Existing studies regarding green space and mental health were mainly with general population and relied on satellite-based imagery, without considering eye-level green space and vegetation types, which is important to elucidate the underlying mechanisms linking green space and health. To improve green space exposure assessment, a machine learning model was evaluated and applied to investigate the associations between street green space and socioeconomic factors. Microsoft Bing Maps images in conjunction with deep learning was used to measure street green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. The results show that street view imagery coupled with deep learning can accurately and efficiently measure eye-level street green space and distinguish vegetation types (i.e., tree, low-lying vegetation, grass); street view data reflect different aspects of natural environments compared to satellite imagery. In Los Angeles County, lower socioeconomic status and racial/ethnic minority communities had substantively less street green space.

Relationships between green space and postpartum depression (PPD) has not been studied. I investigated the relationships between PPD and green space and examined the mediation effect of physical activity during pregnancy. Clinical data were obtained for 415,020 pregnancies in southern California (2008-2018) from Kaiser Permanente Southern California. PPD was based on both diagnostic codes and prescription medications. Multiple indicators were used to characterized green space exposure, including street view-based green space and vegetation types, satellite-based measures (i.e., NDVI, land-cover green space, and tree canopy cover), and proximity to the nearest park. The results indicate that street green space and tree coverage were associated with a decreased risk of PPD. Protection and restoration of trees may translate into a more pronounced reduction of PPD in southern California. Physical activity could be considered as one of the plausible pathways linking green space to depression (mediation effect: 9.6% -15.6%).

To further explore the underlying mechanism, an experimental study was conducted to examine physiological and affective responses to green space on stress recovery among pregnant women, using simulated green space exposure through virtual reality (VR). Participants (n=63) were randomly assigned to view one of three, 5-min, VR videos with different green space levels (i.e., low, moderate, and high) after a laboratory stressor, the Trier Social Stress Test. Physiological stress responses and self-reported affect were measured during the experiment. This study demonstrated that even a short immersion in VR green space environments, especially park-like setting, could effectively ease both physiological and affective stress and improve mental health during pregnancy.

This study contributes to the improvement of green space exposure assessment methodology for health studies, and provide evidence of the relationship between green space and maternal mental health during postpartum period, and potential pathways.

Hypertensive disorders in pregnancy (HDP) are a leading cause of maternal and fetal morbidity and mortality world-wide. According to a recent CDC report, HDP have increased by 3% in 2019, implicating an association with environmental etiology. There are numerous reports of adverse health effects caused by fine particulate matter (PM2.5), particularly on respiratory and cardiovascular health; however, their effects on changed CVS dynamic during pregnancy and associated HDP is unclear yet. Basic pathophysiology of HDP is largely unknown as of today. Since HDP is a cluster of different conditions including milder to severe variety, associations with air pollutants to both mild and severe categories of HDP examined separately will shed light on pathophysiology of HDP spectrum. Such understanding will help to develop preventive measures to reduce environmental contributor to HDP and HDP related unwanted pregnancy outcomes.

The associations between pollution and mild and severe categories of HDP are understudied in literature. Most of the existing studies are based on taking HDP as an umbrella diagnosis outcome, and results of these studies are ambiguous regarding the associations between air pollution and HDP. This is the first large study to examine the relationship between ambient air pollution with gestational hypertension (GH) and preeclampsia- eclampsia (PE-E) separately with personal exposure data of PM2.5 total mass and its constituents. This is a retrospective cohort study with an extensive literature review on patient population of Southern California Kaiser Permanente (KPSC) that includes 15 hospitals and 234 medical offices across Southern California with personal exposure data from residential addresses using fine-resolution geoscience-derived models’ data for PM2.5 total mass and its constituents for the period of 2008 to 2017. Data on demographic characteristics, including race, smoking status, insurance type, income category, residential history, medical records, birth records for maternal age, first pregnancy status, and individual lifestyle were collected from KPSC electronic health record (EHR) system. Information on gestational hypertension (GH), preeclampsia (PE) and eclampsia (E) were collected from the EHR using International Classification of Disease 9th and 10th revision (ICD-9 and ICD-10) codes for diagnosis. The descriptive statistics of selected demographic and pregnancy characteristics and air pollution levels were conducted that showed a total of 431,800 cases, with a prevalence of 4.7% (n= 20310) for GH, 5.3% (n=22,678) for preeclampsia and 0.1% (n=313) for eclampsia. There were no significant age differences between GH group and PE-E group. Prevalence of both GH and PE-E were highest among Hispanic mothers (44.1% and 54.1%, respectively), followed by non-Hispanic White mothers (34.6% and 21.1%, respectively). There were no significant differences in the prevalence of either of the conditions based on mothers’ education level, household income, smoking status, and season of conception. Prevalence of both the conditions were higher in primiparous women than respective controls [GH: 53.7% (non-GH: 40.8%) and PE-E: 56.6% (non-PE-E: 40.3%)]. Additionally, while the prevalence of both the conditions were less among underweight [GH: 0.9% (non-GH: 2.6%), PE-E:1.5%, (non-PE-E: 2.5%)] and normal body mass index (BMI) [GH: 22.9% (non-GH: 44.9%), PE:27.5%, (non-PE-E: 43.7% )] mothers than non-cases, there was no differences in the prevalence of either GH or PE-E among overweight mothers and higher prevalence of both the conditions among obese mothers [GH: 21.3% (non-GH: 14.5%), PE-E:21%, (non-PE-E: 14.8%)]. Associations between air pollution exposures during entire pregnancy and the risk of GH and PE-E was estimated by multivariate Cox regression analysis. Positive associations were observed between PE-E and black carbon, PM2.5 total mass and organic matter [Hazard Ratio (HR):1.12, CI:1.07-1.16, HR: 1.06, CI:1.03-1.08 and HR:1.06, CI:1.03-1.08 respectively]. Sensitivity analysis was conducted after further adjusting the model for pre-pregnancy Body Mass Index (BMI), primiparity and season, results consistent with that of the base model were observed for both GH and PE-E groups. To investigate whether differences in the associations among different race/ethnicity, pre-pregnancy BMI categories, mothers of different smoking habits and mothers from different economic condition exist, stratified analysis was conducted by race/ethnicity, maternal pre-pregnancy BMI, mother’s smoking status and house-hold income categories. It was observed that slightly low risks of PE-E are associated with exposures to sulphate among White mothers with significant heterogenicity (Cochrane p value: 0.04), but for the GH group, maximum negative associations were observed among Asian American mothers in exposure to most of the pollutants with significant heterogenicity (Cochrane p value: <0.01). There were no significant differences in the risks of PE-E among mothers of different BMI categories; However, the GH group shows most negative associations to exposure ammonium in underweight mothers and to exposure to PM2.5 total mass, nitrate, organic matter and black carbon in the groups of normal body weight category, all with significant heterogenicity (Cochrane p value <0.05). For smoking subgroup analysis, the current smoker category shows no significant associations of risks in exposure to any of the pollutants (Cochrane p value: <.05). For the GH group, negative associations are observed in exposure to black carbon most among current smoker mothers, followed by past and never smoker pregnant women. For the subgroup analysis of house-hold income, highest risk of PE-E is seen among mothers with household income than $43,667 in exposure to black carbon with significant heterogenicity (Cochrane p value: <.01); while in the GH group, upper economic condition mothers (household income > $71, 591) were seen to be most protective of risk of developing GH in exposure to black carbon (Cochrane p value: <.01). This study shows that exposure to air pollution with PM2.5 total mass and two of its constituents, black carbon and organic matter, increases risks to develop severe forms of HDP, while there is no statistically significant association with milder HDP.

Introduction: Wildfires, intensified by climate change, emit diverse pollutants, including PM2.5, fine particulate matter, which are implicated in respiratory health effects. The pollutant, fine particulate matter, refers to particles with an aerodynamic diameter of 2.5 micrometers or less (PM2.5). PM2.5 particles from wildfires can deeply penetrate the respiratory system and bloodstream, presenting substantial health hazards. Current regulatory frameworks often fail to differentiate between wildfire specific and ambient PM2.5, despite evidence indicating elevated health risks from wildfires. The concept of "smoke waves," representing periods of varying durations and heightened PM2.5 concentrations resulting from wildfires, offers a novel approach to characterizing exposure. This study seeks to examine the association between wildfire specific PM2.5 exposure and respiratory health outcomes.Methods: A time series analysis was conducted to assess the association of wildfire- specific PM2.5 exposure with respiratory health risk by examining respiratory-related hospitalizations and emergency department visits across various zip codes in the years 2017, 2018, and 2020. Daily concentrations of PM2.5 were analyzed alongside daily 6 hospitalization and emergency visit data. The Generalized Estimating Equations (GEE) model with Poisson regression was employed, with the primary exposure variable being wildfire specific PM2.5 exposure and the outcome variable being respiratory health risk. An autoregressive covariance structure (AR (1)) was included, and an offset term was incorporated by taking the logarithm of the total population. Demographic, temporal, and meteorological covariates were accounted for in the main analysis. All statistical analyses were conducted using SAS 9.4 (SAS Institute Inc.). Results: In the primary investigation, a notable positive association was observed between overall respiratory diseases and the presence of wildfire specific PM2.5 particulate matter. This association persisted across various lag periods with relative risk estimates ranging from 1.022 to 1.048 across various lag times. Upon disaggregating the data to examine disease-specific outcomes like asthma and chronic obstructive pulmonary disease (COPD), a consistent positive relationship emerged with wildfire-specific PM2.5 levels for emergency department visits and hospitalizations across all lag periods with relative risk estimates ranging from 1.025 to 1.048 across various lag times. Conclusion: We observed a strong association between wildfire specific PM2.5 exposure and respiratory health outcomes, showing a consistent positive association across different lag periods. Elevated risks were observed for overall respiratory disease, including asthma and COPD. It was also found that personal behaviors may assist in mitigating risk during smoke waves with increased intensity and similar duration. These results support discerning wildfire PM2.5 from ambient levels and highlight the need to further understand this association and its potential consequences.

Air pollution exposure has been known to increase the risk of several adverse birth outcomes, including postpartum depression (PPD) and gestational diabetes mellitus (GDM). In March 2020, the COVID-19 pandemic caused the state of California to shut down to prevent the spread of the virus. This led to changes in air pollution concentrations. Understanding how these changes influenced the association between air pollution and both PPD and GDM will provide further information into the health impacts seen during the COVID-19 pandemic period. Therefore, we sought to investigate the association between air pollution exposure and the risk of PPD and GDM. We also incorporated the impacts of experiencing the pandemic during and after pregnancy to assess whether these associations changed. Patient records from Kaiser Permanente Southern California (KPSC) electronic health records (EHRs) provided health data, socioeconomic status (SES) data, and residential address histories. Pollution data for monthly averages of particulate matter with an aerodynamic diameter ≤ 2.5 µg/m3 (PM2.5), particulate matter with an aerodynamic diameter ≤ 10 µg/m3 (PM10), nitrogen dioxide (NO2), and 8-hour ozone (O3) was spatiotemporally linked to participant residential addresses. A discrete time approach with pooled logistic regression was used for PPD models, while Cox proportional hazard models were used for GDM analyses. Subgroup analyses were performed with Cox proportional hazard models, and to investigate whether experiencing the pandemic before conception, after birth, or during pregnancy altered the associations with air pollution. We found increased risks of PPD with exposure to O3 during pregnancy and in the postpartum period; PM2.5 had significantly negative associations with PPD risk in the late pregnancy and postpartum periods. Hispanic, white, or multi-racial/other mothers, or mothers with higher incomes were more susceptible to PPD risk with ozone exposure. Positive associations between GDM risk and exposure to PM2.5, PM10, and O3 were found; no significant associations were seen with NO2 exposure, except for negative associations with second trimester exposure. Mothers with more than a college education had greater associations between GDM and exposure to all pollutants, including NO2. For PPD analyses, mothers were grouped by COVID time: not impacted (gave birth before March 2019), postpartum (gave birth between March 2019-March 2020), pregnant (pregnant in March 2020), or conceived after (date of conception after March 2020). Experiencing the COVID-19 pandemic while pregnant or in the 12-month postpartum period was positively associated with exposure to PM10 and O3. For GDM analyses, COVID time groups included: not impacted (gave birth before March 2020), pregnant (pregnant in March 2020), or conceived after (date of conception after March 2020). GDM risk was positively associated with exposure to PM2.5 for mothers who were pregnant when the pandemic began. Overall, this study shows the associations between air pollution exposure and the risk of PPD and GDM have changed since the COVID-19 outbreak. The results were different from our group’s previous studies, which found positive associations between PM exposure and PPD, and positive associations between NO2 exposure and GDM. These differences may be explained by decreases in ambient air pollution levels during the COVID-19 pandemic period, or by altered PM2.5 constituent levels. Future studies should continue to investigate the association between air pollution and maternal health during the pandemic period, especially the association with PM2.5 constituent levels.

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