49

Exposure Assessment of Biomass Burning: A Scoping Review on
Current and Emerging Approaches
Sary VALENZUELA1, Keith Alexius WANGKAY1
Xerxes SEPOSO*1,2 and Geminn Louis C. APOSTOL1
1

School of Medicine and Public Health, Ateneo de Manila University
Don Eugenio Lopez Sr. Medical Complex Ortigas Avenue, Brgy Ugong, 1604 Pasig City, Philippines
2
Department of Hygiene, Graduate School of Medicine, Hokkaido University
Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan*
E-mail: seposo.xerxestesoro@pop.med.hokudai.ac.jp

Abstract
Biomass burning is the main source of air pollution in many countries and has been strongly linked tomany morbid and mortal health outcomes. This scoping review aims to explore the current and emerging
approaches linking health and air pollution from biomass burning. A literature search through PubMed was
conducted to identify studies linking health and air pollution from biomass burning through the use of air
quality data collection methods. A total of 197 studies were initially found, but after screening, only 57 studies
were included. The most common methodology employed for air quality data was through atmospherictransport models (specifically GEOS-Chem) (59.65%), followed by remote sensing through satellite imaging(50.88%), then by direct site monitors (33.33%). A single approach was used by 56.14%, while the rest
employed a blended approach (43.86%), likely due to the inherent limitations of each data collection method,necessitating supplementary or novel approaches. However, the bulk of existing literature uses methods that are
calibrated for the global north (75.44%), leaving behind the global south, which bears the brunt of air pollution
health impacts due to its socioeconomic and geographic vulnerabilities, worsened by climate change. There is aneed to recalibrate or validate these models to increase the reliability of results for the global south, as well as
explore the possibility of further developing these air pollution modeling initiatives to not only contribute tosurveillance, but directly further policy development and public health programming (i.e., the creation of early
warning systems).
Key words: agricultural burning, air pollution modeling, biomass burning, health, public health,vegetation burning

1. Introduction
1.1 Background and Context
In recent decades, there has been a noticeable spikein the incidence of biomass burning and open burning ona global scale. It is the largest source of black carbon
globally at 42% (Yao et al., 2023). Central to this surge isthe compounding influence of climate change (Adams andKanaroglou, 2016; Albertson et al., 2010; Reddington
et al., 2021). Biomass burning in agricultural settings,
often used as a traditional land management or
agricultural practice, has intensified in response tochanging climate conditions, especially in tropical regions,causing serious air quality issues in East Asia (Adrianto
et al., 2019; Johnson et al., 2020; Sheldon and Sankaran,
Global Environmental Research
27/2023 49-60
printed in Japan2017). Farmers, particularly in regions with subsistence
agriculture, resort to controlled burning to clear fields,
eliminate pests and enhance soil fertility (Reddington
et al., 2021).
As climate change amplifies droughts and extends
the fire season, these controlled burns can quickly escalate
into widespread burning, engulfing vast tracts of land,
homes and infrastructure, necessitating mass evacuations
and overwhelming firefighting efforts. High temperatures,
dry conditions, increased lightning strikes and otherextreme weather events increase the vulnerability offorests (>200 hectares) to large fires and the frequency ofthese fires (Gaboriau et al., 2023). Such wildfires result ina cascading array of public health consequences, fromacute injuries and respiratory illnesses to long-term©2023 AIRIES50

S. VALENZUELA et al.

mental health impacts stemming from trauma anddisplacement.
Biomass burning, including forest fires, wildfires,
agricultural fires, residential energy production and wasteburning, is the main source of air pollution in several
countries like Canada, Africa, South America, Australia,
Europe and Southeast Asia. Humans are responsible forabout “90% of biomass burning with only a small
percentage of natural fires contributing to the total amountof vegetation burned” (Chen et al., 2017; Karanasiou
et al., 2021). In Southeast Asia, the practice of biomassburning is widely used for both occupational andnon-occupational functions, most especially in rural areas(Amnuaylojaroen and Parasin, 2023). In Indonesia,
agricultural burning destroyed almost a quarter of thecountry’s forests in 20 years (Sheldon and Sankaran,2017).
However, practical use of biomass burning has also
led to a significant rise in vegetation fires across multiple
Southeast Asian countries. From 2000 to 2010, Myanmar
and Laos for instance experienced an increase in fire
hotspots by 471% and 2383%, respectively, according to a
study by Amnuaylojaroen and Parasin (2023). The same
study noted that Vietnam also experienced a significant
increase of 777.2% from 2000 to 2020, while Thailand
demonstrated a relatively stable number of fire hotspots,
even experiencing a small decrease of 10% from 2010 to2020. A peak increase in these emissions seems to occur
early each year in Southeast Asia (Khodmanee andAmnuaylojaroen, 2021; Amnuaylojaroen et al., 2023).
Regardless of the source, biomass burning has
become a significant cause of air pollution, with global,
regional and local impacts on air quality and public health.
Airborne particulate matter (PM) and ground-level ozone
have been largely demonstrated as key contributors to theglobal burden of mortality and disease (Sorek-Hamer
et al., 2020; Vohra et al., 2021). In 2019, 4.2 million
premature deaths were attributed to ambient outdoor air
pollution, with 89% of these coming from the Southeast
Asia and Western Pacific regions (World HealthOrganization, 2022).
Previous reviews have reported positive associations
between wildfire smoke exposure and respiratory healtheffects, specifically exacerbations of asthma and chronic
obstructive pulmonary disease (Adrianto et al., 2019;
Albertson et al., 2010; Johnson et al., 2020; Oliveri et al.,2017). There is also a growing body of research linking
ambient air pollution to cardiovascular health outcomes(Johnson et al., 2020; Karanasiou et al., 2021). Certain
demographic groups, including children, the elderly andindividuals with pre-existing health conditions, have beenfound to be particularly vulnerable to the negative effectsof air pollution (Albertson et al., 2010; Karanasiou et al.,2021).

1.2 Rationale for the Scoping Review
Air pollution modeling has emerged as an
indispensable tool in predicting and estimating publichealth outcomes attributable to biomass burning,
especially within the changing landscape of climate trends.
These models offer the capacity to delineate thedispersion of pollutants, assess exposure levels andproject the spatial and temporal distribution of healthimpacts. Exposure assessments for PM have been limitedby the availability of ground monitoring stations; hence,technology has led to the innovation of methods of
estimating air pollution using atmospheric, chemical andsatellite modeling to more accurately depict temporal andspatial variations, demonstrate historical associations, andpredict future health outcomes (Johns et al., 2012;
Sorek-Hamer et al., 2020). However, it is disconcerting tonote that such modeling endeavors have been
predominantly concentrated in the global north, often
neglecting regions disproportionately affected byvegetation or wildfires in the global south.
From an equity standpoint, the burden of increasing
biomass burning is disproportionately shouldered by
disadvantaged
andmarginalized
communities.
Anthropogenic activities in agriculturally-producing
countries and ideal tropical weather conditions haveshaped fire-prone land conditions, especially in Africa andSoutheast Asia (Liu et al., 2015; Reddington et al., 2021;
Ren et al., 2021; Taylor, 2010). Climate change threatens
to extend and increase the severity of biomass burning
through prolonged droughts, increased greenhouse gas
emissions and other instabilities in ecosystems (i.e.,changes in biodiversity, decreased soil fertility)(Reddington et al., 2021; Taylor, 2010). Similarly,
marginalized populations living in wildfire-prone regions,
often with limited access to resources and infrastructure,
are at a greater disadvantage. Their ability to evacuatesafely, access healthcare or recover from the aftermath of
wildfires is severely constrained (Liu et al., 2015; Thomas
et al., 2022). In fact, the annual mortality in low- to
middle-income countries is predicted to increase by morethan 100% by 2060 (Holloway et al., 2021).
This stark geographical disparity in air pollution
modeling has not only hindered a comprehensive
understanding of public health impacts but also
contributed to knowledge gaps in assessing theconsequences of biomass burning on marginalized
communities. Consequently, these communities remain
disproportionately vulnerable, with inadequate data andresources to address the impending threats posed by bothbiomass burning and climate change. This scoping review
aims to explore the current and emerging approaches forassessing exposure to biomass burning and its use inestablishing associations to public health outcomes. A
clearer understanding of the advantages anddisadvantages of these approaches in predicting specifichealth outcomes and public health impacts would benefit

Review on Exposure Assessment of Biomass Burning

the research designs of future studies on atmosphericmodeling and possible early warning systems.

2. Methodology
A literature search through PubMed was conductedusing the following terms: “biomass burning,” “forest fire,”“vegetation fire” and “wildfire.” This was used in
combination with the terms “public health,” “health,”“morbidity” and “mortality.” To link the study to air
pollution and modeling, the terms “air pollution,” “airquality,” “atmospheric modeling,” “model” and“modeling” were also used to gather studies that exploredthe connection between biomass burning, air pollution
and public health impacts (both long- and short-term),
through the use of atmospheric modeling methods. Any
articles that did not attempt to establish links between
health and air pollution from biomass burning through theuse of atmospheric modeling methods were excluded (i.e.,systematic reviews, guidelines, perspectives, otherscoping reviews). After appropriate selection, thefollowing data were extracted from the articles: authors,location, year conducted, health impact measured andatmospheric modeling technique used.
The database search identified 197 papers. We thenexcluded five duplicates (i.e., papers identified by morethan one search). We eliminated papers that did not meetthe inclusion criteria by first screening the titles (42papers excluded), by examining abstracts (72 papers
excluded), and then by examining the full articles (21papers excluded) (Fig. 1). The final review included 57
studies of human health impacts of biomass burning,which can be found in Annex 1.

51

3. Results and Discussion
3.1 Descriptive Statistics
The three main categories of air quality datacollection included direct site monitors, satellite imaging(i.e., remote sensing) and air quality models (i.e.,chemical transport models). More than half of thesestudies used a single approach (32/57 studies, 56.14%),
while the rest employed a blended approach (25/57
studies, 43.86%), using at least two of the three datacollection methods. Among the three, the majority of ourreviewed articles (33/57 studies, 57.89%) used chemicaltransport models for their data collection, the mostpopular specific model being GEOS-Chem (Goddard
Earth Observing System-Chemistry), followed by
WRF-Chem (Weather Research and ForecastingChemistry). After chemical transport models, the next
most commonly used data source was satellite imaging(30/57 studies, 52.63%), most popularly through MODIS(Moderate Resolution Imaging Spectroradiometer). The
least commonly used data source was direct station
monitors (20/57, 35.08%).
There were 43 countries and regions included in thisreview. The studies spanned the continents of North
America, South America, Europe, Asia and Australia andemphasized the health impacts of air pollution in densely
populated urban regions, industrial zones and areas prone
to wildfires. The focus of the studies was on areas in the
global north in 75.44% of the studies (43/57 studies), with
39.53% focusing on the USA (17/43 studies), with
California, then Colorado being the most studied states.
This is likely due to richness of research infrastructure,
funding opportunities, data availability, diverse range of

Fig. 1 Article selection process following PRISMA.

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S. VALENZUELA et al.

geographic and environmental settings, and collaborative
research culture. Meanwhile, the Pacific region,
particularly Southeast Asia, was not extensively
represented in the studies, with only 14 out of the 57(24.56%) articles focusing on countries considered part of
the global south. This is likely due to limited researchresources, absence of comprehensive air quality
monitoring networks (save for Thailand where station
monitors are abundant) (Cândido Da Silva et al., 2014;
Mueller et al., 2021; Othman et al., 2022; Pothirat, et al.
2021), and lack of collaborative opportunities andpartnerships. Additionally, only four articles out of the 19(7.02%) that employed direct monitors were also focusedon countries considered in the global south. The resultsfrom the different modeling methods used can be seen in
Fig. 2, alongside the geographical distribution of thesestudies in Fig. 3.

3.2 Station Monitoring
Station monitoring is widely considered the gold
standard for air quality information in most developed
countries. This involves the use of devices that directly
detect air quality within their vicinity. Station monitoring
offers the advantage of directly measuring highly accurate
data at a more local level, allowing for long-term andreliable data for air quality (Holloway et al., 2021; Xieet al., 2017). The disadvantage of station monitoring isthat it is limited by the number and placement of thestations, as well as the quality of the measuring devices
themselves. They are often sparsely distributed over urban
areas due to high initial and maintenance costs, thereby
limiting possible areas for real-time mapping andassessment. This is especially true in rural areas where
vegetation and open burning usually occurs (Adams andKanaroglou, 2016; Holloway et al., 2021; Mueller et al.,2021; Xie et al., 2017). The density of monitoring stations

Fig. 2 Venn diagram of air quality modelling methods.

Fig. 3 Articles by region/location.

Review on Exposure Assessment of Biomass Burning

may also greatly vary. For example, in the USA, there arethousands of monitoring stations for various atmosphericmeasurements, yet only 20% of US counties have at least
one monitor (Holloway et al., 2021). Globally, more thanhalf (60%) of all countries have no regular PM2.5
monitoring, and only 10% of all countries have more than
three monitors per million inhabitants (Martin et al.,2019). The spatial representativeness of air quality
monitoring stations may also vary significantly (220 to4500km2), highlighting the need for qualitative and ad hoc
assessments to understand the limitations and complexityof the results (Mueller et al., 2021; Piersanti et al., 2015).
Multiple articles incorporate the use of station
monitors as their source of air quality data. For example,
Othman et al. (2022) described in detail the use of an
AS-LUNG V.2 outdoor sensing device, which canmeasure particulate matter levels, CO2, temperature andrelative humidity. Mueller et al. (2021) on the other handutilized ground monitors to assign exposure and noted
limited spatial accuracy in capturing differences withinsmaller subdivisions of its area of interest in Thailand.
Portable devices have been increasing in popularity for
their relatively low cost and high spatial and temporal
resolution, but rely on standardized data collection under
different weather conditions (Xie et al., 2017). Data
quality relies on the consistency of the mobile monitoring
unit, i.e., an outfitted industrial van with pollution
monitors, a GPS device and a laptop. Data collection is
also limited by interrupted data and the logistical inability
to cover all areas simultaneously (Xie et al., 2017).
Leveraging mobile air pollution monitoring techniques
may be complimentary to areas with limited monitors(Adams and Kanaroglou, 2016).
Open-source monitoring data are also available
through resources like OpenAQ (OpenAQ, n.d.), which
aim to provide free access to air quality data for analysis
that may lead to advocacies. Studies like Sannigrahi et al.
(2022) utilized OpenAQ to determine the effects of forest
fires on the west coast of the United States on theincidence COVID-19. OpenAQ station density varies byregion, however, being concentrated mostly in the UnitedStates, Europe and Japan as of writing, possibly limiting
its use for many countries, especially those of the global
south.
3.3 Chemical Transport Models
Chemical transport models (CTMs) are a class ofnumerical models used in air pollution modeling tosimulate the dispersion, transport and chemicaltransformation of air pollutants in the atmosphere. They
estimate coverage of air pollution concentrations by
combining emissions, pollutant transport, chemical
reactions and physical processes (e.g., deposition) in theatmosphere in space and time (Koman et al., 2022). The
advantages of CTMs are that they aggregate data fromvarious sources such as satellite measurements and53

ground-based observations into their simulations,providing a potentially more holistic picture of multiple
datasets. Additionally, this modeling technique is popularfor its powerful ability to perform long-term simulations
and demonstrate the temporal evolution of theatmospheric composition (Monge-Sanz and Chipperfield,2006). The disadvantage of CTMs is that they requiresignificantly intensive computational demands, especiallywhen running at high spatial and temporal resolutions.
CTMs may also be limited by lack of input, as describedby Magzamen et al. (2021), where the lack of in situ
monitors in mountains and plains has limited the scope of
potential study. CTMs also operate under certain
assumptions, potentially resulting in data that might beconsidered oversimplified or with spatial and temporal
uncertainties. Burke et al. (2021) mention that CTMs
provide an alternative approach to linking local pollution
concentrations to specific fire activities, aside from thoseof satellite imaging and direct monitors. However, theyalso mentioned that model-related assumptions anduncertainties may lead to dramatic discrepancies (either
overestimation or underestimation) in downstream
exposure estimates. Hence, CTMs are typicallycross-referenced and validated with data either fromsatellite imaging or ground-based monitors, as is the casein Wu, et al. (2023) and Crippa, et al. (2016). Very often
CTMs are used in conjunction with other data sources for
air pollution.
Close proximity of the studied locations with densely
populated urban areas may affect CTMs, as urban smoke
emissions may have similar emission footprints to thoseof biomass burning (Le et al., 2022; Wu et al., 2023). The
vertical distribution of emissions, secondary chemical
reactions and the confounding effect of other pollutants
such as ozone and nitrogen dioxide contribute to thelimitations of CTMs (Bachwenkizi et al., 2021; Kollanus
et al., 2017; Wu et al., 2023). Certain strategies andmethods, including kriging and land-use regression, may
be employed to adjust the model's raw output and increase
the accuracy of the model (Valari et al., 2011).
Subsequent performance model evaluation in a study
by Ballesteros-Gonzales et al. (2020) found that
overpredictions and underpredictions were apparent forvarious meteorological conditions (i.e., wind speed,
humidity) and air pollutant concentrations (i.e., ozone,
carbon dioxide) by as much as twice the actual
measurement. Despite the potential for inaccuracies,
CTMs remain an indispensable tool in the assessment of
air quality. These models are also regularly updated toenhance their performance and accuracy over time.
There are several CTM models. These include
GEOS-Chem (Goddard Earth Observing SystemChemistry) and WRF-Chem (Weather Research andForecasting-Chemistry), both of which use a variety of
inputs including satellite, ground-based and othermeteorological datasets to simulate the transport of gases

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and aerosols in the atmosphere. GEOS-Chem in particularincludes data about oxidant-aerosol chemistry andreanalysis meteorology from NASA’s Global Modelingand Assimilation Office (GMAO). Datasets and regional
inventories come from all areas where these are available
to the USA, including Europe, Asia and Africa, allowing
for regional simulations that allow a better match to the
population’s spatial distribution (Bachwenkizi et al.,2021; Chen et al., 2021; Linares et al., 2018; Vohra et al.,2021; Wu et al., 2023).
WRF-Chem was employed by Crippa et al. (2016) intheir study to determine the relationship between wildfires
and mortality in equatorial Asia, noting that the model
was able to capture the temporal and spatial variability
within Singapore and Sumatra when compared to the datacollection at local monitoring stations. One study(Ballesteros-González et al., 2020) employed WRF-Chem
to assess the effects of open biomass burning on pollutant
concentration and estimate potential health impacts
associated with wildfires in northern South America.
Higher spatial horizontal-grid resolutions might beadvantageous in difficult topographical and highly
mountainous locations like the northern Andes, theyobserved, in order to capture the spatial variability of air
pollution particles over smaller areas.
Other types of CTMs include the Community
Multiscale Air Quality Modeling System (CMAQ)
employed by Stowell et al. (2019) and Zou et al. (2022).
This is a photochemical transport model for estimating
fire-specific air pollution concentrations and is able toinclude and distinguish all categories of PM sources. The
modeling system uses a three-dimensional grid-basedmodel resulting in raster data grid cells over the studied
area. One study (Koman et al., 2022), however, observedthat CMAQ would be useful in exposure assessments foraging health outcomes due to the model's ability toseparate wildfire PM2.5 from other sources of biomassburning in both monitored and unmonitored locations,though overestimation of the severity of wildfire impacts
may occur in juxtaposition to the data collected by routine
surface monitors. Other CTMs in use are the Atmospheric
Dispersion Modelling System (ADMS), developed by the
Cambridge Environmental Research Consultants (Le et al.,2022), and the SILAM model employed by Kollanus et al.
(2017). The study by Kollanus (2017) in particular notedthat the model underestimated the observed PM levels as
it omitted wind-blown dust, secondary organic aerosols
and aerosol-bound water in its simulations.
3.4 Satellite-data-based Modeling
Satellite-based modeling and data collection is anapproach to air pollution modeling that leverages remotesensing data from Earth-observing satellites to monitor,
analyze and model air quality and atmospheric conditions.
This modeling technique measures aerosol optical depth(AOD) and other markers of particulate loading in theatmosphere instead of detecting PM2.5 directly(Holloway et al., 2021). This approach offers several
advantages, as it provides a wealth of information on aregional to global scale (NASA, n.d.; Sohrabinia andKhorshiddoust, 2007). They are spatially consistent andmay have high temporal resolution and data coverage,
complementing the spatial gaps in traditional surface
monitor networks (Holloway et al., 2021; Mijling and Van
Der A, 2012). Satellite sensor sensitivity, however, varies.
The quality and granularity of the data also depend on thesatellite technology involved. Spatial resolutions mayvary between ∼1 to ∼50 km and temporal data coverage
from hourly to once a day or every few days, dependingon whether polar-orbiting or geospatial satellite
technology is used (Holloway et al., 2021).
Unlike ground monitoring stations, satellite-retrieved
data offer more reliable, consistent information regardlessof local conditions at a more affordable price, making it abetter option in low- to middle-income countries(Sohrabinia and Khorshiddoust, 2007). They candemonstrate dynamic changes even over the last few
decades as well as show the regional and intercontinental
conditions downwind of transported pollution (Holloway
et al., 2021; Sorek-Hamer et al., 2020). Plume monitoring
can link fire activity to receptor regions, proving it aninvaluable source of information for both short-term
air-quality forecasting and early warning disaster response(Burke et al., 2021; Holloway et al., 2021; Sorek-Hamer
et al., 2020).
The most popular satellite imaging device is MODIS,
which is attached to NASA’s Terra and Aqua satellites
launched in 1999 and 2002, respectively. MODIS captures
data in 36 spectral bands, covering a wide range of
wavelengths from visible to thermal infrared (NASA,n.d.). It has moderate spatial resolution (hence the name“Moderate Resolution Imaging Spectroradiometer”),
allowing it to provide detailed observations of the Earth's
surface and atmosphere on a global scale. The instrument
is capable of collecting data on a daily basis, making it
valuable for studying Earth’s dynamic processes andchanges over time (NASA, n.d.; Sorek-Hamer et al.,2020). Other satellite retrieving technology includes
NASA’s Fire Information for Resource Management
System (FIRMS) which uses data from MODIS (Sheldon
and Sankaran, 2017), and Modern-Era Retrospective
Analysis for Research and Applications, Version 2(MERRA-2) (Wani et al., 2021).
The most significant constraint of satellite data for
health applications is the lack of surface-level informationand individual-level information (Holloway et al., 2021;
Sorek-Hamer et al., 2020). Kollanus et al. (2017), whoused a blended method for his study, noted that satellite
modeling approaches were limited by uncertainties indiurnal variations in fire intensity and challenges inobserving small vegetation fires. They were unableprecisely to measure smoke density or separate smoke

Review on Exposure Assessment of Biomass Burning

higher in vertical transmission and thus found it difficult
to link to exposure-health response relationships alone(Burke et al., 2021). Conversely, Holloway et al. (2021)
noted that newer satellite technology has been able todetect emissions from single power plants or industrial
facilities and these will likely be further improved to beable to detect individual fires in the near future. Mueller,
et al. (2021) utilized NASA’s Visible Infra-red Imaging
Radiometer Suite sensor on the daily number of fires inThailand, measuring both PM10 and O3, in addition to
ground-monitoring data. They noted the inability to
distinguish effects on the measured health outcome of lowbirth weight between exposure levels of either chemical.
They are also limited by the long-term changes in satellite
functionality, resulting in challenges in establishingconnections between satellite trends and groundmonitoring stations (Holloway et al., 2021).
3.5 Blended Models
Blended models use a combination of different
methods to determine air pollution characteristics and data
sources. Given the large uncertainties in air pollution dataarising from the limitations of a single method, theapplication of a blended model may significantly enhance
the accuracy and reliability of the data collected (Johnson
et al., 2020; Pennington et al., 2019). One study(Jegasothy et al., 2023) for example used MODIS(satellite imaging) to estimate PM2.5 emitted by bushfires.
They did a two-stage study where they first measured
PM2.5 from monitoring sites to check for air quality, thenvisually confirmed any measurements exceeding the 95th
percentile through MODIS if the source was indeed frombushfires, albeit with the potential for false negatives if
the smoke produced was too thin. Magzamen et al. (2021)
similarly used satellite imaging to determine the presenceof smoke plumes while using surface monitors for actual
PM2.5 measurements.
Another emerging approach is the use of all theprevious models in conjunction with machine learning for
improved accuracy. Reid et al. (2015) employed a uniqueapproach of combining CTMs with outputs from satellite
imaging and meteorological data used in conjunction withgradient-boosted machine learning to provide accurate
predictions of out-of-sample air pollution concentrations.
O’Neill et al. (2021) and Zou et al. (2022) used a similar
approach, using multiple data sources (CTM with satellite
imaging) while applying three machine-learning
approaches that similarly improved predictions whencompared to surface monitors. The addition of machinelearning however will likely require even morecomputational power than CTMs alone.

4. Conclusions and Recommendations
The objective of our review was to provide a concise
overview of the available resources used when exploring55

the link between air pollution produced from biomassburning and its potential impact on public health. Our
study revealed that air pollution caused by biomassburning can be observed and studied through a diverse
array of data sources and modeling approaches. Fromchemical (atmospheric) transport models to satellite
observations, many researchers employ various
techniques—usually in combination—to understand thecomplex dynamics of pollutants released during biomassburning events. Broadly categorized, the three main
methods of quantifying and collecting air quality data arethrough direct site/station monitors, satellite data and air
quality models (i.e., CTMs), each with its own strengths
and weaknesses. In our findings, the use of combinationsor blended approaches have been the prevailing method
for collecting air pollution data. It is likely that theinherent limitations of each data collection method
necessitate supplementary approaches, and also pave theway for the birth of novel techniques, as in the case ofmachine-learning algorithms that improve the accuracy of
existing approaches. Both the traditional and the novel
approaches would benefit from qualitative and ad hoc
assessments to grasp their limitations and the complexityof their results, especially when used to makegeneralizations for real-life scenarios that could
potentially impact public health policy later on.
While certain data collection methods may be
impractical for many regions—particularly that ofmonitoring stations, which are limited mostly toresource-abundant countries—alternative and freemethods are available through the use of NASA's satellite
imaging as well as through simulations, as seen in CTMs.
These advancements will hopefully alleviate the need forsuch stations to provide quality evidence when linking air
pollution to negative health impacts. While the emergenceof machine learning holds significant promise toimproving the accuracy of these methods when compared
to direct local measurements of air quality seen inmonitoring stations, the bulk of existing literature uses air
pollution modeling methods that are calibrated for the
global north, leaving behind the global south, whichsuffers from more frequent biomass burning and bears thebrunt of air pollution health impacts due to climate change.
Additionally, most modeling methods were developed forthe primary purpose of surveillance and not necessarily
directly considered for policy development or publichealth programming. These technological advancements
can potentially be leveraged to create systems that cancommunicate risks in a timely manner. Recalibration orvalidation of these models is needed to increase thereliability of the results for the global south, as well as toexplore the possibility of further developing these air
pollution modeling initiatives to create early warning
systems, especially in the global south, where monitoring
stations are fewer and further in between.
It is likely that our study was limited in terms of true

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deep technical understanding of the various datacollection methods employed by the various reviewed
articles, potentially missing out on the nuanced strengths
and weaknesses of each method. However, it is notwithin our objective to delve into the technical aspects ofeach method, but to inform the reader of potential avenues
for data collection for future studies. Moreover, theaddition of health impact assessments adds another layerof complexity to these studies, and we hope that ourreview may potentially reveal potential health impacts
that may have been overlooked and require further
investigation.
Acknowledgments
We wish to thank Global Environmental Research fortheir invitation to contribute to this publication andacknowledge the support of the Ateneo Center for
Research and Innovation of the Ateneo School of
Medicine and Public Health and Hokkaido University inthe study's creation and development.
References
Adams, M.D. and Kanaroglou, P. S. (2016) Mapping real-time air
pollution health risk for environmental management: Combining
mobile and stationary air pollution monitoring with neuralnetwork models. Journal of Environmental Management, 168:
133–141. https://doi.org/10.1016/j.jenvman.2015.12.012
Adrianto, H.A., Spracklen, D.V. and Arnold, S.R. (2019)
Relationship between fire and forest cover loss in Riau Province,
Indonesia between 2001 and 2012. Forests, 10(10): 889.
https://doi.org/10.3390/f10100889
Albertson, K., Aylen, J., Cavan, G. and McMorrow, J. (2010)
Climate change and the future occurrence of moorland wildfires in
the Peak District of the UK. Climate Research, 45: 105–118.
https://doi.org/10.3354/cr00926
Amnuaylojaroen, T., Inkom, J., Janta, R. and Surapipith, V. (2023)
Long range transport of southeast Asian PM2.5 pollution to
Northern Thailand during high biomass burning episodes.
Sustainability, 2020: 12(23). https://doi.org/10.3390/su1223
10049
Amnuaylojaroen, T. and Parasin, N. (2023) Perspective onparticulate matter: From biomass burning to the health crisis inmainland southeast Asia. Toxics, 11(7): 553. https://doi.org/
10.3390/toxics11070553
Bachwenkizi, J., Liu, C., Meng, X., Zhang, L., Wang, W., Van
Donkelaar, A., Martin, R.V., Hammer, M.S., Chen, R. and Kan, H.
(2021) Fine particulate matter constituents and infant mortality in
Africa: A multicountry study. Environment International, 156:
106739. https://doi.org/10.1016/j.envint.2021.106739
Ballesteros-González, K., Sullivan, A. P. and Morales-Betancourt, R.
(2020) Estimating the air quality and health impacts of biomassburning in northern South America using a chemical transportmodel. Science of The Total Environment, 739: 139755.
https://doi.org/10.1016/j.scitotenv.2020.139755
Burke, M., Driscoll, A., Heft-Neal, S., Xue, J., Burney, J. and Wara,
M. (2021) The changing risk and burden of wildfire in the UnitedStates. Proceedings of the National Academy of Sciences, 118(2),e2011048118. https://doi.org/10.1073/pnas.2011048118
Cândido Da Silva, A.M., Moi, G.P., Mattos, I.E. and Hacon, S.D.S.
(2014) Low birth weight at term and the presence of fineparticulate matter and carbon monoxide in the Brazilian Amazon:
A population-based retrospective cohort study. BMC Pregnancy
and Childbirth, 14(1): 309. https://doi.org/10.1186/1471-239314-309
Chakrabarti, S., Khan, M. T., Kishore, A., Roy, D. and Scott, S.P.
(2019) Risk of acute respiratory infection from crop burning inIndia: Estimating disease burden and economic welfare fromsatellite and national health survey data for 250000 persons.
International Journal of Epidemiology, 48(4), 1113–1124.
https://doi.org/10.1093/ije/dyz022
Chen, G., Guo, Y., Yue, X., Tong, S., Gasparrini, A., Bell, M.L.,
Armstrong, B., Schwartz, J., Jaakkola, J.J.K., Zanobetti, A.,
Lavigne, E., Nascimento Saldiva, P.H., Kan, H., Royé, D.,
Milojevic, A., Overcenco, A., Urban, A., Schneider, A., Entezari,
A., Li, S. et al. (2021) Mortality risk attributable towildfire-related PM2·5 pollution: A global time series study in 749
locations. The Lancet Planetary Health, 5(9): e579–e587.
https://doi.org/10.1016/S2542-5196(21)00200-X
Chen, J., Li, C., Ristovski, Z., Milic, A., Gu, Y., Islam, M.S., Wang,
S., Hao, J., Zhang, H., He, C., Guo, H., Fu, H., Miljevic, B.,
Morawska, L., Thai, P., Lam, Y.F., Pereira, G., Ding, A., Huang, X.
and Dumka, U.C. (2017) A review of biomass burning: Emissions
and impacts on air quality, health and climate in China. Science of
The Total Environment, 579: 1000–1034. https://doi.org/10.1016/
j.scitotenv.2016.11.025
Crippa, P., Castruccio, S., Archer-Nicholls, S., Lebron, G.B., Kuwata,
M., Thota, A., Sumin, S., Butt, E., Wiedinmyer, C. and Spracklen,
D.V. (2016) Population exposure to hazardous air quality due tothe 2015 fires in Equatorial Asia. Scientific Reports, 6(1): 37074.
https://doi.org/10.1038/srep37074
Fadadu, R.P., Grimes, B., Jewell, N.P., Vargo, J., Young, A.T.,
Abuabara, K., Balmes, J.R. and Wei, M.L. (2021) Association of
wildfire air pollution and health care use for atopic dermatitis andItch. JAMA Dermatology, 157(6), 658. https://doi.org/
10.1001/jamadermatol.2021.0179:
Gaboriau, D.M., Chaste, É., Girardin, M.P., Asselin, H., Ali, A.A.,
Bergeron, Y. and Hély, C. (2023) Interactions within the
climate-vegetation-fire nexus may transform 21st century boreal
forests in northwestern Canada. iScience, 26(6): 106807.
https://doi.org/10.1016/j.isci.2023.106807
Hein, L., Spadaro, J.V., Ostro, B., Hammer, M., Sumarga, E.,
Salmayenti, R., Boer, R., Tata, H., Atmoko, D. and Castañeda, J.-P.
(2022) The health impacts of Indonesian peatland fires.
Environmental Health, 21(1), 62. https://doi.org/10.1186/s12940022-00872-w
Holloway, T., Miller, D., Anenberg, S., Diao, M., Duncan, B., Fiore,
A.M., Henze, D.K., Hess, J., Kinney, P.L., Liu, Y., Neu, J.L.,
O’Neill, S.M., Odman, M.T., Pierce, R.B., Russell, A.G., Tong, D.,West, J.J. and Zondlo, M.A. (2021) Satellite monitoring for air
quality and health. Annual Review of Biomedical Data Science,
4(1):417–447.
https://doi.org/10.1146/annurev-biodatasci110920-093120
Jegasothy, E., Hanigan, I.C., Van Buskirk, J., Morgan, G.G.,
Jalaludin, B., Johnston, F.H., Guo, Y. and Broome, R.A. (2023)
Acute health effects of bushfire smoke on mortality in Sydney,
Australia. Environment International, 171: 107684. https://doi.
org/10.1016/j.envint.2022.107684
Johns, D.O., Stanek, L.W., Walker, K., Benromdhane, S., Hubbell, B.,
Ross, M., Devlin, R.B., Costa, D.L. and Greenbaum, D.S. (2012)
Practical advancement of multipollutant scientific and risk
assessment approaches for ambient air pollution. Environmental
Health Perspectives, 120(9): 1238–1242. https://doi.org/10.1289/
ehp.1204939
Johnson, A.L., Abramson, M.J., Dennekamp, M., Williamson, G.J.
and Guo, Y. (2020) Particulate matter modelling techniques for
epidemiological studies of open biomass fire smoke exposure: A

Review on Exposure Assessment of Biomass Burning
review. Air Quality, Atmosphere & Health, 13(1): 35–75.
https://doi.org/10.1007/s11869-019-00771-z
Karanasiou, A., Alastuey, A., Amato, F., Renzi, M., Stafoggia, M.,
Tobias, A., Reche, C., Forastiere, F., Gumy, S., Mudu, P. andQuerol, X. (2021) Short-term health effects from outdoor exposure
to biomass burning emissions: A review. Science of The Total
Environment, 781: 146739. https://doi.org/10.1016/j.scitotenv.
2021.146739
Khodmanee, S. and Amnuaylojaroen, T. (2021) Impact of biomassburning on ozone, carbon monoxide, and nitrogen dioxide in
Northern Thailand. Frontiers in Environmental Science, 9: 641877.
https://doi.org/10.3389/fenvs.2021.641877
Kollanus, V., Prank, M., Gens, A., Soares, J., Vira, J., Kukkonen, J.,
Sofiev, M., Salonen, R.O. and Lanki, T. (2017) Mortality due tovegetation fire-originated PM2.5 exposure in Europe—
Assessment for the years 2005 and 2008. Environmental Health
Perspectives, 125(1): 30–37. https://doi.org/10.1289/ EHP194
Koman, P.D., Billmire, M., Baker, K.R., Carter, J.M., Thelen, B.J.,
French, N.H.F. and Bell, S.A. (2022) Using wildland fire smoke
modeling data in gerontological health research (California, 2007–2018). Science of The Total Environment, 838, 156403.
https://doi.org/10.1016/j.scitotenv.2022.156403
Le, H.A., Khoi, N.Q. and Mallick, J. (2022) Integrated emission
inventory and modelling to assess the distribution of particulatematters from rice straw open burning in Hanoi, Vietnam.
Atmospheric Pollution Research, 13(5): 101416. https://doi.org/
10.1016/j.apr.2022.101416
Linares, C., Carmona, R., Salvador, P. and Díaz, J. (2018) Impact onmortality of biomass combustion from wildfires in Spain: A
regional analysis. Science of The Total Environment, 622–623:
547–555. https://doi.org/10.1016/j.scitotenv.2017.11.321
Liu, J.C., Pereira, G., Uhl, S.A., Bravo, M.A. and Bell, M.L. (2015)
A systematic review of the physical health impacts from
non-occupational exposure to wildfire smoke. Environmental
Research, 136: 120–132. https://doi.org/10.1016/j.envres.2014.
10.015
Magzamen, S., Gan, R.W., Liu, J., O’Dell, K., Ford, B., Berg, K., Bol,
K., Wilson, A., Fischer, E.V. and Pierce, J.R. (2021) Differential
cardiopulmonary health impacts of local and long‐range transportof wildfire smoke. GeoHealth, 5(3): e2020GH000330.
https://doi.org/10.1029/2020GH000330
Martin, R.V., Brauer, M., Van Donkelaar, A., Shaddick, G., Narain,
U. and Dey, S. (2019) No one knows which city has the highest
concentration of fine particulate matter. Atmospheric
Environment: X(3): 100040. https://doi.org/10.1016/j.aeaoa.
2019.100040
Mijling, B. and Van Der A, R.J. (2012) Using daily satellite
observations to estimate emissions of short‐lived air pollutants ona mesoscopic scale. Journal of Geophysical Research:
Atmospheres, 117(D17): 2012JD017817. https://doi.org/10.1029/
2012JD017817
Monge-Sanz, B.M. and Chipperfield, M.P. (2006). Chemical
Transport Modelling. ECMWF/GEO Workshop on Atmospheric
Reanalysis.
https://www.ecmwf.int/sites/default/files/elibrary/
2006/11221-chemical-transport-modelling.pdf(accessed
14
November 2023)
Mueller, W., Tantrakarnapa, K., Johnston, H.J., Loh, M., Steinle, S.,
Vardoulakis, S. and Cherrie, J.W. (2021) Exposure to ambientparticulate matter and biomass burning during pregnancy:
Associations with birth weight in Thailand. Journal of Exposure
Science & Environmental Epidemiology, 31(4): 672–682.
https://doi.org/10.1038/s41370-021-00295-8
NASA. (n.d.). MODIS Specifications. https://modis.gsfc.nasa.gov/
about/specifications.php
Oliveri Conti, G., Heibati, B., Kloog, I., Fiore, M. and Ferrante, M.
(2017). A review of AirQ Models and their applications for57

forecasting the air pollution health outcomes. Environmental
Science and Pollution Research, 24(7): 6426–6445.
https://doi.org/10.1007/s11356-016-8180-1
O’Neill, S.M., Diao, M., Raffuse, S., Al-Hamdan, M., Barik, M., Jia,
Y., Reid, S., Zou, Y., Tong, D., West, J.J., Wilkins, J., Marsha, A.,
Freedman, F., Vargo, J., Larkin, N.K., Alvarado, E. and Loesche, P.
(2021) A multi-analysis approach for estimating regional healthimpacts from the 2017 Northern California wildfires. Journal of
the Air & Waste Management Association, 71(7): 791–814.
https://doi.org/10.1080/10962247.2021.1891994
OpenAQ (n.d.) OpenAQ. https://explore.openaq.org/
Othman, M., Latif, M.T., Hamid, H.H.A., Uning, R., Khumsaeng, T.,
Phairuang, W., Daud, Z., Idris, J., Sofwan, N.M. and Lung, S.-C.C.
(2022) Spatial–temporal variability and health impact ofparticulate matter during a 2019–2020 biomass burning event inSoutheast
Asia.
Scientific
Reports,
12(1):
7630.
https://doi.org/10.1038/s41598-022-11409-z
Owili, P., Lien, W.-H., Muga, M. and Lin, T.-H. (2017) The
Associations between Types of Ambient PM2.5 and Under-Fiveand Maternal Mortality in Africa. International Journal ofEnvironmental Research and Public Health, 14(4): 359.
https://doi.org/10.3390/ijerph14040359
Pennington, A.F., Strickland, M.J., Gass, K., Klein, M., Sarnat, S.E.,
Tolbert, P.E., Balachandran, S., Chang, H. H., Russell, A.G.,
Mulholland, J.A. and Darrow, L.A. (2019) Source-apportioned
PM2.5 and cardiorespiratory emergency department visits:
Accounting for source contribution uncertainty. Epidemiology,30(6): 789–798. https://doi.org/10.1097/EDE.0000000000001089
Piersanti, A., Vitali, L., Righini, G., Cremona, G. and Ciancarella, L.
(2015) Spatial representativeness of air quality monitoring
stations: A grid model based approach. Atmospheric Pollution
Research,
6(6):953–960.
https://doi.org/10.1016/j.apr.
2015.04.005
Pothirat, C., Chaiwong, W., Liwsrisakun, C., Bumroongkit, C.,
Deesomchok, A., Theerakittikul, T., Limsukon, A.,
Tajarernmuang, P. and Phetsuk, N. (2021) The short-term
associations of particular matters on non-accidental mortality andcauses of death in Chiang Mai, Thailand: A time series analysis
study between 2016–2018. International Journal ofEnvironmental Health Research, 31(5), 538–547. https://doi.org/
10.1080/09603123.2019.1673883
Reddington, C.L., Conibear, L., Robinson, S., Knote, C., Arnold, S.R.
and Spracklen, D.V. (2021) Air pollution from forest andvegetation fires in Southeast Asia disproportionately impacts thepoor. GeoHealth, 5(9): e2021GH000418. https://doi.org/10.1029/
2021GH000418
Reid, C.E., Jerrett, M., Petersen, M.L., Pfister, G.G., Morefield, P.E.,
Tager, I.B., Raffuse, S.M. and Balmes, J.R. (2015) Spatiotemporal
prediction of fine particulate matter during the 2008 Northern
California wildfires using machine learning. Environmental
Science & Technology, 49(6): 3887–3896. https://doi.org/10.1021/
es505846r
Ren, Y., Shen, G., Shen, H., Zhong, Q., Xu, H., Meng, W., Zhang, W.,
Yu, X., Yun, X., Luo, Z., Chen, Y., Li, B., Cheng, H., Zhu, D. andTao, S. (2021) Contributions of biomass burning to global andregional SO2 emissions. Atmospheric Research, 260: 105709.
https://doi.org/10.1016/j.atmosres.2021.105709
Sannigrahi, S., Pilla, F., Maiti, A., Bar, S., Bhatt, S., Kaparwan, A.,
Zhang, Q., Keesstra, S. and Cerda, A. (2022) Examining the statusof forest fire emission in 2020 and its connection to COVID-19
incidents in West Coast regions of the United States.
Environmental Research, 210: 112818. https://doi.org/10.1016/
j.envres.2022.112818
Sheldon, T.L. and Sankaran, C. (2017) The impact of Indonesian
forest fires on Singaporean pollution and health. American
Economic Review, 107(5): 526–529. https://doi.org/10.1257/

58

S. VALENZUELA et al.

aer.p20171134
Sohrabinia, M. and Khorshiddoust, A.M. (2007) Application ofsatellite data and GIS in studying air pollutants in Tehran. Habitat
International, 31(2): 268–275. https://doi.org/10.1016/j.habitatint.
2007.02.003
Sorek-Hamer, M., Chatfield, R. and Liu, Y. (2020) Review:
Strategies for using satellite-based products in modeling PM2.5
and short-term pollution episodes. Environment International,
144: 106057. https://doi.org/10.1016/j.envint.2020.106057
Stowell, J.D., Geng, G., Saikawa, E., Chang, H.H., Fu, J., Yang,
C.-E., Zhu, Q., Liu, Y. and Strickland, M.J. (2019) Associations ofwildfire smoke PM2.5 exposure with cardiorespiratory events in
Colorado 2011–2014. Environment International, 133, 105151.
https://doi.org/10.1016/j.envint.2019.105151
Taylor, D. (2010) Biomass burning, humans and climate change inSoutheast Asia. Biodiversity and Conservation, 19(4): 1025–1042.
https://doi.org/10.1007/s10531-009-9756-6
Thomas, A.S., Escobedo, F.J., Sloggy, M.R. and Sánchez, J.J. (2022)
A burning issue: Reviewing the socio-demographic andenvironmental justice aspects of the wildfire literature. PLOS ONE,17(7): e0271019. https://doi.org/10.1371/journal.pone.0271019
Valari, M., Menut, L. and Chatignoux, E. (2011) Using a chemistrytransport model to account for the spatial variability of exposure
concentrations in epidemiologic air pollution studies. Journal of
the Air & Waste Management Association, 61(2): 164–179.
https://doi.org/10.3155/1047-3289.61.2.164
Vohra, K., Vodonos, A., Schwartz, J., Marais, E.A., Sulprizio, M.P.
and Mickley, L.J. (2021) Global mortality from outdoor fineparticle pollution generated by fossil fuel combustion: Resultsfrom GEOS-Chem. Environmental Research, 195: 110754.
https://doi.org/10.1016/j.envres.2021.110754
Wani, M.A., Mishra, A.K., Sharma, S., Mayer, I.A. and Ahmad, M.
(2021) Source profiling of air pollution and its association withacute respiratory infections in the Himalayan-bound region ofIndia. Environmental Science and Pollution Research, 28(48):68600–68614. https://doi.org/10.1007/s11356-021-15413-0
World Health Organization (2022, December 19) Ambient (outdoor)
air pollution. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health#:~:text=The%20combi
ned%20effects%20of%20ambient,premature%20deaths%20worl
dwide%20in%202019 (accessed 14 November 2023)
Wu, Y., Li, S., Xu, R., Chen, G., Yue, X., Yu, P., Ye, T., Wen, B., De
Sousa Zanotti Stagliorio Coêlho, M., Saldiva, P.H.N. and Guo, Y.
(2023) Wildfire-related PM2.5 and health economic loss ofmortality in Brazil. Environment International, 174: 107906.
https://doi.org/10.1016/j.envint.2023.107906
Xie, X., Semanjski, I., Gautama, S., Tsiligianni, E., Deligiannis, N.,
Rajan, R., Pasveer, F. and Philips, W. (2017) A review of urban air
pollution monitoring and exposure assessment methods. ISPRSInternational Journal of Geo-Information, 6(12): 389.
https://doi.org/10.3390/ijgi6120389
Yao, W., Zhao, Y., Chen, R., Wang, M., Song, W. and Yu, D. (2023)
Emissions of toxic substances from biomass burning: A review ofmethods and technical influencing factors. Processes, 11(3): 853.
https://doi.org/10.3390/pr11030853
Zou, J., Lu, N., Jiang, H., Qin, J., Yao, L., Xin, Y. and Su, F. (2022)
Performance of air temperature from ERA5-Land reanalysis incoastal urban agglomeration of Southeast China. Science of The
Total Environment, 828: 154459. https://doi.org/10.1016/
j.scitotenv.2022.154459
Zou, Y., O’Neill, S.M., Larkin, N.K., Alvarado, E.C., Solomon, R.,
Mass, C., Liu, Y., Odman, M.T. and Shen, H. (2019) Machinelearning-based integration of high-resolution wildfire smoke
simulations and observations for regional health impact
assessment. International Journal of Environmental Research andPublic Health, 16, 2137. https://doi.org/10.3390/ijerph16122137

Annex 1 Full list of publications included in the study.
Study
Location
Ademu, et al., California,
2022
USA
Alman, et al.,
2016

Colorado,
USA

Study duration
February 1 –December 31,
2020
June 5 – July 6,
2012

Augusto,et al., 2020
Bachwenkizi,
et al., 2021

Portugal

October 2017

Mortality

15 Africancountries

2005 – 2015

Infant mortality

Ballesteros-G Northern
onzales, et al., South
2020, 2020
America

Chakrabarti,
et al., 2019
Chen, et al.,
2021

India

749 cities in
43 countries
and regions,
globally
Cobelo, et al., Brazil
2023
Crippa, et al.,2016
Da Silva,
et al., 2014

Equatorial
Asia
Mato Grosso,
BrazilHealth Outcome
Confirmed COVID-19 cases
Respiratory and cardiovascularemergency department visits

February 2010 –February 2018

All-cause mortality
Cardiovascular and respiratory
mortality
Chronic Obstructive Pulmonary
Disease (COPD)
Respiratory emergency visits
September 2013 – Seeking treatment for AcuteFebruary 2014
Respiratory Infection
2000 – 2016
All-cause mortality
Cardiovascular mortality
Respiratory mortality
2003 – 2018

Mortality

September 1 –December 2015
July 1, 2004 –December 31,
2005

All-cause Mortality

Fadadu, et al., San
November 2018
2021
Francisco,
USA
Faustini, et al., 10 southern
2003 – 20102019
Europeancities in Spain,
France, Italy
and Greece
Ford, et al.,Contiguous
early 21st century
2018
United States (2000–2010)(without
midcentury
Hawaii)(2040–2050)
late century(2090–2099)

Data Collection Method
Station monitoring
Weather Research and Forecasting Model with Chemistry (WRF-Chem);
Model for Ozone and Related chemical Tracers (MOZART-4);
National Center for Environmental Protection’s North American Mesoscale Forecast System(NCEP/NAM);
NCAR Fire Inventory (FINN);
SMARTFIRE framework;
EPA surface network
Satellite imaging using the Navy Aerosol Analysis and Prediction System (NAAPS)
Station monitoring (52 fixed air quality monitoring stations)Chemical transport model (GEOS-Chem);
Satellite monitoring;
Ground based observations
Chemical transport model using Weather Research and Forecasting Model coupled with
Chemistry(WRF-Chem)

Moderate-Resolution Imaging Spectroradiometer (MODIS)
Chemical transport model (GEOS-Chem)

Fire Information for Resource Management System (FIRMS) provided by NASA;
Copernicus Atmosphere Monitoring Service (CAMS);
Meteorological variables from the ERA-Interim model;
Pixel-based classification of Landsat satellite images by MapBiomas
Weather Research and Forecasting model (version 3.5) with Chemistry (WRF-Chem)

Low birth weight at term

Coupled Aerosol and Trace Gas Transport Model to the Brazilian Developments of theRegional Atmospheric Modeling System (CATT-BRAMS Model) and Center for Weather
Forecasting and Climate Studies of the National Institute for Space Research(INPE-CPTEC)
Weekly clinic visit counts for AD or Station monitoring and satellite-based smoke plume density scores rom the Bay Area Air
itch
Quality Management District
Natural mortality
Cardiovascular mortality
Respiratory mortality

Navy Aerosol Analysis and Prediction System (NAAPS) model;
Satellite measurements and fire-related smoke plumes

All-cause mortality

Community Atmospheric Model v4 with an interactive gas‐aerosol scheme (CAM‐Chem);
Community Earth System Model (CESM);
Community Land Model (CLM) v4.5

Review on Exposure Assessment of Biomass Burning
Study
Gao, et al.,2023

Location
UK Biobank
subsetGoncalves,
et al., 2023

Study duration
2004 – 2010

Health Outcome

Data Collection Method
Chemical transport model (GEOS-Chem) (version 12.0.0);
Inverse Distance Weighted (IDW) method;
Deep ensemble machine learning (DEML) framework
COVID-19 morbidity, mortality, and MOD043K (MYD043K) products;
severity
Nasa (LAADS DAAC), Collection v. 6.1 (C6);Non-linear prediction model and the higher-resolution MODIS AOD
Cardiorespiratory emergency
Chemical transport model simulation (GEOS-Chem)
hospital visitsAll-cause adult mortality
Satellite remote sensing (MODIS);
Infant mortality
Chemical transport model simulation (GEOS-Chem)
Severe asthma attacks in children
Asthma-related hospital admissions
Respiratory hospital admissions
Lost work days for the current laborforce
Respiratory physician visits
PM10 TEOM instruments;
Asthma-specific visits
Moderate Resolution Imaging Spectroradiometer (MODIS)
Respiratory hospital admissions
Mortality

5 Brazilian
AmazonstatesHeaney, et al., California,
2022
USA
Hein, et al.,Indonesia
2022

2020 – 2021

Henderson. et Britishal., 2011
Columbia,
CanadaJuly 1 –September 30,
2003

Ignotti, et al.,2010

BrazilianAmazon2004 – 2005

Jegasothy,
et al., 2023

Sydney,Australia

2019 – 2020

Hospitalization due to respiratory
disease in children under five years
old and Elderly people (over 64
years of age)
Hospitalization due to childbirthAll-cause mortality

Johnson andGarcia-Menendez, 2020
Johnston
et al., (2007)

North
Carolina,
USA
Darwin,Australia

November 2016

Mortality and morbidity
Respiratory
Cardiovascular admissions

Johnston,
et al., 2012
Kollanus etal., 2017

GlobalFire seasons of
2000,
2004, 2005
1997 – 2006
2005 and 2008

Mortality risk

2009 – 2018

Influenza

Europeanregions (27countries)
Landguth,Montana,
et al., 2020
USA
Leibel, et al., San Diego
2020
County, USA
Li, et al., 2023 Global (48countries)

2004 – 20092013 – 2017

59

Mortality

December 7 – 16, Pediatric respiratory visits
2017
2003 – 2014
Acute respiratory illness symptoms
in children under 5y

Linares, et al., 1 province in 2004 – 2009
2018
Spain

Provincial daily natural-cause
mortality

Magzamen,
et al., 2021

Coupled aerosol and trace gas transport model for the Brazilian development of theRegional Atmospheric Modeling System Model (CATT-BRAMS)

Satellite imagery;
Monitoring stations using inverse-distance weighting and cross-referenced extreme days
(95th percentile or above)
U.S. Environmental Protection Agency's (U.S. EPA) Air Quality System for 172 monitors;
Satellite-based Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT);
Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem)
Rupprecht and Patashnick Tapered Element Oscillating Microbalance (TEOM) series 1400a
MODIS (Satellite)
GEOS-Chem (Chemical Transport Model)
Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data;
System for Integrated modeLling of Atmospheric coMposition (SILAM) chemical transportmodel
Air quality monitoring stations;
Satellite imagery via Moderate Resolution Imaging Spectroradiometer (MODIS)Satellite imagery via Moderate Resolution Imaging Spectroradiometer (MODIS)
Chemical transport model (GEOS-Chem)
NAAPS (Navy Aerosol Analysis and Prediction System) Global Aerosol Model(Atmospheric numerical);Geostationary satellite;
Satellite imagery via Moderate Resolution Imaging Spectroradiometer (MODIS)
Environmental Protection Agency Air Quality System monitors for the western UnitedStates (kriged PM2.5 surface at a 15 × 15 km resolution);
Satellite-based smoke plume estimates via National Oceanic and Atmospheric
Administration's Hazard Mapping System
Ambient air pollutant data from ground-based monitors;
Biomass burning from satellite remote sensing data (NASA’s Visible Infra-red Imaging
Radiometer Suite sensor)

Colorado,USA

2010 – 2015

Cardiopulmonary hospitalizations
and deaths

Mueller, et al., Thailand
2021

2015 – 2018

Low-birth weight (LBW) (<2500 g)

Nguyen, et al., Queensland,
2021
New South
Wales,
Victoria andSouthAustralia

November 1,
Mortality
2019 – January 8, Respiratory and cardiovascular2020
diseases hospitalizations

Weather Research and Forecasting with Chemistry (WRF-Chem) model;
Moderate Resolution Imaging Spectroradiometer (MODIS);
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites

O'Keefe, et al Victoria,
2016
Australia

Not specified

Morbidity and mortality

E-sampler Aerosol Monitor (MetOne Instruments Inc. Grants Pass OR)

O'Neill, et al., Northern
2021
California,
USA

October 8 – 20,
2017Multiple-cause mortality

Othman, et al., Thailand,2022
Malaysia
Owili, et al., 54 countries
2017
in Africa
Pan, et al.,
USA
2023

2019–2020

Deposition of Particles in
Respiratory Tract
Under-five mortality
Maternal mortality
Premature mortality

Regional modeling (WRF-CMAQ) with data fusion and three machine learning methods;
GOES-16 Advanced Baseline Imaging (ABI);
Visible Infrared Imaging Radiometer Suite (VIIRS);
Moderate Resolution Imaging Spectroradiometer (MODIS);Fire Radiative Power (FRP);US Environmental Protection Agency (EPA) Air Quality System
AS-LUNG V.2 Outdoor sensing device

Pennington,
et al., 2019

Atlanta,Georgia,USA

2000 – 2015
April – October
2012, 2013, and2014
1998 – 2010

Pothirat, et al., Chiang Mai, 2016 – 2018
2021
ThailandPullabhotla,
Africa2004 – 2018
et al., 2023
Rappold,
42 contiguous June 1 – July 14,
et al., 2011
counties in2008
eastern North
Carolina,
USA

Reddington,
et al., 2021

Southeast Asia 2003 – 2015
(including
MainlandSoutheast Asiaandsouth-eastern
China)

Respiratory emergency department
visitsAll-cause mortality
Infant mortality
AsthmaChronic obstructive pulmonary
diseasePneumonia and acute bronchitis
Upper respiratory tract infections
Heart failure
Cardiac dysrhythmia
Myocardial infarction
All-cause mortality and morbidity

Satellite imaging via Moderate Resolution Imaging Spectroradiometer (MODIS)Fire inventory from NCAR (FINN);
CMAQ model
Receptor-based source apportionment methods (Chemical mass balance with organic
molecular markers, Chemical mass balance with gas-based constraints, and Positive matrix
factorization);
Chemical transport model (Community Multiscale Air Quality [CMAQ]);
Street monitor
Single sampling station (located at center of municipal areas of Maung Chiang Mai district,
Chiang Mai, Thailand)Satellite-derived burned area
Geostationary operational environmental satellite instruments

Global Model of Aerosol Processes (GLOMAP);
Weather Research and Forecasting model coupled with Chemistry (WRF-Chem)

60

Study
Reid, et al.,2019
Sannigrahi,
et al., 2022
Sheldon andSankaran,
2017

S. VALENZUELA et al.

Location
USA(California)

Study duration
Health Outcome
June – September All respiratory health outcomes
2008(Asthma, COPD, Pneumonia, AcuteBronchitis, Acute Respiratory
Infections)West Coast of August 1 –Daily COVID-19 morbidity andthe USA
October 30, 2020 mortality
Singapore2010 – mid 2016

Acute upper respiratory tract
infections
Acute conjunctivitis
Acute diarrhea
Chickenpox
Cardiorespiratory acute events

Data Collection Method
Chemical transport model;
Satellite;
Machine Learning AI
OpenAQ platform (retrieved the latest and up-to-date air quality data from multiple sources
such as government/institution air quality monitoring stations and low-cost open-air quality
sensors)
Satellite fire data using NASA's Fire Information
for Resource Management System (FIRMS)

Stowell, et al., Colorado,
2019
USA

May – August
2011-2014

Viswanathan,et al., 2006

San Diego,
USA

Vohra et al.,(2021)

Globe

September 28– December 6,
2003
2012

Hospital emergency room visits forasthma, respiratory problems, eyeirritation, and smoke inhalation
Global mortality

Wani, et al.,2021

Himalayan
region of
India
Africa2013 – 2017

Acute respiratory infections

Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)

2001 – 2014

Lifetime Cancer Risk

BaP inventory (based on GEOS-Chem), CanMETOP

510
immediate
regions in
BrazilAustralia

2000 – 2016

All-cause, cardiovascular, andrespiratory mortality

Chemical transport model (GEOS-Chem);
Global Fire Emissions Database (GFED);
Ground monitored data and machine learning

2000 – 2008

Selected genetic markers (26 CpGs
and 33 DMRs)

Xue, et al.,South Asia
2023
Ye, et al., 2022 510
immediate
regions in
BrazilYin, 2023
MainlandSoutheast
Asia
Zhang, et al., USA
2023

2000 – 2014

Stillbirth

2000 – 2015

All-cause mortality
Cardiovascular mortality
Respiratory mortality

Satellite observations;Chemical transport models (GEOS-Chem);
Ground-based observations
Chemical transport model simulations;
Satellite observations
Chemical transport model (GEOS-Chem)

1990 – 2019

Premature mortality

Wu, et al.,2022
Wu, et al.,2023
Xu, et al.,2023

Zhang, et al.,2023

New South
Wales,
Australia

January 1, 1998 – Dementia
December 31,
20162015 – 2019
Preterm birthTerm low birth weightMaternal medical conditions

Zou, et al.,2019

Pacific
Northwest

August –December 2017

Multiple-cause mortality

Sary VALLENZUELA
Sary Valenzuela is a licensed physician, writerand researcher, specializing in environmental
public health for the ASMPH Center of Researchand Innovation (ACRI). She was one of theprincipal writers for several of their research
projects, creating risk communication plans andconducting policy analyses and literature reviews on topics such asenvironmental health, solid waste management and climate change. She
has also served as marketing director and is currently a board member of
One Million Lights PH, an multi-awarded, globally-recognized non-profit
organization that provides solar energy to communities around thePhilippines that would otherwise have little to no access to electricity. She
has coordinated and organized 10 or more on-site distributions, providing
more than 1,000 solar lights to several geographically-isolated provinces.
She built the organization’s digital presence and social media communityfrom the ground up in less than one year.

Keith Alexius WANGKAY
Keith Wangkay graduated from De La Salle
University with a Bachelor’s degree in Biology
and the University of the East Ramon Magsaysay
Memorial Center Inc. with a medical degree. He
is currently affiliated with public health services,
specifically for HIV and AIDS, where regarding
which the Philippines is one of the few countries
with a rising epidemic.

Community Multiscale Air Quality Modeling System (CMAQ)
AOD Model (derived from Multi-angle Implementation of Atomospheric Correction(MMAIAC)
Station monitoring
Chemical transport model (GEOS-Chem)

Satellite fire observations and emission inventories;
Global Exposure Mortality Model (GEMM)
Spatiotemporal model
Chemical transport model

Satellite imaging (Multi-Angle Implementation of Atmospheric Correction satellite aerosol
optical depth);
U.S. Environmental Protection Agency AirNow;
Weather Research and Forecasting (WRF) model version 3.7;
Community Multiscale Air Quality (CMAQ) model

Chemical transport model (GEOS-Chem) with
Reanalysis meteorological data

Xerxes SEPOSO
Xerxes Seposo is an educator and researcher. As
an environmental epidemiologist, he has authoredand co-authored more than 100 publications todate that have been used as evidence in several
key reports such as IPCC AR6 and the
Association of Academies and Societies of
Sciences in Asia (AASSA) report on climate change and health.

Geminn Louis C. APOSTOL
Geminn Apostol is a physician, educator, andresearcher specializing in environmental publichealth, climate-resilient health systems andsocio-ecological determinants of health. He has
been engaged in various public health initiatives
on mainstreaming environmental health research,
service delivery and advocacy in the Philippines. He currently serves as anexpert for the Philippine Inter-Agency Committee on Environmental Health(IACEH), where he helps convene government, academia, civil society andthe private sector towards generating evidence, developing policies andoperationalizing programs for environmental protection and public healthpromotion.

(Received 14 November 2023, Accepted 22 December 2023)