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At least 127 records · Page 7

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗

Variable wildfire impacts on the seasonal water temperatures of western US streams: A retrospective study

Recent increases in the burn area and severity of wildfires in the western US have raised concerns about the impact on stream water temperature–a key determinant of cold-water fish habitats. However, the effect on seasonal water temperatures of concern, including winter and summer, are not fully understood. In this study, we assessed the impact of wildfire burns at Boulder Creek (Oregon), Elk Creek (Oregon), and Gibbon River (Wyoming) watersheds on the downstream winter and summer water temperatures for the first three post-fire years. To obtain results independent of the choice of the analytical method, we evaluated the consequence of each burn using three different statistical approaches that utilize local water temperature data. Our results from the three approaches indicated that the response of water temperatures to wildfire burns varied across seasons and sites. Wildfire burns were associated with a median increase of up to 0.56°C (Standard Error; S.E. < 0.23°C) in the summer mean water temperatures (MWT) and 62 degree-day Celsius (DDC; S.E. < 20.7 DDC) in the summer accumulated degree days (ADD) for the three subsequent years across studied stream sites. Interestingly, these burns also corresponded to a median decrease of up to 0.49°C (S.E. < 0.45°C) in the winter MWT and 39 DDC (S.E. < 40.5 DDC) in the winter ADD for the same period across sites. Wildfire effects on the downstream water temperatures diminished with increasing site distance from the burn perimeter. Our analyses demonstrated that analytical methods that utilize local watershed data could be applied to evaluate fire effects on downstream water temperatures.

54 ENVIRONMENTAL SCIENCES↗

Data and scripts associated with a manuscript on a meta-analysis synthesizing stream biogeochemical response to wildfires across space and time (v2)

This data package is associated with the publication “Catchment characteristics modulate the influence of wildfires on nitrate and dissolved organic carbon in lotic systems across space and time: A meta-analysis” submitted to Global Biogeochemical Cycles (Cavaiani et al. 2025). This study uses meta-analytical techniques to evaluate the effect of wildfire on in-stream responses in burned and unburned watersheds. The study aims to provide additional insight into the range of responses and net influences that wildfires have on hydro-biogeochemistry across broad spatial scales, burn extents, and the persistence of water-quality change. This study compiles data and metadata from 18 total publications that includes 1) surface water geochemistry data (dissolved organic carbon; nitrate), 2) climate classifications, 3) year of the wildfire, 4) the time lag between when the fire occurred and when the sampling occurred, and 5) study design of the publication. In total, this meta-analysis draws data that spans 8 climate guilds, 3 biomes, 62 watersheds, and 20 unique wildfires. See Sites_meta_data.csv for citations of the papers used in this meta-analysis. All R scripts and the associated data can also be found on GitHub at This data package was originally published in March 2024. It was updated in April 2025 (v2; new and modified files). See the change history section in the readme for more details. This data package contains five primary folders that include the following: (1) inputs; (2) output for analysis; (3) initial plots; (4) R scripts; and (5) GIS data. The data package also contains a data dictionary (dd) that provides column header definitions and a file-level metadata (flmd) file that describes every file. The “inputs” folder contains a list of all publications identified during the formal web search and an indication of whether each publication was included in the final analysis. Additionally, it includes site-level metadata, catchment characteristics, and GIS data for all publications included in the final analysis. The “Output_for_analysis” folder contains all data frames and figures generated from each R script used for additional data analysis. The “initial_plots” folder includes all exploratory figures that will be included in a supplemental and figures that will be submitted with the manuscript for publication. The “R_scripts” folder contains the scripts that perform all the data manipulations, statistical analyses, and plots. The “gis_data” folder includes shape files for each fire included in this meta-analysis. This data package contains the following file types: csv, pdf, jpeg, cpg, dbf, prj, shp, shp.ea.iso.xml, shp.iso.xml, shx.

54 ENVIRONMENTAL SCIENCES↗

Reducing the risks of wildfires

Anyone who has lived in New Mexico for at least a year realizes that the Land of Enchantment has six seasons. In addition to the four seasons everyone else has, we have a wildfire season, which typically comes during late spring and early summer. Wildfires sometimes even last through the monsoon season, when thunderstorms are lighter than usual. Wildfires, it seems, are inevitable in Los Alamos and the surrounding communities that make up part of northern New Mexico. In recent history, the 2000 Cerro Grande Fire destroyed 150,000 acres (including 7,500 acres of Laboratory property) and 235 Los Alamos homes. In 2011, the Las Conchas Fire burned more than 156,000 acres and threatened Los Alamos and the Lab. To minimize the chances of wildfires and to strengthen defenses against wildfires, the Laboratory every year carries out various fire-mitigation strategies throughout its 42 square miles.

54 ENVIRONMENTAL SCIENCES↗

Post-Wildfire Mobilization of Organic Carbon

Wildfires significantly alter watershed functions, particularly the mobilization of organic carbon (OC). This study investigated OC mobility and the physicochemical characteristics of wildfire-impacted soils and ashes from the northern California and Nevada fires (Dixie, Beckworth, Caldor). Organic carbon in wildfire-derived ashes (9.2–57.3 mg/g) generally exceeded levels in the background soils (4.3–24.4 mg/g), except at the Dixie fire sites. The mobile OC fraction varied from 0.0093 to 0.029 in ashes and 0.010 to 0.065 in soils, though no consistent trend was observed between the ashes and soils. Notably, the ash samples displayed lower OC mobility compared with the soils beneath them. A negative correlation was found between the mobile OC fraction and bulk OC content. Wildfire increased the total amount of mobile OC substantially by 5.2–574% compared to the background soils. Electron paramagnetic resonance (EPR) spectra confirmed the presence of environmentally persistent free radicals (EPFRs), which correlated with observed redox reactivity. Additionally, X-ray absorption near edge structure (XANES) and X-ray fluorescence (XRF) imaging revealed that Fe(II) oxidation in soils beneath the ashes may have enhanced the OC mobility, likely driven by pyrogenic carbon and free radicals. These findings enhance our understanding of post-wildfire OC mobilization and the impact of ash–soil physicochemical properties on watershed health.

organic carbon↗

Wildfire Risk Support via Satellite-Derived Vegetation Health and Land Surface Model Soil Moisture

Land Surface Model (LSM) and evaporative demand products provide advanced lead time to wildfire conditions that complement traditional fire indices and represent short-term changes that add context to overall, long-term drought conditions. Established fire indices typically use weather indicators (i.e., precipitation, temperature) and estimated dead fuel moisture to indirectly obtain land surface and sub-surface characterization. The convergence of LSM shallow and deep-layer soil moisture output and satellite-derived vegetation health combine to provide a tool for stakeholders to examine trends in the state of land surface conditions that can help assess wildfire threat. Satellite remote sensing data can constrain near-real time vegetation characteristics within the LSM and/or provide a derived stress index in order to help characterize the wildfire risk. In addition, a percentile product of soil moisture is derived from a comparison of current LSM conditions to the historical record of the LSM in order to put the current conditions in perspective relative to the season and geographic region. The 2015 season as well as the 2018 Camp Fire Complex in California were examined in terms of the changes in LSM soil moisture and vegetation states. Satellite vegetation health consistently showed decreases a month prior to wildfire initiation. Additionally, maximum changes in total column soil moisture corresponded with the greatest concentration of fire locations. While soil moisture deficits occurred in the shallow layers across northern California in 2018, significant deficits at all sub-surface levels were seen ahead of the Camp Fire event. This presentation will demonstrate the complementary value of LSM output and satellite measured vegetation health to diagnose short-term deficits in sub-surface soil moisture and the rapid decline in vegetation health which precedes large wildfire events.

Wildfire↗

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗

Managing Power Systems-Induced Wildfire Risks Using Optimal Scheduled Shutoffs: Preprint

The growing demands for electricity and the increase in extreme weather conditions are putting unprecedented pressure on our electrical grids. Oftentimes, this pressure leads to electrical components failures which might ignite wildfires. This work develops a novel model to balance the reliability of power networks operations and risks of wildfires ignition by optimizing the operational schedule of power transmission networks considering time-varying risk measures that consider exogenous and operational factors. Energy storage systems are considered to deliver power during peak wildfire hours and enable temporal load shifting. The problem is formulated as a mixed-integer linear program that maximizes a weighted sum of the served power demand and the reduction in grid-induced wildfires risk. The results demonstrate the ability of the model to reduce wildfires risks significantly without considerable load shedding.

energy scheduling↗

Integrated Transmission-Distribution Multi-Period Switching for Wildfire Risk Mitigation: Improving Speed and Scalability with Distributed Optimization: Preprint

With increasingly severe wildfire conditions driven by climate change, utilities must manage the risk of wildfire ignitions from electric power lines. During "public safety power shutoff'" events, utilities de-energize power lines to reduce wildfire ignition risk, which may result in load shedding. Distributed energy resources provide flexibility that can help support the system to reduce load shedding when lines are de-energized. We investigate a coordinated transmission-distribution optimization problem that balances wildfire risk mitigation and load shedding. We model distribution systems that include battery energy storage systems which may support loads when transmission lines are de-energized. This multi-period integrated transmission-distribution optimal switching problem jointly optimizes line switching decisions, the generators' setpoints, load shedding, and the batteries' states of charge, resulting in significant computational challenges. To improve scalability, we decompose the problem over both space and time and apply a distributed optimization algorithm. Using a large-scale synthetic California test case with realistic distribution models and real wildfire risk data, we show that distributed optimization can solve large-scale multi-period switching problems that are otherwise intractable for centralized solvers. We also discuss challenges and future directions for improving the distributed algorithm's convergence performance as the number of time periods increases.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger Over the Conterminous United States

Abstract Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US.

54 ENVIRONMENTAL SCIENCES↗

Machine learning-based observation-constrained projections reveal elevated global socioeconomic risks from wildfire

Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries.

54 ENVIRONMENTAL SCIENCES↗

Experimentally determined traits shape bacterial community composition one and five years following wildfire

Abstract Wildfires represent major ecological disturbances, burning 2–3% of Earth’s terrestrial area each year with sometimes drastic effects above- and belowground. Soil bacteria offer an ideal, yet understudied system within which to explore fundamental principles of fire ecology. To understand how wildfires restructure soil bacterial communities and alter their functioning, we sought to translate aboveground fire ecology to belowground systems by determining which microbial traits are important post-fire and whether changes in bacterial communities affect carbon cycling. We employed an uncommon approach to assigning bacterial traits, by first running three laboratory experiments to directly determine which microbes survive fires, grow quickly post-fire and/or thrive in the post-fire environment, while tracking CO 2 emissions. We then quantified the abundance of taxa assigned to each trait in a large field dataset of soils one and five years after wildfires in the boreal forest of northern Canada. We found that fast-growing bacteria rapidly dominate post-fire soils but return to pre-burn relative abundances by five years post-fire. Although both fire survival and affinity for the post-fire environment were statistically significant predictors of post-fire community composition, neither are particularly influential. Our results from the incubation trials indicate that soil carbon fluxes post-wildfire are not likely limited by microbial communities, suggesting strong functional resilience. From these findings, we offer a traits-based framework of bacterial responses to wildfire.

54 ENVIRONMENTAL SCIENCES↗

Radiative impact of record-breaking wildfires from integrated ground-based data

The radiative effects of wildfires have been traditionally estimated by models using radiative transfer calculations. Assessment of model-predicted radiative effects commonly involves information on observation-based aerosol optical properties. However, lack or incompleteness of this information for dense plumes generated by intense wildfires reduces substantially the applicability of this assessment. Here we introduce a novel method that provides additional observational constraints for such assessments using widely available ground-based measurements of shortwave and spectrally resolved irradiances and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges. We apply our method to quantify the radiative impact of the record-breaking wildfires that occurred in the Western US in September 2020. For our quantification we use integrated ground-based data collected at the Atmospheric Measurements Laboratory in Richland, Washington, USA with a location frequently downwind of wildfires in the Western US. We demonstrate that remarkably dense plumes generated by these wildfires strongly reduced the solar surface irradiance (up to 70% or 450 Wm -2 for total shortwave flux) and almost completely masked the sun from view due to extremely large AOD (above 10 at 500 nm wavelength). We also demonstrate that the plume-induced radiative impact is comparable in magnitude with those produced by a violent volcano eruption occurred in the Western US in 1980 and continental cumuli.

54 ENVIRONMENTAL SCIENCES↗

Wildfire and power grid nexus in a changing climate

Global wildfire events have had increasingly severe impacts in recent years, particularly in the western USA, driven by extreme fire-weather conditions, fuel accumulation and multiple ignition sources. Wildfires sparked by power lines tend to be larger and more destructive, as they often occur during high winds, which accelerate the spread of fires. Moreover, efforts to contain wildfires frequently result in power outages, causing considerable economic disruption. Here, in this Review, we examine wildfire risks related to power-line-induced ignitions, infrastructure damage, climate-induced environmental impacts, grid operational risks, real-time grid management risks, vegetation management risks, and financial and funding risks in the context of a changing climate and their interdependence with power grid infrastructures. We then explore the resilience of power grids under wildfire threats, looking at risk analysis, prediction and mitigation strategies. The Review also shares practical insights and experiences in the USA to inform researchers, policymakers and industry professionals.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Wildfire‐Induced Losses of Soil Particulate and Mineral‐Associated Organic Carbon Persist for Over 4 Years in a Chaparral Ecosystem

ABSTRACT Wildfires can lower soil carbon (C) stocks directly through combustion, but also indirectly during post‐fire recovery if microbial C demands outpace photosynthetic C inputs. However, how much C is respired by soil microorganisms post‐fire may depend on wildfire effects on particulate organic carbon (POC; mostly plant material accessible to microbes) and/or mineral‐associated organic carbon (MAOC; considered C protected by minerals from decomposers), meaning assessment of wildfire impacts on these pools is necessary to predict microbial decomposition rates and, thus, the fate of soil C. Here, we measured POC, MAOC, pyrogenic organic matter C, plant cover, extracellular enzyme activity (EEA), and microbial community abundance and composition 17 days, and 1, 3, and 4 years after the Holy Fire burned 94 km 2 of fire‐adapted chaparral. The wildfire immediately decreased POC by 50% (from 51 ± 21 to 26 ± 6 g C kg −1 ) and MAOC by 33% (from 9.3 ± 0.9 to 6.3 ± 0.9 g C kg −1 ), consistent with MAOC being less vulnerable to loss than POC. POC decreased by another 38% 1 year post‐fire, consistent with increases in microbial abundance and EEA suggesting increased microbial decomposition. Between 1 and 4 years after the fire, cover of the dominant shrub (Arctostaphylos glandulosa) increased from 3.9% ± 1.6% to 16% ± 5.4% (compared to 58% ± 4.6% in unburned plots), marking the end of net soil C losses. Still, soil C did not increase between 1 and 4 years post‐fire, suggesting plant C inputs did not outpace microbial respiration, a finding consistent with isotopically heavier C from microorganisms raising bulk soil δ 13 C values. As global changes favor increases in wildfire frequency and severity, C losses via combustion and decomposition may outpace plant C inputs during the first 4 years post‐fire in chaparral, slowing the replenishment of soil C stocks.

Biodiversity & Conservation↗

IAM-FIRE: a Climate Emulator–Based Framework to Project Wildfire Impacts and Risks for Integrated Assessment Models

Most Integrated Assessment Models (IAMs) underrepresent dynamic feedbacks from climate-driven disturbances such as wildfires, potentially overestimating the permanence of land-based carbon sinks. In particular, representing the impacts of forest fires is becoming increasingly important, as these are expected to intensify in the coming years. We introduce IAM-FIRE (Integrated Assessment Model – Fire Impacts & Risks Emulator), a novel framework that enables the projection of wildfire burned area (BA) and carbon emissions (CE) directly from IAM outputs. IAM-FIRE combines a spatial climate emulator, land-use downscaling, vegetation productivity modelling, and an empirical fire model to generate global annual wildfire impacts for arbitrary socioeconomic and emissions scenarios at 0.5° resolution for the period 2020–2100. Calibrated against GFEDv5 observations and using inputs from the Global Change Analysis Model (GCAM), we report projections BA and CE derived from IAM-FIRE for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-6.6 and SSP5-7.6. The model reproduces historical global trends for total BA, including the observed global decline since the early 2000s, and for forest BA. Projected fire trajectories differ strongly among scenarios: total BA range from declines under SSP1-2.6 (-3.36 Mha yr-1) to increases under SSP3-6.6 (+1.6 Mha yr-1). Corresponding total CE show a similar divergence ranging from -15 to +10.6 TgC yr-1. Socioeconomic development exerts a dominant suppressing effect on wildfire impacts while climate change and CO2-driven increases in vegetation productivity amplify fire risk, particularly under high-emissions pathways. Compared with CMIP6 fire-enabled Earth System Models, IAM-FIRE exhibits greater sensitivity to radiative forcing and a stronger role for human-driven fire suppression, highlighting substantial structural uncertainties in future fire projections. By providing a computationally efficient and internally consistent approach to represent wildfire impacts within IAMs, IAM-FIRE enables systematic exploration of fire–climate–land feedbacks and supports improved assessments of mitigation permanence and climate risks in future integrated scenarios.

Rouhette, Theo↗

Porting a fast-running wildfire plume model for use in national security and climate applications

In this project, the code for the fast-running Freitas wildfire plume model was successfully ported into the NARAC system, creating an operations-ready capability for predicting the transport of hazardous smoke from large wildfires. “Porting” refers to copying a section of code from one software package to another while accounting for unique dependencies in each software package. In this case, the code was ported from the Weather Research and Forecasting (WRF) Model, which is used extensively by the project team. To verify correct implementation, model results from the updated NARAC system were compared to those from WRF and the High-Resolution Rapid Refresh (HRRR) forecast model, which also employs the Freitas scheme. The NARAC User Interface (UI) and code documentation were also updated to include the new wildfire plume option, and a wildfire case was added to the NARAC test suite. The code was used in a real-time, semi-operational setup to predict smoke transport during the Cerro Pelado fire near Los Alamos National Laboratory in May 2022. The Freitas plume model was also tested for LLNL climate applications. Although the code was not ported into the Energy Exascale Earth System Model (E3SM), it was tested offline using E3SM atmospheric inputs, and results were compared to existing high-resolution plume predictions from large-eddy simulations in WRF. The code implementation and testing completed during this project will enable future work predicting smoke transport from large wildfires in LLNL national security and climate mission spaces.

54 ENVIRONMENTAL SCIENCES↗

Molecular level characterization of wildfire aerosol induced changes to plant health and value

The potential impacts of wildfire smoke on plant health and value are of increasing concern, due both to increasing awareness, and more frequent wildfire events. Wildfires produce substantial amounts of atmospheric pollution in smoke and particulate matter which can travel thousands of miles. These smoke events can blanket entire agricultural regions and cause impacts to plant development. One notable example of this is ‘smoke taint’ in wine, where vines, exposed to smoke, absorb many small volatile phenolic (VP) compounds. These VPs can be metabolized (glycosylated) and transported throughout the plant. Later, during downstream processing (fermentation of the grape musts), these glycosylated VPs can be hydrolyzed to re-release and volatilize the aroma-active phenolic compounds. The final product, thus, smells of smoke. Despite the obvious commercial and general research interests here, the nature of wildfire impacts on plant health are incompletely understood. Using established metabolomics and organic matter (aerosol) characterization methods, the overall aim of this proposal is thus to study wildfire smoke impacts on plant health and value, in the context of a model system (V. vinifera). This project closed early, and this report reflects some of the limited work conducted prior to conclusions. Specifically, this report discusses some sample preparation and initial MALDI-mass spectrometry analysis.

54 ENVIRONMENTAL SCIENCES↗