Prevalence and predictors of colon and prostate cancer screening among volunteer firefighters: The United States Firefighter Cancer Assessment and Prevention Study.

BACKGROUND: Although firefighters have increased risk for colon and prostate cancer, limited information exists on screening practices for these cancers in volunteer firefighters who compose two-thirds of the US fire service. We estimated the prevalence of colon and prostate cancer screening among volunteer firefighters using eligibility criteria from 4 evidence-based screening recommendations and evaluated factors influencing screening. METHODS: We evaluated colon (n = 569) and prostate (n = 498) cancer screening prevalence in a sample of US volunteer firefighters using eligibility criteria from the US Preventive Services Taskforce (USPSTF), National Fire Protection Association, American Cancer Society, and National Comprehensive Cancer Network. We assessed associations with fire service experience, demographics, and cancer risk perception based on USPSTF guidelines. RESULTS: For those eligible based on USPSTF guidelines, colon and prostate cancer screening prevalence was 51.7% (95% CI: 45.7, 57.8) and 48.8% (95% CI: 40.0, 57.6), respectively. Higher odds of colon and prostate cancer screening were observed with older age and with some college education compared to those with less education. Fire service experience and cancer risk perception were not associated with screening practices. CONCLUSION: This is the first large study to assess colon and prostate cancer screening among US volunteer firefighters based on different screening guidelines. Our findings suggest gaps in cancer prevention efforts in the US volunteer fire service. Promoting cancer screening education and opportunities for volunteer firefighters by their fire departments, healthcare professionals, and public health practitioners, may help to address the gaps.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models.

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology.

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.

Prevalence and predictors of skin cancer screening among a sample of US volunteer firefighters.

BACKGROUND: Firefighters have a higher risk of melanoma incidence and mortality compared to the general population. In the United States (US), the National Fire Protection Association recommends all firefighters receive annual skin cancer screening through visual skin examination by a clinician. However, there is limited information on skin cancer screening practices among volunteer firefighters who comprise two-thirds of the US fire service. METHODS: This cross-sectional study of 552 US volunteer firefighters estimated the prevalence of skin cancer screening and evaluated associations with their fire service experience, demographics, sun protection practices, and cancer risk perception. RESULTS: The prevalence of receiving skin cancer screening among volunteer firefighters was 26.1% (95% confidence interval [CI]: 22.4, 29.8). The odds of being screened for skin cancer, compared to not being screened, were twice as high for firefighters who used sunscreen (odds ratio [OR]: 2.35, 95% CI: 1.48, 3.73) and who perceived their skin likely to burn with prolonged sun exposure (OR: 1.81, 95% CI: 1.10, 3.00). Older age, some college education, and family history of skin cancer were also positively associated with skin cancer screening. A positive exposure-response relationship was observed between more monthly firefighting calls and receiving screening. Cancer risk perception was not associated with screening. CONCLUSION: To our knowledge, this is the first large study to assess skin cancer screening among US volunteer firefighters. Our findings suggest gaps in skin cancer prevention efforts in the volunteer fire service. Additional assessment of skin cancer prevention practices within volunteer fire departments could help address these gaps.

Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign.

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 µg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.

Distinct Ultrafine Particle Profiles Associated with Aircraft and Roadway Traffic.

The Mobile ObserVations of Ultrafine Particles study was a two-year project to analyze potential air quality impacts of ultrafine particles (UFPs) from aircraft traffic for communities near an international airport. The study assessed UFP concentrations within 10 miles of the airport in the directions of aircraft flight. Over the course of four seasons, this study conducted a mobile sampling scheme to collect time-resolved measures of UFP, CO2, and black carbon (BC) concentrations, as well as UFP size distributions. Primary findings were that UFPs were associated with both roadway traffic and aircraft sources, with the highest UFP counts found on the major roadway (I-5). Total concentrations of UFPs alone (10-1000 nm) did not distinguish roadway and aircraft features. However, key differences existed in the particle size distribution and the black carbon concentration for roadway and aircraft features. These differences can help distinguish between the spatial impact of roadway traffic and aircraft UFP emissions using a combination of mobile monitoring and standard statistical methods.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study.

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)-which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Energy consumption of using HEPA-based portable air cleaner in residences: A monitoring study in Seattle, US.

Portable air cleaners (PACs), offering both auto and manual (adjustable) operation modes, are commonly used in residences. Compared with adjustable mode, auto mode's advantage of reducing indoor PM2.5 has been previously demonstrated. This study examines the energy consumption of such PACs in six residences recruited in Seattle, United States, and compares the power consumption between auto and adjustable modes. Each residence went through a one-week-long PAC filtration session under auto and adjustable modes, respectively. PAC power consumption, indoor PM2.5, temperature, and relative humidity (RH) were measured at 10-second intervals in each residence. A linear mixed-effects regression (LMER) model was used to compare the PAC power consumption between the two modes after adjusting for indoor PM2.5, temperature, and RH. Results show that the mean (standard deviation) PAC power consumption under adjustable and auto modes were 7.0 (3.5) and 6.8 (2.6) W, respectively. The average monthly energy consumption of continuous PAC operation was estimated to be ~5 kWh for both modes. Based on the LEMR model, PAC power consumption under auto mode was approximately 3% larger than that under adjustable mode, after adjusting for living-room PM2.5, temperature, and RH levels. The implications for PAC operation mode selection in residential environments were discussed.

Residential cooking-related PM2.5: Spatial-temporal variations under various intervention scenarios.

Some cooking events can generate high levels of hazardous PM2.5. This study assesses the dispersion of cooking-related PM2.5 throughout a naturally-ventilated apartment in the US, examines the dynamic process of cooking-related emissions, and demonstrates the impact of different indoor PM2.5 mitigating strategies. We conducted experiments with a standardized pan-frying cooking procedure under seven scenarios, involving opening kitchen windows, using a range hood, and utilizing a portable air cleaner (PAC) in various indoor locations. Real-time PM2.5 concentrations were measured in the open kitchen, living room, bedroom (door closed), and outdoor environments. Decay-related parameters were estimated, and time-resolved PM2.5 emission rates for each experiment were determined using a dynamic model. Results show that the 1-min mean PM2.5 concentrations in the kitchen and living room peaked 1-7 min after cooking at levels of 200-1400 µg/m3, which were more than 9 times higher than the peak bedroom levels. Mean (standard deviation) kt for the kitchen, ranging from 0.58 (0.02) to 6.62 (0.34) h-1, was generally comparable to that of the living room (relative difference < 20%), but was 1-5 times larger than that of the bedroom. The range of PM2.5 full-decay time was between 1-10 h for the kitchen and living room, and from 0 to > 6 h for the bedroom. The PM2.5 emission rates during and 5 min after cooking were 2.3 (3.4) and 5.1 (3.9) mg/min, respectively. Intervention strategies, including opening kitchen windows and using PACs either in the kitchen or living room, can substantially reduce indoor PM2.5 levels and the related full-decay time. For scenarios involving a PAC, placing it in the kitchen (closer to the source) resulted in better efficacy.

Using Vehicles' Rendezvous for In Situ Calibration of Instruments in Fleet Vehicle-Based Air Pollution Mobile Monitoring.

This study examines the feasibility of the in situ calibration of instruments for fleet vehicle-based mobile monitoring of ultrafine particles (UFPs) and black carbon (BC) by comparing rendezvous vehicle measurements. Two vehicles with identical makes and models of UFP and BC monitors as well as GPS receivers were sampled within 140 m of each other for 2 h in total during winter in Seattle, Washington. To identify an optimal intervehicle distance for rendezvous calibration, 6 different buffers within 0-140 m for UFP monitors and 5 different buffers within 0-90 m for BC monitors were chosen, and the results of calibration were compared against a reference scenario, which consisted of mobile colocation measurements with both sets of the UFP and BC monitors deployed in one of the vehicles. Results indicate that the optimal distances for rendezvous calibration are 10-80 m for UFP monitors and 0-30 m for BC monitors. In comparison with the mobile colocation calibration, the rendezvous calibration shows a normalized root mean squared deviation of 6-14% and a normalized mean absolute deviation of 4-8% for these monitors. Criteria for applying a rendezvous calibration approach are presented, and an extension of this approach to an instrumented fleet of mobile monitoring vehicles is discussed.

Use of low-cost PM monitors and a multi-wavelength aethalometer to characterize PM2.5 in the Yakama Nation Reservation.

Rural lower Yakima Valley, Washington is home to the reservation of the Confederated Tribes and Bands of the Yakama Nation, and is a major agricultural region. Episodic poor air quality impacts this area, reflecting sources of particulate matter with a diameter of less than 2.5 micrometers (PM2.5) that include residential wood smoke, agricultural biomass burning and other emissions, truck traffic, backyard burning, and wildfire smoke. University of Washington partnered with the Yakama Nation Environmental Management Program to investigate characteristics of PM2.5 using 9 months of data from a combination of low-cost optical particle counters and a 5-wavelength aethalometer (MA200 Aethlabs) over 4 seasons and an episode of summer wildfire smoke. The greatest percentage of hours sampled with PM2.5 >12 µg/m3 occurred during the wildfire smoke episode (59%), followed by fall (23%) and then winter (21%). Mean (SD) values of Delta-C (µg/m3), which has been posited as an indicator of wood smoke, and determined as the mass absorbance difference at 375-880nm, were: summer - wildfire smoke 0.34 (0.52), winter 0.27 (0.32), fall 0.10 (0.22), spring 0.05 (0.11), and summer - no wildfire smoke 0.04 (0.14). Mean (95% confidence interval) values of the absorption Ångström exponent, an indicator of the wavelength dependence of the aerosol, were: winter 1.5 (1.2-1.8), summer - wildfire smoke 1.4 (1.0-1.8), fall 1.3 (1.1-1.4), spring 1.2 (1.1-1.4), and summer - no wildfire smoke 1.2 (1.0-1.3). The trends in Delta-C and absorption Ångström exponents are consistent with expectations that a higher value reflects more biomass burning. These results suggest that biomass burning is an important contributor to PM2.5 in the wintertime, and emissions associated with diesel and soot are important contributors in the fall; however, the variety of emissions sources and combustion conditions present in this region may limit the utility of traditional interpretations of aethalometer data. Further understanding of how to interpret aethalometer data in regions with complex emissions would contribute to much-needed research in communities impacted by air pollution from agricultural as well as residential sources of combustion.

Private well stewardship within a rural, agricultural Latino community: a qualitative study.

BACKGROUND: Nitrate contamination in groundwater disproportionately impacts agricultural Latino communities, creating a significant hazard for Latinos that rely on private wells. Private well users must conduct water testing and other well stewardship behaviors to ensure that their well water is safe to drink. This study sought to identify the key factors impacting private well water testing behavior in rural, agricultural Latino communities. METHODS: We conducted 4 focus groups with private well users, 2 in Spanish and 2 in English. We recruited 37 participants from the Lower Yakima Valley, Washington State, a rural, agricultural community with a large Latino population and elevated nitrate concentrations in groundwater. A semi-structured interview guide was developed to capture factors impacting testing as guided by the Risk, Attitudes, Norms, Ability, and Self-Regulation (RANAS) model. Inductive thematic analysis was conducted by two coders to identify common themes. RESULTS: Themes emerged around the factors impacting well stewardship, including well water testing, treatment, and maintenance, and were not specific to nitrate contamination. Private well users reported many of the same factors reported in other communities, with the exception of home repair experience and challenges around landlords and neighbors on shared wells, which have not been reported previously. In addition to landlords and neighbors, lack of actionable information, economic limitations, and lack of technical support emerged as factors that made well stewardship burdensome for individuals. The majority of participants reported using bottled water, including many who used point-of-use or point-of-entry water treatment systems. CONCLUSIONS: The burden of well stewardship in rural, agricultural Latino communities may suggest the need for interventions at the community, county, or state levels and not at the individual level alone. Additionally, the role of landlords, neighbors on shared wells, and home repair experience in well stewardship represent important areas of exploration for researchers and public health practitioners.

Ambient Air Quality Measurements from a Continuously Moving Mobile Platform: Estimation of Area-Wide, Fuel-Based, Mobile Source Emission Factors Using Absolute Principal Component Scores.

We have applied the absolute principal component scores (APCS) receptor model to on-road, background-adjusted measurements of NOx, CO, CO2, black carbon (BC), and particle number (PN) obtained from a continuously moving platform deployed over nine afternoon sampling periods in Seattle, WA. Two Varimax-rotated principal component features described 75% of the overall variance of the observations. A heavy-duty vehicle feature was correlated with black carbon and particle number, whereas a light-duty feature was correlated with CO and CO2. NOx had moderate correlation with both features. The bootstrapped APCS model predictions were used to estimate area-wide, average fuel-based emission factors and their respective 95% confidence limits. The average emission factors for NOx, CO, BC and PN (14.8, 18.9, 0.40 g/kg, and 4.3×1015 particles/kg for heavy duty vehicles, and 3.2, 22.4, 0.016 g/kg, and 0.19×1015 particles/kg for light-duty vehicles, respectively) are consistent with previous estimates based on remote sensing, vehicle chase studies, and recent dynamometer tests. Information on the spatial distribution of the concentrations contributed by these two vehicle categories relative to background during the sampling period was also obtained.

The Impact of Multipollutant Clusters on the Association Between Fine Particulate Air Pollution and Microvascular Function.

BACKGROUND: Prior studies including the Framingham Heart Study have suggested associations between single components of air pollution and vascular function; however, underlying mixtures of air pollution may have distinct associations with vascular function. METHODS: We used a k-means approach to construct five distinct pollution mixtures from elemental analyses of particle filters, air pollution monitoring data, and meteorology. Exposure was modeled as an interaction between fine particle mass (PM2.5), and concurrent pollution cluster. Outcome variables were two measures of microvascular function in the fingertip in the Framingham Offspring and Third Generation cohorts from 2003 to 2008. RESULTS: In 1,720 participants, associations between PM2.5 and baseline pulse amplitude tonometry differed by air pollution cluster (interaction P value 0.009). Higher PM2.5 on days with low mass concentrations but high proportion of ultrafine particles from traffic was associated with 18% (95% confidence interval: 4.6%, 33%) higher baseline pulse amplitude per 5 µg/m and days with high contributions of oil and wood combustion with 16% (95% confidence interval: 0.2%, 34%) higher baseline pulse amplitude. We observed no variation in associations of PM2.5 with hyperemic response to ischemia observed across air pollution clusters. CONCLUSIONS: PM2.5 exposure from air pollution mixtures with large contributions of local ultrafine particles from traffic, heating oil, and wood combustion was associated with higher baseline pulse amplitude but not hyperemic response. Our findings suggest little association between acute exposure to air pollution clusters reflective of select sources and hyperemic response to ischemia, but possible associations with excessive small artery pulsatility with potentially deleterious microvascular consequences.

Estimating Causal Associations of Fine Particles With Daily Deaths in Boston.

Many studies have reported associations between daily particles less than 2.5 µm in aerodynamic diameter (PM2.5) and deaths, but they have been associational studies that did not use formal causal modeling approaches. On the basis of a potential outcome approach, we used 2 causal modeling methods with different assumptions and strengths to address whether there was a causal association between daily PM2.5 and deaths in Boston, Massachusetts (2004-2009). We used an instrumental variable approach, including back trajectories as instruments for variations in PM2.5 uncorrelated with other predictors of death. We also used propensity score as an alternative causal modeling analysis. The former protects against confounding by measured and unmeasured confounders and is based on the assumption of a valid instrument. The latter protects against confounding by all measured covariates, provides valid estimates in the case of effect modification, and is based on the assumption of no unmeasured confounders. We found a causal association of PM2.5 with mortality, with a 0.53% (95% confidence interval: 0.09, 0.97) and a 0.50% (95% confidence interval: 0.20, 0.80) increase in daily deaths using the instrumental variable and the propensity score, respectively. We failed to reject the null association with exposure after the deaths (P =0.93). Given these results, prior studies, and extensive toxicological support, the association between PM2.5 and deaths is almost certainly causal.

PM2.5 and survival among older adults: effect modification by particulate composition.

BACKGROUND: Fine particulate (PM2.5) air pollution has been consistently linked to survival, but reported effect estimates are geographically heterogeneous. Exposure to different types of particle mixtures may explain some of this variation. METHODS: We used k-means cluster analyses to identify cities with similar pollution profiles, (ie, PM2.5 composition) across the United States. We examined the impact of PM2.5 on survival, and its variation across clusters of cities with similar PM2.5 composition, among Medicare enrollees in 81 US cities (2000-2010). We used time-varying annual PM2.5 averages, measured at ambient central monitoring sites, as the exposure of interest. We ran by-city Cox models, adjusting for individual data on previous cardiopulmonary-related hospitalizations and stratifying by follow-up time, age, gender, and race. This eliminates confounding by factors varying across cities and long-term trends, focusing on year-to-year variations of air pollution around its city-specific mean and trend. We then pooled the city-specific effects using a random effects meta-regression. In this second stage, we also assessed effect modification by cluster membership and estimated cluster-specific PM2.5 effects. RESULTS: We followed more than 19 million subjects and observed more than 6 million deaths. We found a harmful impact of annual PM2.5 concentrations on survival (hazard ratio = 1.11 [95% confidence interval = 1.01, 1.23] per 10 µg/m). This effect was modified by particulate composition, with higher effects observed in clusters containing high concentrations of nickel, vanadium, and sulfate. For instance, our highest effect estimate was observed in cities with harbors in the Northwest, characterized by high nickel, vanadium, and elemental carbon concentrations (1.9 [1.1, 3.3]). We observed null or negative associations in clusters with high oceanic and crustal particles. CONCLUSIONS: To the best of our knowledge, this is the first study to examine the association between PM2.5 composition and survival. Our findings indicate that long-term exposure to fuel oil combustion and power plant emissions have the highest impact on survival.

Quantifying the contributions of road and air traffic to ambient ultrafine particles in two urban communities.

Traffic-related activities are widely acknowledged as a primary source of urban ambient ultrafine particles (UFPs). However, a notable gap exists in quantifying the contributions of road and air traffic to size-resolved and total UFPs in urban areas. This study aims to delineate and quantify the traffic's contributions to size-resolved and total UFPs in two urban communities. To achieve this, stationary sampling was conducted at near-road and near-airport communities in Seattle, Washington State, to monitor UFP number concentrations during 2018-2020. Comprehensive correlation analyses among all variables were performed. Furthermore, a fully adjusted generalized additive model, incorporating meteorological factors, was developed to quantify the contributions of road and air traffic to size-resolved and total UFPs. The study found that vehicle emissions accounted for 29% of total UFPs at the near-road site and 13% at the near-airport site. Aircraft emissions contributed 14% of total UFPs at the near-airport site. Notably, aircraft predominantly emitted UFP sizes below 20 nm, while vehicles mainly emitted UFP sizes below 50 nm. These findings reveal the variability in road and air traffic contributions to UFPs in distinct areas. Our study emphasizes the pivotal role of traffic layout in shaping urban UFP exposure.

Use of Portable Air Cleaners in Washington State Schools: A Qualitative Analysis Based on the Technology Acceptance Model.

BACKGROUND: The US government allocated over $2.5 billion in "Elementary and Secondary School Emergency Relief (ESSER)" funds to Washington State for COVID-19 response and ventilation improvements. Despite available funding, gaps persist in supporting schools to successfully use portable air cleaners (PACs). We evaluated PAC needs within King County, Washington and characterized factors influencing schools' purchase and use of PACs. METHODS: Public Health-Seattle & King County (PHSKC) assessed school's ventilation systems and IAQ improvements through a survey (N = 17). Separately, semi-structured interviews (N = 13) based on the technology acceptance model (TAM) were conducted with school personnel. A thematic analysis using inductive and deductive coding was conducted and logistic regression models assessed the predictive capability of the TAM. RESULTS: The PHSKC survey findings informed our recommendations. Positive attitudes, knowledge, and beliefs in ease of use and effectiveness of PACs were facilitators to PAC use. While barriers included a lack of training, education, and concerns about PAC maintenance and sustainability. TAM constructs of perceived usefulness (PU) and perceived ease of use (PEU) were predictive of having the intention to use PACs in schools. CONCLUSIONS: There is a critical need for solutions to circumvent challenges to implementing PACs in schools. This characterization provides insight for promoting PAC use in IAQ-impacted schools.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

BACKGROUND: Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. OBJECTIVE: Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. METHODS: We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. RESULTS: The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- R 2 = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and R 2 = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- R 2 = 0.51 (with LCS). IMPACT: We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Wildfire smoke exposure and emergency department visits in Washington State.

Wildfires are increasing in prevalence in western North America due to changing climate conditions. A growing number of studies examine the impact of wildfire smoke on morbidity; however, few evaluate these impacts using syndromic surveillance data that cover many emergency departments (EDs). We used syndromic surveillance data to explore the effect of wildfire smoke exposure on all-cause respiratory and cardiovascular ED visits in Washington state. Using a time-stratified case crossover design, we observed an increased odds of asthma visits immediately after and in all five days following initial exposure (lag 0 OR: 1.13; 95% CI: 1.10, 1.17; lag 1-5 ORs all 1.05 or greater with a lower CI of 1.02 or higher), and an increased odds of respiratory visits in all five days following initial exposure (lag 1 OR: 1.02; 95% CI: 1.00, 1.03; lag 2-5 ORs and lower CIs were all at least as large) comparing wildfire smoke to non-wildfire smoke days. We observed mixed results for cardiovascular visits, with evidence of increased odds emerging only several days following initial exposure. We also found increased odds across all visit categories for a 10 µg m-3 increase in smoke-impacted PM2.5. In stratified analyses, we observed elevated odds for respiratory visits among ages 19-64, for asthma visits among ages 5-64, and mixed risk estimates for cardiovascular visits by age group. This study provides evidence of an increased risk of respiratory ED visits immediately following initial wildfire smoke exposure, and increased risk of cardiovascular ED visits several days following initial exposure. These increased risks are seen particularly among children and younger to middle-aged adults.