Local and regional smoke impacts from prescribed fires

Abstract

Abstract. Smoke from wildfires poses a significant threat to affected communities. Prescribed burning is conducted to reduce the extent and potential damage of wildfires, but produces its own smoke threat. Planners of prescribed fires model the likely dispersion of smoke to help manage the impacts on local communities. Significant uncertainty remains about the actual smoke impact from prescribed fires, especially near the fire, and the accuracy of smoke dispersal models.To address this uncertainty, a detailed study of smoke dispersal was conducted for one small (52 ha) and one large (700 ha) prescribed fire near Appin in New South Wales, Australia, through the use of stationary and handheld pollution monitors, visual observations and rain radar data, and by comparing observations to predictions from an atmospheric dispersion model. The 52 ha fire produced a smoke plume about 800 m high and 9 km long. Particle concentrations (PM 2.5 ) reached very high peak values (> 400 µg m −3 ) and high 24 h average values (> 100 µg m −3 ) at several locations next to or within ∼ 500 m downwind from the fire, but low levels elsewhere. The 700 ha fire produced a much larger plume, peaking at ∼ 2000 m altitude and affecting downwind areas up to 14 km away. Both peak and 24 h average PM 2.5 values near the fire were lower than for the 52 ha fire, but this may be because the monitoring locations were further away from the fire. Some lofted smoke spread north against the ground-level wind direction. Smoke from this fire collapsed to the ground during the night at different times in different locations. Although it is hard to attribute particle concentrations definitively to smoke, it seems that the collapsed plume affected a huge area including the towns of Wollongong, Bargo, Oakdale, Camden and Campbelltown (∼ 1200 km 2 ). PM 2.5 concentrations up to 169 µg m −3 were recorded on the morning following the fire. The atmospheric dispersion model accurately predicted the general behaviour of both plumes in the early phases of the fires, but was poor at predicting fine-scale variation in particulate concentrations (e.g. places 500 m from the fire). The correlation between predicted and observed varied between 0 and 0.87 depending on location. The model also completely failed to predict the night-time collapse of the plume from the 700 ha fire.This study provides a preliminary insight into the potential for large impacts from prescribed fire smoke to NSW communities and the need for increased accuracy in smoke dispersion modelling. More research is needed to better understand when and why such impacts might occur and provide better predictions of pollution risk.

Preface: Natural hazard event analysis for risk reduction and adaptation

“…The model was not able to predict the night-time collapse of the plume from the large fire. Price et al (2016) conclude that prescribed fires might have large impacts on communities, which challenges science to improve the accuracy in smoke dispersion modelling. More research is needed to better understand when and why such impacts might occur to provide better predictions of pollution risk.…”

“…In a similar vein, Price et al (2016) examine prescribed burning as manmade events to test and improve models, assess pollution risk and derive guidance for mitigating wildfires. Prescribed burning is conducted to reduce the extent and potential damage of wildfires, but it produces its own smoke threat.…”

“…Prescribed burning is conducted to reduce the extent and potential damage of wildfires, but it produces its own smoke threat. Price et al (2016) undertake a detailed study of smoke dispersal for one small and one large prescribed fire in New South Wales, Australia. The approach comprised the utilization of stationary and handheld pollution monitors, visual observations and rain radar data as well as a comparison of the observations with predictions from an atmospheric dispersion model.…”

“…Therefore, planning for adaptation needs to be agile in order to take up these possible changes. In view of increasing trends in natural hazards, Read and Vogel (2016) propose the application of hazard function analysis as an alternative methodology for describing the probabilistic behaviour of non-stationary natural hazards. Hazard function analysis, with applications in medicine, economics, engineering and many other disciplines, offers a well-established set of tools for conducting a time to event analysis and for understanding the distribution of failure times for a given process.…”

“…Hazard function analysis comprises three primary functions: (1) the hazard function, which represents the failure rate; (2) the survival function, which describes the exceedance probability for the random variable time; and (3) the cumulative hazard function, which accounts for the total number of failure events over time. Read and Vogel (2016) link the probabilistic properties of the random variable time to failure with the proper-ties of the exceedance probability for a non-stationary natural hazard event. The two-parameter generalized Pareto distribution is used to introduce stationary and non-stationary models for natural hazard events for which the hazard function analysis relationships are derived.…”