- Algal blooms in a river-dominated estuary and nearshore region of Florida, USA: the influence of regulated discharges from water control structures on hydrologic and nutrient conditions , HYDROBIOLOGIA (2023)
- Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes , ENVIRONMENTAL MODELLING & SOFTWARE (2023)
- Optimization of the number and locations of the calibration stations needed to monitor soil moisture using distributed temperature sensing systems: A proof-of-concept study , JOURNAL OF HYDROLOGY (2023)
- Patterns and drivers of nutrient trends in flood-impacted surface waters: Insights from Bayesian modeling approaches , (2023)
- TESTING THE AGREEMENT BETWEEN A TRADITIONAL AND UAV-BASED METHOD FOR QUANTIFYING SKIPS IN SUBOPTIMAL COTTON STANDS , JOURNAL OF THE ASABE (2023)
- Vulnerability of Estuarine Systems in the Contiguous United States to Water Quality Change Under Future Climate and Land-Use , EARTHS FUTURE (2023)
- Can a simple water quality model effectively estimate runoff-driven nutrient loads to estuarine systems? A national-scale comparison of STEPLgrid and SPARROW , ENVIRONMENTAL MODELLING & SOFTWARE (2022)
- Characterizing soil water content variability across spatial scales from optimized high-resolution distributed temperature sensing technique , JOURNAL OF HYDROLOGY (2022)
- Implementing FAIR data management practices in shellfish sanitation , AQUACULTURE REPORTS (2022)
- Managing nitrogen legacies to accelerate water quality improvement , NATURE GEOSCIENCE (2022)
The Science and Technologies for Phosphorus Sustainability (STEPS) Center is a convergence research hub for addressing the fundamental challenges associated with phosphorus sustainability. The vision of STEPS is to develop new scientific and technological solutions to regulating, recovering and reusing phosphorus that can readily be adopted by society through fundamental research conducted by a broad, highly interdisciplinary team. Key outcomes include new atomic-level knowledge of phosphorus interactions with engineered and natural materials, new understanding of phosphorus mobility at industrial, farm, and landscape scales, and prioritization of best management practices and strategies drawn from diverse stakeholder perspectives. Ultimately, STEPS will provide new scientific understanding, enabling new technologies, and transformative improvements in phosphorus sustainability.
My long-term research goal is to be a leading engineering scientist in the study of nearshore water quality dynamics across diverse human, ecological, and policy landscapes. My immediate research goal is to develop fundamental understanding on the ways tidal floods and stormwater runoff interact to influence the rates and magnitudes of fecal bacteria loading in nearshore waters in order to develop predictive models that inform the design of sustainable coastal and stormwater infrastructure. My long-term educational career goal is to equip students of diverse backgrounds with the training and computational skills needed to harness the power of data science to advance environmental sustainability in their communities.
A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain
The ability to continuously monitor fecal bacteria concentrations in nearshore waters through field sensing has the potential to transform the way in which bacteria-driven public health risks are anticipated, mitigated, and managed by allowing for near real-time detection and the creation of high-quality datasets from which forecast models can be developed. Advances in freshwater monitoring reveal that fecal contamination can be predicted using data collected via high-frequency water quality sondes, but additional research is needed to extend these frameworks to coastal waters. We propose to observe water quality conditions every 15 minutes in Bald Head Creek, North Carolina, a tributary of the Cape Fear River, using a multiparameter sonde (YSI EXO2). The sonde will include sensors to monitor conductivity, temperature, dissolved oxygen, pH, turbidity, total algae (phycocyanin, phycoerythrin, and chlorophyll), fluorescent dissolved organic matter, tryptophan-like fluorescence, and water depth. In addition to observing water quality variables, we will analyze creek water samples for fecal indicator bacteria, antibiotic resistant bacteria, amino acids, and particulate and dissolved organic carbon isotopic signatures across four intensive field campaigns. Data collected via this project will be used to develop an innovative observation and machine learning modeling framework for predicting fecal contamination at high frequencies. Insights gained through the project will be shared with local and federal partners (e.g., Village of Bald Head Island, NC Coastal Federation, FDA Division of Seafood Science).
Aquaculture, the rearing and harvesting of organisms in water environments, is a rapidly expanding industry that now produces more seafood than all wild caught fisheries worldwide. This inevitable growth must be steered towards sustainable production practices, which requires intensive monitoring in areas that are difficult and potentially dangerous to access. The vision of this project is to improve the efficiency and sustainability of near-shore aquaculture production through integrating a flexible, customizable, multi-task vehicle fleet, consisting primarily of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs), with a biologically-relevant framework for accelerated prototyping. This project will use oyster production along the Eastern US shoreline as a case study and testbed.
Coastal ecosystems are uniquely vulnerable to changes in freshwater quantity and quality. With a warming climate, changes in freshwater discharge into estuaries will interact with rising sea levels. Coastal natural resource managers need guidance on the potential impacts and vulnerabilities to better manage the risks to species and habitat. This project will provide guidance and recommendations for resource managers on the development of ecological flow targets that protect coastal ecosystems and wildlife while accounting for changes in freshwater availability and sea level rise.
Accurate monitoring for changes in pest susceptibility to insecticidal toxins expressed in genetically engineered agronomic crops is currently an ineffective process limited by both scale and scope of deployment. Although long-term scientific and social change will be necessary to minimize pest resistance evolution, understanding near-term shifts in susceptibility through novel monitoring will also be essential to enable more effective resistance management strategies. To address this limitation on resistance monitoring, we propose to develop and deploy real-time pheromone-based sensor platforms to indicate patterns of lepidopteran pest activity in landscapes. We will use cotton bollworm (Helicoverpa zea Boddie) as a case study to develop and refine automated monitoring tools designed to detect shifts in pest susceptibility.
The modern availability of novel data analytics and cost-effective high-performance computing creates unique opportunities to tap into the wellspring of potential offered by big data for creating decision-making tools that inform sustainable agroecosystem management. When coupled with climate, land use, and policy-related data streams through analytics, long-term monitoring data can be applied to develop data-driven decision-support tools designed for land and water resource managers, but foundational research is needed to develop such data-rich decision-support platforms. As a case study, this research will develop a data-to-decision pipeline for nearshore water quality management in support of shellfish agroecosystem protection. Shellfish growing areas are regularly screened for coliform bacteria to inform on-the-fly decision-making by regulators who are evaluating the sanitation of cultured shellfish, which has led to the accrual of a vast record of spatiotemporal bacterial observations. These national-scale data remain poorly explored and underutilized due to challenges associated with analyzing big, multi-scale data, but could be mined to develop critically-needed decision-support platforms.
Inconsistent quality and aesthetics in agricultural crops can result in increased consumer and producer food waste, reduced industry resiliency and decreased farmersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ and growersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ profit, poor consumer satisfaction, and inefficiencies across the supply chain. Although there are opportunities to characterize and quantify sources of phenotypic variability across the agricultural supply chain - from cultural practices of growers and producers to storage and handling by distributors - the data available to allow for assessment of horticultural quality drivers are disparate and disconnected. The absence of data integration platforms that link heterogeneous datasets across the supply chain precludes the development of strategies and solutions to constrain variability in produce quality. This projectÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s central hypothesis is that multi-dimensional produce data can be securely integrated and used to optimize management practices in the field while simultaneously adding value across the entire food supply chain. We propose to develop multi-modal sensing platform along with a trust-based, data management, integration, and analytics framework for systematic organization and dynamic abstraction of heterogeneous data across the supply chain of agricultural crops. The projects short term goals are to (1) engage growers to refine research and extension priorities; (2) develop a first-of-its-kind modular imaging system that responds to grower needs by analyzing existing and novel multi-dimensional data; (3) establish the cyberinfrastructure, including analytics and blockchain, to make meaningful inference of the acquired data as related to management practices while ensuring data security; (4) deploy the sensing system at NCSUÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s Horticultural Crops Research Station in Clinton, NC and on a large-scale system at a major commercial farm and distribution facility, and (5) extend findings to producers and regulators through NC Cooperative Extension. The proposed sensing and cyberinfrastructure platforms will be crop-agnostic and our findings will be transferable to other horticultural crops produced in NC and beyond.
Wetland monitoring in North Carolina (NC) has a 14-year history conducted by NC Department of Environment and Natural Resources (DENR) for the first 10 years and North Carolina State University (NCSU) for the last 4 years. We will use the outcomes and lessons learned from these monitoring efforts to develop and implement a pilot, volunteer wetland monitoring program administered by NCSU with support from the Carolina Wetlands Association and RTI. This pilot program will establish the procedures and methods for a sustainable, citizen-based wetland monitoring program that can be expanded in future years. In Task 1, we will determine the methods used to monitor the hydrology, water quality, soil chemistry, vegetation and biota based on the outcomes of the September 2019 meeting of wetland experts from WPDG No. CD-00D25014. In Tasks 2 and 3, we will develop a project QAPP and SOPs for monitoring activities and provide training to volunteers including representatives from local and state agencies. In Task 4, we will establish 10-12 monitoring stations within undisturbed, protected wetland sites recognized as Wetland Treasures of the Carolina. Task 5 will develop a data portal for volunteers to share data, provide tools to visualize results, and develop applications to enhance the volunteer experience. In Task 6, we will develop and populate a database compatible with NC DEQÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s data structure and submit the data to NC DEQÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s for use in their wetland and other water programs. In Task 7, we will disseminate information and results through a quarterly newsletter, website, reporting to the EPA, and annual meetings, where we will also provide training to prospective volunteers. The outputs include a pilot volunteer wetland monitoring and outreach program, training materials and SOPs, data portal and supporting website, and various education materials (factsheets, newsletters, webinars). The data from these sites will determine the condition of different wetland types and demonstrate/quantify services provided by different wetland types. By becoming a sustainable volunteer citizen-based wetland monitoring program, this program will launch a state-wide wetland monitoring network and establish baseline metrics for wetland restoration, and protection.