Search Results (61)

The application of image recognition technology plays a vital role in agricultural disease identification. Existing approaches primarily rely on image classification, object detection, or semantic segmentation. However, a major challenge in current semantic segmentation methods lies in accurately identifying small target objects. In this study, common tobacco leaf diseases—such as frog-eye disease, climate spots, and wildfire disease—are characterized by small lesion areas, with an average target size of only 32 pixels. This poses significant challenges for existing techniques to achieve precise segmentation. To address this issue, we propose integrating two attention mechanisms, namely cross-feature map attention and dual-branch attention, which are incorporated into the semantic segmentation network to enhance performance on small lesion segmentation. Moreover, considering the lack of publicly available datasets for tobacco leaf disease segmentation, we constructed a training dataset via image splicing. Extensive experiments were conducted on baseline segmentation models, including UNet, DeepLab, and HRNet. Experimental results demonstrate that the proposed method improves the mean Intersection over Union (mIoU) by 4.75% on the constructed dataset, with only a 15.07% increase in computational cost. These results validate the effectiveness of our novel attention-based strategy in the specific context of tobacco leaf disease segmentation. Full article

Older adults may experience worse wildfire fine particulate matter (PM_2.5) smoke-related health effects due to conditions such as Alzheimer’s disease and related dementias (ADRDs). We evaluated whether wildfire PM_2.5 was associated with acute hospitalizations among older adults with ADRD, linking modeled daily wildfire PM_2.5 concentrations and circulatory, respiratory, anxiety, and depression hospitalizations from 2006 to 2016. We employed a case-crossover design and conditional logistic regression to estimate associations between lagged daily wildfire PM_2.5 and hospitalizations. Also, we stratified cause-specific models by age, sex, emergency hospitalization status, and zip code-level urbanicity and poverty. The 1,546,753 hospitalizations among Medicare enrollees with ADRD were most coded for circulatory (71.7%), followed by respiratory (43.6%), depression (2.9%), and anxiety (0.7%) endpoints. We observed null associations between wildfire PM_2.5 and circulatory, respiratory, and anxiety hospitalizations over the six days following exposure. Same-day wildfire PM_2.5 was associated with decreased depression hospitalizations (rate ratio = 0.94, 95% CI: 0.90, 0.99). We saw some effect measure modifications by emergency hospitalization status and urbanicity. There were some stratum-specific effects for age, but the results remained mostly null. Future studies should use improved methods to identify ADRD and examine recent years with higher wildfire concentrations. Full article

Breathing in fine particulate matter of diameter less than 2.5 µm (PM_2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM_2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m³ and 35.5 µg/m³) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m³ threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R² of 0.914, particularly excelling in forecasting high PM_2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM_2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins. Full article

The exacerbation of wildfires caused by global warming poses a significant threat to human health and environmental integrity. This review examines the particulate matter (PM) and gaseous pollutants resulting from fire incidents and their impacts on individual health, with a specific focus on the occupational hazards faced by firefighters. Of particular concern is the release of carbon-containing gases and fine particulate matter (PM_2.5) from forest fires and urban conflagrations, which exceed the recommended limits and pose severe health risks. Firefighters exposed to these pollutants demonstrate an elevated risk of developing pulmonary and cardiovascular diseases and cancer compared to the general population, indicating an urgent need for enhanced protective measures and health management strategies for firefighters. Through a meticulous analysis of the current research findings, this review delineates future research directions, focusing on the composition and properties of these pollutants, the impacts of fire-emitted pollutants on human health, and the development of novel protective technologies. Full article

Environmental disasters are extreme environmental processes such as earthquakes, volcanic eruptions, landslides, tsunamis, floods, cyclones, storms, wildfires and droughts that are the consequences of the climate crisis due to human intervention in the environment. Their effects on human health have alarmed the global scientific community. Among them, autoimmune diseases, a heterogeneous group of disorders, have increased dramatically in many parts of the world, likely as a result of changes in our exposure to environmental factors. However, only a limited number of studies have attempted to discover and analyze the complex association between environmental disasters and autoimmune diseases. This narrative review has therefore tried to fill this gap. First of all, the activation pathways of autoimmunity after environmental disasters have been analyzed. It has also been shown that wildfires, earthquakes, desert dust storms and volcanic eruptions may damage human health and induce autoimmune responses to inhaled PM2.5, mainly through oxidative stress pathways, increased pro-inflammatory cytokines and epithelial barrier damage. In addition, it has been shown that heat stress, in addition to increasing pro-inflammatory cytokines, may also disrupt the intestinal barrier, thereby increasing its permeability to toxins and pathogens or inducing epigenetic changes. In addition, toxic volcanic elements may accelerate the progressive destruction of myelin, which may potentially trigger multiple sclerosis. The complex and diverse mechanisms by which vector-borne, water-, food-, and rodent-borne diseases that often follow environmental diseases may also trigger autoimmune responses have also been described. In addition, the association between post-disaster stress and the onset or worsening of autoimmune disease has been demonstrated. Given all of the above, the rapid restoration of post-disaster health services to mitigate the flare-up of autoimmune conditions is critical. Full article

Forest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping. However, it neglects the quality of the temporal trajectory and its change trends for forest disturbance mapping. Therefore, the aim of this paper is to improve LandTrendr (iLandTrendr) for forest disturbance mapping by optimizing its temporal trajectories and the post-processing of detection results. Specifically, the temporal trajectory of complex forest disturbance types was optimized using the Savitzky–Golay (SG) filter with constraints. That is, the smooth value generated from the SG filter for the disturbance year was replaced by the satellite observations when the nonlinear abrupt signals were included in the multi-temporal data. The forest disturbance detected by LandTrendr was further modified using the consistency of spectral variation trends. A case study using iLandTrendr to detect forest disturbance in Yunnan Province was conducted. Compared to the LandTrendr method, which has an overall accuracy (OA) of 35.88%, iLandTrendr generated forest disturbance mapping with an OA of 89.32%, which was significantly higher. The total mapped area of disturbance was 1,985,820.9 km², accounting for 49.69% of the total area. The disturbances were predominately caused by natural factors, such as wildfires, pests and diseases, and forest degradation, accounting for 85.31% of the total disturbed area. iLandTrendr can quickly and accurately detect the occurrence year of complex forest disturbance types and can be extended for the forest disturbance mapping of a large area. Full article

Wildfires generate substantial smoke containing fine particulate matter (PM_2.5) that adversely impacts health. This study develops machine learning models integrating pre-wildfire factors like weather and fuel conditions with post-wildfire health impacts to provide a holistic understanding of smoke exposure risks. Various data-driven models including Support Vector Regression, Multi-layer Perceptron, and three tree-based ensemble algorithms (Random Forest, Extreme Gradient Boosting (XGBoost), and Natural Gradient Boosting (NGBoost)) are evaluated in this study. Ensemble models effectively predict PM_2.5 levels based on temperature, humidity, wind, and fuel moisture, revealing the significant roles of radiation, temperature, and moisture. Further modelling links smoke exposure to deaths from chronic obstructive pulmonary disease (COPD) and lung cancer using age, sex, and pollution type as inputs. Ambient pollution is the primary driver of COPD mortality, while age has a greater influence on lung cancer deaths. This research advances atmospheric and health impact understanding, aiding forest fire prevention and management. Full article

There is a consensus that fire should be actively managed in tropical savannas to decrease wildfire risks, firefighting costs, and social conflicts as well as to promote ecosystem conservation. Selection and participatory monitoring of the effects of fire on cultural keystone species may be an efficient way to involve local stakeholders and inform management decisions. In this study, we investigated the effects of different fire regimes on a cultural keystone species in Central Brazil. With the support of diverse multiethnic groups of local fire brigades, we sampled Hancornia speciosa (Apocynaceae) populations across a vast regional range of 18 traditional territories (Indigenous Lands and Quilombola Territories) as well as four restricted Protected Areas. We considered areas under wildfires (WF), prescribed burns (PB) and fire exclusion (FE) and quantified tree mortality, canopy damage, loss of reproductive structures and fruit production following a simplified field protocol. Areas with H. speciosa populations were identified and classified according to their fire history, and in each sampled area, adult plants were evaluated. We hypothesized that WF would have larger negative impact on the population parameters measured, while FE would increase plant survival and fruit production. We found that tree mortality, canopy damage, and loss of reproductive structures were higher in areas affected by wildfires, which also had the lowest fruit production per plant compared to PB and FE areas, corroborating our hypotheses. However, we also found higher mortality in FE areas compared to PB ones, probably due to plant diseases in areas with longer FE. Considering these results and that the attempts to exclude fire from fire-prone ecosystems commonly lead to periodic wildfires, we argue that the Integrated Fire Management program in course in federal Protected Areas in Brazil—based on early dry season prescribed fires—is a good management option for this, and likely other, cultural keystone species in the Brazilian savanna. Full article

Over recent decades, the Arctic has begun facing a range of climate-related challenges, from rising temperatures to melting ice caps and permafrost thaw, with significant implications for ecosystems and human well-being. Addressing the health impacts of these issues requires a comprehensive approach, integrating scientific research, community engagement, and policy interventions. This study conducts a literature review to assess the effects of climate change on human health in northern latitudes and to compile adaptation strategies from the Arctic countries. A literature search was performed between January and April 2024 for papers published after 2000, using the electronic databases Web of Science, Pubmed, Science Direct, Scopus, Google Scholar, and eLibrary.RU, with specific questions formulated to direct the search: (i) What are the climate changes? (ii) How does climate change affect human health? (iii) What adaptation measures and policies are required? The key phrases “climate change”, “human health”, “adaptation practices”, and “Arctic” were employed for searching. Ultimately, 56 relevant studies were identified, reviewing health risks such as infectious diseases, mental health issues, and diseases connected with extreme weather events; wildfires and their associated pollution; permafrost degradation; pure water; and food quality. The paper also examines mitigation and adaptation strategies at all levels of governance, emphasizing the need for international cooperation and policy action to combat negative health outcomes, investments in healthcare infrastructure, emergency preparedness, and public health education. Incorporating diverse perspectives, including Indigenous knowledge, Community-Based Adaptation, EcoHealth and One Health approaches, is crucial for effectively addressing the health risks associated with climate change. In conclusion, the paper proposes adaptation strategies to mitigate the health impacts of climate change in the Arctic. Full article

The Brazilian Amazon, a vital tropical region, faces escalating threats from human activities, agriculture, and climate change. This study aims to assess the relationship between forest fire occurrences, meteorological factors, and hospitalizations due to respiratory diseases in the Legal Amazon region from 2009 to 2019. Employing simultaneous equation models with official data, we examined the association between deforestation-induced fires and respiratory health issues. Over the studied period, the Legal Amazon region recorded a staggering 1,438,322 wildfires, with 1,218,606 (85%) occurring during August–December, known as the forest fire season. During the forest fire season, a substantial portion (566,707) of the total 1,532,228 hospital admissions for respiratory diseases were recorded in individuals aged 0–14 years and 60 years and above. A model consisting of two sets of simultaneous equations was constructed. This model illustrates the seasonal fluctuations in meteorological conditions driving human activities associated with increased forest fires. It also represents how air quality variations impact the occurrence of respiratory diseases during forest fires. This modeling approach unveiled that drier conditions, elevated temperatures, and reduced precipitation exacerbate fire incidents, impacting hospital admissions for respiratory diseases at a rate as high as 22 hospital admissions per 1000 forest fire events during the forest fire season in the Legal Amazon, 2009–2019. This research highlights the urgent need for environmental and health policies to mitigate the effects of Amazon rainforest wildfires, stressing the interplay of deforestation, climate change, and human-induced fires on respiratory health. Full article

Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R² value of 0.572 and RMSE of 9.98 µg/m³. The corresponding R² and RMSE values for ‘held-out data’ were 0.487 and 10.53 µg/m³. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure. Full article

Wildfires are a global natural phenomenon. In North America, wildfires have not only become more frequent, but also more severe and longer in duration, a trend ascribed to climate change combined with large fuel stores left from modern fire suppression. The intensification of wildfire activity has significant implications for planetary health and public health, as exposure to fine particulate matter (PM_2.5) in wildfire smoke is linked to adverse health effects. This review focuses on respiratory morbidity from wildfire smoke exposure. Inhalation of wildfire PM_2.5 causes lung injury via oxidative stress, local and systemic inflammation, airway epithelium compromise, and increased vulnerability to infection. Wildfire PM_2.5 exposure results in exacerbations of pre-existing asthma and chronic obstructive pulmonary disease, with an escalation in healthcare utilization, including emergency department visits and hospitalizations. Wildfire smoke exposure may be associated with asthma onset, long-term impairment of lung function, and increased all-cause mortality. Children, older adults, occupationally-exposed groups, and possibly women are the most at risk from wildfire smoke. Future research is needed to clarify best practices for risk mitigation and wildfire management. Full article

Coast redwoods (Sequoia sempervirens) are long-lived trees that create deep shade and litter layers, and have limited recruitment from seedlings. Botrytis cinerea is an airborne fungal pathogen that attacks redwood seedlings. B. cinerea lives as a saprotroph in dead plant matter or as a necrotroph in live tissue. In the coast redwood forest, accumulated leaf litter may provide inoculum for subsequent infections, limiting redwood seedling recruitment. Here, we examine the response of B. cinerea to fire in the coast redwood forest. We measured the abundance of airborne B. cinerea spores in paired burned and unburned plots using a selective and diagnostic medium. In a greenhouse experiment, we grew seedlings in four different treatments: (1) burned soil with no leaf litter, (2) unburned soil with no leaf litter, (3) burned soil with leaf litter collected from the burn plot, (4) unburned soil with leaf litter collected from the unburned plot. Spore trapping showed no difference in the abundance of airborne spores in the paired plots. The seedling experiment showed that disease was greatest and survival lowest when grown in burned soil; leaf litter collected from burned plots reduced survival while leaf litter from not-burned plots increased survival. These results indicate that fire did not affect airborne B. cinerea and post-fire conditions did not provide favorable growth conditions for coast redwood seedlings. Full article

Wildfires emit large quantities of air pollutants into the atmosphere. As wildfires increase in frequency, intensity, duration, and coverage area, the emissions from these fires have become a significant control issue and health hazard for residential populations, especially vulnerable groups. A critical barrier to addressing the health impacts of air pollution caused by wildfires lies in our limited understanding of its true extent. This problem is expected to be exacerbated by additional factors such as the anticipated increase in wildfire intensity due to climate change, and the associated rise in fine particulate matter (PM_2.5) in wildfire smoke, which, according to recent toxicological studies, could be more harmful than typical ambient PM_2.5. The primary goal of our study is to develop a novel statistical framework that enables the forecasting of future emissions from active wildfires. This research aims to address the unquantified impacts of wildfire emissions and is a priority research area for many US federal agencies, e.g., NIEHS, US EPA, and NOAA. The framework integrates physicochemical models of emissions and satellite observations with forecasting models based on spatial statistics and machine learning models. Through the incorporation of these diverse datasets, we aim to improve the accuracy and reliability of our predictions regarding the spatio-temporal distribution of wildfire emissions. The potential human health impacts resulting from poor air quality during wildfires are also explored. By modeling the relationship between environmental exposures and disease risk, the burden of disease attributed to both short- and long-term impacts of exposure to wildfire events will be assessed. Full article

Restoring current ponderosa pine (Pinus ponderosa Dougl. Ex P. and C. Laws)-dominated forests (also known as “dry forests”) to spatially resilient stand structures requires an adequate understanding of the overstory spatial variation of forests least impacted by Euro-American settlers (also known as “reference conditions”) and how much contemporary forests (2016) deviate from reference conditions. Because of increased tree density, dry forests are more spatially homogeneous in contemporary conditions compared to reference conditions, forests minimally impacted by Euro-American settlers. Little information is available that can be used by managers to accurately depict the spatial variation of reference conditions and the differences between reference and contemporary conditions. Especially, forest managers need this information as they are continuously designing management treatments to promote contemporary dry forest resiliency against fire, disease, and insects. To fill this knowledge gap, our study utilized field inventory data from reference conditions (1934) along with light detection and ranging and ground-truthing data from contemporary conditions (2016) associated with various research units of Blacks Mountain Experimental Forest, California, USA. Our results showed that in reference conditions, above-ground biomass—a component of overstory stand structure—was more spatially heterogeneous compared to contemporary forests. Based on semivariogram analyses, the 1934 conditions exhibited spatial variation at a spatial scale < 50 m and showed spatial autocorrelation at shorter ranges (150–200 m) compared to those observed in contemporary conditions (>250 m). In contemporary conditions, prescribed burn with high structural diversity treatment enhanced spatial heterogeneity as indicated by a greater number of peaks in the correlograms compared to the low structural diversity treatment. High structural diversity treatment units exhibited small patches of above-ground biomass at shorter ranges (~120 to 440 m) compared to the low structural diversity treatment units (~165 to 599 m). Understanding how spatial variation in contemporary conditions deviates from reference conditions and identifying specific management treatments that can be used to restore spatial variation observed in reference conditions will help managers to promote spatial variation in stand structure that has been resilient to wildfire, insects, and disease. Full article