Development of the crop residue and rangeland burning in the 2014 National Emissions Inventory using information from multiple sources.
ABSTRACT: Biomass burning has been identified as an important contributor to the degradation of air quality because of its impact on ozone and particulate matter. One component of the biomass burning inventory, crop residue burning, has been poorly characterized in the National Emissions Inventory (NEI). In the 2011 NEI, wildland fires, prescribed fires, and crop residue burning collectively were the largest source of PM2.5. This paper summarizes our 2014 NEI method to estimate crop residue burning emissions and grass/pasture burning emissions using remote sensing data and field information and literature-based, crop-specific emission factors. We focus on both the postharvest and pre-harvest burning that takes place with bluegrass, corn, cotton, rice, soybeans, sugarcane and wheat. Estimates for 2014 indicate that over the continental United States (CONUS), crop residue burning excluding all areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay occurred over approximately 1.5 million acres of land and produced 19,600 short tons of PM2.5. For areas identified as Pasture/Grass, Grassland Herbaceous, and Pasture/Hay, biomass burning emissions occurred over approximately 1.6 million acres of land and produced 30,000 short tons of PM2.5. This estimate compares with the 2011 NEI and 2008 NEI as follows: 2008: 49,650 short tons and 2011: 141,180 short tons. Note that in the previous two NEIs rangeland burning was not well defined and so the comparison is not exact. The remote sensing data also provided verification of our existing diurnal profile for crop residue burning emissions used in chemical transport modeling. In addition, the entire database used to estimate this sector of emissions is available on EPA's Clearinghouse for Inventories and Emission Factors (CHIEF, http://www3.epa.gov/ttn/chief/index.html ).Estimates of crop residue burning and rangeland burning emissions can be improved by using satellite detections. Local information is helpful in distinguishing crop residue and rangeland burning from all other types of fires.
Project description:Crop residue burning is a common land management practice that results in emissions of a variety of pollutants with negative health impacts. Modeling systems are used to estimate air quality impacts of crop residue burning to support retrospective regulatory assessments and also for forecasting purposes. Ground and airborne measurements from a recent field experiment in the Pacific Northwest focused on cropland residue burning was used to evaluate model performance in capturing surface and aloft impacts from the burning events. The Community Multiscale Air Quality (CMAQ) model was used to simulate multiple crop residue burns with 2?km grid spacing using field-specific information and also more general assumptions traditionally used to support National Emission Inventory based assessments. Field study specific information, which includes area burned, fuel consumption, and combustion completeness, resulted in increased biomass consumption by 123 tons (60% increase) on average compared to consumption estimated with default methods in the National Emission Inventory (NEI) process. Buoyancy heat flux, a key parameter for model predicted fire plume rise, estimated from fuel loading obtained from field measurements can be 30% to 200% more than when estimated using default field information. The increased buoyancy heat flux resulted in higher plume rise by 30% to 80%. This evaluation indicates that the regulatory air quality modeling system can replicate intensity and transport (horizontal and vertical) features for crop residue burning in this region when region-specific information is used to inform emissions and plume rise calculations. Further, previous vertical emissions allocation treatment of putting all cropland residue burning in the surface layer does not compare well with measured plume structure and these types of burns should be modeled more similarly to prescribed fires such that plume rise is based on an estimate of buoyancy.
Project description:Amazonian deforestation from slash-and-burn practices is a significant contributor to biomass burning within Brazil. Fires emit carbonaceous aerosols that negatively impact human health by increasing fine particulate matter (PM2.5) exposure. These negative effects on health compound the already detrimental climatological and ecological impacts. Despite high biomass burning emissions in Brazil and the international attention drawn by the relaxation of Amazon protections in 2019, little is known about the health impacts from PM2.5 exposure attributable to these fires. We estimate PM2.5-related premature deaths in Brazil associated with biomass burning, focusing on temporal, interannual, and spatial trends. We find that during the fire season of 2019, 4,966 (2,427, 8,340) premature deaths were attributable to fire emissions making up 10% (5, 17) of all PM2.5-related premature deaths in Brazil. Between the 2019 and 2018 seasons, fire emissions increased by 1.37 Tg (1.00, 2.18) or 115% (60, 201), which was responsible for an increase in health impacts of 2,109 (965, 3,623) premature deaths or 74% (54, 98). Biomass burning emissions throughout Brazil contribute significantly to premature deaths, with the largest burning events occurring in northwestern Brazil. The impact of fires on PM2.5-related premature deaths is highest in heavily populated regions despite their fires being 1 to 2 orders of magnitude smaller than the largest burning events. Results from this study characterize the extent to which elevated PM2.5 exposure levels owing to fires affect public health in Brazil and present an additional, public health-focused, support for increased Amazon protections.
Project description:Open burning is illegal in Ukraine, yet Ukraine has, on average, 300 times more fire activity per year (2001-2019) than most European countries. In 2016 and 2017, 47% of Ukraine was identified as cultivated area, with a total of 70% of land area dedicated to agricultural use. Over 57% of all active fires in Ukraine detected using space-borne Visible Infrared Imaging Radiometer Suite (VIIRS) during 2016 and 2017 were associated with pre-planting field clearing and post-harvest crop residue removal, meaning that the majority of these fires are preventable. Due to the small size and transient nature of cropland burns, satellite-based burned area (BA) estimates are often underestimated. Moreover, traditional spectral-based BA algorithms are not suitable for distinguishing burned from plowed fields, especially in the black soil regions of Ukraine. Therefore, we developed a method to estimate agricultural BA by calibrating VIIRS active fire data with exhaustively mapped cropland reference areas (42 958 fields). Our study found that cropland BA was significantly underestimated (by 30%-63%) in the widely used Moderate Resolution Imaging Spectroradiometer-based MCD64A1 BA product, and by 95%-99.9% in Ukraine's National Greenhouse Gas Inventory. Although crop residue burns are smaller and emit far less emissions than larger wildfires, reliable monitoring of crop residue burning has a number of important benefits, including (a) improving regional air quality models and the subsequent understanding of human health impacts due to the proximity of crop residue burns to urban locations, (b) ensuring an accurate representation of predominantly smaller fires in regional emission inventories, and (c) increasing awareness of often illegal managed open burning to provide improved decision-making support for policy and resource managers.
Project description:Northwestern India is known as the "breadbasket" of the country producing two-thirds of food grains, with wheat and rice as the principal crops grown under the crop rotation system. Agricultural data from India indicates a 25% increase in the post-monsoon rice crop production in Punjab during 2002-2016. NASA's A-train satellite sensors detect a consistent increase in the vegetation index (net 21%) and post-harvest agricultural fire activity (net ~60%) leading to nearly 43% increase in aerosol loading over the populous Indo-Gangetic Plain in northern India. The ground-level particulate matter (PM2.5) downwind over New Delhi shows a concurrent uptrend of net 60%. The effectiveness of a robust satellite-based relationship between vegetation index-a proxy for crop amounts, and post-harvest fires-a precursor of extreme air pollution events, has been further demonstrated in predicting the seasonal agricultural burning. An efficient crop residue management system is critically needed towards eliminating open field burning to mitigate episodic hazardous air quality over northern India.
Project description:The large-scale burning of crop residues in the North China Plain (NCP), one of the most densely populated world regions, was recently recognized to cause severe air pollution and harmful health effects. A reliable quantification of the magnitude of these fires is needed to assess regional air quality. Here, we use an eight-year record (2005-2012) of formaldehyde measurements from space to constrain the emissions of volatile organic compounds (VOCs) in this region. Using inverse modelling, we derive that satellite-based post-harvest burning fluxes are, on average, at least a factor of 2 higher than state-of-the-art bottom-up statistical estimates, although with significant interannual variability. Crop burning is calculated to cause important increases in surface ozone (+7%) and fine aerosol concentrations (+18%) in the North China Plain in June. The impact of crop fires is also found in satellite observations of other species, glyoxal, nitrogen dioxide and methanol, and we show that those measurements validate the magnitude of the top-down fluxes. Our study indicates that the top-down crop burning fluxes of VOCs in June exceed by almost a factor of 2 the combined emissions from other anthropogenic activities in this region, underscoring the need for targeted actions towards changes in agricultural management practices.
Project description:Wildland fires are a major source of fine particulate matter (PM2.5), one of the most harmful ambient pollutants for human health globally. To represent the influence of wildland fire emissions on atmospheric composition, regional and global chemical transport models rely on emission inventories developed from estimates of burned area (i.e. fire size and location). While different methods of estimating annual burned area agree reasonably well in the western U.S. (within 20-30% for most years during 2002-2014), estimates for the southern U.S. can vary by more than a factor of 5. These differences in burned area lead to significant variability in the spatial and temporal allocation of emissions across fire emission inventory platforms. In this work, we implement wildland fire emission estimates for 2011 from three different products - the USEPA National Emission Inventory (NEI), the Fire Inventory of NCAR (FINN), and the Global Fire Emission Database (GFED4s) - into the Community Multiscale Air Quality (CMAQ) model to quantify and characterize differences in simulated PM and ozone concentrations across the contiguous U.S. (CONUS) due to the fire emission inventory used. The NEI is developed specifically for the U.S., while both FINN and GFED4s are available globally. We find that NEI emissions lead to the largest increases in modeled annual average PM2.5 (0.85 ?g m-3) and April-September maximum daily 8-h ozone (0.28 ppb) nationally compared to a "no fire" baseline, followed by FINN (0.33 ?g m-3 and 0.22 ppb) and GFED4s (0.12 ?g m-3 and 0.17 ppb). Annual mean enhancements in wildland fire pollution are highest in the southern U.S. across all three inventories (over 4 ?g m-3 and 2 ppb in some areas), but show considerable spatial variability within these regions. We also examine the representation of five individual fire events during 2011 and find that of the two global inventories, FINN reproduces more of the acute changes in pollutant concentrations modeled with NEI and shown in surface observations during each of the episodes investigated compared to GFED4s. Understanding the sensitivity of modeling fire-related PM2.5 and ozone in the U.S. to burned area estimation approaches will inform future efforts to assess the implications of present and future fire activity for air quality and human health at national and global scales.
Project description:<h4>Unlabelled</h4>Environment and Climate Change Canada's FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2-July 15, and August 15-31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of -7.3 µg m(-3) and 3.1 µg m(-3)), it showed better forecast skill than the RAQDPS (MB of -11.7 µg m(-3) and -5.8 µg m(-3)) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m(-3) also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR).<h4>Implications</h4>Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.
Project description:The world’s 2.5 billion poorest people - small farmers living at the far fringe of the developing world – and their billion or so slightly better off neighbors burn 10.5 billion metric tonnes (tonnes) of crop waste annually. Smoke from their fires reddens the sun, closes airports, shuts schools and governments – and kills millions of people (World Health Organization (WHO). who.int/health-topics/air-pollution#tab=tab_1). Their fires release 16.6 billion tonnes of CO2, and emit 9.8 billion tonnes CO2e, 1.1 billion tonnes of smog precursors and 66 million tonnes of PM2.5. (Akagi et al., Atmospheric Chem Physics 4039-4071, 2011; Environmental Protection Agency, epa.gov/ghgemissions/understanding-global-warming-potentials; Food and Agriculture Organization, FAOSTAT, http://www.fao.org/faostat/en/#data) [See Attachments 1–3. For details of the Attachments, please see the section below entitled “Availability of data and materials.”]. No one yet has stopped the burning. Seminars, health warnings, bans, threats, jailings, shootings – nothing has worked, because not one has offered farmers a better alternative. This is the story of how Warm Heart, a small, community development NGO, learned enough about small farmers’ plight to collaborate with them to develop the technology, training and social organization to mobilize villages to form biochar social enterprises. These make it profitable for farmers to convert crop waste into biochar, reducing CO2e, smog precursor and PM2.5 emissions, improving health and generating new local income – in short, to address the big three SDGs (1, 2 and 3) from the bottom. Warm Heart, however, wanted more; it wanted a system so appealing that it would spread by imitation and not require outside intervention. Based on what it has learned, Warm Heart wants to teach others that the knowledge to stop the smoke and improve the quality of one’s life does not require outside experts and lots of money. It wants to teach that anyone can learn to create a more sustainable world by themselves. This article traces the experiential learning process by which Warm Heart and its partners achieved their goals and shares Warm Heart’s open source solution. It serves four purposes. The article closely explores an experiential learning process. It details the underlying logic, workings and consequences of crop waste burning in the developing world. It demonstrates the application of this knowledge to the development of a sustainable – even profitable – solution to this global problem that does not require costly outside intervention but can be undertaken by local communities and small NGOs anywhere. Finally, it models how local communities, small NGOs and social investors can turn this global problem into a profitable business opportunity.
Project description:Prescribed pasture burning plays a critical role in ecosystem maintenance in tallgrass prairie ecosystems and may contribute to agricultural productivity but can also have negative impacts on air quality. Volatile organic compound (VOC) concentrations were measured immediately downwind of prescribed tallgrass prairie fires in the Flint Hills region of Kansas, United States. The VOC mixture is dominated by alkenes and oxygenated VOCs, which are highly reactive and can drive photochemical production of ozone downwind of the fires. The computed emission factors are comparable to those previous measured from pasture maintenance fires in Brazil. In addition to the emission of large amounts of particulate matter, hazardous air pollutants such as benzene and acrolein are emitted in significant amounts and could contribute to adverse health effects in exposed populations.
Project description:Wildland fire emissions are routinely estimated in the US Environmental Protection Agency’s National Emissions Inventory, specifically for fine particulate matter (PM2.5) and precursors to ozone (O3); however, there is a large amount of uncertainty in this sector. We employ a brute-force zero-out sensitivity method to estimate the impact of wildland fire emissions on air quality across the contiguous US using the Community Multiscale Air Quality (CMAQ) modelling system. These simulations are designed to assess the importance of wildland fire emissions on CMAQ model performance and are not intended for regulatory assessments. CMAQ ver. 5.0.1 estimated that fires contributed 11% to the mean PM2.5 and less than 1% to the mean O3 concentrations during 2008–2012. Adding fires to CMAQ increases the number of ‘grid-cell days’ with PM2.5 above 35 ?g m?3 by a factor of 4 and the number of grid-cell days with maximum daily 8-h average O3 above 70 ppb by 14%. Although CMAQ simulations of specific fires have improved with the latest model version (e.g. for the 2008 California wildfire episode, the correlation r = 0.82 with CMAQ ver. 5.0.1 v. r = 0.68 for CMAQ ver. 4.7.1), the model still exhibits a low bias at higher observed concentrations and a high bias at lower observed concentrations. Given the large impact of wildland fire emissions on simulated concentrations of elevated PM2.5 and O3, improvements are recommended on how these emissions are characterised and distributed vertically in the model.