Abstract

Many populated valleys in the western United States experience increased concentrations of particulate matter with diameter of less than 2.5 μm (PM2.5) during winter stagnation conditions. Further study into the chemical components composing wintertime PM2.5 and how the composition and level of wintertime PM2.5 are related to meteorological conditions can lead to a better understanding of the causes of high PM2.5 and aid in development and application of emission controls. The results can also aid in short-term air-pollution forecasting and implementation of periodic emission controls such as burning bans. This study examines relationships between PM2.5 concentrations and wintertime atmospheric stability (defined by heat deficit) during snow-covered and snow-free conditions from 2000 to 2013 for five western U.S. urbanizations: Salt Lake City, Utah; Reno, Nevada; Boise, Idaho; Missoula, Montana; and Spokane, Washington. Radiosonde data were used where available to calculate daily heat deficit, which was compared with PM2.5 concentration for days with snow cover and days with no snow cover. Chemically speciated PM2.5 data were compared for snow-cover and snow-free days to see whether the chemical abundances varied by day category. Wintertime PM2.5 levels were highly correlated with heat deficit for all cities except Spokane, where the airport sounding does not represent the urban valley. For a given static stability, snow-cover days experienced higher PM2.5 levels than did snow-free days, mainly because of enhanced ammonium nitrate concentrations. Normalizing average PM2.5 to the heat deficit reduced year-to-year PM2.5 variability, resulting in stronger downward trends, mostly because of reduced carbonaceous aerosol concentrations. The study was limited to western U.S. cities, but similar results are expected for other urban areas in mountainous terrain with cold, snowy winters.

1. Introduction

Wintertime suspended particulate matter with aerodynamic diameters of less than 2.5 μm (PM2.5) concentrations are frequently higher than normal in many western U.S. valleys. These valleys experience strong temperature inversions, frequently enhanced by the presence of snow cover, and are described as “valley cold pools” (Whiteman and McKee 1977, 1978; Reeves and Stensrud 2009; Silcox et al. 2012). Cold-pool stagnation allows a gradual buildup of pollutants over a period of several days. These accumulations are related to 24-h average PM2.5 concentrations of greater than 35 μg m−3 (Watson and Chow 2002; Gillies et al. 2010; Silcox et al. 2012; Wang et al. 2012), for which multiple occurrences result in violation of a National Ambient Air Quality Standard (NAAQS; EPA 2013). Owing to their cold temperatures and proximity to forests, many mountain valleys, especially those at high elevation, are affected by wintertime emissions from residential wood combustion (RWC) (Ward et al. 2004; Rinehart et al. 2006; Schreuder et al. 2006; Chen et al. 2007; Chow et al. 2007b; Ward and Lange 2010; Allen et al. 2011; Ward et al. 2012; Kelly et al. 2013). As a result of improved stove technology, homeowner education, and forecast-based burning bans, RWC emissions have decreased, but they are still important PM2.5 contributors in mountain valleys. Larger urban areas, such as Salt Lake City, Utah, have a broad mix of residential, industrial, and mobile source emissions that add to increased wintertime PM2.5 under stagnation conditions.

Previous studies (Wang et al. 2012; Chen et al. 2012b) related excessive PM2.5 levels to differences between surface and high-elevation temperatures and the “heat deficit,” defined as the heat necessary to dry adiabatically mix a layer of air. Chen et al. (2012b) found enhanced ammonium nitrate during periods of snow cover and strong inversions associated with PM2.5 of greater than 35 μg m−3 in Reno, Nevada. Whiteman et al. (2014) found that high PM2.5 concentrations in Salt Lake City were much more likely on days with snow cover than on days without snow cover.

The objective of this study is to investigate whether the conclusions noted above for Reno apply to other urban areas with differing population totals and densities, different emission characteristics, and differing topographic settings in the western United States. If similar results occur, an argument can be made that the relationships may be broadly applicable to other settings worldwide in urbanized valleys with cold snowy winters. In particular, this study investigates the following questions: 1) Is heat deficit related to PM2.5 concentration? 2) Does snow cover enhance PM2.5 concentrations, and, if so, is ammonium nitrate the main reason for the enhanced PM2.5? 3) Can the large year-to-year variability of average winter PM2.5 concentrations be reduced by controlling for average heat deficit, thus clarifying trends?

The analysis contrasts the Reno situation with those of Boise, Idaho; Missoula, Montana; Salt Lake City; and Spokane, Washington. These cities were selected for their data availability and their differing population, topographic, and emissions situations. The 2010 metropolitan statistical area populations for the cities ranged from 111 807 for Missoula to 1 124 197 for Salt Lake City. These cities also had periods of high wintertime PM2.5 that exceeded the current National Ambient Air Quality Standards. Wintertime PM2.5 averages are examined with and without normalizing for yearly variations in atmospheric stability to evaluate the effects of emission reduction measures.

2. Method

Meteorological and air-quality data were acquired for January, February, November, and December 2000–13 for the five cities. Atmospheric stability was characterized by the heat deficit for Boise, Reno, Salt Lake City, and Spokane and by differences between valley and ridge temperatures T for Missoula (where radiosondes are not launched). Heat deficit H (J m−2; Whiteman et al. 1999) is given by

 
formula

where h is the height of the surrounding mountain ridge above the valley floor, Cp is the specific heat for air at constant pressure, ρ(z) is air density at height z, θh is the potential temperature at ridge height, and θ(z) is the potential temperature at height z. The heat deficit, the amount of energy required to mix the layer from the valley floor to the surrounding ridgeline dry adiabatically, was calculated 2 times per day at 0000 and 1200 UTC and was summed for each layer between two radiosonde heights from the valley floor to the ridgeline. The surface height, ridgeline height, and layer thickness for each city are shown in Table 1.

Table 1.

Surface height, ridge height, and layer thickness for stability calculations.

Surface height, ridge height, and layer thickness for stability calculations.
Surface height, ridge height, and layer thickness for stability calculations.

Daily 0700 LST snow depth at the airport monitors was acquired online in 2014 from the National Climatic Data Center Summary of the Day data (http://www.ncdc.noaa.gov/cdo-web/), and each day was classified as a “snow cover” day if the depth exceeded 2.5 cm. PM2.5 data (Table 2) were obtained online in 2014 from the U.S. Environmental Protection Agency (EPA) (http://www.epa.gov/ttn/amtic/speciepg.html; http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm) and included one-day-in-three or one-day-in-six 24-h averages from EPA’s Chemical Speciation Network (CSN; Chow et al. 2010) for mass, elements, ions, and carbon as well as nonspeciated PM2.5 mass measurements on hourly to one-in-six-day sampling frequencies. Major PM2.5 components [ammonium sulfate, ammonium nitrate, organic mass (OM), elemental carbon (EC), fine soil, and salt] were calculated from CSN data according to the Interagency Monitoring of Protected Visual Environments (IMPROVE) formula (Green et al. 2012). CSN data experience a discontinuity for organic carbon (OC) and EC before and after 2008 because the original carbon-analysis protocol (Peterson and Richards 2002) was replaced by the “IMPROVE_A” protocol (Chow et al. 2007a, 2011) for compatibility with the IMPROVE visibility network (Watson 2002). For this analysis, OM is calculated as 1.4 × OC rather than 1.8 × OC (Pitchford et al. 2007), because fresh urban aerosols are believed to contain fewer unmeasured oxygenated compounds than do the aged aerosols measured at IMPROVE sites.

Table 2.

Core-based statistical area (CBSA), site name, latitude, longitude, elevation, date range, and EPA site identifier (AQS ID) for the PM2.5 mass and chemical speciation sites that were used in this study. Dates in regular font are for PM2.5 mass only; dates in italics are for CSN speciated data.

Core-based statistical area (CBSA), site name, latitude, longitude, elevation, date range, and EPA site identifier (AQS ID) for the PM2.5 mass and chemical speciation sites that were used in this study. Dates in regular font are for PM2.5 mass only; dates in italics are for CSN speciated data.
Core-based statistical area (CBSA), site name, latitude, longitude, elevation, date range, and EPA site identifier (AQS ID) for the PM2.5 mass and chemical speciation sites that were used in this study. Dates in regular font are for PM2.5 mass only; dates in italics are for CSN speciated data.

3. Results

a. Relationship between PM2.5 and heat deficit for snow-cover and no-snow-cover days

Figure 1 compares PM2.5 levels with the daily heat deficit for all cities, stratified by days with and without snow cover. It was found for all cities that the difference in slopes for snow-cover and no-snow-cover days was statistically significant (the standard error of the slopes for snow-cover and no-snow-cover days did not overlap). For all cities except Missoula, the 95% confidence intervals in the slopes for snow-cover days and for no-snow-cover days did not overlap either. For Missoula, delta T is shown instead of heat deficit because there are no nearby radiosonde measurements. There is some justification for using the difference in temperature from a valley-floor location to a ridge temperature because Chen et al. (2012b) found that for Reno there was a good relationship between PM2.5 and delta T between a valley-floor weather station and one located a few hundred meters higher on the western slope of the Carson Range. Comparisons of PM2.5 and heat deficit, PM2.5 and delta T, and heat deficit and delta T were made to evaluate delta T’s applicability as a surrogate for heat deficit. For the days with corresponding delta T, heat-deficit, and PM2.5 data (338 days between December of 2002 and January of 2013), the squared correlation between delta T and PM2.5 was 0.55, that between heat deficit and PM2.5 was 0.58, and that between heat deficit and delta T was 0.48. From these results, it appears to be possible to use delta T from appropriately selected stations as a surrogate for heat deficit.

Fig. 1.

PM2.5 vs heat deficit or delta T (for Missoula) for Salt Lake City, Reno, Boise, Spokane, and Missoula for snow-cover days (SD > 0) and for no-snow-cover (SD = 0) days (n is number of days).

Fig. 1.

PM2.5 vs heat deficit or delta T (for Missoula) for Salt Lake City, Reno, Boise, Spokane, and Missoula for snow-cover days (SD > 0) and for no-snow-cover (SD = 0) days (n is number of days).

For all cities except Spokane, moderate relationships are evident between PM2.5 and heat deficit or delta T (for Missoula), with squared correlation coefficients of 0.31–0.59. For Salt Lake City, Reno, and Boise, the correlation between PM2.5 and heat deficit was higher for snow-cover days than for days without snow cover. In addition, the slope was higher for snow-cover days, meaning that, for a given heat deficit, PM2.5 was higher on a day with snow cover. Most of the high PM2.5 concentrations (>35 μg m−3) for Salt Lake City, Reno, Boise, and Missoula occurred on days with snow cover. It is not apparent why the relationship between PM2.5 and heat deficit is much weaker for Spokane than for the other cities. The location of the radiosonde release at Spokane International Airport is about 13 km to the west-southwest of the air-quality-monitoring stations, however. The airport is located on a plateau that is ~135 m higher than the air-quality stations, which are located near the bottom of the valley in close proximity to the Spokane River. It appears that heat deficit determined from the radiosonde is not well related to the conditions that affect the air-quality stations.

Table 3 compares average PM2.5 concentration for snow-cover days with that for no-snow-cover days. Later, we show average PM2.5 divided by the average heat deficit for the same conditions (Table 5, below). Normalizing by heat deficit controls variations in stability between snow-cover days and no-snow-cover days. At Missoula and Spokane ~50% of the days had snow cover, at Salt Lake City ~35% of the days had snow cover, and at Reno and Boise ~15% of the days had snow cover. For all cities except Spokane, snow-cover days had significantly higher PM2.5 than did no-snow-cover days. All cities with heat-deficit data had significantly higher heat deficits on snow-cover days (Table 4). Missoula had a higher average delta T for snow-cover days (−4.2° ± 0.2°C) than for no snow-cover days (−5.8° ± 0.3°C). Higher average heat deficits account for some (but not all) of the higher PM2.5 on days with snow cover. Table 5 shows average PM2.5 divided by the average heat deficit for snow-cover days and for no-snow-cover days. Normalizing by heat deficit is a way to control for any variation in stability for snow-cover days and for no-snow-cover days. PM2.5 normalized by heat deficit was higher for snow-cover days in Salt Lake City, Reno (slightly), and Boise but lower at Spokane. The results for Salt Lake City and Boise (and, to a lesser extent, Reno) indicate that something else in addition to strong static stability is contributing to the higher PM2.5 concentrations on snow-cover days.

Table 3.

Average and uncertainty of average PM2.5 concentrations (μg m−3) for days with snow cover and days without snow cover for each city. Uncertainty in all tables represents the standard error of the mean.

Average and uncertainty of average PM2.5 concentrations (μg m−3) for days with snow cover and days without snow cover for each city. Uncertainty in all tables represents the standard error of the mean.
Average and uncertainty of average PM2.5 concentrations (μg m−3) for days with snow cover and days without snow cover for each city. Uncertainty in all tables represents the standard error of the mean.
Table 4.

Average heat deficit (MJ m−2) for days with and without snow cover.

Average heat deficit (MJ m−2) for days with and without snow cover.
Average heat deficit (MJ m−2) for days with and without snow cover.
Table 5.

Average PM2.5 concentration divided by average heat deficit [μg (MJ m)−1] for days with and without snow cover, by city.

Average PM2.5 concentration divided by average heat deficit [μg (MJ m)−1] for days with and without snow cover, by city.
Average PM2.5 concentration divided by average heat deficit [μg (MJ m)−1] for days with and without snow cover, by city.

In Salt Lake City, Reno, and Missoula, most days exceeding the NAAQS were on snow-cover days (Table 6). For Boise, “exceedance” days were about evenly split between days with snow cover and days with no snow cover. Spokane had more exceedances on days without snow cover. Table 6 also shows that, for all cities except Spokane, days with snow cover were more likely to have PM2.5 exceedances than were days without snow cover. It is not known why Spokane was more likely to have PM2.5 > 35 μg m−3 on no-snow-cover days. All 16 days with no snow cover with PM2.5 > 35 μg m−3 at Spokane occurred in November, 11 of which were in November of 2002. Perhaps prescribed fires explain these high concentrations.

Table 6.

Number and percent (in parentheses) of days with daily average PM2.5 > 35 μg m−3 for snow-cover days and for no-snow-cover days, by city.

Number and percent (in parentheses) of days with daily average PM2.5 > 35 μg m−3 for snow-cover days and for no-snow-cover days, by city.
Number and percent (in parentheses) of days with daily average PM2.5 > 35 μg m−3 for snow-cover days and for no-snow-cover days, by city.

It is expected that wind speeds are lighter and stability is greater for days with high heat deficit, because stronger winds would better mix the atmosphere and lead to reduced heat deficit. This possibility is considered explicitly for Salt Lake City. Figures 2 and 3 show average vertical wind speed and temperature profiles for Salt Lake City, stratified by heat deficit (in increments of 2 MJ m−2). Wind speed decreases as heat deficit increases (although the wind speeds for heat deficits of 4–6 and 6–8 MJ m−2 are about the same). For all heat-deficit categories except <4 MJ m−2, temperature increases from the surface to the level about 600 m above the surface.

Fig. 2.

Average wind speed vs height stratified by heat deficit for Salt Lake City. Data are shown for the most frequently reported radiosonde heights, stratified by heat deficit in increments of 2 MJ m−2.

Fig. 2.

Average wind speed vs height stratified by heat deficit for Salt Lake City. Data are shown for the most frequently reported radiosonde heights, stratified by heat deficit in increments of 2 MJ m−2.

Fig. 3.

As in Fig. 2, but for average temperature.

Fig. 3.

As in Fig. 2, but for average temperature.

To summarize the results for this section, heat deficit is related well to PM2.5 concentration except at Spokane, and heat deficit and PM2.5 levels are higher on days with snow cover than on days without snow cover. Most of the increase in PM2.5 on snow-cover days can be explained by the increase in heat deficit.

b. Chemical composition of PM2.5 on snow-cover and no-snow-cover days

For all sites, reconstructed fine mass averaged 102.6% of measured fine mass, ranging from 93.4% at Missoula to 112.5% at Spokane. That the averages were near 100% for all sites indicates that the methods used to estimate major chemical components (e.g., organic mass) were reasonable. Table 7 shows average concentrations of major components of PM2.5 for snow-cover days and for no-snow-cover days.

Table 7.

Average concentrations (μg m−3) of major PM2.5 components for snow-cover days and for no-snow-cover days: OM = 1.4 × OC, (NH4)2SO4 = 1.375 × , NH4NO3 = 1.29 × , soil = 2.2Al + 2.49Si + 1.63Ca + 1.94Ti + 2.42Fe, and salt = 1.8 × Cl.

Average concentrations (μg m−3) of major PM2.5 components for snow-cover days and for no-snow-cover days: OM = 1.4 × OC, (NH4)2SO4 = 1.375 × , NH4NO3 = 1.29 × , soil = 2.2Al + 2.49Si + 1.63Ca + 1.94Ti + 2.42Fe, and salt = 1.8 × Cl−.
Average concentrations (μg m−3) of major PM2.5 components for snow-cover days and for no-snow-cover days: OM = 1.4 × OC, (NH4)2SO4 = 1.375 × , NH4NO3 = 1.29 × , soil = 2.2Al + 2.49Si + 1.63Ca + 1.94Ti + 2.42Fe, and salt = 1.8 × Cl−.

All cities had higher average concentrations of ammonium nitrate on days with snow cover as compared with days without snow cover. Salt Lake City, Reno, and Missoula also had higher OM on snow-cover days, but the increases in ammonium nitrate exceeded the OM increase. Formation of ammonium nitrate and retention in the particulate form are enhanced by high relative humidity and cold temperatures (Stelson and Seinfeld 1982). The equilibrium between ammonia and nitric acid gaseous precursors and ammonium nitrate particles is also sensitive to the precursor gas concentrations, which are not quantified for these cities. More complete wintertime studies (Watson et al. 1994; Lurmann et al. 2006) in lower-elevation (no snow) valleys have shown higher ammonium nitrate values under more humid conditions (after rainfall) in which nitric acid, which derives from photochemical reactions involving oxides of nitrogen emissions, is the limiting precursor. Although not analyzed here, changes in cloud cover would affect incoming solar radiation and could affect the impact of photochemical reactions on new particle formation. Tables 8 and 9 show that days with snow cover have lower average surface temperature and higher average surface relative humidity than do days without snow cover.

Table 8.

Average winter daily temperature (°C) for snow-cover days and for no-snow-cover days for each city.

Average winter daily temperature (°C) for snow-cover days and for no-snow-cover days for each city.
Average winter daily temperature (°C) for snow-cover days and for no-snow-cover days for each city.
Table 9.

Average winter daily relative humidity (%) for snow-cover days and for no-snow-cover days for each city.

Average winter daily relative humidity (%) for snow-cover days and for no-snow-cover days for each city.
Average winter daily relative humidity (%) for snow-cover days and for no-snow-cover days for each city.

Figures 4 and 5 show average diurnal patterns in temperature and relative humidity at Reno for snow-cover days and for no-snow-cover days. Snow-cover days were colder than no-snow-cover days for all hours, but the difference was especially large in the afternoon. Higher albedo on snow-cover days resulted in less radiation absorbed at the surface and led to increased stability. The increased stability on snow-cover days inhibited vertical mixing, thereby keeping temperatures low. Since solar energy tends to be reflected or goes into melting snow rather than into raising the temperature on snow-cover days, temperatures did not rise very much during the daytime. The colder temperatures persisting throughout snow-cover days formed and maintained nitrate in the particulate form. On days without snow cover, the much warmer daytime temperatures may cause some of the ammonium nitrate to evaporate. On no-snow-cover days, relative humidity in Reno dropped, on average, to below 40% in the afternoon, shifting the gas–particle partition toward the gas phase. On snow-cover days, the average relative humidity stayed above 60%.

Fig. 4.

Average winter diurnal patterns in temperature for snow-cover days and for no-snow-cover days in Reno (2000–13).

Fig. 4.

Average winter diurnal patterns in temperature for snow-cover days and for no-snow-cover days in Reno (2000–13).

Fig. 5.

As in Fig. 4, but for relative humidity.

Fig. 5.

As in Fig. 4, but for relative humidity.

In summary, the higher PM2.5 levels on snow-cover days (for all cities except Spokane) are mostly due to higher concentrations of ammonium nitrate that results from the colder, more humid conditions on snow-cover days, along with lower wind speeds and less vertical mixing (higher heat deficit).

c. PM2.5 concentration trends

Both Salt Lake City and Reno show downward PM2.5 trends (Fig. 6), but there is a low correlation between year and average winter PM2.5. A much stronger trend is found when PM2.5 is normalized by the average heat deficit (Fig. 7). For normalization, the winter average PM2.5 for a given year is multiplied by the ratio of winter average heat deficit for that year to the winter average heat deficit for the entire 2000–13 period. The squared correlation coefficient between normalized PM2.5 and year increases to 0.59 at Salt Lake City and 0.91 at Reno. The slope steepens to −0.55 μg m−3 yr−1 at Salt Lake City and −0.51 μg m−3 yr−1 at Reno.

Fig. 6.

Trend in winter average PM2.5 concentrations for Salt Lake City, Reno, Boise, and Spokane (2000–13).

Fig. 6.

Trend in winter average PM2.5 concentrations for Salt Lake City, Reno, Boise, and Spokane (2000–13).

Fig. 7.

As in Fig. 6, but normalized by winter average heat deficit. Normalized PM2.5 is yearly average PM2.5 multiplied by the 2000–13 average heat deficit and divided by the year-specific average heat deficit.

Fig. 7.

As in Fig. 6, but normalized by winter average heat deficit. Normalized PM2.5 is yearly average PM2.5 multiplied by the 2000–13 average heat deficit and divided by the year-specific average heat deficit.

Figures 6 and 7 show similar relationships for Boise and Spokane. The correlation between year and PM2.5 and the slope of the decline are stronger when PM2.5 is normalized by heat deficit. Even when normalizing by heat deficit, however, there is still large year-to-year variability in PM2.5 for Boise. Table 10 summarizes the slopes and statistical significance of PM2.5 and heat-deficit-normalized PM2.5 trends. When normalizing for heat deficit, the trends for Salt Lake City, Reno, and Spokane are significant at the 95% level.

Table 10.

Slope and statistical significance of trends in winter PM2.5 and PM2.5 normalized by average heat deficit for 2000–13 using a Theil (1950ac) regression. Thiel regression calculates the slope for all pairs of years and selects the median to represent the slope, thereby minimizing the effects of outliers. Slopes that are statistically significant (P < 0.05) are shown in boldface type.

Slope and statistical significance of trends in winter PM2.5 and PM2.5 normalized by average heat deficit for 2000–13 using a Theil (1950a–c) regression. Thiel regression calculates the slope for all pairs of years and selects the median to represent the slope, thereby minimizing the effects of outliers. Slopes that are statistically significant (P < 0.05) are shown in boldface type.
Slope and statistical significance of trends in winter PM2.5 and PM2.5 normalized by average heat deficit for 2000–13 using a Theil (1950a–c) regression. Thiel regression calculates the slope for all pairs of years and selects the median to represent the slope, thereby minimizing the effects of outliers. Slopes that are statistically significant (P < 0.05) are shown in boldface type.

The results for this section show that trends in PM2.5 are highly statistically significant (downward), but only after controlling for year-to-year variations in heat deficit.

d. Trends in speciated concentrations

Trends in the PM2.5 chemical constituents (Figs. 811) demonstrate which ones are responsible for decreases in mass concentrations. All show downward trends (except for Boise EC), but OM shows the most rapid decrease. Trend rates for all components are summarized in Table 11.

Fig. 8.

Trend in organic mass (1.4 × OC) normalized by heat deficit for Salt Lake City, Reno, and Boise.

Fig. 8.

Trend in organic mass (1.4 × OC) normalized by heat deficit for Salt Lake City, Reno, and Boise.

Fig. 9.

As in Fig. 8, but for elemental carbon.

Fig. 9.

As in Fig. 8, but for elemental carbon.

Fig. 10.

As in Fig. 8, but for ammonium sulfate.

Fig. 10.

As in Fig. 8, but for ammonium sulfate.

Fig. 11.

As in Fig. 8, but for ammonium nitrate.

Fig. 11.

As in Fig. 8, but for ammonium nitrate.

Table 11.

Squared correlation coefficient, Thiel regression slope (μg m−3 yr−1), and significance of regression slope by major component and normalized component (preceded by “n” label) for Salt Lake City, Reno, and Boise. Slopes that are statistically significant (P < 0.05) are shown in boldface type. Here, “amm” indicates ammonium.

Squared correlation coefficient, Thiel regression slope (μg m−3 yr−1), and significance of regression slope by major component and normalized component (preceded by “n” label) for Salt Lake City, Reno, and Boise. Slopes that are statistically significant (P < 0.05) are shown in boldface type. Here, “amm” indicates ammonium.
Squared correlation coefficient, Thiel regression slope (μg m−3 yr−1), and significance of regression slope by major component and normalized component (preceded by “n” label) for Salt Lake City, Reno, and Boise. Slopes that are statistically significant (P < 0.05) are shown in boldface type. Here, “amm” indicates ammonium.

Salt Lake City and Reno had steady and statistically significant downward trends in EC, which is consistent with nationwide trends (Murphy et al. 2011; Chen et al. 2012a; Hand et al. 2013), but Boise curiously had slight (although not statistically significant) EC increases. All cities had decreases in ammonium sulfate, but only the Salt Lake City and Reno trends were significant at the 95th percentile confidence level (significance P = 0.05). Statistically significant decreases in normalized ammonium nitrate occurred for Salt Lake City and Boise, but there was no significant trend for Reno.

4. Summary and conclusions

PM2.5 concentrations from five U.S. valleys in the Intermountain West showed a strong relationship with atmospheric stability, as expressed by heat deficit and the valley-to-ridge temperature difference. These measures explained about 30%–60% of the variance in daily average wintertime PM2.5 concentrations. The presence of snow cover was also associated with higher PM2.5 concentrations than occurred on days without snow cover. This resulted from two factors: 1) there was increased stability on snow-cover days and 2) for a given stability snow-cover days had higher PM2.5 mainly because of enhanced particulate nitrate levels. All five cities studied showed statistically significant increases in particulate nitrate on snow-cover days. Colder temperatures and higher relative humidity occurred on snow-cover days with enhanced formation and retention of particulate nitrate.

PM2.5 concentrations from 2000 to 2013 decreased, but with much year-to-year variability. When PM2.5 concentrations were normalized to the average heat deficit, however, the downward trends became stronger (except for Boise) and had greater statistical significance. The three cities with greater than 10 yr of chemically speciated PM2.5 data (Salt Lake City, Reno, and Boise) all had strong downward trends in organic mass (ranging from −0.45 to −0.70 μg m−3 yr−1 for concentrations normalized by heat deficit). After normalizing for heat deficit, statistically significant declines also occurred in Salt Lake City and Boise for particulate nitrate and in Salt Lake City and Reno for elemental carbon and particulate sulfate. Decreases in organic mass accounted for the bulk of the decreases in PM2.5 mass concentration for each of the three cities with long-term speciated data.

Except for Missoula, this analysis was limited to cities with both radiosonde observations and chemically speciated PM2.5 data. Similar analyses would help to inform the reasons for high PM2.5 concentrations for other valley locations where PM2.5 is measured (speciated or not) and an atmospheric stability parameter can be obtained, such as through differences in temperature between sites at different elevations. This would include cities throughout the world that are located in valleys and experience cold winters with periods of snow followed by stagnant conditions.

A finding of help to regulatory agencies is that to clearly identify long-term trends in winter PM2.5 concentrations, which are examined to determine control-strategy effectiveness, it is necessary to remove the effect of stability changes from year to year, which can easily mask trends.

Acknowledgments

The Washoe County Health District provided partial support of the analysis for Reno.

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