Geothermal, Oceanic, Wildfire, Meteorological and Anthropogenic Impacts on PM 2.5 Concentrations in the Fairbanks Metropolitan Area

The impacts of low and high-frequency variability from teleconnections between large scale atmospheric processes and local weather as well as emissions changes on concentrations of particulate matter of 2.5 µm or less in diameter ([PM 2.5 ]) were examined for the Fairbanks Metropolitan Area (FMA). October to March and May to August mean [PM 2.5 ] were 1.8 and 3.1 µg·m −3 higher for positive than negative annual mean Pacific Decadal Oscillation. Annual mean [PM 2.5 ] were 3.8 µg·m −3 lower for positive than negative Southern Oscillation Index. On 1999-2018 average, [PM 2.5 ] decreased 2.9 µg·m −3 ·decade −1 . On average over October to March, decadal and inter-annual variability caused higher or similar differences in mean observed [PM 2.5 ] and its species than emission-control measures. The 2006 implementation of Tier 2 for new vehicles decreased observed sulfate concentrations the strongest (~4.95 µg·m −3 ·decade −1 ) of all occurred emissions changes. On average, observed [PM 2.5 ] showed elevated values at all sites when wind blew from directions of hot springs. The same was found for the sulfate, ammonium and non-metal components of PM 2.5 . Observations showed that these geothermal waters contain sulfate, ammonia, boric acid and non-metals. Hot springs of such composition are known to emit hydrogen sulfide and ammonia that can serve as precursors for ammonium and sulfate aerosols.

with air aloft. Despite Fairbanks has no major industry [12], emissions of gaseous compounds like CO, sulfur dioxide (SO 2 ), and nitrogen dioxides (NO 2 ) and aerosols from traffic, power-generation and heating accumulate underneath these often multi-day inversions. Here chemical reactions, aerosol physical and chemical processes form secondary pollutants and aerosols.
Improved engine technology and the turn-over of the vehicle fleet as well as a mandatory biennial check of vehicle emissions eventually "solved" the CO air-quality issue [8]. The new technology led to more efficient combustion processes thereby reducing CO emissions. The more than 150 percent increase of the prices for gasoline and diesel fuel in the first decade of this century may have decreased fuel consumption by individual traffic, and may have contributed to reduced CO emissions as well.
In case of PM 2.5 , such simple solution must not be expected even though solid fuel appliances like wood-and coal-fired stoves, and hydronic heaters have become more efficient. None of the potential direct (wood-stove change-out, introduction of natural gas), indirect (introduction of low sulfur fuel) and multiple emission-control measures (replacing wood with gas heating and use of low sulfur fuel) simulated for October 1, 2008 to March 31, 2009 with the Weather Research and Forecasting (WRF) model inline coupled with chemistry packages (WRF/Chem) [13] would provide design values below 35 µg·m -3 [14]; benefits for air quality would vary in persistence among measures [14]. Exchanging 2930 N. Mölders [15]. Highest reductions on any exceedance day ranged from 1.7 to 2.8 μg·m −3 ; relative response factors were consistently low (~0.95) for all PM 2.5 species and months [15]. The 2008-design value of 44.7 μg·m −3 would be reduced to 42.3 μg·m −3 [14]. Sensitivity studies suggested that the benefits of a wood-burning device change-out program heavily relies on the accuracy of the estimates on how many devices exist that can be exchanged [15].
Substituting all wood by gas heating would reduce PM 2.5 emissions by ~11% yielding a design value of 38.9 μg·m −3 . Burning low-sulfur fuel in oil-fired furnaces and facilities would reduce total SO 2 and PM 2.5 emissions by ~23% and 15%, respectively, and the design value to 42.8 μg·m −3 [14]. Concurrent replacement of wood-heating with gas heating and introduction of low-sulfur fuel would reduce SO 2 and PM 2.5 emissions by ~36 and 19%, respectively, and the design value to 39.3 μg·m −3 . The benefits of using low-sulfur fuel depended the strongest on the meteorological regime. Unfortunately, the benefits of the multiple emissions-control measures generally fail to be the sum of the benefits of the respective single measures [14].  [16]. Highest concentrations occurred in the same locations in both simulations. Point-source emissions influenced [PM 2.5 ] at breathing height strongest about 10 -12 km in their downwind [16].
Various studies linked wildfire smoke, especially particulate matter at the micron-to sub-micron size, ozone (O 3 ) and volatile organic compounds (VOC) with increased risks of respiratory disease, cardiovascular diseases and mortality [1] [2] [17] [18] [19]. In the taiga region around the FMA, boreal wildfires are a natural component of the landscape evolution. Over the past decades, the annual wildfire activity and area burned have increased [20]. During the 2004-and 2005-wildfire seasons, for instance, Fairbanks 24-h average [PM 2.5 ] was up to a factor of 20 and 11 higher than the current NAAQS. In both fire seasons, air quality became hazardous. Thus, from a health perspective, the PM 2.5 problem in the FMA may be more severe during the wildfire seasons than during extreme multi-day inversions in winter.
Obviously, besides local emissions, external factors like the general circulation, meteorology, geography and geological processes influence [PM 2.5 ] in the FMA. The goal of our study was to determine the magnitude of these external (i.e. non-manageable) factors in comparison to observed [PM 2.5 ] changes in response to well-known emissions changes.

Experimental Design
We hypothesized that in the FMA, the magnitude of [PM 2.5 ] is due to a combination of the general circulation, synoptic and mesoscale features as well as weather-related natural and anthropogenic emissions, and if so, low-frequency variability and its impacts on local weather (and hence local emissions and [PM 2.5 ]) could pretend/dilute changes occurring in response to emissions changes in observed [PM 2.5 ].

Data Sources and Processing
We downloaded public-available surface-meteorological, fuel, lysimeter and radiosonde data of Fairbanks, air-quality data of the FMA, fire [21] and anthropogenic emissions [12] data as well as data of the Pacific Decadal Oscillation The PDO, NP and SOI indices describe different aspects of the general circulation known to influence weather in Alaska via large-scale teleconnections [22] [11]. Teleconnections are preferred modes of low-frequency (long time scale) variability. The NP is the monthly area-weighted sea-level pressure over 30N to 65N and 160E to 140W [23]. It measures inter-annual to decadal variations of the atmospheric circulation. The SOI is the normalized pressure difference between Tahiti and Darwin [24]. It

Data Analysis
To test our hypothesis air quality had to be examined in a climatological sense.
Therefore, we determined climatology of PM 2.5 , its speciation, meteorological To assess low frequency impacts we examined monthly means of [PM 2.5 ] for correlations with the NP, SOI and PDO. In our study, all correlations were examined for their significance at the 95% confidence level (p < 0.05) using paired two-tailed t-tests [29]. Following common practice [29], the number of data was considered in determining p for small samples.
Since [PM 2.5 ] can be high due to fires as well as anthropogenic emissions, we examined warm (May to August) and cold (October to March) seasonal means as well as monthly means of concentrations and meteorological features. In the following, May to August and October to March are called the warm and cold season, respectively.
At the PM 2.5 monitoring sites, some meteorological data (wind speed, pressure, minimum, maximum and mean hourly air and dew point temperatures) were recorded. However, an assessment of the synoptic (meso-α-scale) and meso-β-scale (temporal scales of 1 to 3 days) influences on [PM 2.5 ] requires a full suite of meteorological and-in the fire season-fire-relevant data. Therefore, we used the data from the Bureau of Land Management (BLM) Fairbanks site (64.83667N, 147.615W). In addition to 10-m wind speed and direction, 2-m air temperature, 2-m relative humidity, precipitation and pressure, this site also had long-term records on fire-relevant data like daily mean, maximum and minimum fuel temperatures and humidity as well as daily accumulated radiation at the surface. The latter is of interest for inversions caused by radiation deficit, and for photolysis that may initialize some summer aerosol formation paths. Also smoke may reduce solar radiation reaching the surface and photolysis rates.
Daily means and higher moments [29]  Finally, to assess potentially overlooked emission sources we turned to the literature and data from Alaska sites outside the FMA as available.

Climatological Features
Fairbanks is the largest city in the Interior. No other conurbations exist in a radius of 417 km ( Figure 1). In the FMA, daily totals of solar radiation reaching the top of the atmosphere (TOA) and Earth's surface are low between early November and end of February (Figure 2(a)). Solar radiation reaching the surface peaks earlier than at the TOA as humidity and cloudiness increases from March towards August. In the fire season (May-August), high particle loading due to fires may also reduce the solar radiation at the surface [22]. The huge amounts of water vapor released from evaporation and sublimation of soil water and ice due to fires burning on permafrost also may decrease solar radiation at the surface [31].
Usually, the snowfall season is September to May (Figure 2 According to the 1981-2010 climate record at Fairbanks International Airport (134 m, 64.8039N, 147.8761W), daily mean temperatures below and above freezing occur from September through April and May through August, respectively ( Figure 2(b)). Rarely temperatures are below −40˚C or above 28˚C. From early November to end of February, near-surface air temperatures are usually below −10˚C. The diurnal temperature range is about 9 K in November and 13 K in February [22]. Summers are cool and humid with the maximum of monthly precipitation in August. March is typically the driest months. September to April relative humidity varies between 40% and 90%. Wind speed is generally low through the year with June having the highest monthly average wind speed (6.35 The FMA's frequent SBI in winter are among the strongest anywhere [8] and persist much longer than in mid-latitudes [7]. Daytime and nighttime SBI occurred on about 82% of the days in December and January, and on about 68% of  the days in November, and from February to April during 1957 to 2008 [32]. According to the 2000 to 2009 radiosonde data, SBI occurred 67% of the time in winter with a mean height of 377 m; SBI occurred with one, two, three, or four simultaneous elevated inversions (EI) in 84.86%, 48.49%, 21.23%, and 7.99% of the 2326 events, respectively [33]. The first EI layer above a SBI formed under anticyclonic conditions at a mean height of 1249 m, under warm-air-advection at a mean height of 1049 m and combined synoptic situations 35.8%, 22% and 23.4% of the time [33]. Data from the Global Fire Emission Database [21] showed strong year-to-year variation of annual totals of PM 2.5 emissions from fires (e.g.  (Figure 3(d)). Annual totals of Alaska PM 2.5 emissions from fires exceeded those for anthropogenic sources typically by several orders of magnitude (e.g. Figure 3). On average over the area shown in Figure 3    were 16.8 ± 12.2 µg·m −3 and 11.5 ± 40.9 µg·m −3 , respectively ( Table 1). As indicated by the standard deviations, summer maximum concentrations can exceed winter maximum concentrations by more than an order of magnitude depending on the severity of upwind fires; worst air-quality conditions occurred due to advection of pollutants from wildfires, not anthropogenic emissions trapped under winter surface-inversions. Data from the first tribal-owned air-quality network in the Yukon Flats showed similar behavior [34].
The high standard deviations of concentrations (Table 1) have various reasons. In summer, a huge year-to-year variability exists for the location of fires relative to the sites, in the area burned (cf. Figure 3), the number of lightning strikes, the type of synoptic scale weather pattern, the levels at which the smoke is transported and the kind of fuel burned. In winter, emissions differed strongly between mild and extremely cold episodes, the number of Chinook situations, as  [35] and NAAQS of 35 μg·m −3 not to be exceeded [36] are superimposed.

Decadal to Seasonal Scale Impacts
Previous work showed that there was a shift from a cooler to a warmer regime in 1976 [38].  (Table B1).   (Table B1). The same was true for the warm season.  (Table B2). Warm season mean [PM 2.5 ] was 3.1 μg·m −3 higher for positive than negative PDO (Table B2). Wildfires and subsidence inversions occur more often in warm than in cool summers. Subsidence inversions reduce the volume in which pollutants accumulate. This means that even when there were no emissions the mass of pollutants per cubic meter increased (see also section 3.6). Consequently, warm season mean [PM 2.5 ] increased.
Positive correlation between temperature and PDO and the negative correlation between temperature and emissions together with the trend of decreasing temperature (e.g. Figure 5 (Table B1). In January and February, weak, negative, but still significant correlations existed. Marginal and weak (both significant at 95% confidence or higher), negative correlations occurred during the warm and cold season, respectively (Table B1).  (Table B2). In the former case, the weather in the FMA was governed more often by the semi-permanent Canadian High, i.e. storms passed farther south; while in the latter case, the Hawaiian High was strong shifting the tracks of Aleutian Lows northward.
Except for metals, the 2005-2014 speciation climatology showed distinct seasonality and annual courses (Figures 8(a)-(e)) due to the different emissions sources, their strengths and fractional contribution to emitted PM 2.5 and precursor gases as well as weather conditions. Interestingly, significant correlation (95% confidence or higher) existed between sulfate, ammonium and non-metals ( Figure 8(f)). Except metals and OC, all PM 2.5 species concentrations increased as temperatures decreased in fall, peaked in January (coldest month), and de-creased as temperatures increased (compare Figure 2(b), Figure 8(e)). Organic carbon had a secondary peak in July due to fires and small contributions from biogenic emissions. Its concentrations were smallest in late spring and early fall. In late spring, the fire season has not yet started and spatial heating by wood is already reduced. In early fall, the fire season is already over or slowing down and space heating is still sparse.
Non-metals hardly varied among years for April to September and year-to- year differences were largest in December (Figure 8(e)). EC showed lowest inter-annual variability in April and May, and slight variability in summer. In fall, EC inter-annual variability increased to peak in December. OC showed largest inter-annual variability in August due to rain and second largest in December.
OC barely varied among years during breakup (April, May).    [40]. Thermodynamic conditions cooler and wetter than 14˚C and 60%, respectively, limit the chemical reactions of the system [39] [40]. They favor ammonium-nitrate formation on sulfate-and/or carbon-dioxide-containing particles.
When the concentrations of the latter particles is high (35 - [37]. Typically, kinetic rate constants increase exponentially. In the cold season, monthly total solar radiation at the surface, and monthly means of wind speed, 2-m air temperature, maximum and minimum temperatures correlated negatively, moderately to weakly with mean [PM 2.5 ], but at 95% or higher confidence (Table B3). Species concentrations were higher at low wind speeds in winter than in summer. In the warm season, monthly means of 10-m wind speed and [PM 2.5 ] correlated the strongest. However, the paired two-tailed t-test indicated a 95% probability of an accidental correlation.
Monthly mean maximum fuel temperature was weakly, and monthly mean minimum relative humidity was negatively correlated with [PM 2.5 ]. Both correlations were significant at 95% confidence. Low relative humidity during April

Impact of Daily Meteorological Conditions
Wildfire smoke impacts the radiation budget [41]. Over 2/1999 to 3/2018, daily [PM 2.5 ] showed weak, negative, but significant correlation to daily total solar radiation at the surface (Table B3). At daily mean temperatures below −20˚C, [PM 2.5 ] were clustered below 20 μg·m −3 (e.g. Figure 6(f) A strong correlation existed between temperatures below −20˚C and some PM 2.5 species (Table B3). Here sulfate was prominent due to increased emissions of primary sulfate and SO 2 precursors from power generation and heating. As temperature increases, gas-to-particle conversion slows down [37].
At high relative humidity, the uptake of water vapor by aerosols promoted December. Of course, the chemical hydro-thermodynamic processes of the system HNO 3 -NH 3 -NH 4 NO 3 -sulfate partly played a role. In addition, as wind speed increases, so does mixing. Segregation close to emission sources that may limit chemical hydro-thermodynamic processes [42], has less impact on the reactions at high than low wind speeds [42].
We calculated the correlation between wind directions and concentrations using all available wind direction data, i.e. not individual wind-direction sectors.
No significant correlation was found except for NO 2 with R = −0.179 (Table   B3). Nitrate barely depended on wind direction, while ammonium and sulfate both peaked in the sectors 135˚ -225˚ and 270˚ -340˚ (Figure 9(d)). At all sites, also [PM 2.5 ] increased when wind came from these directions (Figure 9(e)).    increased with daily mean temperature which can be explained by increasing emissions from wildfires. In August, the rainiest month (Figure 2(b)), rain over several days and decreasing temperatures may lead to onset of space heating.
The amount of metals increased when relative humidity was less than 75%.
Precipitation showed weak negative, but significant correlation with NO 2 , O 3 , SO 2 , CO and PM 2.5 (Table B3) due to uptake into drops, washout and scavenging.

Spatial Distribution
Mobile [PM 2.5 ] measurements and measurements performed at various sites for a cold season or more, revealed the impact of local emissions [44]. Figure 10 displays a spatial composite of cold season mean [PM 2.5 ] from measurements at the sites. Note that mobile measurements suggested an additional hot spot be-

Relations between Reactants of Precursors, Precursors and Particle Species
In   Figure 11(a)). In fall, a snow cover temporally can increase photolysis rates leading to a slight increase in [O 3 ]. This behavior was found also for other high latitude cities [47].
Over the entire period, significant, but weak (R = −0.347) and marginal (R =

0.180) correlation occurred between [PM 2.5 ] and [O 3 ] as well as between [CO]
and [PM 2.5 ], respectively (   [NO 2 ] was lowest at the end of August due to scavenging by rain, increased in September due to biogenic emissions from soils and showed a secondary minimum once a snow cover established (compare Figure 2(b), Figure 11(a)). Measurements of all precursor gases and aerosols overlapped only for six months (cf. Table A2). Despite NO 2 is a precursor to nitrate aerosol, their positive correlation was weak, although significant (Table 3).
Unfortunately, no observation data on NH 3 , a precursor gas for NH 4 [49]. In the outskirts of the FMA, many dog kennels exist with often more than ten animals.
On average, daily maximum [SO 2 ] was larger in winter than summer ( Figure   11(c)). Sulfate concentrations were also lower in summer than winter (Figure 9(f), Figure 11(b), Figure 11(d)). The mean SO 2 to sulfate ratio was nearly 1:16 in summer vs. 1:8 in winter despite gas-to-particle conversion increases with decreasing temperature (Figure 2(a), Figure 11(d)). Gas-to-particle conversions take time, for which sulfate concentrations showed a lower increase than [SO 2 ].
Since SO 2 emissions and gas-to-particle conversion are lower in summer than winter ( Figure 11(d)  Sulfate correlated less with its precursor SO 2 than with ammonium ( Table 3). The high positive correlation between sulfate and ammonium suggests a common source. Fires emit PM 2.5 and its precursor gases NO 2 , VOC, and NH 3 as well as small amounts of SO 2 . However, in the FMA, observed [NH 3 ] were typically lower than sulfate concentrations in both summer and winter (cf. Figure 8(g), Figure 11(b)). Furthermore, in 2005 to 2014 (period of speciation data), wildfires burned in different directions from the FMA (cf. also Figure 4). Thus, emissions from wildfires fail to explain the distinct preference for elevated concentrations for specific wind sectors. Since the fire season is May thru September, wildfires also fail to explain the winter correlations (Figure 10(a)).
In the cold season 2008/09, wind direction and sulfate showed low, but significant correlation at the Fairbanks (R = 0.142), North Pole (R = 0.058) and Peger Rd (R = −0.103) sites (cf. Figure 8(g), Figure 11(a)). The different signs are due to local primary emissions dominating the PM 2.5 composition. Using only data for wind directions of elevated concentrations (defined here as mean plus one standard deviation) yielded R = −0.193, R = −0.185, R = −0.167 for wind directions and sulfate (all significant at >95% confidence). Correlations between these wind directions and elevated ammonium were smaller than for sulfate, but significant as well.
The correlation of sulfate concentrations observed for these directions at different sites were R = 0.890, R = 0.873 and R = 0.764 for the Fairbanks vs. North Pole, North Pole vs. Peger Rd, and Peger Rd vs. Fairbanks sites (at 95%, 90% and 90% confidence), respectively. Correlation coefficients for ammonium were R = 0.759, R = 0.699 (both significant), and R = 0.772 (non-significant) for these pairs of sites. For correlations of other species among these three sites during the 2008/09 cold season see Table B4.
Ozone concentrations showed distinct differences with wind direction for both the warm and cold season (Figure 9(e)). In the ENE to W sectors, cold wildfires may contribute to O 3 formation. The stronger mixing during the warm than cold season also may contribute to the difference seen between these seasons.
There are 10 hot springs within less than 180 km of Fairbanks in the WNW and ENE to ESE sectors (Figure 1). Besides minerals these geothermal sites hold sulfate and ammonia; the 1912, 1917, 1972 and 1992 analyses of the healing water at Chena Hot Springs, for instance, indicated 89, 78, 68, and 56.1 ppm sulfate dissolved in water, respectively; in 1972, 2.7 ppm ammonia dissolved in the healing water were reported [50].
Only few studies on gases emanating from hot springs exist. Gas samples collected at hot springs in Yellowstone Park never contained detectable amounts of SO 2 , but all contained H 2 S [51]. This finding is consistent with the higher water solubility and lower pK of SO 2 than H 2 S (1.9 vs. 6.88). Many gas samples also contained notable amounts of NH 3 [52].
In the atmosphere, H 2 S oxidizes to SO 2 [51]. According to thermodynamic results, reaction of H 2 S with O 3 produces SO 2 + H 2 O with the lowest value of Gibbs energy (ΔG˚ = −645.84 kJmol −1 ) aka free enthalpy [43]. Cold season [O 3 ] was on average lower when winds came from the directions of hot springs (Figure 9(g)). In the warm season, biogenic emissions yield large amounts of VOC, which also reacts with O 3 . Furthermore, NO from photolysis affects [O 3 ]. These differences in potential reaction paths can explain the quite different behavior of O 3 as a function of wind direction.
Unfortunately, at none of the known Interior hot-springs, gas measurements were performed. However, at Chena, Manley, Tolovana and Hutlinana Hot Springs slight H 2 S odor has been observed.
We analyzed the [PM 2.5 ] collected in the Yukon Flats for September 2017 to April 2018 [34]. The sites at Beaver (population of 84) and Circle (population of 104) are close to the Dall and Circle Hot Springs (Figure 1). At Beaver, slightly elevated [PM 2.5 ] occurred for winds from the 45˚ to 60˚ and 130˚ to 225˚ sectors (Figure 12(c)). The sector between about 130˚ and 180˚ is downwind of hot springs, while the other sectors with elevated [PM 2.5 ] are due to the village. At Circle, elevated [PM 2.5 ] were observed for the sectors around 200˚ and 130˚ that correspond to the major directions of downwind hot springs (compare Figure 1, Figure 12(d)). Despite Chalkyitsik (population of 69) and Ft. Yukon (population of 583) are farther away from hot springs than Circle and Beaver, [PM 2.5 ] were slightly elevated when the winds blew from the directions of hot springs Open Journal of Air Pollution (Figure 12(e) and Figure 12(f)). The peak at around 270˚ for Ft. Yukon was related to the airport where huge cargo planes and small aircrafts of several airlines take off/land once a week and daily, respectively. In September, the Canadian border fire still burned and caused the peak in [PM 2.5 ] around 300˚ observed at Chalkyitsik [34].
Based on 1) the similar water composition of the Interior and some Yellowstone hot springs, 2) the presence of H 2 S at Interior hot springs, 3) the findings at the sites of the Tribal Air Quality Network in the Yukon Flats, and the fact that when the FMA was in the downwind of hot springs 4) elevated sulfate and ammonium concentrations and 5) elevated [PM 2.5 ] occurred at all sites in the FMA concurrently (Figure 9(c), Figure 12(a) and Figure 12(b), 6) the high correlation between ammonium, sulfate and non-metals (Table 3), 7) the twice as high SO 2 to sulfate ratio in summer when [O 3 ] are up, than in winter when [O 3 ] are low (Figure 11), 8) the moderate, negative, but significant correlation between sulfate and ozone (Table 3), and 9) the reduced [O 3 ] in the wind-direction sectors where sulfate concentrations were elevated (Figure 11(e)), as well as 10) the results from WRF/Chem studies [14], one may assume that some of the sulfate and ammonium aerosols might have geothermal origin.

Short-Time Scale: Examples of Typical Winter Weather Conditions
The  (Figure 13(a) and Figure 13(b)). The inversions capped the near-surface air layers and hindered the exchange of polluted air with less polluted air aloft. Gaseous and particulate matter accumulated in the stagnant air (Figure 13(c) and Figure 13(d)). According to the radiosonde soundings (Figure 13(b)), a light destabilization of the near-surface layer started in the afternoon of January 1 (Alaska Standard Time) and persisted, at least, for 24 hours. During this time, [PM 2.5 ] decreased below the NAAQS (Figure 13(d)). Then the wind calmed down and slightly took up after January 6 before calming down again. Similar happened in the other case; wind was calm until February 9, increased for about a day and was calm again (Figure 13(a)). Snowfall occurred on February 18 to 22, 2008 and on January 14 to 16,2009. In both cases, the snow event reduced the [PM 2.5 ] (Figure 13(c) and Figure 13(d)).
Subsidence inversions frequently occur in the Interior [33]. On the contrary to the two cases discussed above, they are governed by the synoptic scale. Table 4 illustrates an example of the retrieval of multi-layered temperature inversions

Impact of Emission Changes on PM2.5 Climatology
To compare the changes in concentrations due to meteorology and low frequency variability with those from known emissions changes we determined the trends. In general, these trends are numerical results. Like for the correlations, these trends are given with several digits to see impacts. By no means are these digits an indicator of the measurement accuracy.
We determined changes in [PM 2.5 ] caused by altered emissions for comparison with those due to influences from large-scale teleconnections. On average from 1999 to 2018, cold season [PM 2.5 ] decreased by 0.1796 μg·m −3 ·y −1 (Table 5).

Tier 2 Emission Standards for New Vehicles
In the 2005-2014 climatology, PM 2.5 composition changed at various scales and differed among seasons (e.g. Figure 8).

Low Sulfur Fuel for Rural Areas
The tightened sulfur-fuel standard for rural Alaska coincided with the wood-stove change-out program. Nevertheless, we examined whether an impact of this measure on [PM 2.5 ] was observable in the FMA. The number of vehicles using diesel with highway/rural standard while traveling in the FMA was small compared to the local vehicle fleet. Results of WRF/Chem simulations showed that reduced fuel sulfur content would decrease [PM 2.5 ] downwind of the FMA with marginal benefits close to the urban emission sources [14]. No measurements existed outside of the FMA at distances where-if at all-differences could be expected according to the WRF/Chem simulations. Based on these facts, we may assume that the impact of low sulfur fuel for rural areas on the urban sulfate aerosol most likely was negligible.

Boom of Woodstove Additions in 2007
Prior to and after the rapid increase in fuel prices in 2007, cold season [PM 2.5 ] increased 0.0717 and decreased 0.1527 μg·m −3 ·y −1 , respectively (  (Table 5) and 6.7 ppb·y −1 (Figure 14). Prior to 2007, cold season [NH 4 ] increased 0.151 μg·m −3 ·y −1 , while they decreased by 0.1117 μg·m −3 ·y −1 thereafter (Table 5).     Figure 14, Table 5).    Table B4. Correlations of speciation concentrations and aerosol precursor-gas concentrations with daily accumulated radiation R s ↓, daily mean T, maximum T max and minimum T min air temperatures, daily mean, maximum and minimum fuel temperatures T fuel , T fuel,max , T fuel,min , as well as daily mean, maxiumum and minimum relative humidity RH, RH max , RH min , mean wind speed, v, wind gusts, v gust and wind direction, dir as observed at the BLM site. Bold values indicate significant correlation at 95% confidence or higher according to a two-tailed paired t-test. Note that correlations represent different observation periods (see Table A2 for times of data availability