NEWRnet Undergraduate Research Internships Project Descriptions
2016 NEWRnet Research Project Descriptions
- University of Delaware, Newark, DE
- 01- Understanding Human Behavior and Water Quality: Applications of Experimental Economics
- 02- Storm Events and Climate Variability Impacts on Water Quality and Watershed Processes
- 03- Patterns of discharge, nutrient concentrations, and nutrient loads in Murderkill River Watershed (Delaware)
- University of Vermont, Burlington, VT
- 04- Continuous Water Quality Monitoring in the Lake Champlain Basin
- 05- Using the Social-Ecological Gaming and Simulation Laboratory to Understand Decision Making That Will Influence Watershed Pollution
- Salve Regina University in Newport, RI
- 06- Biological Assessment of Urban Watersheds in Rhode Island
- University of Rhode Island, Kingston, RI
- 07- Testing the limits of next-generation water sensors
- 08- Collecting and validating real-time water quality sensor data in three RI watersheds of differing land use
- 09- Autonomous surface vessel (ASV) surveys and software development
- 10- Understanding Human Decisions Related to Water Quality
University of Delaware in Newark, DE
Project Code: 01
Title: Understanding Human Behavior and Water Quality: Applications of Experimental Economics
Co-mentors:
Dr. Kent Messer, University of Delaware, Applied Economics and Statistics
Dr. Maik Kecinski, University of Delaware, Applied Economics and Statistics
Research Description: The Center for Experimental and Applied Economics is looking for a research intern to assist with the design, administration, and analysis of a series of experiments related to stakeholder responses to water quality information. This internship is ideal for students with an interest in behavioral and decision sciences as related to environmental issues. Your involvement may include discussing and researching hypotheses and design details, recruiting and scheduling participants, designing and preparing experimental materials, administering experimental sessions in the lab or in the field, conducting basic data analysis, and preparing curriculum based on the research.
Research Questions:
1) How can high resolution water quality sensor data be communicated to stakeholders, and incorporated into a policy environment to improve decision making?; 2) How do individuals respond to information about water contamination risks?; 3) 3) How do individuals behave and respond to environmental change and risk related to global warming and sea level rise?
Student Learning Objectives:
1) Design and administration of economic experiments; 2) Data analysis and presentation; and 3) Developing learning materials based on research.
Prerequisites:
Courses: Microeconomics. Some environmental economics would be helpful. Interest in human behavior in economic and environmental contexts. Microsoft office. Some background in programming, data visualization, and economic experiments would be useful.
Work Environment:
Field and Laboratory
Back to top
University of Delaware in Newark, DE
Project Code: 02
Title: Storm Events and Climate Variability Impacts on Water Quality and Watershed Processes
Co-Mentors:
Dr. Shreeram Inamdar, University of Delaware, Plant and Soil Sciences
Erin Johnson, M.S. Candidate, University of Delaware, Water Science & Policy
Chelsea Krieg, M.S. Candidate, University of Delaware, Water Science & Policy
Research Description: We will have multiple research projects that will investigate how large storm events, droughts, and seasonal weather events impact water quality and watershed processes. Water quality will be evaluated for carbon, nitrogen, and other solutes, both in dissolved and particulate/sediment forms. Students will use high-frequency, in-situ, water quality sensors, traditional stream water sampling, and laboratory analyses of water samples to evaluate changes in water quality. Student interns will work with graduate students and postdocs to address key project questions. Work will be conducted in an experimental watershed minutes away from the University of Delaware campus.
Research Questions:
1) How does storm magnitude and intensity affect sediment composition and water quality in streams?
2) How does stream water quality change with drainage scale and landuse?
3) How do physical and chemical characteristics of stream runoff water and sediments influence its biological character (bioavailability and microbial composition)?
Student Learning Objectives:
1) Develop instrumentation and analytical skills to use in-situ electronic sensors. Learn how water quality sampling is conducted in watersheds.
2) Assess which environmental forcings and factors influence water quality.
3) Learn new laboratory techniques to analyze water quality samples and investigate what these water quality variables mean for aquatic ecosystem functions.
Prerequisites:
Hydrology, Water Quality, Watershed and Stream Sampling, Laboratory Analyses.
Preferred majors: Environmental Engineering, Geology, Geography, Agriculture, Biology, Chemistry
Work Environment:
Watershed/field site as well as Water Analytical Laboratory
Back to top
University of Delaware in Newark, DE
Project Code: 03
Title: Patterns of discharge, nutrient concentrations, and nutrient loads in Murderkill River Watershed (Delaware)
Co-Mentors:
Dr. A. Scott Andres, University of Delaware and Delaware Geological Survey, Watershed Hydrology, Hydrogeology, Nutrient loading
Dr. William Ullman, University of Delaware (Lewes), Marine Science and Policy
Dr. Chris Main, University of Delaware, College of Earth, Ocean and Environment
Research Description: There are many environmental factors that influence nutrient concentrations in freshwater bodies and loads from these waterbodies to downstream receiving waters. These factors operate over very different time scales and, as a consequence, lead to contrasting water quality outcomes. The installation of the Coursey Pond Water Quality Observatory allows us to determine and examine short term variability in concentrations, discharge, and loads from the Murderkill Watershed. There is opportunity to research the high frequency (30-minute) data in the context of historical monthly data from Coursey Pond and its tributary, Killens Pond.
We are seeking a student to evaluate available water quality and discharge data for these two ponds to examine seasonal and inter-annual patterns, the effects of seasonality and storms. Simple graphical and numerical time series analytical methods will be employed to evaluate the data. Students with GIS skills will be encouraged to relate water quality changes and trends to long-term trends associated with changing patterns of land and water use in the watershed.
The long-term goal of this project is to develop techniques for evaluating variability in water quality data collected by high-frequency NEWRNet instrumentation in the context of longer term variability and trends for use at all NEWRNet sites.
Research Questions:
1) What are the relationships between concentrations, water discharge, and loads of nitrogen and carbon and how are these relationships affected by weather and seasonality?; 2) How does the character of dissolved organic carbon, as determined by UV-Vis adsorption spectra, vary with hydrology and weather?
Student Learning Objectives:
1) Growth in data analysis and presentation skills; 2) Exposure to practices supporting the operation and maintenance of automated water quality sensor systems and standard water quality and discharge monitoring.
Prerequisites:
We are looking for a student with interests in water quality and an academic background that includes aspects of mathematics, statistics, computational science, and/or GIS applications.
Work Environment:
Office, lab, and field
Back to top
University of Vermont in Burlington, VT
Project Code: 04
Title: Continuous Water Quality Monitoring in the Lake Champlain Basin
Co-mentors:
Matthew Vaughan, PhD Candidate, University of Vermont, Rubenstein School of Natural Resources
Ryan Sleeper, Master’s Candidate, University of Vermont, Rubenstein School of Natural Resources
Research Team:
Dr. Andrew Schroth, University of Vermont, Geology
Dr. Andrew Vermilyea, Castleton State College, Natural Sciences
Research Description: Two undergraduate students will join graduate students Mathew Vaughan (UVM) and Ryan Sleeper (UVM), Professor Andrew Schroth (UVM), and Professor Andy Vermilyea (Castleton) for the third field season of deploying chemical sensors in waterways entering Lake Champlain as part of the North East Water Resources Network. Students will work in both field and lab settings throughout the summer. Much of the field work will be related to monitoring our sensor installations and helping with installation early in the summer. Students will visit the sites to grab water samples for nitrate, dissolved organic matter, phosphorus, and other nutrients to calibrate the in-situ sensors. Subsequent analyses on grab samples will be performed in the lab. Individual student projects will emerge from this work to better understand how landscape changes in Vermont affect the nutrient loads into Lake Champlain. Students will work with post docs and graduate students at UVM and learn how to operate various laboratory instruments related to water chemistry. If students are interested they can also participate in data telemetry from the sensor location to a server and processing this large amount of data.
Research Questions:
1) How will landscape changes in Vermont affect the nutrient load into Lake Champlain and the resulting eutrophication?; 2) Is there a high level of data quality from the continuous sensors under all conditions and seasons?; 3) Does the character of dissolved organic matter vary between Vermont landscapes; are there differences in carbon availability as a nutrient?
Student Learning Objectives:
1) Calibration of in-situ sensors; 2) Environmental sampling techniques; and 3) Quantification of aquatic nutrients.
Prerequisites:
A background in environmental or other physical science is required. Preference given to incoming junior or senior level students. Experience with both lab and field work highly preferred (class setting is OK).
Work Environment:
Field, laboratory, computer
Back to top
University of Vermont in Burlington, VT
Project Code: 05
Title: Using the Social-Ecological Gaming and Simulation Laboratory to Understand Decision Making That Will Influence Watershed Pollution
Mentor:
Dr. Scott Merrill, University of Vermont, Plant and Soil Science
Research Team:
Dr. Chris Koliba, University of Vermont, Community Development and Applied Economics
Dr. Asim Zia, University of Vermont, Community Development and Applied Economics
Research Description: The Social-Ecological Gaming and Simulation (SEGS) Lab is a transdisciplinary research lab focused on modeling and simulating Social-Ecological Systems (SESs). The SEGS lab focuses on designing interactive games and brings in subjects to the lab-space to play the games, generate the data and then assimilate the data using SES modeling. Field games and online games are also designed that advance the knowledge base for understanding the dynamics and evolution of SESs.
Student interns will contribute to SEGS lab activities. Activities to be undertaken over the summer include assistance with design and implementation of interactive games that will examine farmer decision making and the resulting impacts on watersheds in Vermont, as well as activities that will increase the SEGS lab online footprint (e.g., building online content). An overview of computer simulation modeling will be provided. Interns will pilot experimental economics games and work with PIs to plan for field experiments.
Research Questions:
1) How will spatial components, such as distance from a farm to a river, influence decisions that will result in changes to river pollution?; 2) Will more realistic scenarios and/or visualizations influence decisions that will result in changes to river pollution?; 3) Will participants playing a static version of our watershed pollution movement game make decisions resulting in different water quality than those playing a dynamic version?
Student Learning Objectives:
1) Learn about social-ecological simulations and gaming; 2) Participate in implementation of experimental games; and 3) Will require learning to code in R or in AnyLogic for agent-based modeling (java based).
Prerequisites:
Quantitative background (required). Enthusiasm (required).
Work Environment:
Computer Lab
Back to top
Salve Regina University in Newport, RI
Project Code: 06
Title: Biological Assessment of Urban Watersheds in Rhode Island
Mentor: Dr. Jameson Chace, Salve Regina University, Biology
Research Description: The third year of NEWRNET research in Rhode Island will focus on completing the macroinvertebrate biological assessments and analyzing the biotic indices with chemical and physical stream parameters. Focal areas include Bailey Brook, Maidford River and Cork Brook. Students will assist in survey designs, data collection, database management, statistical analysis, GIS coverage correction and landscape analysis..
Research Questions:
1) Complete third year of macro invertebrate biological assessments to assess stream quality; 2) Correlate biological indices with chemical, physical and landscape level dynamics.
Student Learning Objectives:
1) Field sampling design; 2) Macroinvertebrate identification and analysis of biotic indices of stream quality; and 3) Statistical analysis, reporting and presentation of results.
Prerequisites:
REQUIRED: Biology, Chemistry or Environmental Studies major; At least completed sophomore year; Ability to climb slippery banks, wade in moving rivers, get bitten by insects and still manage to smile
Work Environment:
Field
Back to top
University of Rhode Island in Kingston, RI
Project Code: 07
Title: Testing the limits of next-generation water sensors
Co-Mentors:
Dr. Jason Dwyer, University of Rhode Island, Chemistry (Bioanalytical Chemistry, Nanotechnology, Sensors)
Buddini Karawdeniya, PhD Candidate, University of Rhode Island, Chemistry (Bioanalytical Chemistry, Nanotechnology, Sensors)
Research Description: The researcher will (1) create and explore new materials chemistry (including nanofabrication) methods to fabricate and optimize chemical sensing platforms suitable for water analysis, and (2) design and carry out experiments to test performance limits of the sensors. Experiments will be carried out using both advanced, lab-based instrumentation and portable detection platforms. Focus areas include surface-enhanced Raman spectroscopy (SERS) and nanopore-based single molecule sensing. This project is best suited to a student with a strong background in chemistry (or closely related discipline that has included some university-level training in chemistry).
Research Questions:
1) What chemical sensor platforms, platform modifications, and approaches will deliver robust, reliable, and low-cost aquatic chemical sensing of a host of analytes?; 2) What approaches to sensor design and use best aid the transition from analyzing laboratory standards to analyzing real, and simulated, environmental samples?
Student Learning Objectives:
1) Design and operating principles of modern chemical instrumentation; 2) Materials science approaches to chemical sensor modification and fabrication; 3) Design and analysis of chemical sensing experiments, including the analysis of chemically complex samples.
Prerequisites:
Students with training in chemistry, physics, or engineering. The work will require fine motor control and the physical manipulation of small components. The research facilities are in an old building, with a physical layout (e.g. countertops without clearance underneath) that presents access barriers.
Required: one year of freshman chemistry, including a laboratory component. Strongly preferred: Prior experience in surface-enhanced Raman spectroscopy (SERS) and nanopore-based single molecule sensing. Upper year students preferred.
Work environment:
Chemistry laboratory with occasional field work.
Back to top
University of Rhode Island in Kingston, RI
Project Code: 08
Title: Collecting and validating real-time water quality sensor data in three RI watersheds of differing land use
Co-Mentors:
Dr. Art Gold, University of Rhode Island, Natural Resources Science (Hydrology and Water Quality)
Kelly Addy, University of Rhode Island, Natural Resources Science (Hydrology and Water Quality)
Research Description: The URI Watershed Hydrology Lab (WHL) has partnered up with the Universities of Delaware and Vermont to develop an integrated network of advanced water sensors to gather real-time, high-frequency water quality data to understand the drivers of local and regional water quality. These sensors are a promising tool to learn more about aquatic ecosystems at temporal and spatial scales previously untenable. The results will contribute to improved water quality management for drinking water reservoirs and estuaries.
This summer, the intern will immerse him/herself in cutting-edge, high-tech stream water quality monitoring at three RI streams within watersheds dominated by different land use – rural, agricultural and urban. Activities will include: maintaining and calibrating the sensors, measuring water flow, retrieving automatically collected water samples, processing water samples for several parameters in the lab, and exploring data management and synthesis. Our intern will also connect with their peers in RI, DE and VT to discuss other components of the project and regional trends in data.
Research Questions:
1) What is the impact of events on water quality of streams in watersheds of different land use?; 2) Can we use sensor collected stream data to predict the likelihood of disinfectant by product generation in drinking water facilities?; 3) What are the important diel patterns in oxygen, carbon, and nitrogen within streams?
Student Learning Objectives:
1) Maintain arrays of water quality sensors, data loggers, automatic water samplers and other hydrological equipment; 2) Assist in organizing, displaying, synthesizing, and analyzing high frequency multi-parameter water quality data; and 3) Coordinate watershed-wide water quality sampling campaigns to determine hotspots of contamination and assure the quality of data.
Prerequisites:
We would like to have a student with experience is Microsoft Office products, able to carry heavy equipment, willing to stream sample in inclement weather conditions, able to swim, able to periodic driving to research site in own vehicle with mileage reimbursement, and detailed-oriented. Hydrology or electronics experience or coursework is a plus.
Work Environment:
Field and Laboratory
Back to top
University of Rhode Island in Kingston, RI
Project Code: 09
Title: Autonomous surface vessel (ASV) surveys and software development
Mentor: Dr. Chris Roman, University of Rhode Island, Graduate School of Oceanography (Marine vehicles, water quality)
Dr. Stephen Licht, University of Rhode Island, Ocean Engineering
Research Description: This project will entail working with an Autonomous Surface Vessel (ASV) to compete surveys in estuaries, lakes and ponds. The ASV is able to run automated survey patterns while collecting a variety of environmental parameters (e.g. depth, temperature, salinity, turbidity, pH, nitrate, chlorophyll). The selected student will participate in surveys, process data and help write new software to improve the operation of the vehicle. We will be integrating new sensors into the vehicle, finalizing a profiling winch system and potentially coordinating surveys with a second ASV. In all cases the goal of the project is to develop operational patterns and methods to best utilize the capabilities of the autonomous system in conjunction with other static water monitoring sensors.
Research Questions:
1) What is the actual spatial resolution of the various environmental sensors after accounting for sampling rate, vehicle speed and sensor time constants?; 2) Evaluate the utility of the ASV to measure a control volume surrounding a stream input to a pond or lake; and 3) How can the ASV data be used to evaluate changing water properties over time using repeat transects?
Student Learning Objectives:
1) Gain experience completing field surveying using robotic platforms; 2) Gain experience writing software for robotic systems; and 3) Gain experience processing spatial and temporal data.
Prerequisites:
Preferred: Software experience (Python, Matlab), junior/senior level, some field experience or technical aptitude.
Work environment:
Field and laboratory
Back to top
University of Rhode Island in Kingston, RI
Project Code: 10
Title: Understanding Human Decisions Related to Water Quality
Co-Mentors:
Dr. Emi Uchida, University of Rhode Island, Environmental and Natural Resource Economics
Dr. Todd Guilfoos, University of Rhode Island, Environmental and Natural Resource Economics
Dr. Simona Trandafir, University of Rhode Island, Environmental and Natural Resource Economics
Haoran Miao, University of Rhode Island, Environmental and Natural Resource Economics
Research Description: The goal of this research is to better understand how people's decisions related to water quality changes when they are presented with better information about water quality. The specific objective of this summer project is to design a laboratory and/or a field experiment to test specific hypotheses related to this research. Specific activities may involve designing and implementing an experiment and/or a survey instrument, analyzing the data, and constructing stylized models of decision making.
Research Questions:
1) How do residents and farmers make decisions that relate to water quality?; 2) How does information affect people's decisions related to water quality?; and 3) What types of monetary and non-monetary incentives affect people's decisions related to water quality?
Student Learning Objectives:
1) Gain insights into factors that influence decisions that affect water quality; 2) Designing experiments and surveys; and 3) Programming and data analysis.
Prerequisites:
Major: Environmental economics, microeconomics, or a related field. Required skills and experience: Excellent writing and speaking skills. Any background in statistics, programming, data analysis, and experimental economics is preferred.
Work environment:
Field or computer lab
Back to top