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Background: Fine particulate matter (PM2.5) exposure is an important environmental risk for maternal and children's health, with peak exposures especially those derived from primary combustion hypothesized to pose greater risk. Identifying PM2.5 peaks and their contributions to personal exposure remains challenging. This study measured personal PM2.5 exposure, characterized primary combustion peaks, and investigated their determinants during and after pregnancy and among Hispanic women in Los Angeles, CA. Methods: Continuous personal PM2.5 exposure, Global Positioning System geolocation, and ecological momentary assessment surveys were collected from 63 women for 4 consecutive days in their 1st trimester, 3rd trimester and 4–6 months postpartum. Based on the shape of PM2.5 time-series, primary combustion peaks were identified, characterized (number, duration, area under the curve [AUC]), and linked with locations they occurred in. Zero-inflated generalized mixed-effect models were used to examine the spatial and temporal determinants of PM2.5 peak exposures. Results: A total of 490 PM2.5 peaks were identified from 618 person-days of monitoring. Spending an additional minute at parks and open spaces was related to smaller (AUC decreased 3.1 %, 95 % CI: 1.5 %–4.6 %) and shorter (duration decreased 1.7 %, 0.5 %–2.9 %) PM2.5 peak exposure. An additional minute in vehicular trips also related to smaller and shorter peak exposure (AUC and duration decreased 2.5 %, 1.2 %–3.7 % and 1.8 %, 1.0 %–2.6 %, respectively). However, an additional minute at industrial locations was associated with greater number (3.6 %, 2.0 %–5.2 %), AUC (1.6 %, 0.1 %–3.2 %) and duration (1.0 %, 0.0 %–2.1 %) of personal PM2.5 peak exposure. Conclusions: This study demonstrates the potential to statistically identify exposure to primary combustion PM2.5 peaks and understand their determinants from personal monitoring data. Results suggest that visits to parks and open spaces may minimize PM2.5 peak exposures, while visiting industrial locations may increase them in and around pregnancy.

Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 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 PM2.5 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 PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8–10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Abstract In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-h personal PM2.5 exposure monitoring sub-study. The mass contributions of six major sources of personal PM2.5 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 PM2.5 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 PM2.5 exposure was 21.3 (14.4) µg/m3. 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 PM2.5 mass. This study found evidence that major sources of personal PM2.5 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.

Background Diet during pregnancy may be a potential intervention for preventing hypertensive disorders of pregnancy that disproportionally burdens Hispanic/Latina women. Methods and Results The MADRES (Maternal And Developmental Risks from Environmental and Social stressors) study (n=451) is a prospective pregnancy cohort of predominantly low‐income Hispanic/Latina women in Los Angeles, California, who completed up to 2 staff‐administered 24‐hour dietary recalls in the third trimester of pregnancy. Hypertensive disorders of pregnancy were abstracted from medical records and based on a physician's diagnosis or systolic or diastolic blood pressure (≥140 or ≥90 mm Hg, respectively) at ≥2 consecutive prenatal visits. Using multivariable logistic regression, we evaluated associations of 2 previously derived dietary patterns in this population (solid fats, refined grains, and cheese and vegetables, oils, and fruit) and the Healthy Eating Index 2015 with (1) gestational hypertension, (2) preeclampsia, and (3) any hypertensive disorder of pregnancy (either gestational hypertension or preeclampsia). In separate models, we additionally tested interactions with prepregnancy body mass index. Comparing highest‐to‐lowest quartiles, the solid fats, refined grains, and cheese dietary pattern was associated with an increased odds of any hypertensive disorder of pregnancy (odds ratio [OR], 3.99 [95% CI, 1.44–11.0]; Ptrend=0.014) and preeclampsia (OR, 4.10 [95% CI, 1.25–13.5]; Ptrend=0.036), whereas the vegetables, oils, and fruit pattern was associated with reduced odds of preeclampsia (OR, 0.32 [95% CI, 0.10–0.99]; Ptrend=0.041). Among the overweight prepregnancy body mass index category, inverse associations of vegetables, oils, and fruit and Healthy Eating Index 2015 with preeclampsia were more pronounced (both Pinteractions=0.017). Healthy Eating Index 2015 findings were generally nonsignificant. Conclusions While the solid fats, refined grains, and cheese diet was strongly associated with preeclampsia during pregnancy, findings suggest the vegetables, oils, and fruit diet may be more relevant than Healthy Eating Index 2015 for preventing preeclampsia among low‐income Hispanic/Latina women.

Abstract Background Fluoride is ubiquitous in the United States (US); however, data on biomarkers and patterns of fluoride exposure among US pregnant women are scarce. We examined specific gravity adjusted maternal urinary fluoride (MUFsg) in relation to sociodemographic variables and metal co-exposures among pregnant women in Los Angeles, California. Methods Participants were from the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) cohort. There were 293 and 490 women with MUFsg measured during first and third trimesters, respectively. An intra-class correlation coefficient examined consistency of MUFsg between trimesters. Kruskal–Wallis and Mann-Whitney U tests examined associations of MUFsg with sociodemographic variables. Covariate adjusted linear regression examined associations of MUFsg with blood metals and specific gravity adjusted urine metals among a subsample of participants within and between trimesters. A False Discovery Rate (FDR) correction accounted for multiple comparisons. Results Median (IQR) MUFsg was 0.65 (0.5) mg/L and 0.8 (0.59) mg/L, during trimesters one and three respectively. During both trimesters, MUFsg was higher among older participants, those with higher income, and White, non-Hispanic participants than Hispanic participants. MUFsg was also higher for White, non-Hispanic participants than for Black, non-Hispanic participants in trimester three, and for those with graduate training in trimester one. MUFsg was negatively associated with blood mercury in trimester one and positively associated with blood lead in trimester three. MUFsg was positively associated with various urinary metals, including antimony, barium, cadmium, cobalt, copper, lead, nickel, tin, and zinc in trimesters one and/or three. Conclusions MUFsg levels observed were comparable to those found in pregnant women in Mexico and Canada that have been associated with poorer neurodevelopmental outcomes. Lower urinary fluoride levels among Hispanic and non-Hispanic Black participants in MADRES compared to non-Hispanic White participants may reflect lower tap water consumption or lower fluoride exposure from other sources. Additional research is needed to examine whether MUFsg levels observed among pregnant women in the US are associated with neurodevelopmental outcomes.

Abstract Background Evidence suggests organophosphate esters (OPEs) are neurotoxic; however, the epidemiological literature remains scarce. We investigated whether prenatal exposures to OPEs were associated with child neurobehavior in the MADRES cohort. Methods We measured nine OPE metabolites in 204 maternal urine samples (gestational age at collection: 31.4 ± 1.8 weeks). Neurobehavior problems were assessed among 36-month-old children using the Child Behavior Checklist’s (CBCL) three composite scales [internalizing, externalizing, and total problems]. We examined associations between tertiles of prenatal OPE metabolites (> 50% detection) and detect/non-detect categories (

Abstract 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 (NO2), particulate matter

Background We investigated how childhood‐to‐adulthood perceived stress patterns predict adult cardiometabolic risk. Methods and Results This study included 276 participants from the Southern California Children's Health Study (2003–2014), and a follow‐up assessment (2018–2021). Perceived stress (Perceived Stress Scale) was initially reported by participants' parents for themselves during early childhood (mean age, 6.3 years), and later self‐reported during adolescence (13.3 years) and young adulthood (23.6 years). Participants were grouped into 4 stress patterns: consistently high, decreasing, increasing, and consistently low. Cardiometabolic risk was assessed in young adulthood by carotid artery intima‐media thickness, systolic and diastolic blood pressure, obesity, percent body fat, android/gynoid ratio, and glycated hemoglobin. A cardiometabolic risk score was generated by summing the clinically abnormal markers. Multivariable linear and logistic regression models were used to (1) examine the associations between Perceived Stress Scale at 3 time points and adult cardiometabolic risk, and (2) assess the impact of stress pattern on adult cardiometabolic risk. Findings suggested that in adulthood, higher Perceived Stress Scale score was associated with increased overall cardiometabolic risk (β=0.12 [95% CI, 0.01–0.22]), carotid artery intima‐media thickness (β=0.01 [95% CI, 0.0003–0.02]), systolic blood pressure (β=1.27 [95% CI, 0.09–2.45]), and diastolic blood pressure (β=0.94 [95% CI, 0.13–1.75]). Individuals with a consistently high adolescence‐to‐adulthood stress pattern had greater overall cardiometabolic risk (β=0.31 [95% CI, 0.02–0.60]), android/gynoid ratio (β=0.07 [95% CI, 0.02–0.13]), percent body fat (β=2.59 [95% CI, 0.01–5.17]), and greater odds of obesity (odds ratio, 5.57 [95% CI, 1.62–19.10]) in adulthood, compared with those with a consistently low Perceived Stress Scale score. Conclusions Consistently high perceived stress from adolescence to adulthood may contribute to greater cardiometabolic risk in young adulthood.

Abstract 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 $$\pm$$ ± 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 (PM2.5) and less than 10 µm (PM10) in aerodynamic diameter, nitrogen dioxide (NO2), and 8-h maximum ozone (O3)) 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 $$\pm$$ ± 6 old and predominately Hispanic (82%). We identified a significant sensitive window of PM2.5 exposure (per IQR increase of 6 $$\mathrm{\mu g}/\mathrm{m}$$ μ g / m 3) between gestational weeks 4–16 for lower estimated fetal weight $$\beta$$ β averaged4-16 = -8.7 g; 95% CI -16.7, -0.8). Exposure to PM2.5 during gestational weeks 1–23 was also significantly associated with smaller fetal abdominal circumference ( $$\beta$$ β averaged1-23 = -0.6 mm; 95% CI -1.1, -0.2). Additionally, prenatal exposure to PM10 (per IQR increase of 13 $$\mathrm{\mu g}/\mathrm{m}$$ μ g / m 3) between weeks 6–15 of pregnancy was significantly associated with smaller fetal abdominal circumference ( $$\beta$$ β 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.

Affective states play a role in dietary behaviors. Yet, little research has studied within-subjects associations between affect and diet during pregnancy. We examined the acute bidirectional relationships between affect and food intake and moderation by pre-pregnancy body mass index (BMI) in low-income, Hispanic pregnant women using ecological momentary assessment (EMA). Women (N = 57) completed four days of EMA during their first trimester. Women responded to five random prompts per day about their current affect and past two-hour food intake. Higher positive affect (PA) or lower negative affect (NA) predicted greater likelihood of fruit/vegetable consumption in the next two hours in women with lower pre-pregnancy BMI and lower likelihood in women with higher pre-pregnancy BMI. Higher PA predicted less likelihood of fast food consumption in the next two hours in women with lower pre-pregnancy BMI and slightly higher likelihood in women with higher pre-pregnancy BMI. Women with lower pre-pregnancy BMI had higher PA when they reported consuming chips/fries in the past two hours, and women with higher pre-pregnancy BMI had lower PA when they reported consumption of chips/fries in the past two hours. Results showed differential relationships between affect and food intake as a function of pre-pregnancy BMI.

Abstract Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM2.5 which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures.

Abstract 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 (NO2), ozone (O3), and particulate matter (PM10 and PM2.5) 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 NO2 exposure (OR = 2.63, 95% CI: 1.41–4.89) and pregnancy average NO2 (OR = 2.04, 95% CI: 1.13–3.69). Higher second trimester PM2.5 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 PM10 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 NO2 and PM2.5, 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.

Background: In-utero exposure to outdoor particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) is linked with low birthweight. However, previous results are mixed, likely due to measurement error introduced by estimating personal exposure from ambient data. This study investigated the effect of total personal PM2.5 exposure on birthweight and whether it differed when it was more heavily impacted by sources of indoor vs outdoor origin in the MADRES cohort study. Methods: Personal PM2.5 exposure was measured in 205 pregnant women in the 3rd trimester using 48 h integrated, filter-based sampling. Linear regression was used to test the association between personal PM2.5 exposure and birthweight, adjusting for key covariates. Interactions of PM2.5 with variables representing indoor sources of PM2.5, home ventilation, or time spent indoors tested whether the effect of total PM2.5 on birthweight varied when it was more impacted by sources of indoor vs outdoor origin. Results: In a sample of largely Hispanic (81%) pregnant women, total personal PM2.5 was not significantly associated with birthweight (β = 38.6 per 1SD increase in PM2.5; 95% CI:-21.1, 98.2). This association however, differed by home type (single family home: 156.9 (26.9, 287.0), 2-4 attached units:-16.6 (-111.9, 78.7), 5+ units:-62.6 (-184.9, 59.6), missing: 145.4 (-4.1, 294.9), interaction p = 0.028) and by household air conditioner use (none of the time: -27.6 (-101.5, 46.3) vs. some of the time: 139.9 (42.9, 237.0), interaction p = 0.008). Additionally, the effect of personal PM2.5 on birthweight varied by time spent indoors (none or little of the time:-45.1 (-208.3, 118.1) vs. most or all of the time: 57.1 (-7.3, 121.6), interaction p = 0.255). Conclusions: While no significant association between total personal PM2.5 exposure and birthweight was found, there was evidence that multi-unit housing (vs. single-family homes), candle and/or incense smoke, and greater outdoor source contributions to personal PM2.5 were more strongly associated with lower birthweight.

Background: Perfluoroalkyl substances (PFAS) are ubiquitous synthetic chemicals with long half-lives and are known to cross the placenta during pregnancy. We examined the influence of maternal PFAS levels on in utero fetal growth trajectories and assessed whether maternal stress modified these associations. Methods: Blood serum concentrations of five PFAS (PFOS, PFHxS, PFNA, PFOA, PFDA) were measured in 335 prenatal specimens (mean gestational age (GA): 21±9 weeks) in the MADRES cohort. Fetal growth outcomes (head circumference (HC), abdominal circumference (AC), biparietal diameter (BPD), femur length (FL), and estimated fetal weight (EFW)) were abstracted from ultrasound medical records and measured at the 3rd trimester study visit (N = 833 scans, GA range 10–42 weeks, mean 2.4 scans/participant). Adjusted linear mixed models with a GA quadratic growth curve were used for each PFAS exposure and growth outcome. PFOS and PFHxS were modeled continuously (100% sample detection), while PFOA, PFNA, and PFDA were modeled categorically (57–70% sample detection). Scores on the Perceived Stress Scale (PSS) measured in pregnancy were dichotomized at the median (

Introduction and Objectives: Research suggests that perceived immigration policy vulnerability has important health implications. Coupled with the mental and physical stressors accompanying the postpartum period and a growing awareness of the discrimination and structural racism experienced by marginalized communities globally, the coronavirus disease 2019 period may have exacerbated stress among vulnerable populations, specifically postpartum Hispanic/Latina women. This study evaluated perceived immigration policy vulnerability (i.e. discrimination, social isolation, and family threats) in early postpartum Hispanic/Latina women in Los Angeles before and during the coronavirus disease 2019 pandemic. Methods: The Perceived Immigration Policy Effects Scale (PIPES) was administered cross-sectionally at 1 month postpartum to 187 Hispanic/Latina women in the MADRES cohort. Respondents between September 2018 and March 2020 were classified as “pre-pandemic” ( N = 128), between March 2020 and July 2020 as “early pandemic” ( N = 38), and between August 2020 and November 2021 as “later pandemic” ( N = 21). Average PIPES subscale scores were dichotomized into “higher” and “lower” groups (⩽median, >median) and logistic regression models were performed. Results: Approximately half of participants had incomes of

Abstract Background Chronic exposure to air pollutants is associated with increased risk of cardiovascular disease (CVD) among adults. However, little is known about how air pollution may affect the development of subclinical atherosclerosis in younger populations. Carotid artery intima-media thickness (CIMT) is a measure of subclinical atherosclerosis that provides insight into early CVD pathogenesis. Methods In a pilot study of 70 participants from the Southern California Children’s Health Study, we investigated CIMT progression from childhood to adulthood. Using carotid artery ultrasound images obtained at age 10 and follow-up images at age 21–22, we examined associations between childhood ambient and traffic-related air pollutants with changes in CIMT over time and attained adult CIMT using linear mixed-effects models adjusted for potential confounders. Average residential childhood exposures (i.e., birth to time of measurement at 10–11 years) were assigned for regional, ambient pollutants (ozone, nitrogen dioxide, particulate matter, interpolated from regulatory air monitoring data) and traffic-related nitrogen oxides (NOx) by road class (modeled using the CALINE4 line source dispersion model). Traffic density was calculated within a 300-m residential buffer. Results For each 1 standard deviation (SD) increase in childhood traffic-related total NOx exposure, we observed greater yearly rate of change in CIMT from childhood to adulthood (β: 2.17 μm/yr, 95% CI: 0.78–3.56). Increases in annual rate of CIMT change from childhood to adulthood also were observed with freeway NOx exposure (β: 2.24 μm/yr, 95% CI: 0.84–3.63) and traffic density (β: 2.11 μm/yr, 95% CI: 0.79–3.43). Traffic exposures were also related to increases in attained CIMT in early adulthood. No associations of CIMT change or attained level were observed with ambient pollutants. Conclusions Overall, we observed adverse changes in CIMT over time in relation to childhood traffic-related NOx exposure and traffic density in our study population. While these results must be cautiously interpreted given the limited sample size, the observed associations of traffic measures with CIMT suggest a need for future studies to more fully explore this relationship.

Background: Accurate estimation of nitrogen dioxide (NO2) and nitrogen oxide (NOx) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acute health outcomes. For estimation over large regions like California, high spatial density field campaign measurements can be combined with more sparse routine monitoring network measurements to capture spatiotemporal variability of NO2 and NOx concentrations. However, monitors in spatially dense field sampling are often highly clustered and their uneven distribution creates a challenge for such combined use. Furthermore, heterogeneities due to seasonal patterns of meteorology and source mixtures between sub-regions (e.g. southern vs. northern California) need to be addressed. Objectives: In this study, we aim to develop highly accurate and adaptive machine learning models to predict high-resolution NO2 and NOx concentrations over large geographic regions using measurements from different sources that contain samples with heterogeneous spatiotemporal distributions and clustering patterns. Methods: We used a comprehensive Kruskal-K-means method to cluster the measurement samples from multiple heterogeneous sources. Spatiotemporal cluster-based bootstrap aggregating (bagging) of the base mixed-effects models was then applied, leveraging the clusters to obtain balanced and less correlated training samples for less bias and improvement in generalization. Further, we used the machine learning technique of grid search to find the optimal interaction of temporal basis functions and the scale of spatial effects, which, together with spatiotemporal covariates, adequately captured spatiotemporal variability in NO2 and NOx at the state and local levels. Results: We found an optimal combination of four temporal basis functions and 200 m scale spatial effects for the base mixed-effects models. With the cluster-based bagging of the base models, we obtained robust predictions with an ensemble cross validation R2 of 0.88 for both NO2 and NOx [RMSE (RMSEIQR): 3.62 ppb (0.28) and 9.63 ppb (0.37) respectively]. In independent tests of random sampling, our models achieved similarly strong performance (R2 of 0.87–0.90; RMSE of 3.97–9.69 ppb; RMSEIQR of 0.21–0.27), illustrating minimal over-fitting. Conclusions: Our approach has important implications for fusing data from highly clustered and heterogeneous measurement samples from multiple data sources to produce highly accurate concentration estimates of air pollutants such as NO2 and NOx at high resolution over a large region. Keywords: Air pollution, Nitrogen oxides, Spatiotemporal variability, Generalization, Machine learning, Cluster methods

Abstract Background The burden of childhood and adult obesity disproportionally affects Hispanic and African-American populations in the US, and these groups as well as populations with lower income and education levels are disproportionately affected by environmental pollution. Pregnancy is a critical developmental period where maternal exposures may have significant impacts on infant and childhood growth as well as the future health of the mother. We initiated the “Maternal And Developmental Risks from Environmental and Social Stressors (MADRES)” cohort study to address critical gaps in understanding the increased risk for childhood obesity and maternal obesity outcomes among minority and low-income women in urban Los Angeles. Methods The MADRES cohort is specifically examining whether pre- and postpartum environmental exposures, in addition to exposures to psychosocial and built environment stressors, lead to excessive gestational weight gain and postpartum weight retention in women and to perturbed infant growth trajectories and increased childhood obesity risk through altered psychological, behavioral and/or metabolic responses. The ongoing MADRES study is a prospective pregnancy cohort of 1000 predominantly lower-income, Hispanic women in Los Angeles, CA. Enrollment in the MADRES cohort is initiated prior to 30 weeks gestation from partner community health clinics in Los Angeles. Cohort participants are followed through their pregnancies, at birth, and during the infant’s first year of life through a series of in-person visits with interviewer-administered questionnaires, anthropometric measurements and biospecimen collection as well as telephone interviews conducted with the mother. Discussion In this paper, we outline the study rationale and data collection protocol for the MADRES cohort, and we present a profile of demographic, health and exposure characteristics for 291 participants who have delivered their infants, out of 523 participants enrolled in the study from November 2015 to October 2018 from four community health clinics in Los Angeles. Results from the MADRES cohort could provide a powerful rationale for regulation of targeted chemical environmental components, better transportation and urban design policies, and clinical recommendations for stress-coping strategies and behavior to reduce lifelong obesity risk.

Background: Increasingly ensemble learning-based spatiotemporal models are being used to estimate residential air pollution exposures in epidemiological studies. While these machine learning models typically have improved performance, they suffer from exposure measurement error that is inherent in all models. Our objective is to develop a framework to formally assess shared, multiplicative measurement error (SMME) in our previously published three-stage, ensemble learning-based nitrogen oxides (NOx) model to identify its spatial and temporal patterns and predictors. Methods: By treating the ensembles as an external dosimetry system, we quantified shared and unshared, multiplicative and additive (SUMA) measurement error components in our exposure model. We used generalized additive models (GAMs) with a smooth term for location to identify geographic locations with significantly elevated SMME and explain their spatial and temporal determinants. Results: We found evidence of significant shared and unshared multiplicative error (p

Abstract Background Disproportionately high rates of maternal overweight and obesity among the Hispanic population before, during, and after pregnancy pose serious health concerns for both mothers (e.g., preeclampsia, gestational diabetes, weight retention) and children (e.g., elevated lifelong obesity risk). A growing body of evidence implicates environmental exposures (e.g., air pollution, metals) and social stressors (e.g., poverty, violence) in contributing to obesity-related biobehavioral processes, such as physical activity, dietary intake, perceived stress, and cortisol regulation. However, current understanding of the role of environmental exposures and social stressors on obesity-related biobehavioral processes is limited by infrequent, inter-individual measurement, and lack of personal exposure monitoring. Methods The “Maternal and Developmental Risks from Environmental and Social Stressors” (MADRES) real-time and personal sampling study examines the within-subject day-level effects of environmental and social stressors on maternal pre- and post-partum obesity-related biobehavioral responses. Among a cohort of 65 low-income, Hispanic women in urban Los Angeles, this study uses innovative personal, real-time data capture strategies (e.g., ecological momentary assessment [EMA], personal exposure monitoring, geolocation monitoring, accelerometry) to repeatedly assess obesity-related processes during the 1st and 3rd trimester, and at 4–6 months postpartum. Day-level effects of environmental exposures and social stressors on women’s physical activity, diet, perceived stress and salivary cortisol measured across repeated days will be tested using multilevel modeling. Discussion Hispanic women of childbearing age bear a disproportionately high burden of obesity, and this population is also unduly exposed to numerous obesogenic settings. By using innovative real-time data capture strategies, the current study will uncover the daily impacts of environmental and social stressor exposures on women’s obesity-related biobehavioral responses, which over time can lead to excessive gestational weight gain, postpartum weight retention and can pose serious consequences for both mother and child. Findings from the real-time and personal sampling study will identify key mechanistic targets for policy, clinical, and programmatic interventions, with the potential for broad-reaching public health impacts.

Introduction: Estimating PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 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 PM2.5 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 (

Abstract 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 (NOx) using the California line-source dispersion model. Linear mixed effects models assessed in utero or first year near-road freeway and non-freeway NOx exposures and BMI growth after adjusting for age, sex, race/ethnicity, parental education, Spanish questionnaire, and later childhood near-road NOx exposure. Results A two-standard deviation difference in first year of life near-road freeway NOx exposure was associated with a 0.1 kg/m2 (95% confidence interval (CI): 0.03, 0.2) faster increase in BMI growth per year and a 0.5 kg/m2 (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.

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.

Abstract Background The fractional concentration of exhaled nitric oxide (FeNO) is a biomarker of airway inflammation that has proved to be useful in investigations of genetic and epigenetic airway susceptibility to ambient air pollutants. For example, susceptibility to airway inflammation from exposure to particulate matter with aerodynamic diameter