》2005,103(1-3):59-81
Watershed-based sampling design and assessment tools help serve the multiple goals for water quality monitoring required under
the Clean Water Act, including assessment of regional conditions to meet Section 305(b), identification of impaired water
bodies or watersheds to meet Section 303(d), and development of empirical relationships between causes or sources of impairment
and biological responses. Creation of GIS databases for hydrography, hydrologically corrected digital elevation models, and
hydrologic derivatives such as watershed boundaries and upstream–downstream topology of subcatchments would provide a consistent
seamless nationwide framework for these designs. The elements of a watershed-based sample framework can be represented either
as a continuous infinite set defined by points along a linear stream network, or as a discrete set of watershed polygons.
Watershed-based designs can be developed with existing probabilistic survey methods, including the use of unequal probability
weighting, stratification, and two-stage frames for sampling. Case studies for monitoring of Atlantic Coastal Plain streams,
West Virginia wadeable streams, and coastal Oregon streams illustrate three different approaches for selecting sites for watershed-based
survey designs. 相似文献
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ABSTRACTThe main purpose of Green Supply Chain Management (GSCM) is to improve the quality of supply chain management strategies and environmental performance. As per current statistics, the chemical industry is growing fast in Bangladesh. In order to compete for global competition, GSCM is essential in this sector. This paper proposes a systematic approach of structural framework whose aim is to enhance the probability of constructive implementation of GSCM in the field chemical industry in Bangladesh. Therefore, this framework evaluates the appropriate interrelationship along with the drivers of GSCM in the chemical industry. In total, eight drivers were finalized from an associated literature review with the help of survey and by taking expert opinions via the Delphi methodology. In addition to MICMAC analysis, the driving and the dependence powers for all the drivers were determined. Moreover, the structural frameworks for the drivers were developed by means of total interpretive structural modeling (TISM) technique. As a result, the findings indicate that the most significant driver was supplier pressure and willingness and the most important barrier was high cost. Finally, the main objective of this research is expected to help industrial managers to evaluate and understand the critical areas where they should emphasize to implement GSCM in the chemical industry. 相似文献
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