Research Experiences for Undergraduates

Mentor: Yanna Liang

Concerns regarding per- and polyfluoroalkyl substances (PFAS) in the environment have grown dramatically in recent years. While perfluorooctanoic acid (PFOA) and perfluorooctanesulfonate (PFOS) continue to receive the most attention, recognition of the wide diversity of PFAS has increased, and there are suggestions that the fate of perhaps thousands of related PFAS may need to be considered.

To clean up soils contaminated by PFAS as a result of historical use of aqueous film forming foam (AFFF), several approaches, such as excavation/disposal of at offsites, soil washing, thermal and chemical degradation, bioremediation and phytoremediation have been investigated either at field or lab scales. Given the drawbacks associated with these approaches, for example secondary contamination, potentially more toxic and mobile degradation products and long time needed for phytoremediation, stabilization by amending a sorbent that is designed to retain PFAS in soil and prevent their movements to other surrounding locations has gained momentum in recent years.

To develop robust, green, environmentally sustainable, low or zero carbon footprint and inexpensive sorbents that are able to stabilize all types of PFAS in soil, we seek to focus on those based on natural clay and biopolymers. Our recent screening test has identified three out of >20 sorbents that have much better performance than commercially available sorbents with respect to sorption capacity and required contact time.

The REU students will be involved in this line of research. Specifically, the students will gain skills in synthesizing, characterizing and testing different sorbents and studying their effects on stabilizing different types of PFAS in different environments. These sorbents will also be investigated toward removing PFAS in contaminated water. Through working closely with graduate students and postdoctoral researchers in Liang’s lab, these students will enhance their problem-solving skills, acquire experience and expertise for designing innovative solutions addressing critical environmental and societal challenges.

Mentor: Kyoung-Yeol Kim

Conventional wastewater treatment processes focus on destroying components (e.g., organic matter, nutrients, etc.) in waste streams for treatment. However, those components in waste streams are still economically valuable and can be recovered as energy or value-added products using new sustainable wastewater treatment processes.

For example, ammonia is problematic in waste streams since the excessive ammonia in the effluent will cause eutrophication. Instead of removing ammonia in the wastewater, ammonia can be recovered from wastewater using several processes such as air-stripping, ion exchange, electrochemical separation, etc. Ammonia recovery from wastewater is promising since the Haber-Bosch process (N₂+3H₂→2NH₃) is currently used to produce ammonia for fertilizers, but the process is energy-intensive, it accounts for 1-2% of world energy consumption. 

The mentor (Dr. Kyoung-Yeol Kim)’s research team is working on ammonia recovery from waste streams using an electrochemical separation method. The REU students will learn about basic principles of electrochemical processes in week 1-2 to harvest value-added products from waste streams and experience hands-on experiments in the lab in week 3-10.

Students will be running their electrochemical reactors, learn how to fabricate electrodes that can be used for electrochemical ammonia recovery. Ammonia recovery fluxes and energy consumptions will be monitored by students and students will conduct advanced electrochemical analyses such as linear scan voltammetry, cyclic voltammetry, electrochemical impedance spectroscopy, etc. to analyze and evaluate the electrochemical performances of their recovery system.

Mentor: Md. Aynul Bari

Climate change can affect the air we breathe in both ambient and indoor environments. While government and regulatory agencies have focused to tackle urban ambient air pollution, little attention has been paid to assess the quality of air in the U.S. homes. To fill this gap, REU students will measure community-specific near-field (outdoors in home backyard and indoors) air quality exposure in the NYS Capital District.

In the U.S., community air quality observation networks are limited in characterizing the diverse group of air pollutants e.g., black carbon that affect exposure across regional to neighborhood scales. Indoor and outdoor concentrations of BC, CO₂, total volatile organic compounds (TVOC) and fine particulate matter fractions (PM_2.5 µm) will be measured using low-cost sensors. In year 1 and 2, one REU student will work with undergraduate and graduate students in the PI Bari’s lab and contribute to ongoing weekly sampling in 4-6 homes or apartments in each selected neighborhoods during the Summer to collect data from at least 20 homes.

Undergraduate students in PI Bari’s group will continue sampling during Winter and share data to REU students. In year 3, one REU student will apply statistical modeling to find indoor and outdoor predictors of BC, CO₂ and other pollutants. Findings will provide an improved understanding of community-specific air quality problems associated with climate change and promote increased environmental stewardship and community action to reduce air emissions. 

Mentor: Rixiang Huang

Fire is a major force in the Earth system that influences global ecosystems patterns and processes, including the coupled C and nutrient cycles. Understanding the mechanisms of fire’s impacts on the coupled C and nutrient cycling is fundamental for evaluating and modeling the roles of fire in many ecosystem processes (e.g., primary productivity), which is an integral part of earth system modeling.

The Huang group has multiple research projects under this central theme, focusing on the impacts of fire on soil biogeochemical conditions and dynamics of C and nutrient cycling that regulates soil nutrient status and aboveground productivity. A project is devoted to exploring the interactions between charcoal and soil extracellular enzymes and the impacts on enzyme-mediated organic matter decomposition (currently supported by NSF#2120547).

Several undergraduate students majored in biology, chemistry, and environmental engineering have participated in this research, contributing to charcoal production and characterization, enzyme adsorption measurement, and enzyme activity measurement. Another project focuses on the cycling of nutrients in fire burned residues in soils, targeting the interplay between fire regimes, soil properties, and ecosystem characteristics.

This project involves laboratory and field studies and experimental and modeling approaches. REU students can involve in field sampling, simulated biomass burning, chemical analysis of soils and fire residues, and chemical and transport modeling. Through active engagement in these research projects, REU students will learn multiple climate-related subjects, develop essential research skills, and have extensive hand-on experiences on a range of advanced instruments, such as microcalorimetry and nuclear magnetic resonance spectroscopy (NMR). 

Mentor: Yaoze Liu

Significance of the research area

Urbanization leads to increased impervious surfaces - major causes for increased runoff, decreased recession time, decreased groundwater recharge, and decreased base flow, resulting in increased flood risks and combined sewer overflows (CSOs) due to extreme storm events under current climate and future climate change conditions 9-12.

CSOs contain partially treated or untreated stormwater and wastewater from residential, commercial, and industrial areas that would be delivered to waterbodies, causing severe water quality impairments.

To address these challenges, green infrastructure (GI) practices (or low impact development-LID controls), such as bioretention cells or green roofs, are sustainable stormwater management practices that increase community resilience and adaptation to extreme events under current and future climate conditions by restoring urban hydrological processes to reduce flood risks, CSOs, and conventional gray infrastructure needs with significant environmental, social, and economic benefits ^13,14. However, cost-effective urban GI practices implementation strategies need to be created using an efficient systematic approach to reduce the adverse impacts of climate change.

Underlying theoretical framework

Using modeling approaches (Storm Water Management Model-SWMM ^15-17) to create cost-effective urban GI practices implementation strategies using an efficient systematic approach to reduce the adverse impacts of climate change.

Hypotheses

1. The adverse impacts of climate change on urban flood risks can be reduced by at least 50% by optimally implementing GI practices.
2. The adverse impacts of climate change on urban CSOs can be reduced by at least 30% by optimally implementing GI practices.
3. The optimized implementations (types, designs, quantities, and spatial locations) of GI practices are at least 50% more cost-effective than typical implementations of GI practices.

Research questions

1. How will optimally implementing GI practices reduce the adverse impacts of climate change on urban flood risks and CSOs?
2. How to obtain optimized types, designs, quantities, and spatial locations of GI practices?
3. How will optimized implementations of GI practices compare to typical implementations of GI practices?

Mentor: Justin Minder

The Minder research group, in the Department of Atmospheric and Environmental Sciences (DAES), studies processes controlling regional climate change and variability, with a focus on regions of complex terrain. Research projects typically use some combination of high-resolution regional climate modeling, networks of weather station data, gridded climate analyses, and/or satellite remote sensing. Special attention is given to how mountains and inland lakes shape regional climates and climate change impacts. Specific research projects offered will vary year-to-year and depend on student interest. 

• Projecting changes in winter precipitation type over northeast US: How will the mix of winter precipitation types (e.g., snow, sleet, freezing rain, and rain) change in the complex terrain of the northeastern US as the climate warms? We will investigate possible futures and the processes at play using output from high-resolution regional climate simulations and diagnostics methods for determining precipitation type. Station observations will be used to evaluate the models.
   
• Variability and trends in lake-effect snow: How do variations in atmospheric circulations and lake conditions combine to determine how much snow falls in lake-effect snowstorms downwind of the Great Lakes each winter? We will use atmospheric and lake analyses, high-resolution regional climate simulations, and New York State Mesonet station observations. Lessons learned will be used to understand how lake-effect snow may change as the climate warms. 
   
• Elevation-depending climate warming over the Andes mountains: How will the amount of climate warming vary with elevation in the Andes mountains over the upcoming decades and what are the crucial physical processes at play? We will investigate these questions in a set of state-of-the-art high-resolution regional climate model simulations being conducted over South America.
   

Mentor: Sujata Murty

In climate research, long datasets are invaluable. They help establish a climate baseline against we can measure recent changes and illuminate connections between different aspects of the climate system. Yet, instrumental climate data are sparse before the 20th century, particularly throughout Indo-Pacific ocean regions.

Assessing how recent changes in Indo-Pacific climate fit into a long-term context thus requires archives of past climate and environmental changes. One such archive are reef corals. Corals can grow continuously for hundreds of years and acting like meteorological stations in the ocean that continuously record climate and environmental changes during periods when no observational data exists.

As they grow, corals incorporate chemicals from the seawater surrounding the colony into their carbonate skeleton. The concentration of those chemicals varies in relation to changes in temperature, rainfall and ocean circulation.

In this project, students will analyze geochemical data from Indo-Pacific coral samples (e.g., Red Sea, central Indian Ocean) and combine the data with high-resolution ocean model simulations to assess the drivers of changes in Indo-Pacific ocean and climate systems over past centuries.

This project is flexible in nature and can be focused on coral paleoclimate reconstructions, analysis of model output, or a synthesis of both, depending on the interests of the successful applicant.

Led by Sujata Murty, a paleoclimate scientist and oceanographer at the University at Albany (UAlbany), the project is highly interdisciplinary and will integrate paleoclimatology and marine geochemistry with physical oceanography. The successful applicant can expect to gain a hands-on geochemistry laboratory experience, paleoclimate analytical skills and programming experience while participating in weekly lab group meetings and research paper discussions. 

Mentor: James Schwab

The student will be involved in hands-on operation of instruments for the measurement of air pollutant gases and particles. The Schwab research group operates research stations in Albany (at ETEC), at Whiteface Mountain, and on Long Island. The student will learn about all aspects of the measurement process, from selection of instrumentation, air sampling, data acquisition, calibration, data validation, quality assurance, and data processing.

Depending on support and other logistics, the student may have the opportunity to travel to Whiteface Mountain or Heckscher State Park on Long Island to observe and help calibrate instruments at an operating research site. The project will help the student learn about important air pollutants, including ozone, oxides of nitrogen and particulate matter. For secondary pollutants such as ozone and some forms of particulate matter, the student will also learn about formation and destruction mechanisms.

All of this will be placed in the context of meteorological conditions such as temperature, relative humidity, solar insolation, and wind speed and direction. This will help the student understand the interplay of chemistry and meteorology involved in determining air quality. Since ozone and particulate matter (aerosols) also have important implications to radiative forcing, the role of these species in climate science will also be emphasized.

Mentor: Scott Miller

The exchange of momentum, heat, moisture, and trace gases (e.g., CO₂) across the air-sea interface is important for determining the dynamics and composition of the atmosphere. These “fluxes” are driven by complex processes (e.g., atmospheric turbulence) and interactions (e.g., wind-wave coupling) that must be represented using relatively simple formulas, or parameterizations, in weather and climate models.

To develop these parameterizations, we focus on using specialized techniques such as eddy covariance to directly measure fluxes from platforms such as ships and buoys. These data can be used to improve the understanding of processes controlling air-sea exchange. 

Dr. Miller’s lab is working to develop autonomous, buoy-based systems for measuring air-sea exchange between the sea surface and the marine atmosphere (momentum, heat, moisture, carbon dioxide). This capability will support basic studies of air-sea interaction physics and biogeochemistry, including applied fields such as offshore wind energy that address important environmental challenges.

Depending on interests of the REU student, the range of research projects includes development of instrumentation and measurement systems, field deployments, and data analysis/interpretation.

Mentor: Sarah Lu

The Lu research group focuses on aerosol-related research studies. Research projects typically use some combination of regional air quality modeling, gridded weather analyses, ambient measurements from surface networks, and/or satellite remote sensing observations. Research projects will vary year to year depending on interest of REU students.

• Weather and Air quality:  Limited-area numerical models, in conjunction with multi-platform observations, are used to investigate local weather and air quality phenomena across New York State
   
• Aerosol-weather interaction: Numerical models are used to investigate the impact of aerosol-radiation-cloud interaction on medium-range weather forecasts as well as to characterize the impact of aerosol transmittance effects on satellite radiance data assimilation.
   
• Impact of wildfire smoke plumes: Long-range transport of smoke aerosols and their impacts on local air quality are investigated using satellite measurements, ground-based networks, and model products. Machine learning algorithms are applied to characterize key contributors to surface PM concentrations.

Mentor: Sukata Basu

Research projects will vary year to year depending on interest of REU students.
Automated classification of wind profiles in the lower troposphere. Given the high cost of the instruments/infrastructures and other logistical issues, in general, there is a dearth of tall wind profile measurements around the world. During the proposed REU project, the student researcher will use NYS Mesonet profiler data (17 wind lidars) and first classify these observed wind profiles using a machine learning approach called Self-organizing Feature Map (SOM)18.

Subsequently, they will be able to determine if certain classes of wind profiles are more prevalent at specific locations (e.g., coastal site vs. complex terrain). The outcome of this project will be extremely useful for the US wind industry. 

Reliable estimation of extreme wind gusts using convolutional neural networks. In this REU project, we will reformulate the extreme wind gust (EWG) estimation problem as a deep learning-based ‘image regression’ problem. The student researcher will utilize gridded radar reflectivity images from GridRad.org as input to train a suite of convolutional neural networks (e.g., DenseNet, InceptionV3).

Wind gust measurements from the New York State Mesonet Network will be systematically used for both training and validation. The proposed deep learning framework can be easily extended to other parts of the United States. 

Solar energy forecasting using sky imageries and a novel deep learning framework. In the proposed REU project, the student researcher will conduct rigorous assessment of a deep learning-based multivariate regression framework (henceforth dmvrSolar), by making use of the observational data from the NYS Mesonet Profiler Network. This project will facilitate the NYS’s ambitious goal of generating 70% of its electricity generation from renewable sources (including solar energy) by 2030. 

Mentor: Sara Lance

Clouds impact the transport, chemical processing and removal of pollutants from the atmosphere. Predicting changes to the composition of Earth’s atmosphere (or atmospheres of other planets) thus requires understanding the processes by which clouds, gases and aerosols interact under different conditions.  

The Lance research group focuses on these types of fundamental processes using one of three approaches:

1. ambient measurements to uncover relationships in Earth’s atmosphere,
2. modeling studies to evaluate potential mechanisms behind the ambient observations, and
3. laboratory measurements to establish a physical basis for the proposed mechanisms.

Many of the measurements conducted are in-situ observations of microphysical and chemical properties at a specific location and time, but larger scale observations using remote sensing from surface-based or satellite-based platforms are also needed to provide important context and to constrain large scale models. REU students will have the opportunity to contribute to research at all levels, from instrument deployment, calibration, forecasting and data acquisition to data analysis and scientific presentations/publications.
 

 Mentor: Jeff Freedman

• Use of NYSM data to estimate the renewable energy resources - Strong growth of solar wind energy development is driving demand for better characterizing meteorological conditions where physical data is traditionally sparse.
  
  The New York State Mesonet (NYSM), consisting of 127 standard (surface) stations, and 17 enhanced (profiling) sites, provides a comprehensive and continuous data set of key variables (e.g., wind speeds at hub height - 100 m - 150 m; and surface irradiance) for estimating wind and solar energy generation.
  
  Potential work here would include estimating the New York State wind and solar resource using NYSM data and comparing this analysis with existing gridded data sets such as the National Renewable Energy Laboratory’s Wind Integration National Dataset Toolkit and Solar Resource Maps.
   
• Variability of the wind resource - Rapid changes in wind speed or availability of solar power can produce sudden changes in wind and solar power output.
  
  These “ramp events” can lead to power grid instability and the potential for large-scale blackouts. Potential work here includes using NYSM data to analyze the spatial and temporal distribution of ramp events across New York State.
  
  Such an analysis would be of great value to utilities, the New York Independent System Operator (NYISO), and regulatory authorities in the context of the rapid growth of renewable energy across the state.
   
• Climate change and renewable energy - Renewable energy (onshore and offshore wind, utility-scale and distributed solar generation, and hydropower) is supplying a rapidly increasing share of electricity to New York State’s (NYS) power grid.
  
  However, little is known about the potential effects of climate change on the spatial and temporal distribution of renewables in NYS. With support from the NYS Energy Research and Development Authority, the ASRC Renewable Energy Group conducted a comprehensive observational and modeling study of climate change and its potential effects on the state’s renewable energy resources.
  
  Potential work here would involve examining how climate change may affect trends in wind and solar energy “droughts”, extreme events, and changes in electrical power demand.

Mentor: Jorge E. González-Cruz

The Coastal Urban Environmental Research Group (CUERG) conducts research on the climatology of extreme weather for the US Caribbean, comprised of Puerto Rico and the US Virgin Islands, in collaboration with the US National Ocean and Atmospheric Agency (NOAA) under the Climate Adaptation Partnership Program.  

The specific objective is to quantify trends from present and projected records for thermal environments consisting of surface and air temperatures, moisture content, heat index and consequential heat waves. 
 
Expected Outcomes

• Research on long-term past records and of projected future climate of extreme temperatures, humidity and heat index from surface records, satellite data, and of global circulation models to quantify means and extremes.
• To cross-correlate environmental records with social value data including public health and energy demands.
• To quantify trends of means and extremes.

Mentor: Jie Zhang

Studies have identified significant differences in the spatial distribution of air pollutants, including O₃, NO₂, particulate matter impacted by local sources (e.g., traffic, local industries, etc.), and these are in turn associated with a relatively higher heath exposure for underserved and low-income communities.

Most of these communities have been excluded from (i.e., not chosen as sites for) the current routine regulatory monitoring stations due to the limited number of such stations, their cost, infrastructure and personnel requirements.

Our group will build an enhanced air quality measurement network in and near underserved and low-income communities in the Capital Region of New York State using one kind of low-cost-sensors package for inside/outside measurements with five community schools as sites and supplemented by mobile lab measurements.

REU students will help deploy and maintain low-cost sensor packages, participate in data analysis and assist in the communication between our group, participating communities and local environmental agencies.

The research group of Xueying Yu specializes in carbon emissions, air pollution, carbon-climate interactions, and Bayesian optimization. The REU projects leverage satellite data to explore atmospheric chemistry related to these topics.

Carbon Emissions

Carbon dioxide (CO2) and methane (CH4) are key greenhouse gases responsible for climate change. Rising levels from human activities like fossil fuel use and deforestation require urgent mitigation. Students will analyze satellite data to track CO2 and CH4 emissions from urban centers, exploring their climate impact.

Air Pollution

Nitrogen dioxide (NO2), mainly produced by fossil fuel combustion, contributes to poor air quality, respiratory problems, and ecosystem damage. Students will work with satellite-derived data to investigate NO2 sources, transport, and chemical processes, focusing on urban and industrial emissions. This research will help identify pollution hotspots and inform air quality improvement strategies.

By participating in these projects, students will gain hands-on experience in satellite data analysis and learn to interpret atmospheric processes that drive climate and air quality challenges.