Childhood Air Pollution Exposure Associated with Self-Reported Bronchitic Symptoms in Adulthood.

RATIONALE: Few studies have examined the effects of long-term childhood air pollution exposure on adult respiratory health, including whether childhood respiratory effects underlie this relation. OBJECTIVES: To evaluate associations between childhood air pollution exposure and self-reported adult bronchitic symptoms, while considering child respiratory health, in the Southern California Children's Health Study. METHODS: Nitrogen dioxide (NO2), ozone, particulate matter<2.5µm (PM2.5) and <10µm (PM10) exposures assessed using inverse-distance-squared spatial interpolation based on childhood (birth-17 years) residential histories. Bronchitic symptoms (bronchitis, cough, or phlegm in last 12 months) were ascertained via questionnaire in adulthood. Associations between mean air pollution exposure across childhood and self-reported adult bronchitic symptoms were estimated using logistic regression. We further adjusted for childhood bronchitic symptoms and asthma to understand whether associations operated beyond childhood respiratory health impacts. Effect modification was assessed for family history of asthma, childhood asthma, and adult allergies. MEASUREMENTS AND MAIN RESULTS: 1308 participants were included (mostly non-Hispanic White [56%] or Hispanic [32%]). At adult assessment (age mean=32.0 years, standard deviation [SD]=4.7) 25% reported bronchitic symptoms. Adult bronchitic symptoms were associated with NO2 and PM10 childhood exposures. Odds ratios per SD increase: 1.69 (95%CI:1.14,2.49) for NO2 (SD=11.1ppb); 1.51 (95%CI:1.00,2.27) for PM10 (SD=14.2µg/m3). Adjusting for childhood bronchitic symptoms or asthma produced similar results. NO2 and PM10 associations were modified by childhood asthma, with larger associations among asthmatics. CONCLUSION: Childhood NO2 and PM10 exposures were associated with adult bronchitic symptoms. Associations were not explained by childhood respiratory health impacts; however, participants with childhood asthma had stronger associations.

Prenatal ambient air pollution exposure and child weight trajectories from the 3rd trimester of pregnancy to 2 years of age: a cohort study.

BACKGROUND: Prenatal air pollution exposure may increase risk for childhood obesity. However, few studies have evaluated in utero growth measures and infant weight trajectories. This study will evaluate the associations of prenatal exposure to ambient air pollutants with weight trajectories from the 3rd trimester through age 2 years. METHODS: We studied 490 pregnant women who were recruited from the Maternal and Development Risks from Environmental and Social Stressors (MADRES) cohort, which comprises a low-income, primarily Hispanic population in Los Angeles, California. Nitrogen dioxide (NO2), particulate matter < 10 µm (PM10), particulate matter < 2.5 µm (PM2.5), and ozone (O3) concentrations during pregnancy were estimated from regulatory air monitoring stations. Fetal weight was estimated from maternal ultrasound records. Infant/child weight measurements were extracted from medical records or measured during follow-up visits. Piecewise spline models were used to assess the effect of air pollutants on weight, overall growth, and growth during each period. RESULTS: The mean (SD) prenatal exposure concentrations for NO2, PM2.5, PM10, and O3 were 16.4 (2.9) ppb, 12.0 (1.1) µg/m3, 28.5 (4.7) µg/m3, and 26.2 (2.9) ppb, respectively. Comparing an increase in prenatal average air pollutants from the 10th to the 90th percentile, the growth rate from the 3rd trimester to age 3 months was significantly increased (1.55% [95%CI 1.20%, 1.99%] for PM2.5 and 1.64% [95%CI 1.27%, 2.13%] for NO2), the growth rate from age 6 months to age 2 years was significantly decreased (0.90% [95%CI 0.82%, 1.00%] for NO2), and the attained weight at age 2 years was significantly lower (- 7.50% [95% CI - 13.57%, - 1.02%] for PM10 and - 7.00% [95% CI - 11.86%, - 1.88%] for NO2). CONCLUSIONS: Prenatal ambient air pollution was associated with variable changes in growth rate and attained weight from the 3rd trimester to age 2 years. These results suggest continued public health benefits of reducing ambient air pollution levels, particularly in marginalized populations.

The impact of GPS-derived activity spaces on personal PM2.5 exposures in the MADRES cohort.

BACKGROUND: In-utero exposure to particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) is associated with low birth weight and health risks later in life. Pregnant women are mobile and locations they spend time in contribute to their personal PM2.5 exposures. Therefore, it is important to understand how mobility and exposures encountered within activity spaces contribute to personal PM2.5 exposures during pregnancy. METHODS: We collected 48-h integrated personal PM2.5 samples and continuous geolocation (GPS) data for 213 predominantly Hispanic/Latina pregnant women in their 3rd trimester in Los Angeles, CA. We also collected questionnaires and modeled outdoor air pollution and meteorology in their residential neighborhood. We calculated three GPS-derived activity space measures of exposure to road networks, greenness (NDVI), parks, traffic volume, walkability, and outdoor PM2.5 and temperature. We used bivariate analyses to screen variables (GPS-extracted exposures in activity spaces, individual characteristics, and residential neighborhood exposures) based on their relationship with personal, 48-h integrated PM2.5 concentrations. We then built a generalized linear model to explain the variability in personal PM2.5 exposure and identify key contributing factors. RESULTS: Indoor PM2.5 sources, parity, and home ventilation were significantly associated with personal exposure. Activity-space based exposure to roads was associated with significantly higher personal PM2.5 exposure, while greenness was associated with lower personal PM2.5 exposure (ß = -3.09 µg/m3 per SD increase in NDVI, p-value = 0.018). The contribution of outdoor PM2.5 to personal exposure was positive but relatively lower (ß = 2.05 µg/m3 per SD increase, p-value = 0.016) than exposures in activity spaces and the indoor environment. The final model explained 34% of the variability in personal PM2.5 concentrations. CONCLUSIONS: Our findings highlight the importance of activity spaces and the indoor environment on personal PM2.5 exposures of pregnant women living in Los Angeles, CA. This work also showcases the multiple, complex factors that contribute to total personal PM2.5 exposure.

Identifying pre-conception and pre-natal periods in which ambient air pollution exposure affects fetal growth in the predominately Hispanic MADRES cohort.

BACKGROUND: It is well documented that persons of color experience disproportionate exposure to environmental contaminants, including air pollution, and have poorer pregnancy outcomes. This study assessed the critical windows of exposure to ambient air pollution on in utero fetal growth among structurally marginalized populations in urban Los Angeles. METHODS: Participants (N = 281) from the larger ongoing MADRES pregnancy cohort study were included in this analysis. Fetal growth outcomes were measured on average at 32 [Formula: see text] 2 weeks of gestation by a certified sonographer and included estimated fetal weight, abdominal circumference, head circumference, biparietal diameter and femur length. Daily ambient air pollutant concentrations were estimated for four pollutants (particulate matter less than 2.5 µm (PM2.5) and less than 10 µm (PM10) in aerodynamic diameter, nitrogen dioxide (NO2), and 8-h maximum ozone (O3)) at participant residences using inverse-distance squared spatial interpolation from ambient monitoring data. Weekly gestational averages were calculated from 12 weeks prior to conception to 32 weeks of gestation (44 total weeks), and their associations with growth outcomes were modeled using adjusted distributed lag models (DLMs). RESULTS: Participants were on average 29 years [Formula: see text] 6 old and predominately Hispanic (82%). We identified a significant sensitive window of PM2.5 exposure (per IQR increase of 6 [Formula: see text]3) between gestational weeks 4-16 for lower estimated fetal weight [Formula: see text] averaged4-16 = -8.7 g; 95% CI -16.7, -0.8). Exposure to PM2.5 during gestational weeks 1-23 was also significantly associated with smaller fetal abdominal circumference ([Formula: see text] averaged1-23 = -0.6 mm; 95% CI -1.1, -0.2). Additionally, prenatal exposure to PM10 (per IQR increase of 13 [Formula: see text]3) between weeks 6-15 of pregnancy was significantly associated with smaller fetal abdominal circumference ([Formula: see text] averaged6-15 = -0.4 mm; 95% CI -0.8, -0.1). DISCUSSION: These results suggest that exposure to particulate matter in early to mid-pregnancy, but not preconception or late pregnancy, may have critical implications on fetal growth.

Spatiotemporal Imputation of MAIAC AOD Using Deep Learning with Downscaling.

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.

Measuring Spatial and Temporal PM2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors.

Low-cost sensors can provide insight on the spatio-temporal variability of air pollution, provided that sufficient efforts are made to ensure data quality. Here, 19 AirBeam particulate matter (PM) sensors were deployed from December 2016 to January 2017 to determine the spatial variability of PM2.5 in Sacramento, California. Prior to, and after, the study, the 19 sensors were deployed and collocated at a regulatory air monitoring site. The sensors demonstrated a high degree of precision during all collocated measurement periods (Pearson R2 = 0.98 - 0.99 across all sensors), with little drift. A sensor-specific correction factor was developed such that each sensor reported a comparable value. Sensors had a moderate degree of correlation with regulatory monitors during the study (R2 = 0.60 - 0.68 at two sites). In a multi-linear regression model, the deviation between sensor and reference measurements of PM2.5 had the highest correlation with dew point and relative humidity. Sensor measurements were used to estimate the PM2.5 spatial variability, finding an average pairwise coefficient of divergence of 0.22 and a range of 0.14 to 0.33, indicating mostly homogeneous distributions. No significant difference in the average sensor PM concentrations between environmental justice (EJ) and non-EJ communities (p value = 0.24) was observed.

Pregnancy exposure to PM2.5 from wildland fire smoke and preterm birth in California.

BACKGROUND: Wildfires in the Western United States are a growing and significant source of air pollution that is eroding decades of progress in air pollution reduction. The effects on preterm birth during critical periods of pregnancy are unknown. METHODS: We assessed associations between prenatal exposure to wildland fire smoke and risk of preterm birth (gestational age < 37 weeks). We assigned smoke exposure to geocoded residence at birth for all live singleton births in California conceived 2007-2018, using weekly average concentrations of particulate matter ≤ 2.5 µm (PM2.5) attributable to wildland fires from United States Environmental Protection Agency's Community Multiscale Air Quality Model. Logistic regression yielded odds ratio (OR) for preterm birth in relation to increases in average exposure across the whole pregnancy, each trimester, and each week of pregnancy. Models adjusted for season, age, education, race/ethnicity, medical insurance, and smoking of the birthing parent. RESULTS: For the 5,155,026 births, higher wildland fire PM2.5 exposure averaged across pregnancy, or any trimester, was associated with higher odds of preterm birth. The OR for an increase of 1 µg/m3 of average wildland fire PM2.5 during pregnancy was 1.013 (95 % CI:1.008,1.017). Wildland fire PM2.5 during most weeks of pregnancy was associated with higher odds. Strongest estimates were observed in weeks in the second and third trimesters. A 10 µg/m3 increase in average wildland fire PM2·5 in gestational week 23 was associated with OR = 1.034; 95 % CI: 1.019, 1.049 for preterm birth. CONCLUSIONS: Preterm birth is sensitive to wildland fire PM2.5; therefore, we must reduce exposure during pregnancy.

Increased Risk of Gestational Hypertension by Periconceptional Exposure to Ambient Air Pollution and Effect Modification by Prenatal Depression.

BACKGROUND: Air pollution has been associated with gestational hypertension (GH) and preeclampsia, but susceptible windows of exposure and potential vulnerability by comorbidities, such as prenatal depression, remain unclear. METHODS: We ascertained GH and preeclampsia cases in a prospective pregnancy cohort in Los Angeles, CA. Daily levels of ambient particulate matters (with a diameter of ≤10 µm [PM10] or ≤2.5 µm [PM2.5]), nitrogen dioxide, and ozone were averaged for each week from 12 weeks preconception to 20 gestational weeks. We used distributed lag models to identify susceptible exposure windows, adjusting for potential confounders. Analyses were additionally stratified by probable prenatal depression to explore population vulnerability. RESULTS: Among 619 participants, 60 developed preeclampsia and 42 developed GH. We identified a susceptible window for exposure to PM2.5 from 1 week preconception to 11 weeks postconception: higher exposure (5 µg/m3) within this window was associated with an average of 8% (95% CI, 1%-15%) higher risk of GH. Among participants with probable prenatal depression (n=179; 32%), overlapping sensitive windows were observed for all pollutants from 8 weeks before to 10 weeks postconception with increased risk of GH (PM2.5, 16% [95% CI, 3%-31%]; PM10, 39% [95% CI, 13%-72%]; nitrogen dioxide, 65% [95% CI, 17%-134%]; and ozone, 45% [95% CI, 9%-93%]), while the associations were close to null among those without prenatal depression. Air pollutants were not associated with preeclampsia in any analyses. CONCLUSIONS: We identified periconception through early pregnancy as a susceptible window of air pollution exposure with an increased risk of GH. Prenatal depression increases vulnerability to air pollution exposure and GH.

Joint effects of traffic-related air pollution and hypertensive disorders of pregnancy on maternal postpartum depressive and anxiety symptoms.

BACKGROUND: Ambient air pollution has been linked to postpartum depression. However, few studies have investigated the effects of traffic-related NOx on postpartum depression and whether any pregnancy-related factors might increase susceptibility. OBJECTIVES: To evaluate the association between traffic-related NOx and postpartum depressive and anxiety symptoms, and effect modification by pregnancy-related hypertension. METHODS: This study included 453 predominantly low-income Hispanic/Latina women in the MADRES cohort. Daily traffic-related NOx concentrations by road class were estimated using the California LINE-source dispersion model (CALINE4) at participants' residential locations and averaged across pregnancy. Postpartum depressive and anxiety symptoms were evaluated by a validated questionnaire (Postpartum Distress Measure, PDM) at 1, 3, 6 and 12 months postpartum. Multivariate linear regressions were performed to estimate the associations at each timepoint. Interaction terms were added to the linear models to assess effect modification by hypertensive disorders of pregnancy (HDPs). Repeated measurement analyses were conducted by using mixed effect models. RESULTS: We found prenatal traffic-related NOx was associated with increased PDM scores. Specifically, mothers exposed to an IQR (0.22 ppb) increase in NOx from major roads had 3.78% (95% CI: 0.53-7.14%) and 5.27% (95% CI: 0.33-10.45%) significantly higher 3-month and 12-month PDM scores, respectively. Similarly, in repeated measurement analyses, higher NOx from major roads was associated with 3.06% (95% CI: 0.43-5.76%) significantly higher PDM scores across the first year postpartum. Effect modification by HDPs was observed: higher freeway/highway and total NOx among mothers with HDPs were associated with significantly higher PDM scores at 12 months postpartum compared to those without HDPs. IMPACT: This study shows that prenatal traffic-related air pollution was associated with postpartum depressive and anxiety symptoms. The study also found novel evidence of greater susceptibility among women with HDPs, which advances the understanding of the relationships between air pollution, maternal cardiometabolic health during pregnancy and postpartum mental health. Our study has potential implications for clinical intervention to mitigate the effects of traffic-related pollution on postpartum mental health disorders. The findings can also offer valuable insights into urban planning strategies concerning the implementation of emission control measures and the creation of green spaces.

Empirical nitrogen and sulfur critical loads of U.S. tree species and their uncertainties with machine learning.

Critical loads (CLs) of atmospheric deposition for nitrogen (N) and sulfur (S) are used to support decision making related to air regulation and land management. Frequently, CLs are calculated using empirical methods, and the certainty of the results depends on accurate representation of underlying ecological processes. Machine learning (ML) models perform well in empirical modeling of processes with non-linear characteristics and significant variable interactions. We used bootstrap ensemble ML methods to develop CL estimates and assess uncertainties of CLs for the growth and survival of 108 tree species in the conterminous United States. We trained ML models to predict tree growth and survival and characterize the relationship between deposition and tree species response. Using four statistical methods, we quantified the uncertainty of CLs in 95 % confidence intervals (CI). At the lower bound of the CL uncertainty estimate, 80 % or more of tree species have been impacted by nitrogen deposition exceeding a CL for tree survival over >50 % of the species range, while at the upper bound the percentage is much lower (<20 % of tree species impacted across >60 % of the species range). Our analysis shows that bootstrap ensemble ML can be effectively used to quantify critical loads and their uncertainties. The range of the uncertainty we calculated is sufficiently large to warrant consideration in management and regulatory decision making with respect to atmospheric deposition.

Preconceptional and prenatal exposure to air pollutants and risk of gestational diabetes in the MADRES prospective pregnancy cohort study.

Background: Air pollution has been associated with gestational diabetes mellitus (GDM). We aim to investigate susceptible windows of air pollution exposure and factors determining population vulnerability. Methods: We ascertained GDM status in the prospective Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort from Los Angeles, California, USA. We calculated the relative risk of GDM by exposure to ambient particulate matter (PM10; PM2.5), nitrogen dioxide (NO2), and ozone (O3) in each week from 12 weeks before to 24 weeks after conception, adjusting for potential confounders, with distributed lag models to identify susceptible exposure windows. We examined effect modification by prenatal depression, median-split pre-pregnancy BMI (ppBMI) and age. Findings: Sixty (9.7%) participants were diagnosed with GDM among 617 participants (mean age: 28.2 years, SD: 5.9; 78.6% Hispanic, 11.8% non-Hispanic Black). GDM risk increased with exposure to PM2.5, PM10, and NO2 in a periconceptional window ranging from 5 weeks before to 5 weeks after conception: interquartile-range increases in PM2.5, PM10, and NO2 during this window were associated with increased GDM risk by 5.7% (95% CI: 4.6-6.8), 8.9% (8.1-9.6), and 15.0% (13.9-16.2), respectively. These sensitive windows generally widened, with greater effects, among those with prenatal depression, with age ≥28 years, or with ppBMI ≥27.5 kg/m2, than their counterparts. Interpretation: Preconception and early-pregnancy are susceptible windows of air pollutants exposure that increased GDM risk. Prenatal depression, higher age, or higher ppBMI may increase one's vulnerability to air pollution-associated GDM risk. Funding: National Institutes of Health, Environmental Protection Agency.

Health Outcomes in Children Associated with Prenatal and Early-Life Exposures to Air Pollution: A Narrative Review.

(1) Background: The developmental origins of health and disease (DOHaD) hypothesis links adverse fetal exposures with developmental mal-adaptations and morbidity later in life. Short- and long-term exposures to air pollutants are known contributors to health outcomes; however, the potential for developmental health effects of air pollution exposures during gestation or early-childhood have yet to be reviewed and synthesized from a DOHaD lens. The objective of this study is to summarize the literature on cardiovascular and metabolic, respiratory, allergic, and neuropsychological health outcomes, from prenatal development through early childhood, associated with early-life exposures to outdoor air pollutants, including traffic-related and wildfire-generated air pollutants. (2) Methods: We conducted a search using PubMed and the references of articles previously known to the authors. We selected papers that investigated health outcomes during fetal or childhood development in association with early-life ambient or source-specific air pollution exposure. (3) Results: The current literature reports that prenatal and early-childhood exposures to ambient and traffic-related air pollutants are associated with a range of adverse outcomes in early life, including cardiovascular and metabolic, respiratory and allergic, and neurodevelopmental outcomes. Very few studies have investigated associations between wildfire-related air pollution exposure and health outcomes during prenatal, postnatal, or childhood development. (4) Conclusion: Evidence from January 2000 to January 2022 supports a role for prenatal and early-childhood air pollution exposures adversely affecting health outcomes during development. Future studies are needed to identify both detrimental air pollutants from the exposure mixture and critical exposure time periods, investigate emerging exposure sources such as wildfire, and develop feasible interventional tools.

Association Between Ambient Air Pollution and Birth Weight by Maternal Individual- and Neighborhood-Level Stressors.

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

In utero exposure to near-roadway air pollution and autism spectrum disorder in children.

IMPORTANCE: Previous studies have reported associations between in utero exposure to regional air pollution and autism spectrum disorders (ASD). In utero exposure to components of near-roadway air pollution (NRAP) has been linked to adverse neurodevelopment in animal models, but few studies have investigated NRAP association with ASD risk. OBJECTIVE: To identify ASD risk associated with in utero exposure to NRAP in a large, representative birth cohort. DESIGN, SETTING, AND PARTICIPANTS: This retrospective pregnancy cohort study included 314,391 mother-child pairs of singletons born between 2001 and 2014 at Kaiser Permanente Southern California (KPSC) hospitals. Maternal and child data were extracted from KPSC electronic medical records. Children were followed until: clinical diagnosis of ASD, non-KPSC membership, death, or December 31, 2019, whichever came first. Exposure to the complex NRAP mixture during pregnancy was assessed using line-source dispersion models to estimate fresh vehicle emissions from freeway and non-freeway sources at maternal addresses during pregnancy. Vehicular traffic load exposure was characterized using advanced telematic models combining traditional traffic counts and travel-demand models with cell phone and vehicle GPS data. Cox proportional-hazard models estimated hazard ratios (HR) of ASD associated with near-roadway traffic load and dispersion-modeled NRAP during pregnancy, adjusted for covariates. Non-freeway NRAP was analyzed using quintile distribution due to nonlinear associations with ASD. EXPOSURES: Average NRAP and traffic load exposure during pregnancy at maternal residential addresses. MAIN OUTCOMES: Clinical diagnosis of ASD. RESULTS: A total of 6,291 children (5,114 boys, 1,177 girls) were diagnosed with ASD. The risk of ASD was associated with pregnancy-average exposure to total NRAP [HR(95% CI): 1.03(1.00,1.05) per 5 ppb increase in dispersion-modeled NOx] and to non-freeway NRAP [HR(95% CI) comparing the highest to the lowest quintile: 1.19(1.11, 1.27)]. Total NRAP had a stronger association in boys than in girls, but the association with non-freeway NRAP did not differ by sex. The association of freeway NRAP with ASD risk was not statistically significant. Non-freeway traffic load exposure demonstrated associations with ASD consistent with those of NRAP and ASD. CONCLUSIONS: In utero exposure to near-roadway air pollution, particularly from non-freeway sources, may increase ASD risk in children.

Near-roadway air pollution associated with COVID-19 severity and mortality - Multiethnic cohort study in Southern California.

BACKGROUND: Air pollution exposure has been associated with increased risk of COVID-19 incidence and mortality by ecological analyses. Few studies have investigated the specific effect of traffic-related air pollution on COVID-19 severity. OBJECTIVE: To investigate the associations of near-roadway air pollution (NRAP) exposure with COVID-19 severity and mortality using individual-level exposure and outcome data. METHODS: The retrospective cohort includes 75,010 individuals (mean age 42.5 years, 54% female, 66% Hispanic) diagnosed with COVID-19 at Kaiser Permanente Southern California between 3/1/2020-8/31/2020. NRAP exposures from both freeways and non-freeways during 1-year prior to the COVID-19 diagnosis date were estimated based on residential address history using the CALINE4 line source dispersion model. Primary outcomes include COVID-19 severity defined as COVID-19-related hospitalizations, intensive respiratory support (IRS), intensive care unit (ICU) admissions within 30 days, and mortality within 60 days after COVID-19 diagnosis. Covariates including socio-characteristics and comorbidities were adjusted for in the analysis. RESULT: One standard deviation (SD) increase in 1-year-averaged non-freeway NRAP (0.5 ppb NOx) was associated with increased odds of COVID-19-related IRS and ICU admission [OR (95% CI): 1.07 (1.01, 1.13) and 1.11 (1.04, 1.19) respectively] and increased risk of mortality (HR = 1.10, 95% CI = 1.03, 1.18). The associations of non-freeway NRAP with COVID-19 outcomes were largely independent of the effect of regional fine particulate matter and nitrogen dioxide exposures. These associations were generally consistent across age, sex, and race/ethnicity subgroups. The associations of freeway and total NRAP with COVID-19 severity and mortality were not statistically significant. CONCLUSIONS: Data from this multiethnic cohort suggested that NRAP, particularly non-freeway exposure in Southern California, may be associated with increased risk of COVID-19 severity and mortality among COVID-19 infected patients. Future studies are needed to assess the impact of emerging COVID-19 variants and chemical components from freeway and non-freeway NRAP.

The Comprehensive Fire Information Reconciled Emissions (CFIRE) inventory: Wildland fire emissions developed for the 2011 and 2014 U.S. National Emissions Inventory.

Wildland fire emissions from both wildfires and prescribed fires represent a major component of overall U.S. emissions. Obtaining an accurate, time-resolved inventory of these emissions is important for many purposes, including to account for emissions of greenhouse gases and short-lived climate forcers, as well as to model air quality for health, regulatory, and planning purposes. For the U.S. Environmental Protection Agency's 2011 and 2014 National Emissions Inventories, a new methodology was developed to reconcile the wide range of available fire information sources into a single coherent inventory. The Comprehensive Fire Information Reconciled Emissions (CFIRE) inventory effort utilized satellite fire detections as well as a large number of national, state, tribal, and local databases. The methodology and results for CONUS and Alaska were documented and compared against other fire emissions databases, and the efficacy of the overall effort was evaluated. Results show the overall spatial pattern differences and relative seasonality of wildfires and prescribed fires across the country. Prescribed burn emissions occurred primarily in non-summer months were concentrated in the Southeast, Northwest, and lower Midwest, and were relatively consistent year to year. Wildfire emissions were much more variable but occurred primarily in the summer and fall. Overall, CFIRE represents a third of total emitted PM2.5 across all sources in the National Emissions Inventory, with prescribed fires accounting for nearly half of all CFIRE emissions. Compared with other wildland fire emissions inventories derived solely from satellite detections, the CFIRE inventory shows markedly increased emissions, reflecting the importance of the multiple national and regional databases included in CFIRE in capturing small fires and prescribed fires in particular. Implications: Wildland fire emissions inventories need to incorporate multiple sources of fire information in order to better represent the full range of fire activity, including prescribed burns and smaller fires. For the 2011 and 2014 U.S. National Emissions Inventory, a methodology was developed to collect, associate, and reconcile fire information from satellite data as well as a large number of national, regional, state, local, and tribal fire information databases across the country. The resulting emissions inventory shows the importance of this type of integration and reconciliation when compared against other emissions inventories for the same period.

Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke.

INTRODUCTION: Estimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use. METHODS: Using ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model. RESULTS: Ensemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 µg/m3 (R2: 0.94) and test RMSE of 2.29 µg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 µg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers. CONCLUSION: Our method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies.

Assessment of Ambient Air Toxics and Wood Smoke Pollution among Communities in Sacramento County.

Ambient air monitoring and phone survey data were collected in three environmental justice (EJ) and three non-EJ communities in Sacramento County during winter 2016-2017 to understand the differences in air toxics and in wood smoke pollution among communities. Concentrations of six hazardous air pollutants (HAPs) and black carbon (BC) from fossil fuel (BCff) were significantly higher at EJ communities versus non-EJ communities. BC from wood burning (BCwb) was significantly higher at non-EJ communities. Correlation analysis indicated that the six HAPs were predominantly from fossil fuel combustion sources, not from wood burning. The HAPs were moderately variable across sites (coefficient of divergence (COD) range of 0.07 for carbon tetrachloride to 0.28 for m- and p-xylenes), while BCff and BCwb were highly variable (COD values of 0.46 and 0.50). The BCwb was well correlated with levoglucosan (R2 of 0.68 to 0.95), indicating that BCwb was a robust indicator for wood burning. At the two permanent monitoring sites, wood burning comprised 29-39% of the fine particulate matter (PM2.5) on nights when PM2.5 concentrations were forecasted to be high. Phone survey data were consistent with study measurements; the only significant difference in the survey results among communities were that non-EJ residents burn with indoor devices more often than EJ residents.