Urban Metabolism in the Mountains: Emissions of Greenhouse Gases and Pollutants in an Urbanized Mountain Valley

John C. Lin, Professor and Haley Humble  (PhD Student)

Scientific Background

An urban region can be thought about as a giant meta-organism, and it “breathes”, releasing gases and pollutants to the atmosphere.  The atmosphere (especially the boundary layer) is, then, the chamber into which the urban region breathes.  This project seeks to characterize and understand variations in the concentrations of air-quality relevant species and greenhouse gases (primarily CO₂) at measurement sites in the Salt Lake Valley.  The student will make use of the observed concentration time series and examine the diurnal, seasonal, and interannual patterns of the species, as well as correlations between them.   

Student's Role

The student will plot up the observations of CO₂ carried out by our research group at various locations around Utah (see https://air.utah.edu/).  Variability at different timescales (diurnal, seasonal, multi-year trends) will be noted at the different sites.  If there is time, the CO₂ data will be combined with observations of air-quality relevant pollutants (e.g., PM_2.5, NOx, CO) to examine the similarities and differences between these chemical species.

Student Learning Outcomes and Benefits

• Data analysis skills using R
• Generating insightful graphics and visualizations
• Understanding of how emissions and meteorology interact to affect pollutant concentrations in mountain valleys
• Improved written and oral presentation skills

 

APPLY HERE

John Horel, Professor and Colin Johnson, Research Associate

Scientific Background

A new multi-university Center is helping to coordinate research on the impacts of weather extremes on the power grid in the western U.S. and Canada (https://www.price.utah.edu/2023/09/18/university-of-utah-and-university-of-calgary-launch-u-s-canada-center-on-climate-resilient-western-interconnected-grid). Our research group will be involved in developing software tools to access and display weather observations from sites across the West and output from operational weather prediction models. The REALM student working with us will be involved in a mix of computer-based research related to wildfires along with hands-on field work helping to maintain a network of weather stations across northern Utah. The attached image illustrates smoke from wildfires in California spreading into northern Utah as analyzed by the High Resolution Rapid Refresh (HRRR) operational model.

Student's Role

You will participate in research with faculty, staff, and graduate and undergraduate students in the MesoWest group (meso1.chpc.utah.edu/mesowest_overview). Research and development related to data science, machine learning, data assimilation, fire weather, the Great Salt Lake, and air quality along the Wasatch Front are among some of the projects underway. You will help monitor information related to observational data streams in the western United States during the 2023 summer season with particular attention placed on hazardous conditions. Background in the atmospheric sciences is not required but some familiarity with programming using Python, R, or Matlab is preferred.

You also have the opportunity to spend time assisting with fieldwork related to a network of environmental monitoring stations operated by our group in remote locations of northern Utah. You will obtain hands-on experience with how weather equipment is installed, maintained, and operated.

Student Learning Outcomes and Benefits

• be involved in societally-relevant research that has the potential to protect lives and property threatened by future wildfires.
• gain experience in methodologies in collaborative research to examine environmental information.
• increase familiarity with ways to acquire, archive, and visualize environmental information through online instruction and hands-on field work with environmental sensors.
• become familiar with the linux operating system on workstations housed by the Center for High Performance Computing as well as techniques to access data from cloud providers, such as Amazon.

 APPLY HERE

Computational tools for wildfire smoke detection in the western U.S.

Heather Holmes,  Associate Professor

Scientific Background

Over 70 million people live in the western U.S., 24% of the total U.S. population. The population density in this region is low, and correspondingly should have fewer sources of anthropogenic air pollution. However, areas in the western U.S. have acute, elevated levels of air pollution concentrations, exacerbated by unique air pollution sources like wildfire smoke. The number and size of wildfires are increasing due to climate change and severe drought. Wildfire smoke has public health consequences for populations downwind, including altering behaviors (e.g., individuals forgoing outdoor activities) and smoke-induced illnesses. Wildfire smoke often leads to cities having air pollution concentrations that exceed the national ambient air quality standard. Knowing which air pollution events are attributed to smoke plumes is helpful for regulatory agencies and health effects researchers. This goal of this project is to develop a tool to automate the detection of wildfire smoke contributions to poor air quality.

Student’s Role

The student will help develop a scientifically robust method to identify wildfire smoke days in an urban environment. Over the course of this research experience, the student will develop a piece of the computational tool that several other researchers and students are contributing to. The student will use open-access geoscience datasets (i.e., EPA air quality data, meteorological observations, and satellite fire detections) as the foundation of the detection method. Then the student will incorporate atmospheric models and statistical approaches with the datasets to develop a smoke detection algorithm. The result will be a computational tool written in R for air quality agencies and researchers to use for policy and health assessments.

Student Learning Outcomes and Benefits

At the end of this project, the student will be able to:

• Explain the basic physics and chemistry of wildfire smoke plume transport.
• Recognize reputable open source data (e.g., best file format, metadata).
• Implement R codes to read and write large datasets.
• Generate R codes to build statistical models using these large datasets.
• Produce descriptive, graphical visualizations to share findings and communicate scientific meaning.

 APPLY HERE

The Importance of Daytime VOC Oxidation by the Nitrate Radical

Alfred Mayhew, Wilkes Center Post Doctoral Fellow and Jessica Haskins,  Assistant Professor

Scientific Background

Volatile organic compounds (VOCs) are key atmospheric pollutants that can undergo chemical reactions after they are emitted into the atmosphere. Once present in the atmosphere, VOCs enter a complex web of chemical reactions that generally act to oxidise these VOCs. The hydroxyl radical (OH) is generally considered to be the most important atmospheric oxidant, however the nitrate radical (NO₃) can become a major atmospheric oxidant under certain conditions. Most notably, rapid losses of NO₃ during the day often restrict its importance to nocturnal chemistry. However, there is a growing recognition of the importance of NO₃ oxidation in the afternoon period in many polluted environments, where high ozone concentrations reduce the daytime loss of NO₃. The organonitrate species resulting from the oxidation of VOCs by NO₃ are of interest because of their potential to transport NO_x pollution as well as their potential contribution to particulate matter (PM).

Salt Lake City regularly sees high ozone concentrations during the summer months, so daytime NO₃ chemistry may play a role in the oxidation of VOCs in this environment. While the effect may not be as strong as has been observed in previous studies in megacities such as Beijing, this chemistry has received little attention so a dedicated investigation is needed to identify areas where this chemistry is most important.

 Student's Role

The student will begin the project by running the GEOS-Chem chemical transport model to produce high time resolution concentration data for a range of chemical species, including the nitrate radical. The results from this model will then be analysed to identify areas and seasons in which daytime NO₃ chemistry is most important. The student will also review the literature to compile previous research on daytime NO₃ chemistry and identify points of comparison between their model results and findings from previous work. By the end of the project, the student will have a foundational understanding of atmospheric chemical processes and computational based research and will be well placed to build on these skills to pursue research in a range of fields of environmental science.

Student Learning Outcomes and Benefits

By the end of this research experience, the student will be able to:

• Read and review primary scientific literature to direct their own research.
• Describe the chemical processes that control the concentrations of volatile organic compounds (VOCs) in the atmosphere, including key oxidants such as the hydroxy radical (OH) and nitrate radical (NO₃).
• Use basic bash commands to operate software in a Linux environment, including the global chemical-transport model, GEOS-Chem.
• Analyse large datasets using python, including for the production of publication-quality figures.
• Present their research findings to a variety of audiences.

 APPLY HERE

Predicting weather and climate extremes in a chaotic dynamic system with data assimilation and data sciences

Zhaoxia Pu,  Professor

Scientific Background

The nonlinearity of atmospheric evolution makes it very difficult to predict weather and climate extremes (e.g., events that have not often occurred in the past but have happened recently). Modern data assimilation and data science algorithms have the ability to quantify and mitigate uncertainties in the forecasting processes. In this research project, the student will utilize a toy nonlinear dynamic model, data assimilation methods, and deep learning algorithms to explore the predictability of weather and climate extremes.

Student's Role

The student will gain practical hands-on experience in handling a nonlinear numerical model, data assimilation algorithms, and deep learning methods offered or advised by the professors. They will also handle the data and perform analyses to generate research outcomes.

Student Learning Outcomes and Benefits

At the end of this research experience, the student will be able to:

• Gain a significant understanding of prediction, predictability, data assimilation, and deep learning methods.
• Obtain experience in handling time series datasets and using Python programs.
• Become familiar with the scientific literature.
• Practice writing research reports.

 ^{APPLY HERE}

Forest fire burn scar impacts on surface climate

Marysa Lague, Assistant Professor

Scientific Background

Over the past several years, western North America has experienced record burn areas from wildfires. Wildfires contribute to global warming by releasing carbon stored in forests to the atmosphere as CO2. However, wildfires also alter local climate by changing the basic physical properties of the land surface. In the seasons following stand-destroying wildfires, grassy ecosystems develop in the burned areas. These grasses differ from the forests in albedo, leaf area, root depth, and aerodynamic properties. All of these differences alter the fluxes of water and energy between the land and the atmosphere, which has direct implications for local climate independent of CO2-driven warming.

 Student’s Role

In this project, students will use satellite observations of burned regions to determine changes in the land surface over the years before and after a wildfire. Using a conceptual model of the terrestrial surface energy budget, they will estimate how these changes may have impacted surface fluxes of water and energy, and what those changes in fluxes should mean for temperatures. The student will then learn to use a single-column model of the land surface and atmosphere to simulate the change in the land surface from pre- to post-fire conditions. The student will compare the changes in surface and near-surface temperatures from their model simulations to observed temperature changes in burned areas.

Student Learning Outcomes and Benefits

Upon completing this project, the student will:

• Gain knowledge of how vegetation modifies surface climate.
• Gain experience in data analysis using the python programming language.
• Gain experience and skills for their professional development as a scientist.

 APPLY HERE

Machine learning methods for prediction of precipitation in complex mountainous terrain

Court Strong, Professor and Savanna Wolvin (PhD student)

Scientific Background

Prediction of mountain precipitation is an important component of operational forecasting and long-term hydroclimate research, playing a central role in recreation, hazards, and water supply. In this project we seek to understand and predict how precipitation varies with elevation in complex terrain (orographic precipitation gradients, OPG). This project uses high resolution atmospheric modeling, networks of weather station data, and cutting-edge machine learning methods to understand and predict OPG. The research will involve testing and application of methods in the western US and extreme topography of High Mountain Asia.

Student's Role

The student will help with an ongoing project to predict orographic precipitation gradients using machine learning (ML) methods. Potential research focuses include training a ML algorithm to predict OPG, implementing techniques to improve a current ML model, or evaluating the results of a ML model to climatology. The current ML model we are testing is a convolutional neural network — an algorithm designed to evaluate images, speech, or audio.

 

 

 

 

 

 

Student Learning Outcomes and Benefits

• Gain experience implementing machine learning techniques
• Learn how complex terrain affects the distribution of precipitation
• Develop strong Python programming skills in data manipulation, visualization, and analysis
• Increase skills in professional communication and presentation

APPLY HERE

Mountain Snowflake Photography

Tim Garrett, Professor and Erik Pardyjak, Professor

Scientific Background

Simulations of severe weather events and climate extremes are highly sensitive to how fast frozen hydrometeors grow and fall. Current models of these processes are based on a small database of measurements obtained in the Cascade Mountain Range of Washington State over the course of two winters in 1971 and 1972 when just 376 snow particles were sampled within a still-air environment, without considering ambient meteorology. However, even casual observations reveal that snowflakes are denser in warmer air, and that they swirl in turbulent winds, either being swept downward or lingering in the air for much longer than they would otherwise. Our team aims to develop a more sophisticated understanding of how fast precipitation particles fall as a function of temperature and turbulence using new instrumentation we have developed for photographing snowflakes as they fall, and for providing the first direct, automated, measurements of individual hydrometeor mass and density. Our field site is a high elevation location in Alta Ski Area.

Student's Role

The student will focus on investigating the role of meteorology in determining snowflake type using these new photographic and mass and density datasets from the Multi Angle Snowflake Camera and the Differential Emissivity Imaging Distrometer. The student will explore the relationship of atmospheric temperature to hydrometeor mass- and density-size relationships. The analysis will use existing computer programs for analysis of large datasets, and also involve creating figures that show how snowflake types vary with meteorological conditions.

Student Learning Outcomes and Benefits

• Expertise about the types of snow as a function of meteorology
• Improved skill working with Matlab within a Linux environment
• Experience working with a team of researchers in Atmospheric Sciences, Mechanical Engineering, and a startup company that commercializes instrumentation
• Improved skill at developing professional quality figures that describe precipitation in a mountainous region.

APPLY HERE

Air Quality Study in Utah

Gannet Hallar, Professor

Scientific Background:

In the Western US, many continue to face poor air quality in the summer due to both high ozone concentrations and increased aerosol loading due to wildfire smoke.  Both increased ozone and aerosol concentrations have been linked to increased instances of respiratory illness. 

Student’s Role, and Student Learning Outcomes and Benefits:

The REU student will work closely with the Hallar Aerosol Research Team (HART) to investigate aerosol loading associated with wildfire smoke in Utah.   There are two objectives for this summer project. 

1. The student will be engaged in the Salt Lake City Summer Ozone Study (SLC-SOS) with a focus on preparing air quality forecast for logistical field planning.  The SLC-SOS is a collaboration between NOAA Chemical Sciences Laboratory (CSL), Utah Division of Air Quality, the University of Utah and Colorado State University (along with many others). The student’s learning outcome will focus on: gaining experience in how to successfully deploy mobile research equipment to study air quality in an intermountain basin. This campaign will enable undergraduate students to examine profiles of wind, temperature, aerosol properties, and carbon dioxide (CO₂) in the boundary layer in the Salt Lake Valley.  The overall goals of SLC-SOS are to use research mobile measurements (i.e. vans and trailers) to investigate O₃ production along the Wasatch Front between July 1 and August 30, 2024.
2. Next, the student will be engaged with a team focused on understanding the health impacts due to wildfire smoke exposure on days that are classified as exceptional events by the Environmental Protection Agency.  Here the REU student will use and improve data analysis skills (e.g. R or Python), while involved in societally relevant research.    

APPLY HERE