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3.3 Chemical Transport Models may also greatly vary. For example, in the USA, there are thousands of monitoring stations for various atmospheric measurements, yet only 20% of US counties have at least one monitor (Holloway et al., 2021). Globally, more than half (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 to 4500km2), highlighting the need for qualitative and ad hoc assessments to understand the limitations and complexity of 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 can measure particulate matter levels, CO2, temperature and relative humidity. Mueller et al. (2021) on the other hand utilized ground monitors to assign exposure and noted limited spatial accuracy in capturing differences within smaller 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 the incidence COVID-19. OpenAQ station density varies by region, however, being concentrated mostly in the United States, Europe and Japan as of writing, possibly limiting its use for many countries, especially those of the global south. Chemical transport models (CTMs) are a class of numerical models used in air pollution modeling to simulate chemical transformation 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 the atmosphere in space and time (Koman et al., 2022). The advantages of CTMs are that they aggregate data from various sources such as satellite measurements and the dispersion, Review on Exposure Assessment of Biomass Burning and transport ground-based observations simulations, providing a potentially more holistic picture of multiple datasets. Additionally, this modeling technique is popular for its powerful ability to perform long-term simulations and demonstrate the atmospheric composition (Monge-Sanz and Chipperfield, 2006). The disadvantage of CTMs is that they require significantly intensive computational demands, especially when running at high spatial and temporal resolutions. CTMs may also be limited by lack of input, as described by 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 be considered 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 those of satellite imaging and direct monitors. However, they also mentioned that model-related assumptions and uncertainties may lead to dramatic discrepancies (either overestimation or underestimation) in downstream exposure typically cross-referenced and validated with data either from satellite imaging or ground-based monitors, as is the case in 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 those of 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 the limitations of CTMs (Bachwenkizi et al., 2021; Kollanus et al., 2017; Wu et al., 2023). Certain strategies and methods, including kriging and land-use regression, may be employed to adjust the model's raw output and increase the model (Valari et al., 2011). the accuracy of Subsequent performance model evaluation in a study by Ballesteros-Gonzales et al. that overpredictions and underpredictions were apparent for various meteorological conditions (i.e., wind speed, humidity) and air pollutant concentrations (i.e., ozone, carbon dioxide) by as much as 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 to enhance their performance and accuracy over time. There are several CTM models. These include (Goddard Earth Observing System- GEOS-Chem Chemistry) and WRF-Chem (Weather Research and Forecasting-Chemistry), both of which use a variety of inputs including satellite, ground-based and other meteorological datasets to simulate the transport of gases into the temporal evolution of estimates. Hence, CTMs their are (2020) found twice 53

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