BACKGROUND:Cerebral palsy (CP) is a permanent neuromotor disorder with childhood onset, and the etiology for most cases remains unexplained. METHODS:We conducted a population-based case-control study in California during 2000-2015, including all identified CP cases (N = 9,343) from the California's Department of Developmental Services and a control group of 20% random sample of live births without CP (N = 1,560,423). We employed a high-resolution (100 m) spatiotemporal model to estimate prenatal exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), and investigated their impacts on CP through single-pollutant, multiple-pollutant, and chemical-mixture models. Further, we examined the unmeasured confounding bias through the negative control exposure (NCE) design and assessed the potential heterogeneity across CP subtypes. FINDINGS:Prenatal exposures throughout pregnancy to ambient NO2 (per interquartile-range, OR = 1.14, 95% CI, 1.10-1.18) and O3 (OR = 1.08, 95% CI, 1.05-1.11) were associated with higher odds of CP in offspring. A stronger association emerged when three pollutants were modelled as a mixture (OR = 1.23, 95% CI, 1.17-1.29), especially in the first and third trimesters. Potential interactions were noted across pollutants, with associations for NO2 and O3 strengthening in multiple-pollutant adjusted models or within strata of low PM2.5 (i.e., below the median). PM2.5 was positively associated with CP only in strata of low O3 (OR = 1.14, 95% CI, 1.09-1.19). The 36 months post-birth NCE was not associated with CP, suggesting no strong confounding bias. CP involving ataxic and dyskinetic motor dysfunction was more sensitive to O3 exposure. CONCLUSIONS:Prenatal exposure to major ambient air pollutants was associated with offspring CP. Future studies to elucidate the underlying mechanisms are recommended.

BackgroundEmerging evidence links air pollution exposure to metabolic dysfunction; however, few studies have examined diabetes-related mortality in relation to ambient air pollutants using high-resolution exposure data at the population level. In the United States, particularly in large and geographically diverse states such as California, exposure contrasts and population heterogeneity provide an important setting to evaluate these associations.MethodsWe conducted a matched case–control analysis using California Department of Public Health (CDPH) Vital Records (2010–2021). Diabetes-related mortality events (ICD-10 E11) were identified as primary or contributory causes. Decedents (cases) were geocoded to residential addresses, and one-year rolling averages of fine particulate matter (PM2.5) before death were assigned as individual exposures. Each death record was matched to its selected controls based on month and year of birth and race-ethnicity. Controls were identified from the same statewide CDPH mortality database and were eligible because they had not died by the corresponding case’s date of death. Because the number of eligible controls varied across matched strata, controls were randomly sampled within each matched stratum to achieve an overall control-to-case ratio of approximately 2:1 for the study population. The final dataset included 60,824 diabetes-related deaths and 119,053 controls. Exposures were standardized by their interquartile range (IQR) and conditional logistic regression models estimated associations between 1 year rolling average fine particulate matter (PM2.5) exposure and odds of diabetes-related mortality, adjusting for age, sex, race-ethnicity, marital status, and education. Nitrogen dioxide (NO2) was included as a co-pollutant for confounding control.ResultsPM2.5 exposure (per 2.65 μg/m3 IQR increase) was associated with a 18% higher odds of diabetes-related mortality (OR = 1.18; 95% CI: 1.15–1.22) before traffic indicator NO2 adjustment and showed a stronger association with 21% higher odds (OR = 1.21; 95% CI: 1.17–1.25) after NO2 adjustment. Health economics analysis estimated that reducing PM2.5 exposure by its IQR could avoid losses of $31.2 million per 100,000 people.ConclusionHigher ambient PM2.5 exposure was associated with increased odds of diabetes-related mortality in California even after adjustment for NO2 and other impact factors. These findings support the need for continued strengthening of ambient air quality regulations.

California wildfires have grown increasingly frequent and intense over recent decades, raising serious public health concerns. In response, the California Air Resources Board (CARB) 2022 Scoping Plan outlines land management strategies to reduce wildfire risk and associated emissions under various climate change scenarios. This study evaluates the health benefits of CARB's official mitigation pathway, the S3 scenario, compared to a business-as-usual approach, using three global climate models (GCMs) and three future time slices. We apply the GEOS-Chem model to estimate fire-induced PM2.5 concentrations and use the U.S. EPA's BenMAP-CE tool, along with a wildfire-specific chronic mortality dose-response function, to assess associated morbidity and mortality. Results suggest that S3 can significantly reduce fire-related PM2.5 exposure, particularly in northern and central California where concentrations are typically highest-and where S3 treatments are most effective. In 2035 under the second generation Canadian Earth System Model GCM, for instance, S3 is associated with 1,927 fewer premature deaths and substantial reductions in asthma- and respiratory-related emergency room visits. However, health benefits vary by GCM and year, underscoring the influence of meteorological conditions on fire activity and health outcomes. These findings point to the importance of strategically timed and located land management actions and integrating climate variability into future mitigation planning.

BACKGROUND:Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with increasing prevalence. While genetics play a strong causal role, among environmental factors, air pollution (AP) exposure in pregnancy and infancy has been strongly endorsed as a risk factor. However, potential multigenerational impacts through the exposure of the grandmother during her pregnancy remain unexplored. METHODS:Using a multigenerational, population-based cohort from California spanning three decades (1990-2019), we examined the association between grandmother's gestational AP exposure (PM2.5, NO2, O3) and ASD risk in grandchildren using logistic regression per 1-IQR unit increase, adjusting for maternal exposure in pregnancy or infant's exposure in the first year of life. We used continuous AP exposure as well as a categorical variable representing high and/or low (above or below median) exposure levels for both the grandmaternal and maternal pregnancies. Pregnancy and first year of life AP exposures were assigned using a land-use regression model with advanced machine-learning approaches. RESULTS:We observed associations between PM2.5 (OR = 1.07, 95 % CI: 1.05, 1.10) and NO2 (OR = 1.09, 95 % CI: 1.05, 1.13) exposure during the grandmaternal pregnancy and increased ASD risk in the grandchild. However, only for PM2.5 did the increased effect estimates persist after adjusting for maternal pregnancy exposure (OR = 1.05; 95 % CI: 1.02, 1.08). High compared to low exposures in both grandmaternal and maternal pregnancies to PM2.5 (OR = 1.16, 95 % CI: 1.11, 1.23) and NO2 (OR = 1.12, 95 % CI: 1.06, 1.17) showed the strongest joint effects for ASD in the grandchild. CONCLUSIONS:Multigenerational exposure to air pollution, particularly PM2.5 exposure in grandmaternal pregnancy, may influence ASD risk. Our study also suggests that ASD risk due to air pollution may be compounded by multigenerational exposures.

BACKGROUND:Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contributor to the rise in ASD cases. This study investigated how the risk of ASD associated with prenatal and early postnatal exposure to specific air pollutants is modified by key sociodemographic factors, exploring vulnerable exposure periods. METHODS:We conducted a California population-based cohort study of 44,173 ASD cases among 2,371,379 children born between 2013 and 2018 (California birth registry), linked to California Department of Developmental Services (DDS) records to extract ASD diagnoses prior to the end of 2022. Prenatal and 1-year postnatal air pollution exposures [fine particulate matter with aerodynamic diameter <2.5μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3)] were estimated using an advanced land-use regression (LUR) spatiotemporal model with machine learning. Logistic regression was used to estimate odds ratios and 95% confidence intervals for four models: single pollutant at a single period (prenatal or postnatal), multi-pollutant at a single period, single pollutant with dual periods (prenatal and postnatal), and multi-pollutant with dual time period co-adjustment, adjusting for relevant individual and regional covariates. RESULTS:Prenatal and postnatal PM2.5 exposures increased ASD odds in all models. NO2 was associated with ASD pre- and postnatally in single and multi-pollutant models but only postnatally in dual time period models. In contrast, O3 showed the opposite pattern of NO2 with slightly negative associations in single and multi-pollutant models that turned positive for the prenatal period in dual time period models. The postnatal NO2 effect was strongest among Black and Hispanic children, suggesting higher contributions from traffic-related exposures. CONCLUSIONS:Exposure to specific air pollutants during pregnancy and in the postnatal periods is associated with an increased risk of ASD, with sociodemographic differences potentially highlighting exposure hot spots and sources as well as subpopulation vulnerabilities. https://doi.org/10.1289/EHP15573.

Greenspace can promote health via diverse pathways. A common approach to assessing greenspace exposure is to estimate vegetation availability within buffers surrounding locations where people reside or spend time. However, no clear framework for informed buffer selection exists, and choices made show considerable heterogeneity, impeding evidence synthesis and causal inference. In this Personal View conducted by an interdisciplinary panel of experts, we aimed to establish a framework for informed buffer selection for epidemiological studies on greenspace. We began by reviewing available approaches for the selection of buffer types, which range from single fixed-location approaches to high-resolution mobility-based activity-space approaches, as well as different buffer sizes. We then summarised the determinants of buffer type and size selection including health outcomes and underlying mechanisms, study population, contextual factors, and data characteristics. Finally, based on these determinants, we developed recommendations for future research. Buffer type and size selection should be hypothesis driven, reflecting presumed greenspace-health mechanisms. Buffer selection should target activity-based approaches where feasible, and multiple buffer sizes should be tested. Overall, the assessment of greenspace exposure should shift from ad-hoc approaches to personalised, multiscale, and context-specific methods. We call for standardising and reporting the rationale for buffer selection to minimise bias and enhance comparability and evidence synthesis across studies.

BACKGROUND:Autism spectrum disorder (ASD) prevalence has risen steadily in California (CA) over several decades, with environmental factors like air pollution (AP) increasingly implicated. This study investigates associations between prenatal exposure to both criteria AP and traffic-related air toxics and ASD risk for 1990-2018 births. METHODS:Utilizing CA Department of Public Health birth registry data from 1990 to 2018, linked with ASD diagnoses from the CA Department of Developmental Services (n = 13,591,003 children; ASD cases = 138,460, identified from birth year through 2022, allowing for a follow-up ranging from a minimum of 4 to a maximum of 32 years) we assessed prenatal exposure to PM2.5, NO2, O3, and six traffic-related air toxics (benzene, 1,3-butadiene, chromium, lead, nickel, zinc) using machine learning-enhanced land-use regression models. Logistic regression estimated odds ratios (OR) for ASD per interquartile range increase in pollutant levels across four periods (1990-1997, 1998-2004, 2005-2011, 2012-2018). Additionally, analyses were stratified by race/ethnicity, area-level socioeconomic status (SES), and region. RESULTS:Prenatal exposure to PM2.5 (OR:1.10; 95% CI: 1.04, 1.17) and NO2 (OR:1.25; 95% CI:1.16, 1.35) were associated with increased ASD risk, with effect sizes declining over time. When mutually adjusting for NO2, the association between PM2.5 exposure and ASD risk was attenuated, whereas the association with NO2 exposure remained largely unchanged. Among air toxics, benzene (OR:1.55; 95% CI:1.51, 1.59) and nickel (OR:1.32; 95% CI:1.21, 1.45) were strongly associated with ASD, associations persisting across time. Stratified analyses revealed that associations differed by race/ethnicity, SES, and region. Air toxics consistently exhibited elevated ASD risks, highlighting persistent vulnerabilities despite reductions in criteria pollutants. CONCLUSIONS:Prenatal/early-life exposure to AP, especially traffic-related toxics, is linked to increased ASD risk with temporal and spatial variability. While reductions in NO2 and PM2.5 lessen ASD risk, persistent associations with traffic-related pollutants like benzene and nickel highlight the need for targeted interventions.

The increasing operations of port craft and railway locomotives pose significant environmental and health challenges to vulnerable communities in California, where disadvantaged populations often bear a disproportionate burden of pollution exposure. This study employs digital inhaler sensors to monitor individual respiratory health and quantify the impacts of air pollution from port craft and railway activities on rescue medication use. Combining spatially resolved emissions data with meteorological inputs, we utilized advanced dispersion modeling to estimate pollution levels, focusing on fine particulate matter with an aerodynamic diameter equal to or less than 2.5 µm (PM2.5) and nitrogen dioxide (NO2). Our analysis revealed that a 10-µg m-3 increase in PM2.5 concentrations is associated with a 3.42% rise in daily rescue medication use when accounting for NO2 and PM2.5 jointly. The impacts are particularly pronounced in deprivation-high communities, where the effect of PM2.5 on rescue medication use is 3.8 times greater than in less disadvantaged areas. These findings highlight stark disparities in health outcomes, with marginalized populations facing heightened risks due to their proximity to emission sources and lack of adequate environmental safeguards. While substantial research has addressed air pollution impacts from roadway traffic and industrial activities, studies focusing specifically on port craft and railway emissions remain limited. Our study demonstrates the unique health burdens associated with these underexplored sources of pollution. Furthermore, the use of digital health technologies allows for unprecedented spatiotemporal resolution in tracking exposure and respiratory outcomes, providing a more nuanced understanding of the complex interplay between environmental and health dynamics. These results underscore the urgent need for targeted interventions, including cleaner technologies, stricter emission regulations, and policies aimed at mitigating exposure in vulnerable populations. Addressing these disparities is essential to advancing environmental justice and protecting public health.

Background: Air pollution is a global health concern, with fine particulate matter (PM2.5) constituents posing potential risks to human health, including children's neurodevelopment. Here we investigated associations between exposure during pregnancy and infancy to specific traffic-related PM2.5 components with Autism Spectrum Disorder (ASD) diagnosis. Methods: For exposure assessment, we estimated PM2.5 components related to traffic exposure (Barium [Ba] as a marker of brake dust and Zinc [Zn] as a tire wear marker, Black Carbon [BC]) and oxidative stress potential (OSP) markers (Hydroxyl Radical [OPOH] formation, Dithiothreitol activity [OPDTT], reactive oxygen species [ROS]) modeled with land use regression with co-kriging based on an intensive air monitoring campaign. We assigned exposures to a cohort of 444,651 children born in Southern California between 2016 and 2019, among whom 11,466 ASD cases were diagnosed between 2018 and 2022, Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained with logistic regression for single pollutant and PM2.5 mass co-adjusted models, also adjusting for sociodemographic characteristics. Results: Among PM2.5 components, we found the strongest positive association with ASD for our brake wear marker Ba (ORper IQR = 1.29, 95 % CI: 1.24, 1.34). This was followed by an increased risk for all PM2.5 oxidative stress potential markers; the strongest association was with ROS formation (ORper IQR = 1.22, 95 % CI: 1.18, 1.25). PM2.5 mass was linked to ASD in Hispanic and Black children, but not White children, while traffic-related PM2.5 and OSP markers increased ASD risk across all groups. In neighborhoods with the lowest socioeconomic status (SES), associations with ASD were stronger for all examined pollutants compared to higher SES areas. Conclusions: Our findings suggest that brake wear-related PM2.5 and PM2.5 OSP are associated with ASD diagnosis in Southern California. These results suggest that strategies aimed at reducing the public health impacts of PM2.5 need to consider specific sources.

Previous studies of air pollution and respiratory disease often relied on aggregated or lagged acute respiratory disease outcome measures, such as emergency department (ED) visits or hospitalizations, which may lack temporal and spatial resolution. This study investigated the association between daily air pollution exposure and respiratory symptoms among participants with asthma and chronic obstructive pulmonary disease (COPD), using a unique dataset passively collected by digital sensors monitoring inhaled medication use. The aggregated dataset comprised 456,779 short-acting beta-agonist (SABA) puffs across 3,386 people with asthma or COPD, between 2012 and 2019, across the state of California. Each rescue use was assigned space-time air pollution values of nitrogen dioxide (NO2), fine particulate matter with diameter ≤ 2.5 µm (PM2.5) and ozone (O3), derived from highly spatially resolved air pollution surfaces generated for the state of California. Statistical analyses were conducted using linear mixed models and random forest machine learning. Results indicate that daily air pollution exposure is positively associated with an increase in daily SABA use, for individual pollutants and simultaneous exposure to multiple pollutants. The advanced linear mixed model found that a 10-ppb increase in NO2, a 10 μg m-3 increase in PM2.5, and a 30-ppb increase in O3 were respectively associated with incidence rate ratios of SABA use of 1.025 (95 % CI: 1.013-1.038), 1.054 (95 % CI: 1.041-1.068), and 1.161 (95 % CI: 1.127-1.233), equivalent to a respective 2.5 %, 5.4 % and 16 % increase in SABA puffs over the mean. The random forest machine learning approach showed similar results. This study highlights the potential of digital health sensors to provide valuable insights into the daily health impacts of environmental exposures, offering a novel approach to epidemiological research that goes beyond residential address. Further investigation is warranted to explore potential causal relationships and to inform public health strategies for respiratory disease management.

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO 2 ), fine particulate matter (PM 2.5 ), and ozone (O 3 ) across California for 2012–2019. Our findings revealed opposite spatial patterns of NO 2 and PM 2.5 to that of O 3 . We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO 2 and PM 2.5 reductions and the advantaged neighborhoods experienced greatest rising O 3 concentrations. Further, day-to-day exposure variations decreased for NO 2 and O 3 . The disparity in NO 2 exposure decreased, while it persisted for O 3 . In addition, PM 2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.

Growing evidence from ecological studies suggests that chronic exposure to standard air pollutants (PM _2.5 , NO _2 , and ozone) exacerbates risks of coronavirus 2 (COVID-19) incidence and mortality. This study assessed the associations between an expanded list of air pollutants and COVID-19 incidence and mortality in Los Angeles. Annual mean exposure to air pollutants in 2019—including PM _0.1 mass, PM _2.5 mass, PM _2.5 elemental carbon (EC), PM _2.5 tracer from mobile sources, NO _2 , and ozone—were estimated at the ZIP code level in residential areas throughout Los Angeles. Negative binomial models and a spatial model were used to explore associations between health outcomes and exposures in single pollutant and multi-pollutant models. Exposure to PM _0.1 mass, ozone, NO _2 , and PM _2.5 EC were identified as risk factors for COVID-19 incidence and mortality. The results also suggest that PM _2.5 and NO _2 together may have synergistic effects on harmful COVID-19 outcomes. The study provides localized insights into the spatial and temporal associations between species-specific air pollutants and COVID-19 outcomes, highlighting the potential for policy recommendations to mitigate specific aspects of air pollution to protect public health.

The increasing popularity of wood fired heating appliances in cold winter climates has focused attention on assessment of woodsmoke exposures. Pollution from residential wood combustion (RWC) is a major concern in areas with valley topography where nighttime inversions limit the dispersion of pollutants from ground-level sources. An intensive characterization of ambient particulate mater (PM) from RWC was performed in northern New York State during winter 2008–2009 in an area where the 2005 U.S. EPA National Emissions Inventory shows RWC to be the largest source of PM2.5. Measurements of woodsmoke PM were made using optical scattering and absorption techniques during repeated night-time mobile monitoring to provide data with high spatial and temporal resolution; measurements were also made at six fixed sites for the study period to provide temporal context for the mobile measurements. The difference in optical absorption at near-infrared and near-ultraviolet wavelengths was used as a specific marker for woodsmoke PM. Woodsmoke was the only significant contributor to elevated night-time valley PM concentrations during mobile run nights; short-term (3 minute) PM concentrations frequently exceeded 100 µg/m3. Concentrations observed with mobile monitoring were consistently elevated at valley bottoms where the majority of the population lives, and approached zero outside of valleys. Data from fixed sites indicated that woodsmoke levels peaked near midnight, with a secondary peak around 7 AM and a mid-day minimum. These patterns are consistent with RWC use and diurnal patterns of atmospheric dispersion.

Background:Increased particulate matter <2.5 μm (PM2.5) air pollution is associated with adverse cardiovascular outcomes. However, its impact on patients with prior coronary artery bypass grafting (CABG) is unknown. Objectives:The purpose of this study was to evaluate the association between major adverse cardiovascular events (MACE) (defined as myocardial infarction, stroke, or cardiovascular death) and air pollution after CABG. Methods:We linked 26,403 U.S. veterans who underwent CABG (2010-2019) nationally with average annual ambient PM2.5 estimates using residential address. Over a 5-year median follow-up period, we identified MACE and fit a multivariable Cox proportional hazard model to determine the risk of MACE as per PM2.5 exposure. We also estimated the absolute potential reduction in PM2.5 attributable MACE simulating a hypothetical PM2.5 lowered to the revised World Health Organization standard of 5 μg/m3. Results:The observed median PM2.5 exposure was 7.9 μg/m3 (IQR: 7.0-8.9 μg/m3; 95% of patients were exposed to PM2.5 above 5 μg/m3). Increased PM2.5 exposure was associated with a higher 10-year MACE rate (first tertile 38% vs third tertile 45%; P < 0.001). Adjusting for demographic, racial, and clinical characteristics, a 10 μg/m3 increase in PM2.5 resulted in 27% relative risk for MACE (HR: 1.27, 95% CI: 1.10-1.46; P < 0.001). Currently, 10% of total MACE is attributable to PM2.5 exposure. Reducing maximum PM2.5 to 5 μg/m3 could result in a 7% absolute reduction in 10-year MACE rates. Conclusions:In this large nationwide CABG cohort, ambient PM2.5 air pollution was strongly associated with adverse 10-year cardiovascular outcomes. Reducing levels to World Health Organization-recommended standards would result in a substantial risk reduction at the population level.

California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)-NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM 2.5 ), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM 2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM 2.5 estimates. We used a concentration-response function for PM 2.5 , applying ZIP code–level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM 2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

California faces several serious direct and indirect climate exposures that can adversely affect public health, some of which are already occurring. The public health burden now and in the future will depend on atmospheric greenhouse gas concentrations, underlying population vulnerabilities, and adaptation efforts. Here, we present a structured review of recent literature to examine the leading climate risks to public health in California, including extreme heat, extreme precipitation, wildfires, air pollution, and infectious diseases. Comparisons among different climate-health pathways are difficult due to inconsistencies in study design regarding spatial and temporal scales and health outcomes examined. We find, however, that the current public health burden likely affects thousands of Californians each year, depending on the exposure pathway and health outcome. Further, while more evidence exists for direct and indirect proximal health effects that are the focus of this review, distal pathways (e.g., impacts of drought on nutrition) are more uncertain but could add to this burden. We find that climate adaptation measures can provide significant health benefits, particularly in disadvantaged communities. We conclude with priority recommendations for future analyses and solution-driven policy actions.