Wildfires have caused immense damage to the environment, property and human safety in recent years. Fortunately, the deployment of firefighting aircraft, particularly water bombers, has emerged as one of the most effective strategies for combating wildfires. However, the intricate environment of forest fire sites, characterized by thermal turbulence and canyon winds, presents a formidable flight risk for firefighting aircraft. To address this issue, this study conducted a comprehensive risk assessment of firefighting aircraft operating in a wildfire environment, analyzing the impact of thermal turbulence and canyon winds using a finite element simulation method. This approach not only bridges the research gap in the field of flight safety but also evaluates the environmental risks encountered by firefighting aircraft when entering forest fire sites. Our findings underscore thermal turbulence and canyon winds as potential hazards for aircraft flying over mountainous terrain, elucidating the effect of thermal turbulence on aircraft engine intakes and quantifying the total lift loss incurred during encounters with canyon winds featuring non-uniform airflow velocities. Specifically, thermal turbulence can induce instability and vibration in aircraft engines, whereas canyon winds can generate updrafts and downdrafts that may compromise aircraft structures or lead to lift loss. Furthermore, we cite several references to emphasize the multifaceted risks associated with the forest fire site environment, encompassing temperature gradients, thunderstorms, and air pollution. Such comprehensive wildfire data can be invaluable in assessing the flight safety of firefighting aircraft during water-dropping missions in forest fire scenarios.

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Estuaries are the important interface between the land and sea, providing significant environmental, economic, cultural and social values. However, they face unprecedented pressures including eutrophication, harmful algal blooms, habitat loss, and extreme weather due to climate change. Here we present an open access, quality-controlled water quality dataset collected from twelve diverse estuaries spanning 1000 km along the southeastern Australian coastline. Water depth, temperature and salinity data were collected across two years (2018-2021) capturing drought, wildfire and flood periods, using high accuracy Seabird MicroCAT field sensors located within oyster leases. These fully autonomous instruments collected and transmitted data every 10 minutes before downstream quality checking and uploading onto a public website. Simultaneous, high-resolution, longitudinal environmental data collected across multiple estuaries throughout a range of extreme weather events are exceptionally rare in the Southern Hemisphere, yet provide an invaluable resource for the aquaculture industry, researchers and environmental regulators alike.

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model-Deepbiosphere-not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

The rapid reduction of forests due to environmental impacts such as deforestation, global warming, natural disasters such as forest fires as well as various human activities is an escalating concern. The increasing frequency and severity of forest fires are causing significant harm to the ecosystem, economy, wildlife, and human safety. During dry and hot seasons, the likelihood of forest fires also increases. It is crucial to accurately monitor and analyze the large-scale changes in the forest cover to ensure sustainable forest management. Remote sensing technology helps to precisely study such changes in forest cover over a wide area over time. This research analyzes the impact of forest fires over time, identifies hotspots, and explores the environmental factors that affect forest cover change. Sentinel-2 imagery was utilized to study changes in Brunei Darussalam\'s forest cover area over five years from 2017 to 2022. An object-based approach, Simple Non-Iterative Clustering (SNIC), is employed to cluster the region using NDVI values and analyze the changes per cluster. The results indicate that the area of the clusters reduced where fire incidence occurred as well as the precipitation dropped. Between 2017 and 2022, the increased forest fires and decreased precipitation levels resulted in the change in cluster areas as follows: 66.11%, 69.46%, 68.32%, 73.88%, 77.27%, and 78.70%, respectively. Additionally, hotspots in response to forest fires each year were identified in the Belait district. This study will help forest managers assess the causes of forest cover loss and develop conservation and afforestation strategies.

In this study, spatiotemporal analysis of forest fires in Turkiye was undertaken, with a specific focus on the large-scale atmospheric systems responsible for causing these fires. For this purpose, long-term variations in forest fires were classified based on the occurrence types (i.e. natural/lightning, negligence/inattention, arson, accident, unknown). The role of large-scale atmospheric circulations causing natural originated forest fires was investigated using NCEP/NCAR Reanalysis sea level pressure, and surface wind products for the selected episodes. According to the main results, Mediterranean (MeR), Aegean (AR), and Marmara (MR) regions of Turkiye are highly susceptible to forest fires. Statistically significant number of forest fires in the MeR and MR regions are associated with global warming trend of the Eastern Mediterranean Basin. In monthly distribution, forest fires frequently occur in the MeR part of Turkiye during September, August, and June months, respectively, and heat waves are responsible for forest fires in 2021. As a consequence of the extending summer Asiatic monsoon to the inner parts of Turkiye and the location of Azores surface high over Balkan Peninsula result in atmospheric blocking and associated calm weather conditions in the MeR (e.g. Mugla and Antalya provinces). When this blocking continues for a long time, southerly winds on the back slopes of the Taurus Mountains create a foehn effect, calm weather conditions and lack of moisture in the soil of Antalya and Mugla settlements trigger the formation of forest fires.

UNASSIGNED: To evaluate the association between wildfire exposure in pregnancy and spina bifida risk.
UNASSIGNED: This retrospective cohort study used the California Office of Statewide Health Planning and Development Linked Birth File with hospital discharge data between 2007 and 2010. The Birth File data were merged with the California Department of Forestry and Fire Protection data of the same year. Spina bifida was identified by its corresponding ICD-9 code listed on the hospital discharge of the newborn. Wildfire exposure was determined based on the zip code of the woman\'s home address. Pregnancy was considered exposed to wildfire if the mother lived within 15 miles of a wildfire during the pregnancy or within 30 days prior to pregnancy.
UNASSIGNED: There were 2,093,185 births and 659 cases of spina bifida between 2007 and 2010. The births were analyzed using multivariable logistic regression models and adjusted for potential confounders. Exposure to wildfire in the first trimester was associated with higher odds of spina bifida (aOR= 1.43 [1.11-1.84], p-value = 0.01). Wildfire exposure 30 days before the last menstrual period and during the second and third trimesters were not associated with higher spina bifida risk.
UNASSIGNED: Wildfire exposure has shown an increased risk of spina bifida during the early stages of pregnancy.

We investigated the enduring consequences of a wildfire on nematode diversity and abundance in a pine forest, employing a slope gradient approach. Our primary objective was to determine the extent of post-fire alterations in the nematode community 3 years after the incident, to understand if the ecosystem has returned to its pre-fire state or has transitioned to a distinctive ecological environment. Three distinct burned pine forest sites at varying elevations were sampled to capture short-scale soil property variations due to slope gradients, while unburned forest sites served as controls. A consistent pattern emerged where the lowest altitude sites exhibited the highest nematode abundances, although still lower than unburned sites. Fire-induced changes were profound, shifting from fungivore dominance in unburned sites to bacterivore and herbivore dominance in burned sites. Alterations in soil properties post-fire, particularly reduced organic matter and nitrogen content, were closely associated with nematode community shifts. Water availability played a crucial role with lower moisture levels at higher elevations impacting nematode populations. Structural differences in the nematode community primarily resulted from fire disturbance rather than altitude. This study emphasizes the persistent and transformative impact of wildfire on nematode communities, highlighting the intricate interplay between ecological disturbances, soil properties, and nematode trophic dynamics.

Fire agencies across the United States must make complex resource allocation decisions to manage wildfires using a national network of shared firefighting resources. Firefighters play a critical role in suppressing fires and protecting vulnerable communities. However, they are exposed to health and safety risks associated with fire, smoke inhalation, and infectious disease transmission. The COVID-19 pandemic further complicated these risks, prompting fire agencies to propose resource management adaptations to minimize COVID-19 exposure and transmission. It is unclear if and how the pandemic may have operationally influenced wildland firefighting personnel resource use given compounding wildfire and COVID-19 risks. Therefore, we developed generalized linear mixed models that were fit using multiple integrated datasets to detect changes in personnel resource use for years prior to and during the COVID-19 pandemic, while controlling for historical fire and landscape conditions, societal risks, and management objectives. Analyses of observed and predicted firefighting resource use revealed reductions in the mean personnel resources used per wildfire per day during the pandemic for models developed across the western U.S. and for various western U.S. fire regions. Notably, the Northern California and the Great Basin Coordination Centers showed statistically significant reductions in ground personnel use during the COVID-19 pandemic. Learning from wildland fire management strategies and resource use trends that occurred during the COVID-19 pandemic, fire agencies can better anticipate resource constraints that may arise during the compounding threats of severe wildland fire activity and infectious disease outbreaks to proactively prepare and adapt suppression management strategies.

The development of tools to quickly identify the fate of damaged trees after a stress event such as a wildfire is of great importance. In this context, an innovative approach to assess irreversible physiological damage in trees could help to support the planning of management decisions for disturbed sites to restore biodiversity, protect the environment and understand the adaptations of ecosystem functionality. The vitality of trees can be estimated by several physiological indicators, such as cambium activity and the amount of starch and soluble sugars, while the accumulation of ethanol in the cambial cells and phloem is considered an alarm sign of cell death. However, their determination requires time-consuming laboratory protocols, making the approach impractical in the field. Biosensors hold considerable promise for substantially advancing this field. The general objective of this review is to define a system for quantifying the plant vitality in forest areas exposed to fire. This review describes recent electrochemical biosensors that can detect plant molecules, focusing on biosensors for glucose, fructose, and ethanol as indicators of tree vitality.