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Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. Applying the statistical model predicts that prescribed burns of 1000 ha can be expected to cause air quality exceedances over an area of ~3500 ha. Cool weather that reduces the risk of fire escape, has the highest potential for polluting nearby communities, and fires that burn into the night are particularly bad.

Smoke plume dynamics involve various smoke processes and mechanics in the atmosphere and provide the scientific foundation for the development of tools to simulate and predict smoke and its environmental and human impacts. The increasing occurrence of wildfires and the demands for more extensive application of prescribed fires in the U.S. have posed great challenges and immediate actions for advancing smoke plume dynamics and improving smoke predictions and impact assessments to mitigate smoke impacts. Numerous efforts have been made recently to address these needs and challenges. This paper synthesizes advances in smoke plume dynamics research mainly conducted in the U.S. in the recent decade, identifies gaps, and suggests future research needs. The main advances include smoke data collections from comprehensive field campaigns, new satellite products, improved understanding of smoke plume properties and chemistry, structure and evolution, evaluation and improvement of smoke modeling and prediction systems, the development of coupled smoke models, and applications of machine-learning techniques. The major remaining gaps are the lack of comprehensive simultaneous measurements of smoke, fuels, fire, and atmospheric interactions during wildfires, high-resolution coupled modeling systems of these components, and real-time smoke prediction capacity. The findings from this synthesis study are expected to support smoke research and management to meet various challenges under increasing wildland fires and impacts.

The plume rising height of a ship will directly affect the maximum ground concentration and distance from the source caused by flue gas emission. Ship movement has an important effect on plume rising, but it is often ignored in previous studies. We simulated the weakening effect caused by ship movement by considering the influence of four main parameters (wind speed, ship speed, flue gas exit velocity, and flue gas exit temperature) on the smoke plume rising height, using the computational fluid dynamics (CFD) model (PHOENICS version 6.0 CHAM, London, UK). The main parameters affecting the difference in plume rising height between stationary and moving sources for the same parameter settings are the wind speed and the ship speed. Therefore, we established two simplified calculation methods that corrected the flue gas exit velocity (Vexit′) and the flue gas exit temperature (T′) for approximately simulating the smoke plume rising height of the moving ship using the formula of a stationary ship. Verification cases indicated that the corrected Vexit′ (the average of relative error is 5.48%) and the corrected T′(the average of relative error is 60.07%) not only saved calculation time but also improved the simulation accuracy compared with the uncorrected stationary source scheme (the average of relative error is 135.38%). Of these correction methods, the scheme with corrected Vexit′ is more effective. The intention is to provide some references for the field experimentation of moving ship plume rising in different ports in the future and to further study the mechanism of moving ship plume rising.

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.

Weather is an important factor that determines smoke development, which is essential information for planning smoke field measurements. This study identifies the synoptic systems that would favor to produce the desired smoke plumes for the Fire and Smoke Model Evaluation Experiment (FASMEE). Daysmoke and PB-Piedmont (PB-P) models are used to simulate smoke plume evolution during the day time and smoke drainage and fog formation during the nighttime for hypothetical prescribed burns on 5–8 February 2011 at the Stewart Army Base in the southeastern United States. Daysmoke simulation is evaluated using the measured smoke plume heights of two historical prescribed burns at the Eglin Air Force Base. The simulation results of the hypothetical prescribed burns show that the smoke plume is not fully developed with low plume height during the daytime on 5 February when the burn site is under the warm, moist, and windy conditions connected to a shallow cyclonic system and a cold front. However, smoke drainage and fog are formed during the nighttime. Well-developed smoke plumes, which rise mainly vertically, extend to a majority portion of the planetary boundary layer, and have steady clear boundaries, appear on both 6 and 7 February when the air is cool but dry and calm during a transition between two low-pressure systems. The plume rises higher on the second day, mainly due to lighter winds. The smoke on 8 February shows a loose structure of large horizontal dispersion and low height after passage of a deep low-pressure system with strong cool and dry winds. Smoke drainage and fog formation are rare for the nights during 5–8 February. It is concluded that prescribed burns conducted during a period between two low-pressure systems would likely generate the desired plumes for FASMEE measurement during daytime. Meanwhile, as the fire smolders into the night, the burns would likely lead to fog formation when the burn site is located in the warm and moist section of a low-pressure system or a cold front.

Wildfires emit large amounts of black carbon and light-absorbing organic carbon, known as brown carbon, into the atmosphere. These particles perturb Earth’s radiation budget through absorption of incoming shortwave radiation. It is generally thought that brown carbon loses its absorptivity after emission in the atmosphere due to sunlight-driven photochemical bleaching. Consequently, the atmospheric warming effect exerted by brown carbon remains highly variable and poorly represented in climate models compared with that of the relatively nonreactive black carbon. Given that wildfires are predicted to increase globally in the coming decades, it is increasingly important to quantify these radiative impacts. Here we present measurements of ensemble-scale and particle-scale shortwave absorption in smoke plumes from wildfires in the western United States. We find that a type of dark brown carbon contributes three-quarters of the short visible light absorption and half of the long visible light absorption. This strongly absorbing organic aerosol species is water insoluble, resists daytime photobleaching and increases in absorptivity with night-time atmospheric processing. Our findings suggest that parameterizations of brown carbon in climate models need to be revised to improve the estimation of smoke aerosol radiative forcing and associated warming.Atmospheric short-wave absorption due to wildfire smoke is caused predominantly by dark brown carbon particles, according to observations from smoke plumes in the United States.

Long-range-transported Canadian smoke layers in the stratosphere over northern France were detected by three lidar systems in August 2017. The peaked optical depth of the stratospheric smoke layer exceeds 0.20 at 532 nm, which is comparable with the simultaneous tropospheric aerosol optical depth. The measurements of satellite sensors revealed that the observed stratospheric smoke plumes were transported from Canadian wildfires after being lofted by strong pyro-cumulonimbus. Case studies at two observation sites, Lille (lat 50.612, long 3.142, 60 m a.s.l.) and Palaiseau (lat 48.712, long 2.215, 156 m a.s.l.), are presented in detail. Smoke particle depolarization ratios are measured at three wavelengths: over 0.20 at 355 nm, 0.18–0.19 at 532 nm, and 0.04–0.05 at 1064 nm. The high depolarization ratios and their spectral dependence are possibly caused by the irregular-shaped aged smoke particles and/or the mixing with dust particles. Similar results are found by several European lidar stations and an explanation that can fully resolve this question has not yet been found. Aerosol inversion based on lidar 2α+3β data derived a smoke effective radius of about 0.33 µm for both cases. The retrieved single-scattering albedo is in the range of 0.8 to 0.9, indicating that the smoke plumes are absorbing. The absorption can cause perturbations to the temperature vertical profile, as observed by ground-based radiosonde, and it is also related to the ascent of the smoke plumes when exposed in sunlight. A direct radiative forcing (DRF) calculation is performed using the obtained optical and microphysical properties. The calculation revealed that the smoke plumes in the stratosphere can significantly reduce the radiation arriving at the surface, and the heating rate of the plumes is about 3.5 K day−1. The study provides a valuable characterization for aged smoke in the stratosphere, but efforts are still needed in reducing and quantifying the errors in the retrieved microphysical properties as well as radiative forcing estimates.

Just before midnight on August 12, 2015, violent explosions and massive fires occurred in the Tianjin Harbor, China, releasing extensive amounts of toxic gas and smoke, debris, and mineral dust into the atmosphere. Atmospheric damage resulted from the long-range transport of air pollutants (LRTAP) in neighboring areas and countries. It has been found that the smoke plumes circled around the Huabei Plain in the lee of the Taihang Mountains, the Shandong Peninsula, and the Bohai Sea before reaching the Yellow Sea and the Korean Peninsula. The transport of widespread smoke plumes in the Yellow Sea region was evidenced from detailed analyses of images from various satellites including NOAA, MODIS, Himawari, and MTSAT. Satellite images clearly showed the generation of smoke emissions from Tianjin, the entire covering of smoke plumes over the Yellow Sea and nearby shore areas, and the LRTAP to the Korean Peninsula. The deposit of soil dust after the trace of rainfall confirmed LRTAP from the explosions and large fires in Tianjin. Also, air quality measurements of particulate matter (PM) 10, PM2.5, O 3 , CO, and visibility showed the atmospheric impact of widespread smoke plumes from Tianjin.

We present an analysis of over 23,000 globally distributed wildfire smoke plume injection heights derived from Multi-angle Imaging SpectroRadiometer (MISR) space-based, multi-angle stereo imaging. Both pixel-weighted and aerosol optical depth (AOD)-weighted results are given, stratified by region, biome, and month or season. This offers an observational resource for assessing first-principle plume-rise modelling, and can provide some constraints on smoke dispersion modelling for climate and air quality applications. The main limitation is that the satellite is in a sun-synchronous orbit, crossing the equator at about 10:30 a.m. local time on the day side. Overall, plumes occur preferentially during the northern mid-latitude burning season, and the vast majority inject smoke near-surface. However, the heavily forested regions of North and South America, and Africa produce the most frequent elevated plumes and the highest AOD values; some smoke is injected to altitudes well above 2 km in nearly all regions and biomes. Planetary boundary layer (PBL) versus free troposphere injection is a critical factor affecting smoke dispersion and environmental impact, and is affected by both the smoke injection height and the PBL height; an example assessment is made here, but constraining the PBL height for this application warrants further work.

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM2.5 concentrations near the source region but higher AOD and PM2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %–30 % near the source regions and 5 %–10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 µg m−3) predictions. Higher plume heights generally produce larger downwind PM2.5 exceedance areas. The PM2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM2.5 concentration and predicted PM2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.