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    Scientific Research Paper:
    Nature Sustainability
nature sustainability
https://doi.org/10.1038/s41893-025-01533-9
Article
A full list of affiliations appears at the end of the paper.
 e-mail: shanshan.li@monash.edu
Respiratory risks from wildfire-specific PM2.5
across multiple countries and territories
Under a warming climate, wildfires are becoming more frequent and
severe. Multicountry studies evaluating associations between wildfire fine
particulate matter (PM2.5) and respiratory hospitalizations are lacking.
Here we evaluate the short-term effects of wildfire-specific PM2.5 on
respiratory hospitalizations from 1,052 communities across Australia,
Brazil, Canada, Chile, New Zealand, Vietnam, Thailand and Taiwan, during
2000–2019. A 1 µg m−3 increase in wildfire-specific PM2.5 was associated
with increased hospitalization risks for all-cause respiratory, asthma,
chronic obstructive pulmonary disease, acute upper respiratory infection,
influenza and pneumonia by 0.36%, 0.48%, 0.38%, 0.42%, 0.79% and
0.36%, respectively. Higher risks were observed among populations ≤19 or
≥60 years old, from low-income or high non-wildfire PM2.5 communities,
and residing in Brazil, Thailand, Taiwan and Vietnam. Australia and New
Zealand exhibited a greater hospitalization risk for asthma associated with
wildfire-specific PM2.5. Compared with non-wildfire PM2.5, wildfire-specific
PM2.5 posed greater hospitalization risks for all respiratory diseases and
a greater burden of asthma. Wildfire-specific PM2.5 contributed to 42.4%
of PM2.5-linked respiratory hospitalizations, dominating in Thailand.
Overall, the substantial contribution of wildfire-specific PM2.5 to respiratory
hospitalizations demands continued mitigation and adaptation efforts
across most countries. Intervention should be prioritized for influenza,
children, adolescents, the elderly and populations in low-income or
high-polluted communities.
In recent decades, wildfires, encompassing all types of fire in vegetated
landscapes, have been increasingly reported worldwide. This trend is
particularly evident in Australia, the Brazilian Amazon, Europe, Russia,
Canada and the western United States1. Notably, Canada experienced
a record-breaking series of wildfires in 2023, which burned an esti-
mated 185,000 km2, impacting all provinces and territories2. Severe
wildfires were also experienced in the United States and Europe dur-
ing the 2023 fire season3. Climate change is expected to increase the
frequency, duration and intensity of extreme wildfire events4. The
global fire-prone area is projected to increase by 29%, by the end of
the twenty-first century5. This may result in amplified socioeconomic
and health burdens6. Wildfires harm human health primarily through
wildfire smoke (for example, toxic gases and particles), of which ~90%
of the total particle mass is comprised of fine particulate matter with
diameters ≤2.5 µm (PM2.5)7.
Compared to urban-sourced PM2.5, the higher submicronic particle
content8 and distinct chemical composition of wildfire-specific PM2.5
probably contribute to its increased toxicity at equivalent doses1,
potentially posing a greater threat to human health, with respira-
tory complications as a particular concern6,9. Despite the substantial
burden of wildfire-related health impacts on low- and middle-income
countries (LMICs)10, research investigating the health consequences
Received: 22 April 2024
Accepted: 21 February 2025
Published online: xx xx xxxx
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A list of authors and their affiliations appears at the end of the paper

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Respiratory hospitalization risk and wildfire-specific PM2.5
Wildfire-specific PM2.5 generated persistently elevated risks in res-
piratory hospitalizations on the current day and the previous day
of wildfire-specific PM2.5 exposure (lag 0 and lag 1) (Extended Data
Fig. 1). The hospitalization risk of respiratory diseases peaked on the
day of exposure (relative risk (RR) = 1.0030, 95% confidence interval
(CI) 1.0027–1.0033), followed by a lag of 1 day (RR = 1.0006, 1.0005–
1.0008), per 1 µg m−3 increase in wildfire-specific PM2.5. The risks were
markedly lower and the CIs contained 1, when longer lag day periods
were investigated (Extended Data Fig. 1). Therefore, in the final analyses,
we focused on cumulative RR over the lag of 0–1 days, expressed as RR.
When considering the pooled cumulative C–R relationship
between wildfire-specific PM2.5 and respiratory hospitalizations dur-
ing the lag 0–1 days, we observed a monotonically increasing linear
or supralinear curve for all categories of respiratory hospitalizations
examined. Overall, the RR of respiratory hospitalization from differ-
ent causes responded linearly to the elevating wildfire-specific PM2.5.
This finding was corroborated by community-specific quasi-Bayesian
information criterion (qBIC) values. In >92% of the study communities,
linear models demonstrated a better fit than nonlinear models, as
indicated by lower qBIC values (Supplementary Table 3). The strongest
relationship was observed for influenza, where the RR exhibited a nota-
ble initial increase before 20 µg m−3, followed by a gentler incline up to
50 µg m−3. Similar C–R patterns were observed for AURI, asthma and
pneumonia. The C–R curve of COPD exhibited the most pronounced
degree of linearity (Fig. 2).
Overall, wildfire-specific PM2.5 presented consistent positive
associations, for each of the respiratory hospitalizations examined.
A 1 µg m−3 increase in wildfire-specific PM2.5, during the lag 0–1 days,
was significantly associated with elevated risks in all-cause respira-
tory (RR = 1.0036, 1.0032–1.0038), asthma (RR = 1.0048, 1.0040–
1.0057), COPD (RR = 1.0038, 1.0032–1.0042), AURI (RR = 1.0042,
1.0032–1.0050), influenza (RR = 1.0079, 1.0059–1.0096) and pneu-
monia (RR = 1.0036, 1.0032–1.0040) (Table 1). Sensitivity analyses
confirmed the robustness of our results (Supplementary Figs. 1 and 2
and Supplementary Table 8).
We found that age, community income level, community
non-wildfire PM2.5 level and country or territory significantly modi-
fied the association between wildfire-specific PM2.5 and respiratory
hospitalization risks (Fig. 3 and Supplementary Table 5). Specifically,
individuals ≤19 or ≥60 years old presented a greater hospitalization
risk in all-cause respiratory disease and pneumonia, compared with
those aged 20–59 years old. Greater risks were observed in communi-
ties with high non-wildfire PM2.5 levels for all-cause respiratory disease,
COPD and pneumonia. In contrast, low non-wildfire PM2.5 commu-
nities presented a higher hospitalization risk for asthma. Further-
more, individuals in low-income communities experienced higher
hospitalization risks from all-cause respiratory diseases, COPD and
pneumonia, compared to higher-income communities. Conversely,
greater hospitalization risks for asthma were observed in high-income
communities. At the country and territory level, residents of Taiwan
generally experienced higher cause-specific risks than other countries
and territories. Whereas greater risks were observed, for all-cause
respiratory disease in Brazil, Thailand and Vietnam; asthma in New
Zealand and Australia; COPD, influenza and pneumonia in Brazil; and
AURI in Canada (Supplementary Table 5).
Annual attributable burden
Overall, 25,321 (20,478–30,114) all-cause respiratory hospitaliza-
tions were attributed to wildfire-specific PM2.5 annually, equating to
an attributable fraction (AF) of 1.42% (1.15–1.69%). For each year, on
average, up to 1.49% (0.86–2.10%) of asthma, 1.30% (0.89–1.70%) of
COPD, 1.77% (0.88–2.64%) of AURI, 2.84% (0.90–4.41%) of influenza and
1.57% (1.24–1.89%) of pneumonia hospitalizations, were attributed to
wildfire-specific PM2.5. When community income levels were examined,
of wildfire-specific PM2.5 in these regions remains surprisingly limited.
Previous investigations have largely been conducted in high-income
countries, such as the United States and Canada, focusing on a specific
local area or fire event9,11–17. This focus potentially hinders the generaliz-
ability of findings to other countries disproportionately burdened by
wildfires, such as Chile and Brazil. Furthermore, studies in LMICs are
imperative to inform targeted intervention and resource allocation
directed towards areas with the greatest need, ultimately contributing
to global health equity. To date, only two multicountry studies have
investigated the impacts of wildfire-specific PM2.5 on respiratory health.
One investigated a single outcome (acute respiratory infection) within
a specific population (children)18 and the other one focused solely on
all-cause respiratory mortality19, without covering other major types
of respiratory diseases.
Additionally, few multicountry studies have quantified the
spatiotemporal burden of respiratory diseases attributable to
wildfire-specific PM2.5, which limits their ability to account for the
dynamic vulnerability of populations over time. Further, previous
investigations used heterogeneous modelling approaches. This poses
methodological challenges in establishing a more representative
association between short-term wildfire-specific PM2.5 exposure
and respiratory diseases. Finally, few existing literature compared
the association of respiratory hospitalizations with wildfire and
non-wildfire PM2.5 exposure. Therefore, a systematic analysis of the
risks and burden of cause-specific respiratory diseases associated
with wildfire-specific PM2.5 is warranted, to provide more compara-
ble evidence, by applying a unified analytic framework to different
communities.
Here, by leveraging hospitalization data from eight countries and
territories, over 20 years, we aimed to: (1) characterize the concen-
tration–response (C–R) relationship between wildfire-specific PM2.5
and hospitalization risks for all-cause respiratory, asthma, chronic
obstructive pulmonary disease (COPD), acute upper respiratory
infection (AURI), influenza and pneumonia; (2) identify vulnerable
populations; (3) quantify the spatiotemporal trends of respiratory
hospitalizations attributable to wildfire-specific PM2.5; and (4) compare
the health impacts of wildfire and non-wildfire PM2.5 on respiratory
hospitalizations.
Results
Summary statistics
Annually, 1,777,941 all-cause respiratory, 130,618 asthma, 223,661
COPD, 97,275 AURI, 38,502 influenza and 692,691 pneumonia hospi-
talizations were included in our analyses. These data covered 1,052
communities, in eight countries and territories, during different
periods of 2000–2019. On average, there were approximately five
all-cause respiratory hospitalizations, two pneumonia hospitaliza-
tions and less than one hospitalization from asthma, COPD, AURI
and influenza per day (Supplementary Table 1). Countries and ter-
ritories contributed to a mean of 16.5 years (s.d. = 5.2) of the data.
Brazil contributed the highest number of respiratory hospitaliza-
tions, followed by Thailand (Supplementary Table 2). Overall, males,
children and adolescents, the elderly ≥60 years and individuals from
low-income communities or areas with higher non-wildfire PM2.5 lev-
els bore a slightly larger proportion of respiratory hospitalizations
relative to their counterparts (Supplementary Table 2). The median
daily concentration of wildfire-specific PM2.5 and non-wildfire PM2.5
across study communities was 1.4 (interquartile range (IQR) 3.6,
range 0.0–169.2) µg m−3 and 13.4 (IQR 11.0, range 0.0–198.4) µg m−3,
respectively. The median daily concentration of non-wildfire PM2.5
was highest in Chile, followed by Thailand and Taiwan (Supplemen-
tary Table 1). The average daily concentration of wildfire-specific
PM2.5 was pronounced in communities of Brazil, Thailand, Chile,
New South Wales Australia, Vietnam and Northwest Canada (Fig. 1
and Supplementary Table 1).

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a larger attributable burden was observed in low-income communi-
ties for most respiratory diseases and in middle-income communi-
ties for asthma. When examined at the country and territory level, the
highest cause-specific AFs were found in Taiwan, ranging from 2.29%
(1.82–2.76%) for all-cause respiratory diseases to 3.78% (1.65–5.86%)
for AURI. In contrast, a relatively lower AF was observed in Canada for
asthma (0.45% (0.12–0.77%)), COPD (0.19% (0.04–0.33%)) and AURI
(0.62% (0.18–1.05%)) (Table 2 and Supplementary Table 7).
Comparisons between wildfire-specific and non-wildfire PM2.5
Wildfires only constituted a small portion (17.1%) of ambient PM2.5,
compared to non-wildfire sources, with a median concentration of
1.4 (IQR 3.6) µg m−3 and 13.4 (IQR 11.0) µg m−3, respectively. However,
wildfire-specific PM2.5 was associated with a significantly greater risk
and contributed to a substantial proportion of hospitalizations for
all the major types of respiratory diseases examined, compared with
non-wildfire PM2.5 (Fig. 4 and Supplementary Tables 10 and 11).
The spatiotemporal pattern of annual AF of all-cause respiratory
hospitalization due to wildfire-specific PM2.5 varied. We found an over-
all increasing trend in annual AF. This pattern extended to Australia,
Canada, Chile, New Zealand and Taiwan during 2000–2019. Taiwan
experienced an exceptional rise during the most recent 5 years. In con-
trast, the annual AF in Brazil and Vietnam decreased over 2005–2019
and 2000–2019, respectively (Extended Data Fig. 2).
Taiwan
Vietnam
Chile
Australia
Brazil
0
5
10
15
20
25
No data
Canada
Average daily wildfire-specific PM2.5 (µg m–3)
Thailand
New Zealand
Fig. 1 | Geographical context of the wildfire-specific PM2.5 and study communities. The spatial distribution of average daily wildfire-specific PM2.5 (µg m−3) across
1,052 study communities in Canada, Brazil, New Zealand, Thailand, Australia, Chile, Vietnam and Taiwan during 2000–2019.

Nature Sustainability
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For most study periods, wildfire-specific PM2.5 contributed to a
substantial proportion of PM2.5-related respiratory hospitalizations,
especially in Brazil, Chile, New Zealand, Thailand and Taiwan. In Aus-
tralia, the annual AF difference (wildfire AF minus non-wildfire AF)
exhibited a continuous upward trend from 2005 to 2019, cumulating in
wildfire very close to non-wildfire for the first time in 2015–2019. In Bra-
zil, wildfire-specific PM2.5 carried a greater annual AF than non-wildfire
PM2.5 in 2010–2014, with the annual AF difference declining over time. A
notable reduction was exhibited in the contribution of wildfire-specific
PM2.5 to the PM2.5-related AF from 2010–2014 to 2015–2019. Declining
annual AF of wildfire PM2.5 in Canada and New Zealand (2005–2019)
was accompanied by a growing annual AF difference, especially in
New Zealand, where wildfire smoke emerged as the dominant source
of PM2.5-linked respiratory hospitalizations. From 2005 to 2014, Chile
experienced a rising trend in annual AF of wildfire PM2.5. In Taiwan, both
annual AF of wildfire-specific PM2.5 and AF differences increased from
2000 to 2019, respectively. In Vietnam, annual AF of wildfire-specific
and non-wildfire PM2.5 increased from 2000 to 2019, with their differ-
ences decreasing (Extended Data Fig. 2).
Discussion
To the best of our knowledge, this is the largest study investigating
the association between acute exposure to wildfire-specific PM2.5
and cause-specific respiratory hospitalizations. Overall, short-term
wildfire-specific PM2.5 exposure was linearly associated with elevated
risks in various respiratory hospitalizations, especially for influ-
enza, individuals ≤19 or ≥60 years old, populations in low-income or
high-polluted communities and residents of Brazil, Vietnam, Thai-
land and Taiwan. An estimated 1.42% (1.15–1.69%) of all-cause res-
piratory hospitalizations were attributable to wildfire-specific PM2.5,
increasing overall, in Australia, Taiwan and Vietnam during various
periods between 2010 and 2019. Compared with non-wildfire PM2.5,
wildfire-specific PM2.5 posed a greater hospitalization risk for all the
major type of respiratory diseases. Wildfire emerged as a notable source
of PM2.5-linked respiratory hospitalizations overall, in Brazil, Chile, New
Zealand, Thailand and Taiwan.
Our findings of the linear C–R relationship20,21 and the elevated
risks of respiratory hospitalizations with short-term exposure to
wildfire-specific PM2.5 were broadly in line with previous studies11–19.
However, they mostly focused on several cities or territories within a
single country and reported highly heterogeneous risk estimates. For
example, a study in Darwin, Australia, found a 9.1% (RR = 1.091, 1.023–
1.163) increase in all-cause respiratory hospitalizations per 10 µg m−3
increase in PM2.5 during bushfire events, one day after exposure22.
Another study of the 2003 southern California wildfires found a 2.8%
(RR = 1.028, 1.014–1.041) increase in respiratory hospitalizations for
10 µg m−3 increase in moving average PM2.5 exposure during the wildfire
period23. Others found that a 10 μg m−3 increase in wildfire-related PM2.5
was associated with a 5.09% (RR = 1.0509, 1.0473–1.0544) increase in
respiratory hospital admissions over 0–1 days after the exposure in
Brazil during 2000–201514. The effect varied across studies possibly
due to factors including study population and period, wildfire severity,
exposure assessment and time window and modelling strategy. Our
unified assessment approach across countries and territories ensured
robust comparability and avoids potential publication bias.
The underlying mechanism for the health effects of
wildfire-specific PM2.5 remains unclear. Nonetheless, the potential
pathways by which PM2.5 affects the respiratory system may also apply
to wildfire-specific PM2.5, including injury from free radical peroxida-
tion, unbalanced intracellular calcium homeostasis and inflamma-
tion24. Other mechanisms for influenza and asthma are provided in
Supplementary Discussion 1. In particular, the greater susceptibility
of influenza could be due to greater transmission and gene modifica-
tion of the influenza virus. Specifically, ambient PM2.5 is suggested as
a direct transmission mode for influenza virus infection to the human
alveolar epithelium, with nearly half (47%) of inhaled PM2.5 reaching the
alveolar epithelium, the primary target site for influenza infection25.
Furthermore, PM2.5 may not only act as a carrier but also influence the
virus itself. PM2.5 components potentially modify the influenza virus
genome25 and pre-exposure to PM2.5 may alter the antiviral response
of bronchial epithelial cells, increasing their susceptibility to infec-
tion26. The unique characteristics of wildfire-specific PM2.5, including
b    Asthma
c    COPD
a    All-cause respiratory
1.4
1.2
1.0
1.4
1.2
1.0
0
10
20
30
40
50
0
10
20
30
40
50
0
10
20
Wildfire-specific PM2.5 (µg m–3)
Wildfire-specific PM2.5 (µg m–3)
Wildfire-specific PM2.5 (µg m–3)
30
40
50
2 × 10
6
1 × 10
6
0
2 × 10
6
1 × 10
6
0
e    Influenza
f    Pneumonia
d    AURI
RR (95% CI)
RR (95% CI)
No. of observations
No. of observations
Fig. 2 | The pooled C–R curves for respiratory hospitalization risks from
wildfire-specific PM2.5 exposure during the lag 0–1 days. a–f, C–R curves for
all-cause respiratory disease (a), asthma (b), COPD (c), AURI (d), influenza (e)
and pneumonia (f). The centre of the error bands represents the mean effect
estimates comparing days with lower versus higher wildfire-specific PM2.5 levels
across 1,052 study communities, with error bands indicating uncertainty ranges
derived from the 95% CIs of 1,000 Monte Carlo samples. All the models were
controlled for temperature, relative humidity, non-wildfire PM2.5, day of week,
seasonality and long-term trend.

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Table 1 | Cumulative relative risk of cause-specific respiratory hospitalizations (95% CI) associated with per 1 µg m−3 increase
in wildfire-specific PM2.5 exposure during the lag 0–1 days across countries and territories
Subgroups
All-cause respiratorya
Asthmaa
COPDa
AURIa
Influenzaa
Pneumoniaa
Overall
1.0025 (1.0023,
1.0028)
1.0035 (1.0028,
1.0041)
1.0027 (1.0023,
1.0031)
1.0029 (1.0024,
1.0035)
1.0056 (1.0042,
1.0070)
1.0025 (1.0022, 1.0028)
Country/territory
  Australia
1.0014 (1.0009,
1.0019)a
1.0063 (1.0049,
1.0077)a
1.0024 (1.0014,
1.0034)a
1.0001 (0.9988,
1.0015)a
1.0053 (1.0011,
1.0095)a
1.0007 (0.9997, 1.0016)a
  Brazil
1.0030 (1.0027,
1.0034)a
1.0032 (1.0023,
1.0042)a
1.0037 (1.0031,
1.0044)a
1.0039 (1.0027,
1.0051)a
1.0057 (1.0032,
1.0082)a
1.0029 (1.0025, 1.0033)a
  Canada
1.0006 (0.9999,
1.0014)a
1.0038 (1.0010,
1.0066)a
1.0015 (1.0003,
1.0027)a
1.0052 (1.0015,
1.0089)a
1.0037 (0.9899,
1.0176)a
1.0007 (0.9996, 1.0019)a
  Chile
1.0006 (1.0001,
1.0010)a
1.0001 (0.9973,
1.0029)a
1.0003 (0.9998,
1.0007)a
1.0000 (0.9981,
1.0020)a
1.0006 (0.9989,
1.0024)a
1.0005 (1.0000, 1.0011)a
  New Zealand
1.0005 (0.9994,
1.0016)a
1.0076 (1.0044,
1.0108)a
0.9957 (0.9928,
0.9987)a
1.0003 (0.9972,
1.0033)a
1.0040 (0.9867,
1.0216)a
0.9991 (0.9967, 1.0015)a
  Thailand
1.0025 (1.0022,
1.0028)a
1.0020 (1.0013,
1.0027)a
1.0019 (1.0014,
1.0023)a
1.0031 (1.0025,
1.0037)a
1.0049 (1.0040,
1.0058)a
1.0024 (1.0019, 1.0029)a
  Taiwan
1.0120 (1.0094,
1.0146)a
1.0766 (0.9985,
1.1607)a
1.0196 (1.0059,
1.0335)a
1.0200 (1.0084,
1.0318)a
1.0415 (0.8775,
1.2363)a
1.0165 (1.0135, 1.0195)a
  Vietnam
1.0024 (1.0009,
1.0040)a
NA
NA
NA
NA
NA
A Wald-type test was used to test the significance of the effect modifications. NA is resulted from data paucity. aA two-sided P for difference across countries and territories <0.05.
RR (95% CI)
a
Sex
Female
Male
0.998
1.000
1.002
1.004
1.006
1.008
1.010
1.012
1.014
b
Age (years)
≤19
20–59
≥60
0.998
1.000
1.002
1.004
1.006
1.008
1.010
1.012
1.014
c
Non-wildfire PM2.5
Low
High
RR (95% CI)
All-cause
Asthma
AURI
Influenza
Pneumonia
COPD
0.994
0.996
0.998
1.000
1.002
1.004
1.006
1.008
1.010
1.012
1.014
d
0.994
0.996
0.998
1.000
1.002
1.004
1.006
1.008
1.010
1.012
1.014
All-cause
Asthma
AURI
Influenza
Pneumonia
COPD
Income level
Low
income
Middle
income
High
income
*
*
*
*
*
*
*
*
*
*
Fig. 3 | Cumulative relative risk for respiratory hospitalizations associated
with a 1 µg m−3 increase in wildfire-specific PM2.5 during lag 0–1 days.
a–d, Relative risk modified by sex (a), age (b), community non-wildfire PM2.5 level
(c) and community income level (d). The centre of the error bars represents
the mean effect estimates comparing days with lower versus higher wildfire-
specific PM2.5 levels across 1,052 study communities, with error bars indicating
uncertainty ranges derived from the 95% CIs of 1,000 Monte Carlo samples.
All the models were controlled for temperature, relative humidity, non-wildfire
PM2.5, day of week, seasonality and long-term trend. Income level was categorized
according to tertiles of the community-level GDP per capita: GDP per capita ≤
tertile 1 as low income; tertile 1 < GDP per capita ≤ tertile 2 as middle income;
and GDP per capita > tertile 2 as high income. Non-wildfire PM2.5 level was
classified according to the 50th percentile of non-wildfire PM2.5 concentration
distributions (17.25 µg m−3): average daily non-wildfire PM2.5 during the study
period ≤17.25 µg m−3 as low non-wildfire PM2.5 level; and >17.25 µg m−3 as high non-
wildfire PM2.5 level. A Wald-type test was used to test the significance of the effect
modifications. *A two-sided P for the difference is statistically significant (<0.05).

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its chemical composition and long-range transport capability, might
further facilitate this process, potentially increasing human suscepti-
bility to influenza infection.
Aligned with a previous study in Southern California, 200323,
we found that individuals ≤19 or ≥60 years old demonstrated a
greater vulnerability to respiratory hospitalization associated with
wildfire-specific PM2.5. Such stronger associations among children
and adolescents might be explained by environmental, behavioural
(for example, prolonged outdoor activities) and physiological factors
(for example, underdeveloped detoxification systems)27. However,
previous studies have yielded conflicting results. A meta-analysis
suggested that young people might be less vulnerable to adverse res-
piratory effects from wildfire smoke exposure than are adults28. This
discrepancy warrants further investigation.
Socioeconomic status played a complex role in the association
between wildfire-specific PM2.5 and respiratory diseases. Individuals
from disadvantaged communities experienced substantially amplified
risks in respiratory hospitalization associated with wildfire-specific
PM2.5, supported by a previous study in Northern California29. This
greater vulnerability may be multifactorial, possibly including more
childhood respiratory infections, lower housing conditions and indoor
air quality, deficient nutrition and occupational exposures30. Further
unravelling of how socioeconomic status modulates wildfire-specific
PM2.5-related health effects demands use of multidimensional measures
of socioeconomic status.
Non-wildfire PM2.5 significantly modified the association between
wildfire-specific PM2.5 and respiratory hospitalizations. Consistent
findings were observed across most respiratory diseases, indicating
that individuals residing in areas with higher non-wildfire PM2.5 levels
were more susceptible to wildfire smoke. This was probably due to
impaired lung function20 affected by chronic exposure to PM2.5 from
other sources. This finding is in line with a recent study from North
Carolina, United States, reporting that individuals residing in areas with
higher chronic PM2.5 exposure may exhibit heightened susceptibility
to hospitalization during acute PM2.5 spikes31. Additionally, chronic
inflammation resulting from regular exposures to higher levels of air
pollution31 may further exacerbate the population susceptibility, dur-
ing short-term increases in wildfire-specific PM2.5.
We observed significant spatiotemporal variations in hospital-
ization risks from respiratory diseases, related to wildfire-specific
PM2.5. This may be attributed to differences in exposure levels of
wildfire-specific and non-wildfire PM2.5, climate conditions, popula-
tion susceptibility, basic health status and socioeconomic status (Sup-
plementary Discussion 2). Specifically, as we discussed before, areas
with higher levels of air pollution may bear greater health risks from
wildfire smoke due to impaired lung function and chronic inflamma-
tion. This could contribute to the higher respiratory hospitalization
rates in Taiwan, Brazil, Thailand and Vietnam, regions with historically
higher median concentrations of non-wildfire PM2.5. Additionally, the
pronounced annual concentrations of wildfire-specific PM2.5 may also
play a role, a finding recently reported31. It is impractical for residents
to stay indoors or seek shelter for extended periods, during prolonged
wildfire smoke pollution events, as would be feasible for shorter periods,
such as in Brazil, Thailand and Vietnam. What is more, housing design
also varies across countries and territories. Hotter climates (tropical and
subtropical) tend to have more open housing, which offers less protec-
tion from outdoor pollution. It is noteworthy that stronger associations
of wildfire-specific PM2.5 with asthma hospitalization were found in
Australia and New Zealand. This may be explained by poorer resident
adaptability, substantial asthma burdens32,33 and high concentrations of
various allergens (for example, pollen, dust mites and fungal spores)34.
Furthermore, wildfire-specific PM2.5 toxicity can vary by biomass type
and fire intensity across communities35. This could also contribute to
the geographical disparities in the observed effect estimates.
During the study period, the proportions of respiratory hospi-
talizations attributable to wildfire-specific PM2.5 increased overall, in
Australia, Taiwan and Vietnam. This rising burden could be attributable
to increased population vulnerability and exposure to wildfire-specific
PM2.5 (refs. 36,37). However, a decreasing trend was exhibited in coun-
tries commonly considered wildfire-prone areas (for example, Brazil,
Canada and Chile). This may be due to improved resident adaptation
ability, increased protective measures taken by residents and a long
history of wildfire risk management38. Nonetheless, this does not dimin-
ish the severity or the need for addressing the wildfire-related health
burden in these regions. While both Brazil and Chile have witnessed
declines in the proportion of respiratory hospitalizations attributed
to wildfire-specific PM2.5 exposure, wildfires remained a important
contributor to PM2.5-linked respiratory hospitalizations in Brazil and
the dominant source in Chile. This highlights a greater threat of PM2.5
from wildfire than non-wildfire sources.
The public health significance of PM2.5 from wildfire and
non-wildfire sources could be different. A previous study reported
Table 2 | Annual attributable cases of cause-specific respiratory hospitalizations (N, 95% CI) associated with wildfire-specific
PM2.5 exposure during the lag 0–1 days by community income level and countries and territories
Subgroups
All-cause respiratory
Asthma
COPD
AURI
Influenza
Pneumonia
Overall
25,321 (20,478, 30,114)
1,949 (1,120, 2,739)
2,913 (1,994, 3,811)
1,724 (858, 2,566)
1,083 (342, 1,682)
10,866 (8,600, 13,105)
Income level
  Low income
16,676 (14,889, 18,423)
1,171 (810, 1,528)
2,133 (1,788, 2,468)
1,162 (865, 1,453)
786 (630, 937)
6,736 (5,819, 7,643)
  Middle income
9,867 (8,320, 11,445)
757 (538, 972)
1,197 (864, 1,522)
621 (429, 809)
389 (170, 595)
4,363 (3,485, 5,247)
  High income
872 (542, 1,200)
227 (173, 280)
188 (98, 277)
73 (12, 133)
188 (98, 277)
140 (6, 276)
Country/territory
  Australia
485 (323, 646)
186 (147, 224)
142 (86, 197)
4 (−36, 42)
36 (8, 62)
46 (−16, 106)
  Brazil
13,327 (11,924, 14,722)
1,333 (946, 1,715)
1,829 (1,513, 2,140)
712 (504, 918)
510 (297, 718)
6,666 (5,777, 7,558)
  Canada
220 (−21,461)
59 (16, 102)
143 (30, 254)
71 (21, 119)
12 (−44, 53)
56 (−35, 146)
  Chile
1,454 (323, 2,567)
10 (−226, 219)
80 (−62, 219)
7 (−279, 280)
22 (−39, 80)
545 (−24, 1,085)
  New Zealand
61 (−75, 198)
107 (63, 149)
−85 (−146, −26)
4 (−42, 50)
11 (−36, 57)
−22 (−80, 35)
  Thailand
6,575 (5,733, 7,408)
350 (227, 471)
925 (712, 1,134)
959 (781, 1,134)
596 (495, 694)
2,305 (1,876, 2,729)
  Taiwan
4,634 (3,675, 5,575)
10 (0, 17)
161 (49, 267)
239 (104, 370)
54 (−236, 222)
1,990 (1,646, 2,340)
  Vietnam
817 (322, 1,308)
NA
NA
NA
NA
NA
All the models were controlled for temperature, relative humidity, non-wildfire PM2.5, day of week, seasonality and long-term trend. NA is resulted from data paucity.

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that wildfire-specific PM2.5 may pose greater health risks than PM2.5
from other sources39. This may be due to the potential heightened
toxicity of wildfire-specific PM2.5, accounting for an increased pres-
ence of smaller particles (for example, submicrometre particles
and ultrafine particles) and a higher concentration of oxidative and
pro-inflammatory components (for example, polycyclic aromatic
hydrocarbons and aldehydes)1. This coincided with our findings that
wildfire-specific PM2.5 posed a greater hospitalization risk for various
respiratory diseases than did non-wildfire PM2.5. Therefore, continued
mitigation efforts are warranted to attenuate and reverse the rising
difference in the proportion of respiratory hospitalizations due to
wildfire and non-wildfire particles, especially in Chile, New Zealand,
Thailand, Taiwan and Vietnam.
As by far the largest study on the association between wildfire-
specific PM2.5 and respiratory hospitalizations, this study has several
strengths. First, we used an extensive multicountry dataset with high
statistical power and a unified well-established two-stage analytical
framework, including both the exposure assessment method and risk
assessment model. This ensured the robustness, generalizability and
comparison of the results across countries and territories. No previ-
ous study has done this comprehensive consistent assessment, for
cause-specific respiratory hospitalization with wildfire-specific PM2.5.
Second, the communities included in this study were predominantly
regions with a history of wildfires and thereby serve as areas suitable
for robust investigations on wildfire PM2.5 and respiratory health. Third,
we identified vulnerable populations from different causes, which can
inform the development of targeted interventions and aid in addressing
environmental injustice. Fourth, the spatiotemporal assessment of the
respiratory health burden attributable to wildfire-specific PM2.5 allows
for a better understanding of the extent to which wildfire air pollution
has affected different populations, in multiple countries and territories
over varying periods. This may aid in resource allocation and more
cost-effective policy-making. Finally, we compared the effect esti-
mates, AF and spatiotemporal burden of respiratory hospitalizations
attributable to wildfire and non-wildfire PM2.5 as few previous studies
have reported such findings40. This provided supportive quantitative
evidence on the stronger detrimental health effects of PM2.5 from
wildfire relative to other sources.
Some limitations should be acknowledged. Despite extensive spa-
tiotemporal coverage, our dataset was still incomplete. For example,
some country- and territory-specific effect estimates may not be fully
nationally representative as a result of the inclusion of a part of the com-
munities. Specifically, Taiwan only had data from six municipalities;
Australia only had data from New South Wales; and data from some
communities did not cover the full study period. Moreover, although
PM2.5 is the key component of wildfire smoke mixtures, other air pol-
lutants such as ozone were not considered in this analysis. However,
our results changed minimally when we controlled for wildfire-specific
ozone in the model across all investigated causes. This suggests that
the presence of wildfire-specific ozone has a negligible influence on
the estimated effects of wildfire-specific PM2.5 on respiratory hos-
pitalizations. Additionally, community-level wildfire-specific PM2.5
exposures were used in the analysis, which may not fully reflect per-
sonal exposures because of variations in housing quality and personal
protective behaviours. The grid resolution used may not fully capture
spatial variability, potentially leading to exposure measurement errors.
To address this, we used population-weighted exposure. Overall, our
results should be conservative given that a coarser exposure assign-
ment may bias the effect estimates towards the null41. Meanwhile, the
potential measurement error introduced by the uncertainties of the fire
emission inventory, the GEOS-Chem simulations and machine learning
models may also underestimate the effect estimates. Future studies
with expanded health data, coupled with broader spatiotemporal
coverage and more effect modifiers and air pollutants, could further
advance the assessment of the health impacts of wildfire-specific
air pollution.
Public health implications
Wildfire impacts are still likely to intensify as the global climate
continues to warm1. In the context of the notable contribution of
wildfire-specific PM2.5 exposure to respiratory burdens, prioritizing
robust mitigation and adaptation strategies across diverse countries
and territories emerges as a critical public health imperative, especially
for diseases, populations and areas experiencing a greater suscepti-
bility. Some solutions consist of raising awareness of wildfire-related
health risks targeted at policy-makers, clinicians and the public, improv-
ing emission control, closely monitoring wildfire air pollution levels
and strengthening community preparedness through resource plan-
ning and allocation.
Methods
Hospitalization and socioeconomic data
On the basis of the international classification of diseases nineth (ICD-9)
revision or ICD-10 codes, we obtained daily hospitalization data for
all-cause respiratory diseases, asthma, COPD, AURI, influenza and
pneumonia across 1,052 communities in eight countries and territo-
ries, with a median area of 5,185.3 km2 (IQR 11,664.2 km2) and a median
population of 139,256 (IQR 271,027). The hospitalization data for all
All-cause
Asthma
AURI
COPD
Influenza
Pneumonia
b
Attributable fraction (%, 95% CI)
0
2.50
5.00
7.50
a
RR (95% CI)
*
*
*
*
*
*
1.000
1.002
1.005
1.008
1.010
Wildfire-specific PM2.5
Non-wildfire PM2.5
Wildfire-specific PM2.5
Non-wildfire PM2.5
Fig. 4 | Comparisons in the health impacts of wildfire-specific and non-wildfire
PM2.5 on respiratory hospitalization. a, The relative risks of hospitalizations for
cause-specific respiratory disease per 1 µg m−3 increase in wildfire-specific and
non-wildfire PM2.5. b, The AFs of hospitalizations for cause-specific respiratory
diseases associated with wildfire-specific and non-wildfire PM2.5. The centre of
the error bars represents the mean effect estimates comparing days with lower
versus higher wildfire-specific PM2.5 levels across 1,052 study communities,
with error bars indicating uncertainty ranges derived from the 95% CIs of 1,000
Monte Carlo samples. Models for wildfire-specific PM2.5 were controlled for
temperature, relative humidity, non-wildfire PM2.5, day of week, seasonality and
long-term trend. Models for non-wildfire PM2.5 were controlled for temperature,
relative humidity, wildfire-specific PM2.5, day of week, seasonality and long-term
trend. A Wald-type test was used to test the significance of the difference between
effect estimates of wildfire-specific and non-wildfire PM2.5. *A two-sided P for
difference <0.05.

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countries and territories fell within the period 2000 to 2019, with dif-
fering start and/or end dates: for Australia, Brazil and New Zealand,
hospitalization data were from 2000 to 2019; for Canada, data were
from 2005 to 2019; for Chile, data were from 2001 to 2019; for Thai-
land, data were from 2015 to 2019; for Taiwan, data were from 2000
to 2018; and for Vietnam, data were from 2002 to 2015. Additional
details on data collection, ICD codes and data coverage are provided
in Supplementary Methods 1.
We estimated the community-level gross domestic product (GDP)
per capita, by dividing the total GDP by the population count in each com-
munity, using data from the global gridded GDP (10 × 10 km2) (ref. 42)
and the gridded population of the world (GPW, v.4, 1 × 1 km2) (ref. 43).
Community-specific population and total GDP were calculated by
aggregating the corresponding values of grid cells within each com-
munity. We then categorized community-specific socioeconomic
status, based on tertiles of GDP per capita, across study communities:
GDP per capita ≤ tertile 1 as low income; tertile 1 < GDP per capita ≤ ter-
tile 2 as middle income; and GDP per capita > tertile 2 as high income.
We also assigned the community non-wildfire PM2.5 level according to
the 50th percentile of non-wildfire PM2.5 concentration distribution
across study communities (≤17.25 µg m−3 as low level and >17.25 µg m−3
as high level)44.
Finally, we analysed a multicountry daily hospitalization dataset
of cause-specific respiratory diseases, across 1,052 communities, in
eight countries and territories, during different periods of 2000–
2019, characterized by age, sex, community income level, community
non-wildfire PM2.5 level and country and territory. More information
on data collection and processing is in Supplementary Methods 1.
Wildfire-specific PM2.5 and meteorological data
As detailed in our previous work37, daily concentrations of
wildfire-specific PM2.5 at a 0.25° × 0.25° (~28 × 28 km2) spatial reso-
lution during 2000–2019, were estimated using a chemical trans-
port model (GEOS-Chem) and calibrated through a machine learning
approach. Briefly, wildfire-specific PM2.5 was estimated as differences
in GEOS-Chem-derived simulations for total PM2.5, under two sce-
narios with and without wildfire emissions. The GEOS-Chem-derived
estimates of total PM2.5 from the original 2.0° × 2.5° spatial resolution
(~220 × 280 km2) were interpolated to 0.25° × 0.25° grid-cell resolution
to match other auxiliary variables used in machine learning calibra-
tion, using inverse distance weighted spatial interpolation10,37,45. Then
we trained a random forest machine learning model to calibrate the
GEOS-Chem-derived estimates of total PM2.5 against ground-level
daily total PM2.5 measurements, with daily gridded (0.25° × 0.25°
resolution) meteorological parameters (mean/minimum/maximum
temperature, temperature variations, relative humidity, wind speed,
precipitation, air pressure and ultraviolet radiation) and calendar
year, calendar month, day of week, day of year, longitude and latitude
as auxiliary variables37. According to tenfold cross-validation, the ran-
dom forest-calibrated total PM2.5 concentrations accounted for 91.0%
(root mean squared error = 8.5 µg m−3) of the variability observed in
ground-level measurements37. Finally, the calibrated wildfire-specific
PM2.5 concentrations were estimated by multiplying the calibrated total
PM2.5 concentrations with the GEOS-Chem-derived wildfire-to-all ratio
in each 0.25° × 0.25° grid cell.
We obtained the daily ambient temperature and dewpoint tem-
perature from the European Centre for medium-range weather fore-
casts reanalysis v.5 (ERA5)46, at the 0.25° × 0.25° grid-cell resolution.
These data were used to calculate the daily relative humidity for each
community. Details on the collection and calculation of meteorologi-
cal data are shown in Supplementary Methods 2. Subsequently, three
community-specific, daily data values were assigned to each study
community: the population-weighted wildfire-specific PM2.5 concen-
trations, temperature and relative humidity. For communities smaller
than the 0.25° × 0.25° grid, the value assigned to such a community is
the value of the grid cell it falls entirely within. The value assigned to a
community exceeding the grid size is the average value across all grid
cells covered at least partly by that community.
Statistical analysis
We implemented a standard two-stage analytic framework, to estimate
the association between short-term exposure to daily wildfire-specific
PM2.5 and cause-specific respiratory hospitalization. The two-stage
design is a flexible and computationally efficient analytical frame-
work commonly used in environmental epidemiology to model large,
heterogeneous data from multiple communities19,47–49. This approach
allows for separate modelling of community-specific characteristics,
preserving crucial local nuances and mitigating potential biases that
could arise from pooling diverse data into a single model (Supplemen-
tary Methods 3).
In the first stage, we used a quasi-Poisson regression with a dis-
tributed lag nonlinear model in each community, with the following
equation:
Yit ∼Poisson (μit) log (μit) = cb (Fire_PM2.5it, lag = 7)
+ ns (Tempit, d.f. = 6) + ns (RHit, d.f. = 3) + nonfire_PM2.5it
+ ns (Timeit, d.f. = 7/year) + βDOWit + α
Where Yit denotes the hospitalization counts in community i on day t;
cb is a distributed lag model incorporated with a crossbasis47 function
of a linear term over the current and previous seven (lag 0–7) days and
two internal knots placed at equally spaced values in the log scale of
lag days; ns is the natural cubic spline function; and d.f. is the degree of
freedom. To account for the confounding effects of temperature and
relative humidity (RH), we included moving averaged daily ambient
temperature (Temp) and RH during lag 0–7 days in the model. Daily
non-wildfire PM2.5 concentrations were adjusted to control for the
potential confounding effects of PM2.5 from other sources; time vari-
able was fitted in an ns term of seven degrees of freedom per year to
account for long-term trends and seasonality; DOW is the categorical
variable for day of the week to control for weekly pattern and β is its
coefficient; and α is the intercept.
In the second stage, we pooled the community-specific cumula-
tive effect estimates over the lag period within each country and ter-
ritory, using a random-effect meta-analytic model. This allowed us to
generate the country- and territory-specific estimates, as per a 1 µg m−3
increment in wildfire-specific PM2.5. Our preliminary analyses revealed
moderate heterogeneity in effect estimates across communities for
cause-specific respiratory hospitalization (I2 34–58%) (Supplementary
Table 4). Consequently, we pooled the community-specific estimates
to derive overall estimates.
To characterize the C–R relationship between wildfire-specific
PM2.5 and respiratory hospitalization, we fitted a B-spline function with
two knots at the 25th and 75th percentiles of mean wildfire-specific PM2.5
concentration distributions50–52. For each country and territory, indi-
vidual community percentiles were first averaged to obtain country-
and territory-level values, which were then used to compute an overall
average across all countries and territories50–52. To further test the
linearity of the C–R relationships, we compared community-specific
qBIC values of linear models with nonlinear models (Supplementary
Methods 4). We conducted various sensitivity analyses to assess the
robustness of our results (Supplementary Methods 5).
To explore potential effect modifiers, we performed subgroup
analyses by age, sex, community income level and community
non-wildfire PM2.5 level. We tested the significance of the effect modi-
fications using the Wald-type test53.
Under a causal assumption, we estimated attributable cases and AF
of respiratory hospitalizations attributable to short-term exposure to
wildfire-specific PM2.5, using the pooled country- and territory-specific

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https://doi.org/10.1038/s41893-025-01533-9
effect estimates, as detailed elsewhere54. Briefly, we calculated the
respiratory hospitalizations attributable to wildfire-specific PM2.5 in
each community, using pooled country- and territory-specific effect
estimates. Subsequently, we divided the total hospitalizations attrib-
utable to wildfire-specific PM2.5 by the total hospitalizations across all
the study communities or communities within a specific country and
territory, to obtain the overall and country- and territory-specific AF.
We quantified the uncertainty in attributable cases and AF using 95%
empirical CI, calculated with 1,000 Monte Carlo simulations54. We
further explored the temporal trends of attributable burden, which
were first calculated on the basis of year-specific effect estimates and
then aggregated by four different periods: 2000–2004, 2005–2009,
2010–2014 and 2015–2019.
To compare the health impacts of wildfire-specific PM2.5 and
non-wildfire PM2.5, we estimated the hospitalization risk for all respira-
tory diseases included, as per 1 µg m−3 increase in non-wildfire PM2.5. We
quantified the proportion of respiratory hospitalizations attributable
to non-wildfire PM2.5 during the study period. We measured the differ-
ence in the proportion of respiratory hospitalizations attributable to
wildfire-specific and non-wildfire PM2.5, as AF of wildfire-specific PM2.5
minus AF of non-wildfire PM2.5 (termed AF difference), across different
periods of countries and territories.
All analyses were implemented by R software (v.4.0.5 for Windows)
using mixmeta53 and dlnm55 R packages. A two-sided P < 0.05 was deter-
mined as statistically significant.
Inclusion and ethics
This study has been approved by the Monash University Human
Research Ethics Committee.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
Publicly available data are found here: the global gridded GDP (https://
www.nature.com/articles/sdata20184); the GPW (https://cmr.earth-
data.nasa.gov/search/concepts/C1597158029-SEDAC.html); surface
temperature and ambient dewpoint temperature from ERA5 (https://
www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5);
modern-era retrospective analysis for research and applications v.2
(MERRA-2) data, biomass burning emissions inventory of global fire
emissions database v.4.1 (GFED v.4.1) data and anthropogenic emis-
sions inventory of EDGAR v.4.2 data that support the GEOS-Chem
model development and wildfire-specific PM2.5 simulation in this study
are available from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/,
https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html
and http://edgar.jrc.ec.europa.eu/, respectively. For respiratory hos-
pitalization data, the authors are not permitted to share the data.
Interested researchers should contact the corresponding author S.L.
(shanshan.li@monash.edu) on a case-by-case basis. Source data are
provided with this paper.
Code availability
Analysis codes are available from the corresponding authors on request
and will be shared on https://github.com/Yiwen-Zhang-259/Multicoun-
try_Wildfire_Respiratory_Hospitalization.git.
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Acknowledgements
This study is supported by Australian Research Council
(DP210102076) and Australian National Health & Medical Research
Council (APP2000581). Yiwen Zhang is supported by the NHMRC
e-Asia Joint Research Program Grant (GNT2000581). R.X. is
supported by Monash Faculty of Medicine Nursing and Health
Science (FMNHS) Bridging Postdoctoral Fellowships 2022 and
VicHealth Postdoctoral Research Fellowships 2022. W. Huang and
T.Y. are supported by China Scholarship Council funds (W. Huang,
202006380055; T.Y., 201906320051). S.L. was supported by an
Emerging Leader Fellowship (GNT2009866) of the Australian
National Health and Medical Research Council. Y.G. was supported
by Career Development Fellowship (GNT1163693) and Leader
Fellowship (GNT2008813) of the Australian National Health and
Medical Research Council.
Author contributions
S.L. and Y.G. designed the study and are co-senior authors.
Yiwen Zhang conducted the statistical analysis and took lead in
drafting the manuscript and interpreting the results. R.X. estimated
the wildfire-specific PM2.5 exposure data. R.X. and W. Huang provided
substantial scientific input in interpreting the results and editing of
the manuscript. R.X., W. Huang, T.Y., P.Y., W.Y., Y.W., Y.L., Z.Y., B.W., K.J.,
J.S., M.J.A., A.J., Y.G. and S.L. revised and edited the manuscript. Yiwen
Zhang, R.X., W. Huang, T.Y., P.Y., W.Y., Y.W., Y.L., Z.Y. and B.W. cleaned
the data. A.C., B.J., D.G., E.L., F.H.J., G.M., L.D.K., Ying Zhang, G.G.M.,
J.H., J.A., Y.L.G., L.M., M.S.Z.S.C., P.H.N.S., P.M., P.B., S.H., W. Hu and
D.P. provided the data and contributed to the submitted version of the
manuscript. The corresponding author attests that all listed authors
meet authorship criteria and that no others meeting the criteria have
been omitted.

Nature Sustainability
Article
https://doi.org/10.1038/s41893-025-01533-9
Funding
Open access funding provided by Monash University.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41893-025-01533-9.
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41893-025-01533-9.
Correspondence and requests for materials should be addressed to
Shanshan Li.
Peer review information Nature Sustainability thanks Santu Ghosh,
Colleen Reid and Jennifer Stowell for their contribution to the peer
review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.
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© The Author(s) 2025, corrected publication 2025
1Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
2Monash Biomedicine Discovery Institute, Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia.
3School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia. 4School of Population Health, the University of
New South Wales, Sydney, New South Wales, Australia. 5School of Biological, Earth & Environmental Sciences, the University of New South Wales,
Sydney, New South Wales, Australia. 6School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada. 7Environmental Health
Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada. 8Menzies Institute for Medical Research, University of Tasmania, Hobart,
Tasmania, Australia. 9Sydney School of Public Health, The University of Sydney, Sydney, New South Wales, Australia. 10Public Health Research Analytics
and Methods for Evidence, Public Health Unit, Sydney Local Health District Camperdown, Sydney, New South Wales, Australia. 11School of Clinical
Medicine, University of New South Wales, Sydney, New South Wales, Australia. 12Faculty of Health and Medical Sciences, University of Western Australia,
Crawley, Western Australia, Australia. 13School of Earth, Atmosphere and Environment, Monash University, Melbourne, Victoria, Australia. 14Environmental
and Occupational Medicine, National Taiwan University and National Taiwan University Hospital, Taipei, Taiwan. 15School of Earth and Atmospheric
Sciences, Queensland University of Technology, Brisbane, Queensland, Australia. 16Department of Pathology, School of Medicine, University of São
Paulo, São Paulo, Brazil. 17School of Medicine, University of the Andes (Chile), Las Condes, Región Metropolitana, Las Condes, Chile. 18School of Public
Health, The University of Adelaide, Adelaide, South Australia, Australia. 19Department of Public Health, University of Otago, Wellington, New Zealand.
20School of Public Health & Social Work, Queensland University of Technology, Brisbane, Queensland, Australia. 21School of Public Health, University of
Queensland, Brisbane, Queensland, Australia. 22These authors contributed equally: Yuming Guo, Shanshan Li.
 e-mail: shanshan.li@monash.edu
Yiwen Zhang
  1, Rongbin Xu
  1, Wenzhong Huang
  1, Tingting Ye
  1, Pei Yu
  1, Wenhua Yu
  1, Yao Wu
  1,
Yanming Liu
  1, Zhengyu Yang
  1, Bo Wen
  1, Ke Ju1, Jiangning Song
  2, Michael J. Abramson
  3, Amanda Johnson1,
Anthony Capon
  3, Bin Jalaludin
  4, Donna Green5, Eric Lavigne6,7, Fay H. Johnston
  8, Geoffrey G. Morgan
  9,
Luke D. Knibbs9,10, Ying Zhang
  9, Guy Marks
  11, Jane Heyworth12, Julie Arblaster
  13, Yue Leon Guo
  14,
Lidia Morawska
  15, Micheline S. Z. S. Coelho
  16, Paulo H. N. Saldiva16, Patricia Matus17, Peng Bi
  18, Simon Hales
  19,
Wenbiao Hu20, Dung Phung
  21, Yuming Guo
  1,22 & Shanshan Li
  1,22

Nature Sustainability
Article
https://doi.org/10.1038/s41893-025-01533-9
Extended Data Fig. 1 | The respiratory hospitalization risks associated with a
1 µg/m3 increase in wildfire-specific PM2.5 over the lag 0–7 days. The center
of the error bars represents the mean effect estimates comparing days with
lower versus higher wildfire-specific PM2.5 levels across 1052 study communities,
with error bars indicating uncertainty ranges derived from the 95% CIs of 1,000
Monte Carlo samples. The models controlled for temperature, relative humidity,
non-wildfire PM2.5, day of week, seasonality and long-term trend. Abbreviations:
PM2.5, particulate matter with diameter ≤ 2.5 µm; CI, confidence interval.

Nature Sustainability
Article
https://doi.org/10.1038/s41893-025-01533-9
Extended Data Fig. 2 | Spatiotemporal pattern of average annual attributable
fraction (%) of all-cause respiratory hospitalizations, attributed to wildfire-
specific and non-wildfire PM2.5, during 2000–2019. 2000–04, years from
2000 to 2004; 2005–09, years from 2005 to 2009; 2010–14, years from
2010 to 2014; 2015–19, years from 2015 to 2019. The black points and line
curves represent the temporal trend of difference between the proportion of
hospitalizations attributable to wildfire-specific PM2.5 and non-wildfire PM2.5,
measured as AF of wildfire-specific PM2.5 minus AF of non-wildfire PM2.5. Models
for wildfire-specific PM2.5 were controlled for temperature, relative humidity,
non-wildfire PM2.5, day of week, seasonality and long-term trend. Models for
non-wildfire PM2.5 were controlled for temperature, relative humidity, wildfire-
specific PM2.5, day of week, seasonality and long-term trend. Abbreviations: AF,
attributable fraction; AUS, Australia; BRA, Brazil; CAN Canada; CHL, Chile; NZL,
New Zealand; THA, Thailand; TWN, Taiwan; and VNM, Vietnam.

    Scientific Research Findings:

Answer: We found that wildfire-specific PM2.5 emerged as a significant source of respiratory hospitalization risks from short-term PM2.5 exposure. It posed a greater risk for all major types of respiratory diseases than non-wildfire PM2.5. Annually, around 25,321 respiratory hospitalizations were attributable to wildfire-specific PM2.5, with its proportion relative to total respiratory hospitalizations increasing in Australia, Vietnam and Taiwan during 2000‒2019. Wildfire’s substantial contribution to PM2.5-linked respiratory hospitalizations demands continued relief efforts across most countries and territories. Vulnerable disease types and populations needing targeted intervention were influenza, children and adolescents, older individuals, individuals in low-income and high-polluted communities, and residents of Brazil, Thailand, Taiwan and Vietnam. Our findings can be generalized to regions sharing similar socio-demographic characteristics with our study communities. Despite extensive spatiotemporal coverage, this study cannot be interpreted as a global representative because it only included seven countries and territories with the best data availability.