During the harsh winter conditions of the Yukon Flats as well as during smoke episodes of the fire season in summer, residents of the villages of Eastern Interior of Alaska’s Yukon Flats valley spend the majority of their time indoors. Exposure to fine particulate matter of 2.5 μm or less in diameter (PM2.5) is well known as health-adverse and can occur indoors as well as outdoors . Poor indoor air quality was linked to respiratory problems especially in children, cancer, sick-building syndrome, fatigue, headache and about 6% - 9% reduced performance of office work, among others  .
Indoor emissions of PM2.5 stem from cooking, wood stoves, candles, household and office appliances, smoking, incense, insect repellent coils, cleaning, animals and heating . Fine particulate matter may form by gas-to-particle conversion from precursor gases (e.g. nitrogen oxides, sulfur dioxide, volatile organic compounds) that stem from wood smoke, outgassing of building material, and furniture or vehicle emissions in houses with attached or built-in garages. Wear and tear also contribute to indoor particles. Small amounts of silicates, mold and pollen may exist as well.
Indoor emissions of PM2.5 have less space to dilute and disperse as compared to emissions outside. Indoor PM2.5 concentrations also depend on the duration and intensity of ventilation, number of occupants and their activity, airflow as well as pollutant dynamics like first order removal and sorptive interaction processes  . The construction type affects both—indoor emissions from outgassing and type of ventilation (natural only, natural and mechanical). Mechanical ventilation encompasses kitchen and bathroom exhaust fans or hoods. The flow rate differs among systems depending on their purpose, size, building type and volume as well as the system’s location. Natural ventilation occurs by air flow through open windows or doors. It is driven by the pressure gradients between inside and outside temperature differences and wind . Natural ventilation varies with weather conditions and season. Its impact on indoor air quality also depends on outdoor air quality.
Particle loss rates by Brownian diffusion, gravitational settling, interception, and impaction vary with particle size . Brownian diffusion dominates for ultrafine particles with diameters smaller than about 0.1 μm. Interception, impaction, and gravitational settling dominate the removal of particles exceeding this diameter  . Due to activities by the occupants particles may re-suspend after deposition.
The non-existence of other natural air cleansing mechanisms (e.g. removal by precipitation) contributes to high indoor concentrations unless air-purifier or filter is used. A recent study showed that indoor exposure to emitted PM2.5 (per unit mass) can exceed that of exposure to outdoor emissions by two to three orders of magnitude .
The majority of indoor air quality studies have focused on residences in the contiguous U.S. and Europe. Only few studies exist that examined air quality in industrial, school , office or commercial buildings   . A recent study in New England revealed that homes with a woodstove had 20.6% higher PM2.5 concentrations than those without. PM2.5 concentrations were higher in homes with old stoves, non-EPA-certified stoves, and when burning wet or mixed (vs. dry) wood than those with new EPA-certified stoves burning dry wood . In rural, tribal communities, especially when they are off the road network, old, pot-bellied wood stoves still exist for heating and cooking.
Studies on wildfire smoke impacts mainly focused on urban communities in highly populated areas with distinct firefighting in place . Wildfires are a natural element of the landscape evolution of the Interior and typically occur between May and September in Interior Alaska. Due to the sparse population of the Eastern Interior and undeveloped road network, however, wildfires are watched and only fought actively when they might endanger historic heritage places or burn properties. This so-called “let burn policy” exposes residents to wildfire smoke—often at unhealthy concentrations—over extended periods  . Furthermore, studies suggested that the frequency and extent of wildfire might increase in the future .
To assess the vulnerability of communities in the Yukon Flats, indoor aerosol concentration data were collected in various types of buildings in conjunction with simultaneous outdoor measurements in the largest community of the Yukon Flats (to ensure privacy). The objectives were to assess differences and delays between indoor and outdoor air quality, and to identify ways to reduce exposure.
2. Experimental Design
To accomplish our goals, we conducted a survey of home and business construction type and heating appliances in the communities of the Yukon Flats. Based on the survey results 15 representative homes and five representative business buildings were chosen in Ft. Yukon. Fort Yukon is the largest and oldest community in the Yukon Flats (66.56667N, 145.2581W, 134 m). It encompasses 583 inhabitants in 246 households. The number of people, more than quadruples in summer due to tourists and fire-fighters who are stationed there during the May to September wildfire season . Fort Yukon is located on the north bank of the Yukon River in the center of the Yukon Flats.
To monitor the outdoor air quality and weather conditions, we deployed four EPA-suggested equivalent method monitors and meteorological measuring devices strategically outside the city of Ft. Yukon (Figure 1).
Aerosol indoor PM monitors were located in the five business buildings at 1.52 m (5 ft) and in the homes at 0.61 m (2 ft) and 1.52 m height. These heights correspond to breathing height of adults when sleeping and being up, respectively. Measurements were taken from 14 March 2019 to 30 September 2019.
Figure 1. Map of Ft. Yukon and its vicinity to show the locations of the outdoor monitoring sites (red crosses). Note that locations of indoor monitoring sites cannot be shown for privacy reasons. Gray buildings are public buildings like school, power plant, council buildings, etc., yellow buildings are residencies. The dashed line indicates the Yukon River and Yolada slough bank. Solid lines are roads or streets.
Based on the survey two different home types—cabins and frame houses—were chosen for this study. In Ft. Yukon, cabins are a combination of regional endowed style and modern assembly changes. Frame homes are the common plywood, insulation, and tine structural types normally seen everywhere in the US in rural areas. All homes used woodstoves and furnace with temperature monitor for heating.
Office/commercial buildings with and without monitor were considered as well.
2.2. Specifications of the Instruments
A Met One Instruments’ BAM-1022, Decagon’s EM50 meteorological monitor, Davis cup anemometer, VP-4 ambient temperature/barometric pressure/relative humidity combination sensor were deployed at each of the four outdoor sites. The BAM-1022 were in an enclosed housing to protect the air intake vents from horizontally blown snow and dust, and solar radiation (Figure 2(a)). Air was taken in at 2 m height. BGI VSCC Very Sharp Cut Cyclone (BX-808) particle size separator was set to let only particles equal to 2.5 μm or smaller to pass. The combination sensor recorded temperature, barometric pressure and relative humidity at 2 m height. At 5 m height, precipitation and total in-coming radiation were observed. The Decagon EM50 measured temperature, pressure, and relative humidity at 10 m height. Wind speed and direction were measured at 10 m height as well. Table 1 summarizes the instrument specifications. The real-time measurement interval was 5 min for all devices.
Figure 2. Photos of (a) one outdoor site and (b) the indoor monitor in the lab.
Table 1. Specifications of the deployed instruments.
The Dylos DC-1700 indoor particle monitor is a highly accurate class 1 laser particle counter, and complies with 21CFR1040.10 and 11 (Figure 2(b)). The DC-1700 can display both actual particle concentrations, which relates to the International Standard (ISO) for indoor air (Table 2), as well as mass concentrations, which relates to EPA standards (Table 3). Herein, an indoor aerosol concentration of 1000 μg·m−3, for instance, would correspond to 100,000 particles of 0.5 micron or smaller in diameter, and would indicate very poor air quality.
2.3. Quality Assurance/Quality Control and Data Processing
The data files of each of the outdoor sites were synchronized for time and combined. The same quality assurance/quality control (QA/QC) protocol as described
Table 2. ISO for indoor air quality for particle concentrations.
Table 3. Mass concentration and EPA 24-h outdoor standard.
in  was applied for both the indoor and outdoor observations. Missing data were tagged as such; false and/or data beyond an instrument’s range were discarded and tagged as such.
2.4. Data Analysis
To ensure the privacy of the home and business owners, we striped the data from all information that could be used for identification (location, owner, renter, number of occupants, year of construction, etc.). In doing so, we averaged the recorded data over all measurements from homes of same construction and over all offices/commercial buildings with same heating appliance type. The indoor concentrations served to quantify the long-term means of exposure.
For all quantities, we determined the hourly, daily, monthly and seasonal means and standard deviations. We compared the 24-h means of indoor and outdoor PM2.5 concentrations to the ISO rating scales (Table 2, Table 3) and concentrations found in the literature. Daily means were also assessed using the current U.S. National Ambient Air Quality Standard (NAAQS) for 24-h average for PM2.5 of 35 μg·m−3  and to the World Health Organization (WHO) recommended 3-day average of 25 μg·m−3  that should not be exceeded. In accord with , the standard deviations served as an indicator of the temporal variations of PM2.5 and other quantities over a time interval of interest (hour, day, month, season).
The outdoor concentration and wind direction data at individual sites were analyzed to assess the contributions of emissions from the various sources from within the village and from outside.
To attribute sources, we calculated hourly PM2.5 concentration means as a function of wind direction using the WHO and EPA recommended algorithm   .
To examine the relation of indoor and outdoor air quality and its dependence on the weather conditions, we averaged the meteorological and air quality data of the four outdoor PM2.5 monitors as well. The hourly means over the four outdoor PM2.5 concentrations were also used to identify surface-based inversions (SBI), their onset and dissipation as well as duration and the air flow in and out of the community following  . These hourly means also served to examine differences in the degree of pollution as compared to indoors.
Furthermore, we calculated monthly mean diurnal courses and 24-h means of PM2.5 concentrations for the outdoors, residences at large, cabins, frame houses, office/commercial buildings as well as office/commercial buildings with and without temperature monitor. Ratios of indoor to outdoor (1/O) concentrations were determined as well.
3. Results and Discussion
3.1. Emission Sources
The National Emissions Inventory 2017 (NEI2017) reports only the annual totals of the Yukon-Koyukuk borough without distinction between the airsheds of the Yukon Flats and the Koyukuk region. Annual total emissions of the borough were 8254, 8093, 402, 6054, 1825, 15 and 1 metric tons for halocarbon (HC), carbon monoxide (CO), nitrogen oxides (NOx), particulate matter less or equal to 10 µm in diameter (PM10), PM2.5, sulfur oxide (SOx) and ammonia (NH3), respectively.
In Ft. Yukon, large emission sources for PM2.5 and its precursor gases stem from burning grade-1 diesel for heating the city office buildings, corporation buildings, stores, businesses, the Tribal Council and Tribal Consortium buildings, regional health clinic and laundry-mat . These buildings are all in the center of Ft. Yukon (Figure 1). Emissions vary with time of day, day of the week, time of the year, holidays and outside weather conditions. Another source for emissions of PM2.5 and its precursor gases is the biomass power plant.
Residences use furnaces burning grade-1 diesel fuel and/or woodstoves for heating in winter. Typically, wood- and diesel burning occur during the day and night, respectively. Due to the far north location sporadic heating may occur also in the other seasons. In addition, anthropogenic emissions of fine particulate matter and precursors occur from outdoor cooking of dog food and during the various subsistence seasons from smoking within the village and/or its surroundings  .
In Ft. Yukon, large vehicle fleets of private and municipal vehicles exist that use gas or diesel. Cold starts cause higher emissions  in the cold season (October to April) than in the warm season (May to September). Idling of vehicles to warm up the engine contributes to the cold and transition seasons’ traffic emissions. Off-road vehicles are mostly snow mobiles and four-wheelers. Traffic emissions on the Yukon stem from boats in summer and snow mobiles in winter. Air traffic emissions stem from multiple daily scheduled flights and during the fire season from large retardant cargo carriers  . These aircrafts burn leaded fuel.
Sources of silicate aerosols are the city road systems and dust-uptake by wind from river gravel bars   .
The analysis of the wind direction data and outdoor PM2.5 concentrations confirmed the findings of  regarding the major contributors to local air concentrations.
Common indoor emission sources of particulate matter and its precursors are cooking, woodstoves, fur and hide tanning, fire places, outgassing of building material, carpets and furniture and in houses with built-in or attached garages, vehicle and engine emissions.
The indoor emissions vary by age of building, carpet and furniture types, type and age of heating and cooking devices, number, age and activities of household members, among other things. No emission data were recorded. Of course, the amount of indoor emissions and outdoor concentration and hence vicinity to outdoor emission sources as well as the air exchange between inside and outside air affect indoor concentrations. Measurements at two schools located in Hanoi, Vietnam, for instance, revealed indoor average concentrations of PM2.5 and PM10 of 49.4 µg·m−3 and 59.7 µg·m−3 in one neighborhoods and 7.9 µg·m−3 and 10.8 µg·m−3 in another, respectively . Similarly, in our Ft. Yukon campaign, outdoor and indoor air quality also showed differences dependent on the vicinity to major emissions sources. Smoking and cooking were found to increase indoor and kitchen PM2.5 concentrations rapidly to 1280 µg·m−3 and 3000 µg·m−3, respectively .
3.2. Meteorological Conditions
Daily means of 10 m height wind speed rarely exceeded 5 m·s−1 (Figure 3(a)). At 10 m height, daily means of relative humidity varied between 33% and 96% and were 59% on average over all days. Daily means of relative humidity at 2 m height ranged between 32% and 95%, but were on average around 57% (Figure 3(a)). At this height, daily mean temperatures were between −11˚C and 24.5˚C (Figure 3(a)). At 10 m height, these values were −11.4˚C and 24.9˚C, respectively. Total accumulated solar downward radiation varied between 80 kW·m−2·h−1 and 1247 kW·m−2·h−1 depending on the day of year and atmospheric conditions including cloudiness and aerosol optical depth (see Figure 3(b)).
3.3. Surface-Based Temperature Inversions
Following , we used hourly means of the outdoor temperature measurements at 10 m and 2 m height as an indicator for the occurrence of surface-based
Figure 3. Daily mean outdoor meteorological and air quality conditions during the campaign averaged over the four sites: (a) 2 m height temperature and 10 m height wind speed, (b) 2 m height relative humidity and solar radiation, and (c) outdoor PM2.5 conditions.
temperature inversions during the respective days of the study. For each day the start and end time as well as the duration of the surface inversion were determined (e.g. Figure 4).
Figure 4. Daily local times with (bars) and without inversions at Ft. Yukon as obtained from the hourly averages of 10 m and 2 m temperatures differences over all four outdoor sites for (a) March, (b) April, (c) May, (d) June, (e) July and (f) September. Days without bars indicate missing data. August is not shown due to the high amount of missing data.
All days with complete temperature data had surface-based inversion (e.g. Figure 4). The duration throughout a day varied from March to September. The majority of all SBI occurred through the late night and early morning, local time. Some multiday SBI occurred in the shoulder season. Occasionally, more than one SBI formed per day. Durations of SBI were typically shorter in April than in the other months due to increased wind speed. Furthermore, the onset of melting led to differential heating and convection broke the SBI.
3.4. Exposure to PM2.5
3.4.1. Outdoor Air Quality
The daily data of hourly means of temperatures and the derived onset, dissipation and duration of inversions were utilized to separate the measured outdoor PM2.5 concentrations into two categories, namely those (a) occurring during a surface-based inversion (SBI) event and (b) those occurring during hours without an SBI (Table 4).
Following the EPA guidance, we calculated the 24-h mean outdoor PM2.5 concentrations from hourly mean concentrations. To assess the exposure on a monthly basis we averaged over the daily 24-h means of the respective month (2nd column in Table 4). Since inversions only existed for parts of the day (see e.g. Figure 4) and started or ended not necessarily to the full hour, means for times with and without inversions were calculated for each month based on the 5-min mean raw data that were kept after the QA/QC. These monthly means for times with and without the presence of SBI are up to about factor 2 higher than the monthly means calculated from 24-h means calculated in accord with the NAAQS (Table 4).
The difference between the monthly mean of 24-h means and the monthly means for times with or without inversions in the following tables is that the latter two cover not the full amount of hours of the months. This means that they are expressed in terms of means for the times with or without inversions. The monthly mean of 24-h average PM2.5 concentrations is given by
where and are the concentrations during times with and without inversions, respectively. Furthermore, ΔtSBI/(ΔtSBI + Δtno) and Δtno/(ΔtSBI + Δtno) are the ratios of the times with and without inversions to the time in the month. Note that ΔtSBI = 1 − Δtno..
On average, outdoor air quality improved from March to May (Figure 5). Due to increasing air temperatures emissions from heating went down as spring progressed. After the onset of the wildfire season (end of May), ambient monthly PM2.5 concentrations increased in June (Table 4). Also wind speeds increased as compared to the earlier months and picked up dusts from unpaved roads and the Yukon and Porcupine riverbanks. Nevertheless, March to June monthly mean PM2.5 concentrations remained below 25 µg·m−3. Monthly mean outdoor
Table 4. Monthly means and their standard deviations of daily 24-h mean outdoor PM2.5 concentrations averaged over all four sites, and means and standard deviations calculated from all hours without (no inversion) and all hours with surface based inversions (SBI) using data from all four sites.
Figure 5. Temporal evolution of monthly mean PM2.5 concentration.
PM2.5 concentrations reached 55.3 µg·m−3 in July due to wildfires (Table 4). This means the 24-h NAAQS was exceeded and led to unhealthy for sensitive groups to hazardous conditions on various days. In August, the rain season set on for which outdoor air quality improved. In September, outdoor air quality slightly decreased because decreasing air temperatures caused emissions from heating. Furthermore, inversion durations increased (Figure 4).
On average, monthly mean PM2.5 concentrations were higher during surface-based inversions than at other times (Table 4). During times with and without SBI, PM2.5 concentrations decreased from March to May as less heating was need because of increasing outdoor temperatures. In June, the wildfire season set on and led to increases in concentration during both SBIs and inversion-free times. Concentrations during time with and without inversions differed least in September (3.2 µg·m−3) followed by May (8.7 µg·m−3). The difference was largest (42.0 µg·m−3) in July (cf. Table 5, Table 6) which can be explained by advection of wildfire smoke.
Over the timeframe of available data, the US NAAQS of 35 µg·m−3 for the 24-h mean was exceeded nearly 45% of the time. The WHO recommended 24 h-value of 25 µg·m−3 not to be exceeded for three consequent days was violated as well.
Table 5. Means of baselines of indoor PM2.5 concentration values at 0.61 m height averaged over all hours without (no inversion) and all hours with surface based inversions (SBI) averaged over all residences, cabins and frame houses within a month.
Note -.- indicates no data.
Table 6. Monthly means of baselines of indoor 24-h PM2.5 concentration values at 1.52 m averaged over all hours without (no inversion) and all hours with surface based inversions (SBI) and their spatial standard deviations for various types of residences.
Note -.- indicates no data available.
When looking at hourly means, PM2.5 concentrations were higher than 25 µg·m−3 nearly 91% of the time during SBI and about 80% of the time when no SBI existed at Ft. Yukon. During the 2017 strong fire season in the Yukon Flats , 5-min mean outdoor concentrations were occasionally a magnitude or more, higher than the hourly means. The maximum hourly mean PM2.5 concentrations over all four sites in March to September 2019 were 71.5 µg·m−3, 79.8 µg·m−3, 63.6 µg·m−3, 289.3 µg·m−3, 792.3 µg·m−3, 22.8 µg·m−3, and 66.7 µg·m−3, respectively. Outdoor air quality was on average better than in the record fire season of 2017 (cf. ).
3.4.2. Indoor Air Quality
On average over all indoor sites, a diurnal course can be detected in office/commercial buildings and at both heights for residences (Figure 6). Typically, indoor concentrations were higher during the day than at night which broadly coincides times of no SBI and with SBI (Table 5, Table 6). This finding is expected as people move around in their residences thereby re-suspending particles into the air that had already settled on the floor or other surfaces. Various daytime activities like cooking, smoking or feeding of woodstove could lead
Figure 6. Temporal evolution of monthly mean diurnal course of PM2.5 concentrations.
to notable emissions of particles. In late June and July, mosquitoes become an annoyance in the Yukon Flats and are fought with mosquito pics. Mosquito pics contribute to PM2.5 concentrations in June, July and August (JJA). These activities cannot be readily controlled nor documented due to privacy issues as well as indigenous cultural distrust of being monitored.
In residences, indoor monthly mean concentrations at 0.61 m were lowest in June and highest in July (Table 7). Indoor concentrations at 1.52 m were lowest in July and highest in April and May. On average over all residences, indoor concentrations were higher at 0.61 m than at 1.52 m height (e.g. Figure 6(a)). Averaged over all residencies, monthly mean PM2.5 concentrations were 12.5 µg·m−3, 40.1 µg·m−3, 75.3 µg·m−3, and 46.1 µg·m−3 higher at 0.61 m than at 1.52 m in April, May July and August, respectively. This distribution can be partly explained by gravitational settling. The hourly mean values at those heights showed weak correlation (21%). These findings mean that exposure to high concentrations was on average over all residences higher when laying down than when standing or walking around (cf. Table 7).
The exposure at both levels was higher than the median of 6.65 µg·m−3 found by  for households with woodstoves in New England. The values of the lower and upper range of mean hourly indoor PM2.5 concentrations (Figure 6) were lower than the 112 - 416 µg·m−3 observed by  in homes in Beijing, China in winter. PM2.5 concentrations in office/commercial buildings in Ft. Yukon were on average very unhealthy for some hours on a daily basis in June and August and were even hazardous most of July. Exposure of employees, clients and customer was much higher than found for these groups in other places. Measurements in an office building in Guangzhou, China, for instance, showed only 3% and 1% of the time very unhealthy and hazardous condition in JJA . In Dublin, Ireland, indoor PM2.5 concentrations during working hours were typically below 25 µg·m−3 with highest values in naturally ventilated shops .
Table 7. Monthly means of baselines of indoor PM2.5 concentration values averaged over all days, all hours and their standard deviations.
Note -.- indicates no data available.
3.4.3. Indoor vs. Outdoor Air Quality
Indoor PM2.5 concentrations were influenced not only by continuous and temporal indoor emission sources, the residents’ activities and the degree of mixing related to the residents’ motions, but also by exchange between inside and outside air.
In contrast to all other months, June concentrations were lower (14.4 µg·m−3) at 0.61 m than 1.52 m, which can be explained by transport of polluted air from nearby wildfires into the community. Typically, in wildfire smoke, PM2.5 concentrations decrease from the maximum values towards the ground as well as with height. Consequently, outdoor air has higher concentrations at the heights of vents than at the gaps between doors and the floor.
In comparison to outdoors, indoor concentrations of PM2.5 were usually much larger in magnitude (cf. Tables 4-6). Monthly mean ratios of indoor to outdoor concentrations (I/O) in residences were 4.8, 5.3 and 12.2 at 0.61 m in March, April and September, respectively. Ratios were lowest in May, June and July with 1.4, 2.0 and 2.4, respectively. In August, I/O reached its maximum of 21. In August, the increased humidity and rainfall scavenged particles from the air thereby reducing outdoor concentrations while indoor emissions accumulated in empty homes. The increased humidity also leads to swelling of particles. This means PM2.5 concentrations may decrease, when the grown particles exceed 2.5 µm in diameter. The high I/O ratios can be explained by indoor emissions from wood burning for cooking and heating, recreational smoking and use of mosquito pics. In Guangzhou and Dublin, for instance, I/O were below 1  .
In the analysis of the impact of inversions on indoor concentrations, the question of air exchange arises. To compare indoor and outdoor concentrations of PM2.5 during times with and without SBI the temporal lag between indoor and outdoor concentrations has to be determined first. We applied a lag-time correlation method  at various temporal lags using the means over the hourly outdoor concentrations and those averaged over all hourly indoor concentrations as input data. The method revealed a maximum correlation for a lag-time of 1-hour and 2 hours for residences at 0.61 m and 1.52 m heights (e.g. Figure 7), respectively. It showed a time-lag of 1 hour for office/commercial buildings.
When people are not home in their residents, the floor to door gaps, and other interior-to-exterior connections (e.g. cooking hoods, vents) have a greater influence at floor levels where air settles and is cooler than at 1.52 m. The 1.52 m level has a longer lag-time due to dead air and diffusion time.
Based on these findings and the determined onset and ending times of the SBIs within each 24-hour period (e.g. Figure 4), we adjusted the timing of indoor concentrations to account for the lag-time for the residences at both heights and the office/commercial buildings. Doing so synchronized the indoor concentrations with that outdoors for times with and without inversions.
On average over all residencies, indoor concentrations were higher during times without inversions than during SBIs at 0.61 m height in March, April and September (Table 5). From May to July, indoor concentrations at 0.61 m height were lower during times without inversion than during times with inversions. Averaged over the few available March days and all residences, the means were
Figure 7. Correlation between mean (over all sites) indoor and outdoor PM2.5 concentrations at various time lags and heights.
33.8 µg·m−3 and 137.4 µg·m−3 under conditions of no inversions and SBIs, respectively. The mean of the few available data in September during times of SBIs was 76.2 µg·m−3, while not enough data existed to calculate a mean for non-inversion conditions.
These findings can be explained as follows. In April and September, heating is still and already again needed. Woodstoves release PM2.5 and its precursor gases during operation. Typically, they are used for heating during the day and are fed once more prior to bedtime. Once all wood is burned, these emissions stop. When indoor temperatures fall below the monitor-set threshold, the furnace turns on. The emissions from diesel heating mostly leave via the chimney. Furthermore, settled particles are not re-suspended when the residents are sleeping. Consequently, PM2.5 concentrations were lower during nighttime than daytime. Since, most inversions occurred at night (cf. Figure 4), indoor concentrations were lower during inversions than no inversion conditions in the heating season (Table 5). People are awake the majority of the time during the day when no surface inversion exists. Consequently, due to their activities PM2.5 concentration levels were highest indoors during this time.
3.4.4. Relation between Indoor Air Quality and Building Type
To examine the relation between construction types and building use, we averaged the hourly mean indoor PM2.5 concentrations for each construction type and measurement level. Furthermore, we calculated hourly concentration means for residencies and office building by averaging over all respective sites.
Typically, hourly mean values at 0.61 m and 1.52 m heights correlated weakly (27.4%) and hardly (1.5%) for frame houses and cabins, respectively (Figure 7(b)). This means that indoor air quality of frame houses is stronger related to outdoor air quality than indoor air quality of cabins. On average, in frame houses, concentrations were higher at 0.61 m than 1.52 m, while the opposite was observed in cabins (cf. Figure 6(b), Figure 8). Daily 24-h mean indoor PM2.5 concentrations at both heights for the greater part exceeded the NAAQS frequently (Figure 8).
On average, cabin indoor PM2.5 concentrations at 0.61 m height were higher during times without inversions than during SBIs (Table 5). The same is true for frame houses in April and September, while the opposite was true from May to August (Table 5).
At 1.52 m height, means of both cabin and frame house indoor PM2.5 concentrations were higher for times with than without inversions in all months (Table 6). Cabins provided the lowest exposure at both heights (Table 5, Table 6). No data were available at 0.61 m height for cabins in March and September. Only few PM2.5 data were available at 0.61 m height for frame houses in March. This means the March values for frame houses are the same as those for residencies, namely 33.8 µg·m−3 and 137.4 µg·m−3 during times without and with SBI, respectively. The mean indoor PM2.5 concentrations over all September data during inversions were 76.2 µg·m−3 at 0.61 m height.
Figure 8. Temporal evolution of 24-h mean PM2.5 concentrations.
These insights suggest that in frame houses, sleeping on top-bunks seems to be beneficial for health except during wildfires. Also cabins seem to be superior over frame houses in keeping outside PM2.5 concentrations at bay.
Recall no measurements were made at 0.61 m height in office/commercial buildings because nobody sleeps in them. In April and May, concentrations at 1.52 m were higher in residences than office/commercial buildings both during times with and without SBI (cf. Table 6). At night, office/commercial buildings are closed meaning that particulate matter settled. Consequently, 1.52 m concentrations went down as compared to the times without SBI, i.e. during the day. The higher concentrations in residences than office/commercial buildings during times without SBI can be explained by the different kind of indoor activities, emissions sources and emission rates at home and work.
In JJA, monthly mean and 24-h mean PM2.5 concentrations in office/commercial buildings exceeded those in residences (cf. Table 6, Table 7, Figure 8, Figure 9). For both, concentrations were higher during SBI than during times without SBI (Table 6). The reasons for this shift in concentration behavior are as follows. In JJA, residents were involved in activities related to the various harvest seasons
Figure 9. Temporal evolution of 24 h-mean PM2.5 concentrations averaged over all office/commercial buildings with and without temperature monitor.
(e.g. fishing, berry picking, hunting). Hence, they stayed less time inside or even stayed outside of town in seasonal camps. Consequently, resuspension due to inside activities were reduced in residencies as compared to April and May, when residents spent more time inside. Absence also meant less emissions from mosquito pics as compared to offices/commercial buildings.
As aforementioned, in summer, the population quadruples due to tourists and fire fighters stationed at Ft. Yukon. Thus, office and commercial buildings have not only more public traffic in JJA than April and May, but also longer business hours to serve the increased demand. Furthermore, more emissions occur inside due to increased amounts of merchandise, use of equipment and cooling. Mosquito pics serve to hold mosquitoes at bay during the day. The increase in customers also leads to an increase in activities for restocking. During the day, customer traffic led to air exchange between indoor and outdoor air. At night, no such ventilation existed. Thus, the increased indoor emissions accumulated inside. Consequently, inside concentrations during the nighttime SBI exceeded those observed at daytime.
3.4.5. Impact of Temperature Monitors in Office/Commercial Buildings
Various studies indicated that in Fairbanks, wood burning is the major cause for exceedances of the outdoor NAAQS (e.g.  ). Simulations with the Weather Research Forecast model coupled with chemistry packages (WRF-Chem) showed that using both low sulfur fuel and EPA certified wood stoves reduced the concentrations of PM2.5 as compared to the old devices used in Fairbanks . Therefore, we examined the impacts of temperature monitors on indoor air quality of office/commercial buildings. All residences had a monitor and burned both wood and diesel, while office/commercial buildings only burned fuel.
In office/commercial buildings without temperature monitor, monthly and 24-h mean PM2.5 concentrations were up to more than two orders of magnitude higher than in office buildings with monitors during JJA (Table 7, Figure 9). This means that using a temperature monitor could reduce exposure of employees, clients and customers in summer. In April, office and commercial buildings without monitor have lower PM2.5 concentrations than those with monitor. This finding can be explained by the monitor and hence furnace reacting to changes in indoor temperature caused by opening and closing of doors during business hours. However, in both cases, PM2.5 concentrations remain in the healthy range.
4. Conclusions and Recommendations
The exposure of rural communities in the Yukon Flats to PM2.5, concentrations was assessed by measurements at 20 indoor and four outdoor sites from March 2019 to September 2019 using Ft Yukon as a testbed. In residences, PM2.5 was measured at 0.61 m and 1.52 m heights; while in office/commercial buildings, data were taken at 1.52 m. Outdoor observations included meteorological data in addition to PM2.5.
On average, PM2.5 outdoor concentrations were well below 25 µg·m−3 and decreased from March to May due to decreasing inversion durations and heating. June outdoor PM2.5 concentrations were below this value except when wildfire smoke was advected. July mean outdoor concentration of PM2.5 was 55.3 µg·m−3. When in the downwind of wildfires, outdoor PM2.5 concentrations often exceed the 24-h US NAAQS of 35 µg·m−3 and in some hours even reached hazardous conditions.
While indoor air quality was moderate in March, it decreased towards summer to hazardous levels on monthly mean and also exceeded those observed outside for notable amounts of time for all building types. Typically, concentrations were lower in cabins than frame houses. In frame houses, PM2.5 concentrations were higher at 0.61 m than 1.52 m, while the opposite was true in cabins.
Based on these findings one has to conclude that 1) The new log cabins in Fort Yukon provide better indoor air quality conditions than modern frame homes; 2) Sleeping on bunk beds would be beneficial for health in frame houses; 3) Yukon Flats communities are exposed to hazardous indoor PM2.5 concentrations all summer.
On average, there was a one-hour time-lag between changes of indoor and outdoor air quality conditions. Cabins had the lowest correlation between indoor and outdoor PM2.5 concentrations followed by frame houses and office/commercial buildings with the highest correlation. Therefore, one has to conclude that frame homes have a higher ventilation and air exchange than cabins, and that frequent opening and closing of doors during hours of customer traffic increased air exchange. The additional emissions from increased amounts of merchandise and/or increased use of equipment in office/commercial buildings and mosquito pics caused hazardous PM2.5 concentrations at breathing level. Implementation of a temperature monitor could reduce indoor PM2.5 concentrations by about two orders of magnitude. According to a study in Libby, Montana a change-out program for wood and cooking stoves could improve indoor air quality by 53% .
We thank the residents, business owners and office manager of Ft. Yukon who volunteered their homes and office/commercial buildings as measurement sites, and the anonymous reviewers for fruitful discussion and comments. The Tribal Resilience Program, NSF SOARS program grant #1641177 and the Mellow Graduate Student Fellowship as well as the State of Alaska provided financial support for this study.
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