Browsing by Author "Shen, Zhongzhe"

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    Enhancing Landscape Performance Measurement Using Smart Devices, Data Visualization, and Longitudinal Tracking
    Shen, Zhongzhe (Virginia Tech, 2024-05-28)
    This dissertation explores the use of smart devices to measure the environmental landscape performance (LP) of landscape projects. It proposes and evaluates an alternative landscape performance measurement framework (ALPMF) with smart device assistance. By providing new measurement methods and tools, it aims to fill some existing and potential gaps in LP and promote its development. LP has been proposed in landscape architecture to measure landscape projects' sustainable benefits. Due to LP research's short development history, some gaps exist, including one-time measurements, a lack of standard evaluation methods, and insufficient measurement tools. Given the advantages of smart devices in data collection and the successful application of smart devices in other design-related fields, this dissertation explores their feasibility as assessment tools in environmental LP studies. It begins by analyzing each LP research case's report listed on the Landscape Performance Series (LPS) website to explore the limitations of traditional measurement methods and tools. Following a survey of professionals' perspectives on LP metrics. Based on the survey results, the researcher selects certain air quality and water quality LP metrics as variables (air temperature, humidity, carbon dioxide, particulate matter, total dissolved solids, and electronic conductivity) for subsequent experiments. Two experiments explore smart devices' strengths and limitations in collecting LP data and measuring landscape projects' LP in terms of accuracy, real-time, spatial resolution, and longitudinal analysis. The researcher proposes the ALPMF and conducts a comparative study with the traditional landscape performance measurement framework (TLPMF) to measure a project's LP. By comparing methods, tools, and results, the study examines the advantages and effectiveness of the ALPMF to a certain extent and explores its limitations. The research results show that smart devices and the ALPMF can provide more accurate, real-time, spatial resolution, and longitudinal LP data. The results also demonstrate the effectiveness of the ALPMP. Furthermore, this dissertation offers several insights and suggestions for further developing smart devices and the ALPMF in LP and landscape architecture. This dissertation fills some research gaps and provides new tools and methods for future LP measurement. It contributes to improving landscape projects' sustainable values and refining the landscape architectural design guidelines. As an interdisciplinary study, it also provides an example of the intersection of landscape architecture with other disciplines, such as mechanical engineering and computer science. It helps to broaden the knowledge boundary of landscape architecture.
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    Improving Landscape Performance Measurement: Using Smart Sensors for Longitudinal Air Quality Data Tracking
    Shen, Zhongzhe; Kim, Mintai (Wichmann Verlag, 2022-06)
    As addressing climate changes become a pressing issue in landscape architecture, the importance of landscape performance (LAP) became an important topic. An essential part of LAP is accessing data. Some data are easily accessible in the landscape architecture field, but some are not, such as air quality data. When such data are available in the landscape architecture field, they are often not of high enough quality, regarding scale, adequation, and precision. Also, there are sometimes financial barriers to getting the data. The research team explores an alternative way of collecting longitudinal air quality data to improve LAP measurement, using the Arduino-based cheaply made smart sensors installed on-site over time. The research team conducted experiments in nine comparison sites, collected and analyzed air quality data, including temperature, humidity, equivalent carbon dioxide (eCO2), volatile organic compounds (TVOCs), and fine particulate matter (PM2.5). The result shows that compared to publicly available data, longitudinal data collected by smart sensors are more accurate, dense, and frequent. This study investigates the strengths and capacities of using smart sensors for longitudinal air quality data tracking and offers an alternative way of providing data evidence for sustainable design to mitigate some climate changes issues.
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    Longitudinal Water Pollution Monitoring and Retention Pond Capacity Assessment Using Smart Devices
    Shen, Zhongzhe; Kim, Mintai (Wichmann Verlag, 2023-05)
    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 pollution reduction 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.
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    Quantifying Sustainability and Landscape Performance: A Smart Devices Assisted Alternative Framework
    Shen, Zhongzhe; Peng, Xingjian; Du, Chenlong; Kim, Mintai (MDPI, 2023-09-04)
    This research investigates gaps in current methods and tools in landscape performance research and presents a smart device-assisted alternative framework for performance assessment. Against the background of increasing attention to sustainability, landscape performance has emerged as a novel research focus on sustainability, with the objective of precisely quantifying sustainable performance. However, certain shortcomings persist within this field. This research conducts a comprehensive review of pertinent literature and analyzes deeply the performance metrics and case studies cataloged by the Landscape Performance Series (LPS). Additionally, an examination of quantitative tools is undertaken by surveys. The study finds several issues in current landscape performance research: imbalance development, inconsistent methods, one-time measurement, insufficient tools, and inaccurate and unreliable quantified results. Based on the advantages of smart devices in gathering sustainable data and previous research results, this research presents an alternate framework for conducting landscape performance research, which incorporates smart devices. In addition, it presents a set of recommendations for advancing research on landscape performance. This study could contribute to improving the diversity and accuracy of landscape performance quantification and contribute to future performance research. It assists in the refinement of landscape performance research and the achievement of sustainable development goals.