Early life exposure to air pollution is associated with adverse health outcomes in children however few studies have investigated children's air pollution exposures in urban settings in sub-Saharan Africa (SSA). We measured fine particulate matter (PM2.5) and carbon monoxide (CO) in homes of infants in Nairobi, Kenya and conducted exploratory analysis of exposure factors. Questionnaires captured household characteristics and self-reported air pollution exposures. Indoor and outdoor 24-hour (24 h) concentrations were measured inside and 1 m outside the house. PM2.5 was sampled using standard gravimetric procedures; CO was measured with direct-reading electrochemical sensors. Forty-eight homes were sampled at median infant age 11.5 months (range 0.8-26.2 months). During sampling, 66.7%, 18.8%, 10.4% and 10.4% of mothers, respectively, reported using liquefied petroleum gas (LPG), ethanol, electricity, and kerosene for cooking. Median indoor and outdoor 24 h PM2.5 concentrations (n = 39) were 39.9 ug/m3 (range, 12.8-519.6 ug/m3) and 23.3 ug/m3 (range, 2.6-68.2 ug/m3), respectively. Most PM2.5 concentrations (97% of indoor; 79% of outdoor) exceeded the World Health Organization (WHO) 24 h air quality guideline (AQG) of 15 ug/m3. Median indoor (n = 47) and outdoor (n = 41) 24 h mean CO concentrations were 0.7 ppm (range, 0-33.9 ppm) and 0.0 ppm (range, 0-1.0 ppm), respectively. Mean indoor CO concentrations exceeded the WHO 24 h AQG of 6.2 ppm in 9% of homes. Despite frequent use of cooking fuels considered to be clean such as LPG and ethanol, PM2.5 and CO levels in infant homes in urban SSA often exceeded the WHO AQGs. Expanded studies of children's air pollution exposures in urban SSA are needed to build awareness and inform policy.

Rapid health outcome data acquisition using existing questionnaires can accelerate time-sensitive wildfire research. We intended to create a health questionnaire library that contains readily deployable questionnaires for researchers, public health agencies, and other groups interested in rapid data collection during wildfire events. In this paper, we describe the methodology used to identify relevant self-reported health questionnaires and develop the structured questionnaire library, which serves as a centralized platform for wildfire researchers seeking to quickly design health assessment instruments. This method can also facilitate rapid questionnaire-based data collection following other disasters. To do so we performed the following tasks:•A systematic literature review of wildfire exposure and health studies to 1) identify health outcome categories associated with wildfire and smoke exposure and 2) extract questionnaires used for health outcomes related to wildfire exposure.•A secondary search of existing questionnaire repositories to identify additional relevant health instruments.•A structured organization of questionnaires (n = 100) by eight health outcome categories (mental health = 60, respiratory health = 19, overall health = 17, sleep = 10, cardiovascular health = 4, allergy = 1, irritation (eye, throat, skin) = 2, and metabolic health = 1) into a wildfire health questionnaire library.

Recently, the misuse of fentanyl and methamphetamine has increased in the United States. These drugs can be consumed via smoking a powder, which can subsequently contaminate air and surfaces with drug residue. With limited access to safe consumption sites, this misuse often occurs in public spaces such as public transit, leading to potential secondhand exposures among transit operators and riders. In the Pacific Northwest, transit operators have reported acute health symptoms and safety concerns regarding these drug exposures. Researchers conducted an exposure assessment, sampling air and surfaces for fentanyl and methamphetamine. A total of 78 air samples and 89 surface samples were collected on 11 buses and 19 train cars from four transit agencies in the Pacific Northwest. Fentanyl was detected above the limit of quantification (LOQ) in 25% of air samples (range of concentrations > LOQ: 0.002 to 0.14 µg/m3) and 38% of surface samples (range of concentrations > LOQ: 0.011 to 0.47 ng/cm2), while methamphetamine was detected in 100% of air samples (range: 0.003 to 2.32 µg/m3) and 98% of surface samples (range of concentrations > LOQ: 0.016 to 6.86 ng/cm2) The highest fentanyl air sample (0.14 µg/m3) was collected in the passenger area of a train for 4 hr, and would exceed the ACGIH® 8-hr TWA TLV® of 0.1 µg/m3 if conditions remained the same for the unsampled period. No surface samples exceed the ACGIH fentanyl surface level TLV (10 ng/cm2). The prevalence of fentanyl and methamphetamine on public transit highlights the need to protect transit operators from secondhand exposure and from the stress of witnessing and responding to smoking events. Future work is needed to evaluate the utility of engineering and administrative controls such as ventilation and cleaning upgrades in reducing exposures on transit, as well as the utility of training and increased workplace support for operators in addressing their health and well-being after observing or responding to drug use events.

ObjectiveThis study investigates whether transit operators' risk perceptions of workplace exposure to drug use incidents, occupational stress, and job satisfaction were associated with intent to leave their job.MethodsA cross-sectional survey of operators from union locals (WA, OR) assessed perceived risk of drug exposures, occupational stress, job satisfaction, and intent to leave (N = 273). Ordinal logistic regression models were developed for intent to leave.ResultsMost operators were bus drivers. Higher perceived risk was significantly positively associated with greater intent to leave; stress and job satisfaction attenuated this relationship. Higher stress and lower job satisfaction were significantly associated with greater intent to leave across models.ConclusionsOperator turnover can potentially be reduced by increasing supports targeting risk perceptions of drugs, such as training, or by providing supports (eg, mental health resources) that reduce stress and improve job satisfaction.

Introduction Air pollution is linked with poor neurodevelopment in high-income countries. Comparable data are scant for low-income countries, where exposures are higher. Longitudinal pregnancy cohort studies are optimal for individual exposure assessment during critical windows of brain development and examination of neurodevelopment. This study aims to determine the association between prenatal ambient air pollutant exposure and neurodevelopment in children aged 12, 24 and 36 months through a collaborative, capacity-enriching research partnership.Methods and analysis This observational cohort study is based in Nairobi, Kenya. Eligibility criteria are singleton pregnancy, no severe pregnancy complications and maternal age 18 to 40 years. At entry, mothers (n=400) are administered surveys to characterise air pollution exposures reflecting household features and occupational activities and provide blood (for lead analysis) and urine specimens (for polycyclic aromatic hydrocarbon (PAH) metabolites). Mothers attend up to two additional antenatal study visits, with urine collection, and infants are followed through age 36 months for annual neurodevelopment and caregiving behaviour assessment, and child urine and blood collection. Primary outcomes are child motor skills, language and cognition at 12, 24 and 36 months, and executive function at 36 months. The primary exposure is urinary PAH metabolite concentrations. Additional exposure assessment in a subset of the cohort includes residential indoor and outdoor air monitoring for fine particulate matter (PM2.5), carbon monoxide (CO), ultrafine particles (UFP) and black carbon (BC).Ethics and dissemination This study was approved by the Kenyatta National Hospital - University of Nairobi Ethics and Research Committee, and the University of Washington Human Subjects Division. Results are shared at annual workshops.

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is a U.S. federal nutrition assistance program that provides low-income pregnant, breastfeeding, and postpartum women, infants, and children up to age 5 with supplemental foods, healthcare referrals, and nutrition education. At least 20

The COVID-19 pandemic resulted in reduced air travel in the United States (US). In the ensuing years after the pandemic, air travel rebounded to near pre-pandemic levels. This provided an opportunity to assess the impact of fewer flights on community noise using monitoring data from six major airports in the US. Flight data for the airports were obtained from the US Department of Transportation (DOT) for the 5-year period before, during, and after the pandemic (2018-2022). Noise levels were assessed at monitoring sites surrounding airports (SFO, LAX, ORD, JFK, LGA, and EWR) in San Francisco, CA; Los Angeles, CA; Chicago, IL; New York City, NY; and Newark, NJ for the same period. Linear models and generalized additive models (GAMs) were used to investigate the changes in Lden and Ldn noise metrics by year and by flight operations. Percentage reduction in flights ranged from 43.9 - 56.2% less in the year of the pandemic (2020) compared to pre-pandemic peak levels. Average noise levels were also found to range from 3.0 - 5.4 dBA lower in 2020 compared to the peak levels before the pandemic. Both flight traffic and noise levels have increased since 2020. Linear regressions and GAMs both indicated lower noise levels at each airport during the pandemic, and associations between numbers of flights and noise levels. Based on linear models, the point estimate effect of air traffic on annual noise level was a 0.8 to 2.7 dBA change per 100,000 annual flights. These findings extend those of previous studies that have also documented noise reductions during the pandemic. This is one of the few studies that evaluated noise reductions in US airport communities in relationship to reduced air traffic across multiple years. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at: https://data.sfgov.org/Transportation/Aircraft-Noise-Climates/qxw2-ncq3/about_data https://www.lawa.org/lawa-environment/noise-management/lawa-noise-management-lax/california-state-airport-noise-standards-quarterly-reports-and-contour-maps https://www.flychicago.com/community/ORDnoise/ANMS/Pages/ANMSreports.aspx https://aircraftnoise.panynj.gov/reports/

Importance Flooding is a major environmental hazard, with events increasing in intensity and frequency in the context of climate change. Floods cause significant health and economic impacts, particularly among vulnerable populations, including older adults. However, comprehensive analyses of the health consequences of flooding remain limited. Objective To evaluate the morbidity and health care costs among Medicare beneficiaries associated with flood exposure in the US. Design, Setting, and Participants This retrospective cohort study analyzed emergency department (ED) use and unplanned hospitalization among Medicare beneficiaries 65 years or older living in zip code tabulation areas (ZCTAs) that were exposed to large-scale flood events from January 1, 2008, to December 31, 2017. This analysis was conducted from April 3 to December 15, 2023. Exposure The primary exposure was the presence of a flood as recorded in the Multisourced Flood Inventories, a spatially distributed flood database. Main Outcomes and Measures A conditional fixed-effects regression approach was used to explore the incidence of all-case and cause-specific ED visits and hospitalizations before and after floods. The primary outcomes measured were the incident rate ratios (IRRs) and associated 95% CIs. Attributable risk percentages and estimated attributable excess visits were calculated. Stratified analyses were performed for evaluation of effect modification. Health care costs associated with these events were measured and standardized to 2017 US dollars. Results Among 11 801 527 Medicare beneficiaries 65 years or older (mean [SD] age, 74.4 [7.6] years; 56.3% female), the rate of all-cause ED visits and hospital admissions increased by 4.8% (IRR, 1.05; 95% CI, 1.04-1.05) and 7.4% (IRR, 1.07; 95% CI, 1.07-1.08) after flood exposure, respectively. The mean ZCTA-level cost was $3230 (95% CI, $3198-$3261) per ED visit and $11 310 (95% CI, $11 252-$11 367) for hospitalizations. The national costs to the Medicare system were estimated to be $69 275 429 (95% CI, $63 010 840-$76 315 210) for ED visits and $191 409 579 (95% CI, $172 782 870-$206 181 300) for hospitalizations. Stratified analyses highlighted greater impacts for certain demographic groups, including adults older than 85 years, and specific seasonal patterns. Conclusions and Relevance In this cohort study of Medicare beneficiaries 65 years or older, flood exposure was associated with increased health care use and costs, underscoring the need for targeted public health strategies and improved disaster preparedness, especially for older adults. These findings contribute to a more comprehensive understanding of the health-related costs of flooding and can be used to inform future climate change resilience and health care planning.

Mobile monitoring strategies are increasingly used to provide fine spatial estimates of multiple air pollutant concentrations. This study demonstrates a novel approach using positive matrix factorization (PMF) applied to multipollutant mobile monitoring data to assess source-specific air pollution exposures and to estimate associated emission factors. Data were collected from one-year mobile monitoring, with an average of 26 repeated measures of size-resolved particle number counts (PNC), PM2.5, BC, NO2, and CO2 at 309 sites in Seattle from 2019 to 2020. PMF was used to characterize underlying source-related factors. The sources associated with these six factors included emissions from aviation, diesel trucks, gasoline/hybrid vehicles, oil combustion, wood combustion, and accumulation mode aerosols. Fuel-based emission factors for three transportation-related sources were also estimated. This study reveals that PNC of ultrafine particles with size <18, 18-42, and 42-178 nm was dominated by features associated with aircraft, diesel trucks, and both oil and wood combustion. Gasoline and hybrid vehicles contributed the most to CO2 and NO2 concentrations. This approach can also be extended to other metropolitan areas, enhancing the exposure assessment in epidemiology studies.

Epidemiological studies typically rely on exposure assessments based on ambient PM2.5 concentrations at participants' home addresses. However, these approaches neglect personal exposures indoors and across different non-residential microenvironments. To address this problem, our study combined low-cost sensors and GPS to conduct two-week personal PM2.5 monitoring in 168 adults recruited from the Washington State Twin Registry between 2018 and 2021. PM2.5 mass concentration, size-resolved particle number concentration, temperature, humidity, and GPS coordinates were recorded at 1-min intervals, providing 5,161,737 data points. We used GPS coordinates and a processing algorithm for automatic classification of microenvironments, including seven land use types and vehicles, and time spent indoors/outdoors. The low-cost sensors were calibrated in-situ, using regulatory monitoring data within 600 m of participants' outdoor measurements (R2 = 0.93). A linear mixed model was used to estimate the associations of multiple spatiotemporal factors with personal exposure concentrations. The average PM2.5 exposure concentration was 8.1 ± 15.8 μg/m3 for all participants. Indoor exposure concentration was higher than outdoor exposure level, and indoor exposure dose contributed 77 % to the total exposure. Exposures in residential and industrial land use had a higher concentration than in other areas, and accounted for 69 % of the total exposure dose. Furthermore, personal exposure concentration was the highest during winter and evening hours, possibly due to cooking and heating-related behaviors. This study demonstrates that personal monitoring can capture spatiotemporal variations in PM2.5 exposure more accurately than home-based approaches based on ambient air quality, and suggests opportunities for controlling exposures in certain microenvironments.

High-level exposure to indoor air pollutants (IAPs), including volatile organic compounds (VOCs), has substantially contributed to the burden of disease in China over the past two decades. However, the source contributions to the indoor VOC-related health burden remain unknown. This study utilized a novel approach based on positive matrix factorization (PMF) of indoor multipollutant data to estimate the source-specific residential VOC concentrations and associated burden of disease. Indoor concentrations of 39 VOCs were collected repeatedly in different seasons from 2016 to 2017 in 249 residences across nine cities in China. In 2017, the disability-adjusted life years (DALYs) attributable to residential VOC exposure across nine provinces in China reached 134.2 (95% UI: 65.7 - 225.0) per 100,000, resulting in financial costs of 28.1 (13.8 - 47.1) billion CNY. Contributions to indoor VOC concentrations from six indoor sources and three outdoor sources were derived by PMF. The top three sources, i.e., wood building materials and furniture, outdoor vehicle exhaust, and cooking and indoor combustion, accounted for 42.7%, 25.9%, and 11.0% of the VOC-attributable DALYs, which suggests prioritizing controlling these sources in China. This approach can be extended to other IAPs and provide fundamental data for future cost-benefit analysis of source control interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by The National Key Project of the Ministry of Science and Technology, China (2023YFC3708403), The New Chongqing YC Project (CSTB2024YCJH-KYXM0088), and The National Key Project of the Ministry of Science and Technology, China (Grant No. 2016YFC0700500). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Human Subjects Division (HSD) of University of Washington (UW) waived ethical approval for this work. The UW HSD determined that this study does not involve human subjects (IRB ID: STUDY00023774), and review and approval by the University of Washington Institutional Review Board (IRB) is not required. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

Two sound level maps currently exist for the contiguous United States. One was developed by the National Park Service (NPS) using machine learning methods and sound pressure level monitoring data, and the other by the Bureau of Transportation Statistics (BTS) using transportation noise models of roadway, aviation, and rail sources. Developed for different purposes, each has distinct strengths and weaknesses. This study aimed to compare the two models, develop a hybrid model integrating both, and evaluate its performance against field measurements. Linear regression with data from 378 NPS field sites was used to relate the NPS L50 metric to Leq. A positive association was observed, and the resulting regression equation was used to convert L50 to Leq. Comparing BTS 2018 and 2020 with the converted NPS model, we found strong correlation and small bias between BTS years (Pearson's r = 0.90, Spearman's rho = 0.88, bias = 0.3 dBA), but larger differences between BTS and NPS, with BTS levels on average ~6 dBA higher. A hybrid model was created by filling censored BTS areas with converted NPS Leq values. Evaluation against 708 NPS measurements and 757 metropolitan measurements showed good performance (bias = 0.4 dBA, MAE = 5.0 dBA for NPS; bias = -0.5 dBA, MAE = 3.8 dBA for metropolitan sites). Using the hybrid model, we estimated that ~36.4 million people (11.1% of the U.S. population) are exposed above 55 dB Leq. The hybrid model provides a resource to inform noise-related environmental health research, policy, and planning. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.

Higher levels of body mass index (BMI), particularly for those who have obesity defined as class II and III, are correlated with excess risk of all-cause mortality in the USA, and these risks disproportionately affects marginalized communities impacted by systemic racism. Redlining, a form of structural racism, is a practice by which federal agencies and banks disincentivized mortgage investments in predominantly racialized minority neighborhoods, contributing to residential segregation. The extent to which redlining contributes to current-day wealth and health inequities, including obesity, through wealth pathways or limited access to health-promoting resources, remains unclear. Our quasi-experimental study aimed to investigate the generational impacts of redlining on wealth and body mass index (BMI) outcomes. We leveraged the Panel Study of Income Dynamics (PSID) and Home Owners’ Loan Corporation (HOLC) maps to implement a geographical regression discontinuity design, where treatment assignment is randomly based on the boundary location of PSID grandparents in yellowlined vs. redlined areas and used outcome measures of wealth and mean BMI of grandchildren. To estimate our effects, we used a continuity-based approach and applied data-driven procedures to identify the most appropriate bandwidths for a valid estimation and inference. In our fully adjusted model, grandchildren with grandparents living in redlined areas had lower average household wealth (β = − 35,419; 95

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.

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.

Background While the adverse health effects of civil aircraft noise are relatively well studied, impacts associated with more intense and intermittent noise from military aviation have been rarely assessed. In recent years, increased training at Naval Air Station Whidbey Island, USA has raised concerns regarding the public health and well-being implications of noise from military aviation.Objective This study assessed the public health risks of military aircraft noise by developing a systematic workflow that uses acoustic and aircraft operations data to map noise exposure and predict health outcomes at the population scale.Methods Acoustic data encompassing seven years of monitoring efforts were integrated with flight operations data for 2020-2021 and a Department of Defense noise simulation model to characterize the noise regime. The model produced contours for day-night, nighttime, and 24-h average levels, which were validated by field monitoring and mapped to yield the estimated noise burden. Established thresholds and exposure-response relationships were used to predict the population subject to potential noise-related health effects, including annoyance, sleep disturbance, hearing impairment, and delays in childhood learning.Results Over 74,000 people within the area of aircraft noise exposure were at risk of adverse health effects. Of those exposed, substantial numbers were estimated to be highly annoyed and highly sleep disturbed, and several schools were exposed to levels that place them at risk of delay in childhood learning. Noise in some areas exceeded thresholds established by federal regulations for public health, residential land use and noise mitigation action, as well as the ranges of established exposure-response relationships.Impact statement This study quantified the extensive spatial scale and population health burden of noise from military aviation. We employed a novel GIS-based workflow for relating mapped distributions of aircraft noise exposure to a suite of public health outcomes by integrating acoustic monitoring and simulation data with a dasymetric population density map. This approach enables the evaluation of population health impacts due to past, current, and future proposed military operations. Moreover, it can be modified for application to other environmental noise sources and offers an improved open-source tool to assess the population health implications of environmental noise exposure, inform at-risk communities, and guide efforts in noise mitigation and policy governing noise legislation, urban planning, and land use.

Background In California, climate change and competing water demands are intensifying the desiccation of the Salton Sea, a large land-locked “sea” situated near the southeastern rural US-Mexico border region known as the Imperial Valley. Methods To examine the possible effects of living near a saline lake on children's respiratory health, we analyzed the relationship between respiratory health symptoms and ambient PM concentrations among a predominantly Latino/Hispanic cohort of 722 school age children. Guardians completed a survey of their child's wheeze and respiratory health symptoms over the past 12 months, adapted from the International Study of Asthma and Allergies in Childhood (ISAAC). Exposure to dust storm hours (hourly concentrations >150 μg/m3 for PM10) was estimated using a network of regulatory monitors. Results Between 2017 and 2019, children were exposed to 98 to 395 dust event hours annually. We observed disparate effects for dust events and wheeze among children living near the Salton Sea. Every additional 100 dust storm hours per year among children living near the Sea (<11 km) was associated with a 9.5 percentage point increase in wheeze (95% CI: 3.5, 15.4), a 4.6 percentage point increase in bronchitic symptoms (95% CI: 0.18, 10.2) and a 6.7 percentage point increase in sleep disturbance due to wheeze (95% CI: 0.96, 12.4). Similarly, increases in PM10 were also associated with greater reported wheeze and bronchitic symptoms among those living near the Sea, compared to children living ≥11 km from the Sea. There was no association of dust storms or PM10 with wheeze or bronchitic symptoms among the children residing farther from the Sea. Conclusion We observed stronger adverse impacts of PM10 and dust events on respiratory health among those living closer to the drying Salton Sea, compared to children living farther away. In this community of predominantly low-income residents of color, these impacts raise environmental justice concerns about the effects of the drying Salton Sea on public health.

ABSTRACT 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.

Portable air cleaners (PACs) equipped with high-efficiency particulate air (HEPA) filters are recommended to reduce indoor particulate matter (PM) exposure from wildfire smoke, particularly in regions like the Pacific Northwest, where seasonal wildfires affect air quality. While many studies have evaluated the long-term effectiveness of HEPA PACs, few have focused on the effects of dust loading and their performance in filtering woodsmoke over extended periods. This study investigated the impact of filter dust loading on the performance of a HEPA PAC (Winix C535, Winix America) in reducing woodsmoke particles. Filters were pre-loaded with varying amounts of ASHRAE ISO 12103–1 A2 fine test dust, and an exposure chamber was used to assess clean air delivery rate (CADR), airflow rate, and power consumption. Results indicated a significant decline in PAC performance with increasing filter loading, highlighting the importance of regular filter replacements to maintain effective operation. Based on simulations considering dynamic indoor PM2.5 concentration, in a typical scenario with a 90-m² room, baseline PM2.5 emission rates, and continuous operation at fan speed Level 2 (5-year mean indoor PM2.5: 2.99 μg/m3), it would take over 5 years for the PAC filters to accumulate 46 g of dust – an amount associated with a significant drop in CADR observed in the study. These findings suggest that the commonly recommended 1-year replacement schedule by manufacturers may be overly conservative for such conditions. By utilizing indoor air quality sensors to monitor PM concentrations, users can tailor filter replacement schedules to maintain optimal PAC performance in real-world environments.