scholarly journals Screening and Ranking Methodology Applied to Biochars Aimed at Acidic and Calcareous Sandy Soil Improvement

2021 ◽  
Author(s):  
Eva Farkas ◽  
Viktória Feigl ◽  
Emese Vaszita ◽  
Nikolett Uzinger ◽  
Márk Rékási ◽  
...  
Keyword(s):  
Decision Support ◽  
Sandy Soil ◽  
Decision Support Tool ◽  
Soil Improvement ◽  
Positive Influence ◽  
Support Tool ◽  
Fiber Sludge ◽  
Different Origins ◽  
By Products ◽  
Selection And Ranking

The application of biochar (the by-product of biomass pyrolysis), as a soil amendment has been accepted as a sustainable solution to improve soil quality. The current study aims to establish a decision support tool for characterizing, ranking, and selecting biochars of different origins for soil improvement, thereby contributing to the development of a systematic approach, which lacks in the existing literature.The development of a Multi-Criteria Decision Support Approach applying a banded and weighted rating and scoring system allowed the selection and ranking of various biochars suitable for improving sandy soils before application. First, 14 selected, different biochar products (produced from industrial by-products, herbaceous, wood-based and manure-based feedstocks) were characterized with several physicochemical, biological and ecotoxicological methods taking into account both the technological and the environmental efficiency aspects of biochar utilization. Then, a system for the assessment and ranking of biochars for acidic, and calcareous neutral sandy soil improvement was developed, which could be flexibly adapted to different soil problems as well. Based on their performance in the tests, scores from (−5) to (+5) were assigned to each biochar. As a result, the grain husks and paper fiber sludge biochar was ranked as the most suitable for both acidic and neutral calcareous sandy soil improvement, with 55 and 43 scores, respectively (from the maximum 100). The applicability of this innovative multicriteria scoring-ranking system, as a tool for potential biochar users, was verified in microcosms and field-scale experiments, demonstrating the positive influence of this biochar on the acidic sandy soil.

A Decision Support Tool for Evaluating Big Data Investment in Transport: Empirical evidence from European use cases of NOESIS project

2020 ◽  
Author(s):  
Christos Katrakazas ◽  
Natalia Sobrino ◽  
Ilias Trochidis ◽  
Jose Manuel Vassallo ◽  
Stratos Arampatzis ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Empirical Evidence ◽  
Decision Support Tool ◽  
Use Cases ◽  
Support Tool

A Decision Support Tool for Wastewater Catchment Management

2007 ◽  
Vol 2 (2) ◽  
Author(s):  
S.E. Walters ◽  
D. Savic ◽  
R.J. Hocking
Keyword(s):  
Decision Support ◽  
Asset Management ◽  
Capital Investment ◽  
Management Strategies ◽  
Sewage Treatment ◽  
Decision Support Tool ◽  
Catchment Management ◽  
Clean Water ◽  
Support Tool ◽  
South West

The water industry over the years has primarily focussed on upgrading and investing in clean water provision. However, as research into the science and management of clean water services has progressed rapidly, wastewater provision and services has been slower. Focus, though, is now shifting within Industry and Research into wastewater services. The water regulator, Ofwat, for England and Wales demands the Sewerage Undertakers demonstrate efficient management of wastewater systems in order to obtain funding for Capital Investment projects. South West Water, a Water Service Provider and Sewerage Undertaker located in the South West of England, identified a need gap in their asset management strategies for wastewater catchments. This paper will introduce the production of a Decision Support Tool, DST, to help SWW proactively manage their Wastewater Catchments, examining Sewage Treatment Works, Pumping Stations and Networks. The paper will discuss some concepts within the DST, its production, testing and a brief case study. The DST provides a framework for prioritising catchments to optimise investment choices and actions. The Tool ranks catchments utilising Compromise Programming, CP, as well as AHP Pair-wise comparisons for preference weights. The DST incorporates Asset models, a Whole life Costing Module, as well as a Decay and Intervention Module.

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