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. 2026 Mar 11;10(3):e2025GH001389. doi: 10.1029/2025GH001389

Shorter Recovery Periods Between Smoke Waves: A Spatio‐Temporal Analysis in California (2006–2020)

Caitlin G Jones‐Ngo 1,✉, Chen Chen 1, Rosana Aguilera 1, Miriam E Marlier 2, Tarik Benmarhnia 1
PMCID: PMC12977299  PMID: 41821847

Abstract

The increasing frequency of wildfires in California, fueled by climate change through hotter, drier conditions, poses uncertain public health risks due to repeated wildfire smoke exposure. This study explores the “recovery period,” the time between smoke waves, which may offer respite from smoke impacts, including health risks and adaptation demands. We examine trends in wildfire smoke wave frequency, duration, and recovery periods in California from 2006 to 2020, aiming to assess repeated exposures and develop a framework to evaluate associated health risks via recovery periods. We define a smoke wave as two or more consecutive days with wildfire‐specific fine particulate matter (PM2.5) > 1 μg/m3, at the census tract level. Recovery periods are calculated as the days between smoke waves, ending with the first wave of 2021. We also examine community characteristics such as income, race, and education. From 2006 to 2010 to 2016–2020, we observed a 60% reduction in recovery periods, an 85% increase in smoke events, and longer durations. Spatial variability was substantial across census tracts, with the greatest reductions in recovery periods in Southern and Central Valley regions. Northern California, with the shortest recovery periods, showed minimal changes. Communities with higher proportions of minority race groups, single female householders, and lower incomes experienced the largest reductions in recovery period length. This study introduces a framework to assess the repeated impacts of smoke waves, highlighting changing spatio‐temporal patterns. Incorporating recovery periods into health risk assessments can guide public health strategies to address compounding risks from wildfire smoke.

Keywords: wildfire smoke, exposure, recovery period, compound events

Plain Language Summary

Wildfires in California are becoming more frequent due to climate change, but the risks of repeated exposure to smoke are not well understood. This study examines the “recovery period,” the time between smoke waves when air quality improves. We aimed to understand how often smoke waves occur, how long they last, and how much time people have to recover. Using data from 2006 to 2020, we explored how smoke wave patterns differ across communities, considering factors like race, ethnicity, and socioeconomic conditions. Our findings show that recovery periods have shortened by 60% and the number of smoke events has increased by 85%. Areas in Southern and Central California saw the largest reductions in recovery periods, while Northern California, which already had the shortest recovery times, saw minimal changes. We also found that communities with higher proportions of racial minorities, lower household incomes, and more single female‐headed households experienced the greatest reductions in recovery periods. This study introduces a new approach to understanding the repeated health effects of wildfire smoke and provides insights that can inform public health strategies to address the growing risks from increasingly frequent wildfires.

Key Points

  • Recovery period, the time between wildfire smoke waves, are shortening as smoke waves become more frequent in California

  • Recovery periods reduced more in Southern and Central California, while Northern California consistently had shorter recovery times

  • Communities with larger reductions in recovery periods had more racial minorities, lower income, and more single female‐headed households

1. Introduction

In recent decades, California has witnessed a staggering surge in both the frequency and intensity of wildfires. Smaller fires have driven increases in fire frequency by nearly 200% from the 1920s to late 2010s, while extreme mega‐fires (>10,000 ha) are primarily responsible for the dramatic increases in burned area during that period (Li & Banerjee, 2021). Climate change and variability, land management decisions, and anthropogenic pressures are worsening these wildfire extremes. Warming temperatures, compounded with changes in precipitation regimes, have contributed to longer wildfire seasons, driven by earlier spring fires (Westerling, 2016), and extreme fire weather conditions in autumn (Goss et al., 2020). Furthermore, climate change is projected to continue amplifying extreme fire conditions through the end of this century (Abatzoglou & Williams, 2016; MacDonald et al., 2023; Williams et al., 2019). The changing nature of large wildfires in California is underscored by multiple record‐breaking years in the last decade (Keeley & Syphard, 2021).

Unprecedented wildfire seasons in recent years have led to wide‐ranging health and environmental impacts across the globe (Bowman et al., 2017; Johnston et al., 2024), including in the United States (Higuera & Abatzoglou, 2021), Canada (Jain et al., 2024; Li et al., 2025), Australia (Boer et al., 2020), Europe (Li et al., 2024), and South America (de Oliveira‐Junior et al., 2021; Villagra & Paula, 2021). The growing threat of wildfire, particularly in the context of climate change and human encroachment into fire‐prone landscapes (Chen, Wu, et al., 2024, Chen, Schwarz et al., 2024), is compounded by social and place‐based vulnerabilities. Wildfires can pose greater risks to disadvantaged communities that are ill‐equipped to adapt to smoke harms (Chas‐Amil et al., 2022; Davies et al., 2018; Wigtil et al., 2016). Additionally, the mobility of wildfire smoke transports risks to broader communities (Palaiologou et al., 2019; Vargo et al., 2023; Wu et al., 2018). California's vast socioeconomic and demographic diversity, including some of the most densely populated regions in the United States, alongside its varied landscapes, shapes exposure to wildfire smoke and communities' adaptive capacity (Cooley et al., 2012). An assessment of population exposures to wildfire smoke in California, 2011 to 2021, revealed a 350% increase in the 5‐year average of person‐day exposures to heavy‐density smoke (Vargo et al., 2023). Wildfire smoke even reversed decades of progress in air pollution in California and is becoming the main source of fine particles emissions, classified as wildfire‐specific fine particulate matter (PM2.5) (Burke et al., 2023).

A growing body of evidence highlights the health burden associated with wildfire smoke exposure. A recent meta‐analysis shows substantial evidence linking wildfire smoke PM2.5 to acute effects of all‐cause mortality and respiratory morbidity, along with some, though inconsistent, evidence of cardiovascular impacts (Gould et al., 2024). Authors also found health studies have been limited in characterizing the dynamic and episodic nature of wildfire smoke exposures. In addition, wildfire smoke PM2.5 has been shown to be more harmful on population health than PM2.5 emitted from other sources; for example, in Southern California, a 10 μg/m3 increase in wildfire PM2.5 was associated with a 1.3%–10% increase in respiratory hospitalizations, compared to 0.67%–1.3% for non‐wildfire PM2.5 (Aguilera et al., 2021). In addition to this context, spatial and temporal patterns of wildfire smoke exposure warrant further investigation, including extended periods of wildfire smoke exposure and multiple wildfire smoke events that spatially or temporally compound.

These patterns align with a growing public health concern regarding compound climate hazards, that is, the combination of multiple hazards or drivers across time and/or space (Simpson et al., 2023; Zscheischler et al., 2020). For instance, dry, hot temperatures during extreme heat events can amplify wildfire episodes (Libonati et al., 2022; Vitolo et al., 2019). A few recent studies show compound wildfire smoke and extreme heat events are associated with synergistic health effects for all‐cause, respiratory, and cardiovascular morbidity and mortality (Chen, Wu, et al., 2024, Chen, Schwarz et al., 2024; Patel et al., 2019; Rahman et al., 2022; Uttajug et al., 2024) as well as preterm birth (Ha et al., 2024). However, wildfire smoke events can spatially or temporally compound with other wildfire smoke events as well. Simultaneous wildfire events in the Western United States, or spatially compounding hazards, are projected to double by 2051–2080, foreshadowing dangers for strained firefighting resources (Abatzoglou et al., 2021; Podschwit & Cullen, 2020). Additionally, studies on repeated smoke exposure for firefighters indicate that cumulative smoke inhalation during wildfire season exacerbates airway inflammation and reduces lung function, although it is not conclusive as to whether the reduction persists or returns to normal during the off‐season (D’Evelyn et al., 2022). However, repeated wildfire smoke exposures, or temporally compounding wildfire smoke events has not been described outside of firefighter populations.

As climate change continues to increase the frequency of wildfires in California and expose millions of individuals, the extent to which communities experience repeating wildfire smoke exposures remains unclear. Traditional assessment methods may underestimate risk if they do not consider the temporal and spatial dynamics of multiple smoke events (Zscheischler et al., 2018). Wildfire smoke events can last days, also referred to as smoke waves (Liu et al., 2016). A study characterizing long‐term wildfire smoke exposures in California found exposures to smoke waves over a year are significantly higher in rural areas (Casey, Kioumourtzoglou, et al., 2024). Consistent with other wildfire smoke vulnerability assessments (Davies et al., 2018; Jung et al., 2024; Wigtil et al., 2016), this study showed exposure to smoke waves can be disproportionate between disadvantaged and non‐disadvantaged communities, though this trend differed across years. It is also important to consider whether repeating wildfire smoke events may disproportionately burden highly exposed, and disadvantaged communities.

As wildfire smoke events increase, the time between smoke waves may be shortening. We define this interval as the recovery period, the time between smoke waves that may provide essential respite and recovery for the impacts of wildfire smoke exposure. Shorter recovery periods could heighten the risk of compounding health effects and present repeated adaptation demands, such as avoiding outdoor smoke exposure, evacuating to smoke free regions, or bearing the financial burden of indoor air filtration. In this study, we investigated spatio‐temporal patterns in smoke wave frequency, duration, and recovery periods across California from 2006 through 2020, using a spatially resolved daily wildfire‐specific estimate of fine particulate matter. We aimed to quantify repeated wildfire smoke exposures and identify communities experiencing shorter intervals between smoke waves in recent years. By characterizing these patterns, we sought to improve understanding of the dynamic nature of smoke exposure and provide a framework for future research into associated health outcomes and risk mitigation strategies, such as early warning systems and public health response actions.

2. Materials and Methods

We conducted a spatio‐temporal analysis of repeated wildfire smoke waves in California, 2006 to 2020. We examined wildfire smoke exposures at the census tract level combined with community characteristics from the US Census American Community Survey (ACS) estimates, including income, race and ethnicity, educational attainment, and age. To facilitate comparison over time, we stratified our study period into three periods (2006–2010, 2011–2015, and 2016–2020).

2.1. Smoke Wave Exposures

For our main analysis, we used wildfire‐specific estimates of fine particulate matter (PM2.5) at the census tract level, as provided by Casey, Benmarhnia, and Aguilera (2024). The methodology for generating these wildfire‐specific PM2.5 estimates was originally described by Aguilera et al. (2023) for ZIP Code Tabulation Areas (ZCTAs). Aguilera et al. employed a suite of machine learning models (gradient boosting, random forest, and deep learning techniques), along with a broad array of predictor variables such as air quality monitor data, aerosol optical depth, land cover, and meteorological conditions, to generate daily PM2.5 concentrations. Non‐wildfire‐related PM2.5 concentrations were imputed using a chained random forest algorithm, and the difference between modeled PM2.5 concentrations and non‐wildfire PM2.5 values is referred to as the wildfire‐specific PM2.5.

Using the wildfire‐specific PM2.5, smoke waves were defined as two or more consecutive days of smoke above pre‐specified thresholds. For our primary analysis, we selected a threshold of >1 μg/m3 to indicate detectable wildfire smoke exposure while reducing the influence of background noise and model uncertainty at very low concentrations. This definition is sensitive to relatively short, localized events that may be more frequent and impact the overall smoke free periods that provide respite. In sensitivity analyses, we evaluated two alternative thresholds to assess the robustness of smoke wave detection across exposure intensities: (a) the tract‐specific 90th percentile of wildfire‐PM2.5 values over the study period; and (b) a fixed threshold of >15 μg/m3, included to facilitate comparison with prior definitions of higher‐intensity smoke waves in the literature (Casey, Kioumourtzoglou, et al., 2024). We then calculated duration, frequency, and recovery period for smoke waves (Figure S1 in Supporting Information S1). Recovery period is calculated as the number of days from the previous smoke wave end date and the next smoke wave start date for each census tract. The final recovery period ends with the first smoke wave event of 2021 for each tract. It is important to note that census tracts with no smoke waves in 2021 (N = 7) have recovery periods extending to the end of the 2021 calendar year. We then conducted descriptive analysis of frequency, duration, and recovery period length for smoke waves over time and examine the spatial distribution with descriptive mapping.

2.2. Community Characteristics

We used ACS 5‐year estimates from 2006 to 2010 and 2016–2020 to investigate community level characteristics which may be disproportionate for repeated wildfire smoke exposures. This included educational attainment (population with high school education or less), race (population of White, Black, Asian, Pacific Islander, Native American, other race, and two or more races) and ethnicity (population of Hispanic or White, non‐Hispanic), population 65 years and older, households with a single female householder and no spouse present, and economic factors (median household income, unemployment rate, and percent living below poverty). We standardized estimates using z‐score for analysis with smoke wave data. We then compared the mean standardized sociodemographic factors by changes in recovery period from early years (2006–2010) to later years (2016–2020) using a 3‐level categorical variable for recovery period change (large decrease, moderate decrease, and minimal/no decrease or increase). A large decrease in recovery period indicates the number of days between smoke waves shortened by over 72%; moderate decrease is 10%–72%; and no/minimal change or increase is a 0%–10% decrease or an increase, that is, longer recovery periods.

3. Results

The average length of recovery periods across the study period and all tracts was 128 days, though there was a broad range for individual tracts from 2 days to multiple years without smoke waves (Table 1). From 2006 to 2020, California census tracts averaged 3–4 smoke wave events that were, on average, 3–4 days long.

Table 1.

Summary Statistics of Smoke Wave Recovery Period, Frequency, and Duration Across California Census Tracts From 2006 to 2020 and Stratified by Five‐Year Periods

Minimum 1st quartile Median Mean 3rd quartile Maximum
Recovery periods (days)
All years 2 5 20 128 228 2,912
2006–2010 2 7 44 208.8 282 2,912
2011–2015 2 10 50 164.9 301 1,567
2016–2020 2 4 12 76.3 64 1,940
Frequency (N)
All years 1 2 3 3.6 5 20
2006–2010 1 1 2 2.7 3 14
2011–2015 1 1 2 2.8 4 20
2016–2020 1 3 5 5 7 17
Duration (days)
All years 2 2.3 3 3.7 4 83
2006–2010 2 2.5 3.3 3.6 4.4 17.8
2011–2015 2 2 2.4 2.6 3 21
2016–2020 2 2.5 3.2 4.5 5.4 83
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Temporal variability in smoke wave characteristics was evident across the study period. In terms of exposure intensity, average tract‐level wildfire PM2.5 during smoke wave events for 2006–2020 was 6.45 μg/m3, although maximum wildfire PM2.5 during events reached as high as 421.1 μg/m3. Many tracts experienced their highest mean wildfire PM2.5 during smoke wave events in 2018 (Figure S2 in Supporting Information S1). By other measures, 2020 was an exceptional year for smoke wave events, with the annual average tract‐level smoke wave duration across California nearly doubling relative to previous study years (Figure S3 in Supporting Information S1).

Changes in the 5‐year averages across California census tracts from early years (2006–2010) to later years (2016–2020) showed shorter recovery periods between smoke waves (Figure S4 in Supporting Information S1), accompanied by longer durations (Figure S2 in Supporting Information S1) and increased frequencies of events (Figure S5 in Supporting Information S1; Table 1). Between these periods, the frequency of smoke wave events within census tracts increased by roughly 85%, with the highest frequency in 2017. At the same time, the recovery period shortened by over 60%.

Year‐to‐year variability of smoke wave patterns were not homogenous spatially across California census tracts. The spatial distribution of smoke wave patterns (frequency, duration, and recovery period) fluctuated, with shifts in smoke wave patterns across different geographic regions over time. Figure 1 shows the spatial distribution of recovery period length by year. Notably, the large smoke wave events in 2020 led to higher exposures across the state and much shorter periods between events (Figure 1). Following the break in smoke waves that many census tracts experienced starting in 2009, the time between smoke waves continued to shorten (Figure 2). Spatial patterns of smoke wave duration and frequency are available in the Appendix (Figures S6 and S7 in Supporting Information S1).

Figure 1.

Figure 1

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Average recovery period (days) between smoke waves for California census tracts, from 2006 to 2020. Darker colors indicate shorter time between smoke wave events. Some regions may experience no smoke waves during a year (indicated by blank, gray spaces); thus, the time between events can be longer than one year (365 days).

Figure 2.

Figure 2

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Mean, minimum, and maximum recovery periods averaged across California census tracts for each year, 2006 to 2020. Recovery period is the number of days between smoke waves for each tract, which is shown by the year of the previous smoke wave's end date; some tracts may have greater than one year (365 days) between smoke waves.

Our results show Northern California was consistently impacted by smoke waves in each year, often with the shortest recovery periods between events, thus, not experiencing as much change compared to Southern California and Central Valley over time. The Southern and Central regions had the biggest changes to recovery period length in recent years (Figure S8 in Supporting Information S1). Many census tracts in Southern and Central regions also had a long period without any smoke waves following 2009. However, smoke wave events spiked across the entire state in 2020, when census tracts experienced an average of about 46 smoke wave days during that year (Table S1 in Supporting Information S1).

The degree of reduction in recovery period length also differed by characteristics of census tracts during this timeframe (Figure 3). We observed larger reductions of recovery period from early years to later years for communities with higher proportions of minority race groups (Black, Asian, Hispanic, and Other race), single female householders, and lower median household income. Communities experiencing large decreases in recovery period length were primarily concentrated in Southern California, including densely populated coastal and inland urban centers. On the other hand, tracts with larger populations of White, multiracial (two or more race), and elderly adults (65 years and older) had lesser reductions, or even increases, in recovery period length. Many of these tracts were located in the Sierra Nevada and Northern California regions, which frequently experienced wildfire smoke waves throughout the study period. Our findings indicate that communities in Southern California with greater socioeconomic vulnerabilities are now experiencing a larger shift to shorter intervals between smoke wave events in recent year.

Figure 3.

Figure 3

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Mean sociodemographic characteristics of census tracts, standardized with z‐score, by categories of recovery period change from early years (2006–2010) to later years (2016–2020). A large decrease in recovery period indicates the number of days between smoke waves shortened by over 72%; moderate decrease is 10%–72%; and no/minimal change or increase is a 0%–10% decrease or an increase, that is, longer recovery periods.

4. Discussion

This study presents a new framework for analyzing repeating impacts of smoke waves in California and shows that spatio‐temporal patterns are changing in recent years. A novel measure of recovery periods is of particular importance, as the time between smoke wave events within California census tracts is shortening. Shorter recovery periods lead to compounding smoke events, providing less time for communities to recover and imposing repeated adaptation demands. The shift to shorter recovery periods is largely occurring in Southern California and the Central Valley regions. Although not explored in this analysis, these regions contend with different fire regimes including lower intensity prescribed fires as well as less frequent, high intensity wildfires. Prescribed fires have been shown to abate wildfire smoke exposure (Kiely et al., 2024; Schollaert et al., 2024); thus, it is important to consider tradeoffs in the shifting temporal dynamics of prescribed fire contributions to more frequent, though lower level smoke exposures. On the contrary, Northern California has consistently had shorter periods between smoke waves throughout this 15‐year period. Worsening wildfire seasons, such as the record‐breaking events in 2020, have resulted in an increase of compounding smoke events across the state.

Changes in recovery period between smoke waves were also accompanied by increases in the frequency and duration of smoke waves in California, with clear spatial variability. Throughout the study period, Northern California consistently experienced longer and more frequent smoke wave events. Despite relatively stable recovery periods over time, communities in this region remained highly exposed to wildfire smoke. Many of these communities are located within or near the wildland‐urban interface, where continued residential development increases vulnerability to both smoke and wildfire hazards (Chen, Wu, et al., 2024, Chen, Schwarz et al., 2024; Kumar et al., 2022). As such, repeated smoke exposures in Northern California underscore the need for sustained adaptation measures, such as land use planning, community preparedness, and investment in protective infrastructure, to manage long‐standing and recurrent risk.

In contrast, Southern California showed the most dramatic shortening in recovery period length over the study years, indicating a substantial shift toward more frequent wildfire smoke exposures with less time for recovery between events. These regions, including densely populated urban centers in coastal and inland areas, are characterized by high levels of social and environmental vulnerability (Cooley et al., 2012). The increasing frequency of repeated smoke exposures in these areas introduces novel public health challenges, particularly for populations already facing structural disadvantages that may limit their capacity to adapt. This presents a growing threat of compounding wildfire smoke events, and highlights the importance of targeted early warning systems and community‐specific interventions for compounding smoke exposure risks. Considering the differing regional realities will help to tailor public health protections and preparedness efforts: long‐term resilience building in consistently affected regions like Northern California, and proactive adaptation to newly emerging threats in Southern California.

In this context, recovery periods may play a key role in determining resilience to repeated wildfire smoke events. However, the current literature on the health effects of repeated wildfire smoke exposure is limited. One study found cumulative exposures to smoke during a wildfire season exacerbated respiratory health effects among firefighters, suggesting multiple smoke events can have accumulating impacts (Swiston et al., 2008). Another study implemented focus group discussions in unincorporated communities of Southern California in which residents described a range of health impacts from repeat smoke experiences, including respiratory and dermatological effects, sinus and eye irritation, and headaches (Hopfer et al., 2024). The study also highlighted concern about repeat smoke experiences amidst compounding risks within these communities, including poor waste management practices, outdoor occupational risks, and financial insecurities. Therefore, it is reasonable to suspect repeating wildfire smoke waves can temporally compound and exacerbate health and resiliency within communities. However, additional epidemiologic research is needed to broaden the knowledge on repeat smoke effects within the general population. Additionally, epidemiologic studies could build off this proposed recovery period framework to better understand how periods of respite modulate the relationship of compounding smoke events.

Our findings also emphasize the importance of viewing wildfire smoke recovery dynamics through the lens of compounding social and health vulnerabilities. Community characteristics were related to differences in recovery period reductions: census tracts with higher proportions of minority racial and ethnic groups, lower income levels, and single female householders experienced the largest reductions in recovery periods over time. While the health implications of shorter recovery periods remain uncertain without further epidemiologic research, these patterns raise concern for potential disproportionate impacts. These populations may be less equipped to adapt to a growing burden of repeated smoke exposure due to structural and resource‐based limitations, such as inadequate housing, limited access to healthcare, and occupational or caregiving constraints. Although California's diverse population and landscapes are unique, other parts of the United States and the world are also confronting intensifying wildfire seasons (Bowman et al., 2017; Johnston et al., 2024). Applying this recovery period framework to other regions could offer valuable insights into how exposure frequency intersects with local vulnerability profiles.

This study does have limitations. We used a specific set of definitions to measure smoke wave characteristics across years, however, it may also be valuable to consider cumulative concentrations of wildfire smoke PM2.5 exposures, seasonal differences in smoke wave characteristics, contributions of individual fire types (wildfire vs. prescribed fire) and the role of climate migration. There is evidence that repeated wildfires provides higher motivation for climate migration to lower risk areas (Mueller et al., 2009). While this is limited to wildfire events, which pose a greater threat to property than smoke, it is important to consider patterns of population movement when assessing repeated wildfire smoke over a long timeframe. Community dynamics may change over time and thus, our assessment of differential smoke wave impacts by community level characteristics is limited. Our findings may underestimate true exposure disparities, since communities at greater risk may have compounding social and place‐based disadvantages which can relate to increased susceptibility, greater exposure risk, or reduced adaptive capacity. These disadvantages may create greater vulnerabilities for communities experiencing frequent wildfire smoke exposure with little respite.

Additionally, the wildfire‐specific PM2.5 estimates used in this study are subject to uncertainty. These data were modeled using machine learning methods integrating satellite‐derived smoke plume information and land‐use and meteorological variables. While this approach has demonstrated strong predictive performance (Aguilera et al., 2023), some limitations remain. For example, satellite data may underestimate smoke plume extent due to cloud cover, and the method may, to a small extent, misclassify wildfire versus non‐wildfire PM2.5. Specific sources such as agricultural burns and prescribed fires, as well as chemical speciation of PM2.5, were not differentiated. Although such uncertainties are expected to be relatively infrequent, they may affect the precision of exposure estimates, particularly in years with extensive regional smoke coverage or limited ground monitoring data.

5. Conclusions

While many studies have focused on the acute effects of short‐term wildfire smoke exposure, fewer address the temporally compounding nature of wildfire smoke in California. This study introduces a framework to evaluate the recovery period between smoke waves, which may be a crucial factor in resiliency of communities affected by repeated wildfire smoke events. We show recovery periods are shortening with increasing frequencies and durations of wildfire smoke waves in California. It is important to incorporate this measure into the design of epidemiologic studies considering the episodic nature of wildfire smoke events over time. As smoke waves become longer and more frequent, it is essential to focus on factors that modulate adaptive capacity. Thus, improved public health strategies can be developed to manage the compounding risks.

Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

Supporting information

Supporting Information S1

GH2-10-e2025GH001389-s001.docx (1.3MB, docx)

Acknowledgments

This work was supported by Climate Action 2023 UC Innovation and Entrepreneurship Awards R02CE6859 and by the Office of Environmental Health Hazard Assessment (23‐E0031). MEM acknowledges support from the Climate and Wildfire Research Initiative at UCLA.

Data Availability Statement

Data for census tract level wildfire‐specific fine particulate matter are available in Casey, Benmarhnia, and Aguilera (2024).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Casey, J. , Benmarhnia, T. , & Aguilera, R. (2024). Daily census tract‐level wildfire fine particulate matter concentrations for California, 2006‐2020 (Version V1) [Dataset]. Harvard Dataverse. 10.7910/DVN/CICODO [DOI]

Supplementary Materials

Supporting Information S1

GH2-10-e2025GH001389-s001.docx (1.3MB, docx)

Data Availability Statement

Data for census tract level wildfire‐specific fine particulate matter are available in Casey, Benmarhnia, and Aguilera (2024).

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