Category: Model Improvements Current & Ongoing Studies

Air quality models are used to predict atmospheric conditions in the future. They are based on meteorological conditions, chemistry mechanisms, and inventories of reported emissions. Models are tested for correctness by comparing their results to observed atmospheric characteristics, such as PM2.5 concentrations during high pollution periods in the past. The following studies show various ways in which models are being improved to better reflect observations of the atmosphere in Utah.

Current & Recently Completed Studies

The Salt Lake Regional Smoke, Ozone and Aerosol Study (SAMOZA)

The University of Washington, Utah State University and the University of Montana will conduct a detailed study of ozone (O3) and fine particulate matter (PM2.5) in the Salt Lake Valley (SLV). Using new VOC observations, plus existing measurements of NOx, CO and PM2.5, they will use a variety of analyses to understand O3 formation and the sources of PM2.5 in the SLV during the summertime season. In addition, they will conduct photochemical modeling and statistical/machine learning analyses to improve our understanding of O3 photochemistry. We expect to gain significant new policy-relevant insights on what controls high concentrations of O3 in the SLV during both smoke-influenced and non-smoke conditions.

  • Principal Investigator: Dan Jaffe (University of Washington)
  • Funded by Science for Solutions Research Grant: $280,516
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Improving Smoke Detection and Quantifying the Wildfire Smoke Impacts on Local Air Quality Using Modeling and Machine Learning Techniques

Though it can be easy to tell that wildfire smoke has negative impacts on urban air quality, there is no tool to quantitatively measure wildfire impacts, nor to identify whether exceedance days are due to wildfire smoke or other emissions. The first scope of this work will develop a new plume rise model to estimate the plume injection heights for larger wildfires, which will improve simulations of smoke transport and downwind air pollution concentrations. The second scope of this work is to use CTM (chemical transport model) ensemble simulations to determine wildfire smoke contributions to local air quality using source apportionment techniques. The last scope of this work is to develop a fast-response tool to identify federal standards (NAAQS) exceedance days with large contributions from wildfire smoke.

  • Principal Investigator: Heather Holmes (University of Utah)
  • Funded by Science for Solutions Research Grant: $61,738
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Improved Vegetation Data for the Biogenic Emission Inventory of Wasatch Front

The goal of this project is to improve numerical predictions of regional ozone and aerosol distributions in the Wasatch Front by developing more accurate estimates of biogenic volatile organic carbon (BVOC) emissions for the urban areas within the Northern Wasatch Front. Specifically, this project will upgrade modeled MEGAN (Model of Emissions of Gasses and Aerosols from Nature) BVOC emission estimates by analyzing high-resolution satellite imagery using machine learning, object-based classifications that are calibrated and assessed by field observations. Such techniques have already successfully been applied in Texas and California. These techniques will improve MEGAN landcover inputs for the Wasatch Front region including time-varying Leaf Area Index (LAI), growth form fractions (tree, shrub, crops, herbaceous plants) and tree species composition (e.g., relative abundance of oaks, poplars, pines, spruce, etc). The benefit of this project will be an improved MEGAN emission model for the Wasatch Front that is available for use in air quality models that are critical for our scientific understanding and the development of effective regulatory strategies.

  • Principal Investigator: Tejas Shah (Ramboll US Consulting)
  • Funded by Science for Solutions Research Grant: $124,797
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Development of a WRF-based Urban Canopy Model for the Greater Salt Lake City Area

Brigham Young University will conduct a two-year project that will utilize state-of-the-science meteorological modeling with land use descriptions of the Great Salt Lake area to characterize impacts of urban growth on local meteorological conditions. Model methodology and usage will be documented so air quality modelers can use existing or self-developed future results for additional urban growth and air pollutant assessments.

  • Principal Investigator: Bradley Adams (BYU)
  • Funded by Science for Solutions Research Grant: $59,411
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Assessing Wintertime Ozone Prediction Sensitivity to Photochemical Mechanism

Ramboll and the Utah State University – Bingham Research Center (BRC) will conduct a study to thoroughly investigate wintertime ozone prediction sensitivity in the Uinta Basin among two current photochemical mechanisms using a consistent modeling platform. Recent air quality modeling conducted by BRC using different modeling systems indicates that the Regional Atmospheric Chemistry Mechanism (RACM) produces much higher ozone concentrations than the Carbon Bond (CB) mechanisms. Ramboll and the BRC will comprehensively test and understand RACM2 performance in simulating wintertime ozone in the Uinta Basin relative to the CB version 6 (CB6) mechanism currently implemented in the CAMx air quality model used by the Utah Division of Air Quality.

  • Principal Investigators: Greg Yarwood (Ramboll), Seth Lyman (Utah State University)
  • Funded by Science for Solutions Research Grant: $98,048
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Improving WRF/CMAQ Model Performance using Satellite Data Assimilation Technique for the Uintah Basin

This study will test if satellite observations of vegetation and land use can be used to improve photochemical model performance in the Uintah basin. An improved model will help inform emission reduction strategies and regulatory action.

  • Principal Investigators: Huy Tran, Trang Tran (USU)
  • Funded by Science for Solutions Research Grant: $38,392
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