SEARCH · Search NASA

Results for “fire detection”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

All sources NASA NTRS DOE OSTI

DOE OSTI2024

The impact of plant‐derived fire management prescriptions on fire‐responsive bird species

Abstract In fire‐prone regions, the occurrence of some faunal species is contingent on the presence of resources that arise through post‐fire plant succession. Through planned burning, managers can alter resource availability and aim to provide the conditions required to promote biodiversity. Understanding how species occurrence changes at different spatial and temporal scales after fire is essential to achieve this goal. However, many fire prescriptions are guided primarily by the responses of fire‐sensitive plants when setting tolerable fire intervals. This approach assumes that maintaining floristic diversity will satisfy the requirements of fauna. We surveyed bird species in two semi‐arid vegetation types across an environmental gradient in south‐eastern Australia. We conducted four surveys at each of 253 sites across a 75‐year chronosequence of time since fire and used generalized additive mixed models to examine changes in the occurrence of birds in response to time since fire. Model predictions were compared to plant‐derived fire prescriptions currently guiding fire management in the region. Time since fire was a significant predictor for 18 of 28 species modeled, in at least one vegetation type, over a gradient of 1.3° of latitude. We detected considerable variation in the responses of some species, both between vegetation types and geographically within a vegetation type. Our evaluation of plant‐derived fire prescriptions suggests that the intervals considered acceptable for maintaining floristic diversity may not be sustainable for populations of birds requiring longer unburnt vegetation, with 6 of the 12 species assessed attaining a mean occurrence probability of 20.3% by the minimum tolerable fire interval, and 57.3% by the maximum tolerable fire interval, in their respective vegetation types. Our findings highlight the potential vulnerability of fire‐responsive bird species if fire prescriptions are applied in a manner that fails to account for the slow development of habitat resources needed by some species, and the variation detected within the responses of species. This highlights the need for species‐specific data collected at an appropriate spatial scale to inform management plans.

Makdissi, Rhys↗

DOE OSTI2025

Detecting Short Circuits: Post Accident Electric Vehicle Battery Safety Check

Fast and accurate detection of soft short circuits (SCs) in the battery packs of damaged electric vehicles is needed by first responders and mechanics to mitigate the potential risk from battery fires that may occur hours, days, or weeks after an accident. Here, this paper presents an SC-detection algorithm for potentially damaged lithium-ion batteries that works quickly and without a priori knowledge of the battery-pack chemistry, capacity, state of charge, or state of health. The proposed universal SC-detection algorithm is designed to be implemented on an inexpensive handheld device that can connect to and monitor the voltages of all cells in a pack. Transient filtering and linear-quadratic state observation provide estimates of normalized SC current for every cell in the pack. Cells with SC-current estimates outside a sigma-based threshold are detected. Simulations, experiments, and electric vehicle (EV) crash data are used to verify the speed, sensitivity, and accuracy of the method, demonstrating 96% accurate detection of 0.0027 C SCs in under 1 h for 5S cell groups in the lab and no false positives for crashed Volkswagen, Chevrolet, and Tesla vehicles without SCs.

25 - ENERGY STORAGE↗

DOE OSTI2025

5G integrated edge computing platform for efficient component monitoring in coal-fired power plants

This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. 2. In this study, we show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging

Drought can have pervasive and wide-spread impacts to forest health, as evidenced in several severe events occurring over the recent decades. Extensive forest die-off due to drought can impair the ecological functioning of forests, impacting habitat, water yield and quality from forested lands, and altering forest fire dynamics and intensity. Satellite remote sensing provides an effective means for detecting and monitoring spatial patterns of forest mortality over large areas, exploiting free and open long-term image archives available at a range in spatial and temporal resolutions. While remotely sensed surface reflectances and vegetation indices have been widely used to study optical response of forest canopies to drought events, retrievals of evapotranspiration (ET) derived from thermal satellite imagery – particularly at resolutions approaching crown scale - can provide insights into cumulative tree stresses that can incite disease and trigger mortality. In this study, we applied a multi-sensor satellite data fusion approach to estimate daily 30-m resolution ET and an associated Evaporative Stress Index (ESI) to study drought-induced mortality in a temperate forest at the Missouri Ozark AmeriFlux (MOFLUX) site, located in the central United States. The study covered the period from 2010 to 2014, including an exceptional drought year of 2012. Modeled ET agreed well with eddy flux measurements from the MOFLUX tower, with average monthly relative errors of 15%. Plot-scale ESI, describing temporal anomalies in the ratio of actual-to-reference ET, was used as an index of relative forest health to investigate relationships between forest mortality and drought severity. ESI showed good agreement with observed predawn leaf water potential, especially during the drought year. Furthermore, plot-scale ESI was also correlated with the subsequent year's tree mortality, suggesting the importance of considering the forest health condition prior to drought when studying drought-induced forest impacts. This study demonstrates the utility of multi-year ET remote sensing data at the stand or plot scale as an indicator of forest health and as a predictor of future mortality due to drought.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Disk Failure Dataset from the Campaign Storage System

This dataset consists of 1,389 disk (HDD) failure events collected from the Campaign storage system at LANL. The Campaign system supported various compute platforms throughout its lifespan, including Cielo, Fire, Ice, and notably, the Trinity supercomputer. Each recorded event includes its detection timestamp (in ISO 8601 format) and details such as its location within the storage systemâ€”rack, enclosure, and drive slot number. The data, spanning from May 4, 2021, to July 25, 2023 (2 years, 2 months, and 22 days), represents failure events from the terminal years of Campaign's operational period, accounting for 26% of its total operational time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Magnetostrictive materials for enhanced sensors and electronic components

Magnetostriction is a property of magnetic materials that causes them to change their shape or dimensions when their magnetization changes. Low-cost, mechanically robust magnetostrictive sensors would be valuable for a wide range of applications addressing DOE's energy, environmental, and national security missions. Several examples include monitoring internal conditions of pipelines, enhancing implantable systems for the human body, and improving the electrical grid by providing real time sensors for detecting high impedance faults that may act as ignition sources for forest fires. Historically, magnetostrictive materials tended to be expensive and mechanically brittle, but recent developments in iron-based alloys, especially those that include dilute solutions of rare earth elements, have demonstrated useful magnetostriction values, mechanical robustness, and low costs. This project investigated addition of dilute cerium doping on the magnetostrictive properties of Fe-Ga and Fe-Al alloys. Advanced manufacturing techniques were applied to these materials to take advantage of rapid cooling of the alloy to stabilize desired phases and to make near net shape coupons. These coupons develop significant texturing, offering a route to target microstructures with exceptional performance.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Application of multidimensional structural characterization to detect and describe moderate forest disturbance

The study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical-based remote sensing platforms, which are often above-canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Leak detection in a subcritical boiler

Thermal power plants experience cycling duty leading to the fatigue of the boiler and heat exchanger tubes. As a result, tube failures occur frequently in coal fired fleets leading to forced outages. Furthermore, because the tube leaks have been the major source of unwanted shutdowns and the number of outages is increasing, present work focuses on the detection and isolation of the leak in a subcritical boiler based upon the process data from a commercial coalfired power plant. The mass balance equation around the steam drum was analyzed using timeseries data collected from a 300 MW power plant. The ratio of the feed water mass flow rate to the steam mass flow rate was defined as a key parameter for detecting leaks. The difference in slope between the feedwater and steam mass flow rate during the normal and faulty operations was established as the upper control limit for real time monitoring. To reduce false alarm rates that arise when raw signal is directly compared against the threshold due to common process fluctuations, an optimal filter was derived for smoothing. It was found that the optimal filter reacted much more quickly to process changes than an exponential moving average filter, around 8 h earlier on average. Occurrence of relatively high false alarm rates even in the filtered responses was related to the cycling of the boiler from the base load condition. Variable threshold was established to keep false alarm rates to the minimum while maintaining the leak detection rate. Finally, the leak was located at the economizer and this could readily be isolated by investigating the magnitude of the mass flow rates ratio and the temperature at the economizer outlet.

42 ENGINEERING↗

DOE OSTI2023

Anthropogenic climate change impacts exacerbate summer forest fires in California

Record-breaking summer forest fires have become a regular occurrence in California. Observations indicate a fivefold increase in summer burned area (BA) in forests in northern and central California during 1996 to 2021 relative to 1971 to 1995. While the higher temperature and increased dryness have been suggested to be the leading causes of increased BA, the extent to which BA changes are due to natural variability or anthropogenic climate change remains unresolved. Here, we develop a climate-driven model of summer BA evolution in California and combine it with natural-only and historical climate simulations to assess the importance of anthropogenic climate change on increased BA. Our results indicate that nearly all the observed increase in BA is due to anthropogenic climate change as historical model simulations accounting for anthropogenic forcing yield 172% (range 84 to 310%) more area burned than simulations with natural forcing only. We detect the signal of combined historical forcing on the observed BA emerging in 2001 with no detectable influence of the natural forcing alone. In addition, even when considering fuel limitations from fire-fuel feedbacks, a 3 to 52% increase in BA relative to the last decades is expected in the next decades (2031 to 2050), highlighting the need for proactive adaptations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations

In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.

Edge computing↗

DOE OSTI2026

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Atmospheric methane (CH4) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and enables early detection of climate‐driven feedbacks from wetlands, fires, permafrost, agriculture, and fossil‐fuel emissions.

Ciais, P↗

DOE OSTI2024

Quantitative Risk Assessment for Hydrogen-Powered Locomotive Refueling

Hydrogen powered locomotives are being explored to reduce emissions in rail applications. The risks of operations like refueling should be understood to ensure safe environments for workers and members of the public. Sensitivity analyses were conducted using HyRAM+ to identify major drivers of risk and compare effects of system parameters on individual risk. The consequences of jet fires from full-bore leaks dominated the risk, compared to explosions or smaller leaks. Pipe size, leak detection capability, and leak frequencies of system components greatly affected risk while overpressure modeling parameters and ambient conditions had little effect. The effects of personal protective equipment (PPE) materials on individual risk were quantified by reducing the individual’s exposure time or absorbed thermal dose. PPE only showed a risk reduction in low-risk cases. This study highlighted target areas for risk mitigation, including leak detection equipment and component maintenance, and indicated that the minimal effects of other parameters on risk may not justify prescriptive requirements for refueling operators.

08 HYDROGEN↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗

DOE OSTI2024

Spread Spectrum Time Domain Reflectivity for String Monitoring in PV Power Plants (Final Technical Report)

This final report describes the methods and results of applying Spread Spectrum Time Domain Reflectivity (SSTDR) for String Monitoring in PV Power Plants for DE-EE0008169. The project created a new system for both detecting and locating electrical faults in photovoltaic systems. In this work, we address photovoltaic electric faults that are both common and costly. Based on interviews with photovoltaic power plant owners, operators, and maintainers, three types of faults are common and of significant interest: disconnects, ground faults, and arc faults. Disconnects can originate from many sources. They are often due to everyday events, such as lawnmowing (accidentally running over a cable), animals eating through the cables, or degradation that occurs over time due to corrosion or general degradation. Ground faults occur when the cables (for example, due to frayed insolation) connect to the ground, relaying current into the ground. These faults are particularly problematic since the ground faults are often intermittent. That is, ground faults commonly appear during rain storms due to a change in soil conductivity and then disappear when the rain ends. This makes the ground fault difficult to find because while current systems can detect the overall change in voltage and current associated with a ground fault, technicians are necessary to locate the fault. As a result, ground faults may disappear before the technician arrives at the power plant. Hence, locating and fixing ground faults often require multiple trips. We also study arc faults, which can result when nearby conductors create an arc of electrical current through the air. While less common, arc faults can be extremely dangerous. The energetic electrical arc can cause fires and destroy equipment, costing significant damage. Overall all three types of faults cost owners and operators money, either from the destruction of equipment or from technician time. Furthermore, while devices exist for detecting ground faults (ground fault circuit interrupters) and arc faults (arc fault circuit interrupter), these systems only search patterns of electrical current that correspond to each fault. This information cannot be used to locate the fault. In addition, these protection systems experience nuisance trips due to nearby electromagnetic interference, such as from a lawn mower or other motors that produce significant amounts of electromagnetic radiation. Hence, the overall goal of this project is to create an SSTDR tool that provides photovoltaic power plants with more reliable fault detection in addition to the localization of faults. SSTDR works by transmitting electrical signals into the photovoltaic string. Those signals reflect from impedance discontinuities (i.e., disconnects, ground faults, and arc faults). These faults are then detected by measuring the presence of a reflection at the SSTDR and can be located by identifying the location of that reflection in time. In addition, unlike current protection systems, these systems do not experience nuisance trips since their low amplitude, high frequency, and coded signal can by analyzed without interference from the regular operational voltage on the photovoltaic string.

14 SOLAR ENERGY↗

DOE OSTI2026

A Data-driven Phytotechnology Framework for Identification and Remediation of Leached-Metals-Contaminated Soil Near Coal Ash Impoundments

This project developed and evaluated advanced remote sensing and machine learning approaches to identify, monitor, and address environmental contamination associated with coal combustion residual (CCR) impoundments and landfills at coal-fired power plants. In Phase I, historical and multi-temporal Sentinel-2 satellite imagery, groundwater monitoring data, and environmental variables were integrated to detect vegetation stress potentially caused by toxic metal leaching from coal ash disposal sites. Multiple vegetation and biophysical indices were analyzed to determine their effectiveness in identifying abnormal vegetation growth patterns linked to contamination. Results from case studies conducted at coal ash–impacted power plant sites in North Carolina and Virginia demonstrated that satellite-based vegetation monitoring can serve as an effective early indicator of environmental stress associated with metals such as arsenic, cadmium, cobalt, lead, lithium, radium, and thallium.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

An experimental and kinetic modeling study of the pyrolysis of isoprene, a significant biogenic hydrocarbon in naturally occurring vegetation fires

Isoprene dominates the carbon flux emitted by vegetation and constitutes 40% of non-methane biogenic emissions worldwide. Despite pyrolysis experiments at temperatures above 1000 K showing a link between isoprene combustion and aromatic species formation, comprehensive mechanistic research on isoprene is scarce in the literature. Here, in this work, we carry out an experimental and theoretical study to build, for the first time, a chemical kinetic model describing isoprene pyrolysis. The formation of polycyclic aromatic hydrocarbon (PAH) precursor species, often observed in vegetation fire plumes, is partially explained by isoprene pyrolysis experiments and theoretical modeling. Molecular dynamics (MD) simulations unveil reaction pathways from allylic isoprenyl radicals to allene and cyclopentadiene (CPD) intermediates, two relevant species detected in the experiments. Rate constants for these identified pathways are calculated using variational transition state theory to update the kinetic model, which is validated against single-pulse shock tube (SPST), and jet-stirred reactor (JSR) experimental data in the temperature range of 850–1690 K. The kinetic model presents satisfactory agreement with the SPST experimental data, and a reaction pathway analysis shows that association of propargyl radicals results in benzene formation. The JSR pathway analysis also identifies the prominent reactions for CPD, benzene, styrene, and toluene formation. Our model does not reproduce the CPD experimental profiles, indicating that additional studies are necessary. Overall, our findings advance the understanding of isoprene pyrolysis and its related atmospheric pollutants in naturally occurring vegetation fires where smoldering and oxygen-deficient combustion processes are present.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗