Journal of Digital Landscape Architecture

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Longitudinal Water Pollution Monitoring and Retention Pond Capacity Assessment Using Smart Devices

This study experiment uses low-cost smart devices to longitudinally monitor the level of common water pollutants, such as electrical conductivity (EC) and total dissolved solids (TDS), in a retention pond, and assess and quantify a retention pond's capacity for pollution reduction. Landscape performance (LAP) is an important and emerging topic that quantifies the impacts of design practices and helps to improve future designs. Although previous research has suggested that retention ponds can aid in cleaning surface runoff before water is discharged into downstream systems, most of this research has been theoretical, with few studies measuring the water cleaning capacity of retention ponds. In this study, the research team installs several smart devices with various sensors at each inlet and outlet of the retention pond water system. Environmental data is collected continuously and can be accessed by researchers at any time through an SD storage card. This research presents an alternative way for professionals to evaluate water quality and provides a method for quantifying a retention pond's pollutionreduction ability. The results of this study can potentially improve the existing environmental performance monitoring system, provide evidence-based data to guide future retention pond projects, and serve as a reference for landscape teaching to enhance the competence of future environmental professionals.

Autor / Author: Shen, Zhongzhe; Kim, Mintai
Institution / Institution: Virginia Tech, Virginia/USA; Virginia Tech, Virginia/USA
Seitenzahl / Pages: 8
Sprache / Language: Englisch
Veröffentlichung / Publication: JoDLA – Journal of Digital Landscape Architecture, 8-2023
Tagung / Conference: Digital Landscape Architecture 2023 – Future Resilient Landscapes
Veranstaltungsort, -datum / Venue, Date: Dessau Campus of Anhalt University, Germany 24-05-23 - 27-05-23
Schlüsselwörter (de):
Keywords (en): Smart devices, water quality, retention ponds, landscape performance, longitudinal tracking
Paper review type: Full Paper Review
DOI: doi:10.14627/537740007
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