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Air pollution is a major public health threat that is associated with asthma, cardiovascular disease, respiratory disease and all-cause mortality. Among the most important acute air pollution events occurring each year are celebrations involving fireworks, such as the 4th of July holiday in the United States. In this community-engaged study, academic partners and residents collaborated to collect indoor and outdoor PM2.5 concentration measurements in the disadvantaged city of Santa Ana, California, using low-cost AtmoTube sensor devices before, during and after the July 4th firework celebration, while also examining July 4th data extracted from the PurpleAir sensor network across over a hundred other cities in southern California. Average outdoor PM2.5 concentrations on July 4th were found to be three-to-five times higher than baseline, with hourly concentrations exceeding 160 μg/m3. Outdoor averages were roughly 30% to 100% higher than indoor levels. The most polluted cities exhibited 15-times higher PM2.5 levels compared with the least contaminated cities and were often those where household-level fireworks were legal for sale and use. Race/ethnicity was found to be the leading predictor of July 4th-related air pollution across three counties in southern California, with greater PM2.5 being associated with higher proportions of Hispanic residents and lower proportions of White residents. The findings from this study underscore the importance of environmental justice as it relates to firework-related air pollution exposure, and the critical role city- and county-level firework policies play in determining exposure. [ABSTRACT FROM AUTHOR]

Major wildfires and heatwaves have begun to increase in frequency throughout much of the United States, particularly in western states such as California, causing increased risk to public health. Air pollution is exacerbated by both wildfires and warmer temperatures, thus adding to such risk. With climate change and the continued increase in global average temperatures, the frequency of major wildfires, heat days, and unhealthy air pollution episodes is projected to increase, resulting in the potential for compounding risks. Risks will likely vary by region and may disproportionately impact low-income communities and communities of color. In this study, we processed daily particulate matter (PM) data from over 18,000 low-cost PurpleAir sensors, along with gridMET daily maximum temperature data and government-compiled wildfire perimeter data from 2018–2020 in order to examine the occurrence of compound risk (CR) days (characterized by high temperature and high PM2.5) at the census tract level in California, and to understand how such days have been impacted by the occurrence of wildfires. Using American Community Survey data, we also examined the extent to which CR days were correlated with household income, race/ethnicity, education, and other socioeconomic factors at the census tract level. Results showed census tracts with a higher frequency of CR days to have statistically higher rates of poverty and unemployment, along with high proportions of child residents and households without computers. The frequency of CR days and elevated daily PM2.5 concentrations appeared to be strongly related to the occurrence of nearby wildfires, with over 20% of days with sensor-measured average PM2.5 > 35 μg/m3 showing a wildfire within a 100 km radius and over two-thirds of estimated CR days falling on such days with a nearby wildfire. Findings from this study are important to policymakers and government agencies who preside over the allocation of state resources as well as organizations seeking to empower residents and establish climate resilient communities. [ABSTRACT FROM AUTHOR]

Insolvent firms usually file for formal bankruptcy protection under either liquidation or reorganization. Reorganization aims to save viable failing firms whereas liquidation focuses on filtering out unviable failing firms. This paper theoretically and empirically investigates the determinants of formal bankruptcy resolution. We present a concise theory to reveal the theoretical boundary between liquidation and reorganization, which reflects how industry characteristics, judicial bias, and firm characteristics affect the outcome of bankruptcy resolution. By using the commercial bankruptcy data on US courts (2000–2016), we validate the proposed theory. In empirical tests, we deploy discrete-choice models to address the main predictions derived from theory and conduct robustness checks (e.g. placebo test). We document that firms are more likely to be reorganized when their industry is experiencing prosperity. Firms in asset-heavy industries (e.g. hotels, mining, and oil) tend to be reorganized. Formal resolution of bankruptcy cases handled by courts in Alaska and Hawaii are more likely to be reorganization than is the case in other states; however, firms that file bankruptcy petitions in California courts are more likely to face liquidation. Finally, larger and more transparent firms are more likely to be reorganized. [ABSTRACT FROM AUTHOR]

Inhaled particles and gases can harm health by promoting chronic inflammation in the body. Few studies have investigated the relationship between outdoor air pollution and inflammation by race and ethnicity, socioeconomic status, and lifestyle risk factors. We examined associations of particulate matter (PM) and other markers of traffic-related air pollution with circulating levels of C-reactive protein (CRP), a biomarker of systemic inflammation. CRP was measured from blood samples obtained in 1994–2016 from 7,860 California residents participating in the Multiethnic Cohort (MEC) Study. Exposure to PM (aerodynamic diameter ≤2.5 μm [PM 2.5 ], ≤10 μm [PM 10 ], and between 2.5 and 10 μm [PM 10-2.5 ]), nitrogen oxides (NO x , including nitrogen dioxide [NO 2 ]), carbon monoxide (CO), ground-level ozone (O 3), and benzene averaged over one or twelve months before blood draw were estimated based on participants' addresses. Percent change in geometric mean CRP levels and 95% confidence intervals (CI) per standard concentration increase of each pollutant were estimated using multivariable generalized linear regression. Among 4,305 females (55%) and 3,555 males (45%) (mean age 68.1 [SD 7.5] years at blood draw), CRP levels increased with 12-month exposure to PM 10 (11.0%, 95% CI: 4.2%, 18.2% per 10 μg/m3), PM 10-2.5 (12.4%, 95% CI: 1.4%, 24.5% per 10 μg/m3), NO x (10.4%, 95% CI: 2.2%, 19.2% per 50 ppb), and benzene (2.9%, 95% CI: 1.1%, 4.6% per 1 ppb). In subgroup analyses, these associations were observed in Latino participants, those who lived in low socioeconomic neighborhoods, overweight or obese participants, and never or former smokers. No consistent patterns were found for 1-month pollutant exposures. This investigation identified associations of primarily traffic-related air pollutants, including PM, NO x , and benzene, with CRP in a multiethnic population. The diversity of the MEC across demographic, socioeconomic, and lifestyle factors allowed us to explore the generalizability of the effects of air pollution on inflammation across subgroups. [Display omitted] • Traffic-related air pollutants were associated with C-reactive protein in a multiethnic population. • Associations with C-reactive protein were stronger for 12- versus 1-month pollutant exposures. • Some differences were seen by demographic, socioeconomic, and lifestyle factors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective To determine the risk of gestational diabetes (GDM) and preeclampsia associated with various community resources. Methods An ecological study was performed in Los Angeles and Orange counties in California. Fast food restaurants, supermarkets, grocery stores, gyms, health clubs and green space were identified using Google © Maps Extractor and through the Southern California Association of Government. California Birth Certificate data was used to identify cases of GDM and preeclampsia. Unadjusted and adjusted risk ratios were calculated using negative binomial regression. Results There were 9692 cases of GDM and 6288 cases of preeclampsia corresponding to incidences of 2.5 and 1.4 % respectively. The adjusted risk of GDM was reduced in zip codes with greater concentration of grocery stores [relative risk (RR) 0.95, 95 % confidence interval (CI) 0.92-0.99] and supermarkets (RR 0.94, 95 % CI 0.90-0.98). There were no significant relationships between preeclampsia and the concentration of fast food restaurants, grocery store, supermarkets or the amount of green space. Conclusion The distribution of community resources has a significant association with the risk of developing GDM but not preeclampsia. [ABSTRACT FROM AUTHOR]

Background: The incidence of both gestational diabetes mellitus and preeclampsia is on the rise; however, these pregnancy complications may not be systematically reported. This study aimed to examine differences in reporting of preeclampsia and gestational diabetes between hospital records and birth certificate data, and to determine if such differences vary by maternal socioeconomic status indicators.Methods: We obtained over 70,000 birth records from 2001 to 2006 from the perinatal research database of the Memorial Care system, a network of four hospitals in Los Angeles and Orange Counties, California. Memorial birth records were matched to corresponding state birth certificate records and analyzed to determine differential rates of reporting of preeclampsia and diabetes. Additionally, the influence of maternal socioeconomic factors on the reported incidence of such adverse pregnancy outcomes was analyzed. Socioeconomic factors of interest included maternal education levels, race, and type of health insurance (private or public).Results: It was found that the birth certificate data significantly underreported the incidence of both preeclampsia (1.38% vs. 3.13%) and diabetes (1.97% vs. 5.56%) when compared to Memorial data. For both outcomes of interest, the degree of underreporting was significantly higher among women with lower education levels, among Hispanic women compared to Non-Hispanic White women, and among women with public health insurance.Conclusion: The Memorial Care database is a more reliable source of information than birth certificate data for analyzing the incidence of preeclampsia and diabetes among women in Los Angeles and Orange Counties, especially for subpopulations of lower socioeconomic status. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability.Methods: We obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight.Results: Higher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced exposure or higher socioeconomic status with lower vulnerability.Conclusions: Our Bayesian models effectively combined a priori knowledge with training data to infer the posterior association of air pollution with term birth weight and to evaluate the influence of the tract-level factors on spatial variability of such association. This study contributes new findings about non-linear influences of socio-demographic factors, land-use patterns, and unaccounted exposures on spatial variability of the effects of air pollution. [ABSTRACT FROM AUTHOR]

Abstract: Lead (Pb) poisoning causes permanent neurologic and developmental disorders and remains an important environmental health problem for US children, despite removal of Pb from gasoline and household paints. To better understand the contribution of Pb from historical traffic and residential Pb based paint to soil Pb concentrations in Los Angeles, we analyzed 550 soil Pb samples from south central Los Angeles County, CA, in relation to land-use patterns (commercial, industrial, residential, and parks and open areas) and proximity to freeways, highways, and major arterials. House age variables (surrogates of historical Pb-based paint) and traffic index variables (surrogates of historical traffic) were created at different buffer distances (10–5000m). Total and bioavailable Pb concentrations near freeways and major arterials were significantly higher than those collected elsewhere. Total and bioavailable Pb concentrations were highly correlated (r=0.96) after the removal of one outlier. Both parcel-age related variables and traffic variables were important predictors of current soil bioavailable Pb concentrations. Average age of parcels within 30m and length of small streets within 3000m explained 57% and 38% of the variance, respectively, in soil bioavailable Pb concentrations in residential areas away from freeways and major arterials (N=44). Road length of freeways within 750m explained 28% of bioavailable Pb concentrations in parks and open areas (N=26). Multi-variable regression models predicted 16–61% of the variances in bioavailable Pb concentrations, depending on land-use type and spatial relationship to roadways. Based on these models a map of spatial distributions of soil Pb concentrations was created for the Los Angeles area that shows promise as a screening tool to evaluate continued Pb poisoning in children. [Copyright &y& Elsevier]

The Ports of Los Angeles and Long Beach are the entry point for almost half of all cargo containers entering the United States. The use of diesel trucks to move Port-related goods has raised significant public health concerns associated with black carbon and other air pollutants. It is difficult to reliably estimate people''s exposure to vehicle-related pollutants due to the narrow impact zone of traffic, usually within 200–300 m downwind of major roadways. Previous studies suffer from the lack of traffic count data on surface streets and the lack of neighborhood-level population data. We examined seasonal and annual average exposures of particulate matter less than 2.5 μm (PM2.5) and elemental carbon (EC) at a neighborhood scale for communities heavily impacted by diesel trucks near these ports. We assembled a traffic-activity database that distinguishes gasoline and diesel vehicles on both freeways and surface streets, by consolidating information from several sources, including our own field measurements. The CALINE4 model was used to estimate residential exposure of the study population to PM2.5 and EC. Parcel property data were used to allocate Census block group (BG) population to increase spatial resolution. The annual average PM2.5 and EC exposure due to local traffic was 3.8 and 0.4 μg m−3, respectively. On average, surface streets contributed a little more than freeways (55% vs. 45% for EC and 57% and 43% for PM2.5). Light-duty vehicles contributed significantly more than heavy-duty trucks for PM2.5 (61% vs. 39%), but slightly less than heavy-duty trucks for EC (49% vs. 51%). Community mean population exposure was similar using parcel, census block, and BG population data, but extreme values and standard deviations varied significantly at different spatial resolutions. The intake fraction for the study population was in the range of 1.0–2.2 × 10−5 by vehicle type, roadway type, and season. [Copyright &y& Elsevier]

Objectives. We assessed child care facilities' proximity to heavily traveled roadways in an attempt to estimate the extent of potential exposure of young children to vehicle-related pollution in this understudied microenvironment. Methods. We examined approximately 24000 licensed child care facilities in California located within 200 m of heavily traveled roadways. Results. Approximately 57 000 of the available slots in California child care centers (7% of the overall capacity) are in facilities located within 200 m (650 ft) of roadways averaging 50000 or more vehicles per day, and another 172000 (21%) are in facilities located within 200 m of roadways averaging 25000 to 49000 vehicles per day. Facilities providing care to infants or preschool-aged children and facilities located in disadvantaged areas were more often situated in mediumor high-traffic areas. Conclusions. Additional research is needed to further clarify the significance of the child care microenvironment in terms of potential childhood exposures to vehicle-related pollutants. Design strategies, notification standards, and distance-based siting restrictions should be considered in the facility licensing process and in land use and transportation planning. (Am J Public Health. 2006;96: 1611-1617. doi:10.2105/AJPH.2005.077727) [ABSTRACT FROM AUTHOR]

• Wildfires contribute 92 % of Naphthalene, 82 % of PM_PAH emissions in study period. • Model performance improved for PM2.5 and PAHs prediction with proposed method. • PM_PAH impacts broader areas, while naphthalene's effect is localized to fire zones. • In fire season, 32 % (particles) and 10 % (gas) of PAH exposure linked to wildfires. In recent years, wildfires in California have increased in frequency and intensity due to climate change and prolonged drought. The air pollutants released by wildfires cause significant health consequences, among which polycyclic aromatic hydrocarbons (PAHs) are particularly toxic. Estimating PAH emissions from wildfires is challenging due to variability in vegetation types. In this study, we estimate PAH emission rates across California at a high resolution, based on laboratory-measured PAH emission rates from 22 different vegetation types and detailed vegetation mapping. By combining these estimates with biomass burning data from the NCAR Fire Inventory, the Community Multiscale Air Quality Modeling System simulates PAH concentrations for the 2017 fire season. The modeling results compare favorably to measurements from three PAH monitoring sites in California. The peak PAH emissions from wildfire events are up to be 80 times higher in the gas phase and 32 times higher in the particle phase compared to a case without fire emissions. The population-weighted PAH concentrations from the fire case (0.053 µg/m3) are 47 % higher compared to a non-fire case (0.036 µg/m3) in the particle phase and 11 % higher in the gas phase (9.82 ppt compared to 8.83 ppt) during the study period. While highly depended on the meteorological condition, the simulated spatial distribution indicates that gas-phase PAHs are less likely to travel long distances from the fire source and are prone to aging into the particle phase during transport. Consequently, populations are more likely to be exposed to particle-phase PAHs during wildfire events. This finding has important implications for understanding the health impacts of wildfire-induced PAH concentrations, as particle-phase PAHs may have different toxicological effects compared to gas-phase PAHs. [ABSTRACT FROM AUTHOR]

Prior studies suggested that air pollution exposure may increase the risk of Parkinson's Disease (PD). We investigated the long-term impacts of traffic-related and multiple sources of particulate air pollution on PD in central California. Our case-control analysis included 761 PD patients and 910 population controls. We assessed exposure at residential and occupational locations from 1981 to 2016, estimating annual average carbon monoxide (CO) concentrations - a traffic pollution marker - based on the California Line Source Dispersion Model, version 4. Additionally, particulate matter (PM 2.5) concentrations were based on a nationwide geospatial chemical transport model. Exposures were assessed as 10-year averages with a 5-year lag time prior to a PD diagnosis for cases and an interview date for controls, subsequently categorized into tertiles. Logistic regression models were used, adjusting for various factors. Traffic-related CO was associated with an increased odds ratio for PD at residences (OR for T3 vs. T1: 1.58; 95% CI: 1.20, 2.10; p -trend = 0.02) and workplaces (OR for T3 vs. T1: 1.91; 95% CI: 1.22, 3.00; p -trend <0.01). PM 2.5 was also positively associated with PD at residences (OR for T3 vs. T1: 1.62; 95% CI: 1.22, 2.15; p -trend <0.01) and workplaces (OR for T3 vs. T1: 1.85; 95% CI: 1.21, 2.85; p -trend <0.01). Associations remained robust after additional adjustments for smoking status and pesticide exposure and were consistent across different exposure periods. We found that long-term modeled exposure to local traffic-related air pollution (CO) and fine particulates from multiple sources (PM 2.5) at homes and workplaces in central California was associated with an increased risk of PD. [Display omitted] • We investigated impacts of modeled 10-year air pollution exposure on PD. • CO as a marker of traffic-related exposure was associated with increased PD risk. • PM 2.5 exposure from multiple sources was also associated with increased PD risk. • Effect estimates were larger for workplace than residential address exposures. • Smoking and pesticide exposure adjustment did not change air pollution associations. [ABSTRACT FROM AUTHOR]

Associations between residential greenness and improved birth weight have been increasingly reported, but underlying mechanisms and interactions with other environmental exposures are still unclear. To study the relationships between low birth weight (LBW, <2500 g), residential greenness, and the potential influence of air pollution in these relationships (interaction and mediation) in California, over the period 2001–2008. Residential greenness around maternal homes was characterized using the Normalized Difference Vegetation Index (NDVI). Complementary indicators of air pollution exposure reflected its main components. Birth weight and maternal characteristics were obtained from birth certificate records. In this case-cohort study, associations between greenness and LBW were investigated using multi-level Poisson regression with random effect at the hospital level. We investigated potential interaction of greenness and air pollutants on both additive and multiplicative scales. Mediation analyses were conducted to estimate the potential contribution of local variations in air pollutant concentrations associated with greenness on LBW risk. In total 72,632 LBW cases were included. A reduction of LBW risk was associated with an increase in NDVI (adjusted risk ratio per inter-quartile range in NDVI: 0.963; 95% confidence interval: 0.947; 0.978). We observed no interaction between NDVI and air pollution on LBW risk. The estimated mediating effect of fine particulate matter in the impact of greenness on LBW was 12%. This large study confirms that residential greenness is associated with a reduced risk of LBW and suggests that greenness might benefit to LBW partly through a local reduction in air pollution. • Residential greenness is associated with a reduced risk of low birth weight. • This association is stronger in socioeconomically deprived population. • This association is observed in urban but not rural areas. • There is no significant interaction between greenness and air pollution on risk. • Greenness might reduce risk partly through a local reduction in particle exposure. [ABSTRACT FROM AUTHOR]

Accurate estimation of nitrogen dioxide (NO 2) and nitrogen oxide (NO x) 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 NO 2 and NO x 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. In this study, we aim to develop highly accurate and adaptive machine learning models to predict high-resolution NO 2 and NO x concentrations over large geographic regions using measurements from different sources that contain samples with heterogeneous spatiotemporal distributions and clustering patterns. 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 NO 2 and NO x at the state and local levels. 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 NO 2 and NO x [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. 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 NO 2 and NO x at high resolution over a large region. • Challenge of fusing multi-source and heterogeneous data for NO x estimation in large regions. • Cluster-based bagging to reduce bias in training samples and errors in prediction. • Grid search to find optimal combination of parameters with best model performance. • High spatiotemporal resolution NO 2 and NO x estimation with high accuracy. • Robust model generalization based on independent tests. [ABSTRACT FROM AUTHOR]

Though fine particulate matter (PM 2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM 2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM 2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM 2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM 2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM 2.5 presents a challenge since measurements of the smoke contribution to PM 2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM 2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM 2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM 2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM 2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM 2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies. [ABSTRACT FROM AUTHOR]

Introduction Intrauterine growth restriction has been associated with exposure to air pollution, but there is a need to clarify which sources and components are most likely responsible. This study investigated the associations between low birth weight (LBW, < 2500 g) in term born infants (≥ 37 gestational weeks) and air pollution by source and composition in California, over the period 2001–2008. Methods Complementary exposure models were used: an empirical Bayesian kriging model for the interpolation of ambient pollutant measurements, a source-oriented chemical transport model (using California emission inventories) that estimated fine and ultrafine particulate matter (PM 2.5 and PM 0.1 , respectively) mass concentrations (4 km × 4 km) by source and composition, a line-source roadway dispersion model at fine resolution, and traffic index estimates. Birth weight was obtained from California birth certificate records. A case-cohort design was used. Five controls per term LBW case were randomly selected (without covariate matching or stratification) from among term births. The resulting datasets were analyzed by logistic regression with a random effect by hospital, using generalized additive mixed models adjusted for race/ethnicity, education, maternal age and household income. Results In total 72,632 singleton term LBW cases were included. Term LBW was positively and significantly associated with interpolated measurements of ozone but not total fine PM or nitrogen dioxide. No significant association was observed between term LBW and primary PM from all sources grouped together. A positive significant association was observed for secondary organic aerosols. Exposure to elemental carbon (EC), nitrates and ammonium were also positively and significantly associated with term LBW, but only for exposure during the third trimester of pregnancy. Significant positive associations were observed between term LBW risk and primary PM emitted by on-road gasoline and diesel or by commercial meat cooking sources. Primary PM from wood burning was inversely associated with term LBW. Significant positive associations were also observed between term LBW and ultrafine particle numbers modeled with the line-source roadway dispersion model, traffic density and proximity to roadways. Discussion This large study based on complementary exposure metrics suggests that not only primary pollution sources (traffic and commercial meat cooking) but also EC and secondary pollutants are risk factors for term LBW. [ABSTRACT FROM AUTHOR]

Objective: Neighborhood 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.Method: Participants (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.Results: Both 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.Conclusion: Our 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. [ABSTRACT FROM AUTHOR]

[Display omitted] Within the Multiethnic Cohort (MEC), we examined the association between air pollution and mortality among African American, European American, Japanese American, and Latina American women diagnosed with breast cancer. We used a land use regression (LUR) model and kriging interpolation to estimate nitrogen oxides (NO x , NO 2) and particulate matter (PM 2.5 , PM 10) exposures for 3,089 breast cancer cases in the MEC, who were diagnosed from 1993 through 2013 and resided largely in Los Angeles County, California. Cox proportional hazards models were used to examine the association of time-varying air pollutants with all-cause, breast cancer, cardiovascular disease (CVD), and non-breast cancer/non-CVD mortality, accounting for key covariates. We identified 1,125 deaths from all causes (474 breast cancer, 272 CVD, 379 non-breast cancer/non-CVD deaths) among the 3,089 breast cancer cases with 8.1 years of average follow-up. LUR and kriged NO X (per 50 ppb) and NO 2 (per 20 ppb), PM 2.5 (per 10 µg/m3), and PM 10 (per 10 µg/m3) were positively associated with risks of all-cause (Hazard Ratio (HR) range = 1.13–1.25), breast cancer (HR range = 1.19–1.45), and CVD mortality (HR range = 1.37–1.60). Associations were statistically significant for LUR NO X and CVD mortality (HR = 1.60; 95% CI: 1.08–2.37) and kriged NO 2 and breast cancer mortality (HR = 1.45; 95% CI 1.02–2.07). Gaseous and PM pollutants were positively associated with breast cancer mortality across racial/ethnic group. In this study, air pollutants have a harmful impact on breast cancer survival. Additional studies should evaluate potential confounding by socioeconomic factors. These data support maintaining clean air laws to improve survival for women with breast cancer. [ABSTRACT FROM AUTHOR]

Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and childhood disorders. High-resolution metabolomics (HRM) has previously been employed to identify metabolic responses to traffic-related air pollution in adults, including pregnant women. Thus far, no studies have examined metabolic effects of air pollution exposure in utero on neonates. We retrieved stored neonatal blood spots for 241 children born in California between 1998 and 2007. These children were randomly selected from all California birth rolls to serve as birth-year matched controls for children with retinoblastoma identified from the California cancer registry for a case control study of childhood cancer. We estimated prenatal traffic-related air pollution exposure (particulate matter less than 2.5 μm (PM 2.5)) during the third-trimester using the California Line Source Dispersion Model, version 4 (CALINE4) based on residential addresses recorded at birth. We employed untargeted HRM to obtain metabolic profiles, and metabolites associated with air pollution exposure were identified using partial least squares (PLS) regression and linear regressions. Biological effects were characterized using pathway enrichment analyses adjusting for potential confounders including maternal age, race/ethnicity, and education. In total we extracted 4038 and 4957 metabolite features from neonatal blood spots in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 reverse phase columns (negative ion mode), respectively. After controlling for confounding factors, partial least square regression (Variable Importance in Projection (VIP) ≥ 2) selected 402 HILIC positive and 182 C18 negative features as statistically significantly associated with increasing third trimester PM 2.5 exposure. Using pathway enrichment analysis, we identified metabolites in oxidative stress and inflammation pathways as being altered, primarily involving lipid metabolism. The metabolite features and pathways associated with air pollution exposure in neonates suggest that maternal exposure during late pregnancy contributes to oxidative stress and inflammation in newborn children. [ABSTRACT FROM AUTHOR]

• California has high variability in PM 2.5 sources, meteorology and topography. • We used ensemble deep learning with multisource big data to improve PM 2.5 estimates. • We reliably imputed missing satellite AOD and fused wildfire dispersion estimates. • Our model achieved high PM 2.5 prediction performance with uncertainty estimates. 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. 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. Ensemble deep learning to predict PM 2.5 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 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/m3). 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. 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. [ABSTRACT FROM AUTHOR]

• Exposure to residential green space is associated with decreased risk of PTB. • Green space has stronger protective associations with PTB in the second trimester. • There are positive interactions between green space and air pollution on PTB. Recent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born < 37 completed gestational weeks). In addition, windows of susceptibility during pregnancy have not been explored and potential interactions of green space with air pollution exposures during pregnancy are still unclear. To evaluate the relationships between green space and PTB, identify windows of susceptibility, and explore potential interactions between green space and air pollution. Birth certificate records for all births in California (2001–2008) were obtained. The Normalized Difference Vegetation Index (NDVI) was used to characterized green space exposure. Gestational age was treated as a time-to-event outcome; Cox proportional hazard models were applied to estimate the association between green space exposure and PTB, moderately PTB (MPTB, gestational age < 35 weeks), and very PTB (VPTB, gestational age < 30 weeks), after controlling for maternal age, race/ethnicity, education, and median household income. Month-specific green space exposure was used to identify potential windows of susceptibility. Potential interactions between green space and air pollution [fine particulate matter < 2.5 µm (PM 2.5), nitrogen dioxide (NO 2), and ozone (O 3)] were examined on both additive and multiplicative scales. In total, 3,753,799 eligible births were identified, including 341,123 (9.09%) PTBs, 124,631 (3.32%) MPTBs, and 22,313 (0.59%) VPTBs. A reduced risk of PTB was associated with increases in residential NDVI exposure in 250 m , 500 m , 1000 m , and 2000 m buffers. In the 2000 m buffer, the association was strongest for VPTB [adjusted hazard ratio (HR) per interquartile range increase in NDVI: 0.959, 95% confidence interval (CI): 0.942–0.976)], followed by MPTB (HR = 0.970, 95% CI: 0.962–0.978) and overall PTB (HR = 0.972, 95% CI: 0.966–0.978). For PTB, green space during the 3rd − 5th gestational months had stronger associations than those in the other time periods, especially during the 4th gestational month (NDVI 2000 m : HR = 0.970, 95% CI: 0.965–0.975). We identified consistent positive additive and multiplicative interactions between decreasing green space and higher air pollution. This large study found that maternal exposure to residential green space was associated with decreased risk of PTB, MPTB, and VPTB, especially in the second trimester. There is a synergistic effect between low green space and high air pollution levels on PTB, indicating that increasing exposure to green space may be more beneficial for women with higher air pollution exposures during pregnancy. [ABSTRACT FROM AUTHOR]

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. • Massive non-random missingness limits satellite AOD applications. • Residual learning of deep network boosts training efficiency for AOD imputation. • Residual network reliably downscales coarse-scale reanalysis data. • Adjusted satellite AOD using elevation and coordinates better correlates with AERONET AOD. • Residual network generalizable to impute missing satellite or environmental data [ABSTRACT FROM AUTHOR]

Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and neurodevelopmental disorders. By utilizing high-resolution metabolomics (HRM), we investigated perturbations of the maternal serum metabolome in response to traffic-related air pollution to identify biological mechanisms. We retrieved stored mid-pregnancy serum samples from 160 mothers who lived in the Central Valley of California known for high air particulate levels. We estimated prenatal traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 μm) during first-trimester using the California Line Source Dispersion Model, version 4 (CALINE4) based on residential addresses recorded at birth. We used liquid chromatography-high resolution mass spectrometry to obtain untargeted metabolic profiles and partial least squares discriminant analysis (PLS-DA) to select metabolic features associated with air pollution exposure. Pathway analyses were employed to identify biologic pathways related to air pollution exposure. As potential confounders we included maternal age, maternal race/ethnicity, and maternal education. In total we extracted 4038 and 4957 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for confounding factors, PLS-DA (Variable Importance in Projection (VIP) ≥2) yielded 181 and 251 metabolic features (HILIC and C18, respectively) that discriminated between the high (n = 98) and low exposed (n = 62). Pathway enrichment analysis for discriminatory features associated with air pollution indicated that in maternal serum oxidative stress and inflammation related pathways were altered, including linoleate, leukotriene, and prostaglandin pathways. The metabolomic features and pathways we found to be associated with air pollution exposure suggest that maternal exposure during pregnancy induces oxidative stress and inflammation pathways previously implicated in pregnancy complications and adverse outcomes. • High-resolution metabolomics detected metabolic signals associated with air pollution in maternal mid-pregnancy serum. • High levels of air pollution in early pregnancy affected oxidative stress and inflammatory activities. • Metabolism derived markers pointed to mechanisms in which air pollution may cause adverse pregnancy and birth outcomes. [ABSTRACT FROM AUTHOR]

Background: Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy., Methods and Findings: A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochran's Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP., Conclusions: Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Gestational diabetes mellitus (GDM) is a major pregnancy complication affecting approximately 14.0% of pregnancies around the world. Air pollution exposure, particularly exposure to PM 2.5 , has become a major environmental issue affecting health, especially for vulnerable pregnant women. Associations between PM 2.5 exposure and adverse birth outcomes are generally assumed to be the same throughout a large geographical area. However, the effects of air pollution on health can very spatially in subpopulations. Such spatially varying effects are likely due to a wide range of contextual neighborhood and individual factors that are spatially correlated, including SES, demographics, exposure to housing characteristics and due to different composition of particulate matter from different emission sources. This combination of elevated environmental hazards in conjunction with socioeconomic-based disparities forms what has been described as a "double jeopardy" for marginalized sub-populations. In this manuscript our analysis combines both an examination of spatially varying effects of a) unit-changes in exposure and examines effects of b) changes from current exposure levels down to a fixed compliance level, where compliance levels correspond to the Air Quality Standards (AQS) set by the U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality guideline values. Results suggest that exposure reduction policies should target certain "hotspot" areas where size and effects of potential reductions will reap the greatest rewards in terms of health benefits, such as areas of southeast Los Angeles County which experiences high levels of PM 2.5 exposures and consist of individuals who may be particularly vulnerable to the effects of air pollution on the risk of GDM., 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 © 2023 Elsevier Inc. All rights reserved.)

Background: Significant mortality and morbidity in pregnant women and their offspring are linked to premature rupture of membranes (PROM). Epidemiological evidence for heat-related PROM risk is extremely limited. We investigated associations between acute heatwave exposure and spontaneous PROM., Methods: We conducted this retrospective cohort study among mothers in Kaiser Permanente Southern California who experienced membrane ruptures during the warm season (May-September) from 2008 to 2018. Twelve definitions of heatwaves with different cut-off percentiles (75th, 90th, 95th, and 98th) and durations (≥ 2, 3, and 4 consecutive days) were developed using the daily maximum heat index, which incorporates both daily maximum temperature and minimum relative humidity in the last gestational week. Cox proportional hazards models were fitted separately for spontaneous PROM, term PROM (TPROM), and preterm PROM (PPROM) with zip codes as the random effect and gestational week as the temporal unit. Effect modification by air pollution (i.e., PM 2.5 and NO 2 ), climate adaptation measures (i.e., green space and air conditioning [AC] penetration), sociodemographic factors, and smoking behavior was examined., Results: In total, we included 190,767 subjects with 16,490 (8.6%) spontaneous PROMs. We identified a 9-14% increase in PROM risks associated with less intense heatwaves. Similar patterns as PROM were found for TPROM and PPROM. The heat-related PROM risks were greater among mothers exposed to a higher level of PM 2.5 during pregnancy, under 25 years old, with lower education and household income level, and who smoked. Even though climate adaptation factors were not statistically significant effect modifiers, mothers living with lower green space or lower AC penetration were at consistently higher heat-related PROM risks compared to their counterparts., Conclusion: Using a rich and high-quality clinical database, we detected harmful heat exposure for spontaneous PROM in preterm and term deliveries. Some subgroups with specific characteristics were more susceptible to heat-related PROM risk., 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 © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Major wildfires and their smoke pose a threat to public health and are becoming more frequent in the United States, particularly in California and other populated, fire-prone states. Therefore, it is crucial to understand how California residents view wildfires and engage in risk-reducing behaviors during wildfire events. Currently, there is a knowledge gap concerning this area of inquiry. We disseminated a 40-question cross-sectional survey to explore wildfire perception and knowledge along with related risk-reducing measures and policies among 807 adult residents in the fire-prone region of Orange County, California. Results demonstrated that nearly all (>95%) participants had (or knew someone who had) previously experienced a wildfire. Female gender, knowing a wildfire victim and reporting to have a general interest/passion for environmental issues were the three factors most strongly associated with (1) wildfires (and smoke) being reported as a threat, (2) participants' willingness to evacuate if threatened by a nearby wildfire, and (3) participants' willingness to support a wildfire-related tax increase ( p < 0.05). The majority (57.4%) of participants agreed that the occurrence of wildfires is influenced by climate change, with the most commonly reported risk-reducing actions (by 44% of participants) being informational actions (e.g., tracking the news) rather than self-motivated physical safety actions (e.g., using an air purifier) (29%). The results of this study can help to inform decision- and policy-making regarding future wildfire events as well as allow more targeted and effective public health messaging and intervention measures, in turn helping to reduce the risk associated with future wildfire/smoke episodes.

Rationale: Although the contribution of air pollution to lung cancer risk is well characterized, few studies have been conducted in racially, ethnically, and socioeconomically diverse populations. Objectives: To examine the association between traffic-related air pollution and risk of lung cancer in a racially, ethnically, and socioeconomically diverse cohort. Methods: Among 97,288 California participants of the Multiethnic Cohort Study, we used Cox proportional hazards regression to examine associations between time-varying traffic-related air pollutants (gaseous and particulate matter pollutants and regional benzene) and lung cancer risk ( n  = 2,796 cases; average follow-up = 17 yr), adjusting for demographics, lifetime smoking, occupation, neighborhood socioeconomic status (nSES), and lifestyle factors. Subgroup analyses were conducted for race, ethnicity, nSES, and other factors. Measurements and Main Results: Among all participants, lung cancer risk was positively associated with nitrogen oxide (hazard ratio [HR], 1.15 per 50 ppb; 95% confidence interval [CI], 0.99-1.33), nitrogen dioxide (HR, 1.12 per 20 ppb; 95% CI, 0.95-1.32), fine particulate matter with aerodynamic diameter <2.5 μm (HR, 1.20 per 10 μg/m 3 ; 95% CI, 1.01-1.43), carbon monoxide (HR, 1.29 per 1,000 ppb; 95% CI, 0.99-1.67), and regional benzene (HR, 1.17 per 1 ppb; 95% CI, 1.02-1.34) exposures. These patterns of associations were driven by associations among African American and Latino American groups. There was no formal evidence for heterogeneity of effects by nSES ( P heterogeneity > 0.21), although participants residing in low-SES neighborhoods had increased lung cancer risk associated with nitrogen oxides, and no association was observed among those in high-SES neighborhoods. Conclusions: These findings in a large multiethnic population reflect an association between lung cancer and the mixture of traffic-related air pollution and not a particular individual pollutant. They are consistent with the adverse effects of air pollution that have been described in less racially, ethnically, and socioeconomically diverse populations. Our results also suggest an increased risk of lung cancer among those residing in low-SES neighborhoods.

Background: Traffic and industrial emissions are associated with increased pediatric asthma morbidity. However, few studies have examined the influence of city industrial zoning on pediatric asthma outcomes among minoritized communities with limited access to air monitoring. Methods: In this cross-sectional analysis of 39,974 school-aged students in Santa Ana, CA, we investigated the effect of proximity to areas zoned for industrial use on pediatric asthma prevalence, physical fitness, school attendance, and standardized test scores. Results: The study population was 80.6% Hispanic, with 88.2% qualifying for free/reduced lunch. Compared to students living more than 1 km away from industrial zones, those living within 0.5 km had greater odds of having asthma (adjusted OR 1.21, 95% CI 1.09 to 1.34, p < 0.001). Among children with asthma, those living between 0.5−1.0 km had greater odds of being overweight or obese (aOR 1.47, 95% CI 1.00, 2.15, p = 0.047). Industrial zone proximity was not significantly associated with worse fitness and academic outcomes for students with asthma. Conclusion: These findings suggest that industrial zone proximity is associated with increased pediatric asthma in a predominantly Latino community in Southern California.

Objectives. To (1) examine the disparity in availability of PurpleAir low-cost air quality sensors in California based on neighborhood socioeconomic status (SES) and exposure to fine particulate matter smaller than 2.5 micrometers (PM 2.5 ), (2) investigate the temporal trend of sensor distribution and operation, and (3) identify priority communities for future sensor distribution. Methods. We obtained census tract-level SES variables and PM 2.5 concentrations from the CalEnviroScreen4.0 data set. We obtained real-time PurpleAir sensor data (July 2017-September 2020) to examine sensor distribution and operation. We conducted spatial and temporal analyses at the census tract level to investigate neighborhood SES and PM 2.5 concentrations in relation to sensor distribution and operation. Results. The spatial coverage and the number of PurpleAir sensors increased significantly in California. Fewer sensors were distributed in census tracts with lower SES, higher PM 2.5 , and higher proportions of racial/ethnic minority populations. Furthermore, a large proportion of existing sensors were not in operation at a given time, especially in disadvantaged communities. Conclusions. Disadvantaged communities should be given access to low-cost sensors to fill in spatial gaps of air quality monitoring and address environmental justice concerns. Sensor purchasing and deployment must be paired with regular maintenance to ensure their reliable performance. ( Am J Public Health . 2022;112(3):434-442. https://doi.org/10.2105/AJPH.2021.306603).

Background: Epidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents., Objectives: To assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents., Methods: Detailed clinical data were obtained for 395,927 pregnancies in southern California (2008-2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter < 2.5 μm (PM 2.5 ), <10 μm (PM 10 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) were estimated using kriging interpolation of Environmental Protection Agency's routine monitoring station data, while PM 2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon) were estimated using a fine-resolution geoscience-derived model. A multilevel logistic regression was used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of air pollution and PM component mixtures. Main analyses adjusted for maternal age, race/ethnicity, education, median family household income, pre-pregnancy BMI, smoking during pregnancy, insurance type, season of conception and year of delivery., Results: The incidence of GDM was 10.9% in the study population. In single-pollutant models, we observed an increased odds for GDM associated with exposures to PM 2.5 , PM 10 , NO 2 and PM 2.5 constituents. The association was strongest for NO 2 [adjusted odds ratio (OR) per interquartile range: 1.176, 95% confidence interval (CI): 1.147-1.205)]. In multi-pollutant models, increased ORs for GDM in association with one quartile increase in air pollution mixtures were found for both kriging-based regional air pollutants (NO 2 , PM 2.5 , and PM 10 , OR = 1.095, 95% CI: 1.082-1.108) and PM 2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon, OR = 1.258, 95% CI: 1.206-1.314); NO 2 (78%) and black carbon (48%) contributed the most to the overall mixture effects among all krigged air pollutants and all PM 2.5 constituents, respectively. The risk of GDM associated with air pollution exposure were significantly higher among Hispanic mothers, and overweight/obese mothers., Conclusion: This study found that exposure to a mixture of ambient PM 2.5 , PM 10 , NO 2 , and PM 2.5 chemical constituents was associated with an increased risk of GDM. NO 2 and black carbon PM 2.5 contributed most to GDM risk., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although evidences showed an overall reduction in outdoor air pollution levels across the globe due to COVID-19-related lockdown, no comprehensive assessment was available for indoor air quality during the period of stay-at-home orders, despite that the residential indoor environment contributes most to personal exposures. We examined temporal and diurnal variations of indoor PM 2.5 based on real-time measurements from 139 indoor-outdoor co-located low-cost PurpleAir sensor sets across California for pre-, during, and post-lockdown periods in 2020 and "business-as-usual" periods in 2019. A two-step method was implemented to systematically control the quality of raw sensor data and calibrate the sensor data against co-located reference instruments. During the lockdown period, 17-24% higher indoor PM 2.5 concentrations were observed in comparison to those in the 2019 business-as-usual period. In residential sites, a clear peak in PM 2.5 concentrations in the afternoon and elevated evening levels toping at roughly 10 μg·m -3 was observed, which reflects enhanced human activity during lunch and dinner time (i.e., cooking) and possibly more cleaning and indoor movement that increase particle generation and resuspension in homes. The contribution of indoor-generated PM 2.5 to total indoor concentrations increased as high as 80% during and post-lockdown periods compared to before lockdown.

Wildfires can be detrimental to urban and rural communities, causing impacts in the form of psychological stress, direct physical injury, and smoke-related morbidity and mortality. This study examined the area burned by wildfires over the entire state of California from the years 2000 to 2020 in order to quantify and identify whether burned area and fire frequency differed across Census tracts according to socioeconomic indicators over time. Wildfire data were obtained from the California Fire and Resource Assessment Program (FRAP) and National Interagency Fire Center (NIFC), while demographic data were obtained from the American Community Survey. Results showed a doubling in the number of Census tracts that experienced major wildfires and a near doubling in the number of people residing in wildfire-impacted Census tracts, mostly due to an over 23,000 acre/year increase in the area burned by wildfires over the last two decades. Census tracts with a higher fire frequency and burned area had lower proportions of minority groups on average. However, when considering Native American populations, a greater proportion resided in highly impacted Census tracts. Such Census tracts also had higher proportions of older residents. In general, high-impact Census tracts tended to have higher proportions of low-income residents and lower proportions of high-income residents, as well as lower median household incomes and home values. These findings are important to policymakers and state agencies as it relates to environmental justice and the allocation of resources before, during, and after wildfires in the state of California.

Electronic health record data for pediatric members of Kaiser Permanente Southern California were used to identify key variables (ie, the number of emergency department visits and the number of providers) available in early infancy (0-6 months) placing children at higher risk of a maltreatment diagnosis in the first 5 years of life. The analytic sample included 96 462 children age 0 to 5 years born from January 1, 2009, to June 30, 2018. Poisson regression showed that children with ≥2 emergency department visits from birth to 6 months were at twice the risk of a maltreatment diagnosis before age 2 and 5 years compared with those children with no emergency department visits. Children with more continuity of primary care providers (0-5 providers) in the first 6 months of life were at lower risk of a maltreatment diagnosis at 2 years and 5 years than those children who saw multiple providers (6+). Information about medical utilization in early infancy may help physicians and other medical providers identify children at higher risk of maltreatment and prevent future incidents.

Background: Lead (Pb) exposure is a problem that disproportionately impacts low-income communities and communities of color. We applied a community-based participatory research approach to assess the distribution of soil Pb concentrations and related social vulnerabilities across Census tracts in Santa Ana, CA., Methods: Soil Pb samples (n = 1528) were collected by the ¡Plo-NO! Santa Ana! Lead-Free Santa Ana! partnership in 2018 across Santa Ana, CA, at a high spatial resolution and measured using XRF analysis. Pb concentrations were mapped and spatial interpolation was conducted to generate a continuous smoothed map of soil Pb concentrations across the city. American Community Survey data was used to examine Pb across Census tracts based on social and economic factors, and to allow for the development of a Cumulative Risk Index to identify areas at high risk of health impacts., Results: Soil Pb concentrations varied by landuse type and socioeconomic factors. Census tracts with a median household income below $50,000 had over five times higher soil Pb concentrations than high-income Census tracts. Soil samples collected in tertiles with the highest percent children, residents without health insurance, renter-occupied housing units, and lowest percent college educated residents had 90.0%, 96.1%, 75.2%, and 87.0% higher Pb concentrations on average, respectively, compared to their counterparts. Overall, 52.7% of residential samples had Pb concentrations in excess of the 80 ppm California EPA recommendation, and 11 Census tracts were characterized as high risk according to our Cumulative Risk Index., Discussion: This study underscores the need for precautionary measures relating to disturbances of the soil, particularly for areas where children play outside, given children's higher absorption of lead. It also informs environmental justice initiatives and identifies vulnerable subpopulations at greater risk of Pb exposure, thus warranting community-driven recommendations for policies and initiatives to remediate soil Pb and protect public health and health equity., 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 © 2020 Elsevier B.V. All rights reserved.)

Previous studies using different exposure methods to assess air pollution and breast cancer risk among primarily whites have been inconclusive. Air pollutant exposures of particulate matter and oxides of nitrogen were estimated by kriging (NO x , NO 2 , PM 10 , PM 2.5 ), land use regression (LUR, NO x , NO 2 ) and California Line Source Dispersion model (CALINE4, NO x , PM 2.5 ) for 57,589 females from the Multiethnic Cohort, residing largely in Los Angeles County from recruitment (1993-1996) through 2010. Cox proportional hazards models were used to examine the associations between time-varying air pollution and breast cancer incidence adjusting for confounding factors. Stratified analyses were conducted by race/ethnicity and distance to major roads. Among all women, breast cancer risk was positively but not significantly associated with NO x (per 50 parts per billion [ppb]) and NO 2 (per 20 ppb) determined by kriging and LUR and with PM 2.5 and PM 10 (per 10 μg/m 3 ) determined by kriging. However, among women who lived within 500 m of major roads, significantly increased risks were observed with NO x (hazard ratio [HR] = 1.35, 95% confidence interval [95% CI]: 1.02-1.79), NO 2 (HR = 1.44, 95% CI: 1.04-1.99), PM 10 (HR = 1.29, 95% CI: 1.07-1.55) and PM 2.5 (HR = 1.85, 95% CI: 1.15-2.99) determined by kriging and NO x (HR = 1.21, 95% CI:1.01-1.45) and NO 2 (HR = 1.26, 95% CI: 1.00-1.59) determined by LUR. No overall associations were observed with exposures assessed by CALINE4. Subgroup analyses suggested stronger associations of NO x and NO 2 among African Americans and Japanese Americans. Further studies of multiethnic populations to confirm the effects of air pollution, particularly near-roadway exposures, on the risk of breast cancer is warranted., (© 2019 UICC.)

Although lead has been removed from paint and gasoline sold in the U.S., lead exposures persist, with communities of color and residents in urban and low-income areas at greatest risk for exposure. The persistence of and inequities in lead exposures raise questions about the scope and implementation of policies that address lead as a public health concern. To understand the multi-level nature of lead policies, this paper and case study reviews lead policies at the national level, for the state of California, and for Santa Ana, CA, a dense urban city in Southern California. Through a community-academic partnership process, this analysis examines lead exposure pathways represented, the level of intervention (e.g., prevention, remediation), and whether policies address health inequities. Results indicate that most national and state policies focus on establishing hazardous lead exposure levels in settings and consumer products, disclosing lead hazards, and remediating lead paint. Several policies focus on mitigating exposures rather than primary prevention. The persistence of lead exposures indicates the need to identify sustainable solutions to prevent lead exposures in the first place. We close with recommendations to reduce lead exposures across the life course, consider multiple lead exposure pathways, and reduce and eliminate health inequities related to lead., Competing Interests: The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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 PM 2.5 (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 PM 2.5 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 PM 2.5 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 PM 2.5 exposure at baseline and cumulative exposure during follow-up was significantly associated (p < 0.05) with increased delinquent behavior. The estimated effect sizes (per interquartile increase of PM 2.5 by 3.12-5.18 μg/m 3 ) were equivalent to the difference in delinquency scores between adolescents who are 3.5-4 years apart in age. The adverse effect was stronger in families with unfavorable parent-to-child relationships, increased parental stress or maternal depressive symptoms. Overall, these findings sμggest long-term PM 2.5 exposure may increase delinquent behavior of urban-dwelling adolescents, with the resulting neurotoxic effect aggravated by psychosocial adversities.

Multiple sclerosis (MS) incidence and serum 25-hydroxyvitamin D (25OHD) levels vary by race/ethnicity. We examined the consistency of beneficial effects of 25OHD and/or sun exposure for MS risk across multiple racial/ethnic groups. We recruited incident MS cases and controls (blacks 116 cases/131 controls; Hispanics 183/197; whites 247/267) from the membership of Kaiser Permanente Southern California into the MS Sunshine Study to simultaneously examine sun exposure and 25OHD, accounting for genetic ancestry and other factors. Higher lifetime ultraviolet radiation exposure (a rigorous measure of sun exposure) was associated with a lower risk of MS independent of serum 25OHD levels in blacks (adjusted OR = 0.53, 95% CI = 0.31-0.83; p = 0.007) and whites (OR = 0.68, 95% CI = 0.48-0.94; p = 0.020) with a similar magnitude of effect that did not reach statistical significance in Hispanics (OR = 0.66, 95% CI = 0.42-1.04; p = 0.071). Higher serum 25OHD levels were associated with a lower risk of MS only in whites. No association was found in Hispanics or blacks regardless of how 25OHD was modeled. Lifetime sun exposure appears to reduce the risk of MS regardless of race/ethnicity. In contrast, serum 25OHD levels are not associated with MS risk in blacks or Hispanics. Our findings challenge the biological plausibility of vitamin D deficiency as causal for MS and call into question the targeting of specific serum 25OHD levels to achieve health benefits, particularly in blacks and Hispanics., Competing Interests: Annette Langer–Gould was site principal investigator for two industry-sponsored phase 3 clinical trials (Biogen Idec; Hoffman-LaRoche) and was site PI for one industry–sponsored observation study (Biogen Idec). She receives grant support from the National Institutes of Health, NINDS, PCORI and the National MS Society; Robyn Lucas receives grant support from Cancer Australia and the National Health and Medical Research Council of Australia; Lie Hong Chen declares no conflict of interest; Jun Wu declares no conflict of interest; Edlin Gonzales declares no conflict of interest; Samantha Haraszti declares no conflict of interest; Jessica Smith declares no conflict of interest; Anny Xiang declares no conflict of interest; Hong Quach declares no conflict of interest; Lisa Barcellos declares no conflict of interest.

Background: Racial/ethnic disparities in preterm birth (PTB) are well documented in the epidemiological literature, but little is known about the relative contribution of different social and environmental determinants of such disparities in birth outcome. Furthermore, increased focus has recently turned toward modifiable aspects of the environment, including physical characteristics, such as neighborhood air pollution, to reduce disparities in birth outcomes., Objectives: To apply decomposition methods to understand disparities in preterm birth (PTB) prevalence between births of non-Hispanic black individuals and births of non-Hispanic white individuals in California, according to individual demographics, neighborhood socioeconomic environment, and neighborhood air pollution., Methods: We used all live singleton births in California spanning 2005 to 2010 and estimated PTBs and other adverse birth outcomes for infants borne by non-Hispanic black mothers and white mothers. To compare individual-level, neighborhood-level, and air pollution [Particulate Matter, 2.5 micrometers or less (PM 2.5 ) and nitrogen dioxide (NO 2 )] predictors, we conducted a nonlinear extension of the Blinder-Oaxaca method to decompose racial/ethnic disparities in PTB., Results: The predicted differences in probability of PTB between black and white infants was 0.056 (95% CI: 0.054, 0.058). All included predictors explained 37.8% of the black-white disparity. Overall, individual (17.5% for PTB) and neighborhood-level variables (16.1% for PTB) explained a greater proportion of the black-white difference in birth outcomes than air pollution (5.7% for PTB)., Conclusions: Our results suggest that, although the role of individual and neighborhood factors remains prevailing in explaining black-white differences in birth outcomes, the individual contribution of PM 2.5 is comparable in magnitude to any single individual- or neighborhood-level factor. https://doi.org/10.1289/EHP490.

To assess the burden associated with hypertension, reliable estimates for the prevalence of pediatric hypertension are vital. For this cross-sectional study of 237,248 youths aged 6 to 17 years without indication of secondary hypertension, blood pressure (BP) was classified according to age, sex, and height using standards from the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents as prehypertension with at least 1 BP ≥90th percentile and as hypertension with 3 BPs ≥95th percentile. The prevalence of prehypertension and hypertension were 31.4% and 2.1%, respectively. An additional 21.4% had either 1 (16.6%) or 2 (4.8%) BPs ≥95th percentile. Based on this large population-based study using routinely measured BP from clinical care, a remarkable proportion of youth (6.9%) has hypertension or nearly meets the definition of hypertension with 2 documented BPs in the hypertensive range., (©2013 Wiley Periodicals, Inc.)

Background: The literature on traffic-related air pollution and childhood cancers is inconclusive, and little is known on rarer cancer types., Objectives: We sought to examine associations between childhood cancers and traffic-related pollution exposure., Methods: The present study included children < 6 years of age identified in the California Cancer Registry (born 1998-2007) who could be linked to a California birth certificate (n = 3,590). Controls were selected at random from California birthrolls (n = 80,224). CAlifornia LINE Source Dispersion Modeling, version 4 (CALINE4) was used to generate estimates of local traffic exposures for each trimester of pregnancy and in the first year of life at the address indicated on the birth certificate. We checked our findings by additionally examining associations with particulate matter (≤ 2.5 μm in aerodynamic diameter; PM2.5) pollution measured by community-based air pollution monitors, and with a simple measure of traffic density., Results: With unconditional logistic regression, a per interquartile range increase in exposure to traffic-related pollution during the first trimester (0.0538 ppm carbon monoxide, estimated using CALINE4) was associated with acute lymphoblastic leukemia [ALL; first trimester odds ratio (OR) = 1.05; 95% CI: 1.01, 1.10]; germ cell tumors (OR = 1.16; 95% CI: 1.04, 1.29), particularly teratomas (OR = 1.26; 95% CI: 1.12, 1.41); and retinoblastoma (OR = 1.11; 95% CI: 1.01, 1.21), particularly bilateral retinoblastoma (OR = 1.16; 95% CI: 1.02, 1.33). Retinoblastoma was also associated with average PM2.5 concentrations during pregnancy, and ALL and teratomas were associated with traffic density near the child's residence at birth., Conclusions: We estimated weak associations between early exposure to traffic pollution and several childhood cancers. Because this is the first study to report on traffic pollution in relation to retinoblastoma or germ cell tumors, and both cancers are rare, these findings require replication in other studies.

In the absence of evidence-based guidelines for high blood pressure screening in asymptomatic youth, a reasonable strategy is to screen those who are at high risk. The present study aimed to identify optimal body mass index (BMI) thresholds as a marker for high-risk youth to predict hypertension prevalence. In a cross-sectional study, youth aged 6 to 17 years (n=237,248) enrolled in an integrated prepaid health plan in 2007 to 2009 were classified according to their BMI and hypertension status. In moderately and extremely obese youth, the prevalence of hypertension was 3.8% and 9.2%, respectively, compared with 0.9% in normal weight youth. The adjusted prevalence ratios (95% confidence intervals) of hypertension for normal weight, overweight, moderate obesity, and extreme obesity were 1.00 (Reference), 2.27 (2.08-2.47), 4.43 (4.10-4.79), and 10.76 (9.99-11.59), respectively. The prevalence of hypertension was best predicted by a BMI-for-age ≥94th percentile. These results suggest that all obese youth should be screened for hypertension., (©2013 Wiley Periodicals, Inc.)

Little is known about the impacts of green spaces on pregnancy outcomes. The relationship between green space exposure and preeclampsia has never been studied. We used a hospital-based perinatal database including more than 80,000 births to study the relationships between greenness exposure and three pregnancy outcomes: birth weight in term born infants, preterm deliveries and preeclampsia. Greenness was characterized using the normalized difference vegetation index (NDVI) within circular buffers surrounding maternal homes. Analyses were conducted using generalized estimating equations, adjusted for potential confounders. We observed an increase in birth weight in term born infants and a reduced risk of preterm births associated with an increase in NDVI. No significant association was observed between greenness and preeclampsia. This study provides modest support for beneficial effects of greenness exposure on pregnancy outcomes and calls for confirmation in other study settings., (© 2013 Elsevier Ltd. All rights reserved.)

High concentrations of air pollutants on roadways, relative to ambient concentrations, contribute significantly to total personal exposure. Estimation of these exposures requires measurements or prediction of roadway concentrations. Our study develops, compares, and evaluates linear regression and nonlinear generalized additive models (GAMs) to estimate on-road concentrations of four key air pollutants, particle-bound polycyclic aromatic hydrocarbons (PB-PAH), particle number count (PNC), nitrogen oxides (NOx), and particulate matter with diameter <2.5 μm (PM2.5) using traffic, meteorology, and elevation variables. Critical predictors included wind speed and direction for all the pollutants, traffic-related variables for PB-PAH, PNC, and NOx, and air temperatures and relative humidity for PM2.5. GAMs explained 50%, 55%, 46%, and 71% of the variance for log or square-root transformed concentrations of PB-PAH, PNC, NOx, and PM2.5, respectively, an improvement of 5% to over 15% over the linear models. Accounting for temporal autocorrelation in the GAMs further improved the prediction, explaining 57-89% of the variance. We concluded that traffic and meteorological data are good predictors in estimating on-road traffic-related air pollutant concentrations and GAMs perform better for nonlinear variables, such as meteorological parameters.

Background: Exposure to air pollution is frequently associated with reductions in birth weight but results of available studies vary widely, possibly in part because of differences in air pollution metrics. Further insight is needed to identify the air pollution metrics most strongly and consistently associated with birth weight., Methods: We used a hospital-based obstetric database of more than 70,000 births to study the relationships between air pollution and the risk of low birth weight (LBW, <2,500 g), as well as birth weight as a continuous variable, in term-born infants. Complementary metrics capturing different aspects of air pollution were used (measurements from ambient monitoring stations, predictions from land use regression models and from a Gaussian dispersion model, traffic density, and proximity to roads). Associations between air pollution metrics and birth outcomes were investigated using generalized additive models, adjusting for maternal age, parity, race/ethnicity, insurance status, poverty, gestational age and sex of the infants., Results: Increased risks of LBW were associated with ambient O(3) concentrations as measured by monitoring stations, as well as traffic density and proximity to major roadways. LBW was not significantly associated with other air pollution metrics, except that a decreased risk was associated with ambient NO(2) concentrations as measured by monitoring stations. When birth weight was analyzed as a continuous variable, small increases in mean birth weight were associated with most air pollution metrics (<40 g per inter-quartile range in air pollution metrics). No such increase was observed for traffic density or proximity to major roadways, and a significant decrease in mean birth weight was associated with ambient O3 concentrations., Conclusions: We found contrasting results according to the different air pollution metrics examined. Unmeasured confounders and/or measurement errors might have produced spurious positive associations between birth weight and some air pollution metrics. Despite this, ambient O(3) was associated with a decrement in mean birth weight and significant increases in the risk of LBW were associated with traffic density, proximity to roads and ambient O(3). This suggests that in our study population, these air pollution metrics are more likely related to increased risks of LBW than the other metrics we studied. Further studies are necessary to assess the consistency of such patterns across populations.

Background: High levels of airborne particles from secondhand smoke have been reported in California Indian casinos. Yet, little is known regarding the smoking status of casino patrons, their avoidance of secondhand smoke while visiting, and their views on a hypothetical smoking ban., Methods: Predictors of visiting an Indian casino were assessed among participants of the 2008 California Tobacco Survey (n = 10,397). Exposure to and avoidance of secondhand smoke were subsequently analyzed among a subset of participants who had visited a casino in the year prior to the survey (n = 3,361)., Results: Ethnic minorities, older individuals, current smokers and residents of sparsely populated regions of California were more likely than other demographic groups to visit a tribal casino. Avoidance of secondhand smoke was more frequent among the never smokers than former and current smokers, particularly those who last visited a casino lacking physical separation between non-smoking and smoking sections. The never smokers versus current smokers disproportionately expressed a willingness to extend their stay and visit again if smoking were prohibited., Conclusions: If casinos became smoke free, then it is anticipated that they would be visited by a significantly larger number of Californians, including both patrons and those who otherwise would not have visited a casino.

Background: Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data., Methods: We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model., Results: Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data., Conclusions: Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.