Southeast Asia experiences frequent fires in fuel‐rich tropical peatlands, leading to extreme episodes of regional haze with high concentrations of fine particulate matter (PM_2.5) impacting human health. In a study published recently, the first field measurements of PM_2.5 emission factors for tropical peat fires showed larger emissions than from other fuel types. Here we report even higher PM_2.5 emission factors, measured at newly ignited peat fires in Malaysia, suggesting that current estimates of fine particulate emissions from peat fires may be underestimated by a factor of 3 or more. In addition, we use both field and laboratory measurements of burning peat to provide the first mechanistic explanation for the high variability in PM_2.5 emission factors, demonstrating that buildup of a surface ash layer causes the emissions of PM_2.5 to decrease as the peat fire progresses. This finding implies that peat fires are more hazardous (in terms of aerosol emissions) when first ignited than when still burning many days later. Varying emission factors for PM_2.5 also have implications for our ability to correctly model the climate and air quality impacts downwind of the peat fires. For modelers able to implement a time‐varying emission factor, we recommend an emission factor for PM_2.5 from newly ignited tropical peat fires of 58 g of PM_2.5 per kilogram of dry fuel consumed (g/kg), reducing exponentially at a rate of 9%/day. If the age of the fire is unknown or only a single value may be used, we recommend an average value of 24 g/kg.

During extreme air pollution events, such as bushfires, public health agencies often recommend that vulnerable individuals visit a nearby public building with central air conditioning to reduce their exposure to smoke. However, there is limited evidence that these “cleaner indoor air shelters” reduce exposure or health risks. We quantified the impact of a “cleaner indoor air shelter” in a public library in Port Macquarie, NSW, Australia when concentrations of fine particulate matter (PM_2.5) were elevated during a local peat fire and nearby bushfires. Specifically, we evaluated the air quality improvements with central air conditioning only and with the use of portable high efficiency particulate air (HEPA) filter air cleaners. We measured PM_2.5 from August 2019 until February 2020 by deploying pairs of low-cost PM_2.5 sensors (i) inside the main library, (ii) in a smaller media room inside the library, (iii) outside the library, and (iv) co-located with regulatory monitors located in the town. We operated two HEPA cleaners in the media room from August until October 2019. We quantified the infiltration efficiency of outdoor PM_2.5 concentrations, defined as the fraction of the outdoor PM_2.5 concentration that penetrates indoors and remains suspended, as well as the additional effect of HEPA cleaners on PM_2.5 concentrations. The infiltration efficiency of outdoor PM_2.5 into the air-conditioned main library was 30%, meaning that compared to the PM_2.5 concentration outdoors, the concentrations of outdoor-generated PM_2.5 indoors were reduced by 70%. In the media room, when the HEPA cleaners were operating, PM_2.5 concentrations were reduced further with a PM_2.5 infiltration efficiency of 17%. A carefully selected air-conditioned public building could be used as a cleaner indoor air shelter during episodes of elevated smoke emissions. Further improvements in indoor air quality within the building can be achieved by operating appropriately sized HEPA cleaners.

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM_2.5 concentrations (r² = 0.93–0.99) and good accuracy (mean absolute error (MAE) < 2 μg m⁻³). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r² = 0.87–95) and good accuracy (MAE of 2.5–3.0 μg m⁻³). The PM_2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM_2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.

Background: Air quality modelling of smoke from wildfires requires knowledge of emission factors and how these vary. Aims: Experimental fires were used to test the variation of emission factors with fuel load to improve a smoke forecasting model. Methods: Gas and particle-phase chemical composition of smoke plumes from laboratory-scale fires was measured with different fuel loads and at different stages of fire progression. Key results: Different fine woody debris loads had no significant effect on the emission factors. In contrast, different stages of the fire produced significant differences in emission factors. The lowest emission factors for most species (except carbon dioxide) were observed during the flaming fire front, which accounted for half the total emissions. Importantly, we found that emission ratios relative to carbon monoxide were consistent across different fuel loads and throughout the fire’s progression. Conclusions: By modelling the modified combustion efficiency, it is possible to simultaneously model the emissions of carbon monoxide and the emissions of nearly all other pollutants throughout the progression of a fire. Implications: The emissions of carbon monoxide, fine particulate matter and other key pollutants all scale with the intensity of the fire, simplifying the task of modelling these emitted pollutants downwind of prescribed fires.

While remote sensing has been widely used to estimate vegetation biochemical and structural properties, relatively little work has systematically and experimentally linked vegetative fuel spectral reflectance to independently measured fuel combustibility, free-spreading fire behaviour, and fire emissions. This limits the development and validation of remote sensing products for operational wildland fire applications. We present a pilot-study evaluation of an experimental method that integrates imaging spectroscopy with free-spreading fires in a combustion wind tunnel to investigate relationships between wildland fuel spectral reflectance and combustibility, fire behaviour, and fire emissions. The pilot study used three common Australian fuels at two combustibility levels. Pre- and post-burn imaging spectroscopy observations (400–2500 nm) were collected during 26 experiments, alongside measurements of fuel biochemistry, calorimetry, moisture, rate of spread, combustion efficiency, and gaseous and particulate emissions. Statistically significant differences between fuel type and combustibility were found in fuel moisture, rate of spread, and emissions, with corresponding differences evident in the spectral signatures. Partial least squares regression (PLSR) indicated that pre-fire spectral information was informative for predicting several fire behaviour and emissions metrics. These results demonstrate the feasibility of the proposed methodology and provide a foundation for extending it to a wider range of wildland fuels. Data generated using this methodology have the potential to improve interpretation of remote sensing datasets and inform the design of future satellite instruments, with potential applications in assessing fuel condition, predicting fire behaviour, and estimating wildland fire emissions.