Cumulative Summary of Research Relating to Uinta Basin Air Quality

This page provides a summary of significant research findings that relate to air quality in the Uinta Basin.  It includes outcomes from research performed by scientists at USU, as well as researchers at other institutions, and is referenced so readers can refer to the original documents for more detailed information.  This summary is specific to the Uinta Basin.  It is not meant to be a general summary of air quality research or oil and gas-related air quality impacts. We intend to update this document annually.  the most recent update occurred in November 2021.


Key Components of Ozone-forming Winter Inversions

Significant local ozone production during wintertime requires the following three ingredients, and in the absence of these three ingredients it does not occur (Lyman and Tran, 2015; Mansfield, 2017; Mansfield and Hall, 2013; Mansfield and Hall, 2018; Oltmans et al., 2014; Schnell et al., 2009):

  • Thermal inversions. When not inverted, the atmosphere is well mixed and ozone precursors are rapidly dispersed.
  • Snow cover. The snow surface reflects rather than absorbs solar radiation, the energy source for ozone formation.  This increases the amount of radiation available in the atmosphere for chemical reactions.  Snow cover also acts to stabilize inversions by reflecting sunlight and keeping the surface cool.
  • Precursor emissions. Only organic compounds and oxides of nitrogen (NOx) produced by the oil and gas industry, as opposed to typical urban sources, appear to be capable of generating significant winter ozone.

Evidence that precursors from the oil and gas industry are required comes from the many basins and valleys of the western United States that do not produce winter ozone. They have inversions and snow cover, and some even have strong urban organic compound and NOx emissions (Mansfield and Hall, 2018). Nevertheless, the only known regions to produce winter ozone are the Uintah Basin and the Upper Green River Basin (near Pinedale, Wyoming), both of which have a robust oil and natural gas industry. The specificity of winter ozone to oil and gas emissions no doubt results from differences in chemical processes. See the discussion in the Ambient Air Chemistry section concerning aldehyde photolysis.

Harder to explain is the absence of winter ozone in the Wind River Basin (near Riverton, Wyoming), because all three ingredients are present there. However, gas and oil activity in the Wind River Basin are less than half that of the Uinta Basin. Winter ozone will probably occur there if the petroleum industry grows (Mansfield and Hall, 2018).

Inversion Structure, Extent, and Frequency

A thermal inversion occurs when surface air is colder than the air aloft. Tethersonde measurements of inversion strength do not occur routinely in the Uinta Basin, so we rely on air temperature measurements at a number of different surface sites at different elevations throughout the basin to make this determination (Mansfield and Hall, 2013).

Wintertime ozone formation requires inversion conditions that last more than 24 hours. Multi-day inversion episodes have occurred for as short as two days, and for more than two weeks. Because inversion conditions are not uniform across the Uinta Basin, the length of the inversion is location-dependent

The “bowl-shaped” terrain of the Uinta Basin, combined with a large altitude difference between the lowest location at the middle of the basin and the top of mountain ranges (~2000 m), provide ideal topographic conditions for strong inversion layers to build up and persist over relatively long periods of time (Lyman and Tran, 2015).

Low winds with upslope flow during the day downslope flow during the night are typical during winter inversions (Lyman and Tran, 2015; Schnell et al., 2016).

Available evidence suggests that the inversion top is flat, rather than following elevation contours (Schnell et al., 2016). In spite of this, upslope airflow during the day can push air with high ozone to higher elevations on the basin margins than would be expected from vertical inversion structure alone.

Inversion events are complex, with changing lapse rates within the inversion layer, changing inversion depth, often a complex horizontal structure, and sometimes ice fog (Neemann et al., 2015; Tran et al., 2018).

The horizontal and vertical extent of winter inversions depends on the strength of negative lapse rates (a measure of the temperature difference at the surface and aloft) and on the length of the inversion episode. Inversions tend to form first at the lowest elevations of the Uinta Basin and then build out and up day upon day as the episode continues. The inversion episode ends when a storm breaks up the inversion structure and mixes out the polluted air. This process tends to clear out the highest elevations and the basin margins first, and weak storms sometimes fail to entirely eliminate inversion conditions at the lowest elevations (Lyman and Tran, 2015).

Wintertime inversions in the Uinta Basin frequently extend over several days, often up to a week or more, giving time for ozone precursors to build up. Define an “inversion cluster” as any successive period of days that are all inverted.  The frequency and duration of clusters are both larger in winter and are correlated with the presence of snow cover.  The distribution in cluster size is heavy-tailed, especially in the presence of snow cover.  Although the mean durations in the presence of snow are about 12 and 8 days in December and January, respectively, we have observed four clusters since 1950 with durations of from 41 to 46 days.

By most measures, December and January are the worst months for inversions, but on average, ozone concentrations are higher in February (Mansfield, 2017; Mansfield and Hall, 2018).  This occurs because the lower solar zenith angle and the lengthening day in February provide more sunlight for ozone production.  Ozone production rates are more rapid as the season moves further away from the winter solstice, so weaker inversions later in the winter season are still able to produce ozone, as long as snow cover remains.

Air Pollutant Emissions

Inventoried Anthropogenic Emissions

Regulatory agencies with jurisdiction in the Uinta Basin have worked with industry to develop an oil and gas emissions inventory for 2014, and a new effort for 2017 is underway (UDAQ, 2018). These new emissions inventories have more detailed information about emission sources (e.g., emission types, locations and emission activities) than any other emission database in existence. Thus, this inventory allows for high accuracy in the temporal and spatial allocation of emission sources to the modeling domain. As with previous inventories, however, application of this inventory in our photochemical models still results in underestimation of wintertime ozone.  This underestimation could be due to overestimation of NOx emissions, underestimation of organic compound emissions, or the inability of the meteorological model to accurately simulate winter inversion conditions.

Several measurement-based estimates of a Basin-wide emissions of organic compounds exist, and all estimate much higher emissions than are predicted by official inventories (Ahmadov et al., 2015; Foster et al., 2017a; Karion et al., 2013; Koss et al., 2015; Matichuk et al., 2017; Robertson et al., 2017; Foster et al., 2019).

Methane emissions per well pad in the Uinta Basin the third highest in a study of eight oil and gas-producing basins, and methane emissions normalized by natural gas production were highest in the Uinta Basin.  At least a part of the reason for high production-normalized emissions in the Uinta Basin can be explained by a high number of low-producing wells (Omara et al., 2018).

Wilkey et al. (2016) developed a method to predict emissions from the Uinta Basin oil and gas sector based on energy industry forecast data.

Emissions of NOx and organic compounds, including methane, have declined along with wintertime ozone, especially in the eastern Uinta Basin, from about 2013 through 2021 (Mansfield and Lyman, 2021; Lin et al., 2021).

Produced Water

Organic compound emissions from produced water ponds are around 4% to 14% of inventoried organic compounds (depending on the inventory) (Mansfield et al., 2018).

Emissions from produced water ponds include light hydrocarbons, methanol, aromatics (BTEX), and carbon dioxide. The composition of organic compound emissions from produced water is more reactive than typical oil and gas emissions, meaning that a given quantity of emitted organics is more able to produce ozone than the same quantity of typical oil and gas emissions (Lyman et al., 2018; Mansfield et al., 2018; Tran et al., 2017). 

When added to the 2014 multi-agency oil and gas inventory for the Uinta Basin, emissions from produced water ponds are responsible for 18-35% of locally-produced ozone (i.e., total ozone during an inversion episode minus ozone that was present before the episode began).

Models of emissions of organic compounds from produced water surfaces have been developed and are in use for regulatory applications (Mansfield 2020; USU 2019). An emissions model can be downloaded at

Other Oil and Gas Sources

Ozone-forming organic compounds can leak from subsurface infrastructure at well These compounds migrate to the surface and are emitted into the atmosphere. Well pad soil emissions are controlled by complex and dynamic processes, but overall they make a very small contribution to total emissions from the oil and gas industry (Lyman et al., 2017b). The same is true for plugged and abandoned wells (Townsend‐Small et al., 2016; Williams et al., 2020).  Soil emissions from subsurface leaks are extremely variable, both temporally and spatially (Lyman et al., 2020).

The majority of fugitive emissions from oil and gas wells, including oil and gas wells with emission control devices, come from thief hatches and pressure relief valves on tanks (Mansfield et al., 2017).

Aldehydes and other carbonyl compounds are emitted from combustion sources like pump jacks and burners, and from non-combustion sources, including liquid storage tank venting and glycol dehydrator exhaust (Lyman, 2015b). Ambient air measurements and analysis show that primary emissions of carbonyls and secondary photochemical production in the atmosphere are both important carbonyl sources (Edwards et al., 2014; Koss et al., 2015).

Wells with glycol dehydrators emit more reactive aromatics then wells without on-site dehydrators (Warneke et al., 2014).

Emissions of non-methane organics from land farms (hydrocarbon solid waste disposal facilities) are significant (Lyman and Mansfield 2018).

Emissions of organics are more likely to be large enough to be detectable at new, high-producing oil and gas wells than at older, lower-producing wells. (Lyman et al., 2019).

Well pads with emissions control devices (i.e., combustors and vapor recovery units) on liquid storage tanks are more likely than wells without control devices to have detectable emissions from tanks.  These emissions originate mostly from thief hatches, valves, and piping on tanks, indicating that organics are escaping into the atmosphere before they can reach the control devices (Lyman et al., 2019).

Cold weather leads to poor performance by the optical gas imaging cameras that many operators use to detect and repair organic gas leaks at well pads.  Thus, leaks are less likely to be detected and repaired during winter (Lyman et al., 2019).

Methane emissions from liquids unloadings are estimated to be a small part of total methane emissions from the oil and gas industry in the Uinta Basin (Zaimes et al., 2019).

Other Emission Sources

Snowpack can act as a reservoir for reactive nitrogen compounds, but emissions of reactive nitrogen compounds, including nitrous acid (HONO), from the snow are small compared to emissions from anthropogenic sources (Zatko et al., 2016).

Organic compound emissions from snowpack, on the order of mg m-2 h-1, are known to occur in polar regions (Grannas et al., 2007). In preliminary measurements, we have detected organic compound emissions on the order of mg m-2 h-1 from the Uinta Basin snowpack, some three orders of magnitude higher (Lyman et al., 2017b).  There is also convincing evidence from polar regions that organic compounds cycle in and out of the snowpack, and that they undergo photochemical conversions to formaldehyde (an aldehyde) while adsorbed on the snow (Grannas et al., 2007).  More extensive measurements are needed to better understand snowpack emissions and assess their contributions to Uinta Basin air quality, but we are at least intrigued that snowpack processes may help us explain how oil and gas emissions are able to activate the aldehyde photolysis process mentioned in the atmospheric chemistry section.

Biogenic emissions of organic compounds are unimportant to ozone production during Uinta Basin winters (Sakulyanontvittaya et al., 2012).

Estimates of organic compound emissions due to natural seepage from subsurface reservoirs through the soil and into the atmosphere are poorly constrained. However, natural seepage is unlikely to be an important emission source relative to oil and gas development activities (Mansfield, 2014).

The Bonanza power plant is a large source of NOx. During wintertime inversions, however, the plume’s exit velocity and buoyancy relative to the surrounding air propel the majority of the plume above the inversion layer, so its influence on wintertime ozone production under the inversion is small (Schnell et al., 2016; Tran et al., 2014).

Emissions Speciation

Speciation profiles provide information about the composition of organic compound emissions (e.g., the fraction that is benzene vs. propane, etc.). They are used with emissions inventories to indicate the composition of emitted organic compounds.  For photochemical modeling studies, several different organic compound speciation profiles have been developed and applied for oil and gas emission sources in the Uinta Basin (Lyman, 2015b), including profiles developed in SPECIATE 4.5 (EPA, 2018), profiles developed by the Utah Bureau of Land Management’s Air Resource Management Strategy study (AECOM, 2013) and profiles developed as part of the Western Regional Air Partnership’s WRAP-III (WRAP, 2015).

Some available speciation profiles were developed based on gas composition analysis, rather than from actual emission measurements, and inaccuracies in them likely exist. Other profiles were developed from emission measurements collected in other basins.  Speciation profiles for carbonyls, in particular, are inaccurate, either speciating too much (e.g., flaring profile) or too little carbonyls, and this inaccuracy could be an important reason that photochemical models fail to reproduce high ozone concentrations as observed during ozone pollution events in the Uinta Basin. Photochemical sensitivity tests of oil and gas VOC speciation profiles (Lyman, 2015b) indicated that simulated ozone concentrations are highly sensitive to the amount of formaldehyde and other carbonyls speciated in organic compound speciation profiles.

Ambient Air Chemistry


Ozone during winter inversions in the Uinta basin is formed in the same way as urban, summertime ozone – it forms when sufficient NOX and organic compounds exist in the atmosphere, and when sufficient sunlight exists to energize reactions between the NOX and organic compounds and create ozone.

Ozone formation requires the presence of radicals that catalyze reactions between NOX and organic compounds. For summertime ozone, the main source of radicals is reactions between water vapor and oxygen atoms to form OH radical. During cold winter months, the atmosphere holds less water vapor, and this pathway is curtailed. Instead, radical production is dominated by the photolysis of aldehydes in the atmosphere (Edwards et al., 2013; Edwards et al., 2014; Koss et al., 2015; Lyman, 2015b).

Ozone that forms during winter inversions is spatially correlated primarily with elevation (inverse correlation) and secondly with proximity to oil and gas wells. Wintertime inversions form first, last longest, and have the strongest negative lapse rates at the lowest elevations, which accounts for the strong correlation between ozone concentrations and elevation (Lyman and Tran, 2015).  

Ozone production is more efficient for inversion episodes that occur further away from the winter solstice. This means that, for a given level of precursor pollutants, ozone is produced much more quickly in February and March than in December and January. In March, the EPA standard can be exceeded after just one inversion day, while this may take several days for an early January episode (Lyman et al., 2018b).

Usually, ozone production is more efficiently controlled by reducing either organic compound or NOx Available evidence shows that:

  • Both organic compound and NOx emission controls would have been effective at reducing Uinta Basin winter ozone in 2013, at least in the Wonsits Valley area (i.e., at the Horsepool measurement station; Edwards et al., 2014), though NOx controls would have been more effective (Womack et al., 2019).
  • Photochemistry changes rapidly with the progress of the winter season. NOx controls become increasingly effective (relative to organic compound controls) as the year progresses away from the winter solstice (Lyman et al., 2014; Lyman et al., 2018b).
  • The Salt Lake Valley and the Uinta Basin both have wintertime inversion episodes that are associated with poor air quality.  In the Salt Lake Valley, in contrast to the Uinta Basin, these episodes result in high concentrations of particulate matter, not ozone.  The reason for the difference in air chemistry in the two areas is dramatically different emission profiles.  In the Salt Lake area, emissions of NOx are plentiful and organic compound emissions are relatively few.  The opposite is the case in the Uinta Basin (Womack et al., 2018).

Monitoring stations in the Uinta Basin occasionally measure exceedances of the 70 ppb ozone standard during spring and summer. These exceedances are usually due to events separate from local anthropogenic emission sources, including wildfire smoke and intrusion of ozone-rich air from the stratosphere.

Wintertime ozone concentrations in the Uinta Basin, and the number of exceedance days per year, decreased between about 2013 and 2021.  Available evidence suggests that this decrease was caused by decreases in ozone precursor emissions (Mansfield and Lyman, 2021).

Organic Compounds

The relative concentrations of individual organic compounds (i.e., speciation) in the Uinta Basin atmosphere are similar to the speciation of natural gas, and clearly indicate that the primary source of these compounds is oil and gas production. This is true in populated and remote areas of the Uinta Basin (Stoeckenius et al., 2014).

The spatial distribution of organic compounds in the Uinta Basin is strongly correlated with the distribution of oil and gas wells (Lyman and Tran, 2015).

Emissions from oil wells are richer in heavier organics relative to gas wells (Koss et al., 2015; Warneke et al., 2014), and the oil-producing and gas-producing regions of the Uinta Basin have distinct differences in organic compound speciation.

Concentrations of organic compounds increase during inversion episodes because emitted pollutants are trapped in the shallow layer under the inversion (Edwards et al., 2014).

The atmosphere under the inversion becomes relatively depleted in reactive organic compounds, including many aromatics, during daytime when photochemistry is active. Concentrations of reactive aromatics also decrease over inversion episodes relative to the concentrations of less reactive compounds (Koss et al., 2015).

Urban areas and oil-producing areas have higher concentrations of alkenes than gas-producing areas (Lyman et al., 2014).

Even though aromatic compounds are more reactive, less-reactive light alkanes (including propane, butanes, and pentanes) are much more abundant in the atmosphere and are responsible for a larger percentage of ozone production (Koss et al., 2015; Lyman et al., 2014).

Areas of the Uinta Basin with more oil wells have higher ambient concentrations of reactive organic compounds than areas with more gas wells.  These compounds include ethylene, propylene, and formaldehyde. Nnatural gas-fueled engines may be the source of these compounds (Lyman et al., 2021).

Much has been done to better understand the composition of organic compound emissions from the oil and gas industry, including as study led by the Utah Division of Air Quality (Wilson et al., 2020).  Many Uinta Basin-specific speciation profiles are available in the official EPA SPECIATE database ( 

The buildup of methane in the Uinta Basin atmosphere during inversion episodes has been detected from the TROPOMI satellite instrument (de Gouw et al., 2020).

Reactive Nitrogen

Nitrous acid (HONO) concentrations in the Uinta Basin atmosphere are relatively low, and HONO photolysis is not a major source of radicals (Wild et al., 2016). 

The highest concentrations of NOx are found in the populated areas of the Uinta Basin (Lyman et al., 2014; Schnell et al., 2016). 

Unlike organic compounds, NOx concentrations don’t tend to increase under inversion episodes, relative to inversion-free periods (Wild et al., 2016). 

Atmospheric concentrations of reactive nitrogen follow temporal trends in oil and gas production. From 2012 through 2018, concentrations of total reactive nitrogen (corrected for meteorological influences) in Wonsits Valley, where natural gas development is prominent, follow trends in natural gas production. Concentrations in Roosevelt, which is in proximity to oil development, follow trends in oil production. 

During winter inversions, measurements of NO
x at regulatory monitoring stations do not provide useful information. The measurement systems used at these stations become contaminated with other reactive nitrogen compounds, and so tend to be biased high during wintertime inversions, when levels of those other compounds are high (Lyman and Tran, 2015; Mansfield and Lyman, 2020). 

N2O5 formed from NO2 at night during inversions acts mostly as a sink of NO2 because the N2O5 formed is largely converted to nitric acid.  Most nitric acid formation during inversions occurs from nighttime chemistry.  Nighttime N2O5 formation is the most important removal process for NOX (Lee et al., 2015; Wild et al., 2016) 

Peroxyacyl nitrates (PANs) and nitric acid are the most abundant non-NOx reactive nitrogen species (i.e., NOZ) during inversion episodes (Wild et al., 2016). Both are photochemically formed. Snow cover and very cold temperatures inhibit nitric acid deposition to the ground, and cold temperatures keep PANs from disassociating.

Particulate Matter

Particulate matter (PM5) concentrations increase above background levels during winter inversion episodes in the Uinta Basin, but the Uinta Basin is not in danger of exceeding EPA standards for particulate matter.

The highest concentrations of PM5 during inversion episodes have been observed in populated areas, though increases occur in oil and gas producing areas as well (Lyman et al., 2016).

The largest component of particulate matter in the Uinta Basin is organic compounds. This is true in populated areas and in oil and gas producing areas (Lyman, 2015a; Martin et al., 2011).

Computer Simulations

Meteorological Simulations

Three-dimensional simulations of meteorology are required inputs to photochemical air quality models that are used by regulators and industry to make pollution control decisions.

All the characteristics of inversion layers presented above are typical meteorological aspects of a stable planetary boundary layer in complex terrain and are strongly impacted by micro-scale physical processes rather than just by mesoscale weather patterns that state-of-the-art three-dimensional meteorological models (e.g., Weather Research and Forecasting (WRF)) were designed to simulate. Thus, modeling studies of Uinta Basin winters using WRF as a meteorological model (Ahmadov et al., 2015; Matichuk et al., 2017; Neemann et al., 2015; Tran et al., 2018) are not able to accurately reproduce all the characteristics of inversion layers in the Uinta Basin.

Meteorological models tend to produce warm temperature biases both at the surface and above the ground, overpredicting warm clouds and underpredicting ice clouds, overpredicting inversion depth and underpredicting inversion strengths, and producing down-slope flow at night that is too strong. Those inaccuracies likely negatively affect the ability of photochemical models that rely on meteorological model output to simulate ozone formation in the Uinta Basin.

Standard meteorological models (WRF) poorly represent surface characteristics during winter inversions. Neemann et al. (2015) used observed data to create a prescribed snow cover to correct snow depth and snow water equivalent in their WRF/CMAQ simulation for winter 2013. They also corrected land use parameters for better surface albedo estimation. Manual corrections had to be made to land use data for model applications in the Salt Lake and Utah Valleys to accurately characterize winter conditions in the Great Salt Lake and surrounding urban areas (Foster et al., 2017b).

Data assimilation using satellite-based data is a promising approach to improve photochemical model performance. SNOw Data assimilation System (SNODAS) has been applied for modeling studies in Uintah Basin (Matichuk et al., 2017; Tran et al., 2018). In a study that is unrelated to the Uintah Basin, Ran et al. (2016) demonstrated that incorporating MODIS data, such as leaf area indexes (LAI) and the fraction of absorbed photosynthetically active radiation (FPAR), to the WRF/CMAQ model led to a decrease in ozone deposition velocity and therefore less ozone removal. Tang et al. (2017) utilized MODIS aerosol optical depth to improve CMAQ’s PM5 model performance. Data assimilation approaches in these studies could be applied for modeling exercises in the Uinta Basin. Matichuk et al. (Matichuk et al., 2017) demonstrated that predicted ozone in the Basin increases with the arbitrary decreases in LAI, vegetation index and dry deposition velocity.

Applying observational four-dimensional data assimilation (FDDA) (or nudging) in meteorological simulations improves model performance by correcting warm temperature biases for surface temperature, producing more realistic inversion depth, inversion strength and advection patterns compared to simulations using default configurations (Tran et al., 2018). Nudging is a process by which the model uses measurement or other data to correct itself, thereby making model outputs more realistic. However, observational nudging approaches have not yet been able to correct the overestimation of warm cloud seen in default model configurations, and more work is needed to produce a three-dimensional meteorological model that faithfully reproduces all aspects of inversion episodes that are essential to winter ozone production.

Air Chemistry Simulations

High ozone episodes that occurred during winter 2013 have been the focus of most photochemical modeling efforts to date (Ahmadov et al., 2015; Matichuk et al., 2017; Tran et al., 2015; Tran et al., 2018). Winter 2013 had intense inversion conditions and high ozone concentrations, and during this winter season a number of research groups collaborated in intensive measurement campaigns (Stoeckenius et al., 2014), providing a large dataset for comparison with model results. Thus, the ozone episodes during winter 2013 provide good benchmarks for evaluating photochemical model performance.

Many photochemical modeling studies suggest that discrepancies in emission inventories, including overestimation of NOx emissions and underestimation of organic compound emissions, are one of the major reasons for negative biases in predicted ozone concentrations (Ahmadov et al., 2015; Emery et al., 2015; Matichuk et al., 2017). To obtain simulated ozone concentrations closer to observed values, researchers have scaled up organic compound emissions (Matichuk et al., 2017) or scaled up organic compound emissions and scaled down NOx emissions (Ahmadov et al., 2015; Emery et al., 2015). It is also possible that inadequate representation of meteorological conditions during winter inversions is responsible for poor model performance with respect to ozone formation (Tran et al., 2018).

Other modeling studies have shown that ozone concentrations similar to observed values could be simulated using available emissions inventories with speciation profiles (i.e., the composition of organic compounds in the inventory) that included a relatively high proportion of compounds that are very active in ozone production, especially formaldehyde (Lyman, 2015b; Neemann et al., 2015). Increasing the proportion of formaldehyde, rather than increasing the total amount of organic compound emissions, was enough to bring the predicted ozone in line with observed values. Currently, field measurements of carbonyl emissions do not support the level of formaldehyde emitted in these studies (Lyman, 2015), but emissions information for carbonyls is far from complete.

Different modeling platforms have been used in modeling studies, including CMAQ (Lyman et al., 2016; Matichuk et al., 2017; Tran et al., 2015; Tran et al., 2018), CAMx (Emery et al., 2015) and WRF-Chem (Ahmadov et al., 2015). No study has been conducted to compare the performance of these model platforms in simulating winter ozone in the Basin.

The majority of modeling studies in the Uinta Basin have utilized the Carbon Bond chemical mechanism version 5 (CB05) in CMAQ. The CB6 mechanism, which was first made available in CAMx in 2010, has been updated for winter conditions in the Uinta Basin by Emery et al. (2015). The CB6 mechanism was not available for the CMAQ model platform until version 5.2, which was released in 2017. Modeling studies that compare CMAQ model performance using CB6 over CB05 are still limited, although several studies for summer ozone episodes have suggested better performance in simulating ozone and other chemicals was achieved with the CB6 mechanism (Appel et al., 2016; Sarwar et al., 2013). On the other hand, Emery et al. (2015) reported that predicted ozone by CAMx with the updated CB6 mechanism is 1-3 ppb lower than with the CB05 mechanism. This fact is due to the CB6’s updates to organic nitrate production, which increases as temperature decreases and ultimately leads to a decrease in ozone production. Emissions inventories, including the EPA 2011 National Emissions Inventory and later versions, have also formatted for the CB6 mechanism. Most future modeling studies will likely utilize the CB6 mechanism. SAPRC07 is another popular chemical mechanism for the CMAQ and CAMx model platforms. It has also been adapted for low temperature conditions in the Uinta Basin (Tran et al., 2015).


Air Quality Forecasting and Hindcasting

Regression models of the Uinta Basin ozone system are able to predict when high ozone is likely to occur (Mansfield, 2017; Mansfield and Hall, 2013). Based on a “training set” of ozone data, currently consisting of every winter day from winter 2010 to winter 2018, these models are able to identify the impact of different meteorological variables on ozone levels and to predict the ozone concentration given arbitrary values of the same variables. 

Winter ozone regression models can be used with meteorological forecast data to make a forecast of ozone concentration 24 to 48 hours in advance (Lyman et al., 2017a).

Regression models can also be used in “hindcast” mode. If all the necessary input meteorological variables are available for any given day in the past, the ozone concentration that would have formed on that day, assuming modern oil and gas activity, can be predicted.  This allows one to make statistical estimates about the modern ozone system by accumulating averages over thousands of winter days, assuming each past winter season to be a representative sample.  For example, the Uinta Basin was declared to be in marginal non-attainment based on the three-year average of the fourth-largest daily ozone concentration from calendar years 2015, 2016, and 2017.  The percentage odds of receiving a given non-attainment classification over any given three-year period, using ozone data from 2017 and earilier, is (Lyman et al., 2017a):

  • Attainment: 33.7%
  • Marginal: 28.6%
  • Moderate: 25.4%
  • Serious: 9.9%
  • Severe: 2.3%
  • Extreme: 0.0%
These values have shifted in recent years as wintertime ozone has become less common (Mansfield and Lyman, 2021).  Attainment of the standard is now much more likely.

Health Impacts

Ozone negatively impacts respiratory health. Children, the elderly, and those with respiratory diseases are most vulnerable. For detailed information about the health impacts of impaired air quality, see

Asthma cases at a Uinta Basin hospital were not correlated with ozone concentrations, probably because of the small population size and the confounding influence of other triggers of asthma attacks (Hall et al., 2013). A study in Pinedale, Wyoming, that considered all respiratory ailments did show a correlation between winter ozone and respiratory conditions (Pride et al., 2015).

Adverse birth outcomes do not occur at a greater frequency in the Uinta Basin compared to the rest of the state. The rate of adverse birth outcomes in the Uinta Basin is lower than the national average (UDOH, 2015; UDOH, 2017).

Concentrations of benzene in Roosevelt are higher than thresholds for cancer risk established by EPA and are similar to urban areas in the United States (Hall et al., 2013; Lamb, 2017).


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