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Research Projects

Projects are posted below; new projects will continue to be posted through February. To learn more about the type of research conducted by undergraduates, view the 2017 Research Symposium Abstracts.

This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.

You can browse all the projects on the list, or view only projects in the following categories:

Environmental Science

 

Assessing Nutrient Usage during Harmful Algal Blooms

Research categories:  Chemical, Environmental Science, Life Science
School/Dept.: COS
Professor: Greg Michalski
Preferred major(s): Chemistry, Biology, natural resources
Desired experience:   basic chemistry/biology lab experience

Harmful algal blooms are a serious environmental, economic, and human health issue. They occur when cyanobacteria undergo rapid growth when nutrient availability and physical conditions coincide. There rapid growth and decay can release toxic compounds that is harmful to organism including humans. The project will probe the mechanism of N uptake versus N fixation using isotope techniques. The student will collect field samples, conduct incubation experiments, and analyze chemical and isotopic tracers.

 

Code Optimization and GUI Development for DHM-WM Hydrologic Model

Research categories:  Computer Engineering and Computer Science, Environmental Science
School/Dept.: ABE
Professor: Margaret Gitau
Preferred major(s): Computer Engineering, Computer Science
Desired experience:   Python, GUI design, programing, and testing

The hydrologic model DHM-WM was developed to provide spatial information on hydrologic components for determining critical pollutant source areas. The spatial details provided by the model will help in the development of precise and cost-effective watershed management solutions. A great advantage of DHM-WM is in its simplicity and the small number of parameters that require calibration. However, depending on watershed configuration and computational capacity, DHM-WM can take about 1.5 hours per simulation year this being largely due to the use of ArcGIS functions in Python scripts and the sequential algorithm used in the programing. The goal of this project is to enhance DHM-WM to enable its use by a broad range of users. Specifically to: 1) improve DHM-WM’s computational efficiency by modifying the algorithms and optimizing its code; and, 2) to provide a GUI to facilitate model use.

 

Estimating watershed residence times in artificially-drained landscapes and relation to nutrient concentrations

Research categories:  Environmental Science
School/Dept.: Earth, Atmospheric, and Planetary Sciences (EAPS)
Professor: Lisa Welp
Preferred major(s): EAPS, Chemistry, Natural Resources
Desired experience:   Basic chemistry lab skills, willingness to work outdoors occasionally, and experience with R stats programing language and/or ArcGIS or desire to learn

Nutrient runoff from agricultural lands leads to Harmful Algae Blooms and eutrophication in freshwater ecosystems including the Great Lakes and the Gulf of Mexico. Best Management Practices (BMPs) implemented over the last few decades aim to reduce nutrient transport to streams and rivers. Evaluations of their effectiveness have found mixed results in reducing nutrient concentrations. This could indicate that BMPs are ineffective in certain areas, or simply that the residence time of water and nutrients in the watersheds are long and the effect of BMPs won't be seen for decades. Watershed discharge is a combination of recent precipitation, soil water on the order of a year old, and decades-to-centuries old ground water, and the proportions vary with hydrology and land management resulting in a spectrum of nutrient dynamics within the same land use classification. We aim to investigate the variability in residence times of local watersheds using stable isotope tracers and radon measurements and examine the relationships with nutrient concentration variability. This work will leverage 4 years of existing water stable isotope data and 8 years of nutrient concentrations from citizen scientist collections of streams during Wabash Sampling Blitz organized by the non-profit Wabash River Enhancement Corporation (WREC). We hypothesize that isotope variability in individual watersheds is correlated with residence times.

The scope of this project proposes to analyze the Spring 2018 and Fall 2018 sampling Blitzes for stable isotopes to further constrain the isotopic variability of individual watersheds. Samples will be analyzed for δ18O and δD in the Welp lab using an LGR Triple Isotope Liquid Water Analyzer. An undergraduate student will work under the direction of Prof. Welp, technical staff, and a PhD student to analyze samples and work on statistical analysis of the expanded multi-year data record to analyze watershed isotope and nutrient variability. We will identify watersheds that exhibit particularly large and small isotopic variability and perform additional sampling visits during the summer of 2018. In cooperation with Prof. Marty Frisbee's hydrology lab, we will test streams for radon concentrations to confirm presence/absence of strong groundwater influence. Some groundwater aquifers in the area that recharged before widespread agricultural fertilization have low inorganic N concentrations, but others (typically shallower with younger mean ages) have higher concentrations of N. We will use the Blitz data and these additional observations to examine patterns in varying influence of surface and ground water discharge and sources of N to local waterways.

For more information, contact lwelp@purdue.edu

 

Purdue AirSense: An Air Pollution Sensing Network for West Lafayette

Research categories:  Agricultural, Chemical, Civil and Construction, Computer Engineering and Computer Science, Electronics, Environmental Science, Innovative Technology/Design, Mechanical Systems, Nanotechnology, Physical Science
School/Dept.: Civil Engineering
Professor: Brandon Boor
Preferred major(s): The position is open to students from all STEM disciplines.
Desired experience:   Proficient in Python, Java, MATLAB; experience with Raspberry Pi or Arduino.

Air pollution is the largest environmental health risk in the world and responsible for 7 million deaths each year. We are presently developing a new air pollution sensing network for the Purdue campus to monitor and analyze air pollutants in real-time. We are recruiting an undergraduate student to assist with the development of our Raspberry Pi-based air quality sensor module. You will be responsible for integrating the Raspberry Pi with air quality sensors, developing laboratory calibration protocols, building an environmental enclosure for the sensors, creating modules on our website for real-time data analysis and visualization, and maintaining state-of-the-art aerosol instrumentation at our central air quality monitoring site at the Purdue Agronomy Center for Research and Education (ACRE).

 

Stochastic Storm Generation of Storms and Their Inner Structure

Research categories:  Agricultural, Civil and Construction, Computer Engineering and Computer Science, Environmental Science
School/Dept.: Agricultural & Biological Engineering
Professor: Bernie Engel
Preferred major(s): Agricultural engineering, environmental engineering, computer science

Advanced field and watershed scale hydrologic models for engineering design, soil erosion, land use planning, and global-change research require detailed continuous temporal and spatial inputs of precipitation to execute the hydrologic processes integrated into their formulations. Accurate estimates of processes such as infiltration, runoff routing, and water quality algorithms need precipitation values on the order of minutes apart. In the United States, the National Oceanic and Atmospheric Administration (NOAA) collects 15-min time increment precipitation data in ~2000 locations. However, observed precipitation is yet rarely available in many sites and lack spatial coverage. In ongoing research, a stochastic storm generator developed at Purdue University allows generating storm characteristics such as inter-event time, duration, and volume, as well as within-storm intensities using the available 15-min resolution data. The current project proposes to extend the application of the current version of the storm generator from a single station to a more detailed network of meteorological stations. The final goal seeks to perform a test of available interpolation method between the statistical parameters defining the available locations so that time series of precipitation data in ungauged areas can be generated.

Activities:

1. Collect short-time increment precipitation from NOAA and other sources. The SURF student will learn how to search available precipitation data available in the different agencies.
2. Organize and run a clean-up data analysis. The SURF student will deal with different files containing precipitation data and formats as well as its spatial representation by GIS tools.
3. Identify independent storms over the time period. The SURF student will be able to learn how to run Python, MATLAB, and R scripts and to understand the concepts defining independent rainfall events.
4. Fit storm characteristics (time between storms, duration, and volume) to a suitable storm distribution. The SURF student will be able to perform statistical distribution fitting and how to measure the goodness of fit of the available procedure in the storm generator.
5. Generate correlated storm characteristics by Monte Carlo numerical simulation implemented in a stochastic storm generator develop at the National Soil Erosion Research Laboratory (NSERL). The SURF student will experience the use of complex mathematical algorithms incorporated into the storm generator.
6. Characterize storm patterns of the observed storms.
7. Identify representative patterns of storms by cluster analysis over the storm patterns data. The SURF student will explore the concept of machine learning and cluster analysis.
8. Generate storms patterns by Monte Carlo numerical simulation also implemented in a stochastic storm generator develop at the NSERL. The SURF student will continue experiencing the use of complex mathematical algorithms incorporated into the storm generator.
9. Propose an interpolation method of the storm parameters between the stations previously analyzed. The SURF student will apply available spatial interpolation methods in precipitation statistical parameters.

 

Sustainable Development Goals and Climate Change

Research categories:  Environmental Science
School/Dept.: EAPS
Professor: Matthew Huber
Desired experience:   Quantitative skills, preferable with a background in physics and programming. Some knowledge of broader environmental issues important and atmospheric/ocean/hydrological systems desirable.

Various research projects are available on the Indo-Asian monsoon, the urban heat island effect, land-use change, human heat stress, and agricultural impacts of climate change. Research will involve computer modeling and data analysis. Familiarity with linux/unix and some program is required. Most projects will focus on tropical regions and developing nations.