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BackgroundAccess to water and sanitation is a basic human right; however, in many parts of the world, communities experience water, sanitation, and hygiene (WaSH) insecurity. While WaSH insecurity is prevalent in many low and middle-income countries, it is also a problem in high-income countries, like the United States, as is evident in vulnerable populations, including people experiencing homelessness. Limited knowledge exists about the coping strategies unhoused people use to access WaSH services. This study, therefore, examines WaSH access among unhoused communities in Los Angeles, California, a city with the second-highest count of unhoused people across the nation.MethodsWe conducted a cross-sectional study using a snowball sampling technique with 263 unhoused people living in Skid Row, Los Angeles. We calculated frequencies and used multivariable models to describe (1) how unhoused communities cope and gain access to WaSH services in different places, and (2) what individual-level factors contribute to unhoused people's ability to access WaSH services.ResultsOur findings reveal that access to WaSH services for unhoused communities in Los Angeles is most difficult at night. Reduced access to overnight sanitation resulted in 19% of the sample population using buckets inside their tents and 28% openly defecating in public spaces. Bottled water and public taps are the primary drinking water source, but 6% of the sample reported obtaining water from fire hydrants, and 50% of the population stores water for night use. Unhoused people also had limited access to water and soap for hand hygiene throughout the day, with 17% of the sample relying on hand sanitizer to clean their hands. Shower and laundry access were among the most limited services available, and reduced people's ability to maintain body hygiene practices and limited employment opportunities. Our regression models suggest that WaSH access is not homogenous among the unhoused. Community differences exist; the odds of having difficulty accessing sanitation services is two times greater for those living outside of Skid Row (Adj OR: 2.52; 95% CI: 1.08-6.37) and three times greater for people who have been unhoused for more than six years compared to people who have been unhoused for less than a year (Adj OR: 3.26; 95% CI: 1.36-8.07).ConclusionOverall, this study suggests a need for more permanent, 24-h access to WaSH services for unhoused communities living in Skid Row, including toilets, drinking water, water and soap for hand hygiene, showers, and laundry services.

Acute lymphoblastic leukemia (ALL) is the most common type of cancer in children (age 0-14 years); however, the etiology remains incompletely understood. Several environmental exposures have been linked to risk of childhood ALL, including air pollution. Closely related to air pollution and human development is artificial light at night (ALAN), which is believed to disrupt circadian rhythm and impact health. We sought to evaluate outdoor ALAN and air pollution on risk of childhood ALL. The California Linkage Study of Early-Onset Cancers is a large population-based case-control in California that identifies and links cancer diagnoses from the California Cancer Registry to birth records. For each case, 50 controls with the same year of birth were obtained from birth records. A total of 2,782 ALL cases and 139,100 controls were identified during 2000-2015. ALAN was assessed with the New World Atlas of Artificial Night Sky Brightness and air pollution with an ensemble-based air pollution model of particulate matter smaller than 2.5 microns (PM2.5). After adjusting for known and suspected risk factors, the highest tertile of ALAN was associated with an increased risk of ALL in Hispanic children (odds ratio [OR] = 1.15, 95% confidence interval [CI] 1.01-1.32). There also appeared to be a borderline association between PM2.5 level and risk of ALL among non-Hispanic White children (OR per 10 µg/m3 = 1.24, 95% CI 0.98-1.56). We observed elevated risk of ALL in Hispanic children residing in areas of greater ALAN. Further work is needed to understand the role of ALAN and air pollution in the etiology of childhood ALL in different racial/ethnic groups.

Only two-thirds of Americans meet the recommended 7 hours of sleep nightly. Insufficient sleep and circadian disruption have been associated with adverse health outcomes, including diabetes and cardiovascular disease. Several environmental disruptors of sleep have been reported, such as artificial light at night (ALAN) and noise. These studies tended to evaluate exposures individually. We evaluated several spatially derived environmental exposures (ALAN, noise, green space, and air pollution) and self-reported sleep outcomes obtained in 2012-2015 in a large cohort of 51,562 women in the California Teachers Study. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for sleep duration and latency. After adjusting for age, race/ethnicity, chronotype, use of sleep medication, and self-reported trouble sleeping, ALAN (per 5 millicandela (mcd)/m2 luminance, OR = 1.13, 95% CI: 1.07, 1.20) and air pollution (per 5 μg/m3 PM2.5, OR = 1.06, 95% CI: 1.04, 1.09) were associated with shorter sleep duration (15 minutes) (per 10 decibels, OR = 1.05, 95% CI: 1.01, 1.10). Green space was associated with increased duration (per 0.1 units, OR = 0.41, 95% CI: 0.28, 0.60) and decreased latency (per 0.1 units, OR = 0.55, 95% CI: 0.39, 0.78). Further research is necessary to understand how these and other exposures (e.g., diet) perturb an individuals' inherited sleep patterns and contribute to downstream health outcomes.

Implementation of regulatory standards has reduced exhaust emissions of particulate matter from road traffic substantially in the developed world. However, nonexhaust particle emissions arising from the wear of brakes, tires, and the road surface, together with the resuspension of road dust, are unregulated and exceed exhaust emissions in many jurisdictions. While knowledge of the sources of nonexhaust particles is fairly good, source-specific measurements of airborne concentrations are few, and studies of the toxicology and epidemiology do not give a clear picture of the health risk posed. This paper reviews the current state of knowledge, with a strong focus on health-related research, highlighting areas where further research is an essential prerequisite for developing focused policy responses to nonexhaust particles.

IntroductionEstimating 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.MethodsUsing 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.ResultsEnsemble 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 (

BackgroundPrior studies suggest exposure to oil and gas development (OGD) adversely affects birth outcomes, but no studies have examined flaring-the open combustion of natural gas-from OGD.ObjectivesWe investigated whether residential proximity to flaring from OGD was associated with shorter gestation and reduced fetal growth in the Eagle Ford Shale of south Texas.MethodsWe conducted a retrospective cohort study using administrative birth records from 2012 to 2015 (N=23,487) and satellite observations of flaring activity during pregnancy within 5km of maternal residence. Multivariate logistic and linear regression models were used to estimate associations between four outcomes (preterm birth, small-for-gestational age, continuous gestational age, and term birthweight) and exposure to a low (1-9) or high (≥10) number of nightly flare events, as compared with no exposure, while controlling for known maternal risk factors. We also examined associations with the number of oil and gas wells within 5km using data from DrillingInfo (now Enverus).ResultsExposure to a high number of nightly flare events was associated with a 50% higher odds of preterm birth [odds ratio (OR)=1.50 (95% CI: 1.23, 1.83)] and shorter gestation [mean difference=-1.9 (95% CI: -2.8, -0.9) d] compared with no exposure. Effect estimates were slightly reduced after adjustment for the number of wells within 5km. In stratified models these associations were present only among Hispanic women. Flaring and fetal growth outcomes were not significantly associated. Women exposed to a high number of wells (fourth quartile, ≥27) vs. no wells within 5km had a higher odds of preterm birth [OR=1.31 (95% CI: 1.14, 1.49)], shorter gestation [-1.3 (95% CI: -1.9, -0.8) d], and lower average birthweight [-19.4 (95% CI: -36.7, -2.0) g].DiscussionOur study suggests exposure to flaring from OGD is associated with an increased risk of preterm birth. Our findings need to be confirmed in other populations. https://doi.org/10.1289/EHP6394.

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78-0.81; mean RMSE = 0.013-0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications.

BackgroundAccurate 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.ObjectivesIn 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.MethodsWe 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.ResultsWe 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.ConclusionsOur 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.

The climate-violence relationship has been debated for decades, and yet most of the supportive evidence has come from ecological or cross-sectional analyses with very limited long-term exposure data. We conducted an individual-level, longitudinal study to investigate the association between ambient temperature and externalizing behaviors of urban-dwelling adolescents. Participants (n = 1,287) in the Risk Factors for Antisocial Behavior Study, in California, were examined during 2000-2012 (aged 9-18 years) with repeated assessments of their externalizing behaviors (e.g., aggression, delinquency). Ambient temperature data were obtained from the local meteorological information system. In adjusted multilevel models, aggressive behaviors significantly increased with rising average temperatures (per 1°C increment) in the preceding 1, 2, or 3 years (respectively, β = 0.23, 95% confidence interval (CI): 0.00, 0.46; β = 0.35, 95% CI: 0.06, 0.63; or β = 0.41, 95% CI: 0.08, 0.74), equivalent to 1.5-3.0 years of delay in age-related behavioral maturation. These associations were slightly stronger among girls and families of lower socioeconomic status but greatly diminished in neighborhoods with more green space. No significant associations were found with delinquency. Our study provides the first individual-level epidemiologic evidence supporting the adverse association of long-term ambient temperature and aggression. Similar approaches to studying meteorology and violent crime might further inform scientific debates on climate change and collective violence.

Introduction Secondhand smoke (SHS) exposure during pregnancy is linked to adverse birth outcomes, such as low birth weight and preterm birth. While questionnaires are commonly used to assess SHS exposure, their ability to capture true exposure can vary, making it difficult for researchers to harmonize SHS measures. This study aimed to compare self-reported SHS exposure with measurements of airborne SHS in personal samples of pregnant women. Methods SHS was measured on 48-hour integrated personal PM2.5 Teflon filters collected from 204 pregnant women, and self-reported SHS exposure measures were obtained via questionnaires. Descriptive statistics were calculated for airborne SHS measures, and analysis of variance tests assessed group differences in airborne SHS concentrations by self-reported SHS exposure. Results Participants were 81% Hispanic, with a mean (standard deviation [SD]) age of 28.2 (6.0) years. Geometric mean (SD) personal airborne SHS concentrations were 0.14 (9.41) µg/m3. Participants reporting lower education have significantly higher airborne SHS exposure (p  = .015). Mean airborne SHS concentrations were greater in those reporting longer duration with windows open in the home. There was no association between airborne SHS and self-reported SHS exposure; however, asking about the number of smokers nearby in the 48-hour monitoring period was most correlated with measured airborne SHS (Two + smokers: 0.30 µg/m3 vs. One: 0.12 µg/m3 and Zero: 0.15 µg/m3; p  = .230). Conclusions Self-reported SHS exposure was not associated with measured airborne SHS in personal PM2.5 samples. This suggests exposure misclassification using SHS questionnaires and the need for harmonized and validated questions to characterize this exposure in health studies. Implications This study adds to the growing body of evidence that measurement error is a major concern in pregnancy research, particularly in studies that rely on self-report questionnaires to measure SHS exposure. The study introduces an alternative method of SHS exposure assessment using objective optical measurements, which can help improve the accuracy of exposure assessment. The findings emphasize the importance of using harmonized and validated SHS questionnaires in pregnancy health research to avoid biased effect estimates. This study can inform future research, practice, and policy development to reduce SHS exposure and its adverse health effects. [ABSTRACT FROM AUTHOR]

Animal experiments and cross-sectional human studies have linked particulate matter (PM) with increased behavioral problems. We conducted a longitudinal study to examine whether the trajectories of delinquent behavior are affected by PM2.5 (PM with aerodynamic diameter ≤ 2.5 μm) exposures before and during adolescence. We used the parent-reported Child Behavior Checklist at age 9-18 with repeated measures every ~2-3 years (up to 4 behavioral assessments) on 682 children from the Risk Factors for Antisocial Behavior Study conducted in a multi-ethnic cohort of twins born in 1990-1995. Based on prospectively-collected residential addresses and a spatiotemporal model of ambient air concentrations in Southern California, monthly PM2.5 estimates were aggregated to represent long-term (1-, 2-, 3-year average) exposures preceding baseline and cumulative average exposure until the last assessment. Multilevel mixed-effects models were used to examine the association between PM2.5 exposure and individual trajectories of delinquent behavior, adjusting for within-family/within-individual correlations and potential confounders. We also examined whether psychosocial factors modified this association. The results sμggest that PM2.5 exposure at baseline and cumulative exposure during follow-up was significantly associated (p

Mounting evidence indicates that early-life exposure to particulate air pollutants pose threats to children's cognitive development, but studies about the neurotoxic effects associated with exposures during adolescence remain unclear. We examined whether exposure to ambient fine particles (PM2.5) at residential locations affects intelligence quotient (IQ) during pre-/early- adolescence (ages 9-11) and emerging adulthood (ages 18-20) in a demographically-diverse population (N = 1,360) residing in Southern California. Increased ambient PM2.5 levels were associated with decreased IQ scores. This association was more evident for Performance IQ (PIQ), but less for Verbal IQ, assessed by the Wechsler Abbreviated Scale of Intelligence. For each inter-quartile (7.73 μg/m3) increase in one-year PM2.5 preceding each assessment, the average PIQ score decreased by 3.08 points (95% confidence interval = [-6.04, -0.12]) accounting for within-family/within-individual correlations, demographic characteristics, family socioeconomic status (SES), parents' cognitive abilities, neighborhood characteristics, and other spatial confounders. The adverse effect was 150% greater in low SES families and 89% stronger in males, compared to their counterparts. Better understanding of the social disparities and sexual dimorphism in the adverse PM2.5-IQ effects may help elucidate the underlying mechanisms and shed light on prevention strategies.

ObjectiveNeighborhood greenspace improves mental health of urban-dwelling populations, but its putative neurobehavioral benefits in adolescents remain unclear. We conducted a prospective study on urban-dwelling adolescents to examine the association between greenspace in residential neighborhood and aggressive behaviors.MethodParticipants (n = 1,287) of the Risk Factors for Antisocial Behavior Study, a multi-ethnic cohort of twins and triplets born in 1990 to 1995 and living in Southern California, were examined in 2000 to 2012 (aged 9-18 years) with repeated assessments of their aggressive behaviors by the parent-reported Child Behavior Checklist. Normalized Difference Vegetation Index (NDVI) derived from satellite imagery was used as a proxy for residential neighborhood greenspace aggregated over various spatiotemporal scales before each assessment. Multilevel mixed-effects models were used to estimate the effects of greenspace on aggressive behaviors, adjusting for within-family/within-individual correlations and other potential confounders.ResultsBoth short-term (1- to 6-month) and long-term (1- to 3-year) exposures to greenspace within 1,000 meters surrounding residences were associated with reduced aggressive behaviors. The benefit of increasing vegetation over the range (∼0.12 in NDVI) commonly seen in urban environments was equivalent to approximately 2 to 2.5 years of behavioral maturation. Sociodemographic factors (e.g., age, gender, race/ethnicity, and socioeconomic status) and neighborhood quality did not confound or modify these associations, and the benefits remained after accounting for temperature.ConclusionOur novel findings support the benefits of neighborhood greenspace in reducing aggressive behaviors of urban-dwelling adolescents. Community-based interventions are needed to determine the efficacy of greenspace as a preemptive strategy to reduce aggressive behaviors in urban environments.