Targeting Macrophytes: Optimizing Vegetation Density to Enhance Water Quality within Constructed Wetlands
Author:
McBrady Austin Johnathon1ORCID, Den Walter12ORCID
Affiliation:
1. Water Resources Science and Technology, Department of Natural Sciences, Texas A&M University-San Antonio, One University Way, San Antonio, TX 78224, USA 2. Institute for Water Resources Science and Technology, Texas A&M University, San Antonio, One University Way, San Antonio, TX 78224, USA
Abstract
This study of constructed wetland design investigated relationships between macrophyte species selection and planting density for water quality improvement. A lab-scale wetland was compared against a pilot-scale wetland in San Antonio, Texas, at Mitchell Lake to measure differences in effluent water quality improvement using three native macrophyte species. Using a novel, two-phase method, a targeting macrophyte was identified from among Olney’s bulrush (Schoenoplectus americanus), hardstem bulrush (Schoenoplectus acutus), and California bulrush (Schoenoplectus californicus), based on its marked capability for improving water quality factors, then it was planted in varied majority densities to compare differences in treatment effectiveness. The results showed that the planting density with 50% giant bulrush, 25% Olney’s bulrush, and 25% hardstem improved conductivity removal by 34% and increased dissolved oxygen by 3713% as compared to the Mitchell Lake pilot-scale results. The 70% and 90% majority density plantings (giant bulrush) were not shown to be as effective for the tested parameters, indicating diminishing returns as the vegetation density increasingly becomes a monoculture within the system. The results of this study showed that this complementary approach to wetland design displayed significant improvement in certain treatment parameters than the evenly planted species distribution of the pilot study. These findings demonstrate that the constructed wetland design can be optimized by selecting and planting macrophytes based on their effectiveness in targeting site-specific water quality concerns by capitalizing on their individual traits within complex wetland systems.
Funder
Virginia Tech National Science Foundation
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