Environmental Health Perspectives

We describe the development and public availability of the National Transportation Noise Exposure Map with the goal of estimating population exposures to various noise levels at the census tract level in the United States. The map was created by overlaying the Bureau of Transportation Statistics National Transportation Noise Map with 5-year block group population estimates from the American Community Survey, and aggregating exposed population estimates to the census tract level. Based on the exposure map, an estimated 94.9 million people (29.1 % of the total U. S. population) were exposed to [≥] 45 dB LAeq of transportation-related noise, and approximately 11.9 million (3.6 %) were exposed to [≥] 60 dB in the year 2020.The exposure maps indicate that the greatest population proportion and number of individuals exposed were in California, while generally the map illustrates high proportions of exposure for populations living along major U. S. roadways and in airport communities. The availability of this new exposure map will facilitate the integration of noise exposures into a variety of studies, including regional and national health impact assessments, epidemiologic, and environmental justice studies.

BackgroundEvidence suggests maternal exposure to ambient air pollution increases the risk of stillbirth, but few studies conducted in the United States have evaluated temporally varying exposures or susceptibility across gestational windows. Moreover, the generalizability of existing findings is often limited by restricted geographic coverage or reliance on selected study populations. MethodsUsing Georgia vital records from 2005 to 2014, we conducted a matched case-control study including 8,384 stillbirths and 33,459 live birth controls matched on maternal county of residence and conception month. We used stratified Cox proportional hazards models with time-varying covariates to estimate hazard ratios (HRs) for ten air pollutants across five exposure windows (first month, weekly, and first, second, and third trimester). Our primary analysis included all stillbirths combined, with subgroup analyses separating second and third trimester losses. ResultsStillbirths had a median gestational age of 27 weeks (IQR: 6.67) compared with 38 weeks for live births (IQR: 2.13). Particulate matter showed strong associations in the second trimester exposure window for all stillbirths (PM10: HR = 1.07; 95% CI: 1.04, 1.11; PM2.5: HR = 1.05; 95% CI: 1.01, 1.09). This pattern was consistent for NO2 and NH4, which also exhibited positive associations across early and entire pregnancy exposure windows (first month, first trimester, weekly), with the strongest associations for the second trimester exposures. Associations were larger for second trimester stillbirths, whereas estimates for third trimester stillbirths were largely null or negative. ConclusionsIn this population-based study in Georgia, time-varying ambient air pollution exposures during pregnancy were associated with increased risk of stillbirth, particularly for second trimester exposures and for stillbirths occurring earlier in pregnancy. These findings highlight the importance of considering gestational timing when evaluating environmental risk factors for stillbirth. What this study addsThis study is the first to evaluate maternal ambient air pollution exposure and stillbirth using time-varying exposures on vital records in the state of Georgia. By examining ten air pollutants across multiple gestational windows and subset analyses by timing of stillbirth, we identified second trimester susceptibility to NO2, PM10, PM2.5, and NH4. These findings highlight periods of vulnerability to ambient air pollution during pregnancy.

BackgroundThe rapid evolution and diversity of sensor technologies, coupled with inconsistencies in how sensor metadata is reported across formats and sources, present significant challenges for generating exposomes and exposure health research. ObjectiveDespite the development of standardized metadata schemas, the process of extracting sensor metadata from unstructured sources remains largely manual and unscalable. To address this bottleneck, we developed and evaluated a large language model (LLM)-based pipeline for automating sensor metadata extraction and harmonization from exposure health literature publicly available. MethodsUsing GPT-4 in a zero-shot setting, we constructed a pipeline that parses full-text PDFs to extract metadata and harmonizes output into structured formats. Results: Our automated pipeline achieved substantial efficiency gains in completing extractions much faster than manual review and demonstrated strong performance with average accuracy and precision of 94.74%, recall of 100%, and F1-score of 97.28%. ConclusionsThis study demonstrates the feasibility and scalability of leveraging LLMs to automate sensor metadata extraction for exposure health, reducing manual burden while enhancing metadata completeness and consistency. Our findings support the integration of LLM-driven pipelines into exposure health informatics platforms.

1.Indoor airborne particulates are well-known health hazards and filtration is one common method of reducing exposures. Based on our previously developed Regional Shelter Analysis methodology and parameters that characterize the existing US building stock, we perform a high-level assessment of the potential benefits of upgrading existing filters in furnace and in heating, ventilation, and air conditioning systems using off-the-shelf filters. We use three metrics to assess the improvement: Building Transmission Factor (a measure of protection against outdoor airborne particles), Indoor Normalized Time and Space Integrated Air Concentration (a measure of indoor exposure to indoor-origin airborne particles), and Building Exit Fraction (fraction of indoor airborne particles that are released to the outdoor atmosphere). We also discuss the potential reduction in regional exposures due to particles exiting the building and exposing downwind building occupants. Our modeling indicates that while buildings provide their occupants passive protection against airborne particulate hazards, including but not limited to PM2.5, PM10, and wildfire smoke; improving particle filtration efficiency may further improve this protection. The degree of improvement varies with particle size and building type. Of the building types studied, apartments are predicted to benefit most, with greater than a factor of 2 improvement ([≥]50% reduction in exposures) for 1 {micro}m particle exposures when using MERV 7 to 12 rated filters. Non-residential buildings were notably less responsive to improved filtration but had the highest Building Exit Fractions with 30% to 40% of indoor airborne particles released to the outdoor atmosphere (apartment buildings only released 6% to 9%). Improvements predicted for single family homes were intermediate between apartments and non-residential buildings. Improvements in the Regional Exposure metric are larger, ranging from a factor of 2.5x to 10x for residences (when using MERV 7 to 12 rated filters) and up to 25x for large apartments with MERV 14 or 15 rated filters. The results of our modeling analysis are broadly consistent with the limited experimental data and modeling results available in the literature.

Mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. We carried out a simulation study of fixed-site air quality monitors to better understand how different mobile monitoring designs involving short-term stationary measurements at fixed locations impact the resulting exposure surfaces. We used Monte Carlo resampling to simulate three archetypal monitoring designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each designs land use regression prediction model. The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. A temporally-balanced sampling design is crucial for mobile monitoring campaigns aiming to assess accurate long-term exposure in epidemiologic cohorts. SynopsisAir pollution mobile monitoring campaigns rarely conduct temporally balanced sampling. We show that this results in biased annual average exposure estimates. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/21255641v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@126c8ddorg.highwire.dtl.DTLVardef@14d52e5org.highwire.dtl.DTLVardef@17d390dorg.highwire.dtl.DTLVardef@2cc3d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundAs the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis. ObjectivesTo conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes. MethodWe searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design. Results27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes. DiscussionWe discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

There are large differences in premature mortality in the USA by racial/ethnic, education, rurality, and social vulnerability index groups. Using existing concentration-response functions, particulate matter (PM2.5) air pollution, population estimates at the tract level, and county-level mortality data, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in mortality attributable to PM2.5 were consistently more pronounced between racial/ethnic groups than by education, rurality, or social vulnerability index, with the Black American population having by far the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic White population was attributable to PM2.5 in the years 2000 to 2011.

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-minute intervals, providing 5,161,737 datapoints. 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 g/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. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/25329147v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1f3329aorg.highwire.dtl.DTLVardef@17f2ee6org.highwire.dtl.DTLVardef@e01873org.highwire.dtl.DTLVardef@6558bb_HPS_FORMAT_FIGEXP M_FIG TOC Art C_FIG Highlights[bullet] A total of 168 participants completed two-week personal PM2.5 and GPS monitoring. [bullet]Personal exposure to PM2.5 had substantial spatiotemporal variation. [bullet]Indoor exposure had higher exposure concentration and exposure dose than outdoor. [bullet]Residential/industrial PM2.5 concentration was higher based on regression analysis. [bullet]Home-based exposure assessment cannot capture actual personal exposure patterns.

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the contextual and spatial risk factors for PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified contextual and spatial risk factors from nearly 200 environmental characteristics for 23 PASC symptoms and conditions of eight organ systems. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each contextual and spatial factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) contextual and spatial characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), criteria air pollutants (e.g., sulfur dioxide), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, respiratory, blood, circulatory, endocrine, and other organ systems. Specific contextual and spatial risk factors for each PASC condition and symptom were different across New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular contextual and spatial characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

BackgroundThe Sustainable Development Goals set an ambitious new benchmark for safely managed drinking water services (SMDW), but many countries lack data on the availability and quality of drinking water. ObjectivesTo quantify the availability and microbiological quality of drinking water, monitor SMDW and examine risk factors for E. coli contamination in 20 low-and middle-income countries. MethodsA new water quality module for household surveys was implemented in Multiple Indicator Cluster Surveys. Teams used portable equipment to measure E. coli at the point of collection (PoC, n=48,323) and at the point of use (PoU, n=51,345) and asked respondents about the availability and location of drinking water services. E. coli levels were classified into risk categories and SMDW was calculated at the household- and domain-levels. Modified Poisson regression was used to explore risk factors for contamination. ResultsE. coli was commonly detected at PoC (range 16-90%) and was more likely at PoU (range 20-97%). Coverage of SMDW was 56% points lower than improved drinking water with water quality the limiting factor for SMDW in 14 countries. Detection of E. coli at PoC was associated with use of improved water sources (RR=0.64 [0.52-0.78]) located on premises (RR=0.78 [0.67-0.91]) but not with availability (RR=0.94 [0.82-1.06]). Households in the richest quintile (RR=0.67 [0.50-0.90]) and in communities with high (>75%) improved sanitation coverage (RR=0.95 [0.91-0.98]) were less likely to use contaminated water at PoU whereas animal ownership (RR=1.08 [1.03-1.14]) and rural residence (RR=1.11 [1.03-1.19]) increased risk of contamination. DiscussionWater quality data can be reliably collected in household surveys and can be used to assess inequalities in service levels, to track the SDG indicator of SMDW, and to examine risk factors for contamination. There is an urgent need to implement scalable and sustainable interventions to reduce exposure to faecal contamination through drinking water.

RationaleShort-term exposure to fine particulates (PM2.5) transiently increases the risk of respiratory exacerbations, but the contribution of chronic, long-term particulate exposure to respiratory exacerbations is poorly defined. ObjectivesTo assess long-term effects of PM2.5 exposure on risk of severe respiratory exacerbations. MethodsA longitudinal cohort of current and former smokers with and without COPD were surveyed every six months for severe exacerbation events. PM2.5 concentrations at participant addresses were estimated using satellite, reanalysis, and ground-based monitoring data sources. Measurements and Main ResultsThe relative risk of severe exacerbation increased by a factor of 1.516 (CI: 1.226, 1.873; p = 0.00012) for every 10 g/m3 increase in long-term PM2.5 exposure across all participants. The effect in the non-COPD participants was greater, with a relative risk of 2.639 (CI: 1.840, 3.756; p<0.0001). Significant effect modifiers with greater effect of PM2.5, included prior exacerbations, female sex, and neighborhood characteristics and as well as smoking status, white race, disease severity, asthma diagnosis, and age at enrollment. Significant positive associations for PM2.5 on exacerbations were identified at levels below the EPA primary annual standard for PM2.5 of 9.0 g/m3. ConclusionsPersistent exposure to fine particulates is a significant risk factor for severe respiratory exacerbations in current and former smokers, and in patients with or at risk of COPD. The effect of fine particulates on the risk of severe exacerbations appears to be greater in those current and former smokers without COPD. The EPA annual PM2.5 standard may be inadequate to prevent ongoing lung injury.

Exposure to ambient air pollution contributes substantially to the global burden of disease, and in 2015, ambient exposure to PM2.5 (fine particles with a mass median aerodynamic diameter of less than 2.5 m) was the fifth-ranking risk factor of mortality globally. We analyzed data from the US zip code tabulation areas (N=32047) for 2000-2016 and found strong evidence of inequalities in exposure to PM2.5 among both racial/ethnic and income groups. Most alarming, we found that these inequalities have been increasing over time. From 2010 to 2016 inequalities in the exposure to PM2.5 levels above 8 g/m3 across racial/ethnic, and income groups increased by factors of 1.6 and 4.0 respectively. As shown in our powerful map visualizations, these results indicate that air pollution regulations must not only decrease PM2.5 concentration levels nationwide but also prioritize reducing environmental injustice across the US.

BackgroundHealth research on ambient nitrogen dioxide (NO2) is sparse in Latin America, despite the high prevalence of NO2-associated respiratory diseases in the region. This study describes within-city distributions of ambient NO2 concentrations at high spatial resolution and urban characteristics associated with neighborhood ambient NO2 in 326 Latin American cities. MethodsWe aggregated estimates of annual surface NO2 at 1 km2 spatial resolution for 2019, population counts, and urban characteristics compiled by the SALURBAL project to the neighborhood level (i.e., census tracts). We described the percent of the urban population living with ambient NO2 levels exceeding WHO Air Quality Guidelines. We used multilevel models to describe associations of neighborhood ambient NO2 concentrations with population and urban characteristics at the neighborhood and city levels. FindingsWe examined 47,187 neighborhoods in 326 cities from eight Latin American countries. Of the {approx}236 million urban residents observed, 85% lived in neighborhoods with ambient annual NO2 above WHO guidelines. In adjusted models, higher neighborhood-level educational attainment, closer proximity to the city center, and lower neighborhood-level greenness were associated with higher ambient NO2. At the city level, higher vehicle congestion, population size, and population density were associated with higher ambient NO2. InterpretationAlmost nine out of every 10 residents of Latin American cities live with ambient NO2 concentrations above WHO guidelines. Increasing neighborhood greenness and reducing reliance on fossil fuel-powered vehicles warrant further attention as potential actionable urban environmental interventions to reduce population exposure to ambient NO2. FundingWellcome Trust, National Institutes of Health, Cotswold Foundation

BackgroundThe impact of short-term exposure to fine particulate matter (PM2.5) due to wildland fire smoke on the risk of cardiovascular disease (CVD) remains unclear. We investigated the association between short-term exposure to wildfire smoke PM2.5 and Emergency Department (ED) visits for acute CVD in the Western United States from 2007 to 2018. MethodsED visits for primary or secondary diagnoses of atrial fibrillation (AF), acute myocardial infarction (AMI), heart failure (HF), stroke, and total CVD were obtained from hospital associations or state health departments in California, Arizona, Nevada, Oregon, and Utah. ED visits included those that were subsequently hospitalized. Daily smoke, non-smoke, and total PM2.5 were estimated using a satellite-driven multi-stage model with a high resolution of 1 km. The data were aggregated to the zip code level and a case-crossover study design was employed. Temperature, relative humidity, and day of the year were included as covariates. ResultsWe analyzed 49,759,958 ED visits for primary or secondary CVD diagnoses, which included 6,808,839 (13.7%) AFs, 1,222,053 (2.5%) AMIs, 7,194,474 (14.5%) HFs, and 808,396 (1.6%) strokes. Over the study period from 2007-01-01 to 2018-12-31, the mean smoke PM2.5 was 1.27 (Q1: 0, Q3: 1.29) {micro}g/m3. A 10 {micro}g/m3 increase in smoke PM2.5 was associated with a minuscule decreased risk for AF (OR 0.994, 95% CI 0.991-0.997), HF (OR 0.995, 95% CI 0.992-0.998), and CVD (OR 0.9997, 95% CI 0.996-0.998), but not for AMI and stroke. Adjusting for non-smoke PM2.5 did not alter these associations. A 10 {micro}g/m3 increase in total PM2.5 was linked to a small increased risk for all outcomes except stroke (OR for CVD 1.006, 95% CI 1.006-1.007). Associations were similar across sex and age groups. ConclusionWe identified an unexpected slight lower risk of CVD ED visits associated with short-term wildfire smoke PM2.5 exposure. Whether these findings are due to methodological issues, behavioral changes, or other factors requires further investigation.

We report small-sample evidence from a randomized experiment among a set of urban Ecuadorian households who owned both electric induction and gas stoves. We randomly assigned households to cook only with one stove during a prescribed two-day monitoring period, and then cook only with the other stove in a subsequent two-day period. The order of stove use was randomized, and air pollution was measured during each period. We found that mean 48-hour personal NO2 exposure was 9.9 ppb higher (95% CI, 4.5-15.3) -- a 50% increase over the 48-hour induction mean -- when households were randomized to gas as compared to induction. Mean kitchen area NO2 concentrations were 1 ppb higher (95% CI, 0.4-2.1) (a 6% increase) and mean personal PM2.5 exposure was 11 gm-3 higher (95% CI, -0.1-22.8) (a 44% increase) during study periods when randomized to gas. We use time-resolved cooking and pollution data to illustrate that these differences are driven by LPG cooking, which was associated with a 5.0 ppb increase in 5-minute average NO2 kitchen area concentrations (95% CI, 3.4-6.7) and a 20.8 gm-3 increase in 5-minute average personal PM2.5 exposure (95% CI 8.9-32.6). In contrast, cooking with induction was not associated with changes to short-term NO2 kitchen area concentrations, though it was associated with short-term increased personal PM2.5 exposure (10.8, 95% CI, 5.7-15.9).

Recent studies have linked air pollution to increased risk for behavioral problems during development, albeit with inconsistent findings. Additional longitudinal studies are needed that consider how emotional behaviors may be affected when exposure coincides with the transition to adolescence - a vulnerable time for developing mental health difficulties. This study examines how annual average PM2.5 and NO2 exposure at ages 9-10 years relates to internalizing and externalizing behaviors over a 2-year follow-up period in a large, nationwide U.S. sample of participants from the Adolescent Brain Cognitive Development (ABCD) Study(R). Air pollution exposure was estimated based on the residential address of each participant using an ensemble-based modeling approach. Caregivers answered questions from the Child Behavior Checklist (CBCL) at baseline and annually for two follow-up sessions for a total of 3 waves of data; from the CBCL we obtained scores on internalizing and externalizing problems plus 5 syndrome scales (anxious/depressed, withdrawn/depressed, rule-breaking behavior, aggressive behavior, and attention problems). Zero-inflated negative binomial models were used to examine both the main effect of age as well as the interaction of age with each pollutant on behavior while adjusting for various socioeconomic and demographic characteristics. Overall, the pollution effects moderated the main effects of age with higher levels of PM2.5 and NO2 leading to an even greater likelihood of having no behavioral problems (i.e., score of zero) with age over time, as well as fewer problems when problems are present as the child ages. Albeit this was on the order equal to or less than a 1-point change. Thus, one year of annual exposure at 9-10 years is linked with very small change in emotional behaviors in early adolescence, which may be of little clinical relevance.

BackgroundEnvironment-wide association studies (ExWAS) offer a systematic approach to identifying chemical biomarker-health outcome associations, yet few have applied rigorous multi-stage validation. MethodsWe screened 92 chemical biomarkers against 48 health outcomes in NHANES 2017-2018 (2,796 tests across four screening rounds; not all chemicals were crossed with all outcomes). Associations passing an initial FDR screen were subjected to cross-cycle validation in NHANES 2015-2016--the primary inferential safeguard given the adaptive screening design--followed by dose-response analysis and multiple sensitivity specifications. Survey-weighted regression models adjusted for age, sex, race/ethnicity, poverty-income ratio, BMI, and smoking. ResultsOf 26 associations passing FDR correction, 21 were testable in cross-cycle validation; of these, 15 (71%) replicated with concordant direction and p < 0.05 in a temporally independent NHANES 2015-2016 sample. Of these 15, 14 remained robust after analyte-specific sensitivity checks; urinary creatinine adjustment identified one association (iodine-BMI) as a dilution artifact. Two novel findings emerged: dimethylarsonic acid with uric acid ({beta} = 0.20 mg/dL per log-unit DMA, 95% CI: 0.15-0.26) and urinary perchlorate with BUN ({beta} = 1.21 mg/dL per log-unit perchlorate, 95% CI: 0.97-1.45); a third high-novelty association (methylmercury-waist circumference) is likely explained by fish consumption patterns. ConclusionsMulti-stage ExWAS with cross-cycle validation identified 14 robust chemical-health associations. Two novel findings--DMA-uric acid and perchlorate-BUN--survived all sensitivity checks and warrant prospective investigation.

ABSTRACTO_ST_ABSBackgroundC_ST_ABSReducing household air pollution (HAP) to levels associated with health benefits requires nearly exclusive use of clean cooking fuels and abandonment of traditional biomass fuels. MethodsThe Household Air Pollution Intervention Network (HAPIN) trial randomized 3,195 pregnant women in Guatemala, India, Peru, and Rwanda to receive a liquefied petroleum gas (LPG) stove intervention (n=1,590), with controls expected to continue cooking with biomass fuels (n=1,605). We assessed fidelity to intervention implementation and participant adherence to the intervention starting in pregnancy through the infants first birthday using fuel delivery and repair records, surveys, observations, and temperature-logging stove use monitors (SUMs). ResultsFidelity and adherence to the HAPIN intervention were high. Median time required to refill LPG cylinders was 1 day (interquartile range 0-2). Although 26% (n=410) of intervention participants reported running out of LPG at some point, the number of times was low (median: 1 day [Q1, Q3: 1, 2]) and mostly limited to the first four months of the COVID-19 pandemic. Most repairs were completed on the same day as problems were reported. Traditional stove use was observed in only 3% of observation visits, and 89% of these observations were followed up with behavioral reinforcement. According to SUMs data, intervention households used their traditional stove a median of 0.4% of all monitored days, and 81% used the traditional stove <1 day per month. Traditional stove use was slightly higher post-COVID-19 (detected on a median [Q1, Q3] of 0.0% [0.0%, 3.4%] of days) than pre-COVID-19 (0.0% [0.0%, 1.6%] of days). There was no significant difference in intervention adherence pre- and post-birth. ConclusionFree stoves and an unlimited supply of LPG fuel delivered to participating homes combined with timely repairs, behavioral messaging, and comprehensive stove use monitoring contributed to high intervention fidelity and near-exclusive LPG use within the HAPIN trial.

Legionella is an opportunistic waterborne pathogen that is difficult to eradicate in colonized drinking water pipes. Legionella control is further challenged by aging water infrastructure and lack of evidence-based guidance for building treatment. This study assessed multiple premise water remediation approaches designed to reduce Legionella pneumophila (Lp) within a residential building located in an aging, urban drinking water system over a two-year period. Samples (n=745) were collected from hot and cold-water lines and quantified via most probable number culture. Building-level treatment approaches included three single heat shocks (HS), three single chemical shocks (CS), and continuous low-level chemical disinfection (CCD) in the potable water system. The building was highly colonized with Lp with 71% Lp positivity. Single HS had a statistically significant Lp reduction one day post treatment but no significant Lp reduction one, two, and four weeks post treatment. The first two CS resulted in statistically significant Lp reduction at two days and four weeks post treatment, but there was a significant Lp increase at four weeks following the third CS. CCD resulted in statistically significant Lp reduction ten weeks post treatment implementation. This demonstrates that in a building highly colonized with Lp, sustained remediation is best achieved using CCD. SYNOPSISLong-term Legionella control is difficult to maintain within aging premise plumbing. This study supports continuous low-level building treatment as an effective long-term remediation of a building highly colonized with Legionella. For Table of Contents Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/23292444v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@14d8188org.highwire.dtl.DTLVardef@17313aborg.highwire.dtl.DTLVardef@107188org.highwire.dtl.DTLVardef@18dbdb4_HPS_FORMAT_FIGEXP M_FIG C_FIG

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 (Pearsons r = 0.90, Spearmans 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.