Life cycle optimisation for highway best management practices

2006 ◽  
Vol 54 (6-7) ◽  
pp. 477-484 ◽  
Author(s):  
J.G. Lee ◽  
J.P. Heaney ◽  
D.N. Rapp ◽  
C.A. Pack
Keyword(s):  
Life Cycle ◽  
Best Management Practices ◽  
Management Practices ◽  
Cycle Performance ◽  
Point Source Pollution ◽  
Highway Runoff ◽  
Continuous Simulation ◽  
Best Management ◽  
Simulation Optimisation ◽  
Highway Drainage

Highway runoff can cause a number of water quantity and quality problems. Stormwater management systems for highways have been developed based on a fast drainage for large storm situations. Non-point source pollution from highway runoff is a growing water quality concern. Stormwater quality control needs to be integrated into highway drainage design and operation to reduce the stormwater impacts on the receiving water. A continuous simulation/optimisation model for analysing integrated highway best management practices (BMPs) is presented. This model can evaluate the life cycle performance of infiltration and/or storage oriented highway BMPs. It can be directly integrated with spreadsheet optimisation tools to find the least cost options for implementing BMPs throughout a specified life cycle.

scholarly journals Framework to prioritize watersheds for diffuse pollution management in the Republic of Korea: application of multi-criteria analysis using the Delphi method

2019 ◽  
Vol 19 (12) ◽  
pp. 2767-2779 ◽  
Author(s):  
Gyumin Lee ◽  
Kyung Soo Jun ◽  
Minji Kang
Keyword(s):  
Decision Making ◽  
Point Source ◽  
Best Management Practices ◽  
Delphi Technique ◽  
Management Practices ◽  
Point Sources ◽  
Republic Of Korea ◽  
Point Source Pollution ◽  
Best Management ◽  
Non Point Source Pollution

Abstract. This study aimed to develop a risk-based approach for determining control areas to manage non-point source pollution, developing a framework to prioritize catchments by considering the characteristics of polluted runoff from non-point sources. The best management, decision-making, and scientific approaches, such as the technique for order of preference by similarity to ideal solution (TOPSIS) and the Delphi technique, are required for the designation of control areas and the application of the best management practices to the control areas. Multi-criteria decision-making (MCDM) methods can handle the diversity and complexity of non-point source pollution. The Delphi technique was employed for selecting the assessment criteria/sub-criteria and determining their weights. Sub-criteria for each catchment unit were scored with either a quantitative or qualitative scale. All non-point pollution sources in mainland Republic of Korea were included, with the exception of a few islands, with catchment prioritization and pollution vulnerability evaluations shown as thematic maps. This study contributes to the field by developing a new risk-based approach for ranking and prioritizing catchments; this provides valuable information for the Ministry of Environment to use to identify control areas and manage non-point source pollution.

scholarly journals The Adoption of Best-Management Practices by Louisiana Dairy Producers

2004 ◽  
Vol 36 (1) ◽  
pp. 229-240 ◽  
Author(s):  
Noro C. Rahelizatovo ◽  
Jeffrey M. Gillespie
Keyword(s):  
Data Analysis ◽  
Point Source ◽  
Best Management Practices ◽  
Count Data ◽  
Management Practices ◽  
Negative Binomial ◽  
Farm Size ◽  
Point Source Pollution ◽  
Best Management ◽  
Non Point Source Pollution

This study examines the adoption of best-management practices (BMPs) in terms of the total number of practices implemented up to a certain period, using count data analysis. Poisson and negative binomial regressions were used to examine the likely determinants of producers' decisions to adopt greater numbers of technologies, and the specific case of dairy producers' adoption of BMPs was explored. Our results emphasize the significant effect of producers' awareness of the efforts to control non-point source pollution, information about BMPs, farm size, producer's educational attainment, and risk aversion on the number of BMPs adopted.

scholarly journals Comparison of Three Production Scenarios for Buxus microphylla var. japonica ‘Green Beauty’ Marketed in a No. 3 Container on the West Coast Using Life Cycle Assessment

HortScience ◽  
2017 ◽  
Vol 52 (3) ◽  
pp. 357-365 ◽  
Author(s):  
Dewayne L. Ingram ◽  
Charles R. Hall ◽  
Joshua Knight
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Best Management Practices ◽  
West Coast ◽  
Management Practices ◽  
The United States ◽  
The West ◽  
Best Management ◽  
Buxus Microphylla ◽  
Carbon Dioxide Equivalents

Three scenarios for production of Buxus microphylla var. japonica [(Mull. Arg.) Rehder & E.H. Wilson] ‘Green Beauty’ marketed in a no. 3 container on the west coast of the United States were modeled based on grower interviews and best management practices. Life cycle inventories (LCIs) of input products, equipment use, and labor were developed from the protocols for those scenarios and a life cycle assessment (LCA) was conducted to determine impact of individual components on the greenhouse gas emissions (GHGs) and the subsequent carbon footprint (CF) of the product at the nursery gate and in the landscape. CF is expressed in global warming potential (GWP) for a 100-year period in units of kilograms of carbon dioxide equivalents (kg CO2e). The GWP of the plant from Scenario A (propagation to no. 1 to 3 container) was 2.198 kg CO2e with variable costs of $4.043. Scenario B (propagation to field to no. 3 container) would result in a GWP of 1.717 kg CO2e with variable costs of $2.880 and take a year longer in production than the other two models. The GWP of Scenario C (propagation to no. 1 to no. 2 to no. 3 containers) would be 3.364 kg CO2e with variable costs of $5.733. Containers, transplants/transplanting, irrigation, and fertilization input products and associated activities accounted for the greatest portion of GHG and variable costs in each scenario. Pruning, assembling/load trucks, pesticides, and chlorination were other important components to variable costs of each scenario but had little impact on GWP. Otherwise, the major contributors to GWP are also major contributors to cost.

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