Estimation of hospital visits for respiratory diseases attributable to PM10 from vegetation fire smoke and health impacts of regulatory intervention in Upper Northern Thailand

Abstract

The air quality in Upper Northern Thailand (UNT) deteriorates during seasonal vegetation fire events, causing adverse effects especially on respiratory health outcomes. This study aimed to quantitatively estimate respiratory morbidity from vegetation fire smoke exposure, and to assess the impact of a burning ban enforced in 2016 on morbidity burden in UNT. We computed daily population exposure to fire-originated PM10 and estimated its health burden during a 5-year period from 2014 to 2018 using daily fire-originated PM10 concentration and the concentration–response function for short-term exposure to PM10 from vegetation fire smoke and respiratory morbidity. In subgroups classified as children and older adults, the health burden of respiratory morbidity was estimated using specific effect coefficients from previous studies conducted in UNT. Finally, we compared the health burden of respiratory morbidity before and after burning ban enforcement. Approximately 130,000 hospital visits for respiratory diseases were estimated to be attributable to fire-originated PM10 in UNT from 2014 to 2018. This estimation accounted for 1.3% of total hospital visits for respiratory diseases during the 5-year period, and 20% of those during burning events. Age-specific estimates revealed a larger impact of PM10 in the older adult group. The number of hospital visits for respiratory diseases attributable to fire-originated PM10 decreased from 1.8% to 0.5% after the burning ban policy was implemented in the area. Our findings suggest that PM10 released from vegetation fires is a health burden in UNT. The prohibition of the burning using regulatory measure had a positive impact on respiratory morbidity in this area.

Full text

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Estimation of hospital visits
for respiratory diseases attributable
to PM10 from vegetation re smoke
and health impacts of regulatory
intervention in Upper Northern
Thailand
Athicha Uttajug1,2*, Kayo Ueda1,2,3, Akiko Honda1,3 & Hirohisa Takano1,3
The air quality in Upper Northern Thailand (UNT) deteriorates during seasonal vegetation re events,
causing adverse eects especially on respiratory health outcomes. This study aimed to quantitatively
estimate respiratory morbidity from vegetation re smoke exposure, and to assess the impact of a
burning ban enforced in 2016 on morbidity burden in UNT. We computed daily population exposure
to re-originated PM10 and estimated its health burden during a 5-year period from 2014 to 2018
using daily re-originated PM10 concentration and the concentration–response function for short-
term exposure to PM10 from vegetation re smoke and respiratory morbidity. In subgroups classied
as children and older adults, the health burden of respiratory morbidity was estimated using specic
eect coecients from previous studies conducted in UNT. Finally, we compared the health burden
of respiratory morbidity before and after burning ban enforcement. Approximately 130,000 hospital
visits for respiratory diseases were estimated to be attributable to re-originated PM10 in UNT from
2014 to 2018. This estimation accounted for 1.3% of total hospital visits for respiratory diseases
during the 5-year period, and 20% of those during burning events. Age-specic estimates revealed a
larger impact of PM10 in the older adult group. The number of hospital visits for respiratory diseases
attributable to re-originated PM10 decreased from 1.8% to 0.5% after the burning ban policy was
implemented in the area. Our ndings suggest that PM10 released from vegetation res is a health
burden in UNT. The prohibition of the burning using regulatory measure had a positive impact on
respiratory morbidity in this area.
Vegetation re events, including forest res, grass res, and open eld burning for agricultural practices and
plantation management, are signicant sources of air pollution in many Southeast Asian (SEA) countries1,2.
Due to proximity to the equatorial Pacic Ocean, smoke haze events in these countries are worsened by drought
conditions during the El Nino phenomenon3. Smoke haze events have frequently observed in the maritime SEA
region, including Indonesia, Malaysia, and Singapore since 19974. Recently, Mainland Southeast Asia (MSEA),
which covers the continental land area (i.e., Vietnam, Laos, Cambodia, Myanmar, and upper northern ailand),
has also suered from local and transboundary air pollution due to vegetation res5.
In ailand, smoke haze from vegetation res is a common occurrence across Upper Northern ailand
(UNT) during dry seasons (January to April). Fires are mostly used to clear vegetation to collect non-timber
forest products (i.e., mushroom and bamboo shoot)6. Forests represent the predominant burned area in UNT7.
In order to address this problem, the government has implemented several control measures in UNT since
2004. However, seasonal smoke haze continues aect the area. In 2016, a prohibition of burning using National
OPEN
1Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Kyoto,
Japan. 2Department of Hygiene, Graduate School of Medicine, Hokkaido University, Sapporo, Japan. 3Graduate
School of Global Environmental Studies, Kyoto University, Kyoto, Japan. *email: u.athicha@med.hokudai.ac.jp
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Reserved Forest Act was enforced with the strict penalties8. A previous study reported that the ban led to
decreased burning activities, fewer satellite-re hotspots, and lower PM10 concentrations in the area9.
Previous epidemiological studies have shown that exposure to air pollution emitted from vegetation res
is associated with respiratory health outcomes10–25. Despite the growing epidemiological evidence, few studies
estimated the health burden of vegetation re smoke exposure. One study estimated that more than 300,000
premature deaths are attributable to exposure to PM emitted from vegetation res, with the highest number of
deaths occurring in sub-Saharan Africa and Southeast Asia26. In Southeast Asia, some studies assessed the health
burden of vegetation re smoke in the Maritime region27–32.
To date, no study has quantied the health burden of air pollutants from vegetation res in MSEA, where
the sources of vegetation res dier from other areas (i.e., peatland re in Maritime SEA). e present study
aimed to quantitatively estimate the number of hospital visits for respiratory diseases attributable to short-term
exposure to PM10 from vegetation res in UNT.
Results
Hospital visits for respiratory diseases in UNT. From 2014 to 2018, there were roughly 2 million hos-
pital visits for respiratory diseases annually (Table1). Nearly half of these visits were made by children, and 15%
by older adults. e daily average of total hospital visits for respiratory diseases decreased aer the enforcement
of the 2016 burning ban for a 5-year period and burning days.
Fire-originated and background PM10. Daily average re-originated PM10 concentrations ranged from
58.7 to 171.9μg/m3 across the eight provinces in UNT (mean: 106.5μg/m3), and the numbers of burning days
ranged from 64days (Lamphun) to 122days (Lampang) (Fig.1). e daily average background concentration
Table 1. Summary of hospital visits for respiratory diseases in UNT during 2014–2018.
2014 2015 2016 2017 2018 2014–2018
5-years period
Total count 1,834,682 2,124,322 2,377,088 2,184,780 1,925,402 10,446,274
Daily average 689 728 807 743 690 731
Children (%) 49.2 50.4 50.3 48.6 47.0 49.2
Older adults (%) 14.5 14.1 14.7 16.4 17.5 15.4
Burning days
Total count 173,396 158,019 207,756 51,524 61,717 652,412
Daily average 788 802 802 661 726 756
Children (%) 43.9 45.3 47.2 44.0 41.8 45.1
Older adults (%) 15.3 14.9 15.6 17.8 18.7 15.8
Figure1. Average re-originated PM10 concentration (μg/m3) and total number of burning days. Map was
generated using the package “raster”46 and “tmap”47 of R (version 4.1.3, e R Foundation for Statistical
Computing, Vienna, Austria).
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ranged from 21.3μg/m3 to 30.4μg/m3 (mean: 23.2μg/m3, which was lower than almost one h of the average
re-originated PM10 concentration) (Fig.2). e average re-originated PM10 concentration and the average
number of burning days decreased aer burning ban enforcement from 114.6 to 94.5μg/m3 and 233 to 82days,
respectively (Table2).
Hospital visits for respiratory diseases attributed to re-originated PM10. e estimated number
of hospital visits for respiratory diseases attributable to re-originated PM10 for all ages throughout the study
period was 132,923 (Table2). One third of these hospital visits were made by vulnerable groups (children: 28,937
visits, older adults: 22,207 visits). is estimation of total attributable cases accounted for approximately 1.3% of
total hospital visits for respiratory diseases during the 5-year period and 20.3% during burning days. e pro-
portion of hospital visits attributable to re-originated PM10 was greater in older adults (1.4%) than in children
(0.6%). e incidence rate of attributable cases for all ages and in vulnerable groups by province-year are shown
in Fig.3 and Supplementary Figures (S2 and S3). We observed the largest incidence rates in Lampang (4,244
cases per 100,000 persons for 5-year period) (Fig.3).
Impact of a vegetations re events ban on hospital visits for respiratory diseases. Aer burn-
ing ban enforcement in 2016, the annual average number of hospital visits for respiratory diseases attributable
Figure2. Boxplot of background and vegetation-re-originated PM10 concentration.
Table 2. Fire-originated PM10 concentration and estimated number of hospital visits attributable to re-
originated PM10 during 2014–2018 for eight provinces.
Variables
Study period (year) Burning ban enforcement (annual
average)
2014 2015 2016 2017 2018 2014–2018 B efore (2014–2016) Aer (2017–2018)
Environmental variables
Total number of burning days 244 197 259 78 85 863 233.3 81.5
Average population weighted re-originated
PM10 115 120.7 108.2 98.5 90.4 106.6 114.6 94.5
Number of attributable cases and uncertainty range (thousand)
All ages 36.6 35.9 40.5 9.4 10.5 132.9 37.6 9.9
(23.2–48.6) (22.9–47.3) (25.6–54.0) (5.9–12.5) (6.6–14.0) (84.2–176.5) (24.0–50.0) (6.3–13.3)
Children 7.7 7.9 9.3 1.9 2.0 28.9 8.3 2.0
(0.9–13.9) (0.9–14.1) (1.1–16.6) (0.2–3.4) (0.2–3.7) (3.4–51.7) (1.0–14.9) (0.2–3.6)
Older adults 5.9 5.6 6.7 1.8 2.1 22.2 6.1 2.0
(2.1–9.0) (2.0–8.4) (2.4–10.2) (0.6–2.8) (0.7–3.2) (8.1 -33.7) (2.2–9.2) (0.7–3.0)
Proportion of attributable cases for 5-years period (%)
All ages 2.0 1.7 1.7 0.4 0.5 1.3 1.8 0.5
Children 0.9 0.7 0.8 0.2 0.2 0.6 0.8 0.2
Older adults 2.2 1.9 1.9 0.5 0.6 1.4 2.0 0.6
Proportion of attributable cases for burning days (%)
All ages 21.1 22.7 19.5 18.2 17.0 20.3 21.0 17.6
Children 10.2 11.1 9.5 8.4 7.9 9.4 10.2 8.2
Older adults 22.4 23.9 20.6 20.1 18.4 21.1 22.1 19.2
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to re-originated PM10 decreased from 37,682 to 9944 (approximately 70% reduction from the pre-intervention
period) (Table2), which is consistent with the decrease in the total number of hospital visits during burning days
(Table1). e proportion of total attributable cases for 5-year period decreased by 1.3% (from 1.8% to 0.5%).
Simultaneously, the proportion of attributable cases also decreased by 0.6% and 1.4% for children and older
adults, respectively, aer burning ban enforcement in the area. e decrease in the proportion of attributable
cases during burning days (−3.4%) was greater than that during the 5-year period (−1.3%) for all ages aer ban
enforcement.
Sensitivity results. e sensitivity analysis using a lower cut-o concentration (50μg/m3) of re-orig-
inated PM10 revealed that 2.4% and 12.6% of total hospital visits for respiratory diseases were attributable to
re-originated PM10 during the 5-year period and burning days, respectively (TableS1).
Discussion
The population-weighted daily average concentration of PM10 from vegetation fires across UNT during
2014–2018 was 106.5μg/m3 (range: 58.7–171.9μg/m3). In general, re-originated PM10 concentrations was
lower aer burning ban enforcement in 2016.
Despite the growing concern about air pollution caused by vegetation re events, its far-reaching health
eects are oen ignored. e present study showed that exposure to particles emitted from vegetation re events
throughout UNT poses health risks, such as increased respiratory morbidity, with 132,923 hospital visits (1.3%
of total) being attributed to re-originated PM10 for all ages. Moreover, approximately one-third of these visits
occurred in vulnerable groups. We found that Lampang, which was the province withthe highest PM10 concen-
tration from re events, had the highest incidence rate of attributable cases among UNT region. e number of
hospital visits for respiratory diseases attributable to PM10 decreased aer burning ban enforcement.
Only a few studies have estimated the health burden of exposure to air pollution from vegetation re events,
particularly in terms of morbidity. Previously studies mainly addressed mortality on a global scale or in the
equatorial Southeast Asian region26–29,31–35. Some studies used morbidity as a health outcome, such as a study
in Australia which examined hospitalization for cardiovascular disease and asthma36, and another that targeted
respiratory diseases in the United States37. While the impact of long-term exposure to particles from all sources
in ailand has been reported38, no study has estimated morbidity impacts of short-term exposure to particles
emitted from vegetation re events in MSEA. e study estimated the number of health burden attributable to
re-originated PM10 is needed because air pollution from these events has continuously aected people in UNT
and it may be useful for further policy making.
Quantifying the health burden of air pollution exposure due to vegetation burning may be useful from a
policy-making perspective. We observed decreases in re-originated PM10 concentration, number of burning
days, and number of hospital visits for respiratory diseases attributable to PM10 aer burning ban enforcement.
ese ndings are consistent with previous reports that PM10 concentrations in the area have decreased since the
enforcement of the burning ban policy9. While the policy may have helped reduce toxic components of particles
Figure3. Incidence rates of annual total hospital visits for respiratory diseases attributable to re-originated
PM10 for all ages during 2014–2018 by province. Map was generated using the package “raster”46 and “tmap”47 of
R (version 4.1.3, e R Foundation for Statistical Computing, Vienna, Austria).
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emitted from burning activities, such as carbonaceous aerosols (black and organic carbon), Polycyclic Aromatic
Hydrocarbons (PAHs), and levoglucosan39, it does not appear to oer sustainable measures against smoke haze
events. In fact, we observed increases in re-originated PM10 concentration as well as the number of hospital
visits attributable to PM10 in 2018 (i.e., aer ban enforcement).
In addition to the policy, global climate factors may have inuenced PM10 emission from vegetation res.
e strong El Nino phenomenon was observed during 2015–2016, resulting in dry conditions, followed by La
Nina events (i.e., wet climate) in 201740. During the study period, we estimated the highest number of hospital
visits for respiratory diseases attributable to re-originated PM10 to be approximately 40,000 during the time of
strong El Nino (2015–2016). A previous study also estimated a high global health burden attributable to particles
released from burning sources due to the inuence of El Nino41.
ere are some limitations to this study. In exposure assessment, we estimated the health burden of PM10
exposure using PM10 concentrations derived from ambient air pollution monitoring data, which may not have
accurately reected actual individual exposure. e inaccurate number of hospital visits for respiratory diseases
attributable to re-originated PM10 may be caused from several stages of health burden estimation (i.e., exposure
assessment and applying of concentration response function). To identify burning days, we used a cut-point
reported in a previous study for the occurrence of intensive res. PM10 concentrations on the remaining days
(i.e., non-burning days) were averaged as the background concentration, but small burning events might have
occurred during non-burning days, contributing to the estimated background concentration. However, the
background PM10 concentration did not dier from PM10 concentrations reported for non-burning months
(May-December) in a previous study20.
According to the WHO guideline, the concentration of daily PM10 should not exceeded 50μg/m3. We thus
performed a sensitivity analysis by changing the cut-point from 100μg/m3 to 50μg/m3 in order to capture
more burning events, and to lower the average re-originated PM10 concentration as compared to the principal
analysis. e proportions of estimated hospital visits during the 5-year period did not signicantly dier between
principal and sensitivity analyses, but the proportion during burning days was smaller when using the WHO
guideline concentration. ese results suggest that using the guideline concentration, which has been set based
on ambient air particles, may lead to underestimation.
Despite these limitations, the present study has the following strengths. We used eect coecients obtained
from epidemiological studies that conducted with the same health outcomes in UNT. is might have helped
reduce the uncertainty of health burden estimation attributable to re-originated PM10 because the same factors
were considered such as health care system, vegetation re particle compositions, and behavioral responses to the
smoke haze of people in this area. Another strength is that we estimated the number of hospital visits for respira-
tory diseases attributable to re-originated PM10 in vulnerable groups. We found a larger impact of short-term
exposure to re-originated PM10 among older adults. With the increasing aging population, this study highlights
the need to address the eect of burning events on the health of older people. Our ndings may help prepare for
and implement preventive measures against smoke haze risk in vulnerable populations.
Conclusion
Short-term exposure to PM10 emitted from vegetation re events was associated with approximately 130,000
hospital visits for respiratory diseases in UNT during a 5-year period. In particular, the estimated number
of hospital visits attributable to PM10 was high among older adults. ese ndings may be useful for further
advancing policy-making regarding haze control and overall health and socioeconomic consequences. Moreover,
our results suggest that regulatory actions on vegetation re events had a positive impact on hospital visits for
respiratory diseases in UNT.
Methods
Identication of burning days and estimation of re-originated PM10. Hourly data of PM10
concentration were obtained from 14 air quality monitoring stations in eight provinces of UNT (Chiangrai,
Chiangmai, Lampang, Lamphun, Maehongson, Nan, Phayao, and Phrea), provided by the Pollution Control
Department of ailand, from January 2014 to December 2018. Daily averages were estimated when 75% daily
records were available (at least 18 valid hourly records). Initially, we calculated the population weighted PM10
concentration to rene exposure estimation, as shown in the following equation Eq.(1).
where
Ci
is the PM10 concentration,
Pi
is the population of district i (in each province), and
Ptot
is the total
population of each province42. e population data of each district was retrieved from Gridded Population of
the World, version 443.
We estimated the re-originated PM10 concentration by subtracting background PM10 concentration from
daily average PM10 concentration on burning days:
In this step, we identied burning days based on criteria described in a previous study25. Briey, a burning day
was identied when the number of satellite re hotspots exceeded the 90th percentile of the daily distribution in
the entire region (10 counts) and the daily PM10 concentration in each province was greater than 100μg/m3. e
re hotspot data was obtained from the National Aeronautics and Space Administration Land, Atmosphere Near
real-time Capability for Earth observing system (NASA-LANCE) for Fire Information for Resource Management
(1)
Population weighted PM
10 =
C
i×
P
i
Ptot
(2)
PM
10(fire−sourced)=
PM
10
(
daily
)
−
PM
10
(
background
)
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System (FIRMS)44. In this study, individual re hotpots with lower condence values (< 20%) were excluded
from the analyses to obtain the precise burning point. Moreover, the cut-point of PM10 concentration (> 100μg/
m3) was selected based on the previous study ndings23. e background concentration was derived by averag-
ing the estimated daily PM10 concentrations aer adjusting for day-of-week (DOW) and seasonal patterns on
non-burning days (re hotspot = 0). Adjustments for temporal trends were performed by using a natural cubic
spline of time with 5 degrees of freedom (df) per year and DOW (Supplementary FigureS1). We changed df of
time from 4 to 6 to check the robustness. e minimal adequate model was chosen by the Akaike Information
Criterion and ANOVA tests.
Concentration–response function and morbidity burden assessment. We estimated the number
of hospital visits for respiratory diseases attributable to PM10 for all ages and vulnerable groups (children and
older adults) in each province. Data were obtained from the Ministry of Public Health of ailand for each
province and included demographic information (sex and age), date of visit, and International Classication
of Disease codes for diagnosis (ICD10: J00-J99). We estimated the number of hospital visits for respiratory dis-
eases attributable to re-originated PM10 between January 2014 and December 2018 using methods described
previously33.
where RR is the relative risk of daily average re-originated PM10 concentration on burning days for each prov-
ince. Among few epidemiological studies conducted in this region20,25,45, we used the coecient
β
derived from
Mueller and colleagues because the subjects included in this study are truly represented the population in this
area20. Specically, Mueller and colleagues reported the risk of hospital visits for respiratory diseases to be 1.020
(95% CI: 1.012, 1.028) per 10μg/m3 increase in PM10 for all ages. Accordingly,
β
was calculated as In(1.020)
per 10μg/m3. e same estimation method was used to calculate the health burden in vulnerable groups, that
is, children under age 15 (1.009 (95% CI: 1.001, 1.017) for the risk of hospital visits for respiratory diseases)25
and older adults aged ≥ 65years (1.021 (95% CI: 1.007, 1.035) for the risk of outpatient visits for chronic lower
respiratory diseases)20.
e number of daily hospital visits for respiratory diseases attributable to re-originated PM10 in each prov-
ince was calculated using the following equation:
where HV is the daily number of hospital visits for respiratory diseases. e fraction of risk function (RR-1)/
RR is dened as the population attributable fraction (PAF), which measures the disease burden attributable to a
risk from exposure to re-originated PM10. We summed number of the attributable cases by year and province.
e proportion of attributable cases was estimated from the number of attributable cases divided by the total
number of cases in each year and age group. We also calculated the incidence rate of the attributable cases for
each province using the population data in 2015 derived from the National Statistical Oce of ailand.
Finally, we performed a sensitivity analysis to address the uncertainty of health burden estimation. We esti-
mated the health burden by changing the cut-point for burning days from 100μg/m3 to 50μg/m3 according to
the WHO guideline for daily PM10 concentration in order to capture lower concentration exposure that could
aect health outcomes.
Ethical considerations. is study was exempt from ethical approval by the Ethics Committee of Kyoto
University Graduate School of Engineering (No. 201904), since only secondary and aggregated data were used
in the analyses.
Data availability
e data that support the ndings of this study are available from Permanent Secretary Ministry of Public Health
ailand but restrictions apply to the availability of these data, which were used under license for the current
study, and so are not publicly available. Data are however available from the authors upon reasonable request
and with permission of Permanent Secretary Ministry of Public Health ailand.
Received: 11 March 2022; Accepted: 31 October 2022
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(3)
RR
=exp

β×

PM
10(fire
−
sourced)
(4)
Attributable cases
=HV ×
(RR
−
1)
RR
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Acknowledgements
We thank the Ministry of Public Health, the Pollution Control Department of the Ministry of Natural Resources
and Environment. We would also like to acknowledge LANCE FIRMS, operated by NASA’s Earth Science Data
and Information System (ESDIS), for permission to use their data and imagery, with funding provided by NASA
Headquarters.
Author contributions
e concept of this study was conceived by A.U. and K.U.. A.U. contributed to data collection and management.
Data analysis and initial dra of manuscript was carried out by A.U. and K.U.. All authors contributed to provide
edits of the manuscript.
Funding
This research was performed by the Environment Research and Technology Development Fund S-20
(JPMEER21S12020) of the Environmental Restoration and Conservation Agency Provided by the Ministry of
Environment of Japan.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 022- 23388-2.
Correspondence and requests for materials should be addressed to A.U.
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