Search Results (148)

Amino acids (AAs), a type of nitrogen-based organic compounds in the atmosphere, are directly and indirectly related to climate change, and as their link to allergic diseases becomes more known, the need for quantitative analysis of ultrafine dust (PM_2.5) will become increasingly necessary. When sensing water-soluble AAs using a gas chromatograph combined with a tandem mass spectrometer (GC-MS/MS), derivatization should be considered to increase the volatility and sensitivity of target analytes. In this study, two methods were used to compare and evaluate 13 AA derivatives in PM_2.5 samples: N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide with 1% tert-butyldimethylchlorosilane (MTBSTFA w/1% t-BDMCS), which is preferred for silylation, and ethyl chloroformate (ECF) with methanol (MeOH) for chloroformate derivatization. The most appropriate reaction conditions for these two derivative methods, such as temperature and time, and the analytical conditions of GC-MS/MS for the qualitative and quantitative analysis of AAs were optimized. Furthermore, the calibration curve, detection limit, and recovery of both methods for validating the quantification were determined. The two derivative methods were applied to 23 actual PM_2.5 samples to detect and quantify target AAs. The statistical significances between pairwise measurements of individual AAs detected by both methods were evaluated. This study will help in selecting and utilizing appropriate derivative methods for the quantification of individual AAs in PM_2.5 samples. Full article

The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of data on climate-sensitive diseases and related hospital visits in these areas. This study explored the prevalence of such diseases using the Delphi method through focus group discussions with 493 healthcare professionals from 153 hospitals in 156 upazilas across 21 districts and ten zones. Participants were selected by district Civil Surgeons. Key climate-sensitive diseases identified included malnutrition, diarrhea, pneumonia, respiratory infections, typhoid, skin diseases, hypertension, cholera, mental health disorders, hepatitis, heat stroke, and dengue. Seasonal surges in hospital visits were noted, influenced by factors like extreme heat, air pollution, floods, water contamination, poor sanitation, salinity, and disease vectors. Some diseases were zone-specific, while others were widespread. Regions with fewer hospital visits often had higher disease burdens, indicating under-reporting or lack of access. The findings highlight the need for area-specific adaptation strategies and updates to the Health National Adaptation Plan. Strengthening resilience through targeted investment and preventive measures is crucial to reducing health risks from climate change. Full article

Cadmium is widely recognized as an important environmental toxicant that may give rise to kidney dysfunction, bone disease, and cancer in humans and animals. Kidney dysfunction occurs at very low exposures and is often considered as the most sensitive or critical effect. Cadmium exposures of concern occur in many countries. In low- and middle-income countries with small-scale mining, excessive exposure to cadmium and other metals occurs in occupational and environmental settings. This is of particular importance in view of the growing demand for metals in global climate change mitigation. Since the 1970s, the present authors have contributed evidence concerning the role of metallothionein and other factors in influencing the toxicokinetics and toxicity of cadmium, particularly as it relates to the development of adverse effects on kidneys in humans and animals. The findings gave a background to the development of biomarkers employed in epidemiological studies, demonstrating the important role of metallothionein in protection against cadmium-induced kidney dysfunction in humans. Studies in cadmium-exposed population groups demonstrated how biomarkers of kidney dysfunction changed during 8 years after drastic lowering of environmental cadmium exposure. Other epidemiological studies showed the impact of a good zinc status in lowering the prevalence of cadmium-related kidney dysfunction. Increased susceptibility to Cd-induced kidney dysfunction was shown in a population with high exposure to inorganic arsenic when compared with a group with low such exposure. Several national and international organizations have used part of the reviewed information, but the metallothionein-related biomarkers and the interaction effects have not been fully considered. We hope that these data sets will also be included and improve risk assessments and preventive measures. Full article

Aquaculture is a crucial food-producing sector that can supply more essential nutrients to nourish the growing human population. However, it faces challenges, including limited water quality and space competition. These constraints have led to the intensification of culture systems for more efficient resource use while maintaining or increasing production levels. However, intensification introduces stress risks to cultured organisms by, for instance, overcrowding, waste accumulation, and water quality deterioration, which can negatively affect the growth, health, and immunity of animals and cause diseases. Additionally, environmental changes due to climate and anthropogenic activities further intensify the environmental stress for aquaculture organisms, including crustaceans. Shrimp are one of the most widely cultured and consumed farmed crustacea. Relative to aquatic vertebrates such as fish, the physiology of crustaceans has simpler physiological structures, as they lack a spinal cord. Consequently, their stress response mechanisms follow a single pathway, resulting in less complex responses to stress exposure compared to those of fish. While stress is considered a primary factor influencing the growth, health, and immunity of shrimp, comprehensive research on crustacean stress responses remains limited. Understanding the stress response at the organismal and cellular levels is essential to identify sensitive and effective stress biomarkers which can inform the development of targeted intervention strategies to mitigate stress. This review provides a comprehensive overview of the physiological changes that occur in crustaceans under stress, including hormonal, metabolic, hematological, hydromineral, and phenotypic alterations. By synthesizing current knowledge, this article aims to bridge existing gaps and provide insights into the stress response mechanisms, paving the way for advancements in crustacean health management. Full article

In Kenya, Rift Valley fever (RVF) outbreaks pose significant challenges, being one of the most severe climate-sensitive zoonoses. While machine learning (ML) techniques have shown superior performance in time series forecasting, their application in predicting disease outbreaks in Africa remains underexplored. Leveraging data from the International Livestock Research Institute (ILRI) in Kenya, this study pioneers the use of ML techniques to forecast RVF outbreaks by analyzing climate data spanning from 1981 to 2010, including ML models. Through a comprehensive analysis of ML model performance and the influence of environmental factors on RVF outbreaks, this study provides valuable insights into the intricate dynamics of disease transmission. The XGB Classifier emerged as the top-performing model, exhibiting remarkable accuracy in identifying RVF outbreak cases, with an accuracy score of 0.997310. Additionally, positive correlations were observed between various environmental variables, including rainfall, humidity, clay patterns, and RVF cases, underscoring the critical role of climatic conditions in disease spread. These findings have significant implications for public health strategies, particularly in RVF-endemic regions, where targeted surveillance and control measures are imperative. However, this study also acknowledges the limitations in model accuracy, especially in scenarios involving concurrent infections with multiple diseases, highlighting the need for ongoing research and development to address these challenges. Overall, this study contributes valuable insights to the field of disease prediction and management, paving the way for innovative solutions and improved public health outcomes in RVF-endemic areas and beyond. Full article

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus Steiner & Buhrer (pine wood nematodes, PWN), impacts forest carbon sequestration and climate change. However, satellite-based PWD monitoring is challenging due to the limited spatial resolution of Sentinel’s MSI sensor, which reduces its sensitivity to subtle biochemical alterations in foliage. We have, therefore, developed a slope product index (SPI) for effective detection of PWD using single-date satellite imagery based on spectral gradients in the visible and near-infrared (VNIR) range. The SPI was compared against 15 widely used vegetation indices and demonstrated superior robustness across diverse test sites. Results show that the SPI is more sensitive to changes in chlorophyll content in the PWD detection, even under potentially confounding conditions such as drought. When integrated into Random Forest (RF) and Back-Propagation Neural Network (BPNN) models, SPI significantly improved classification accuracy, with the multivariate RF model achieving the highest performance and univariate with SPI in BPNN. The generalizability of SPI was validated across test sites in distinct climate zones, including Zhejiang (accuracyZ_Mean = 88.14%) and Shandong (accuracyS_Mean = 78.45%) provinces in China, as well as Portugal. Notably, SPI derived from Sentinel-2 imagery in October enables more accurate and timely PWD detection while reducing field investigation complexity and cost. Full article

Allergic diseases, particularly respiratory allergies like asthma and allergic rhinitis, are a growing public health concern influenced by environmental factors such as climate change and air pollution. The exposome framework enables a comprehensive assessment of how lifelong environmental exposures shape immune responses and allergic sensitization. Peru’s diverse ecosystems and climates provide a unique setting to investigate regional variations in allergic sensitization. This study characterized these patterns in five Peruvian regions with distinct climatic, urbanization, and socioeconomic characteristics. A total of 268 individuals from Lima, Piura, Tarapoto, Arequipa, and Tacna were analysed for allergen-specific IgE responses using a multiplex IgE detection system. The results revealed significant geographical differences in sensitization frequencies and serodominance profiles, based on descriptive statistics and supported by Chi-square comparative analysis. House dust mites were predominant in humid regions, while Arequipa exhibited higher sensitization to cat allergens. In Tacna, olive pollen showed notable prevalence alongside house dust mites. Tarapoto’s high humidity correlated with increased fungal and cockroach allergen sensitization. Notably, some allergens traditionally considered minor, such as Der p 5 and Der p 21, reached sensitization prevalences close to or exceeding 50% in certain regions. These findings provide the most detailed molecular characterization of allergic sensitization in Peru to date, highlighting the importance of region-specific allergy management strategies. Understanding environmental influences on allergic diseases can support more effective diagnostic, therapeutic, and preventive approaches tailored to diverse geographical contexts. Full article

Agriculture in India supports millions of livelihoods and is a major force behind economic expansion. Challenges in modern agriculture depend on environmental factors (such as soil quality and climate variability) and biotic factors (such as pests and diseases). These challenges can be addressed by advancements in technology (such as sensors, internet of things, communication, etc.) and data-driven approaches (such as machine learning (ML) and deep learning (DL)), which can help with crop yield and sustainability in agriculture. This study introduces an innovative deep neural network (DNN) approach for identifying leaf diseases in paddy crops at an early stage. The proposed neural network is a hybrid DL model comprising feature extraction, channel attention, inception with residual, and classification blocks. Channel attention and inception with residual help extract comprehensive information about the crops and potential diseases. The classification module uses softmax to obtain the score for different classes. The importance of each block is analyzed via an ablation study. To understand the feature extraction ability of the modules, extracted features at different stages are fed to the SVM classifier to obtain the classification accuracy. This technique was experimented on eight classes with 7857 paddy crop images, which were obtained from local paddy fields and freely available open sources. The classification performance of the proposed technique is evaluated according to accuracy, sensitivity, specificity, F1 score, MCC, area under curve (AUC), and receiver operating characteristic (ROC). The model was fine-tuned by setting the hyperparameters (such as batch size, learning rate, optimizer, epoch, and train and test ratio). Training, validation, and testing accuracies of 99.91%, 99.87%, and 99.49%, respectively, were obtained for 20 epochs with a learning rate of 0.001 and sgdm optimizer. The proposed network robustness was studied via an ablation study and with noisy data. The model’s classification performance was evaluated for other agricultural data (such as mango, maize, and wheat diseases). These research outcomes can empower farmers with smarter agricultural practices and contribute to economic growth. Full article

Cryptosporidiosis is a gastrointestinal disease affecting terrestrial and aquatic vertebrates worldwide. This study investigated molecularly and microscopically the prevalence and the diversity of Cryptosporidium spp. in goats across the Bouira communes, Algeria. A total of 559 fecal samples were collected from 70 farms, representing 16.6% of the regional goat population. Samples were analyzed using microscopy (modified Ziehl-Neelsen staining) and molecular methods (i.e., qPCR and nested PCR targeting the 18S rRNA gene, followed by sequencing). Microscopy detected Cryptosporidium in 6.1% of samples, while qPCR revealed a significantly higher prevalence of 13.6% (p < 0.00001), confirming the superior sensitivity of molecular diagnostics. Spatial analysis identified significant clustering (Moran’s I = 0.330, p = 0.0003), with communes-level prevalence ranging from 6.7% to 45.7%. Infection rates correlated positively with humidity and rainfall but negatively with temperature. Phylogenetic analysis confirmed Cryptosporidium xiaoi as the sole species circulating, showing 100% genetic similarity to global caprine isolates. Despite C. xiaoi’s host adaptation, a GenBank review highlighted six other zoonotic species infecting goats worldwide, underscoring potential cross-species transmission risks. The study emphasizes the need for PCR-based surveillance to assess true prevalence and zoonotic threats, while climatic findings support targeted interventions in high-risk areas. Full article

Climate change poses a significant threat to Ethiopian agriculture, impacting both cereal and livestock production through rising temperatures, erratic rainfall, prolonged droughts, and increased pest and disease outbreaks. These challenges intensify food insecurity, particularly for smallholder farmers and pastoralists who rely on climate-sensitive agricultural systems. This systematic review aims to synthesize the impacts of climate change on Ethiopian agriculture, with a specific focus on cereal production and livestock feed quality, while exploring effective adaptation strategies that can support resilience in the sector. The review synthesizes 50 peer-reviewed publications (2020–2024) from the Climate Change Effects on Food Security project, which supports young African academics and Higher Education Institutions (HEIs) in addressing Sustainable Development Goals (SDGs). Using PRISMA guidelines, the review assesses climate change impacts on major cereal crops and livestock feed in Ethiopia and explores adaptation strategies. Over the past 30 years, Ethiopia has experienced rising temperatures (0.3–0.66 °C), with future projections indicating increases of 0.6–0.8 °C per decade resulting in more frequent and severe droughts, floods, and landslides. These shifts have led to declining yields of wheat, maize, and barley, shrinking arable land, and deteriorating feed quality and water availability, severely affecting livestock health and productivity. The study identifies key on-the-ground adaptation strategies, including adjusted planting dates, crop diversification, drought-tolerant varieties, soil and water conservation, agroforestry, supplemental irrigation, and integrated fertilizer use. Livestock adaptations include improved breeding practices, fodder enhancement using legumes and local browse species, and seasonal climate forecasting. These results have significant practical implications: they offer a robust evidence base for policymakers, extension agents, and development practitioners to design and implement targeted, context-specific adaptation strategies. Moreover, the findings support the integration of climate resilience into national agricultural policies and food security planning. The Climate Change Effects on Food Security project’s role in generating scientific knowledge and fostering interdisciplinary collaboration is vital for building institutional and human capacity to confront climate challenges. Ultimately, this review contributes actionable insights for promoting sustainable, climate-resilient agriculture across Ethiopia. Full article

This study aimed to identify the pathogen of oil palm (Elaeis guineensis) leaf spot disease in Hainan Province, China and examine the effects of environmental factors and fungicide sensitivity on the pathogen. The research confirmed that the pathogen responsible for this novel leaf spot disease was Colletotrichum siamense, marking the first report of this pathogen on oil palm in China. Field observations revealed summer-onset disease symptoms with concomitant leaf damage. The pathogen demonstrated optimal growth at a temperature of 30 °C and pH of 7.0, indicating its adaptability to prevailing climatic conditions in the region. Laboratory tests assessed the effects of various environmental factors on mycelial growth, revealing a marked decline in growth at temperatures below 20 °C and above 35 °C, as well as at acidic pH levels. Fungicide sensitivity assays identified pyraclostrobin, tebuconazole, prochloraz, and carbendazim as the most effective compounds, significantly inhibiting the growth of C. siamense with low EC₅₀ values. These findings provide essential information for developing effective disease management strategies to combat leaf spot disease in oil palm plantations. Full article

Climate conditions in combination with the concentration of pollutants increase the human health stress and exacerbate systemic diseases. The city of Rhodes is a desirable tourist destination that is located in a sensitive climate region of the southeastern Aegean Sea in the Mediterranean region. In this work, hourly recordings from a mobile air quality monitoring system, which is located in an urban area of Rhodes city, are employed in order to measure the concentration of regulated pollutants (${\mathrm{S}\mathrm{O}}_2,{\mathrm{N}\mathrm{O}}_2,{\mathrm{O}}_3,{\mathrm{P}\mathrm{M}}_{10} \mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{P}\mathrm{M}}_{2.5})$ and meteorological factors (pressure, temperature, and relative humidity). The air quality health index (AQHI) and the discomfort index (DI) are calculated to study the impact of air quality and meteorological conditions on human health. The analysis is conducted during a hot summer period, from 29 June to 14 July 2024. During the second half of the studied period, a heatwave episode occurred that affected the bioclimatic conditions over the city. The results show that despite the fact that the concentration of pollutants is lower than the pollutant thresholds (according to Directive 2008/50/EC), the AQHI and DI conditions degrade significantly over the heatwave days. In particular, the AQHI is classified in the “Moderate” class, and the DI indicates that most of the population suffers discomfort. The AQHI and DI simultaneously increase during the days of the heat episode, showing a possible negative synergy for the health risk. Finally, both the day maximum and night minimum temperature are increased (about 0.8 and 0.6 °C, respectively) during the heatwave days as compared to the whole studied period. Full article

As climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these models requires data from diverse sources, including the scientific literature. Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. By framing the selection criteria of articles as a question–answer task and utilising zero-shot chain-of-thought prompting, the proposed method achieves a saving of at least 70% work effort compared to manual screening at a recall level of 95% (NWSS@95%). This was validated across four datasets containing four distinct zoonotic diseases and a critical climate variable (rainfall). The approach additionally produces explainable AI rationales for each ranked article. The effectiveness of the approach across multiple diseases demonstrates the potential for broad application in systematic literature reviews. The substantial reduction in screening effort, along with the provision of explainable AI rationales, marks an important step toward automated parameter extraction from the scientific literature. Full article

Background: Dengue, transmitted by Aedes spp. mosquitoes, poses significant public health challenges in Burkina Faso. This study investigated outbreak thresholds, utilizing historical data since 2018 to explore the climatic impacts on dengue transmission and address knowledge gaps. Methodology: This retrospective ecological study utilized historical and contemporary data from Burkina Faso’s Public Health Ministry (2018–2024) to model dengue outbreak thresholds. A combination of epidemic channel analysis, joinpoint regression, climate–disease relationship analysis, and negative binomial regression was employed to provide comprehensive insights into the factors driving dengue outbreaks. Principal Findings: The incidence of probable dengue cases remained stable, mostly below 5 cases per 100,000 people, except for a sharp surge in week 40 of 2023, peaking at 38 cases per 100,000. This surge was brief, normalizing by week 47, but coincided with a marked increase in mortality, reaching 90 deaths in week 45. Joinpoint regression identified key thresholds, an alert at 2.1 cases per 100,000 by week 41 and an intervention threshold at 19.1 cases by week 44, providing a framework for timely public health responses. Climatic factors significantly influenced dengue transmission, with higher temperatures (RR = 2.764) linked to increased incidence, while higher precipitation (RR = 0.551) was associated with lower case numbers, likely due to disrupted mosquito breeding conditions. Additionally, intermediate precipitation levels showed a complex relationship with higher incidence rates. Conclusions: This study established evidence-based epidemiological thresholds for dengue outbreak detection in Burkina Faso (2018–2024), demonstrating temperature as a primary transmission driver while precipitation showed inverse relationships. Analysis of the 2023 outbreak identified a critical five-week intervention window (weeks 40–45), providing a framework for climate-sensitive early warning systems. These findings advance the understanding of dengue dynamics in West Africa, though future research should integrate geographical and socioeconomic variables to enhance predictive modeling and outbreak preparedness. Full article

Chinese forests, particularly the coniferous forest ecosystems represented by pines, play a crucial role in the global carbon cycle, significantly contributing to mitigating climate change, regulating regional climates, and maintaining ecological balance. However, pine wilt disease (PWD), caused by the pine wood nematode (PWN), has become a major threat to forest carbon stocks in China. This study evaluates the impact of PWN invasion on forest carbon stocks in China using multi-source data and an optimized MaxEnt model, and the study analyzes this invasion’s spread trends and potential risk areas. The results show that the high-suitability area for PWN has expanded from 68,000 km² in 2002 to 184,000 km² in 2021, with the spread of PWN accelerating, especially under warm and humid climate conditions and due to human activities. China’s forest carbon stocks increased from 111.34 billion tons of carbon (tC) to 168.05 billion tC, but the carbon risk due to PWN invasion also increased from 87 million tC to 99 million tC, highlighting the ongoing threat to the carbon storage capacity. The study further reveals significant differences in tree species’ sensitivity to PWN, with highly sensitive species such as Masson’s pine and black pine mainly concentrated in the southeastern coastal regions, while less sensitive species such as white pine and larch show stronger resistance in the northern and southwestern areas. This finding highlights the vulnerability of high-sensitivity tree species to PWN, especially in high-risk areas such as Guangdong, Guangxi, and Guizhou, where urgent and effective control measures are needed to reduce carbon stock losses. To address this challenge, the study recommends strengthening monitoring in high-risk areas and proposes specific measures to improve forest management and policy interventions, including promoting cross-regional joint control, enhancing early warning systems, and utilizing biological control measures, while encouraging local governments and communities to actively participate. By strengthening collaboration and implementing control measures, the health and sustainable development of forest ecosystems can be ensured, safeguarding the forests’ important role in climate regulation and carbon sequestration and contributing to global climate change mitigation. Full article