Recent studies report improved performance of ensemble models over machine learning (ML) models for air pollution estimation, although there is little evidence of their added value in settings with sparsely monitored data. We developed and compared three ML models, a supervised linear regression (SLR) model, and an ensemble model, for estimating annual average PM2.5, PM10, and NO2 in NSW, Australia, a relatively sparsely monitored, low pollution, and large geographic region. We assembled pollutant data from government and project monitors and data on 236 predictors including land use, population, traffic, and satellite observations. We used a three-stage DEML framework: (1) base-ML models (Random Forest (RF), XGBoost, GBM) and a SLR model; (2) meta-learner (RF, XGBoost, GBM, GLMNet); and (3) ensemble. We reserved 10% of data for hold-out validation and conducted 10-fold Cross validation (CV) using the training data sets. We evaluated the models using CV-R 2 and RMSE. DEML models resulted in the best fit for all pollutants; however, improvements over base ML models were modest, indicating the latter, with lower cost of implementation, are valuable for low pollution settings with heterogeneous monitoring density. Choice of CV methods substantially impacted model performance and should be considered, along with setting constraints, when choosing modeling methods.

Ground-level ozone (O3) is a significant public health concern. We developed maps of monthly average 1-h maximum O3 concentrations in New South Wales, Australia (2005-2018), a region with sparse monitoring. For the first time Bayesian Maximum Entropy (BME) blending was used within a Deep Ensemble Machine Learning (DEML) framework for air pollution predictions. The DEML combined geographical predictors in random forest (RF), extreme gradient boosting (XGBoost), and gradient boosted machine (GBM) models with three meta-models. BME blending incorporated observed O3 data into posterior predictions. We generated 2.5 km x 2.5 km resolution gridded surfaces. The DEML estimates achieved an R2 of 0.89 and RMSE of 2.3 ppb in the held-out test dataset at monitors. DEML grid cell predictions (R2: 0.84, RMSE: 3.03 ppb) were improved by BME blending (R2: 0.89, RMSE: 2.49 ppb). Mean bias reduced from -0.7 ppb to -0.4 ppb. This demonstrates high accuracy and precision in a sparsely monitored region.

On 17 March 2018 major grassland fires in south-west Victoria, Australia ignited several peat bogs. The peat fires smouldered for 40 days, generating substantial amounts of smoke. The surrounding communities were exposed to significant concentrations of fine particles (PM2.5), resulting in the need for active interventions (such as the relocation of schools) in order to protect vulnerable communities. The peat fires provided a unique opportunity to review the capabilities of two air pollution forecasting models to assess the impact from the peat fires on nearby communities: the Air Quality Forecasting System (AQFx) and the Accident Reporting and Guiding Operational System (ARGOS). Both systems are used in Victoria to inform emergency management response strategies and community warnings. A key configuration change was made to the smoke emissions module in AQFx from simulating emissions from a planned burn to a sub-surface peat fire. Emissions were derived by using heat maps generated from aerial imagery data and estimates of fuel load determined by the peat bulk density and depth of the peat. The results indicated that AQFx successfully captured most smoke plume events during the simulation period, despite some errors in timing and magnitude. Accurate forecasting was most challenged by calm conditions, and meso-scale meteorological transition events. The ARGOS model performed better at capturing smoke plume dispersion during a meso-scale meteorological transition event due to meteorological forecasts being updated every 6 hours compared to a 24-hour update in AQFx. However, the source geometry in ARGOS meant that emission rates were concentrated within a small number of release points resulting in relatively narrow plumes with a likely overprediction of higher end PM2.5 concentrations. The AQFx system was better suited to area emissions. The ARGOS and AQFx models showed different strengths in providing timely information to emergency response agencies to better manage smoke impacts from smouldering peat fires on communities. The ARGOS model can be set up quickly with preliminary emission estimates that can be adjusted as more accurate and updated information becomes available. Compared to the ARGOS model, AQFx is better suited for area emissions. Implementation of a rapid update cycle in AQFx would further improve forecasts especially during meteorological transition events.

Robust high spatiotemporal resolution daily PM2.5 exposure estimates are limited in Australia. Estimates of daily PM2.5 and the PM2.5 component from extreme pollution events (e.g., bushfires and dust storms) are needed for epidemiological studies and health burden assessments attributable to these events. We sought to: (1) estimate daily PM2.5 at a 5 km × 5 km spatial resolution across the Australian continent between 1 January 2001 and 30 June 2020 using a Random Forest (RF) algorithm, and (2) implement a seasonal-trend decomposition using loess (STL) methodology combined with selected statistical flags to identify extreme events and estimate the extreme pollution PM2.5 component. We developed an RF model that achieved an out-of-bag R-squared of 71.5% and a root-mean-square error (RMSE) of 4.5 µg/m3. We predicted daily PM2.5 across Australia, adequately capturing spatial and temporal variations. We showed how the STL method in combination with statistical flags can identify and quantify PM2.5 attributable to extreme pollution events in different locations across the country.

Air pollution is the leading environmental risk factor for mortality worldwide. In Australia, residential wood heating is the single largest source of pollution in many regions of the country. Estimates around the world and in some limited locations across Australia have shown that the health burden attributable to wood heating PM2.5 is considerable, and that there is great potential to reduce this burden. Here, we aimed to calculate the mortality burden attributable to wood heating emissions (WHE)-related PM2.5 throughout Australia and estimate the potential health benefits of reducing WHE-related air pollution, by replacing wood heaters with cleaner heating technologies. In summary, we used a four -stage process to (1) compile a nationwide WHE inventory, (2) generate annual exposure estimates of WHE-PM2.5, (3) estimate the annual mortality burden attributable to wood heater use across Australia for the year 2015, and (4) assess the potential health benefits of replacing existing wood heaters with cleaner heating technologies. We estimated that population weighted WHE-PM2.5 exposure across Australia for 2015 ranged between 0.62 mu g/m3 and 1.35 mu g/m3, with differing exposures across State/Territories. We estimated a considerable mortality burden attributable to WHE-PM2.5 ranging between 558 (95 % CI, 364-738) and 1555 (95 % CI, 1180-1740) deaths annually, depending on the scenario assessed. We calculated that replacing 50 % of the current wood heater stock, with zero or lower emission technologies could produce relevant health benefits, of between $AUD 1.61 and $AUD 1.93 billion per year (303-364 attributable deaths). These findings provide a preliminary and likely conservative assessment of the health burden of wood heater smoke across Australia, and an estimation of the potential benefits from replacing the current wood heater stock with cleaner technologies. The results presented here underscore the magnitude of the health burden attributable to wood heating in Australia.

Background: People living in Australian cities face increased mortality risks from exposure to extreme air pollution events due to bushfires and dust storms. However, the burden of mortality attributable to exceptional PM2.5 levels has not been well characterised. We assessed the burden of mortality due to PM2.5 pollution events in Australian capital cities between 2001 and 2020. Methods: For this health impact assessment, we obtained data on daily counts of deaths for all non-accidental causes and ages from the Australian National Vital Statistics Register. Daily concentrations of PM2.5 were estimated at a 5 km grid cell, using a Random Forest statistical model of data from air pollution monitoring sites combined with a range of satellite and land use-related data. We calculated the exceptional PM2.5 levels for each extreme pollution exposure day using the deviation from a seasonal and trend loess decomposition model. The burden of mortality was examined using a relative risk concentration-response function suggested in the literature. Findings: Over the 20-year study period, we estimated 1454 (95 % CI 987, 1920) deaths in the major Australian cities attributable to exceptional PM2.5 exposure levels. The mortality burden due to PM2.5 exposure on extreme pollution days was considerable. Variations were observed across Australia. Despite relatively low daily PM2.5 levels compared to global averages, all Australian cities have extreme pollution exposure days, with PM2.5 concentrations exceeding the World Health Organisation Air Quality Guideline standard for 24-h exposure. Our analysis results indicate that nearly one-third of deaths from extreme air pollution exposure can be prevented with a 5 % reduction in PM2.5 levels on days with exceptional pollution. Interpretation: Exposure to exceptional PM2.5 events was associated with an increased mortality burden in Australia's cities. Policies and coordinated action are needed to manage the health risks of extreme air pollution events due to bushfires and dust storms under climate change.

Smoke haze events have increasingly affected Australia’s environmental quality, having demonstrable effects on air quality, climate, and public health. This study employs a hybrid methodology, merging satellite-based aerosol optical depth (AOD) data with Chemical Transport Model (CTM) simulations to comprehensively characterize these events. The AOD data are sourced from the Japan Aerospace Exploration Agency (JAXA), Copernicus Atmosphere Monitoring Service (CAMS), and the Commonwealth Scientific and Industrial Research Organization (CSIRO), and they are statistically evaluated using mean, standard deviation, and root mean square error (RMSE) metrics. Our analysis indicates that the combined dataset provides a more robust representation of smoke haze events than individual datasets. Additionally, the study investigates aerosol distribution patterns and data correlation across the blended dataset and discusses possible improvements such as data imputation and aerosol plume scaling. The outcomes of this investigation contribute to enhancing our understanding of the impacts of smoke haze on various environmental factors and can assist in developing targeted mitigation and management strategies.

Chapter 9 Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World Susan M. O'Neill, Susan M. O'Neill Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorPeng Xian, Peng Xian United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorJohannes Flemming, Johannes Flemming European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorMartin Cope, Martin Cope CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlexander Baklanov, Alexander Baklanov World Meteorological Organization, Geneva, SwitzerlandSearch for more papers by this authorNarasimhan K. Larkin, Narasimhan K. Larkin Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorJoseph K. Vaughan, Joseph K. Vaughan Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, USASearch for more papers by this authorDaniel Tong, Daniel Tong Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, USASearch for more papers by this authorRosie Howard, Rosie Howard Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorRoland Stull, Roland Stull Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorDidier Davignon, Didier Davignon Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorRavan Ahmadov, Ravan Ahmadov CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorM. Talat Odman, M. Talat Odman School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USASearch for more papers by this authorJohn Innis, John Innis EPA Tasmania, Hobart, Tasmania, AustraliaSearch for more papers by this authorMerched Azzi, Merched Azzi Department of Planning and Environment, Government of New South Wales, Sydney, AustraliaSearch for more papers by this authorChristopher Gan, Christopher Gan Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorRadenko Pavlovic, Radenko Pavlovic Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorBoon Ning Chew, Boon Ning Chew Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorJeffrey S. Reid, Jeffrey S. Reid United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorEdward J. Hyer, Edward J. Hyer United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorZak Kipling, Zak Kipling European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorAngela Benedetti, Angela Benedetti European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorPeter R. Colarco, Peter R. Colarco NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorArlindo Da Silva, Arlindo Da Silva NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorTaichu Tanaka, Taichu Tanaka Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, JapanSearch for more papers by this authorJeffrey McQueen, Jeffrey McQueen NOAA National Centers for Environmental Prediction, College Park, Maryland, USASearch for more papers by this authorPartha Bhattacharjee, Partha Bhattacharjee I. M. Systems Group, NWS/NCEP/EMC, College Park, Maryland, USASearch for more papers by this authorJonathan Guth, Jonathan Guth Météo-France, Toulouse, FranceSearch for more papers by this authorNicole Asencio, Nicole Asencio Météo-France, Toulouse, FranceSearch for more papers by this authorOriol Jorba, Oriol Jorba Barcelona Supercomputing Center, Barcelona, SpainSearch for more papers by this authorCarlos Pérez García-Pando, Carlos Pérez García-Pando Barcelona Supercomputing Center, Barcelona, Spain Catalan Institution for Research and Advanced Studies, Barcelona, SpainSearch for more papers by this authorRostislav Kouznetsov, Rostislav Kouznetsov Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMikhail Sofiev, Mikhail Sofiev Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMelissa E. Brooks, Melissa E. Brooks Met Office, Exeter, United KingdomSearch for more papers by this authorJack Chen, Jack Chen Environment and Climate Change Canada, Ottawa, Ontario, CanadaSearch for more papers by this authorEric James, Eric James CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorFabienne Reisen, Fabienne Reisen CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlan Wain, Alan Wain Australian Bureau of Meteorology, Melbourne, Victoria, AustraliaSearch for more papers by this authorKerryn McTaggart, Kerryn McTaggart Department of Environment, Land, Water and Planning, Government of Victoria, Melbourne, Victoria, AustraliaSearch for more papers by this authorAngus MacNeil, Angus MacNeil Forest Practices Authority, Hobart, Tasmania, AustraliaSearch for more papers by this author Susan M. O'Neill, Susan M. O'Neill Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorPeng Xian, Peng Xian United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorJohannes Flemming, Johannes Flemming European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorMartin Cope, Martin Cope CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlexander Baklanov, Alexander Baklanov World Meteorological Organization, Geneva, SwitzerlandSearch for more papers by this authorNarasimhan K. Larkin, Narasimhan K. Larkin Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorJoseph K. Vaughan, Joseph K. Vaughan Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, USASearch for more papers by this authorDaniel Tong, Daniel Tong Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, USASearch for more papers by this authorRosie Howard, Rosie Howard Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorRoland Stull, Roland Stull Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorDidier Davignon, Didier Davignon Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorRavan Ahmadov, Ravan Ahmadov CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorM. Talat Odman, M. Talat Odman School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USASearch for more papers by this authorJohn Innis, John Innis EPA Tasmania, Hobart, Tasmania, AustraliaSearch for more papers by this authorMerched Azzi, Merched Azzi Department of Planning and Environment, Government of New South Wales, Sydney, AustraliaSearch for more papers by this authorChristopher Gan, Christopher Gan Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorRadenko Pavlovic, Radenko Pavlovic Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorBoon Ning Chew, Boon Ning Chew Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorJeffrey S. Reid, Jeffrey S. Reid United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorEdward J. Hyer, Edward J. Hyer United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorZak Kipling, Zak Kipling European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorAngela Benedetti, Angela Benedetti European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorPeter R. Colarco, Peter R. Colarco NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorArlindo Da Silva, Arlindo Da Silva NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorTaichu Tanaka, Taichu Tanaka Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, JapanSearch for more papers by this authorJeffrey McQueen, Jeffrey McQueen NOAA National Centers for Environmental Prediction, College Park, Maryland, USASearch for more papers by this authorPartha Bhattacharjee, Partha Bhattacharjee I. M. Systems Group, NWS/NCEP/EMC, College Park, Maryland, USASearch for more papers by this authorJonathan Guth, Jonathan Guth Météo-France, Toulouse, FranceSearch for more papers by this authorNicole Asencio, Nicole Asencio Météo-France, Toulouse, FranceSearch for more papers by this authorOriol Jorba, Oriol Jorba Barcelona Supercomputing Center, Barcelona, SpainSearch for more papers by this authorCarlos Pérez García-Pando, Carlos Pérez García-Pando Barcelona Supercomputing Center, Barcelona, Spain Catalan Institution for Research and Advanced Studies, Barcelona, SpainSearch for more papers by this authorRostislav Kouznetsov, Rostislav Kouznetsov Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMikhail Sofiev, Mikhail Sofiev Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMelissa E. Brooks, Melissa E. Brooks Met Office, Exeter, United KingdomSearch for more papers by this authorJack Chen, Jack Chen Environment and Climate Change Canada, Ottawa, Ontario, CanadaSearch for more papers by this authorEric James, Eric James CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorFabienne Reisen, Fabienne Reisen CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlan Wain, Alan Wain Australian Bureau of Meteorology, Melbourne, Victoria, AustraliaSearch for more papers by this authorKerryn McTaggart, Kerryn McTaggart Department of Environment, Land, Water and Planning, Government of Victoria, Melbourne, Victoria, AustraliaSearch for more papers by this authorAngus MacNeil, Angus MacNeil Forest Practices Authority, Hobart, Tasmania, AustraliaSearch for more papers by this author Book Editor(s):Tatiana V. Loboda, Tatiana V. LobodaSearch for more papers by this authorNancy H. F. French, Nancy H. F. FrenchSearch for more papers by this authorRobin C. Puett, Robin C. PuettSearch for more papers by this author First published: 20 October 2023 https://doi.org/10.1002/9781119757030.ch9Book Series:Geophysical Monograph Series AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke leading to a range of health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to include smoke and its consequences in atmospheric modeling systems to meet needs such as informing and protecting the public during smoke episodes. This chapter profiles many of the global and regional smoke prediction systems available. It is not an exhaustive list, but rather a profile of many of the systems to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble. Regional and national systems are being developed independently for example in Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization is bringing together global and regional systems to form an ensemble to support countries with smoke issues and who lack resources. For each system we discuss how fire activity information is obtained, how fire emissions are calculated, and how atmospheric transport and chemical transformation of the smoke plume is treated. REFERENCES Adams , C. , McLinden , C. A. , Shephard , M. 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Simulation outputs from chemical transport models (CTMs) are essential to plan effective air quality policies. A key strength of these models is their ability to separate out source-specific components which facilitate the simulation of the potential impact of policy on future air quality. However, configuring and running these models is complex and computationally intensive, making the evaluation of multiple scenarios less accessible to many researchers and policy experts. The aim of this work is to present how Gaussian process emulation can provide a top-down approach to interrogating and interpreting the outputs from CTMs at minimal computational cost. A case study is presented (based on fine particle sources in the southwest of Western Australia) to illustrate how an emulator can be constructed to simultaneously evaluate changes in emissions from on-road transport and electricity sectors. This study demonstrates how emulation provides a flexible way of exploring local impacts of electric vehicles and wider regional effects of emissions from electricity generation. The potential for emulators to be applied to other settings involving air quality research is discussed.

Ambient fine particulate matter <2.5 µm (PM2.5) air pollution increases premature mortality globally. Some PM2.5 is natural, but anthropogenic PM2.5 is comparatively avoidable. We determined the impact of long-term exposures to the anthropogenic PM component on mortality in Australia. PM2.5-attributable deaths were calculated for all Australian Statistical Area 2 (SA2; n = 2310) regions. All-cause death rates from Australian mortality and population databases were combined with annual anthropogenic PM2.5 exposures for the years 2006–2016. Relative risk estimates were derived from the literature. Population-weighted average PM2.5 concentrations were estimated in each SA2 using a satellite and land use regression model for Australia. PM2.5-attributable mortality was calculated using a health-impact assessment methodology with life tables and all-cause death rates. The changes in life expectancy (LE) from birth, years of life lost (YLL), and economic cost of lost life years were calculated using the 2019 value of a statistical life. Nationally, long-term population-weighted average total and anthropogenic PM2.5 concentrations were 6.5 µg/m3 (min 1.2–max 14.2) and 3.2 µg/m3 (min 0–max 9.5), respectively. Annually, anthropogenic PM2.5-pollution is associated with 2616 (95% confidence intervals 1712, 3455) deaths, corresponding to a 0.2-year (95% CI 0.14, 0.28) reduction in LE for children aged 0–4 years, 38,962 (95%CI 25,391, 51,669) YLL and an average annual economic burden of $6.2 billion (95%CI $4.0 billion, $8.1 billion). We conclude that the anthropogenic PM2.5-related costs of mortality in Australia are higher than community standards should allow, and reductions in emissions are recommended to achieve avoidable mortality.

Background Wildland fire (wildfire; bushfire) pollution contributes to poor air quality, a risk factor for premature death. The frequency and intensity of wildfires are expected to increase; improved tools for estimating exposure to fire smoke are vital. New-generation satellite-based sensors produce high-resolution spectral images, providing real-time information of surface features during wildfire episodes. Because of the vast size of such data, new automated methods for processing information are required. Objective We present a deep fully convolutional neural network (FCN) for predicting fire smoke in satellite imagery in near-real time (NRT). Methods The FCN identifies fire smoke using output from operational smoke identification methods as training data, leveraging validated smoke products in a framework that can be operationalized in NRT. We demonstrate this for a fire episode in Australia; the algorithm is applicable to any geographic region. Results The algorithm has high classification accuracy (99.5% of pixels correctly classified on average) and precision (average intersection over union = 57.6%). Significance The FCN algorithm has high potential as an exposure-assessment tool, capable of providing critical information to fire managers, health and environmental agencies, and the general public to prevent the health risks associated with exposure to hazardous smoke from wildland fires in NRT.

We describe an assessment of the impact on mortality of eight major sources of PM2.5 in the Greater Metropolitan Region of Sydney, Australia (GMR). We modeled exposure to PM2.5 for the year July 2010 to June 2011 and estimated the burden of current mortality attributable to these sources. We also estimated the number of life-years that would be produced if emissions from wood heaters and power stations, the two largest emissions sources, were reduced. Wood heaters (assuming a real-world emissions factor of 11.4 g of PM2.5 per kg of wood burned) were the most important source of PM2.5 exposure, responsible for around 24.0% of the total anthropogenic PM2.5 concentration. On-road sources and power stations were also important, responsible for 16.9% and 10.5% of anthropogenic PM2.5 exposure respectively. Around 1.2% of mortality (5,900 YLL) was attributable to long-term exposure to all anthropogenic PM2.5, including 0.3% (1,400 YLL) attributable to wood heater–related PM2.5, 0.2% (990 YLL) to on-road sources and 0.1% (620 YLL) to power stations. Compared to ongoing emissions at 2010/11 levels, we estimated that a sustained reduction in emissions from wood heaters due to the introduction of an emissions standard of 1.5 g of PM2.5 per kilogram of wood burned (real world emissions factor of 3.9 g of PM2.5 per kg of wood burned) and the associated reduction in PM2.5 population exposure would produce 90,000 life-years among the cohort of people alive in 2010/11. Complete removal of sulphur oxide emissions from power stations would produce 14,000 life-years and complete removal of nitrogen oxide emissions would produce 38,000 life-years. A range of sensitivity analyses indicate the true impact of PM2.5 from these sources is likely to be at least as large as these estimates. This assessment shows that eight sources are responsible for more than 60% of exposure to anthropogenic PM2.5 in the Sydney GMR. Although the burden of mortality attributable to each source is relatively small, interventions that achieve sustained reductions in emissions could provide substantial health benefits, which are likely to far outweigh the costs.

The 2014 fire in the Hazelwood open-cut mine of brown coal, located in the State of Victoria (Australia), burned for 45 days. The fire sent dense smoke over the nearby town of Morwell and beyond, resulting in one of the worst air quality incidents in Victoria. Precision air monitoring of PM2.5 (particulate matter 2.5 mu m or less in diameter) and carbon monoxide (CO), which has been reported previously, started a few days after the fire at two locations in Morwell and measured PM2.5 levels up to 19 times higher than the 24-h Australian air quality standard. Because of the sparseness of the monitors and the fact that the fire was most intense prior to the start of the air monitoring, it is likely that the smoke concentrations in Morwell were even greater than measured. Thus, the concentration measurements are insufficient in time and space for a comprehensive study of exposure and health impacts due to the smoke. We reconstruct the hourly spatial distributions of smoke (as represented by PM2.5 and CO) in and around Morwell by first developing a rigorous methodology for estimating the fire emissions, and then using them in a high-resolution prognostic meteorological and dispersion model with local wind data assimilation and an appropriate plume rise mechanism. Larger-scale modelling is also conducted to estimate the background concentrations without the mine fire. The model simulates the number of observed exceedances and the observed maximum concentrations exceeding the air quality standards for both PM2.5 and CO within a factor of 2. At the monitor south of Morwell near the fire, the model predicts hourly PM2.5 and CO concentrations as high as 3730 mu g m(-3) and 58.6 ppm, respectively, in the early phase of the fire; these levels are much higher than those recorded by the subsequent air monitoring. The modelled PM2.5 fields are being used by other researchers to estimate the impact of smoke exposure on health outcomes in the local community.