Brian P. Leaderer’s research while affiliated with Yale-New Haven Hospital and other places

Ambient nitrogen dioxide (NO2) is derived from tailpipe vehicle emission and is linked with various of health outcomes. Personal exposure monitoring is crucial for accurate assessment of the associated disease risks. This study aimed to evaluate the utility of a wearable air pollutant sampler in determining the personal NO2 exposure of school children for comparison with a model-based personal exposure assessment. We employed cost-effective, wearable passive samplers to directly measure personal exposure of 25 children (aged 12-13 years) in Springfield, MA to NO2 over a five-day period in winter 2018. NO2 levels were additionally measured at 40 outdoor sites in the same region using stationary passive samplers. A land use regression (LUR) model was developed based on the ambient NO2 measures, with a good prediction performance (R2 = 0.72) using road lengths, distance to highway, and institutional land area as predictor variables. Time-weighted averages (TWA), which incorporated the time-activity patterns of participants and LUR-derived estimates in children's primary microenvironments (homes, the school and commute paths), were calculated as an indirect measure of personal NO2 exposure. Results indicated that the conventional residence-based exposure estimate approach, often used in epidemiological studies, differed from the direct personal exposure and could overestimate the personal exposure by up to 109 %. TWA improved personal NO2 exposure estimates by accounting for the time activity patterns of individuals, a difference of 5.4 % ± 34.2 % was found for exposures compared to wristband measurements. Nevertheless, the personal wristband measurements exhibited a large variability due to the potential contributions from indoor and in-vehicle NO2 sources. The findings suggest that exposure to NO2 can be highly personalized based on individual activities and contact with pollutants in specific microenvironments, reaffirming the importance of measuring personal exposure.

Introduction Nitrogen dioxide (NO 2 ) is known to be a trigger for asthma exacerbation. However, little is known about the role of seasonal variation in indoor and outdoor NO 2 levels in childhood asthma in a mixed rural-urban setting of North America. Methods This prospective cohort study, as a feasibility study, included 62 families with children (5-17 years) that had diagnosed persistent asthma residing in Olmsted County, Minnesota. Indoor and outdoor NO 2 concentrations were measured using passive air samples over 2 weeks in winter and 2 weeks in summer. We assessed seasonal variation in NO 2 levels in urban and rural residential areas and the association with asthma control status collected from participants’ asthma diaries during the study period. Results Outdoor NO 2 levels were lower (median: 2.4 parts per billion (ppb) in summer, 3.9 ppb in winter) than the Environmental Protection Agency (EPA) annual standard (53 ppb). In winter, a higher level of outdoor NO 2 was significantly associated with urban residential living area ( P = .014) and lower socioeconomic status (SES) ( P = .027). For both seasons, indoor NO 2 was significantly higher ( P < .05) in rural versus urban areas and in homes with gas versus electric stoves ( P < .05). Asthma control status was not associated with level of indoor or outdoor NO 2 in this cohort. Conclusions NO 2 levels were low in this mixed rural-urban community and not associated with asthma control status in this small feasibility study. Further research with a larger sample size is warranted for defining a lower threshold of NO 2 concentration with health effect on asthma in mixed rural-urban settings.

Objective: Triple-crossover randomized controlled intervention trial to test whether reduced exposure to household NO2 or fine particles results in reduced symptoms among children with persistent asthma. Methods: Children (n = 126) aged 5-11 years with persistent asthma living in homes with gas stoves and levels of NO2 15 ppb or greater recruited in Connecticut and Massachusetts (2015-2019) participated in an intervention involving three air cleaners configured for: (1) NO2 reduction: sham particle filtration and real NO2 scrubbing; (2) particle filtration: HEPA filter and sham NO2 scrubbing; (3) control: sham particle filtration and sham NO2 scrubbing. Air cleaners were randomly assigned for 5-week treatment periods using a three-arm crossover design. Outcome was number of asthma symptom-days during final 14 days of treatment. Treatment effects were assessed using repeated measures, linear mixed models. Results: Measured NO2 was lower (by 4 ppb, p < .0001) for NO2-reducing compared to control or particle-reducing treatments. NO2-reducing treatment did not reduce asthma morbidity compared to control. In analysis controlling for measured NO2, there were 1.8 (95% CI -0.3 to 3.9, p = .10) fewer symptom days out of 14 in the particle-reducing treatment compared to control. Conclusions: It remains unknown if using an air cleaner alone can achieve levels of NO2 reduction large enough to observe reductions in asthma symptoms. We observed that in small, urban homes with gas stoves, modest reductions in asthma symptoms occurred using air cleaners that remove fine particles. An intervention targeting exposures to both NO2 and fine particles is complicated and further research is warranted. Registration number: NCT02258893.

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide ($\hbox {NO}_2$) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on $\hbox {NO}_2$ that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using additional local covariates including total traffic volume within 2 km, population density, elevation, and land use characteristics. Predictions from this model greatly improved the bias in the CMAQ estimates, as observed by the much lower mean squared error (MSE) at the $\hbox {NO}_2$ monitor sites. The final model was used to predict the daily concentration of ambient $\hbox {NO}_2$ over the entire state of Connecticut on a grid with pixels of size 300 $\times$ 300 m. A comparison of the prediction map with a similar map for the CMAQ estimates showed marked improvement in the spatial resolution. The effect of local covariates was evident in the finer spatial resolution map, where the contribution of traffic on major highways to ambient $\hbox {NO}_2$ concentration stands out. An animation was also provided to show the change in the concentration of ambient $\hbox {NO}_2$ over space and time for 1994 and 1995.

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide (NO$_2$) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on NO$_2$ that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using additional local covariates including total traffic volume within 2 km, population density, elevation, and land use characteristics. Predictions from this model greatly improved the bias in the CMAQ estimates, as observed by the much lower mean squared error (MSE) at the NO$_2$ monitor sites. The final model was used to predict the daily concentration of ambient NO$_2$ over the entire state of Connecticut on a grid with pixels of size 300 x 300 m. A comparison of the prediction map with a similar map for the CMAQ estimates showed marked improvement in the spatial resolution. The effect of local covariates was evident in the finer spatial resolution map, where the contribution of traffic on major highways to ambient NO$_2$ concentration stands out. An animation was also provided to show the change in the concentration of ambient NO$_2$ over space and time for 1994 and 1995.

Over 4 million Americans live within 1.6 km of an unconventional oil and gas (UO&G) well, potentially placing them in the path of toxic releases. We evaluated relationships between residential proximity to UO&G wells and (1) water contamination and (2) health symptoms in an exploratory study. We analyzed drinking water samples from 66 Ohio households for 13 UO&G-related volatile organic compounds (VOCs) (e.g., benzene, disinfection byproducts [DBPs]), gasoline-range organics (GRO), and diesel-range organics. We interviewed participants about health symptoms and calculated metrics capturing proximity to UO&G wells. Based on multivariable logistic regression, odds of detection of bromoform and dibromochloromethane in surface water decreased significantly as distance to nearest UO&G well increased (odds ratios [OR]: 0.28-0.29 per km). Similarly, distance to nearest well was significantly negatively correlated with concentrations of GRO and toluene in ground water (rSpearman: -0.40 to -0.44) and with concentrations of bromoform and dibromochloromethane in surface water (rSpearman: -0.48 to -0.50). In our study population, those with higher inverse-distance-squared-weighted UO&G well counts within 5 km around the home were more likely to report experiencing general health symptoms (e.g. stress, fatigue) (OR: 1.52, 95%CI: 1.02-2.26). This exploratory study, though limited by small sample size and self-reported health symptoms, suggests that those in closer proximity to multiple UO&G wells may be more likely to experience environmental health impacts. Further, presence of brominated DBPs (linked to UO&G wastewater) raises the question of whether UO&G activities are impacting drinking water sources in the region. The findings from this study support expanded studies to advance knowledge of the potential for water quality and human health impacts; such studies could include a greater number of sampling sites, more detailed chemical analyses to examine source attribution, and objective health assessments.

Identifying periods of increased vulnerability during pregnancy to air pollution with respect to the development of adverse birth outcomes can improve understanding of possible mechanisms of disease development and provide guidelines for protection of the child. Exposure to air pollution during pregnancy is typically based on the residence at delivery, potentially resulting in exposure misclassification and biasing the estimation of critical windows. In this work, we determine the impact of maternal residential mobility during pregnancy on defining weekly exposure to PM10 and the estimation of windows of susceptibility for term low birth weight utilizing birth cohort datasets from Connecticut (1988-2008) that include information on all residential addresses for each woman between conception and delivery. A simulation study is designed to investigate the impact of increasing levels of mobility on critical window identification. Increased PM10 exposure during pregnancy weeks 16-18 is associated with an increased probability of term low birth weight. Ignoring residential mobility when defining weekly exposure has only minor impact on the identification of critical windows for PM10 and term low birth weight in the data application and simulation study. Critical window identification is robust to exposure misclassification caused by ignoring residential mobility in these Connecticut birth cohorts.

Introduction: Traffic-related air pollution causes fatty liver, inflammation and fibrosis in animal models, but there have been few studies in humans. Objectives: To test the hypothesis that traffic-related air pollution causes non-alcoholic fatty liver disease (NAFLD) and increased markers for non-alcoholic steatohepatitis (NASH); and that NAFLD increases liver susceptibility to increased NASH risk. Methods: Data collected prospectively from 74 overweight or obese children were obtained from the Yale Pediatric Obesity Clinic. Traffic-related air pollution was characterized as vehicle traffic volume on major roads within a 1 km residential buffer, and as residential nitrogen dioxide (NO2 ) exposure. Outcomes were hepatic fat fraction (HFF) measured by magnetic resonance imaging, liver enzymes using standard assays and plasma cytokeratin-18 (CK-18) by immunosorbent assays. Results: Significant non-linear relationships with air pollution and CK-18 were found. Plasma CK-18 at follow-up increased from approximately 150 U/L to almost 200 U/L as residential traffic volume increased from 220 000 vehicle-km to 330 000 vehicle-km, after adjustment for baseline CK-18, age and gender. Among patients with NAFLD at baseline, CK-18 increased from 140 U/L to 200 U/L (a 1.5 standard deviation increase in CK-18) as NO2 increased from 8 to 10 ppb. Conclusions: Traffic-related air pollution was associated with CK-18. Effects were larger in children with pre-existing NAFLD at study entry.