Chemical spills and backflow incidents are common threats to drinking water distribution and plumbing systems. Sometimes free product and drinking water with dissolved contaminants can travel through this infrastructure and reach building faucets. When this occurs health officials, system owners, and infrastructure owners rapidly seek information about whether individual constituents became sequestered in certain parts of the systems and how best to remove them. Plastics are an important concern because many are easily permeated by organic compounds which prompts them to leach chemicals into clean water making it unsafe.

In response to drinking water contamination incidents over the past 20 years and requests from health departments and households affected, this project will examine the fate of fuel chemicals in contact with plumbing materials (i.e., pipes, gaskets) and plastic personal effect materials (i.e., baby bottles, plates, cups, etc.). Diesel, gasoline and crude oil are being considered. The student will conduct the contamination experiments, collect water samples and analyze them using state-of-the-art instrumentation. The student will analyze, interpret, and report the information with advisement of one graduate research assistant and two faculty who respond to these types of water contamination incidents.

Other questions that may be explored include the chlorination of the fuel components and formation of disinfectant byproducts, mechanical integrity impacts on the plastic materials, chemical transformations of the leached products. This work directly supports emergency response and recovery activities of the Center for Plumbing Safety.

Chemical Unit Operations, Chemical Catalysis and Synthesis, Engineering the Built Environment, Environmental Characterization, Other

• Environmental and Ecological Engineering
• Chemistry
• Chemical Engineering
• Civil Engineering
• Materials Engineering
• Materials Science
• Plastics Engineering
• Agricultural Engineering
• Pharmacy
• Military Science
• Public Health
• Environmental Health Sciences
• Food Science

Strong internal motivation to learn Basic understanding of chemistry

CE & EEE

Andrew Whelton

Our project is funded by the U.S. Environmental Protection Agency (EPA) and involves an interdisciplinary collaboration between engineers, chemists, and psychologists at Purdue University and New York University (NYU). We will elucidate determinants of indoor dust ingestion in 6- to 24-month-old infants (age range for major postural and locomotor milestones). Specific objectives are to test: (1) whether the frequency and characteristics of indoor dust and non-dust mouthing events change with age and motor development stage for different micro-environments; (2) how home characteristics and demographic factors affect indoor dust mass loading and dust toxicant concentration; (3) how dust transfer between surfaces is influenced by dust properties, surface features, and contact dynamics; and (4) contributions of developmental, behavioral, and socio-environmental factors to dust and toxicant-resolved dust ingestion rates. In addition, the project will (5) create a shared corpus of video, dust, toxicant, and ingestion rate data to increase scientific transparency and speed progress through data reuse by the broader exposure science community.

Our transdisciplinary work will involve: (1) parent report questionnaires and detailed video coding of home observations of infant mouthing and hand-to-floor/object behaviors; (2) physical and chemical analyses of indoor dust collected through home visits and a citizen-science campaign; (3) surface-to-surface dust transfer experiments with a robotic platform; (4) dust mass balance modeling to determine distributions in and determinants of dust and toxicant-resolved dust ingestion rates; and (5) open sharing of curated research videos and processed data in the Databrary digital library and a public website with geographic and behavioral information for participating families.

The project will provide improved estimates of indoor dust ingestion rates in pre-sitting to independently walking infants and characterize inter-individual variability based on infant age, developmental stage, home environment, and parent behaviors. Dust transport experiments and modeling will provide new mechanistic insights into the factors that affect the migration of dust from the floor to mouthed objects to an infant’s mouth. The shared corpus will enable data reuse to inform future research on how dust ingestion contributes to infants’ total exposure to environmental toxicants.

U.S. EPA project overview: https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract_id/11194

Biological Characterization and Imaging, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Human Factors

• No Major Restriction

We are seeking students passionate about studying environmental contaminants and infant exposure to chemicals in the indoor environment. Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, microbiology, physics, thermodynamics, heat/mass transfer, fluid mechanics, developmental psychology.

Lyles School of Civil Engineering

Brandon Boor

Drinking water and sewer pipes are decaying across the nation, and inexpensive methods for repairing these assets are being increasingly embraced. One method called cured-in-place-pipe (CIPP) involves workers chemically manufacturing a new plastic pipe inside an existing damaged pipe. This is the least expensive pipe repair method and, as such, is preferred by utilities and municipalities. The practice is often conducted outdoors and industry ‘best’ practice involves discharging the plastic manufacturing waste into the environment and nearby pipelines. Under some conditions, this waste finds its way into public areas and buildings prompted illnesses and environmental damage. Another consequence can be direct leaching of unreacted chemicals into water or volatilization of chemicals from the new plastic into air.

This project will involve the student working with a graduate student as well as leading experts on plastics manufacturing, chemistry, public health, civil/environmental engineering, and communications. The student will learn plastic manufacturing methods, environmental sampling and analysis methods, and participate in the process of reducing human health and environmental risks of the practice. To complete this work, the student will learn and apply infrastructure, environmental, and public health principles.

Composite Materials and Alloys, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Other

• Chemical Engineering
• Environmental and Ecological Engineering
• Civil Engineering
• Public Health
• Chemistry
• Environmental Health Sciences

Strong interest in learning and applying scientific methods and techniques to help solve a pressing day problem; Basic understand of chemistry; General lab experience desirable as the student will help manufacture plastics in the lab using chemical formulations

CE & EEE

Andrew Whelton

Atmospheric blocking events and 'Bomb Cyclones' are an important contributor to high impact extreme weather events. Both these weather extremes lead to heat waves, cold spells, droughts, and heavy precipitation episodes, which have dire consequences for the public health, economy, and ecosystem. For example, the blocking-induced heat waves of 2003 in Europe led to tens of thousands of human casualties and tens of billions of dollars of financial damage.

Traditionally, prediction of extreme weather events is based on direct numerical simulation of regional or global atmospheric models, which are expensive to conduct and involve a large number of tunable parameters. However, with the rapid rise of data science and machine learning in recent years, this proposed work will apply convolutional neural network to an idealized atmospheric model to conduct predictability analysis of extreme weather events within this model. With this proposed machine-learning algorithm, our project will provide a robust forecast of heat waves and atmospheric blocking with a lead-time of a few weeks. With more frequent record-breaking heat waves in the future, such a prediction will offer a crucial period of time (a few weeks) for our society to take proper preparedness steps to protect our vulnerable citizens.

This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. The undergraduate student will play an active and important role in running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.

Closely advised by Prof. Wang, the student will conduct numerical simulations of an idealized and very simple climate model, and use python-based machine learning tools to predict extreme weather events within the model. Prof. Wang will provide weekly tutorial sessions to teach key techniques along with interactive hands-on sessions. The students will get access to the big datasets on Purdue’s Data Depot, analyze and visualize data of an idealized atmospheric model. The student will use convolutional neural networks (CNNs) to train and assess a Machine-Learning model. The student will further use feature tracking algorithm to backward identify the physical structure in the atmosphere that is responsible for the onset of extreme weather events.

Big Data/Machine Learning, Deep Learning, Energy and Environment, Environmental Characterization, Fluid Modelling and Simulation

• Physics
• Planetary Sciences
• Atmospheric Science/Meteorology
• Computer Science
• Mathematics - Computer Science
• Mathematics
• Environmental Geosciences
• Mechanical Engineering
• Civil Engineering
• Aeronautical and Astronautical Engineering
• Computer Engineering
• Engineering (First Year)
• Multidisciplinary Engineering
• Natural Resources and Environmental Science (multiple concentrations)

Familiar with Machine Learning or prior knowledge of convolutional neural networks (CNNs); Have basic level training on PHYS172 Modern Mechanics or PHYS 15200 Mechanics or equivalent courses from other institutions; Familiar with Python scripting and visualization

Earth, Atmospheric, and Planetary Sciences

Lei Wang

The objective of this project is to utilize state-of-the-art proton transfer reaction mass spectrometry (PTR-MS) to evaluate emissions and exposures of volatile chemicals in buildings. My group is investigating volatile chemical emissions from consumer and personal care products, disinfectants and cleaning agents, and building and construction materials. You will assist graduate students with full-scale experiments with our PTR-MS in our new Purdue zEDGE Tiny House and process and analyze indoor air data in MATLAB.

Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Human Factors, Internet of Things

• No Major Restriction

Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, physics, thermodynamics, heat/mass transfer, and fluid mechanics.

Lyles School of Civil Engineering

Nusrat Jung

Water and energy are tightly linked resources that must both become renewable for a successful future. However, today, water and energy resources are often in conflict with one another, especially related to impacts on electric grids. Further, advances in nanotechnology, material science and artificial intelligence allow for new avenues to improve the widespread implementation of desalination and water purification technology. The team is pursuing multiple projects that aim to explore solar and wind-powered desalination, nanofabricated membranes, light-driven reactions, artificial intelligence control algorithms, and thermodynamic optimization of energy systems. The student will be responsible for fabricating membranes, building hydraulic systems, modeling thermal fluid phenomenon, analyzing data, or implementing control strategies in novel system configurations. More information here: www.warsinger.com

Big Data/Machine Learning, Chemical Catalysis and Synthesis, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Fluid Modelling and Simulation, Material Modeling and Simulation, Nanotechnology, Thermal Technology

• Mechanical Engineering
• Civil Engineering
• Environmental and Ecological Engineering
• Chemistry
• Chemical Engineering
• Materials Engineering

Applicants should have an interest in thermodynamics, water treatment, and sustainability. Applicants with experience in some (not all) of the following are preferred: experimental design and prototyping, manufacturing, Python, LabView, EES, MATLAB, 3D CAD Software, & Adobe Illustrator. Rising Juniors and Seniors are preferred.

Mechanical Engineering

David Warsinger