Fast Eutrophication Assessment For Stormwater Wet Detention Ponds Via Fuzzy Probit Regression Analysis Under Uncertainty

Keywords

Environmental monitoring; Fuzzy synthetic evaluation; Probit regression; Stormwater wet detention ponds; Trophic state classification

Abstract

Stormwater wet detention ponds have been a commonly employed best management practice for stormwater management throughout the world for many years. In the past, the trophic state index values have been used to evaluate seasonal changes in water quality and rank lakes within a region or between several regions; yet, to date, there is no similar index for stormwater wet detention ponds. This study aimed to develop a new multivariate trophic state index (MTSI) suitable for conducting a rapid eutrophication assessment of stormwater wet detention ponds under uncertainty with respect to three typical physical and chemical properties. Six stormwater wet detention ponds in Florida were selected for demonstration of the new MTSI with respect to total phosphorus (TP), total nitrogen (TN), and Secchi disk depth (SDD) as cognitive assessment metrics to sense eutrophication potential collectively and inform the environmental impact holistically. Due to the involvement of multiple endogenous variables (i.e., TN, TP, and SDD) for the eutrophication assessment simultaneously under uncertainty, fuzzy synthetic evaluation was applied to first standardize and synchronize the sources of uncertainty in the decision analysis. The ordered probit regression model was then formulated for assessment based on the concept of MTSI with the inputs from the fuzzy synthetic evaluation. It is indicative that the severe eutrophication condition is present during fall, which might be due to frequent heavy summer storm events contributing to high-nutrient inputs in these six ponds.

Publication Date

2-1-2016

Publication Title

Environmental Monitoring and Assessment

Volume

188

Issue

2

Number of Pages

1-18

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s10661-015-5073-6

Socpus ID

84953222563 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84953222563

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