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54 3.4 Satellite-data-based Modeling and aerosols in the atmosphere. GEOS-Chem in particular includes data about oxidant-aerosol chemistry and reanalysis meteorology from NASA’s Global Modeling and 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) in their 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 data collection 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 be advantageous topographical and highly mountainous locations like the northern Andes, they observed, in order to capture the spatial variability of air pollution particles over smaller areas. in difficult 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 to include and distinguish all categories of PM sources. The modeling system uses a three-dimensional grid-based model resulting in raster data grid cells over the studied area. One study (Koman et al., 2022), however, observed that CMAQ would be useful in exposure assessments for aging health outcomes due to the model's ability to separate wildfire PM2.5 from other sources of biomass burning 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 noted that the model underestimated the observed PM levels as it omitted wind-blown dust, secondary organic aerosols and aerosol-bound water in its simulations. Satellite-based modeling and data collection is an approach to air pollution modeling that leverages remote sensing 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 the S. VALENZUELA et al. atmosphere instead of detecting PM2.5 directly (Holloway et al., 2021). This approach offers several advantages, as it provides a wealth of information on a regional to global scale (NASA, n.d.; Sohrabinia and Khorshiddoust, 2007). They are spatially consistent and may 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 the satellite technology involved. Spatial resolutions may vary between ∼1 to ∼50 km and temporal data coverage from hourly to once a day or every few days, depending on 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 regardless of local conditions at a more affordable price, making it a better option to middle-income countries (Sohrabinia and Khorshiddoust, 2007). They can demonstrate 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 an invaluable 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 and changes 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 information and individual-level information (Holloway et al., 2021; Sorek-Hamer et al., 2020). Kollanus et al. (2017), who used a blended method for his study, noted that satellite modeling approaches were limited by uncertainties in diurnal variations in fire intensity and challenges in observing small vegetation fires. They were unable precisely to measure smoke density or separate smoke in low-

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