Zeshu Zhang - Dissertation Defense

Friday, December 2, 2022
12:30 p.m.
AJC4104
Rachel Chang
301 405 8268
rachel53@umd.edu

Date: Friday, December 2, 2022
Time: 12:30 PM EST
Location: AJC4104 (4th floor conference room)
 
Title: Modeling, Targeting, and Optimization of BMPs for Environmental Health in a Coupled Human-natural System
 
Committee members:
Dr. Hubert Montas, Chair
Dr. Adel Shirmohammadi, Co-chair
Dr. Yang Tao
Dr. Masoud Negahban-Azar
Dr. Paul T. Leisnham, Dean's Representative
 
Abstract:
Pollution is a severe problem throughout the world. For control purposes, it may be classified based on how it enters the environment as either point source pollution or nonpoint source (NPS) pollution. Point source pollution refers to situations where pollutants enter the environment from a single place, like a pipe or a smokestack, and is best controlled at or before that discharge point. NPS pollution, on the contrary, refers to situations where pollutants come from spatially distributed and unconfined locations. NPS pollution is a severe problem worldwide. In the Chesapeake Bay region, surface runoff, sediment, nitrogen, and phosphorus are among the most critical pollution parameters. To increase water quality and control NPS pollution, Best Management Practices (BMPs), such as conservation tillage, rain gardens, or porous pavement, are being implemented in various areas of the Chesapeake Bay basin, but improvements are not as significant as expected, and some areas show degrading trends. Hydrologic models have been developed to simulate the processes that govern the generation and movement of NPS pollutants, and to provide specific guidance on the location and type of BMPs best suited for efficiently improving water quality and conserving water. The approach has shown promise in agricultural areas, but several research questions remain to enhance its broader applicability. One major question is how to translate the approach to suburban and urban areas common in the Chesapeake Bay basin, and whether the spatial distribution of polluting regions may differ from that in agricultural zones. Another question is the degree to which selected BMPs are the most cost-effective for a given targeted pollution reduction and whether an alternative allocation can reduce costs. A third research question is about BMP adoption by stakeholders: can adoption likelihood of BMPs be predicted based on socioeconomic factors, and can it be increased to meet pollution reduction goals?
This dissertation investigates the above questions and generates guidance on cost-effective BMPs and social intervention strategies for BMP adoption across diverse land use types. First, the study characterizes how the spatial distribution of NPS hotpots changes among natural, agricultural, suburban, and ultra-urban watersheds typical of the region, using a hydrologic and water quality model, SWAT (Soil Water Assessment Tool). The results indicate that the spatial distribution of NPS constituents becomes increasingly uniform as urbanization increases. It is found that the spatial distribution of NPS constituents is a function of the major landcover categories in a study site, and that control measures should be adopted accordingly. Second, this study evaluates the cost-effectiveness of eight pre-selected BMPs capable of controlling NPS constituents in different land covers using random, targeted, and optimized allocation methods. Results show that the optimized selection of BMPs at optimized locations achieves the highest-performing BMP allocation plan across different landscape types. In the areas where NPS constituents are highly concentrated (natural or agricultural areas), a lower computational cost method: targeting NPS hotspots by criterion-based methods, is also broadly applicable. Lastly, this research explores the physical and socioeconomic factors that affect stakeholders' BMP adoption and quantifies the impact of these factors based on the RiverSmart Home BMP adoption data for Washington, D.C.  The best regression model (random forest regression: R2=0.67, PBIAS=7.2%) shows that distance to the nearest BMP has the most significant impact on BMP adoption. Other features like median household income, education level, and existing green space area contribute less to BMP adoption. Spatio-temporal simulation of BMP adoption provides social intervention guidelines for increasing adoption locally, thus helping to achieve NPS pollutant reduction targets.
 
 


Audience: Graduate  Faculty  Staff 

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