Measuring long-term exposure to wildfire PM2.5 in California: Time-varying inequities in environmental burden.

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermittent and extreme daily wildfire PM2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM2.5 < 5 µg/m3; 2) days with non-zero wildfire PM2.5; 3) mean wildfire PM2.5 during peak exposure week; 4) smoke waves (≥2 consecutive days with <15 µg/m3 wildfire PM2.5); and 5) mean annual wildfire PM2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM2.5 exposure for several racial/ethnic groups.

The Burden of Wildfire Smoke on Respiratory Health in California at the Zip Code Level: Uncovering the Disproportionate Impacts of Differential Fine Particle Composition.

Wildfires constitute a growing source of extremely high levels of particulate matter that is less than 2.5 microns in diameter (PM2.5). Recently, toxicologic and epidemiologic studies have shown that PM2.5 generated from wildfires may have a greater health burden than PM2.5 generated from other pollutant sources. This study examined the impact of PM2.5 on hospitalizations for respiratory diseases in California between 2006 and 2019 using a health impact assessment approach that considers differential concentration-response functions (CRF) for PM2.5 from wildfire and non-wildfire sources of emissions. We quantified the burden of respiratory hospitalizations related to PM2.5 exposure at the zip code level through two different approaches: (a) naïve (considering the same CRF for all PM2.5 emissions) and (b) nuanced (considering different CRFs for PM2.5 from wildfires and from other sources). We conducted a Geographically Weighted Regression to analyze spatially varying relationships between the delta (i.e., the difference between the naïve and nuanced approaches) and the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). A higher attributable number of respiratory hospitalizations was found when accounting for the larger health burden of wildfire PM2.5. We found that, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13% as a result of not accounting for the higher CRF of wildfire-related PM2.5 throughout California. This underestimation was higher in northern California and areas with higher SVI rankings. The relationship between delta and SVI varied spatially across California. These findings can be useful for updating future air pollution guideline recommendations.

A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006-2020).

Though fine particulate matter (PM2.5) 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 PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 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 PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 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 PM2.5 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 PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 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 PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

Wildfire smoke knows no borders: Differential vulnerability to smoke effects on cardio-respiratory health in the San Diego-Tijuana region.

Exposure to fine particles in wildfire smoke is deleterious for human health and can increase cases of cardio-respiratory illnesses and related hospitalizations. Neighborhood-level risk factors can increase susceptibility to environmental hazards, such as air pollution from smoke, and the same exposure can lead to different health effects across populations. While the San Diego-Tijuana border can be exposed to the same wildfire smoke event, socio-demographic differences may drive differential effects on population health. We used the October 2007 wildfires, one the most devastating wildfire events in Southern California that brought smoke to the entire region, as a natural experiment to understand the differential effect of wildfire smoke on both sides of the border. We applied synthetic control methods to evaluate the effects of wildfire smoke on cardio-respiratory hospitalizations in the Municipality of Tijuana and San Diego County separately. During the study period (October 11th- October 26th, 2007), 2009 hospital admissions for cardio-respiratory diseases occurred in San Diego County while 37 hospital admissions were reported in the Municipality of Tijuana. The number of cases in Tijuana was much lower than San Diego, and a precise effect of wildfire smoke was detected in San Diego but not in Tijuana. However, social drivers can increase susceptibility to environmental hazards; the poverty rate in Tijuana is more than three times that of San Diego. Socio-demographics are important in modulating the effects of wildfire smoke and can be potentially useful in developing a concerted regional effort to protect populations on both sides of the border from the adverse health effects of wildfire smoke.

A Perspective on Pediatric Respiratory Outcomes During California Wildfires Due to Smoke and PM2.5 Exposure.

As wildfires increase in prevalence and intensity across California and globally, it is anticipated that more children will be exposed to wildfire smoke, and thus face associated adverse health outcomes. Here, we provide a concise summary of the respiratory effects of California's wildfires on pediatric healthcare utilization, examine global examples of wildfire smoke exposure within the pediatric population and associated physiological effects, and assess the efficacy of metrics used to measure and communicate air quality during wildfires within the United States and elsewhere.

Mediating Role of Fine Particles Abatement on Pediatric Respiratory Health During COVID-19 Stay-at-Home Order in San Diego County, California.

Lower respiratory tract infections disproportionately affect children and are one of the main causes of hospital referral and admission. COVID-19 stay-at-home orders in early 2020 led to substantial reductions in hospital admissions, but the specific contribution of changes in air quality through this natural experiment has not been examined. Capitalizing on the timing of the stay-at-home order, we quantified the specific contribution of fine-scale changes in PM2.5 concentrations to reduced respiratory emergency department (ED) visits in the pediatric population of San Diego County, California. We analyzed data on pediatric ED visits (n = 72,333) at the ZIP-code level for respiratory complaints obtained from the ED at Rady Children's Hospital in San Diego County (2015-2020) and ZIP-code level PM2.5 from an ensemble model integrating multiple machine learning algorithms. We examined the decrease in respiratory visits in the pediatric population attributable to the stay-at-home order and quantified the contribution of changes in PM2.5 exposure using mediation analysis (inverse of odds ratio weighting). Pediatric respiratory ED visits dropped during the stay-at-home order (starting on 19 March 2020). Immediately after this period, PM2.5 concentrations, relative to the counterfactual values based in the 4-year baseline period, also decreased with important spatial variability across ZIP codes in San Diego County. Overall, we found that decreases in PM2.5 attributed to the stay-at-home order contributed to explain 4% of the decrease in pediatric respiratory ED visits. We identified important spatial inequalities in the decreased incidence of pediatric respiratory illness and found that brief decline in air pollution levels contributed to a decrease in respiratory ED visits.

The socioeconomic landscape of the exposome during pregnancy.

BACKGROUND: While socioeconomic position (SEP) is consistently related to pregnancy and birth outcome disparities, relevant biological mechanisms are manifold, thus necessitating more comprehensive characterization of SEP-exposome associations during pregnancy. OBJECTIVES: We implemented an exposomic approach to systematically characterize the socioeconomic landscape of prenatal exposures in a setting where social segregation was less distinct in a hypotheses-generating manner. METHODS: We described the correlation structure of 134 prenatal exogenous and endogenous sources (e.g., micronutrients, hormones, immunomodulatory metabolites, environmental pollutants) collected in a diverse, population-representative, urban, high-income longitudinal mother-offspring cohort (N = 1341; 2009-2011). We examined the associations between maternal, paternal, household, and areal level SEP indicators and 134 exposures using multiple regressions adjusted for precision variables, as well as potential effect measure modification by ethnicity and nativity. Finally, we generated summary SEP indices using Multiple Correspondence Analysis to further explore possible curved relationships. RESULTS: Individual and household SEP were associated with anthropometric/adiposity measures, folate, omega-3 fatty acids, insulin-like growth factor-II, fasting glucose, and neopterin, an inflammatory marker. We observed paternal education was more strongly and consistently related to maternal exposures than maternal education. This was most apparent amongst couples discordant on education. Analyses revealed additional non-linear associations between areal composite SEP and particulate matter. Environmental contaminants (e.g., per- and polyfluoroalkyl substances) and micronutrients (e.g., folate and copper) showed opposing associations by ethnicity and nativity, respectively. DISCUSSION: SEP-exposome relationships are complex, non-linear, and context specific. Our findings reinforce the potential role of paternal contributions and context-specific modifiers of associations, such as between ethnicity and maternal diet-related exposures. Despite weak presumed areal clustering of individual exposures in our context, our approach reinforces subtle non-linearities in areal-level exposures.

Wildfire smoke impacts respiratory health more than fine particles from other sources: observational evidence from Southern California.

Wildfires are becoming more frequent and destructive in a changing climate. Fine particulate matter, PM2.5, in wildfire smoke adversely impacts human health. Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM2.5. Air quality regulations however assume that the toxicity of PM2.5 does not vary across different sources of emission. Assessing whether PM2.5 from wildfires is more or less harmful than PM2.5 from other sources is a pressing public health concern. Here, we isolate the wildfire-specific PM2.5 using a series of statistical approaches and exposure definitions. We found increases in respiratory hospitalizations ranging from 1.3 to up to 10% with a 10 µg m-3 increase in wildfire-specific PM2.5, compared to 0.67 to 1.3% associated with non-wildfire PM2.5. Our conclusions point to the need for air quality policies to consider the variability in PM2.5 impacts on human health according to the sources of emission.

Fine Particles in Wildfire Smoke and Pediatric Respiratory Health in California.

BACKGROUND AND OBJECTIVES: Exposure to airborne fine particles with diameters ≤2.5 µm (PM2.5) pollution is a well-established cause of respiratory diseases in children; whether wildfire-specific PM2.5 causes more damage, however, remains uncertain. We examine the associations between wildfire-specific PM2.5 and pediatric respiratory health during the period 2011-2017 in San Diego County, California, and compare these results with other sources of PM2.5. METHODS: Visits to emergency and urgent care facilities of Rady's Children Hospital network in San Diego County, California, by individuals (aged ≤19 years) with ≥1 of the following respiratory conditions: difficulty breathing, respiratory distress, wheezing, asthma, or cough were regressed on daily, community-level exposure to wildfire-specific PM2.5 and PM2.5 from ambient sources (eg, traffic emissions). RESULTS: A 10-unit increase in PM2.5 (from nonsmoke sources) was estimated to increase the number of admissions by 3.7% (95% confidence interval: 1.2% to 6.1%). In contrast, the effect of PM2.5 attributable to wildfire was estimated to be a 30.0% (95% confidence interval: 26.6% to 33.4%) increase in visits. CONCLUSIONS: Wildfire-specific PM2.5 was found to be ∼10 times more harmful on children's respiratory health than PM2.5 from other sources, particularly for children aged 0 to 5 years. Even relatively modest wildfires and associated PM2.5 resolved on our record produced major health impacts, particularly for younger children, in comparison with ambient PM2.5.

Wildfire particulate matter in Shasta County, California and respiratory and circulatory disease-related emergency department visits and mortality, 2013-2018.

Wildfire smoke harms health. We add to this literature by evaluating the health effects of California's 2018 Carr Fire and preceding wildfire seasons in Shasta County. METHODS: With data from the Shasta County Health and Human Services Agency, we examined the link between weekly wildfire fine particulate matter (PM2.5) exposure estimated using a spatiotemporal multiple imputation approach and emergency department (ED) visits and mortality using time-series models that controlled for temporal trends and temperature. RESULTS: Between 2013 and 2018, Shasta County experienced 19 weeks with average wildfire PM2.5 ≥5.5 µg/m3 (hereafter, "high wildfire PM2.5 concentration"). Among all Shasta County Zip Code Tabulation Areas (ZCTAs; n = 36), we detected no association between high wildfire PM2.5 concentrations and respiratory or circulatory disease-related ED visits or mortality. Subsequent analyses were confined to valley ZCTAs (n = 11, lower elevation, majority of population, worse air quality in general). In valley ZCTAs, high wildfire PM2.5 was associated with a 14.6% (95% confidence interval [CI] = 4.2, 24.9) increase in same-week respiratory disease-related ED visits but no increase in the subsequent 2 weeks nor on circulatory disease-related mortality or ED visits or all-cause mortality. Two weeks after high wildfire PM2.5 weeks, respiratory disease-related deaths decreased (-31.5%, 95% CI = -64.4, 1.5). The 2018 Carr Fire appeared to increase respiratory disease-related ED visits by 27.0% (95% CI = 4.0, 50.0) over expectation and possibly reduce circulatory disease-related deaths (-18.2%, 95% CI = -39.4, 2.9). CONCLUSIONS: As climate change fuels wildfire seasons, studies must continue to evaluate their health effects, particularly in highly exposed populations.

Hot and cold flavors of southern California's Santa Ana winds: their causes, trends, and links with wildfire.

Santa Ana winds (SAWs) are associated with anomalous temperatures in coastal Southern California (SoCal). As dry air flows over SoCal's coastal ranges on its way from the elevated Great Basin down to sea level, all SAWs warm adiabatically. Many but not all SAWs produce coastal heat events. The strongest regionally averaged SAWs tend to be cold. In fact, some of the hottest and coldest observed temperatures in coastal SoCal are linked to SAWs. We show that hot and cold SAWs are produced by distinct synoptic dynamics. High-amplitude anticyclonic flow around a blocking high pressure aloft anchored at the California coast produces hot SAWs. Cold SAWs result from anticyclonic Rossby wave breaking over the northwestern U.S. Hot SAWs are preceded by warming in the Great Basin and dry conditions across the Southwestern U.S. Precipitation over the Southwest, including SoCal, and snow accumulation in the Great Basin usually precede cold SAWs. Both SAW flavors, but especially the hot SAWs, yield low relative humidity at the coast. Although cold SAWs tend to be associated with the strongest winds, hot SAWs tend to last longer and preferentially favor wildfire growth. Historically, out of large (> 100 acres) SAW-spread wildfires, 90% were associated with hot SAWs, accounting for 95% of burned area. As health impacts of SAW-driven coastal fall, winter and spring heat waves and impacts of smoke from wildfires have been recently identified, our results have implications for designing early warning systems. The long-term warming trend in coastal temperatures associated with SAWs is focused on January-March, when hot and cold SAW frequency and temperature intensity have been increasing and decreasing, respectively, over our 71-year record. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00382-021-05802-z.

Respiratory hospitalizations and wildfire smoke: a spatiotemporal analysis of an extreme firestorm in San Diego County, California.

Wildfire smoke adversely impacts respiratory health as fine particles can penetrate deeply into the lungs. Epidemiological studies of differential impacts typically target population subgroups in terms of vulnerability to wildfire smoke. Such information is useful to customize smoke warnings and mitigation actions for specific groups of individuals. In addition to individual vulnerability, it is also important to assess spatial patterns of health impacts to identify vulnerable communities and tailor public health actions during wildfire smoke events. METHODS: We assess the spatiotemporal variation in respiratory hospitalizations in San Diego County during a set of major wildfires in 2007, which led to a substantial public health burden. We propose a spatial within-community matched design analysis, adapted to the study of wildfire impacts, coupled with a Bayesian Hierarchical Model, that explicitly considers the spatial variation of respiratory health associated with smoke exposure, compared to reference periods before and after wildfires. We estimate the signal-to-noise ratio to ultimately gauge the precision of the Bayesian model output. RESULTS: We find the highest excess hospitalizations in areas covered by smoke, mainly ZIP codes contained by and immediately downwind of wildfire perimeters, and that excess hospitalizations tend to follow the distribution of smoke plumes across space (ZIP codes) and time (days). CONCLUSIONS: Analyzing the spatiotemporal evolution of exposure to wildfire smoke is necessary due to variations in smoke plume extent, particularly in this region where the most damaging wildfires are associated with strong wind conditions.

Santa Ana Winds of Southern California Impact PM2.5 With and Without Smoke From Wildfires.

Fine particulate matter (PM2.5) raises human health concerns since it can deeply penetrate the respiratory system and enter the bloodstream, thus potentially impacting vital organs. Strong winds transport and disperse PM2.5, which can travel over long distances. Smoke from wildfires is a major episodic and seasonal hazard in Southern California (SoCal), where the onset of Santa Ana winds (SAWs) in early fall before the first rains of winter is associated with the region's most damaging wildfires. However, SAWs also tend to improve visibility as they sweep haze particles from highly polluted areas far out to sea. Previous studies characterizing PM2.5 in the region are limited in time span and spatial extent, and have either addressed only a single event in time or short time series at a limited set of sites. Here we study the space-time relationship between daily levels of PM2.5 in SoCal and SAWs spanning 1999-2012 and also further identify the impact of wildfire smoke on this relationship. We used a rolling correlation approach to characterize the spatial-temporal variability of daily SAW and PM2.5. SAWs tend to lower PM2.5 levels, particularly along the coast and in urban areas, in the absence of wildfires upwind. On the other hand, SAWs markedly increase PM2.5 in zip codes downwind of wildfires. These empirical relationships can be used to identify windows of vulnerability for public health and orient preventive measures.

Increase in Pediatric Respiratory Visits Associated with Santa Ana Wind-Driven Wildfire Smoke and PM2.5 Levels in San Diego County.

Rationale: There is significant evidence of increased healthcare utilization from cardiopulmonary causes in adults from exposure to wildfire smoke, but evidence in pediatric age groups is limited.Objectives: To quantify and examine the healthcare utilization effects of the December 2017 Lilac Fire in San Diego County among pediatric patients at the Rady Children's Hospital (RCH) emergency department and urgent care (UC) clinics.Methods: Using data from 2011 to 2017, including data on daily particulate matter <2.5 µm (PM2.5) in an inverse-distance interpolation model and RCH electronic medical records, we retrospectively analyzed pediatric respiratory visits at the RCH emergency department and UC clinics during the Santa Ana wind (SAW)-driven Lilac Fire from December 7 to 16, 2017. An interrupted time series study design was applied as our primary analysis to compare the observed pediatric respiratory visits from December 7 to 16, 2017 to what would have occurred in a counterfactual situation, namely, if the Lilac Fire had not occurred. A complementary descriptive spatial analysis was also used to evaluate the geographic distribution of respiratory visits in relationship to satellite imaging of the Lilac Fire and the associated wind pattern.Results: The Lilac Fire was associated with 16.0 (95% confidence interval [CI], 11.2-20.9) excess respiratory visits per day at the RCH emergency department across all pediatric age groups. Children aged 0 to 5 years had the highest absolute excess respiratory visits per day with 7.3 (95% CI, 3.0-11.7), whereas those aged 6 to 12 years had the highest relative increase in visits, with 3.4 (95% CI, 2.3-4.6). RCH UC clinics had similar results. The top five ZIP codes in San Diego County with the highest standard deviations of age-adjusted respiratory visits were all located generally downwind of the fire perimeter, as expected for the SAW pattern.Conclusions: We have demonstrated an increase in pediatric respiratory visits during the SAW-driven Lilac Fire in San Diego County in a patterned geographic distribution that is attributable to an increase in PM2.5 exposure. Younger children were particularly affected. Climate change is expected to result in more frequent and extensive wildfires in the region and will require greater preparedness and adaptation efforts to protect vulnerable populations, such as young children.

Atmospheric rivers impact California's coastal water quality via extreme precipitation.

Precipitation in California is projected to become more volatile: less frequent but more extreme as global warming pushes midlatitude frontal cyclones further poleward while bolstering the atmospheric rivers (ARs), which tend to produce the region's extreme rainfall. Pollutant accumulation and delivery to coastal waters can be expected to increase, as lengthening dry spells will be increasingly punctuated by more extreme precipitation events. Coastal pollution exposes human populations to high levels of fecal bacteria and associated pathogens, which can cause a variety of health impacts. Consequently, studying the impact of atmospheric rivers as the mechanism generating pulses of water pollution in coastal areas is relevant for public health and in the context of climate change. We aimed to quantify the links between precipitation events and water quality in order to explore meteorological causes as first steps toward effective early warning systems for the benefit of population health in California and beyond. We used historical gridded daily precipitation and weekly multiple fecal bacteria indicators at ~500 monitoring locations in California's coastal waters to identify weekly associations between precipitation and water quality during 2003-09 using canonical correlation analysis to account for the nested/clustered nature of longitudinal data. We then quantified, using a recently published catalog of atmospheric rivers, the proportion of coastal pollution events attributable to ARs. Association between precipitation and fecal bacteria was strongest in Southern California. Over two-thirds of coastal water pollution spikes exceeding one standard deviation were associated with ARs. This work highlights the importance of skillful AR landfall predictions in reducing vulnerability to extreme weather improving resilience of human populations in a varying and changing climate. Quantifying the impacts of ARs on waterborne diseases is important for planning effective preventive strategies for public health.

Quantifying Exposure to Wildfire Smoke Among Schoolchildren in California, 2006 to 2021.

This cross-sectional study quantifies exposure to wildfire particulate matter less than 2.5 µm among schoolchildren in California.

Linking in-stream nutrient flux to land use and inter-annual hydrological variability at the watershed scale.

The significance of nutrient inputs at the watershed scale is best expressed in terms of in-stream processes, compared to evaluating simple field measurements of nutrient inputs. Modeling tools are necessary to consider the complexity of river networks in the determination of the sources and processes by which nutrients are transported at the watershed scale. Mediterranean rivers are potentially vulnerable to climate change (decrease in precipitation and increase of extreme events), and identifying and quantifying nutrient pollution sources and their spatial distribution can improve water resource management at the watershed scale. We apply a hybrid process-based and statistical model (SPARROW, spatially referenced regression on watershed attributes) to a largely disturbed Mediterranean watershed in NE Spain in order to estimate the annual nitrate and phosphate loads reaching the drainage network. The model emphasized the contribution of in-stream processes in nutrient transport and retention, and the inter-annual (7 years) effects of hydrological variability on the export of nutrients from the landscape to water bodies. Although forest and grassland land cover types predominate, agricultural activities and human agglomerations were significant sources of nutrient enrichment. Nutrient flux apportionment was also linked to inter-annual hydrological variability. Exported nutrient load increased in the downstream direction and coincided with decreased in-stream nutrient removal, probably worsened by the significant chemical and geomorphological impairment found in the lower parts of the watershed.