Associations between Wildfire Smoke Exposure and Health-Related Quality of Life: Findings from the Lovelace Smokers Cohort

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Sinclair, Huining Kang, Tyler Eshelman, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7069099/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Respiratory Research → Version 1 posted 14 You are reading this latest preprint version Abstract Background The impact of wildfire smoke (WFS) on air quality across the contiguous US has become geographically widespread. However, the effects of episodic WFS exposure on psychometric measures of mental and physical health remain largely unknown. Objectives To assess the associations between WFS PM 2.5 and black carbon (BC) exposure and psychometric health measures. Methods The St. George's Respiratory Questionnaire (SGRQ) and the 36-Item Short Form Survey (SF-36) were administered to participants in the Lovelace Smokers Cohort in New Mexico to assess psychometric health measures in the past 4 weeks. WFS estimates were calculated against Albuquerque metropolitan area or individual residential addresses for 7-, 15-, 30-, and 60-d prior to questionnaire filling. The associations between exposure and health measures were assessed using linear models. Results Significant associations were observed for all psychometric measures with WFS PM 2.5 and BC exposures estimated for 7-d prior to questionnaire filling. Significant associations remained for WFS exposure estimated up to 30-d prior to questionnaire filling for all SGRQ subdomains and physical health measures of SF-36, but became non-significant for the mental health measures of SF-36 beyond one week prior. Additionally, WFS PM 2.5 exhibited stronger potency than total ambient PM 2.5 . Male participants, individuals with less than a college education, and those exposed to woodsmoke demonstrated heightened vulnerability to WFS. Conclusions Episodic exposure to WFS was associated with worse SGRQ and SF-36 scores, with notable differences in temporal patterns between mental and physical health measures. Our findings also underscore the importance of source-specific risk assessment for air pollution. Wildfire smoke PM2.5 black carbon SGRQ SF-36 Figures Figure 1 Figure 2 Introduction Rising temperatures, prolonged droughts, and increasing vegetation desiccation accompanying climate variability are dramatically intensifying the frequency, duration, and severity of wildfires across North America and globally ( 1 – 3 ). Wildfire smoke (WFS) has had a spatially and temporally profound impact on air quality across the contiguous U.S., stalling or reversing multi-decadal declines in fine particulate matter (PM 2.5 ) concentrations in 35 states, and contributing significantly to the rise in extremely polluted days (daily PM 2.5 >35 µg/m 3 ) in 18 states since 2012 ( 4 , 5 ). The impact of WFS on air quality is no longer transient or negligible, as it has contributed an average of 1 µg/m 3 annually to ambient PM 2.5 concentrations in the eight most affected states in the western and midwestern U.S. since 2016. Moreover, in high-fire years (2017, 2018, and 2020), WFS contributes up to 5 µg/m 3 to annual PM 2.5 concentrations, which is equivalent to roughly half of the total annual average PM 2.5 from all sources across much of the contiguous U.S. ( 5 ). Since 2010, WFS has also significantly increased the black carbon-to-PM 2.5 ratio in Western U.S., thus could potentially elevate the toxicity of PM 2.5 ( 6 – 9 ). Individual wildfire episodes can last anywhere from weeks to months, releasing smoke plumes into the atmosphere that contain large amounts of particles and toxicants. These wildfires, along with the smoke they produce, can significantly affect local air quality and public health. In addition, prevailing winds can carry WFS hundreds or even thousands of miles away from the source. Once the smoke descends, it impacts near-ground air quality, affecting the health of populations far from the original fire sites ( 2 ). Therefore, geographic locations affected by WFS can either be near the burning sites or downwind regions, with air quality deteriorating in an episodic manner. This means that when wildfires are active, air pollutants in these regions can increase temporarily, ranging from weeks to months. Acute health effects from extreme WFS exposure can occur within hours or days, including cardiovascular events, respiratory symptoms and exacerbation, eye irritation, and reduced cognitive performance ( 10 – 14 ). Evidence for chronic health effects after multiple rounds of episodic WFS exposure begins to emerge and supports the etiological link of WFS with lung and brain cancer and incident dementia ( 15 , 16 ). However, evidence supporting the health impacts of episodic WFS exposure, which usually last from weeks to 2–3 months in the contiguous U.S., is very limited. Only one study has reported obstructive airway changes following a 45-day WFS exposure in Seeley Lake, Montana, where daily PM 2.5 levels averaged 220.9 µg/m 3 . Notably, these airway changes persisted even two years after the exposure ( 17 ). Delineating these early changes will inform both acute and chronic health risks, identify vulnerable subgroups, and reveal underlying mechanisms for potential mitigation options. This study aimed to investigate the associations between episodic exposure to WFS and health-related quality of life (HRQoL), which captures multi-dimensional assessment of physical health, mental well-being, and social functioning. Additionally, we assessed individual traits that may sensitize people for WFS health effects. These analyses were conducted using data from the Lovelace Smokers Cohort (LSC), located in Albuquerque, an arid region frequently impacted by WFS from wildfires originated in Gila, Apache, and Apache-Sitgreaves National Forests due to prevailing southwest winds in warm seasons. Methods Study population The LSC is a longitudinal, population-based volunteer cohort with majority of participants enrolled from the greater Albuquerque area of New Mexico from 2001 to 2017 (Fig. 1 ). The primary objective of the LSC was to identify biomarkers in sputum and blood for lung cancer and chronic obstructive pulmonary disease (COPD) development. The design of the LSC has been described elsewhere ( 18 – 20 ). Briefly, a total of 2511 participants aged 40 to 75 years, with at least 10 pack-years of smoking and no prior history of lung cancer, were recruited through local newspaper, radio, and television advertisements. At baseline, participants completed a standardized questionnaire on demographics, tobacco smoking, medical history, diet, as well as quality of life measures (the St. George's Respiratory Questionnaire [SGRQ] and the 36-item short-form health survey [SF-36]). They also underwent pre- and post-bronchodilator spirometry and provided biological samples (blood and sputum). Follow-up visits were conducted approximately every 18 months till November 2017. All participants provided informed consent, and the study was approved by the Western Institutional Review Board. Health-Related Quality of Life Measures Health-related quality-of-life (HRQoL) was assessed using the general health SF-36 questionnaire and the lung disease-specific SGRQ with the recall period of past four weeks ( 21 , 22 ). The SGRQ total score and its activity, symptom, and impact domain subscores range from 0 to 100, with higher score indicating a worse HRQoL ( 23 ). A minimal clinically important difference in SGRQ total score and domain subscores is 4 ( 24 ). The SF-36 encompasses eight domains including physical functioning, role physical, role emotional, social functioning, mental health, vitality, general health perceptions, and bodily pain. The SF-36 scores range from 0 to 100, with higher scores indicating better HRQoL ( 21 ). Confirmatory factor analysis using R package lavaan was used to identify latent constructs representing physical and mental health based on factor structures established in U.S. general populations ( 25 ). These two factor scores were then used in subsequent analyses to reduce dimensionality. WFS assessment The majority of the LSC participants were from the greater Albuquerque area including Bernalillo, Sandoval, Valencia, and Torrance counties, thus we used this area (a total of 18,719 km 2 ) to estimate WFS exposure. First, we calculated the number of smoke days in periods of 7-, 15-, 30-, 60- days prior to questionnaire filling. The 60-day maximal length was selected because majority of WFS episodes that led to elevation in air pollution in Albuquerque lasted two months or shorter. Smoke days were defined as when the study area was overlapped with satellite-detected smoke plumes by at least 1 km², based on data from the National Oceanic and Atmospheric Administration (NOAA) Hazard Mapping System (HMS). Second, we also calculated cumulative area of the greater Albuquerque area overlapped with WFS plumes for individual time periods to estimate the geographic massiveness of the WFS. Third, we assessed WFS exposure using quantitative metrics such as smoke PM 2.5 and smoke black carbon (BC) in ambient air. These quantitative metrics effectively captured near-ground pollutant levels, providing a more precise assessment compared to smoke plume-based methods. We took advantage of recent advances in air quality modeling that provided ambient PM 2.5 and BC data at higher geospatial (1 × 1 km) and temporal (daily) resolution across the contiguous US from 2000 to 2020 ( 6 ). We followed Dr. Child’s published methods to estimate smoke PM 2.5 and smoke BC ( 4 , 5 ). In brief, smoke PM 2.5 and BC were quantified as deviation in PM 2.5 or BC values in smoke days from the median values from non-smoke days in the same month over a three-year period, spanning the year before and the year after. Zero was assigned to smoke days when the subtraction gave negative values, i.e., smoke plumes did not compromise near ground air quality. Average smoke PM 2.5 and smoke BC in 7-, 15-, 30-, 60- days prior to questionnaire filling were calculated. Additionally, Dr. Childs and colleagues developed a machine learning algorithm to predict smoke PM 2.5 levels through integrating a comprehensive dataset spanning ground monitoring records, expanded smoke plume data, fire emission inventories, land use and elevation, meteorology, and satellite aerosol measurements ( 4 , 5 ). This smoke PM 2.5 dataset covered the contiguous US from 2006 to 2020 and has an excellent prediction accuracy, verified specificity for WFS, and sufficient geo-spatial (10 × 10 km) and temporal (daily) resolution for health linkage study ( 4 , 5 ). We applied the county-level daily WFS dataset developed by Dr. Childs and colleagues as an alternative exposure assessment in sensitivity analyses. Geocoding We used residential locations to calculate WFS exposure and as sensitivity analyses to validate results from the greater Albuquerque area. Addresses were geocoded with ArcGIS 10.8, standardizing data and matching it to the Albuquerque Street map to assign latitude and longitude. These geocoded locations helped estimate WFS exposure by assigning average smoke PM 2.5 and BC concentrations based on the corresponding 1 × 1 km grid for each specified time period. Statistical analysis Statistical analyses were conducted in 747 LSC subjects who were enrolled in 2006 (first year when smoke plume data became available) and after and had measures of SGQR and SF-36 at baseline. Linear model was used to assess the associations between WFS measures and SGRQ and SF-36 scores, with adjustment for baseline age, sex, BMI, ethnicity, education level, status and packyears of tobacco smoking, airway obstruction for SGRQ, and baseline comorbidity for SF-36 ( 19 , 26 , 27 ). Due to moderate to high correlations among WFS measures (Supplemental Fig. 1) and among outcomes, instead of setting arbitrary cutoffs for claiming study-wide significance, we chose to weight evidence based on nominal P values as well as consistency across measures and time periods. Interaction analyses were conducted to explore whether the observed associations varied across several candidate factors such as sex, education, current smoking status, woodsmoke exposure (self-reported in response to a question “Have you ever been exposed to woodsmoke for 12 months or longer” as part of the general health survey at study entry of the LSC), chronic mucous hypersecretion, and ethnicity. Interaction analyses were conducted in 7-day WFS – SGRQ or WFS – SF36 associations due to the greatest significance. P values less than 0.1 for the interaction terms were deemed meaningful interactions. These interaction analyses should be viewed as secondary analyses to reduce the issue of multiple comparisons. As a sensitivity analysis, we excluded participants who were followed up during winter (n = 66), as significant WFS events were rare in the Albuquerque area during this season. All analyses were conducted using R (version 4.4.1, Vienna, Austria) in the RStudio (version 2024.9.0.375). Results WFS and Air Quality The greater Albuquerque, the catchment area of the LSC was frequently affected by WFS during summer that predominantly originated from the fires in Apache and Gila National Forest around the Arizona and New Mexico border (Fig. 2 A) ( 28 ). The distance between these fires and Albuquerque metropolitan area is 150 to 200 miles, suggesting aged WFS. The estimated ambient PM 2.5 and BC concentrations were significantly higher in 474 smoke days versus 3909 non-smoke days (6.55 µg/m 3 versus 4.14 µg/m 3 for PM 2.5 and 0.25 µg/m 3 versus 0.16 µg/m 3 for BC). For example, in the summer of 2011, WFS released from the Wallow Fire is the major contributor to elevated ambient PM 2.5 (Fig. 2 B). Study Participants and their WFS Exposure The 747 participants had an average mean (SD) age of 56.9 (9.1) years and included 383 females, 185 Hispanics, and 447 current smokers (Table 1 ). A total of 479 subjects had received some college education and above. A total of 241 subjects self-reported to be “ever woodsmoke exposure for over a year”. Comparisons between people with and without smoke days in a week prior to questionnaire filling did not identify any significant differences for variables under consideration (Table 1 ). Moreover, about 400, 540, and 613 subjects were exposed to smoke days in 15-, 30-, and 60- days prior to questionnaire filling (Table 2 ). Table 1 Demographics of the Study Participants (n = 747) Characteristics All participants 7-day no WFS 7-day with WFS P Value (N = 747) (N = 469) (N = 278) Age, mean (SD), yr 56.87 (9.08) 56.47 (9.03) 57.53 (9.13) 0.12 Sex, No. (%) 0.25 Male 364 (48.73) 221 (47.12) 143 (51.44) Female 383 (51.27) 248 (52.88) 135 (48.56) BMI, mean (SD), kg/m 2 28.78 (6.35) 28.48 (5.88) 29.29 (7.06) 0.09 Ethnicity, No. (%) 0.08 Non-Hispanic 562 (75.23) 343 (73.13) 219 (78.78) Hispanic 185 (24.77) 126 (26.87) 59 (21.22) Education level, No. (%) 0.12 Less than college 268 (35.88) 178 (37.95) 90 (32.37) Some college or above 479 (64.12) 291 (62.05) 188 (67.63) Current smokers, No. (%) 0.20 No 300 (40.16) 180 (38.38) 120 (43.17) Yes 447 (59.84) 289 (61.62) 158 (56.83) Pack-years, mean (SD) 41.41 (20.96) 40.65 (20.44) 42.69 (21.80) 0.20 Woodsmoke, No. (%) 0.91 Never 506 (67.74) 317 (67.59) 189 (67.99) Ever 241 (32.26) 152 (32.41) 89 (32.01) CMH, No. (%) 0.13 No 518 (69.34) 316 (67.38) 202 (72.66) Yes 229 (30.66) 153 (32.62) 76 (27.34) Pulmonary disease, No. (%) 0.13 No 647 (86.61) 413 (88.06) 234 (84.17) Yes 100 (13.39) 56 (11.94) 44 (15.83) Abbreviation: BMI, Body Mass Index; CMH, chronic mucous hypersecretion; SD, standard deviation; WFS, wildfire smoke Table 2 Descriptive Statistics of Wildfire Smoke Across Different Time Windows in the LSC Cohort Exposure * WFS PM 2.5 WFS BC Days affected by WFS Area affected by WFS 7-day # of non-zero, % 278 278 278 278 Mean (SD) 0.37 (0.72) 0.02 (0.04) 2.75 (1.59) 17427 (22301) Median (IQR) 0.03 (0.37) 0.0004 (0.01) 3.00 (3.00) 7436 (21201) 15-day # of non-zero, % 400 400 400 400 Mean (SD) 0.27 (0.58) 0.01 (0.03) 3.96 (3.12) 26726 (37558) Median (IQR) 0.02 (0.22) 0.0005 (0.01) 3.00 (5.00) 9983 (30831) 30-day # of non-zero, % 540 540 540 540 Mean (SD) 0.20 (0.47) 0.01 (0.03) 5.69 (4.86) 37918 (54727) Median (IQR) 0.03 (0.16) 0.006 (0.01) 4.00 (6.00) 14665 (46583) 60-day # of non-zero, % 613 613 613 613 Mean (SD) 0.17 (0.32) 0.01 (0.02) 9.80 (8.12) 67383 (89865) Median (IQR) 0.02 (0.19) 0.0006 (0.01) 9.00 (11.00) 26155 (95921) * PM 2.5 and BC concentrations are measured in µg/m 3 . The number of days affected by smoke is reported in days, and the affected area is measured in km 2 . Means and medians are estimates among visits with any smoke days in defined time periods. WFS PM 2.5 and WFS BC are calculated using the deviation methods with data from Wei et al as the input ( 6 ). Associations between WFS and the HRQoL Significant associations with all four SGRQ scores were identified for WFS PM 2.5 estimated for 7-, 15-, and 30-day time frames with 1 µg/m 3 increase associated with > 4 points of increase in activity, symptom, and total SGRQ scores, a clinically significant alteration (Table 3 ). When using same unit of change (0.05 µg/m 3 ) to quantify impacts of WFS on SGRQ scores, BC exhibited over 20-fold stronger potency in affecting SGRQ scores compared to PM 2.5 (Table 3 ). Similar temporal patterns were also observed for physical health score of SF-36 except that WFS BC’s impact remains significant for the 60-day time window prior to questionnaire filling (Table 4 ). However, the impact of WFS on mental health measure was predominantly seen for WFS estimates a week prior to questionnaire filling (Table 4 ). It was also interesting to note that WFS PM 2.5 was much more potent (> 2.7 fold) in affecting symptom SGRQ scores and physical health measures compared to total ambient PM 2.5 . Association analyses based on the number of WFS day and area affected by WFS can reproduce majority of the associations seen using WFS PM 2.5 or BC (Tables 3 and 4 ). Table 3 Associations of Wildfire Smoke PM 2.5 and BC with SGRQ Scores (N = 747) * Exposure Activity Impacts Symptom Total β (95%CI) P β (95%CI) P β (95%CI) P β (95%CI) P PM 2.5 _7d (per 1 µg/m 3 ) 1.03 (-0.28, 2.34) 0.122 0.52 (-0.27, 1.31) 0.195 1.29 (0.10, 2.48) 0.034 0.84 (-0.11, 1.79) 0.082 PM 2.5 _15d (per 1 µg/m 3 ) 1.23 (-0.14, 2.59) 0.079 0.78 (-0.04, 1.60) 0.064 1.42 (0.17, 2.67) 0.026 1.09 (0.10, 2.09) 0.031 PM 2.5 _30d (per 1 µg/m 3 ) 1.08 (-0.38, 2.54) 0.148 0.63 (-0.25, 1.51) 0.160 1.29 (-0.04, 2.63) 0.058 0.95 (-0.11, 2.02) 0.078 PM 2.5 _60d (per 1 µg/m 3 ) 0.59 (-1.02, 2.19) 0.473 0.36 (-0.60, 1.33) 0.463 0.87 (-0.60, 2.34) 0.245 0.59 (-0.57, 1.76) 0.319 WFS PM 2.5 _7d (per 1 µg/m 3 ) 5.51 (1.86, 9.16) 0.0032 3.27 (1.08, 5.47) 0.0035 6.15 (2.83, 9.48) 0.00031 4.68 (2.04, 7.33) 0.00053 WFS PM 2.5 _15d (per 1 µg/m 3 ) 5.34 (1.43, 9.24) 0.0075 3.16 (0.81, 5.50) 0.0086 4.88 (1.31, 8.45) 0.0075 4.38 (1.56, 7.21) 0.0024 WFS PM 2.5 _30d (per 1 µg/m 3 ) 4.63 (0.35, 8.91) 0.034 2.88 (0.30, 5.45) 0.029 3.40 (-0.52, 7.32) 0.090 3.72 (0.61, 6.82) 0.019 WFS PM 2.5 _60d (per 1 µg/m 3 ) 1.96 (-3.89, 7.81) 0.512 1.68 (-1.84, 5.19) 0.351 1.36 (-3.99, 6.72) 0.618 1.91 (-2.34, 6.15) 0.379 WFS PM 2.5 _7d (per 0.05 µg/m 3 ) 0.28 (0.09, 0.46) 0.0032 0.16 (0.05, 0.27) 0.0035 0.31 (0.14, 0.47) 0.00031 0.23 (0.10, 0.37) 0.00053 WFS PM 2.5 _15d (per 0.05 µg/m 3 ) 0.27 (0.07, 0.46) 0.0075 0.16 (0.04, 0.28) 0.0086 0.24 (0.07, 0.42) 0.0075 0.22 (0.08, 0.36) 0.0024 WFS PM 2.5 _30d (per 0.05 µg/m 3 ) 0.23 (0.02, 0.45) 0.034 0.14 (0.02, 0.27) 0.029 0.17 (-0.03, 0.37) 0.090 0.19 (0.03, 0.34) 0.019 WFS PM 2.5 _60d (per 0.05 µg/m 3 ) 0.10 (-0.19, 0.39) 0.512 0.08 (-0.09, 0.26) 0.351 0.07 (-0.20, 0.34) 0.618 0.10 (-0.12, 0.31) 0.379 WFS BC_7d (per 0.05 µg/m 3 ) 5.31 (1.70, 8.92) 0.0041 3.19 (1.01, 5.36) 0.0041 6.11 (2.82, 9.40) 0.00029 4.55 (1.93, 7.16) 0.00070 WFS BC_15d (per 0.05 µg/m 3 ) 5.38 (1.57, 9.18) 0.0057 3.09 (0.80, 5.38) 0.0084 4.99 (1.51, 8.47) 0.0051 4.37 (1.61, 7.13) 0.0020 WFS BC_30d (per 0.05 µg/m 3 ) 4.87 (0.86, 8.87) 0.017 2.79 (0.38, 5.20) 0.024 3.60 (-0.07, 7.26) 0.055 3.75 (0.85, 6.66) 0.012 WFS BC_60d (per 0.05 µg/m 3 ) 2.90 (-2.69, 8.50) 0.310 1.89 (-1.47, 5.26) 0.271 1.67 (-3.45, 6.80) 0.522 2.37 (-1.70, 6.43) 0.254 WFS days_7d (per 1 day) 0.57 (-0.49, 1.63) 0.290 0.41 (-0.22, 1.05) 0.205 1.21 (0.24, 2.17) 0.014 0.63 (-0.14, 1.40) 0.110 WFS days_15d (per 1 day) 0.22 (-0.35, 0.80) 0.453 0.26 (-0.09, 0.60) 0.142 0.45 (-0.08, 0.97) 0.094 0.30 (-0.12, 0.72) 0.157 WFS days_30d (per 1 day) 0.19 (-0.17, 0.55) 0.293 0.21 (-0.00, 0.43) 0.052 0.22 (-0.11, 0.55) 0.191 0.23 (-0.03, 0.49) 0.085 WFS days_60d (per 1 day) -0.08 (-0.30, 0.13) 0.437 0.01 (-0.12, 0.14) 0.916 -0.01 (-0.21, 0.19) 0.927 -0.02 (-0.18, 0.14) 0.812 WFS area_7d (per IQR) 0.36 (0.04, 0.67) 0.027 0.26 (0.07, 0.44) 0.0083 0.50 (0.22, 0.79) 0.00060 0.35 (0.12, 0.58) 0.0030 WFS area_15d (per IQR) 0.69 (-0.03, 1.41) 0.061 0.53 (0.10, 0.96) 0.017 0.87 (0.21, 1.53) 0.010 0.68 (0.16, 1.21) 0.011 WFS area_30d (per IQR) 0.62 (-0.46, 1.69) 0.260 0.65 (0.00, 1.30) 0.049 0.80 (-0.18, 1.78) 0.112 0.73 (-0.05, 1.51) 0.067 WFS area_60d (per IQR) -0.74 (-2.68, 1.21) 0.458 0.24 (-0.94, 1.42) 0.689 0.22 (-1.58, 2.01) 0.811 -0.01 (-1.43, 1.41) 0.991 Abbreviations: β, regression coefficient; BC, black carbon; CI, confidence interval; PM 2.5 , particle with aerodynamic diameter ≤ 2.5 µm; PM 2.5 _X d, average PM 2.5 concentration during the X days prior to the interview; SGRQ, St. George’s Respiratory questionnaire; WFS, Wildfire smoke. * Models were adjusted for age, sex, ethnicity, BMI, education level, current smokers, pack-years, and prevalence of airway obstruction (defined as FEV1/FVC ratio ≤ 70%). WFS PM 2.5 and WFS BC are calculated using the deviation methods with data from Wei et al as the input ( 6 ). Table 4 Associations of Wildfire Smoke PM 2.5 and BC with SF-36 Score (N = 747) * Exposure Mental Health Score Physical Health Score β (95%CI) P β (95%CI) P PM 2.5 _7d (per 1 µg/m 3 ) -0.043 (-0.092, 0.006) 0.087 -0.063 (-0.111, -0.016) 0.0093 PM 2.5 _15d (per 1 µg/m 3 ) -0.037 (-0.089, 0.014) 0.154 -0.068 (-0.118, -0.018) 0.0079 PM 2.5 _30d (per 1 µg/m 3 ) -0.029 (-0.084, 0.026) 0.300 -0.063 (-0.117, -0.010) 0.020 PM 2.5 _60d (per 1 µg/m 3 ) -0.005 (-0.065, 0.055) 0.866 -0.065 (-0.124, -0.007) 0.029 WFS PM 2.5 _7d (per 1 µg/m 3 ) -0.172 (-0.309, -0.036) 0.014 -0.214 (-0.348, -0.081) 0.0016 WFS PM 2.5 _15d (per 1 µg/m 3 ) -0.125 (-0.272, 0.022) 0.096 -0.192 (-0.335, -0.049) 0.0085 WFS PM 2.5 _30d (per 1 µg/m 3 ) -0.075 (-0.236, 0.086) 0.361 -0.172 (-0.329, -0.016) 0.031 WFS PM 2.5 _60d (per 1 µg/m 3 ) -0.035 (-0.254, 0.184) 0.754 -0.207 (-0.420, 0.006) 0.058 WFS PM 2.5 _7d (per 0.05 µg/m 3 ) -0.009 (-0.015, -0.002) 0.014 -0.011 (-0.017, -0.004) 0.0017 WFS PM 2.5 _15d (per 0.05 µg/m 3 ) -0.006 (-0.014, 0.001) 0.096 -0.010 (-0.017, -0.002) 0.0085 WFS PM 2.5 _30d (per 0.05 µg/m 3 ) -0.004 (-0.012, 0.004) 0.361 -0.009 (-0.016, -0.001) 0.031 WFS PM 2.5 _60d (per 0.05 µg/m 3 ) -0.002 (-0.013, 0.009) 0.754 -0.010 (-0.021, 0.000) 0.058 WFS BC_7d (per 0.05 µg/m 3 ) -0.172 (-0.307, -0.036) 0.013 -0.221 (-0.353, -0.089) 0.0011 WFS BC_15d (per 0.05 µg/m 3 ) -0.135 (-0.278, 0.008) 0.065 -0.201 (-0.341, -0.062) 0.0048 WFS BC_30d (per 0.05 µg/m 3 ) -0.100 (-0.250, 0.050) 0.193 -0.184 (-0.330, -0.038) 0.014 WFS BC_60d (per 0.05 µg/m 3 ) -0.075 (-0.284, 0.135) 0.486 -0.225 (-0.429, -0.021) 0.031 WFS days_7d (per 1 day) -0.042 (-0.081, -0.002) 0.040 -0.043 (-0.081, -0.004) 0.030 WFS days_15d (per 1 day) -0.015 (-0.036, 0.007) 0.179 -0.021 (-0.042, -0.000) 0.048 WFS days_30d (per 1 day) -0.011 (-0.024, 0.002) 0.110 -0.017 (-0.030, -0.004) 0.012 WFS days_60d (per 1 day) -0.001 (-0.009, 0.007) 0.730 -0.006 (-0.014, 0.001) 0.101 WFS area_7d (per IQR) -0.015 (-0.027, -0.004) 0.011 -0.017 (-0.028, -0.005) 0.0041 WFS area_15d (per IQR) -0.023 (-0.050, 0.004) 0.096 -0.032 (-0.059, -0.006) 0.017 WFS area_30d (per IQR) -0.013 (-0.053, 0.027) 0.523 -0.038 (-0.077, 0.001) 0.058 WFS area_60d (per IQR) 0.008 (-0.065, 0.080) 0.837 -0.043 (-0.113, 0.027) 0.228 Abbreviations: β, regression coefficient; BC, BC; CI, confidence interval; PM 2.5 , particle with aerodynamic diameter ≤ 2.5 µm; PM 2.5 _X d, average PM 2.5 concentration during the X days prior to the interview; SF-36, the short form 36 health survey questionnaire; WFS, Wildfire smoke. * Models were adjusted for age, sex, ethnicity, BMI, education level, current smokers, pack-years, and prevalence of comorbidity. WFS PM 2.5 and WFS BC are calculated using the deviation methods with data from Wei et al as the input ( 6 ). Sensitivity analyses Excluding visits occurring in winter when there were few WFS episodes affecting the greater Albuquerque area did not change the associations observed (Table S1 and S2). Association analyses using WFS measures based on residential addresses reproduced the results seen using WFS measures based on four counties. Moreover, stronger magnitudes of association were observed for symptom score of SGRQ for 7-d and 15-d WFS measures and for mental and physical health measures of SF-36 for 15-d WFS measures (Table S3 and S4). Using four-county based WFS PM 2.5 measures from Dr. Childs’ study reproduced the temporal patterns of associations with SF-36 measures, however its associations with SGRQ scores were only observed in 7-d WFS measures (Table S5 and S6). Effect modification We conducted interaction analyses to determine whether the WFS – HRQoL associations were modified by several candidate factors (Table S7 - S13). These analyses were conducted using 7-day WFS measures to ensure optimal power as 7-day measures had most significant associations with HRQoL measures. Most consistent effect modifications were identified for “ever woodsmoke exposure for over a year” as subjects with woodsmoke exposure have elevated vulnerability to WFS induced HRQoL changes compared to people who do not have woodsmoke exposure (Table S7). We also found evidence that males and subjects with less than college education were more vulnerable to WFS induced HRQoL changes (Table S8 and S9). Discussion Through linking daily WFS estimates to psychometric measures collected at baseline visits of the LSC members, we identified that episodic WFS exposures significantly deteriorated psychometric measures of multiple health domains. The impact of WFS exposure on SGRQ scores had similar temporal patterns across all subdomains, i.e. , respiratory symptoms, limitation in physical activity due to breathlessness, and psychosocial disturbances, with significant associations observed for WFS exposure estimated for 7-, 15-, and 30-d prior to questionnaire filling. However, temporal patterns for the impact of WFS exposure on SF-36 differed by subdomains with impacts seen up to 30-d exposure for physical health measures, while only 7-d exposure for mental health measures. The link between WFS exposures and physical health measures of SF-36 and respiratory quality of life measures by the SGRQ is more likely to be driven by inhalation exposure of WFS and lung deposition of BC particles. Phagocytosis of BC by airway macrophages is a major clearance mechanism in the acinar airway, which triggers persistent cytokine/chemokine secretion and generation of other mediators for downstream pulmonary and extra-pulmonary toxicity ( 29 ). Our recent analyses in 88 LSC subjects identified that exposure to elevated PM 2.5 in the air for over a month due to WFS (summer) or residential wood burning (winter) significantly increased macrophage carbon load ( 28 ). Despite the prevalence and persistence of psychological disorders in communities facing wildfire threats, there remains a notable gap in research regarding the effects of WFS on general mental health in populations not directly affected by the fires, but exposed to smoke that travels long distance in atmosphere. We identified the first human evidence supporting alterations of mental health measures after acute exposure to aged WFS. The time frame for triggering alteration in mental health measures was rather short, i.e. , a week prior to questionnaire filling and this impact became non-significant beyond one-week time window of WFS exposure regardless of whether WFS continued or not. A meta-analysis published in 2019 showed that short-term exposure to PM 10 in the air was associated with the risk of completed suicide at a 0-2d cumulative time lag in meta-analysis ( 30 ). There was also some evidence suggesting an association between short-term PM 2.5 or PM 10 exposure and depression-related emergency department visits ( 30 ). A randomized, double-blind, crossover trial in China demonstrated that use of air purification for 9 days significantly reduced indoor PM 2.5 and stress hormones (cortisol, cortisone, epinephrine, and norepinephrine) in blood circulation ( 31 ). Our animal models discovered brain effects such as neuroinflammation, neurometabolomic alterations, and depression-like behavioral changes after inhalation exposure to WFS for 4 hours/day, every other day, for 2 weeks (7 exposures total) at an environmentally relevant level (448 µg/m 3 PM 2.5 ) ( 32 , 33 ). Several key mechanisms linking inhalation exposure of nanoscale particles and brain effects have been proposed and include direct enter of PM 2.5 in brain tissue via the olfactory nerve, circulating mediators and compromised blood brain barrier integrity, and transfer of PM 2.5 through blood gas barrier and circulation ( 29 , 34 ). However, it remains unclear whether the associations with mental health measures we observed are due to mechanisms described above due to the rapid attenuation of associations beyond one-week time window of WFS exposure as well as much lower levels of WFS PM 2.5 in our study population compared to animal exposure study. Further research using blood omics approach in populations with real-world WFS exposure may help decode the mechanisms underlying how episodic WFS exposure affect mental health measures in humans. Combustion-emitted PM is demonstrated to be more harmful to health than PM from non-combustion sources and the literature shows that estimation of combustion emitted PM using source apportionment or BC outperforms total PM 2.5 mass levels for evaluating health risks ( 7 , 35 ). Our analyses provide evidence supporting this premise. The SGRQ scores were most significantly associated with WFS PM 2.5 rather than with total ambient PM 2.5 . For physical health measures, WFS PM 2.5 showed 2.7- to 3.4-fold stronger associations compared to total ambient PM 2.5 , although both PM 2.5 metrics were significantly associated with the outcomes. Stronger potency in inducing lung effects by WFS PM 2.5 compared to PM 2.5 from other sources was reported in animal models with lung lavage cytology and lung histology as the outcome ( 36 ) and in California populations with respiratory hospitalizations as the outcome ( 37 ). Additionally, days with high pollution levels (PM 2.5 ≥35 µg/m 3 ) resulting from WFS were strongly associated with an increased risk of tuberculosis diagnoses in California, unlike those caused by other sources of air pollution ( 38 ). All together, these studies suggest that WFS PM may be more toxic than equal doses from other sources. WFS PM 2.5 is mostly carbonaceous (with 5–20% elemental carbon and at least 50% organic carbon) and has more oxidative potential than ambient urban particulate due to the presence of more polar organic compounds ( 39 , 40 ). It is therefore imperative to differentiate between smoke and non-smoke PM 2.5 when assessing impacts on public health. Moreover, WFS BC also demonstrated stronger associations with all health measures compared to WFS PM 2.5 , suggesting BC constituents may be more potent in inducing health effects compared to non-BC constituents in WFS PM 2.5 . It is also important to note that male participants, those with lower than college education, and those reporting ever woodsmoke exposure were more vulnerable to the adverse physical health impacts of WFS. Under controlled exposure settings, lungs from healthy, young adults 18–40 years of age were affected by WFS in a sex-dependent manner with males having upregulation of inflammatory genes and females having suppression of defense responses ( 41 ). Sex disparity was also observed in woodsmoke induced inflammation in lungs and brains in animal models ( 42 , 43 ). However, we cannot exclude possibility that sex disparity observed in the LSC was due to more outdoor activities and higher exposure in males compared to females. Elevated susceptibility in people with less than college education may be due to their higher likelihood of WFS exposure and lower adoption of protective behaviors during wildfire smoke events. We also found that ever woodsmoke exposure for over a year potentiates the health impacts of WFS and this effect modification is independent of airway obstruction or baseline comorbidities. The biological mechanisms underlying the effect potentiation of WFS exposure warrant further investigation. The findings of this study may be specific to populations in the Southwestern U.S., particularly those exposed to aged WFS originating from wildfires in the Gila, Apache, and Apache-Sitgreaves National Forests. The smoke traveled nearly 200 miles through the atmosphere before reaching the Albuquerque metropolitan area. The toxicity of WFS is influenced by the constituents present, which are determined by factors such as biomass fuel types, metals in the fuel, burning conditions, and secondary reactions during atmospheric transport. Future studies that analyze PM 2.5 constituents in air samples collected during WFS events will provide further insight in associations observed in populations from smoke-prone regions. In summary, our study suggests that episodic exposure to WFS PM 2.5 and BC was associated with poorer respiratory-specific or general health-related quality of life, with notable differences in the temporal relationships between mental and physical health measures. Additionally, WFS PM 2.5 exhibited higher toxicity than total ambient PM 2.5 , underscoring the importance of source-specific risk assessment for air pollution. Moreover, BC constituents may be more potent in inducing health effects compared to non-BC constituents in WFS PM 2.5 . Male participants, individuals with less than a college education, and those exposed to woodsmoke demonstrated heightened vulnerability to WFS. Understanding the factors driving these health disparities is essential for developing precision public health interventions to protect at-risk populations. Abbreviations BC black carbon COPD chronic obstructive pulmonary disease CMH chronic mucous hypersecretion HRQoL health-related quality of life LSC the Lovelace Smokers Cohort PM 2.5 fine particulate matter SF-36 the 36-Item Short Form Survey SGRQ The St. George's Respiratory Questionnaire WFS wildfire smoke. Declarations Ethics approval and consent to participate This study was approved by the Western Institutional Review Board and all participants signed consent forms. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding Research reported in this publication was supported by NIEHS R01 ES035421 and P30 ES032755, NINR 1P20NR021824, NHLBI 1R21HL173388, NIA R01 AG070776, NCI P30 CA118100, NIGMS P50 MD015706, UL1TR001449, and KL2TR001448. Author Contribution Q.W. conducted the exposure assessment, performed the data analysis, summarized the results, and drafted the manuscript. Y.H. and X.G. contributed to the exposure design and evaluation, data visualization, and manuscript review and editing. L.L.S., T.E., and S.Z. contributed to data visualization and manuscript editing. H.K., M.A.P., M.C., J.M.C., Y.Z., and M.J.C. contributed to manuscript review and editing. S.A.B. and S.L. oversaw the study design, interpreted the results, and revised and edited the manuscript. All authors read and approved the final manuscript. 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Cerrato","email":"","orcid":"","institution":"University of New Mexico","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"M.","lastName":"Cerrato","suffix":""},{"id":528400038,"identity":"cdfa7017-b453-4790-819f-8feba1c8e375","order_by":8,"name":"Yiliang Zhu","email":"","orcid":"","institution":"University of New Mexico School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yiliang","middleName":"","lastName":"Zhu","suffix":""},{"id":528400039,"identity":"03c24f63-338c-4d1a-b4b4-2ff9a67c8ffc","order_by":9,"name":"Su Zhang","email":"","orcid":"","institution":"University of New Mexico","correspondingAuthor":false,"prefix":"","firstName":"Su","middleName":"","lastName":"Zhang","suffix":""},{"id":528400040,"identity":"de61b713-523e-4506-91de-fd70100f5b33","order_by":10,"name":"Steven A. Belinsky","email":"","orcid":"","institution":"University of New Mexico","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"A.","lastName":"Belinsky","suffix":""},{"id":528400041,"identity":"d7c2d708-a5c4-4cbf-814d-8bcabdb2dc17","order_by":11,"name":"Matthew J. Campen","email":"","orcid":"","institution":"University of New Mexico","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"J.","lastName":"Campen","suffix":""},{"id":528400042,"identity":"0397eb54-850b-474f-b621-5879533c1228","order_by":12,"name":"Xi Gong","email":"","orcid":"","institution":"University of New Mexico","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Gong","suffix":""},{"id":528400043,"identity":"45e97e5b-0b86-45e1-bb75-0e7e5bbb8c6a","order_by":13,"name":"Shuguang Leng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACdhBRwcDAx8BgABVKIKCFGUScYWBgI00LYxspWuSbmY89/DqvTp5NInkDM++ObQz87DkGeLUYHGZLN5bddtiwTSKtgJn3zG0GyZ43BLQw85hJS247wNgmkWPAzNt2m8HgBgFb5Jv5v0lLzqmzh2uxJ6SF4TAPm+THBuZEhC0ShP1iJs1w7HByG8+zgoNz227zSJx5VoDfYe3NzyR/1NTZ9rMnb3zwtu22HH978gb8DgMCZh4o4wAQ8+BRiACMP4hSNgpGwSgYBSMWAACTjT8GwVDCEQAAAABJRU5ErkJggg==","orcid":"","institution":"University of New Mexico","correspondingAuthor":true,"prefix":"","firstName":"Shuguang","middleName":"","lastName":"Leng","suffix":""}],"badges":[],"createdAt":"2025-07-07 23:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7069099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7069099/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12931-026-03505-9","type":"published","date":"2026-01-14T16:30:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94211583,"identity":"e1a1a10a-926b-4b0d-98bf-d45357326b93","added_by":"auto","created_at":"2025-10-23 15:46:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":162312,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptWildfireSmokeSGRQwithTableRR20250707.docx","url":"https://assets-eu.researchsquare.com/files/rs-7069099/v1/75af4d4b7e53502a3ae35afe.docx"},{"id":94211585,"identity":"fab1f13a-775d-4a79-9a61-31fd4a3ae414","added_by":"auto","created_at":"2025-10-23 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15:46:40","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195811,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7069099/v1/29a79bc71f8a7120a92873a3.html"},{"id":94211581,"identity":"d3fb72c9-44a7-4947-94cd-58945ac21fab","added_by":"auto","created_at":"2025-10-23 15:46:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1011607,"visible":true,"origin":"","legend":"\u003cp\u003eGeospatial distribution of the LSC participants by census track in New Mexico.\u003c/p\u003e","description":"","filename":"Figure1GeospatialdistributionofLSCparticipants.png","url":"https://assets-eu.researchsquare.com/files/rs-7069099/v1/a1a2c5f579431226a253bd1e.png"},{"id":94211582,"identity":"4f042e6a-0b1a-439a-9549-07735bffc947","added_by":"auto","created_at":"2025-10-23 15:46:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3947031,"visible":true,"origin":"","legend":"\u003cp\u003eSmoke day, wildfire smoke PM\u003csub\u003e2.5\u003c/sub\u003e, and total ambient PM\u003csub\u003e2.5 \u003c/sub\u003ein Albuquerque in the summer of 2011. A. MODIS satellite image demonstrates actively burning wildfires in Apache-Sitgreaves National Forests and atmospheric transfer of the smoke from the burn sites to central New Mexico. This imagery represents a \"true color\" band combination of data collected by the MODIS instrument on the NASA Aqua satellite acquired June 6, 2011. Band1=Visible Red (0.620 - 0.670 µm), Band3=Visible Blue (0.459 - 0.479 µm), Band4=Visible Green (0.545 - 0.565 µm). The image represents the three spectral bands scaled to a resolution of 250m per pixel at the equator. B. Wildfire smoke as primary contributor to episodic elevation of ambient PM\u003csub\u003e2.5 \u003c/sub\u003ein Albuquerque in the summer of 2011.\u003c/p\u003e","description":"","filename":"Figure215.png","url":"https://assets-eu.researchsquare.com/files/rs-7069099/v1/76485c3fdfcb40df2f6c87ae.png"},{"id":100616123,"identity":"96772c2c-a0b9-4d5f-9333-19cd1ea81bf2","added_by":"auto","created_at":"2026-01-19 17:40:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5875880,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7069099/v1/dfb71120-aab9-427e-a22f-bfc42ff6fa1b.pdf"},{"id":94211586,"identity":"1ee54a61-130f-4dc2-a14c-a024914a8ec3","added_by":"auto","created_at":"2025-10-23 15:46:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":845800,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementWildfireSmokeSGRQRR20250707.docx","url":"https://assets-eu.researchsquare.com/files/rs-7069099/v1/80ccca20d43a85bfd8e16623.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between Wildfire Smoke Exposure and Health-Related Quality of Life: Findings from the Lovelace Smokers Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRising temperatures, prolonged droughts, and increasing vegetation desiccation accompanying climate variability are dramatically intensifying the frequency, duration, and severity of wildfires across North America and globally (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Wildfire smoke (WFS) has had a spatially and temporally profound impact on air quality across the contiguous U.S., stalling or reversing multi-decadal declines in fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) concentrations in 35 states, and contributing significantly to the rise in extremely polluted days (daily PM\u003csub\u003e2.5\u003c/sub\u003e \u0026gt;35 µg/m\u003csup\u003e3\u003c/sup\u003e) in 18 states since 2012 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The impact of WFS on air quality is no longer transient or negligible, as it has contributed an average of 1 µg/m\u003csup\u003e3\u003c/sup\u003e annually to ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in the eight most affected states in the western and midwestern U.S. since 2016. Moreover, in high-fire years (2017, 2018, and 2020), WFS contributes up to 5 µg/m\u003csup\u003e3\u003c/sup\u003e to annual PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, which is equivalent to roughly half of the total annual average PM\u003csub\u003e2.5\u003c/sub\u003e from all sources across much of the contiguous U.S. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Since 2010, WFS has also significantly increased the black carbon-to-PM\u003csub\u003e2.5\u003c/sub\u003e ratio in Western U.S., thus could potentially elevate the toxicity of PM\u003csub\u003e2.5\u003c/sub\u003e (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIndividual wildfire episodes can last anywhere from weeks to months, releasing smoke plumes into the atmosphere that contain large amounts of particles and toxicants. These wildfires, along with the smoke they produce, can significantly affect local air quality and public health. In addition, prevailing winds can carry WFS hundreds or even thousands of miles away from the source. Once the smoke descends, it impacts near-ground air quality, affecting the health of populations far from the original fire sites (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Therefore, geographic locations affected by WFS can either be near the burning sites or downwind regions, with air quality deteriorating in an episodic manner. This means that when wildfires are active, air pollutants in these regions can increase temporarily, ranging from weeks to months. Acute health effects from extreme WFS exposure can occur within hours or days, including cardiovascular events, respiratory symptoms and exacerbation, eye irritation, and reduced cognitive performance (\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Evidence for chronic health effects after multiple rounds of episodic WFS exposure begins to emerge and supports the etiological link of WFS with lung and brain cancer and incident dementia (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, evidence supporting the health impacts of episodic WFS exposure, which usually last from weeks to 2–3 months in the contiguous U.S., is very limited. Only one study has reported obstructive airway changes following a 45-day WFS exposure in Seeley Lake, Montana, where daily PM\u003csub\u003e2.5\u003c/sub\u003e levels averaged 220.9 µg/m\u003csup\u003e3\u003c/sup\u003e. Notably, these airway changes persisted even two years after the exposure (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Delineating these early changes will inform both acute and chronic health risks, identify vulnerable subgroups, and reveal underlying mechanisms for potential mitigation options.\u003c/p\u003e\u003cp\u003eThis study aimed to investigate the associations between episodic exposure to WFS and health-related quality of life (HRQoL), which captures multi-dimensional assessment of physical health, mental well-being, and social functioning. Additionally, we assessed individual traits that may sensitize people for WFS health effects. These analyses were conducted using data from the Lovelace Smokers Cohort (LSC), located in Albuquerque, an arid region frequently impacted by WFS from wildfires originated in Gila, Apache, and Apache-Sitgreaves National Forests due to prevailing southwest winds in warm seasons.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe LSC is a longitudinal, population-based volunteer cohort with majority of participants enrolled from the greater Albuquerque area of New Mexico from 2001 to 2017 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The primary objective of the LSC was to identify biomarkers in sputum and blood for lung cancer and chronic obstructive pulmonary disease (COPD) development. The design of the LSC has been described elsewhere (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Briefly, a total of 2511 participants aged 40 to 75 years, with at least 10 pack-years of smoking and no prior history of lung cancer, were recruited through local newspaper, radio, and television advertisements. At baseline, participants completed a standardized questionnaire on demographics, tobacco smoking, medical history, diet, as well as quality of life measures (the St. George's Respiratory Questionnaire [SGRQ] and the 36-item short-form health survey [SF-36]). They also underwent pre- and post-bronchodilator spirometry and provided biological samples (blood and sputum). Follow-up visits were conducted approximately every 18 months till November 2017. All participants provided informed consent, and the study was approved by the Western Institutional Review Board.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHealth-Related Quality of Life Measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHealth-related quality-of-life (HRQoL) was assessed using the general health SF-36 questionnaire and the lung disease-specific SGRQ with the recall period of past four weeks (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The SGRQ total score and its activity, symptom, and impact domain subscores range from 0 to 100, with higher score indicating a worse HRQoL (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). A minimal clinically important difference in SGRQ total score and domain subscores is 4 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The SF-36 encompasses eight domains including physical functioning, role physical, role emotional, social functioning, mental health, vitality, general health perceptions, and bodily pain. The SF-36 scores range from 0 to 100, with higher scores indicating better HRQoL (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Confirmatory factor analysis using R package \u003cem\u003elavaan\u003c/em\u003e was used to identify latent constructs representing physical and mental health based on factor structures established in U.S. general populations (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These two factor scores were then used in subsequent analyses to reduce dimensionality.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWFS assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe majority of the LSC participants were from the greater Albuquerque area including Bernalillo, Sandoval, Valencia, and Torrance counties, thus we used this area (a total of 18,719 km\u003csup\u003e2\u003c/sup\u003e) to estimate WFS exposure. First, we calculated the number of smoke days in periods of 7-, 15-, 30-, 60- days prior to questionnaire filling. The 60-day maximal length was selected because majority of WFS episodes that led to elevation in air pollution in Albuquerque lasted two months or shorter. Smoke days were defined as when the study area was overlapped with satellite-detected smoke plumes by at least 1 km², based on data from the National Oceanic and Atmospheric Administration (NOAA) Hazard Mapping System (HMS). Second, we also calculated cumulative area of the greater Albuquerque area overlapped with WFS plumes for individual time periods to estimate the geographic massiveness of the WFS. Third, we assessed WFS exposure using quantitative metrics such as smoke PM\u003csub\u003e2.5\u003c/sub\u003e and smoke black carbon (BC) in ambient air. These quantitative metrics effectively captured near-ground pollutant levels, providing a more precise assessment compared to smoke plume-based methods. We took advantage of recent advances in air quality modeling that provided ambient PM\u003csub\u003e2.5\u003c/sub\u003e and BC data at higher geospatial (1 × 1 km) and temporal (daily) resolution across the contiguous US from 2000 to 2020 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). We followed Dr. Child’s published methods to estimate smoke PM\u003csub\u003e2.5\u003c/sub\u003e and smoke BC (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In brief, smoke PM\u003csub\u003e2.5\u003c/sub\u003e and BC were quantified as deviation in PM\u003csub\u003e2.5\u003c/sub\u003e or BC values in smoke days from the median values from non-smoke days in the same month over a three-year period, spanning the year before and the year after. Zero was assigned to smoke days when the subtraction gave negative values, i.e., smoke plumes did not compromise near ground air quality. Average smoke PM\u003csub\u003e2.5\u003c/sub\u003e and smoke BC in 7-, 15-, 30-, 60- days prior to questionnaire filling were calculated. Additionally, Dr. Childs and colleagues developed a machine learning algorithm to predict smoke PM\u003csub\u003e2.5\u003c/sub\u003e levels through integrating a comprehensive dataset spanning ground monitoring records, expanded smoke plume data, fire emission inventories, land use and elevation, meteorology, and satellite aerosol measurements (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This smoke PM\u003csub\u003e2.5\u003c/sub\u003e dataset covered the contiguous US from 2006 to 2020 and has an excellent prediction accuracy, verified specificity for WFS, and sufficient geo-spatial (10 × 10 km) and temporal (daily) resolution for health linkage study (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). We applied the county-level daily WFS dataset developed by Dr. Childs and colleagues as an alternative exposure assessment in sensitivity analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeocoding\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used residential locations to calculate WFS exposure and as sensitivity analyses to validate results from the greater Albuquerque area. Addresses were geocoded with ArcGIS 10.8, standardizing data and matching it to the Albuquerque Street map to assign latitude and longitude. These geocoded locations helped estimate WFS exposure by assigning average smoke PM\u003csub\u003e2.5\u003c/sub\u003e and BC concentrations based on the corresponding 1 × 1 km grid for each specified time period.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted in 747 LSC subjects who were enrolled in 2006 (first year when smoke plume data became available) and after and had measures of SGQR and SF-36 at baseline. Linear model was used to assess the associations between WFS measures and SGRQ and SF-36 scores, with adjustment for baseline age, sex, BMI, ethnicity, education level, status and packyears of tobacco smoking, airway obstruction for SGRQ, and baseline comorbidity for SF-36 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Due to moderate to high correlations among WFS measures (Supplemental Fig.\u0026nbsp;1) and among outcomes, instead of setting arbitrary cutoffs for claiming study-wide significance, we chose to weight evidence based on nominal P values as well as consistency across measures and time periods. Interaction analyses were conducted to explore whether the observed associations varied across several candidate factors such as sex, education, current smoking status, woodsmoke exposure (self-reported in response to a question “Have you ever been exposed to woodsmoke for 12 months or longer” as part of the general health survey at study entry of the LSC), chronic mucous hypersecretion, and ethnicity. Interaction analyses were conducted in 7-day WFS – SGRQ or WFS – SF36 associations due to the greatest significance. P values less than 0.1 for the interaction terms were deemed meaningful interactions. These interaction analyses should be viewed as secondary analyses to reduce the issue of multiple comparisons. As a sensitivity analysis, we excluded participants who were followed up during winter (n = 66), as significant WFS events were rare in the Albuquerque area during this season. All analyses were conducted using R (version 4.4.1, Vienna, Austria) in the RStudio (version 2024.9.0.375).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eWFS and Air Quality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe greater Albuquerque, the catchment area of the LSC was frequently affected by WFS during summer that predominantly originated from the fires in Apache and Gila National Forest around the Arizona and New Mexico border (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The distance between these fires and Albuquerque metropolitan area is 150 to 200 miles, suggesting aged WFS. The estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e and BC concentrations were significantly higher in 474 smoke days versus 3909 non-smoke days (6.55 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e versus 4.14 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for PM\u003csub\u003e2.5\u003c/sub\u003e and 0.25 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e versus 0.16 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for BC). For example, in the summer of 2011, WFS released from the Wallow Fire is the major contributor to elevated ambient PM\u003csub\u003e2.5\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Participants and their WFS Exposure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 747 participants had an average mean (SD) age of 56.9 (9.1) years and included 383 females, 185 Hispanics, and 447 current smokers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 479 subjects had received some college education and above. A total of 241 subjects self-reported to be \u0026ldquo;ever woodsmoke exposure for over a year\u0026rdquo;. Comparisons between people with and without smoke days in a week prior to questionnaire filling did not identify any significant differences for variables under consideration (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, about 400, 540, and 613 subjects were exposed to smoke days in 15-, 30-, and 60- days prior to questionnaire filling (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics of the Study Participants (n\u0026thinsp;=\u0026thinsp;747)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll participants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7-day no WFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7-day with WFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;747)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;469)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;278)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, mean (SD), yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56.87 (9.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.47 (9.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.53 (9.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e364 (48.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e221 (47.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e143 (51.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e383 (51.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e248 (52.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135 (48.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, mean (SD), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.78 (6.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.48 (5.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.29 (7.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e562 (75.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e343 (73.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e219 (78.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e185 (24.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126 (26.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59 (21.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e268 (35.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e178 (37.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90 (32.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e479 (64.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e291 (62.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e188 (67.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smokers, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e300 (40.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180 (38.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e120 (43.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e447 (59.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e289 (61.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e158 (56.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePack-years, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.41 (20.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.65 (20.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.69 (21.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWoodsmoke, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e506 (67.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e317 (67.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e189 (67.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e241 (32.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e152 (32.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89 (32.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMH, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e518 (69.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e316 (67.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e202 (72.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e229 (30.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e153 (32.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76 (27.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary disease, No. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e647 (86.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e413 (88.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e234 (84.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100 (13.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56 (11.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44 (15.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: BMI, Body Mass Index; CMH, chronic mucous hypersecretion; SD, standard deviation; WFS, wildfire smoke\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Wildfire Smoke Across Different Time Windows in the LSC Cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure *\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWFS BC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDays affected by WFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eArea affected by WFS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7-day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e# of non-zero, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.37 (0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.75 (1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17427 (22301)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03 (0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0004 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 (3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7436 (21201)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15-day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e# of non-zero, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27 (0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.96 (3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26726 (37558)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02 (0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0005 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 (5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9983 (30831)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30-day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e# of non-zero, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e540\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20 (0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.69 (4.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37918 (54727)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03 (0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00 (6.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14665 (46583)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60-day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e# of non-zero, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.17 (0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01 (0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.80 (8.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67383 (89865)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02 (0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0006 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.00 (11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26155 (95921)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* PM\u003csub\u003e2.5\u003c/sub\u003e and BC concentrations are measured in \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. The number of days affected by smoke is reported in days, and the affected area is measured in km\u003csup\u003e2\u003c/sup\u003e. Means and medians are estimates among visits with any smoke days in defined time periods. WFS PM\u003csub\u003e2.5\u003c/sub\u003e and WFS BC are calculated using the deviation methods with data from Wei et al as the input (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations between WFS and the HRQoL\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSignificant associations with all four SGRQ scores were identified for WFS PM\u003csub\u003e2.5\u003c/sub\u003e estimated for 7-, 15-, and 30-day time frames with 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase associated with \u0026gt;\u0026thinsp;4 points of increase in activity, symptom, and total SGRQ scores, a clinically significant alteration (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When using same unit of change (0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) to quantify impacts of WFS on SGRQ scores, BC exhibited over 20-fold stronger potency in affecting SGRQ scores compared to PM\u003csub\u003e2.5\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similar temporal patterns were also observed for physical health score of SF-36 except that WFS BC\u0026rsquo;s impact remains significant for the 60-day time window prior to questionnaire filling (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, the impact of WFS on mental health measure was predominantly seen for WFS estimates a week prior to questionnaire filling (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It was also interesting to note that WFS PM\u003csub\u003e2.5\u003c/sub\u003e was much more potent (\u0026gt;\u0026thinsp;2.7 fold) in affecting symptom SGRQ scores and physical health measures compared to total ambient PM\u003csub\u003e2.5\u003c/sub\u003e. Association analyses based on the number of WFS day and area affected by WFS can reproduce majority of the associations seen using WFS PM\u003csub\u003e2.5\u003c/sub\u003e or BC (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of Wildfire Smoke PM\u003csub\u003e2.5\u003c/sub\u003e and BC with SGRQ Scores (N\u0026thinsp;=\u0026thinsp;747) *\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eActivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eImpacts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eSymptom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e_7d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03 (-0.28, 2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52 (-0.27, 1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.29 (0.10, 2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.84 (-0.11, 1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e_15d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.23 (-0.14, 2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78 (-0.04, 1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.42 (0.17, 2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.09 (0.10, 2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e_30d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08 (-0.38, 2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63 (-0.25, 1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.29 (-0.04, 2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.95 (-0.11, 2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e_60d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59 (-1.02, 2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36 (-0.60, 1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.87 (-0.60, 2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.59 (-0.57, 1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_7d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.51 (1.86, 9.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.27 (1.08, 5.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.15 (2.83, 9.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.00031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.68 (2.04, 7.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.00053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_15d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.34 (1.43, 9.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.16 (0.81, 5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.88 (1.31, 8.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.38 (1.56, 7.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.0024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_30d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.63 (0.35, 8.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.88 (0.30, 5.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.40 (-0.52, 7.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.72 (0.61, 6.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_60d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.96 (-3.89, 7.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.68 (-1.84, 5.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.36 (-3.99, 6.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.91 (-2.34, 6.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_7d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28 (0.09, 0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16 (0.05, 0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.31 (0.14, 0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.00031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.23 (0.10, 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.00053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_15d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27 (0.07, 0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16 (0.04, 0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.24 (0.07, 0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.22 (0.08, 0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.0024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_30d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23 (0.02, 0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14 (0.02, 0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17 (-0.03, 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.19 (0.03, 0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_60d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10 (-0.19, 0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08 (-0.09, 0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.07 (-0.20, 0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.10 (-0.12, 0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS BC_7d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.31 (1.70, 8.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.19 (1.01, 5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.11 (2.82, 9.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.00029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.55 (1.93, 7.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.00070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS BC_15d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.38 (1.57, 9.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.09 (0.80, 5.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.99 (1.51, 8.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.37 (1.61, 7.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.0020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS BC_30d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.87 (0.86, 8.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.79 (0.38, 5.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.60 (-0.07, 7.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.75 (0.85, 6.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS BC_60d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.90 (-2.69, 8.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.89 (-1.47, 5.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.67 (-3.45, 6.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.37 (-1.70, 6.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS days_7d (per 1 day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.57 (-0.49, 1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41 (-0.22, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.21 (0.24, 2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.63 (-0.14, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS days_15d (per 1 day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22 (-0.35, 0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26 (-0.09, 0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.45 (-0.08, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.30 (-0.12, 0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS days_30d (per 1 day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19 (-0.17, 0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21 (-0.00, 0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.22 (-0.11, 0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.23 (-0.03, 0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS days_60d (per 1 day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.08 (-0.30, 0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01 (-0.12, 0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.01 (-0.21, 0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.02 (-0.18, 0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS area_7d (per IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.36 (0.04, 0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26 (0.07, 0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.50 (0.22, 0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.00060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.35 (0.12, 0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.0030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS area_15d (per IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.69 (-0.03, 1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53 (0.10, 0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.87 (0.21, 1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.68 (0.16, 1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS area_30d (per IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62 (-0.46, 1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65 (0.00, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.80 (-0.18, 1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.73 (-0.05, 1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS area_60d (per IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.74 (-2.68, 1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24 (-0.94, 1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.22 (-1.58, 2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.01 (-1.43, 1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations: β, regression coefficient; BC, black carbon; CI, confidence interval; PM\u003csub\u003e2.5\u003c/sub\u003e, particle with aerodynamic diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m; PM\u003csub\u003e2.5\u003c/sub\u003e _X d, average PM\u003csub\u003e2.5\u003c/sub\u003e concentration during the X days prior to the interview; SGRQ, St. George\u0026rsquo;s Respiratory questionnaire; WFS, Wildfire smoke. * Models were adjusted for age, sex, ethnicity, BMI, education level, current smokers, pack-years, and prevalence of airway obstruction (defined as FEV1/FVC ratio\u0026thinsp;\u0026le;\u0026thinsp;70%). WFS PM\u003csub\u003e2.5\u003c/sub\u003e and WFS BC are calculated using the deviation methods with data from Wei et al as the input (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of Wildfire Smoke PM\u003csub\u003e2.5\u003c/sub\u003e and BC with SF-36 Score (N\u0026thinsp;=\u0026thinsp;747) *\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMental Health Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ePhysical Health Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e_7d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.043 (-0.092, 0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.063 (-0.111, -0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e_15d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.037 (-0.089, 0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.068 (-0.118, -0.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e_30d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.029 (-0.084, 0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.063 (-0.117, -0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e_60d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.005 (-0.065, 0.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.065 (-0.124, -0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_7d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.172 (-0.309, -0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.214 (-0.348, -0.081)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_15d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.125 (-0.272, 0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.192 (-0.335, -0.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_30d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.075 (-0.236, 0.086)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.172 (-0.329, -0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_60d (per 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.035 (-0.254, 0.184)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.207 (-0.420, 0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_7d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.009 (-0.015, -0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.011 (-0.017, -0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_15d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.006 (-0.014, 0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.010 (-0.017, -0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_30d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.004 (-0.012, 0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.009 (-0.016, -0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS PM\u003csub\u003e2.5\u003c/sub\u003e_60d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.002 (-0.013, 0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.010 (-0.021, 0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS BC_7d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.172 (-0.307, -0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.221 (-0.353, -0.089)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS BC_15d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.135 (-0.278, 0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.201 (-0.341, -0.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS BC_30d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.100 (-0.250, 0.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.184 (-0.330, -0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS BC_60d (per 0.05 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.075 (-0.284, 0.135)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.225 (-0.429, -0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS days_7d (per 1 day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.042 (-0.081, -0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.043 (-0.081, -0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS days_15d (per 1 day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.015 (-0.036, 0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.021 (-0.042, -0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS days_30d (per 1 day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.011 (-0.024, 0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.017 (-0.030, -0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS days_60d (per 1 day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.001 (-0.009, 0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.006 (-0.014, 0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS area_7d (per IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.015 (-0.027, -0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.017 (-0.028, -0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS area_15d (per IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.023 (-0.050, 0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.032 (-0.059, -0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS area_30d (per IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e-0.013 (-0.053, 0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.038 (-0.077, 0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWFS area_60d (per IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e0.008 (-0.065, 0.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e-0.043 (-0.113, 0.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: β, regression coefficient; BC, BC; CI, confidence interval; PM\u003csub\u003e2.5\u003c/sub\u003e, particle with aerodynamic diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m; PM\u003csub\u003e2.5\u003c/sub\u003e _X d, average PM\u003csub\u003e2.5\u003c/sub\u003e concentration during the X days prior to the interview; SF-36, the short form 36 health survey questionnaire; WFS, Wildfire smoke.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e* Models were adjusted for age, sex, ethnicity, BMI, education level, current smokers, pack-years, and prevalence of comorbidity. WFS PM\u003csub\u003e2.5\u003c/sub\u003e and WFS BC are calculated using the deviation methods with data from Wei et al as the input (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSensitivity analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExcluding visits occurring in winter when there were few WFS episodes affecting the greater Albuquerque area did not change the associations observed (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). Association analyses using WFS measures based on residential addresses reproduced the results seen using WFS measures based on four counties. Moreover, stronger magnitudes of association were observed for symptom score of SGRQ for 7-d and 15-d WFS measures and for mental and physical health measures of SF-36 for 15-d WFS measures (Table S3 and S4). Using four-county based WFS PM\u003csub\u003e2.5\u003c/sub\u003e measures from Dr. Childs\u0026rsquo; study reproduced the temporal patterns of associations with SF-36 measures, however its associations with SGRQ scores were only observed in 7-d WFS measures (Table S5 and S6).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffect modification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted interaction analyses to determine whether the WFS \u0026ndash; HRQoL associations were modified by several candidate factors (Table S7 - S13). These analyses were conducted using 7-day WFS measures to ensure optimal power as 7-day measures had most significant associations with HRQoL measures. Most consistent effect modifications were identified for \u0026ldquo;ever woodsmoke exposure for over a year\u0026rdquo; as subjects with woodsmoke exposure have elevated vulnerability to WFS induced HRQoL changes compared to people who do not have woodsmoke exposure (Table S7). We also found evidence that males and subjects with less than college education were more vulnerable to WFS induced HRQoL changes (Table S8 and S9).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough linking daily WFS estimates to psychometric measures collected at baseline visits of the LSC members, we identified that episodic WFS exposures significantly deteriorated psychometric measures of multiple health domains. The impact of WFS exposure on SGRQ scores had similar temporal patterns across all subdomains, \u003cem\u003ei.e.\u003c/em\u003e, respiratory symptoms, limitation in physical activity due to breathlessness, and psychosocial disturbances, with significant associations observed for WFS exposure estimated for 7-, 15-, and 30-d prior to questionnaire filling. However, temporal patterns for the impact of WFS exposure on SF-36 differed by subdomains with impacts seen up to 30-d exposure for physical health measures, while only 7-d exposure for mental health measures. The link between WFS exposures and physical health measures of SF-36 and respiratory quality of life measures by the SGRQ is more likely to be driven by inhalation exposure of WFS and lung deposition of BC particles. Phagocytosis of BC by airway macrophages is a major clearance mechanism in the acinar airway, which triggers persistent cytokine/chemokine secretion and generation of other mediators for downstream pulmonary and extra-pulmonary toxicity (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Our recent analyses in 88 LSC subjects identified that exposure to elevated PM\u003csub\u003e2.5\u003c/sub\u003e in the air for over a month due to WFS (summer) or residential wood burning (winter) significantly increased macrophage carbon load (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the prevalence and persistence of psychological disorders in communities facing wildfire threats, there remains a notable gap in research regarding the effects of WFS on general mental health in populations not directly affected by the fires, but exposed to smoke that travels long distance in atmosphere. We identified the first human evidence supporting alterations of mental health measures after acute exposure to aged WFS. The time frame for triggering alteration in mental health measures was rather short, \u003cem\u003ei.e.\u003c/em\u003e, a week prior to questionnaire filling and this impact became non-significant beyond one-week time window of WFS exposure regardless of whether WFS continued or not. A meta-analysis published in 2019 showed that short-term exposure to PM\u003csub\u003e10\u003c/sub\u003e in the air was associated with the risk of completed suicide at a 0-2d cumulative time lag in meta-analysis (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). There was also some evidence suggesting an association between short-term PM\u003csub\u003e2.5\u003c/sub\u003e or PM\u003csub\u003e10\u003c/sub\u003e exposure and depression-related emergency department visits (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). A randomized, double-blind, crossover trial in China demonstrated that use of air purification for 9 days significantly reduced indoor PM\u003csub\u003e2.5\u003c/sub\u003e and stress hormones (cortisol, cortisone, epinephrine, and norepinephrine) in blood circulation (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our animal models discovered brain effects such as neuroinflammation, neurometabolomic alterations, and depression-like behavioral changes after inhalation exposure to WFS for 4 hours/day, every other day, for 2 weeks (7 exposures total) at an environmentally relevant level (448 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e PM\u003csub\u003e2.5\u003c/sub\u003e) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Several key mechanisms linking inhalation exposure of nanoscale particles and brain effects have been proposed and include direct enter of PM\u003csub\u003e2.5\u003c/sub\u003e in brain tissue via the olfactory nerve, circulating mediators and compromised blood brain barrier integrity, and transfer of PM\u003csub\u003e2.5\u003c/sub\u003e through blood gas barrier and circulation (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). However, it remains unclear whether the associations with mental health measures we observed are due to mechanisms described above due to the rapid attenuation of associations beyond one-week time window of WFS exposure as well as much lower levels of WFS PM\u003csub\u003e2.5\u003c/sub\u003e in our study population compared to animal exposure study. Further research using blood omics approach in populations with real-world WFS exposure may help decode the mechanisms underlying how episodic WFS exposure affect mental health measures in humans.\u003c/p\u003e\u003cp\u003eCombustion-emitted PM is demonstrated to be more harmful to health than PM from non-combustion sources and the literature shows that estimation of combustion emitted PM using source apportionment or BC outperforms total PM\u003csub\u003e2.5\u003c/sub\u003e mass levels for evaluating health risks (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Our analyses provide evidence supporting this premise. The SGRQ scores were most significantly associated with WFS PM\u003csub\u003e2.5\u003c/sub\u003e rather than with total ambient PM\u003csub\u003e2.5\u003c/sub\u003e. For physical health measures, WFS PM\u003csub\u003e2.5\u003c/sub\u003e showed 2.7- to 3.4-fold stronger associations compared to total ambient PM\u003csub\u003e2.5\u003c/sub\u003e, although both PM\u003csub\u003e2.5\u003c/sub\u003e metrics were significantly associated with the outcomes. Stronger potency in inducing lung effects by WFS PM\u003csub\u003e2.5\u003c/sub\u003e compared to PM\u003csub\u003e2.5\u003c/sub\u003e from other sources was reported in animal models with lung lavage cytology and lung histology as the outcome (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and in California populations with respiratory hospitalizations as the outcome (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Additionally, days with high pollution levels (PM\u003csub\u003e2.5\u003c/sub\u003e \u0026ge;35 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) resulting from WFS were strongly associated with an increased risk of tuberculosis diagnoses in California, unlike those caused by other sources of air pollution (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). All together, these studies suggest that WFS PM may be more toxic than equal doses from other sources. WFS PM\u003csub\u003e2.5\u003c/sub\u003e is mostly carbonaceous (with 5\u0026ndash;20% elemental carbon and at least 50% organic carbon) and has more oxidative potential than ambient urban particulate due to the presence of more polar organic compounds (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). It is therefore imperative to differentiate between smoke and non-smoke PM\u003csub\u003e2.5\u003c/sub\u003e when assessing impacts on public health. Moreover, WFS BC also demonstrated stronger associations with all health measures compared to WFS PM\u003csub\u003e2.5\u003c/sub\u003e, suggesting BC constituents may be more potent in inducing health effects compared to non-BC constituents in WFS PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eIt is also important to note that male participants, those with lower than college education, and those reporting ever woodsmoke exposure were more vulnerable to the adverse physical health impacts of WFS. Under controlled exposure settings, lungs from healthy, young adults 18\u0026ndash;40 years of age were affected by WFS in a sex-dependent manner with males having upregulation of inflammatory genes and females having suppression of defense responses (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Sex disparity was also observed in woodsmoke induced inflammation in lungs and brains in animal models (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). However, we cannot exclude possibility that sex disparity observed in the LSC was due to more outdoor activities and higher exposure in males compared to females. Elevated susceptibility in people with less than college education may be due to their higher likelihood of WFS exposure and lower adoption of protective behaviors during wildfire smoke events. We also found that ever woodsmoke exposure for over a year potentiates the health impacts of WFS and this effect modification is independent of airway obstruction or baseline comorbidities. The biological mechanisms underlying the effect potentiation of WFS exposure warrant further investigation.\u003c/p\u003e\u003cp\u003eThe findings of this study may be specific to populations in the Southwestern U.S., particularly those exposed to aged WFS originating from wildfires in the Gila, Apache, and Apache-Sitgreaves National Forests. The smoke traveled nearly 200 miles through the atmosphere before reaching the Albuquerque metropolitan area. The toxicity of WFS is influenced by the constituents present, which are determined by factors such as biomass fuel types, metals in the fuel, burning conditions, and secondary reactions during atmospheric transport. Future studies that analyze PM\u003csub\u003e2.5\u003c/sub\u003e constituents in air samples collected during WFS events will provide further insight in associations observed in populations from smoke-prone regions.\u003c/p\u003e\u003cp\u003eIn summary, our study suggests that episodic exposure to WFS PM\u003csub\u003e2.5\u003c/sub\u003e and BC was associated with poorer respiratory-specific or general health-related quality of life, with notable differences in the temporal relationships between mental and physical health measures. Additionally, WFS PM\u003csub\u003e2.5\u003c/sub\u003e exhibited higher toxicity than total ambient PM\u003csub\u003e2.5\u003c/sub\u003e, underscoring the importance of source-specific risk assessment for air pollution. Moreover, BC constituents may be more potent in inducing health effects compared to non-BC constituents in WFS PM\u003csub\u003e2.5\u003c/sub\u003e. Male participants, individuals with less than a college education, and those exposed to woodsmoke demonstrated heightened vulnerability to WFS. Understanding the factors driving these health disparities is essential for developing precision public health interventions to protect at-risk populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eblack carbon\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCMH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic mucous hypersecretion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRQoL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehealth-related quality of life\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe Lovelace Smokers Cohort\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efine particulate matter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSF-36\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe 36-Item Short Form Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSGRQ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe St. George's Respiratory Questionnaire\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWFS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ewildfire smoke.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThis study was approved by the Western Institutional Review Board and all participants signed consent forms.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eResearch reported in this publication was supported by NIEHS R01 ES035421 and P30 ES032755, NINR 1P20NR021824, NHLBI 1R21HL173388, NIA R01 AG070776, NCI P30 CA118100, NIGMS P50 MD015706, UL1TR001449, and KL2TR001448.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQ.W. conducted the exposure assessment, performed the data analysis, summarized the results, and drafted the manuscript. Y.H. and X.G. contributed to the exposure design and evaluation, data visualization, and manuscript review and editing. L.L.S., T.E., and S.Z. contributed to data visualization and manuscript editing. H.K., M.A.P., M.C., J.M.C., Y.Z., and M.J.C. contributed to manuscript review and editing. S.A.B. and S.L. oversaw the study design, interpreted the results, and revised and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the dedicated efforts of the staff at Lovelace Biomedical Research Institute in conducting questionnaire-based data collection for the Lovelace Smokers Cohort.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eDe-identified data are available upon execution of a Data Use Agreement with Lovelace Biomedical Research Institute, the data owner.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJones MW, Veraverbeke S, Andela N, Doerr SH, Kolden C, Mataveli G, Pettinari ML, Le Quere C, Rosan TM, van der Werf GR, van Wees D, Abatzoglou JT. Global rise in forest fire emissions linked to climate change in the extratropics. 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Respir Res. 2022;23:236.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeng S, Stidley CA, Willink R, Bernauer A, Do K, Picchi MA, Sheng X, Frasco MA, Van Den Berg D, Gilliland FD, Zima C, Crowell RE, Belinsky SA. Double-strand break damage and associated DNA repair genes predispose smokers to gene methylation. Cancer Res. 2008;68:3049\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKelly A, Rush J, Shafonsky E, Hayashi A, Votova K, Hall C, Piccinin AM, Weber J, Rast P, Hofer SM. Detecting short-term change and variation in health-related quality of life: within- and between-person factor structure of the SF-36 health survey. Health Qual Life Outcomes. 2015;13:199.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones PW. Health status measurement in chronic obstructive pulmonary disease. Thorax. 2001;56:880\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones PW, Quirk FH, Baveystock CM. The St George's Respiratory Questionnaire. Respir Med. 1991;85(Suppl B):25\u0026ndash;31. discussion 33\u0026thinsp;\u0026ndash;\u0026thinsp;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones PW. St. George's Respiratory Questionnaire: MCID. \u003cem\u003eCOPD\u003c/em\u003e 2005; 2: 75\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNgo-Metzger Q, Sorkin DH, Mangione CM, Gandek B, Hays RD. Evaluating the SF-36 Health Survey (Version 2) in Older Vietnamese Americans. J Aging Health. 2008;20:420\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiaz AA, Petersen H, Meek P, Sood A, Celli B, Tesfaigzi Y. Differences in Health-Related Quality of Life Between New Mexican Hispanic and Non-Hispanic White Smokers. Chest. 2016;150:869\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaulin LM, Gassett AJ, Alexis NE, Kirwa K, Kanner RE, Peters S, Krishnan JA, Paine R 3rd, Dransfield M, Woodruff PG, Cooper CB, Barr RG, Comellas AP, Pirozzi CS, Han M, Hoffman EA, Martinez FJ, Woo H, Peng RD, Fawzy A, Putcha N, Breysse PN, Kaufman JD, Hansel NN, for Si. Association of Long-term Ambient Ozone Exposure With Respiratory Morbidity in Smokers. JAMA Intern Med. 2020;180:106\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang M, Hu CJ, Rowe CL, Kang H, Gong X, Dagucon CP, Wang J, Lin Y, Sood A, Guo Y, Zhu Y, Alexis NE, Gilliland FD, Belinsky SA, Yu X, Leng S. Application of artificial intelligence in quantifying lung deposition dose of BC in people with exposure to ambient combustion particles. J Expo Sci Environ Epidemiol 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMostovenko E, Canal CG, Cho M, Sharma K, Erdely A, Campen MJ, Ottens AK. Indirect mediators of systemic health outcomes following nanoparticle inhalation exposure. Pharmacol Ther. 2022;235:108120.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraithwaite I, Zhang S, Kirkbride JB, Osborn DPJ, Hayes JF. Air Pollution (Particulate Matter) Exposure and Associations with Depression, Anxiety, Bipolar, Psychosis and Suicide Risk: A Systematic Review and Meta-Analysis. Environ Health Perspect. 2019;127:126002.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Cai J, Chen R, Zhao Z, Ying Z, Wang L, Chen J, Hao K, Kinney PL, Chen H, Kan H. Particulate Matter Exposure and Stress Hormone Levels: A Randomized, Double-Blind, Crossover Trial of Air Purification. Circulation. 2017;136:618\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScieszka D, Jin Y, Noor S, Barr E, Garcia M, Begay J, Herbert G, Hunter RP, Bhaskar K, Kumar R, Gullapalli R, Bolt A, McCormick MA, Bleske B, Gu H, Campen MJ. Biomass smoke inhalation promotes neuroinflammatory and metabolomic temporal changes in the hippocampus of female mice. J Neuroinflammation. 2023;20:192.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScieszka D, Gu H, Barkley-Levenson A, Barr E, Garcia M, Begay JG, Herbert G, Bhaskar K, McCormick M, Brigman J, Ottens A, Bleske B, Campen MJ. Neurometabolomic Impacts of Modeled Wildfire Smoke and Protective Benefits of Anti-Aging Therapeutics in Aged Female C57bl/6j Mice. \u003cem\u003ebioRxiv\u003c/em\u003e 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAragon MJ, Topper L, Tyler CR, Sanchez B, Zychowski K, Young T, Herbert G, Hall P, Erdely A, Eye T, Bishop L, Saunders SA, Muldoon PP, Ottens AK, Campen MJ. Serum-borne bioactivity caused by pulmonary multiwalled carbon nanotubes induces neuroinflammation via blood-brain barrier impairment. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e 2017; 114: E1968-E1976.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThurston GAY, Ostro B, S\u0026aacute;nchez-Triana E. Are All Air Pollution Particles Equal? \u0026ndash; How Constituents and Sources of Fine Air Pollution Particles (PM2.5. Affect Health; 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWegesser TC, Pinkerton KE, Last JA. California wildfires of 2008: coarse and fine particulate matter toxicity. Environ Health Perspect. 2009;117:893\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAguilera R, Corringham T, Gershunov A, Benmarhnia T. Wildfire smoke impacts respiratory health more than fine particles from other sources: observational evidence from Southern California. Nat Commun. 2021;12:1493.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLinde LR, Readhead A, Barry PM, Balmes JR, Lewnard JA. Tuberculosis Diagnoses Following Wildfire Smoke Exposure in California. Am J Respir Crit Care Med. 2023;207:336\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerma V, Polidori A, Schauer JJ, Shafer MM, Cassee FR, Sioutas C. Physicochemical and toxicological profiles of particulate matter in Los Angeles during the October 2007 southern California wildfires. Environ Sci Technol. 2009;43:954\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdetona O, Reinhardt TE, Domitrovich J, Broyles G, Adetona AM, Kleinman MT, Ottmar RD, Naeher LP. Review of the health effects of wildland fire smoke on wildland firefighters and the public. Inhal Toxicol. 2016;28:95\u0026ndash;139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRebuli ME, Speen AM, Martin EM, Addo KA, Pawlak EA, Glista-Baker E, Robinette C, Zhou H, Noah TL, Jaspers I. Wood Smoke Exposure Alters Human Inflammatory Responses to Viral Infection in a Sex-Specific Manner. A Randomized, Placebo-controlled Study. Am J Respir Crit Care Med. 2019;199:996\u0026ndash;1007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWardhani K, Yazzie S, Edeh O, Grimes M, Dixson C, Jacquez Q, Zychowski KE. Neuroinflammation is dependent on sex and ovarian hormone presence following acute woodsmoke exposure. Sci Rep. 2024;14:12995.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWardhani K, Yazzie S, McVeigh C, Edeh O, Grimes M, Jacquez Q, Dixson C, Barr E, Liu R, Bolt AM, Feng C, Zychowski KE. Systemic immunological responses are dependent on sex and ovarian hormone presence following acute inhaled woodsmoke exposure. Part Fibre Toxicol. 2024;21:27.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wildfire smoke, PM2.5, black carbon, SGRQ, SF-36","lastPublishedDoi":"10.21203/rs.3.rs-7069099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7069099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe impact of wildfire smoke (WFS) on air quality across the contiguous US has become geographically widespread. However, the effects of episodic WFS exposure on psychometric measures of mental and physical health remain largely unknown.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eTo assess the associations between WFS PM\u003csub\u003e2.5\u003c/sub\u003e and black carbon (BC) exposure and psychometric health measures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e The St. George's Respiratory Questionnaire (SGRQ) and the 36-Item Short Form Survey (SF-36) were administered to participants in the Lovelace Smokers Cohort in New Mexico to assess psychometric health measures in the past 4 weeks. WFS estimates were calculated against Albuquerque metropolitan area or individual residential addresses for 7-, 15-, 30-, and 60-d prior to questionnaire filling. The associations between exposure and health measures were assessed using linear models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSignificant associations were observed for all psychometric measures with WFS PM\u003csub\u003e2.5\u003c/sub\u003e and BC exposures estimated for 7-d prior to questionnaire filling. Significant associations remained for WFS exposure estimated up to 30-d prior to questionnaire filling for all SGRQ subdomains and physical health measures of SF-36, but became non-significant for the mental health measures of SF-36 beyond one week prior. Additionally, WFS PM\u003csub\u003e2.5\u003c/sub\u003e exhibited stronger potency than total ambient PM\u003csub\u003e2.5\u003c/sub\u003e. Male participants, individuals with less than a college education, and those exposed to woodsmoke demonstrated heightened vulnerability to WFS.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eEpisodic exposure to WFS was associated with worse SGRQ and SF-36 scores, with notable differences in temporal patterns between mental and physical health measures. Our findings also underscore the importance of source-specific risk assessment for air pollution.\u003c/p\u003e","manuscriptTitle":"Associations between Wildfire Smoke Exposure and Health-Related Quality of Life: Findings from the Lovelace Smokers Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 15:46:35","doi":"10.21203/rs.3.rs-7069099/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-12T18:31:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T13:28:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216316810138079506752708241148929197536","date":"2025-10-06T11:45:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164988111521023219590102919872680021464","date":"2025-10-02T19:56:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147573761414396546196455801603172776212","date":"2025-10-02T05:17:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T16:35:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176050258497301093203129444465340419765","date":"2025-10-01T15:44:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T20:42:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330990951918099937134560221330800970644","date":"2025-07-28T13:48:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88815632501126915187697704198235538509","date":"2025-07-19T17:01:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T14:45:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T18:19:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-08T23:37:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2025-07-07T23:37:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"806f0fad-9095-49d7-8ee8-8def61e8601a","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:05:49+00:00","versionOfRecord":{"articleIdentity":"rs-7069099","link":"https://doi.org/10.1186/s12931-026-03505-9","journal":{"identity":"respiratory-research","isVorOnly":false,"title":"Respiratory Research"},"publishedOn":"2026-01-14 16:30:11","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-10-23 15:46:35","video":"","vorDoi":"10.1186/s12931-026-03505-9","vorDoiUrl":"https://doi.org/10.1186/s12931-026-03505-9","workflowStages":[]},"version":"v1","identity":"rs-7069099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7069099","identity":"rs-7069099","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}