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Oct 9, 2015 - observational data to quantify the turbulent mixing during two CAP episodes in Utah's Salt Lake Valley during February of 2004. Particul...
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Turbulent Fluxes and Pollutant Mixing during Wintertime Air Pollution Episodes in Complex Terrain Heather A. Holmes,*,† Jai K. Sriramasamudram,‡ Eric R. Pardyjak,‡ and C. David Whiteman¶ †

Atmospheric Sciences Program, Department of Physics, University of Nevada, 1664 N. Virginia Street MS0220, Reno, Nevada 89557, United States ‡ Department of Mechanical Engineering and ¶Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah 84112, United States ABSTRACT: Cold air pools (CAPs) are stagnant stable air masses that form in valleys and basins in the winter. Low wintertime insolation limits convective mixing, such that pollutant concentrations can build up within the CAP when pollutant sources are present. In the western United States, wintertime CAPs often persist for days or weeks. Atmospheric models do not adequately capture the strength and evolution of CAPs. This is in part due to the limited availability of data quantifying the local turbulence during the formation, maintenance, and destruction of persistent CAPs. This paper presents observational data to quantify the turbulent mixing during two CAP episodes in Utah’s Salt Lake Valley during February of 2004. Particulate matter (PM) concentration data and turbulence measurements for CAP and non-CAP time periods indicate that two distinct types of mixing scenarios occur depending on whether the CAP is dry or cloudy. Where cloudy, CAPs have enhanced vertical mixing due to top-down convection from the cloud layer. A comparison between the heat and momentum fluxes during 5 days of a dry CAP episode in February to those of an equivalent 5 day time period in March with no CAP indicates that the average turbulent kinetic energy during the CAP was suppressed by approximately 80%.

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INTRODUCTION Several cities in Utah (i.e., Salt Lake City, Ogden, Logan, and Provo) are in nonattainment for at least one National Ambient Air Quality Standard (NAAQS) criteria pollutant.1 Approximately 2.5 million people live in these Utah cities, which are located in complex, mountainous terrain with meteorology favorable for the formation of surface-based temperature inversions, where the air on the ground is colder than the air above.2 In complex terrain, inversions can occur daily, typically forming at or near the ground in the early evening and being destroyed in the morning when surface heating produces convection that erodes the stable nocturnal layer from below. During wintertime, however, persistent or multiday inversions or cold air pools (CAPs) can form within the topography when background winds are weak. In particular, CAP periods are associated with stagnant conditions when slow-moving, highpressure systems dominate the winter climate, and the air quality is worsened by snow cover and lower sun angles that decrease the insolation needed to initiate convection and mixing.3 CAPs can last for days or even weeks, leading to an accumulation of pollutants in the atmosphere.4−9 The temperature inversions during persistent CAPs can be either surfacebased or elevated.10 Due to a variety of reasons (i.e., continentality, orography, or meteorology), these thermal inversions tend to occur in intermountain regions.11,12 The accumulation of pollutants in the atmosphere during persistent CAPs leads to concentrations that can be harmful to human health. Pope et al.13 show a correlation between short-term © XXXX American Chemical Society

exposure of PM2.5 and cardiovascular effects in patients in the Salt Lake Valley (SLV), with an emphasis on acute wintertime episodes of elevated PM2.5. This phenomena is not limited to Utah; there are several cities in the western United States located in intermountain regions with terrain that is conducive to CAP formation and increased PM concentrations (i.e., Los Angeles, Sacramento, Phoenix, Las Vegas, Reno, Denver, and Boise). The many sources of air pollution emissions in urban areas lead to a buildup of pollutant concentrations in CAPs due to reduced mixing and dispersion. This can have an impact on both health and human activities. For example, transportation is commonly disrupted during persistent CAPs because episodes are often accompanied by periods of low clouds and fog, which greatly reduce visibility. Increased pollutant concentrations and reduced visibility associated with CAPs have been documented in regions throughout the western United States, including Denver,14 Boise,8,9 the central valley of California,15,16 and cities in Oregon17 and Utah.3,5,6,18,19 Understanding the fundamental nature of CAPs is not only essential for air-quality forecasting but also for the siting of new businesses, industries, and transportation facilities.20 Received: May 28, 2015 Revised: September 29, 2015 Accepted: October 9, 2015

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required ad hoc turbulence model modifications. Specifically, vertical mixing was turned off in CMAQ to achieve results that more closely reflect the observed PM concentrations during wintertime temperature inversions. For the improvement of atmospheric models, observational data are required to update the empirical parameterizations. There have been many attempts to characterize air flow patterns in the western United States using both archived meteorological data and field campaigns targeted at characterizing air flow and mixing.25−29 These studies characterized pollutant transport in mountainous regions during nonwinter conditions when daily convective boundary layers (CBL) destroyed nighttime inversions. In contrast, there have been fewer studies aimed at characterizing boundary layer evolution and airflows during wintertime CAPs. Two such studies have been conducted, however, in the SLV to investigate pollutant mixing and accumulation; the Urban Trace-gas Emissions Study (UTES)10,30 and the Persistent Cold-Air Pool Study (PCAPS, www.pcaps.utah.edu).19 The previously published UTES study used carbon dioxide mixing ratios and vertical temperature profiles to characterize mixing during CAP periods but did not quantify atmospheric turbulence. The PCAPS study investigated the complex atmospheric dynamics associated with CAP formation and destruction, but no turbulence data have yet been published for PCAPS. Additional understanding of mixing and pollutant accumulation during persistent winter CAPs is needed because these time periods pose the greatest risk to human health (i.e., 24 h PM2.5 exceeding 35 μg m−3).13 Meteorological data combined with turbulence information and continuous pollutant concentration data are required to investigate the physical properties of mixing in cold air pools. This paper investigates the turbulent fluxes of heat and momentum during persistent wintertime CAP episodes in the SLV and relates the findings to continuous pollutant data from ambient monitoring networks. The objectives of the study were to (1) compare fluxes during CAP and non-CAP periods; (2) determine the magnitude of turbulence during CAPs; (3) investigate the temporal variations of momentum, heat, and moisture fluxes and turbulent kinetic energy (TKE); and (4) correlate PM concentration buildup with reduced atmospheric turbulence during CAP periods. Data were collected in the SLV during two persistent CAP episodes that were observed from February 12 to February 16 and from February 20 to February 24 in 2004. The period between the two CAP episodes was characterized as a “mix-out” period and will be discussed further in the results.

Challenges preventing atmospheric models from adequately capturing these air pollution episodes in the western United States are summarized by Baker et al.17 We illustrate this by comparing observations in the Salt Lake Valley to air quality simulations using the U.S. Environmental Protection Agency’s Community Multiscale Air Quality Model (CMAQ), which uses meteorological outputs from a numerical weather prediction (NWP) model and emissions modeling to obtain spatiotemporal fields of species concentrations.21 The CMAQ model incorporates emissions estimates and simulates the physical and chemical processes in the atmosphere without nudging or calibrating the model with observations. Because of the low resolution of the model and inadequate parameterizations of local-scale physical processes, predictions of concentrations in valley cold air pools have high uncertainties. This is illustrated for February 2004 in Figure 1, which shows

Figure 1. Time series of 24 h PM2.5 data at three locations in Northern Utah for modeled (dashed lines) and observed (solid lines) concentrations (see map in Figure 2). The 24 h PM2.5 National Ambient Air Quality Standard is also indicated on the graph. Observations are FRM mass concentrations,34 and numerical results are for 36 km resolution for the continental United States.22

modeled and observed PM2.5 concentrations at three locations along the Wasatch Front at the Logan, Salt Lake, and Lindon sites during a period affected by intermittent cold-air pools. The locations of the three sites are shown in Figure 2. These CMAQ results were accessed from the Public Health Air Surveillance Evaluation (PHASE) project at the Center for Disease Control and Prevention (CDC),22 which uses CMAQ results to develop spatially resolved exposure metrics to investigate the impact of air pollution on human health. The PHASE project evaluated CMAQ results for the continental United States over a 1 year time period, finding that performance statistics were within expected limits. However, if the region and time period of interest are limited to the Wasatch Front during February of 2004, the model performance is seen to degrade and to result in evaluation statistics that are not within the expected range of evaluation metrics found in the literature.23 This is consistent with modeling results in the 2013 Utah Division of Air Quality State Implementation Plan (UDAQ-SIP), where adjustments were made to the emissions inventory data and meteorology inputs to evaluate the uncertainties in the numerical modeling system to improve CMAQ predictions.24 Ultimately, to account for discrepancies between the model results and observations, the SIP modeling

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METHODS The Salt Lake Valley is a partially enclosed valley with the Wasatch Mountains to the east, Oquirrh Mountains to the west, and Traverse Mountains to the south. The mountains create a basin where cold air accumulates on the valley floor, particularly during the winter. The Salt Lake City urban area is located in the northern part of the SLV, and the population of the entire valley is just over 1 million people. This urban area is a significant source of air pollution emissions. Meteorological data (SLC Airport), turbulence data (Kennecott), and air quality data (Hawthorne) are available for the SLV (Figure 2). All data are in Mountain Standard Time (MST). Micrometeorology Measurements. Micrometeorological measurements were made at the Kennecott site (40.5384 °N, 112.0696 °W, 1585 m above sea level) at the southwest end of the SLV in the foothills of the Oquirrh Mountains. A complete B

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Figure 2. Map showing monitoring locations in Northern Utah. Time series of hourly (line) and 24 h (bar) PM2.5 concentrations during the month of February 2004 consisting of two cold air pool periods and one mix-out period. Note: Hourly data from the UDAQ filter dynamics measurement systems−tapered element oscillating microbalance with a 30 °C inlet in Salt Lake City, Hawthorne site and 24 h concentrations are averages of the hourly FEM data. Photo images are copyright Tim Brown and time-science.com. Map data are copyright Google, 2015.

site description can be found in Ramamurthy and Pardyjak.31 This rural site is characterized by shrubs and grass measuring up to 0.5 m with a fallow agricultural field situated north and northwest of the site. The Trans-Jordan landfill is located approximately 1 km to the northeast, and the Kennecott Copper Mine is southwest of the site. Because the site is located at the foot of the Oquirrh Mountains, typical diurnal wind patterns are often observed under fair weather conditions, with upslope (anabatic) flow during the daytime and downslope (katabatic) flow during nighttime.32 The Kennecott site was equipped with three Campbell Scientific Inc. (Logan, UT) CSAT-3 sonic anemometers and thermometers, which measured three components of wind speed and sonic temperature at a frequency of 10 Hz. The sonic anemometers were mounted on a 15 m tower constructed over a 2 m tall platform, with measurements at 5, 7, and 9 m above ground level. A closed-path LICOR 7000 (Lincoln, NE) infrared gas analyzer (IRGA) was used to monitor carbon dioxide (CO2) and water vapor concentrations at 10 Hz. Air was drawn into the IRGA via a 7 m long tube with the inlet colocated with the 9 m CSAT measurement volume. A laminar flow (Re = 375) was maintained in the tubing with a flow rate of approximately 1.5 L min−1. Mean and turbulence quantities from the LICOR and sonic anemometer data were computed using a 5 min linear detrending window to determine turbulent fluctuations. Averaged quantities were calculated over a 30 min time period. The planar fit method by Wilczak et al.33 was used to remove the effects of surface inclination. Routine Monitoring Network Data. A regulatory air monitoring network operated by the Utah Division of Air Quality (UDAQ) and a routine meteorological monitoring network were used to investigate pollutant mixing and to classify the synoptic weather conditions during the experiment. Time series data of meteorological variables such as temper-

ature, humidity, wind speed, and direction and twice-daily rawinsonde soundings from Salt Lake City International Airport (1288 m above sea level) were used in the analysis. Ambient air quality observations were collected by UDAQ and accessed through the EPA Air Quality System (AQS) monitoring network.34 The PM data from the air quality monitoring stations used in this paper were obtained using two data collection methods. Mean daily PM2.5 concentrations (midnight to midnight MST) were collected on filters using the Federal Reference Method (FRM). Additionally, hourly PM2.5 concentrations were obtained using a Federal Equivalent Method (FEM) with a filter dynamics measurement systems−tapered element oscillating microbalance (FDMS− TEOM) with an inlet heated to 30 °C. The hourly concentrations are shown in Figure 2 but are not used quantitatively in the Results section because the heated inlet TEOM measures lower PM mass concentrations than the FRM due to losses from volatile compounds.35

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RESULTS

Comparison of Noncloudy and Cloudy Cold Air Pools. A total of two 5 day long CAPs occurred during February of 2004, the first (CAP1) being from February 12 to February 16 and the second (CAP2) being from February 20 to February 24. Environmental conditions for the two CAPs are shown in Figure 3. These CAPs were separated by a mix-out (February 17−19) associated with low-altitude warm air advection followed by the passage of a cold front.36 Surface wind speeds were weak during both CAP episodes, with variable wind directions. A temporal increase in atmospheric stability occurred within both CAPs, driven primarily by warm air advection aloft, as indicated by increases in potential temperature in the upper valley atmosphere at 2200 m MSL. Stability C

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weak diurnal surface temperature oscillation of 7 or 8 °C, clear or partly cloudy skies with no cloud ceilings within the valley, and daily total global solar radiation of 10−14 MJ m−2. Relative humidity varied from 40 to 90%. Daily PM2.5 concentrations increased monotonically from 35.4 to 94.2 μg m−3 during the 5 day episode. The 94.2 μg m−3 value is the highest daily concentration value ever reported at Hawthorne since observations were begun on January 1, 1999. Snow was present on the valley floor and sidewalls during this episode. The second or moist episode, in contrast, had warmer temperatures, weaker day−night temperature oscillations, generally cloudy skies with cloud ceilings within and just above the valley, correspondingly lower daily incoming solar radiation, higher relative humidity, and lower daily PM2.5 concentrations that varied little over the last 4 days of the episode (∼60 μg m−3). The atmospheric stability was weaker than for CAP1 with lower valley heat deficits. The valley floor was snow-free during this episode. Diurnal variations in near-ground atmospheric stability and near-ground stability variations between the dry and moist CAPs are expected to affect vertical mixing within the cold-air pools. These day−night and dry−moist CAP variations are illustrated in Figure 4 using selected radiosonde soundings from

Figure 4. Sample day and night temperature (solid lines) and dew point temperature (dashed lines) soundings during the two CAP episodes (Feb 13 at 5 MST and 17 MST for CAP1; Feb 21 at 17 MST and Feb 22 at 5 MST for CAP2). Shown for comparison is the dry adiabatic lapse rate, Γd = 9.8 °C km−1, and the approximate ridge line height on the west side of the SLV. Figure 3. Time series of meteorological and air quality data for Feb 2004. Hourly (a) wind speed, (b) wind direction, and (c) temperature at Hawthorne. Hourly (d) relative humidity and (e) cloud ceiling heights (i.e, broken, overcast, or obscured) from SLC airport. Symbols are omitted for hours without a cloud ceiling. Twice-daily (f) potential temperatures at the surface and 2200 mMSL and (g) valley heat deficits from radiosondes at SLC airport. (h) Daily total global radiation at SLC airport and (i) daily FRM PM2.5 concentrations at m MSL cp ρ(z)[θ2200 Hawthorne. Valley heat deficit4 is computed as ∫ 2200 sfc −3 − θ(z)]dz, where ρ is air density (kg m ), cp is the specific heat of air (J kg−1 K−1), θ is potential temperature (K), and z is altitude (m).

the two CAPs. CAP1 was dry, as evidenced by the large dew point depressions. It also experienced strong subsidence heating. The nighttime sounding had a strong inversion in the near-ground layer that would inhibit vertical mixing, but the afternoon sounding had a 150 m deep neutral or dry adiabatic layer caused by convection from the heated ground. This convection occurred despite the snow cover and would produce enhanced vertical mixing during daytime. CAP2 was a moist CAP with generally cloudy skies and no snow cover. Small dew point depressions illustrate the high moisture contents throughout the sounding depths. In the lower valley atmosphere, near-moist adiabatic temperature profiles were present in the lowest 300 m surmounted by elevated temperature inversions. The moist adiabatic layers are thought to be produced by top-down convection2 (i.e., the downward transport of cold air formed by long wave radiation loss both day and night from the cloud tops). Investigators in both the

increases were associated with the approach from the west of high-pressure ridges. The first or dry episode was a classical dry cold-air pool characterized by a surface-based temperature inversion and a strong valley heat deficit4 (a vertically integrated measure of valley atmospheric stability), cold temperatures, a regular but D

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Figure 5. The 30 minute (a) mean wind speed, (b) temperature, (c) sensible heat flux, and (d) turbulent kinetic energy from Feb 11 to Feb 29, 2004, measured at 9.16 m above ground level at the Kennecott site. The shaded regions represent the two persistent CAP episodes.

Salt Lake Valley10 and in the Boise, Idaho basin9 have suggested that vertical mixing will be enhanced in the near-moist-adibatic layer that forms below stratiform clouds within valley CAPs, as compared to noncloudy CAPs with their generally stronger stability. This convection would enhance vertical mixing in the lower portion of cold-air pools during both day and night during cloudy periods. Soundings from the two deep CAPs support the idea that vertical mixing in the near-ground layer may differ significantly between dry and moist CAPs and, during dry CAPS, between night and day. Temporal variations in PM2.5 concentrations (Figure 3) differ between CAP1 and CAP2. Daily PM2.5 concentrations increase monotonically throughout CAP1 but level off during CAP2 when top-down convection produces enhanced mixing below the cloud top. The rates of increase of concentration from the day before the episode to the final day of the episode are 12.3 and 9.6 μg m−3 day−1 for CAP1 and CAP2, respectively. These growth rates are similar to the previously reported values of 65 and 9−11 μg m−3 day−1 4 for the SLV, although computed with a somewhat different definition of episode length. The buildup of CAP2, however, occurs during the first 2 days at a rate of 20.3 μg m−3 day−1 and levels off at approximately 60 μg m−3 from the third to the fifth day. Few micrometeorological or turbulence measurements are available for cold-air pools.37,38 In the present case, however, sonic anemometer data are available from the Kennecott site to compute momentum fluxes, heat fluxes, turbulent kinetic energy, and sonic temperature variance at high sampling frequency. Averaged data from the sonic anemometer can also be used to investigate meteorological variables such as mean wind speed, wind direction, and temperature. A time series of

30 min averaged measurements of mean wind speed (U, m s−1), mean sonic temperature (Ts, °C), sensible heat flux (HS, W m−2) and turbulent kinetic energy (TKE, m2 s−2) at the Kennecott site are shown for the period from February 11 to February 29, 2004 in Figure 5. The lowest wind speeds occur during the CAP1 and CAP2 episodes as mountains act as barriers and stability inhibits the vertical mixing of stronger winds from aloft into the valley. The advection of warm air from the south caused the surface wind speeds to increase on February 17 and again on February 25. While the temperatures were low during both CAP periods, the temperatures during CAP2 were higher than for CAP1 (0 °C versus −10 °C). Warm air advection produces the warmer temperatures during CAP2. Radiation from the sun heats the earth’s surface during daytime, generating warm thermals that raise the temperature of air near the surface. These thermals carry heat from the surface to the surrounding air due to the temperature gradient between the air and the surface (i.e., convection). The vertical turbulent sensible heat flux near the surface is given by HS = ρcpw′T′, where ρ is air density and cp is the specific heat of air at constant pressure. The turbulent fluctuations of vertical velocity and temperature are given by w′ and T′, respectively. The flux of sensible heat is positive when sensible heat is transferred away from the surface and negative when it is transferred from the air toward the surface. Figure 5c shows the 30 min averaged sensible heat flux for February 11−29, 2004. Low daytime values of HS (i.e., less than 100 W m−2 versus the typical daytime maximum values >125 W m−2) during the CAP episodes indicate that the absorbed solar radiation was not sufficient to increase the surface temperature and produce the convection necessary to destroy the persistent CAP. The E

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Figure 6. Daily ensemble averaged data from Feb 11 to Feb 15 (CAP1 period) and Mar 11 to Mar 15 (non-CAP) showing meteorological variables: (a) temperature, (b) normalized temperature, (c) wind speed, and (d) wind direction and turbulence quantities: (e) sensible heat flux, (f) latent heat flux, (g) turbulent kinetic energy, and (h) friction velocity, measured at 9.16 m above ground level at the Kennecott site.

added to destroy the CAP. High negative or downward HS values on February 17, 18, and 26 are due to the advection of warm air over the cool surface. The 30 min HS data show the diurnal variations, where a typical diurnal HS cycle can be seen on February 26 with a daytime maximum exceeding 100 W m−2 and a slight nighttime negative value caused by radiative cooling at the surface. The daytime maximum HS values during CAP1 do not exceed 50 W m−2, while during CAP2, the maximum HS

threshold for CAP destruction can be investigated by comparing the integral of the sensible heat flux (HS) from sunrise to sunset to the heat deficit (shown in Figure 3). If the HS integral is less than the heat deficit the CAP will persist, the integral HS values for CAP1 are less than 0.5 MJ m−2, and for CAP2, less than 1.2 MJ m−2. By comparing these values to the heat deficit values in Figure 3 (CAP1, 2−6 MJ m−2, and CAP2, 2−4 MJ m−2), it can be surmised that there is not enough heat F

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Environmental Science & Technology is greater (≈ 75 W m−2). This indicates that there is more energy added to CAP2 and, therefore, more convection during the second CAP. This is evident in the increased temperature during CAP2 and supports the findings that the two CAP periods have two distinct mixing characteristics, as discussed above, where CAP2 has increased mixing due to top-down convection from the cloud layer and CAP1 has inhibited mixing that is enhanced by the snow cover. Turbulent kinetic energy is a direct measure of the turbulence intensity and is related to momentum, heat, and moisture transport through the boundary layer.39 Turbulent 1 kinetic energy is defined as TKE = 2 (u′2 + v′2 + w′2 ), where u′ and v′ are the turbulent fluctuations of wind velocity components (m s−1) in the along-wind and cross-wind directions, respectively. The maximum value of TKE during a typical daytime period is approximately 1 m2 s−2.39 Figure 5d shows a time series of 30 min mean TKE from February 11 to February 29 where low values of TKE (less than 0.2 m2 s−2) during both persistent CAP episodes indicate low turbulence levels in the boundary layer. Advection of warm air from the south on February 17, 18, 25, and 26 caused an increase in TKE. The daytime TKE is increased compared to nighttime values due to an increase in convective mixing. Comparison of CAP and Non-CAP Periods. Micrometeorological variables for CAP1 from February 11 to February 15, 2004 are compared to the micrometeorological variables from March 11 to March 15, 2004. This time period in March was not under the influence of a persistent CAP, and while the data were collected one month after CAP1, the synoptic conditions for both time periods were similar (i.e., high-pressure systems with strong diurnal cycles). Because the non-CAP time period in March was quiescent, it provided an ideal period for comparison with CAP episodes. A diurnal average was computed using an ensemble average of the 30 min data for each day of the 5 day time period. The ensemble averages were computed for both the February and March episodes to compare diurnal cycles for the CAP1 and non-CAP periods. The variation of mean temperature over the 5 day periods in February and March 2004 are shown in Figure 6a. The March non-CAP period was approximately 15 °C warmer than the CAP1 period. To investigate the variability or range of mean temperature during the CAP and non-CAP periods, we normalized the temperatures by the corresponding mean temperatures for the entire data period (Ts − mean(Ts)) shown in Figure 6b. The CAP1 episode has a much smaller diurnal temperature range than the non-CAP episode. Figure 6e depicts the variation of sensible heat flux during a persistent CAP compared to the sensible heat flux variation during a non-CAP period. The presence of high albedo snow during the CAP1 period reduced the fraction of incoming solar radiation available to heat the surface. As a result, small magnitude sensible heat fluxes were observed during CAP1. Even though the valley weather during the CAP1 and non-CAP periods shown was characterized by clear skies, the average daytime sensible heat flux during CAP1 was only 10.0% of the average sensible heat flux for the non-CAP period. The Kennecott site was located at the west−southwest end of the valley at the foot of the Oquirrh mountains. Under weak synpotic forcing, thermally driven winds dominate; these winds are easterly and southeasterly during the day and westerly at night (see Figure 6d). The much stronger sensible heating of

the air during the non-CAP period (Figure 6e) led to growth of the convective boundary layer and stronger daytime (thermally driven) wind speeds compared to those in CAP1 (Figure 6c). During CAP1, nighttime wind speeds were similar to the nonCAP period but were greater than during the daytime, indicating that the nighttime downslope flows were stronger than the daytime upslope flows. Similarity in the diurnal wind directions for the CAP and non-CAP periods indicates that the thermally driven upslope and downslope (east−west) wind systems dominated over the up-valley and down-valley (north− south) winds at the Kennecott site. Latent heat flux (W m−2) is the result of water phase change and the transport of moisture from the surface to the atmosphere. It can be computed using high-frequency measurements from the sonic anemometer and IRGA as HL = ρLv(w′q′), where Lv is the latent heat of vaporization and q′ is the turbulent fluctuation of specific humidity (kg kg−1). The daytime maximum latent heat flux during the non-CAP was approximately four times the maximum latent heat flux during CAP1 (Figure 6f), while the average latent heat flux during CAP1 was 12.8% of the average latent heat flux during the nonCAP period in spite of snow coverage (i.e., ground moisture source) during CAP1. Insufficient availability of net radiation energy and weak winds are responsible for the low magnitudes of latent and sensible heat fluxes. Because the CAP1 and non-CAP time periods are one month apart, the theoretical daily total extraterrestrial solar radiation computed using Whiteman and Allwine’s40 model for the CAP1 period is only 74.2% of that for the non-CAP period. The integral of the sensible and latent heat fluxes from sunrise to sunset can be normalized by the theoretical solar flux to investigate differences in surface energy fluxes due to solar radiation. For CAP1, these values are 0.015 and 0.005 for HS and HL, respectively, indicating that the surface fluxes are small compared to the incoming solar radiation. The values for the non-CAP period are 0.127 and 0.033 for HS and HL. The comparison of the surface heat fluxes as a fraction of total incoming solar radiation between CAP1 and non-CAP shows that the decrease in CAP1 surface heat fluxes cannot be solely attributed to the reduction in the total incoming solar radiation from the CAP period being one month before the non-CAP period. Generally, solar radiation heats the surface leading to thermals that rise through the atmosphere. This process, coupled with mean wind shear, generates turbulence, typically leading to higher levels of TKE. Under stable conditions, air parcels that are vertically displaced by turbulence experience a downward buoyancy force, causing them to return toward their starting height. Low magnitudes of turbulent kinetic energy result from the suppression of atmosphere turbulence during these stable conditions. Figure 6g depicts the variation of TKE during the CAP1 and non-CAP episodes. TKE is suppressed throughout the CAP1 episode down to typical nocturnal levels. The average TKE over the entire CAP period was 79.5% less than the TKE for the non-CAP episode. The HS and HL measurements presented in this paper can be compared to surface fluxes calculated with improved land surface models that are components of regional weather and air quality models. The surface heat fluxes will be impacted by moisture, terrain, and surface type due to changes in albedo and heat capacity. An important quantity used in numerical applications to parametrize turbulent mixing is the friction velocity (u*, m s−1). The friction velocity can be combined with G

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stratified ABL conditions. The mesoscale phenomena associated with the CAP formation and destruction can be simulated adequately using NWP models.45,46 However, there is a large amount of uncertainty in the NWP models for the microscale physics, particularly the surface-based inversion during persistent CAP episodes.46 It is expected that the uncertainties in simulating the surface inversion will be propagated through to the air quality model leading to underprediction in concentrations during persistent CAP events because the vertical mixing is overpredicted. Hence, future work should specifically investigate the role of subgrid eddy diffusivity parameterizations on cold pool mixing (e.g., the PBL scheme and land surface model). The data presented in this paper represent an opportunity to make quantitative comparisons of surface heat fluxes, friction velocity, and turbulent kinetic energy.

similarity profiles to model the deposition and vertical mixing of pollutants in the atmosphere.11,41 Using the velocity fluctuations from a sonic anemometer, the friction velocity can be calculated using u* = (u′w′2 + v′w′2)1/4. The u* values for the CAP1 and non-CAP periods are compared in Figure 6h, where low friction velocities indicate that turbulence is being suppressed. The maximum daytime friction velocity for the CAP1 period is only 36.2% of that during the non-CAP period. Also, the clear diurnal pattern of u* during the non-CAP period is absent during the CAP1 episode.

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DISCUSSION The experimental investigations of CAP physics presented in this paper have important implications for numerical investigations of air quality and atmospheric physics in regions of complex terrain. Lee and Ngan42 reviewed the coupling of the physics in meteorological and chemical transport models (CTM) for continental air quality applications and stressed the importance of the planetary boundary layer (PBL) parameterizations. The focus of their review was to study the PBL parameterization schemes used to model turbulent mixing and dry deposition of pollutants in five air quality forecasting models. They concluded that future air quality forecasting and model improvements will require data assimilation techniques (e.g., soil moisture) and improvements in turbulent mixing parameterization schemes, including parameter optimization and uncertainty estimation. Parameter optimization has been described by Nielsen−Gammon et al.,43 who rigorously evaluated the sensitivity of the parametrized terms in the PBL equation to the simulated PBL height and surface temperature. They found that a hidden parameter in the formulation for the vertical eddy diffusivity equation had the most impact on PBL height and surface temperature. While this type of investigation is important, it does not address the fundamental issue that the empirical data used to develop the PBL equations and parameterizations are based on experiments in flat, idealized terrain.41,44 An example of the influence of mixing parameterizations on air quality prediction can be found in the 2013 Utah State Implementation Plan, which required the vertical mixing in the atmospheric model to be turned off to adequately model the pollutant concentrations during wintertime temperature inversions.24 These challenges are not isolated to Utah, and there are several regions in the western United States where atmospheric models do not adequately capture the mixing phenomena associated with these airpollution episodes.17 A finer vertical and horizontal grid resolution for the numerical weather prediction and air quality models might improve model performance, but the NWP model will still not be able to resolve atmospheric flows in complex terrain. This is because the model has problems simulating the micrometeorology and decreased mixing associated with increased PM concentrations during CAP episodes due to the PBL parameterizations.17 The PBL equations are simplified on the basis of Monin−Obukhov similarity theory and the equations make use of empirical data that were collected in regions with flat, homogeneous terrain and for an unstable or neutral ABL.44 The complex atmospheric flows experienced in regions with high relief require updated model formulations and parameterizations to simulate subgrid-scale atmospheric physics. To accomplish this, it is necessary to obtain atmospheric turbulence, surface flux, and air quality data from field experiments in regions that experience persistent stably

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AUTHOR INFORMATION

Corresponding Author

*Phone: +1 (775) 784-6712; fax: +1 (775)784-1398; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported by U.S. National Science Foundation grants ATM 02157658 (Pardyjak) and AGS 1160730 (Whiteman), as well as the Office of Naval Research award #N0001411-1-0709, the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program (Pardyjak and Whiteman). Solar radiation data were provided by the National Oceanic and Atmospheric Administration’s (NOAA) Integrated Surface Irradiance Study. Particulate and meteorological data from air quality stations were provided by Utah’s Division of Air Quality, and meteorological data from the airport were provided by NOAA’s National Weather Service.

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DOI: 10.1021/acs.est.5b02616 Environ. Sci. Technol. XXXX, XXX, XXX−XXX