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:
Assessing Nutrient Usage during Harmful Algal Blooms
|Research categories:||Chemical, Environmental Science, Life Science|
|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|
|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.
Drinking water safety and sampling in buildings
|Research categories:||Agricultural, Bioscience/Biomedical, Civil and Construction, Environmental Science, Life Science|
|School/Dept.:||Civil Engineering -AND- Environmental and Ecological Engineering|
|Desired experience:||Science or engineering background Prior lab or field experience with chemical or microbiological analysis preferred, but not required. Students will be trained with all necessary methods. Clear motivation to make a difference Able to effectively work in diverse teams Work hours will be based on the time of day and actual date of prescheduled sampling|
The student will assist graduate students, a postdoctoral research association, and the professor conduct drinking water sampling in buildings. The project's focus is to better understand how drinking water quality changes during a plumbing system's age and also differences in drinking water across buildings. This project will be a mix of field and laboratory work. One study site is located in West Lafayette, IN while others are elsewhere. The student would accompany the researchers to those sites. Prior study can be found here: https://www.ncbi.nlm.nih.gov/pubmed/29253792
Effects of Aging Treatment on the Microstructure, Surface and Mechanical Properties of Food and Pharmaceutical Relevant Materials
|Research categories:||Agricultural, Environmental Science, Material Science and Engineering|
|Preferred major(s):||ABE, MSE, ChE, ME|
|Desired experience:||Physical Chemistry, Thermodynamics, Material properties such as Mechanical Stress and Response of Materials, Mohr's circles, Organic Chemistry, Polymers Statistics. Overall, very motivated student eager to innovate.|
Characterization of the physicochemical, surface and mechanical properties in a wide range of soft materials (food and pharmaceuticals) will be conducted. Of interest, the environmental conditions during manufacturing and storage that could change the properties of materials leading to potential detrimental changes on the performance and quality in the food or pharmaceutical product. The study is directed to the question of what stimulates aging on the microstructures, which might contribute to stability and performance during processing. The microstructure-level controlling surface interactions will be also addressed by using various analytical tools. The bulk properties such as powder flow behavior will be characterized such that structure-property-processing relationships can be established.
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)|
|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 firstname.lastname@example.org
Modeling and Measuring Lead in Residential Hot Water Heaters and Drinking Water
|Research categories:||Civil and Construction, Environmental Science, Other|
|School/Dept.:||Environmental and Ecological Engineering and Civil Engineering|
|Preferred major(s):||Environmental and Ecological Engineering, Civil Engineering with Environmental Engineering concentration|
|Desired experience:||Prefer student who has had previous experience in a 'wet' lab, and studying engineering applications of chemistry. Prefer student with strong academic preparation (course work) in chemistry and environmental engineering.|
In recent years, the presence of lead (Pb) in US drinking water supplies has emerged as a critical human health issue. This is due to the fact that a significant portion of pipes in the distribution system and fittings within premise plumbing contain lead which can then be released into the drinking water supply. To limit lead exposure, the US EPA set a 15 μg/L action limit for lead for drinking water through the Lead and Copper rule in 19911. Over the last 20 years, two major incidents in Flint, Michigan beginning in 2014 and Washington, D.C. from 2001 to 2004 have magnified this issue as lead concentrations in these cities drinking waters began to exceed regulatory limits. Lead concentrations increased in these waters due to changes in the water supply or how the water was disinfected (moving from free chlorine to chloramine use). In Indiana, similar issues regarding lead contamination are of concern since approx. 8% of large drinking water distribution networks in the state contain lead pipes4. In fact, this percentage may be even greater when considering smaller distribution networks as well. To address this problem, certain Indiana municipalities reported that lead contamination is minimized due to their high hardness waters which induce pipe scaling whereas other municipalities have corrosion inhibitors. While this may solve some of the problems, it is clear that a greater understanding is needed to evaluate how lead enters drinking waters in the distribution system and subsequently reaches tap water supplies. One major unexplored area includes our understanding of how lead is affected within residential water heating systems, which are typically found in residential buildings to supply heated water to its residents. An undergraduate researcher will work on batch and flow-through experiments to characterize lead chemistry in systems that model residential homes.
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|
|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).
Remote sensing of soil moisture using P-band Signals of Opportunity: Model development and experimental validation.
|Research categories:||Agricultural, Aerospace Engineering, Computer Engineering and Computer Science, Electronics, Environmental Science, Physical Science|
|Preferred major(s):||ECE, Physics, Geophysics, With appropriate coursework: AAE, ABE, Civil, Geomatics,|
|Desired experience:||Signal processing; Programming: C, Python, MATLAB; Electronic hardware experience preferred; Drivers license and access to car required.|
Root Zone Soil Moisture (RZSM), defined as the water profile in the top meter of soil where most plant absorption occurs, is an important environmental variable for understanding the global water cycle, forecasting droughts and floods, and agricultural management. No existing satellite remote sensing instrument can measure RZSM. Sensing below the top few centimeters of soil requires the use of microwave frequencies below 500 MHz, a frequency range known as “P-band”. A P-band microwave radiometer would require an aperture diameter larger than 10 meters. Launching such a satellite into orbit will present big and expensive technical challenge, certainly not feasible for a low-cost small satellite mission. This range for frequencies is also heavily utilized for UHF/VHF communications, presenting an enormous amount of radio frequency interference (RFI). Competition for access to this spectrum also makes it difficult to obtain the required license to use active radar for scientific use.
Signals of opportunity (SoOp) are being studied as alternatives to active radars or passive radiometry. SoOp re-utilizes existing powerful communication satellite transmissions as “free” sources of illumination, measuring the change in the signal after reflecting from soil surface. In this manner, SoOp methods actually make use of the very same transmissions that would cause interference in traditional microwave remote sensing. Communication signal processing methods are used in SoOp, enabling high quality measurements to be obtained with smaller, lower gain, antennas.
Under NASA funding, Purdue and the Goddard Space Flight Center have developed an airborne prototype P-band remote sensing instrument to demonstrate the feasibility of a future satellite version. Complementing this technology development, a field campaign in the Purdue Agricultural research fields is being planned. This campaign will make reflected signal measurements from towers installed over instrumented fields. Measurements will be obtained over bare soil first, and then throughout the corn or soybean growth cycle. Complementing these remote sensing measurements, a comprehensive set of ground-truth data will also be collected for use in developing models and verifying their performance.
Work under this project will involve installing microwave electronic equipment in the field, writing software for signal and data processing, and making field measurements of soil moisture and vegetation properties.
Students interested in this project should have good programming skills and some experience with C, python and MATLAB. They should also have a strong background in basic signal processing. Experience with building computers or other electronic equipment will also be an advantage. Preference will be given to students who have an interest in applying their skills to solving problems in the Earth sciences, environment, or agriculture.
NOTE: The project will involve regular travel to and from the local research field, so students should have a drivers license and reliable access to a car.
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|
|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.
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|
|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.