Search

Background: Little is known about how childhood exposure to traffic-related air pollution (TRAP) and stress interact to affect adults' cardiometabolic health. We examined this interaction and assessed the impact of over 10 years of childhood TRAP exposure on cardiometabolic health., Methods: From 2018 to 2023, 313 young adults from the Southern California Children's Health Study were enrolled in a follow-up assessment. Using CALINE4 line source dispersion model, average childhood TRAP exposures (from pregnancy to age 13) were estimated for nitrogen oxides (NO x ) from all roads. Traffic density was calculated within a 300-m residential buffer. Cardiometabolic health was assessed in adulthood (mean age 24 ± 1.7) based on blood lipids (total cholesterol, high- and low-density lipoprotein [HDL, LDL], triglycerides), glucose metabolism (fasting glucose, fasting insulin, HbA1c), body composition (BMI, android/gynoid ratio [AG ratio], percent body fat), and blood pressure. A PDAY (Pathobiologic Determinants of Atherosclerosis in Youth) score was generated to evaluate overall cardiometabolic health. Participants' perceived stress was assessed in childhood and adulthood (ages 13 and 24 years, respectively)., Results: Results of mixed effects linear models, adjusted for demographics and smoking status, suggested that each standard deviation increase in childhood exposure to traffic-related total NO x was associated with 0.62 increase in PDAY score (95% Confidence Interval [CI]:0.10,1.14), 0.09% increase in HbA1c (95%CI: 0.04, 0.15), 1.19% increase in percent body fat (95%CI: 0.18, 2.20), and 0.96 kg/m 2 increase in BMI (0.11, 1.80) in adulthood. Among participants with higher childhood stress levels, we observed significant associations of traffic-related total NO x with total cholesterol, HDL, LDL, HbA1c, insulin, and BMI. None of these associations were significant among people with lower stress levels. We observed similar statistically significant associations of traffic density., Conclusion: Long-term childhood exposure to TRAP in childhood may have lasting adverse impacts on cardiometabolic health, especially for children with higher stress levels., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Shohreh Farzan reports financial support was provided by National Institutes of Health. Breton, Bastain, Habre reports financial support was provided by National Institutes of Health. Shohreh Farzan reports a relationship with University of Southern California that includes: employment. Guo, Habre, Xu, Chen, Howland, Gauderman, Bastain, Breton reports a relationship with University of Southern California that includes: employment. Lurmann, Pavlovic reports a relationship with Sonoma Technology Inc that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Ambient air pollution during pregnancy has been linked with postpartum depression up to 12 months, but few studies have investigated its impact on persistent depression beyond 12 months postpartum. This study aimed to evaluate prenatal ambient air pollution exposure and the risk of persistent depression over 3 years after childbirth and to identify windows of susceptibility., Methods: This study included 361 predominantly low-income Hispanic/Latina participants with full-term pregnancies in the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) cohort. We estimated daily residential PM 2.5 , PM 10 , NO 2 , and O 3 concentrations throughout 37 gestational weeks using inverse-distance squared spatial interpolation from monitoring data and calculated weekly averaged levels. Depression was assessed by the 20-item Center for Epidemiologic Studies-Depression (CES-D) scale at 12, 24, and 36 months postpartum, with persistent postpartum depression defined as a CES-D score ≥16 at any of these timepoints. We performed robust Poisson log-linear distributed lag models (DLM) via generalized estimating equations (GEE) to estimate the adjusted risk ratio (RR)., Results: Depression was observed in 17.8 %, 17.5 %, and 13.4 % of participants at 12, 24, and 36 months, respectively. We found one IQR increase (3.9 ppb) in prenatal exposure to NO 2 during the identified sensitive window of gestational weeks 13-29 was associated with a cumulative risk ratio of 3.86 (95 % CI: 3.24, 4.59) for persistent depression 1-3 years postpartum. We also found one IQR increase (7.4 μg/m 3 ) in prenatal exposure to PM 10 during gestation weeks 12-28 was associated a cumulative risk ratio of 3.88 (95 % CI: 3.04, 4.96) for persistent depression. No clear sensitive windows were identified for PM 2.5 or O 3 ., Conclusions: Mid-pregnancy PM 10 and NO 2 exposures were associated with nearly 4-fold increased risks of persistent depression after pregnancy, which has critical implications for prevention of perinatal mental health outcomes., Competing Interests: Declaration of competing interest We declare no actual or potential competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rationale: Few studies have examined the effects of long-term childhood air pollution exposure on adult respiratory health, including whether childhood respiratory effects underlie this relation. Objectives: To evaluate associations between childhood air pollution exposure and self-reported adult bronchitic symptoms while considering child respiratory health in the Southern California Children's Health Study. Methods: Exposures to nitrogen dioxide (NO 2 ), ozone, and particulate matter <2.5 μm and <10 μm in diameter (PM 10 ) assessed using inverse-distance-squared spatial interpolation based on childhood (birth to age 17 yr) residential histories. Bronchitic symptoms (bronchitis, cough, or phlegm in the past 12 mo) were ascertained via a questionnaire in adulthood. Associations between mean air pollution exposure across childhood and self-reported adult bronchitic symptoms were estimated using logistic regression. We further adjusted for childhood bronchitic symptoms and asthma to understand whether associations operated beyond childhood respiratory health impacts. Effect modification was assessed for family history of asthma, childhood asthma, and adult allergies. Measurements and Main Results: A total of 1,308 participants were included (mostly non-Hispanic White [56%] or Hispanic [32%]). At adult assessment (mean age, 32.0 yr; standard deviation [SD], 4.7), 25% reported bronchitic symptoms. Adult bronchitic symptoms were associated with NO 2 and PM 10 childhood exposures. Odds ratios per 1-SD increase were 1.69 (95% confidence interval, 1.14-2.49) for NO 2 (SD, 11.1 ppb) and 1.51 (95% confidence interval, 1.00-2.27) for PM 10 (SD, 14.2 μg/m 3 ). Adjusting for childhood bronchitic symptoms or asthma produced similar results. NO 2 and PM 10 associations were modified by childhood asthma, with greater associations among asthmatic individuals. Conclusions: Childhood NO 2 and PM 10 exposures were associated with adult bronchitic symptoms. Associations were not explained by childhood respiratory health impacts; however, participants with childhood asthma had stronger associations.

Background: Emerging literature suggests that fine particulate matter [with aerodynamic diameter ≤ 2.5 μ m ( PM 2.5 )] air pollution and its components are linked to various neurodevelopmental outcomes. However, few studies have evaluated how PM 2.5 component mixtures from distinct sources relate to cognitive outcomes in children., Objectives: This cross-sectional study investigated how ambient concentrations of PM 2.5 component mixtures relate to neurocognitive performance in 9- to 10-year-old children, as well as explored potential source-specific effects of these associations, across the US., Methods: Using spatiotemporal hybrid models, annual concentrations of 15 chemical components of PM 2.5 were estimated based on the residential address of child participants from the Adolescent Brain Cognitive Development (ABCD) Study. General cognitive ability, executive function, and learning/memory scores were derived from the NIH Toolbox. We applied positive matrix factorization to identify six major PM 2.5 sources based on the 15 components, which included crustal, ammonium sulfate, biomass burning, traffic, ammonium nitrate, and industrial/residual fuel burning. We then utilized weighted quantile sum (WQS) and linear regression models to investigate associations between PM 2.5 components' mixture, their potential sources, and children's cognitive scores., Results: Mixture modeling revealed associations between cumulative exposure and worse cognitive performance across all three outcome domains, including shared overlap in detrimental effects driven by ammonium nitrates, silicon, and calcium. Using the identified six sources of exposure, source-specific negative associations were identified between ammonium nitrates and learning & memory, traffic and executive function, and crustal and industrial mixtures and general cognitive ability. Unexpected positive associations were also seen between traffic and general ability as well as biomass burning and executive function., Discussion: This work suggests nuanced associations between outdoor PM 2.5 exposure and childhood cognitive performance, including important differences in cognition related both to individual chemicals as well as to specific sources of these exposures. https://doi.org/10.1289/EHP14418.

Background: Prenatal air pollution exposure has been associated with individual inflammatory, cardiovascular, and metabolic biomarkers in mothers and neonates. However, studies of air pollution and a comprehensive panel of biomarkers across maternal and cord blood samples remain limited. Few studies used data-driven methods to identify biomarker groupings that converge biomarkers from multiple biological pathways. This study aims to investigate the impacts of prenatal air pollution on groups of biomarkers in maternal and cord blood samples., Methods: In the Maternal And Developmental Risks from Environmental and Social Stressors (MADRES) cohort, 87 biomarkers were quantified from 45 trimester 1 maternal blood and 55 cord blood samples. Pregnancy and trimester 1-averaged concentrations of particulate matter ≤2.5 μm and ≤10 μm in diameter (PM 2.5 and PM 10 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) were estimated, using inverse distance squared weighted spatial interpolation from regulatory air monitoring stations. Traffic-related NO x was assessed using California Line Source Dispersion Model: freeway/highway roads, non-freeway major roads, non-freeway minor roads, and their sum as total NOx. Elastic Net (EN) regression within the rexposome R package was used to group biomarkers and assess their associations with air pollution., Results: In maternal samples, trimester 1-averaged PM 10 was associated with elevated inflammation biomarkers and lowered cardiovascular biomarkers. NO 2 exhibited positive associations with cardiovascular and inflammation markers. O 3 was inversely associated with inflammation, metabolic, and cardiovascular biomarkers. In cord blood, pregnancy-averaged PM 2.5 was associated with higher cardiovascular biomarkers and lower metabolic biomarkers. PM 10 was associated with lower inflammation and higher cardiovascular biomarkers. Total and major road NOx was associated with lower cardiovascular biomarkers., Conclusion: Prenatal air pollution exposure was associated with changes in biomarkers related to inflammation, cardiovascular, metabolic, cancer, and neurological function in both mothers and neonates. This study shed light on mechanisms by which air pollution can influence biological function during pregnancy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Air pollution has been associated with gestational hypertension (GH) and preeclampsia, but susceptible windows of exposure and potential vulnerability by comorbidities, such as prenatal depression, remain unclear., Methods: We ascertained GH and preeclampsia cases in a prospective pregnancy cohort in Los Angeles, CA. Daily levels of ambient particulate matters (with a diameter of ≤10 μm [PM 10 ] or ≤2.5 μm [PM 2.5 ]), nitrogen dioxide, and ozone were averaged for each week from 12 weeks preconception to 20 gestational weeks. We used distributed lag models to identify susceptible exposure windows, adjusting for potential confounders. Analyses were additionally stratified by probable prenatal depression to explore population vulnerability., Results: Among 619 participants, 60 developed preeclampsia and 42 developed GH. We identified a susceptible window for exposure to PM 2.5 from 1 week preconception to 11 weeks postconception: higher exposure (5 µg/m 3 ) within this window was associated with an average of 8% (95% CI, 1%-15%) higher risk of GH. Among participants with probable prenatal depression (n=179; 32%), overlapping sensitive windows were observed for all pollutants from 8 weeks before to 10 weeks postconception with increased risk of GH (PM 2.5 , 16% [95% CI, 3%-31%]; PM 10 , 39% [95% CI, 13%-72%]; nitrogen dioxide, 65% [95% CI, 17%-134%]; and ozone, 45% [95% CI, 9%-93%]), while the associations were close to null among those without prenatal depression. Air pollutants were not associated with preeclampsia in any analyses., Conclusions: We identified periconception through early pregnancy as a susceptible window of air pollution exposure with an increased risk of GH. Prenatal depression increases vulnerability to air pollution exposure and GH., Competing Interests: Disclosures None.

In-utero exposure to fine particulate matter (PM 2.5 ) and specific sources and components of PM 2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM 2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM 2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-h personal PM 2.5 exposure monitoring sub-study. The mass contributions of six major sources of personal PM 2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions evaluated the association between personal PM 2.5 sources/components and birthweight, adjusting for gestational age, maternal age, race, infant sex, parity, diabetes status, temperature, maternal education, and smoking history. Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3295.8 g (484.1) and mean PM 2.5 exposure was 21.3 (14.4) µg/m 3 . A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2 g decrease in birthweight (95% CI - 197.7, - 0.6), and aged sea salt was associated with a 70.1 g decrease in birthweight (95% CI - 141.7, 1.4). Magnesium, sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM 2.5 mass. This study found evidence that major sources of personal PM 2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls., (© 2023. The Author(s).)

Background: Prenatal air pollution exposure may increase risk for childhood obesity. However, few studies have evaluated in utero growth measures and infant weight trajectories. This study will evaluate the associations of prenatal exposure to ambient air pollutants with weight trajectories from the 3rd trimester through age 2 years., Methods: We studied 490 pregnant women who were recruited from the Maternal and Development Risks from Environmental and Social Stressors (MADRES) cohort, which comprises a low-income, primarily Hispanic population in Los Angeles, California. Nitrogen dioxide (NO 2 ), particulate matter < 10 µm (PM 10 ), particulate matter < 2.5 µm (PM 2.5 ), and ozone (O 3 ) concentrations during pregnancy were estimated from regulatory air monitoring stations. Fetal weight was estimated from maternal ultrasound records. Infant/child weight measurements were extracted from medical records or measured during follow-up visits. Piecewise spline models were used to assess the effect of air pollutants on weight, overall growth, and growth during each period., Results: The mean (SD) prenatal exposure concentrations for NO 2 , PM 2.5 , PM 10 , and O 3 were 16.4 (2.9) ppb, 12.0 (1.1) μg/m 3 , 28.5 (4.7) μg/m 3 , and 26.2 (2.9) ppb, respectively. Comparing an increase in prenatal average air pollutants from the 10th to the 90th percentile, the growth rate from the 3rd trimester to age 3 months was significantly increased (1.55% [95%CI 1.20%, 1.99%] for PM 2.5 and 1.64% [95%CI 1.27%, 2.13%] for NO 2 ), the growth rate from age 6 months to age 2 years was significantly decreased (0.90% [95%CI 0.82%, 1.00%] for NO 2 ), and the attained weight at age 2 years was significantly lower (- 7.50% [95% CI - 13.57%, - 1.02%] for PM 10 and - 7.00% [95% CI - 11.86%, - 1.88%] for NO 2 )., Conclusions: Prenatal ambient air pollution was associated with variable changes in growth rate and attained weight from the 3rd trimester to age 2 years. These results suggest continued public health benefits of reducing ambient air pollution levels, particularly in marginalized populations., (© 2023. BioMed Central Ltd., part of Springer Nature.)

The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort Study (EWC), a collaborative research design comprising 69 cohorts in 31 consortia, was funded by the National Institutes of Health (NIH) in 2016 to improve children's health in the United States. The EWC harmonizes extant data and collects new data using a standardized protocol, the ECHO-Wide Cohort Data Collection Protocol (EWCP). EWCP visits occur at least once per life stage, but the frequency and timing of the visits vary across cohorts. As of March 4, 2022, the EWC cohorts contributed data from 60,553 children and consented 29,622 children for new EWCP data and biospecimen collection. The median (interquartile range) age of EWCP-enrolled children was 7.5 years (3.7-11.1). Surveys, interviews, standardized examinations, laboratory analyses, and medical record abstraction are used to obtain information in 5 main outcome areas: pre-, peri-, and postnatal outcomes; neurodevelopment; obesity; airways; and positive health. Exposures include factors at the level of place (e.g., air pollution, neighborhood socioeconomic status), family (e.g., parental mental health), and individuals (e.g., diet, genomics)., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Background: It is well documented that persons of color experience disproportionate exposure to environmental contaminants, including air pollution, and have poorer pregnancy outcomes. This study assessed the critical windows of exposure to ambient air pollution on in utero fetal growth among structurally marginalized populations in urban Los Angeles., Methods: Participants (N = 281) from the larger ongoing MADRES pregnancy cohort study were included in this analysis. Fetal growth outcomes were measured on average at 32 [Formula: see text] 2 weeks of gestation by a certified sonographer and included estimated fetal weight, abdominal circumference, head circumference, biparietal diameter and femur length. Daily ambient air pollutant concentrations were estimated for four pollutants (particulate matter less than 2.5 µm (PM 2.5 ) and less than 10 µm (PM 10 ) in aerodynamic diameter, nitrogen dioxide (NO 2 ), and 8-h maximum ozone (O 3 )) at participant residences using inverse-distance squared spatial interpolation from ambient monitoring data. Weekly gestational averages were calculated from 12 weeks prior to conception to 32 weeks of gestation (44 total weeks), and their associations with growth outcomes were modeled using adjusted distributed lag models (DLMs)., Results: Participants were on average 29 years [Formula: see text] 6 old and predominately Hispanic (82%). We identified a significant sensitive window of PM 2.5 exposure (per IQR increase of 6 [Formula: see text] 3 ) between gestational weeks 4-16 for lower estimated fetal weight [Formula: see text] averaged4-16  = -8.7 g; 95% CI -16.7, -0.8). Exposure to PM 2.5 during gestational weeks 1-23 was also significantly associated with smaller fetal abdominal circumference ([Formula: see text] averaged1-23  = -0.6 mm; 95% CI -1.1, -0.2). Additionally, prenatal exposure to PM 10 (per IQR increase of 13 [Formula: see text] 3 ) between weeks 6-15 of pregnancy was significantly associated with smaller fetal abdominal circumference ([Formula: see text] averaged6-15 = -0.4 mm; 95% CI -0.8, -0.1)., Discussion: These results suggest that exposure to particulate matter in early to mid-pregnancy, but not preconception or late pregnancy, may have critical implications on fetal growth., (© 2022. The Author(s).)

Background: In-utero exposure to particulate matter with aerodynamic diameter less than 2.5 μm (PM 2.5 ) is associated with low birth weight and health risks later in life. Pregnant women are mobile and locations they spend time in contribute to their personal PM 2.5 exposures. Therefore, it is important to understand how mobility and exposures encountered within activity spaces contribute to personal PM 2.5 exposures during pregnancy., Methods: We collected 48-h integrated personal PM 2.5 samples and continuous geolocation (GPS) data for 213 predominantly Hispanic/Latina pregnant women in their 3 rd trimester in Los Angeles, CA. We also collected questionnaires and modeled outdoor air pollution and meteorology in their residential neighborhood. We calculated three GPS-derived activity space measures of exposure to road networks, greenness (NDVI), parks, traffic volume, walkability, and outdoor PM 2.5 and temperature. We used bivariate analyses to screen variables (GPS-extracted exposures in activity spaces, individual characteristics, and residential neighborhood exposures) based on their relationship with personal, 48-h integrated PM 2.5 concentrations. We then built a generalized linear model to explain the variability in personal PM 2.5 exposure and identify key contributing factors., Results: Indoor PM 2.5 sources, parity, and home ventilation were significantly associated with personal exposure. Activity-space based exposure to roads was associated with significantly higher personal PM 2.5 exposure, while greenness was associated with lower personal PM 2.5 exposure (β = -3.09 μg/m 3 per SD increase in NDVI, p-value = 0.018). The contribution of outdoor PM 2.5 to personal exposure was positive but relatively lower (β = 2.05 μg/m 3 per SD increase, p-value = 0.016) than exposures in activity spaces and the indoor environment. The final model explained 34% of the variability in personal PM 2.5 concentrations., Conclusions: Our findings highlight the importance of activity spaces and the indoor environment on personal PM 2.5 exposures of pregnant women living in Los Angeles, CA. This work also showcases the multiple, complex factors that contribute to total personal PM 2.5 exposure., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Importance: Fetal growth is precisely programmed and could be interrupted by environmental exposures during specific times during pregnancy. Insights on potential sensitive windows of air pollution exposure in association with birth weight are needed., Objective: To examine the association of sensitive windows of ambient air pollution exposure with birth weight and heterogeneity by individual- and neighborhood-level stressors., Design, Setting, and Participants: Data on a cohort of low-income Hispanic women with singleton term pregnancy were collected from 2015 to 2021 in the ongoing Maternal and Developmental Risks from Environmental and Social Stressors cohort in Los Angeles, California., Exposures: Daily ambient particulate matter with aerodynamic diameter less than 10 μm (PM10) and aerodynamic diameter less than 2.5 μm (PM2.5), nitrogen dioxide (NO2), and 8-hour maximum ozone were assigned to residential locations. Weekly averages from 12 weeks before conception to 36 gestational weeks were calculated. Individual-level psychological stressor was measured by the Perceived Stress Scale. Neighborhood-level stressor was measured by the CalEnviroScreen 4.0., Main Outcomes and Measures: Sex-specific birth weight for gestational age z score (BWZ). The associations between air pollutant and BWZ were estimated using distributed lag models to identify sensitive windows of exposure, adjusting for maternal and meteorologic factors. We stratified the analyses by Perceived Stress Scale and CalEnviroScreen 4.0. We converted the effect size estimation in BWZ to grams to facilitate interpretation., Results: The study included 628 pregnant women (mean [SD] age, 22.18 [5.92] years) and their newborns (mean [SD] BWZ, -0.08 [1.03]). On average, an interquartile range (IQR) increase in PM2.5 exposure during 4 to 22 gestational weeks was associated with a -9.5 g (95% CI, -10.4 to -8.6 g) change in birth weight. In stratified models, PM2.5 from 4 to 24 gestational weeks was associated with a -34.0 g (95% CI, -35.7 to -32.4 g) change in birth weight and PM10 from 9 to 14 gestational weeks was associated with a -39.4 g (95% CI, -45.4 to -33.4) change in birth weight in the subgroup with high Perceived Stress Scale and high CalEnviroScreen 4.0 scores. In this same group, NO2 from 9 to 14 gestational weeks was associated with a -40.4 g (95% CI, -47.4 to -33.3 g) change in birth weight and, from 33 to 36 gestational weeks, a -117.6 g (95% CI, -125.3 to -83.7 g) change in birth weight. Generally, there were no significant preconception windows for any air pollutants or ozone exposure with birth weight., Conclusions and Relevance: In this cohort study, early pregnancy to midpregnancy exposures to PM2.5, PM10, and NO2 were associated with lower birth weight, particularly for mothers experiencing higher perceived stress and living in a neighborhood with a high level of stressors from environmental pollution.

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8−16 years with moderate-to-severe asthma. We deployed the Biomedical REAI-Time Health Evaluation (BREATHE) Kit developed in the Los Angeles PRISMS Center to continuously monitor personal exposure to particulate matter of aerodynamic diameter < 2.5 µm (PM2.5), relative humidity and temperature, geolocation (GPS), and asthma outcomes including lung function, medication use, and symptoms for 14 days. Hourly ambient (PM2.5, nitrogen dioxide (NO2), ozone (O3)) and traffic-related (nitrogen oxides (NOx) and PM2.5) air pollution exposures were modeled based on location. We used mixed-effects models to examine the association of same day and lagged (up to 2 days) exposures with daily changes in % predicted forced expiratory volume in 1 s (FEV1) and % predicted peak expiratory flow (PEF), count of rescue inhaler puffs, and symptoms. Results: Participants were on average 12.0 years old (range: 8.4−16.8) with mean (SD) morning %predicted FEV1 of 67.9% (17.3%) and PEF of 69.1% (18.4%) and 1.4 (3.5) puffs per day of rescue inhaler use. Participants reported chest tightness, wheeze, trouble breathing, and cough symptoms on 36.4%, 17.5%, 32.3%, and 42.9%, respectively (n = 217 person-days). One SD increase in previous day O3 exposure was associated with reduced morning (beta [95% CI]: −4.11 [−6.86, −1.36]), evening (−2.65 [−5.19, −0.10]) and daily average %predicted FEV1 (−3.45 [−6.42, −0.47]). Daily (lag 0) exposure to traffic-related PM2.5 exposure was associated with reduced morning %predicted PEF (−3.97 [−7.69, −0.26]) and greater odds of “feeling scared of trouble breathing” symptom (odds ratio [95% CI]: 1.83 [1.03, 3.24]). Exposure to ambient O3, NOx, and NO was significantly associated with increased rescue inhaler use (rate ratio [95% CI]: O3 1.52 [1.02, 2.27], NOx 1.61 [1.23, 2.11], NO 1.80 [1.37, 2.35]). Conclusions: We found significant associations of air pollution exposure with lung function, rescue inhaler use, and “feeling scared of trouble breathing.” Our study demonstrates the potential of informatics and wearable sensor technologies at collecting highly resolved, contextual, and personal exposure data for understanding acute pediatric asthma triggers.

Background: Depression is the leading cause of mental health-related morbidity and affects twice as many women as men. Hispanic/Latina women in the US have unique risk factors for depression and they have lower utilization of mental health care services. Identifying modifiable risk factors for maternal depression, such as ambient air pollution, is an urgent public health priority. We aimed to determine whether prenatal exposure to ambient air pollutants was associated with maternal depression at 12 months after childbirth., Methods: One hundred eighty predominantly low-income Hispanic/Latina women participating in the ongoing MADRES cohort study in Los Angeles, CA were followed from early pregnancy through 12 months postpartum through a series of phone questionnaires and in-person study visits. Daily prenatal ambient pollutant estimates of nitrogen dioxide (NO 2 ), ozone (O 3 ), and particulate matter (PM 10 and PM 2.5 ) were assigned to participant residences using inverse-distance squared spatial interpolation from ambient monitoring data. Exposures were averaged for each trimester and across pregnancy. The primary outcome measure was maternal depression at 12 months postpartum, as reported on the 20-item Center for Epidemiologic Studies-Depression (CES-D) scale. We classified each participant as depressed (n = 29) or not depressed (n = 151) based on the suggested cutoff of 16 or above (possible scores range from 0 to 60) and fitted logistic regression models, adjusting for potential confounders., Results: We found over a two-fold increased odds of depression at 12 months postpartum associated with second trimester NO 2 exposure (OR = 2.63, 95% CI: 1.41-4.89) and pregnancy average NO 2 (OR = 2.04, 95% CI: 1.13-3.69). Higher second trimester PM 2.5 exposure also was associated with increased depression at 12 months postpartum (OR = 1.56, 95% CI: 1.01-2.42). The effect for second trimester PM 10 was similar and was borderline significant (OR = 1.58, 95% CI: 0.97-2.56)., Conclusions: In a low-income cohort consisting of primarily Hispanic/Latina women in urban Los Angeles, we found that prenatal ambient air pollution, especially mid-pregnancy NO 2 and PM 2.5 , increased the risk of depression at 12 months after childbirth. These results underscore the need to better understand the contribution of modifiable environmental risk factors during potentially critical exposure periods., (© 2021. The Author(s).)

Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.

Predicting PM 2.5 concentrations at a fine spatial and temporal resolution (i.e., neighborhood, hourly) is challenging. Recent growth in low cost sensor networks is providing increased spatial coverage of air quality data that can be used to supplement data provided by monitors of regulatory agencies. We developed an hourly, 500 × 500 m gridded PM 2.5 model that integrates PurpleAir low-cost air sensor network data for Los Angeles County. We developed a quality control scheme for PurpleAir data. We included spatially and temporally varying predictors in a random forest model with random oversampling of high concentrations to predict PM 2.5 . The model achieved high prediction accuracy (10-fold cross-validation (CV) R 2  = 0.93, root mean squared error (RMSE) = 3.23 μg/m 3 ; spatial CV R 2  = 0.88, spatial RMSE = 4.33 μg/m 3 ; temporal CV R 2  = 0.90, temporal RMSE = 3.85 μg/m 3 ). Our model was able to predict spatial and diurnal patterns in PM 2.5 on typical weekdays and weekends, as well as non-typical days, such as holidays and wildfire days. The model allows for far more precise estimates of PM 2.5 than existing methods based on few sensors. Taking advantage of low-cost PM 2.5 sensors, our hourly random forest model predictions can be combined with time-activity diaries in future studies, enabling geographically and temporally fine exposure estimation for specific population groups in studies of acute air pollution health effects and studies of environmental justice issues., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Introduction: Estimating PM 2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use., Methods: Using ensemble-based deep learning with big data fused from multiple sources we developed a PM 2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM 2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM 2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model., Results: Ensemble deep learning to predict PM 2.5 achieved an overall mean training RMSE of 1.54 μg/m 3 (R 2 : 0.94) and test RMSE of 2.29 μg/m 3 (R 2 : 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM 2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m 3 ). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM 2.5 . The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers., Conclusion: Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM 2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Air quality data from satellites and low-cost sensor systems, together with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much-needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network. To address these issues, the American Thoracic Society, the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, and the National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: 1 ) current and near-term capabilities in air pollution monitoring, 2 ) data assimilation from multiple technology platforms, 3 ) critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network, and 4 ) risk communication and health messaging. Recommendations for research and improved use were identified during the workshop, including a recognition that the integration of data across monitoring technology groups is critical to maximizing the effectiveness (e.g., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together, these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible, and reliable air pollution data and forecasts to health professionals, as well as to all global citizens.

Background: Evidence suggests that childhood near-roadway air pollution (NRAP) exposures contribute to increased body mass index (BMI); however, effects of NRAP exposure during the vulnerable periods including in utero and first year of life have yet to be established. In this study, we examined whether exposure to elevated concentrations of NRAP during in utero and/or first year of life increase childhood BMI growth., Methods: Participants in the Children's Health Study enrolled from 2002 to 2003 with annual visits over a four-year period and who changed residences before study entry were included (n = 2318). Annual height and weight were measured and lifetime residential NRAP exposures including in utero and first year of life periods were estimated by nitrogen oxides (NO x ) using the California line-source dispersion model. Linear mixed effects models assessed in utero or first year near-road freeway and non-freeway NO x exposures and BMI growth after adjusting for age, sex, race/ethnicity, parental education, Spanish questionnaire, and later childhood near-road NO x exposure., Results: A two-standard deviation difference in first year of life near-road freeway NO x exposure was associated with a 0.1 kg/m 2 (95% confidence interval (CI): 0.03, 0.2) faster increase in BMI growth per year and a 0.5 kg/m 2 (95% CI: 0.02, 0.9) higher attained BMI at age 10 years., Conclusions: Higher exposure to early life NRAP increased the rate of change of childhood BMI and resulted in a higher attained BMI at age 10 years that were independent of later childhood exposures. These findings suggest that elevated early life NRAP exposures contribute to increased obesity risk in children.

Importance: Thyroid hormones are critical for fetal growth and development. Prenatal particulate matter (PM) air pollution exposure has been associated with altered newborn thyroid function, but other air pollutants have not been evaluated, and critical windows of exposure are unknown., Objectives: To investigate the association of prenatal exposure to ambient and traffic-related air pollutants with newborn thyroid function and identify critical windows of exposure., Design, Setting, and Participants: This cohort study used data from 2050 participants in the Children's Health Study. Statistical analyses were conducted from 2017 to 2018 using pregnancy and birth data from 1994 to 1997 for a subset of participants recruited from schools in 13 southern California communities in 2002 to 2003 when participants were 5 to 7 years of age. Participants were included in statistical analyses if they could be linked to their newborn blood spot and had complete monthly exposure measures for at least 1 air pollutant across pregnancy., Exposures: Prenatal monthly averages of ambient (PM diameter <2.5 μm [PM2.5] or <10 μm [PM10], nitrogen dioxide, and ozone) and traffic-related (freeway, nonfreeway, and total nitrogen oxides) air pollutant exposures were determined using inverse distance-squared weighting of central monitoring data and the California Line Source Dispersion model, respectively., Main Outcomes and Measures: Newborn heel-stick blood spot total thyroxine (TT4) measures were acquired retrospectively from the California Department of Public Health., Results: Participants included 2050 newborns (50.5% male), with a median (interquartile range) age of 20 (15-29) hours. The majority of newborns were Hispanic white (1202 [58.6%]) or non-Hispanic white (638 [31.1%]). Sixty-six (3.2%) were black and 144 (7.0%) were from other racial/ethnic groups. The mean (SD) newborn TT4 measure was 16.2 (4.3) μg/dL. A 2-SD increase in prenatal PM2.5 (16.3 μg/m3) and PM10 (22.2 μg/m3) was associated with a 1.2-μg/dL (95% CI, 0.5-1.8 μg/dL) and 1.5-μg/dL (95% CI, 0.9-2.1 μg/dL) higher TT4 measure, respectively, in covariate-adjusted linear regression models. Other pollutants were not consistently associated with newborn TT4. Distributed lag models revealed that PM2.5 exposure during months 3 to 7 of pregnancy and PM10 exposure during months 1 to 8 of pregnancy were associated with significantly higher newborn TT4 concentrations (P < .05)., Conclusions and Relevance: Prenatal PM exposure, particularly in early pregnancy and midpregnancy, is associated with higher newborn TT4 concentrations. Future studies should assess the health implications of PM-associated differences in newborn TT4 concentrations.

Background: Traffic-related air pollution (TRAP) exposure has been linked to type 2 diabetes and metabolic dysfunction in humans. Animal studies suggest that air pollutants may alter the composition of the gut microbiota, which may negatively impact metabolic health through changes in the composition and/or function of the gut microbiome., Objectives: The primary aim of this study was to determine whether elevated TRAP exposure was correlated with gut bacterial taxa in overweight and obese adolescents from the Meta-AIR (Metabolic and Asthma Incidence Research) study. The secondary aim was to examine whether gut microbial taxa correlated with TRAP were also correlated with risk factors for type 2 diabetes (e.g., fasting glucose levels). We additionally explored whether correlations between TRAP and these metabolic risk factors could be explained by the relative abundance of these taxa., Methods: Participants (17-19 years; n=43) were enrolled between 2014 and 2016 from Southern California. The CALINE4 line dispersion model was used to model prior year residential concentrations of nitrogen oxides (NOx) as a marker of traffic emissions. The relative abundance of fecal microbiota was characterized by 16S rRNA sequencing and spearman partial correlations were examined after adjusting for body fat percent., Results: Freeway TRAP was correlated with decreased Bacteroidaceae (r=-0.48; p=0.001) and increased Coriobacteriaceae (r=0.48; p<0.001). These same taxa were correlated with fasting glucose levels, including Bacteroidaceae (r=-0.34; p=0.04) and Coriobacteriaceae (r=0.41; p<0.01). Further, freeway TRAP was positively correlated fasting glucose (r=0.45; p=0.004) and Bacteroidaceae and Coriobacteriaceae explained 24% and 29% of the correlation between TRAP and fasting glucose levels., Conclusions: Increased TRAP exposure was correlated with gut microbial taxa and fasting glucose levels. Gut microbial taxa that were correlated with TRAP partially explained the correlation between TRAP and fasting glucose levels. These results suggest that exposure to air pollutants may negatively impact metabolic health via alterations in the gut microbiota., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objectives: Growing evidence indicates that ambient (AAP: NO 2 , PM 2.5 and O 3 ) and traffic-related air pollutants (TRAP) contribute to metabolic disease risk in adults; however, few studies have examined these relationships in children., Methods: Metabolic profiling was performed in 429 overweight and obese African-American and Latino youth living in urban Los Angeles, California. This cross-sectional study estimated individual residential air pollution exposure and used linear regression to examine relationships between air pollution and metabolic outcomes., Results: AAP and TRAP exposure were associated with adverse effects on glucose metabolism independent of body fat percent. PM 2.5 was associated with 25.0% higher fasting insulin (p < 0.001), 8.3% lower insulin sensitivity (p < 0.001), 14.7% higher acute insulin response to glucose (p = 0.001) and 1.7% higher fasting glucose (p < 0.001). Similar associations were observed for increased NO 2 exposure. TRAP from non-freeway roads was associated with 12.1% higher insulin (p < 0.001), 6.9% lower insulin sensitivity (p = 0.02), 10.8% higher acute insulin response to glucose (p = 0.003) and 0.7% higher fasting glucose (p = 0.047)., Conclusions: Elevated air pollution exposure was associated with a metabolic profile that is characteristic of increased risk for type 2 diabetes. These results indicate that increased prior year exposure to air pollution may adversely affect type 2 diabetes-related pathophysiology in overweight and obese minority children., (© 2016 World Obesity Federation.)

Evidence suggests that ambient air pollution (AAP) exposure may contribute to the development of obesity and type 2 diabetes. The objective of this study was to determine whether exposure to elevated concentrations of nitrogen dioxide (NO 2 ) and particulate matter with aerodynamic diameter <2.5 (PM 2.5 ) had adverse effects on longitudinal measures of insulin sensitivity (S I ), β-cell function, and obesity in children at high risk for developing diabetes. Overweight and obese Latino children (8-15 years; n = 314) were enrolled between 2001 and 2012 from Los Angeles, CA, and followed for an average of 3.4 years (SD 3.1 years). Linear mixed-effects models were fitted to assess relationships between AAP exposure and outcomes after adjusting for covariates including body fat percent. Higher NO 2 and PM 2.5 were associated with a faster decline in S I and a lower S I at age 18 years, independent of adiposity. NO 2 exposure negatively affected β-cell function, evidenced by a faster decline in disposition index (DI) and a lower DI at age 18 years. Higher NO 2 and PM 2.5 exposures over follow-up were also associated with a higher BMI at age 18 years. AAP exposure may contribute to development of type 2 diabetes through direct effects on S I and β-cell function., (© 2017 by the American Diabetes Association.)

Unlabelled: We hypothesized that chronic exposures to traffic combustion products may lower bone mineral density (BMD). We found that proximity to freeways was associated with reduced BMD. Our findings suggest that traffic-related pollution may contribute to the occurrence of osteopenia and osteoporosis., Introduction: Adults residing in rural areas have been linked with higher BMD. We aimed to determine if this difference is due in part to air pollution by examining the relationships between traffic metrics and ambient air pollution with total body and pelvic BMD., Methods: Mexican American adults (n = 1,175; mean 34 years; 72 % female) who had participated in the BetaGene study of air pollution, obesity, and insulin resistance were included in this analysis. Total body and pelvic BMD were estimated using dual-energy X-ray absorptiometry. Traffic and ambient air pollutant exposures were estimated at residences using location and ambient monitoring data. Variance component models were used to analyze the associations between residential distance to the nearest freeway and ambient air pollutants with BMD., Results: Residential proximity to a freeway was associated with lower total body BMD (p-trend = 0.01) and pelvic BMD (p-trend = 0.03) after adjustment for age, sex, weight, and height. The adjusted mean total body and pelvic BMD in participants living within 500 m of a freeway were 0.02 and 0.03 g/cm(2) lower than participants living greater than 1,500 m from a freeway. These associations did not differ significantly by age, sex, or obesity status. Results were similar after further adjustment for body fat and weekly physical activity minutes. Ambient air pollutants (NO2, O3, and PM2.5) were not significantly associated with BMD., Conclusions: Traffic-related exposures in overweight and obese Mexican Americans may adversely affect BMD. Our findings indicate that long-term exposures to traffic may contribute to the occurrence of osteoporosis and its consequences.

Background: Hypothalamic-pituitary-adrenal (HPA)-axis dysfunction has been associated with a variety of mental health and cardio-metabolic disorders. While causal models of HPA-axis dysregulation have been largely focused on either pre-existing health conditions or psychosocial stress factors, recent evidence suggests a possible role for central nervous system activation via air pollutants, such as nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM). Therefore, in an observational study of Latino youth, we investigated if monthly ambient NO2, O3, and PM with aerodynamic diameter ≤ 2.5 (PM2.5) exposure were associated with morning serum cortisol levels.Methods: In this cross-sectional study, morning serum cortisol level was assessed after a supervised overnight fast in 203 overweight and obese Latino children and adolescents (female/male: 88/115; mean age: 11.1 ± 1.7 years; pre-pubertal/pubertal/post-pubertal: 85/101/17; BMI z-score: 2.1 ± 0.4). Cumulative concentrations of NO2, O3 and PM2.5 were spatially interpolated at the residential addresses based on measurements from community monitors up to 12 months prior to testing. Single and multi-pollutant linear effects models were used to test the cumulative monthly lag effects of NO2, O3, and PM2.5 on morning serum cortisol levels after adjusting for age, sex, seasonality, social position, pubertal status, and body fat percent by DEXA.Results: Single and multi-pollutant models showed that higher O3 exposure (derived from maximum 8-h exposure windows) in the prior 1-7 months was associated with higher serum morning cortisol (p < 0.05) and longer term PM2.5 exposure (4-10 months) was associated with lower serum morning cortisol levels (p < 0.05). Stratification by pubertal status showed associations in pre-pubertal children compared to pubertal and post-pubertal children. Single, but not multi-pollutant, models showed that higher NO2 over the 4-10 month exposure period associated with lower morning serum cortisol (p < 0.05).Conclusions: Chronic ambient NO2, O3 and PM2.5 differentially associate with HPA-axis dysfunction, a mechanism that may serve as an explanatory pathway in the relationship between ambient air pollution and metabolic health of youth living in polluted urban environments. Further research that uncovers how ambient air pollutants may differentially contribute to HPA-axis dysfunction are warranted. [ABSTRACT FROM AUTHOR]

Summary: Objective: Studies suggest that prenatal exposure to traffic‐related air pollution (TRAP) may contribute to childhood obesity. While exact mechanisms for this association are unknown, circulating adipokines are hypothesized to contribute to early‐life weight gain. Methods: The Maternal and Child Health Study birth cohort included 136 women from the Los Angeles County + University of Southern California Medical Center. This study estimated prenatal residential TRAP exposure and used linear regression analysis to examine associations between adipokines with TRAP exposure and infant weight change (birth to 6 months). Results: A one standard deviation (1‐SD: 2 ppb) increase in prenatal non‐freeway nitrogen oxides was associated with 33% (P = 0.01) higher leptin and 9% higher high molecular weight adiponectin levels (P = 0.07) in cord blood. Leptin levels were 71% higher in mothers who lived <75 m than those living >300 m from major roadways (P = 0.03). A 1‐SD (10 ng mL−1) increase in leptin was associated with a significant increase in infant weight change in female infants (0.62 kg, P = 0.02) but not male infants (0.11 kg, P = 0.48). Conclusions: Higher TRAP exposures were associated with higher cord blood levels of leptin and high molecular weight adiponectin. These adipokines were associated with increased infant weight change in female infants, which may have implications for future obesity risk. [ABSTRACT FROM AUTHOR]