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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

2018

Environmental Sciences

University of Vermont

Green stormwater infrastructure

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Soil Media Co2 And N2O Fluxes Dynamics From Sand-Based Roadside Bioretention Systems, Paliza Shrestha, Stephanie E. Hurley, E. Carol Adair Feb 2018

Soil Media Co2 And N2O Fluxes Dynamics From Sand-Based Roadside Bioretention Systems, Paliza Shrestha, Stephanie E. Hurley, E. Carol Adair

College of Agriculture and Life Sciences Faculty Publications

Green stormwater infrastructure such as bioretention is commonly implemented in urban areas for stormwater quality improvements. Although bioretention systems' soil media and vegetation have the potential to increase carbon (C) and nitrogen (N) storage for climate change mitigation, this storage potential has not been rigorously studied, and any analysis of it must consider the question of whether bioretention emits greenhouse gases to the atmosphere. We monitored eight roadside bioretention cells for CO2-C and N2O-N fluxes during two growing seasons (May through October) in Vermont, USA. C and N stocks in the soil media layers, microbes, and aboveground vegetation were also …


Applying Bayesian Belief Network To Understand Public Perception On Green Stormwater Infrastructures In Vermont, Qing Ren Jan 2018

Applying Bayesian Belief Network To Understand Public Perception On Green Stormwater Infrastructures In Vermont, Qing Ren

Graduate College Dissertations and Theses

Decisions of adopting best management practices made on residential properties play an important role in reduction of nutrient loading from non-point sources into Lake Champlain and other waterbodies in Vermont. In this study, we use Bayesian belief network (BBN) to analyze a 2015 survey dataset about adoption of six types of green infrastructures (GSIs) in Vermont’s residential areas. Learning BBNs from physical probabilities of the variables provides a visually explicit approach to reveal the message delivered by the dataset. Using both unsupervised and supervised machine learning algorithms, we are able to generate networks that connect the variables of interest and …