Soil organic carbon (SOC) in the active layer (0-2 m) of the Tibetan Plateau (TP) permafrost region is sensitive to climate change, with significant implications for the global carbon cycle. Environmental factors-including parent material, climate, vegetation, topography, soil, and human activities-inevitably drive SOC variations. However, vegetation and climate are likely the two most influential factors impacting SOC variations. To test this hypothesis, we conducted experiments using 31 environmental variables combined with the recursive feature elimination (RFE) algorithm. These experiments showed that RFE retained all vegetation variables [Land cover types (LCT), normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)] as well as two climate variables [Moisture index (MI) and drought index (DI)], supporting our hypothesis. We then analyzed the relationship between SOC and the retained vegetation and climate variables using random forest (RF), Shapley additive explanations (SHAP), and GeoDetector models to quantify the independent and interactive drivers of SOC distribution and to identify the optimal conditions for SOC accumulation. The RF model explained 68% and 42% of the spatial variability in SOC at depths of 0-1 m and 1-2 m, respectively, with SOC stocks higher in the southeast and lower in the northwest. Additionally, SOC stock at 0-1 m was significantly higher (p 0.05). Spearman correlation coefficients results indicated that NDVI, LAI, GPP, and MI had highly significant positive correlations with SOC (p < 0.01), whereas DI had a highly significant negative correlation with SOC (p < 0.01). SHAP analysis revealed environmental thresholds for SOC variations, with notable shifts at NDVI (0.40), LAI (7), GPP (250 g C m(-)(2) year(-)(1)), MI (0.40), and DI (0.50). The spatial distribution of these thresholds aligns with the 400 mm equivalent precipitation line. Additionally, GeoDetector results emphasized that interactions between climate and vegetation factors enhance the explanatory power of individual variables on SOC variations. The swamp meadow type, with an NDVI range of 0.73-0.84, LAI range of 11.06-15.94, and MI range of 0.46-0.56, was identified as the most favorable environment for SOC accumulation. These findings are essential for balancing vegetation and climate conditions to sustain SOC levels and mitigate climate change-driven carbon release.

Predicting slope movement has become a great challenge, especially in the Himalayan region, as such natural hazards cause great damage. Machine Learning (ML) models can help in the prediction of landslide hazards. Despite the capabilities of ML models in predicting landslide hazards, most existing approaches are deficient in capturing changes in weather conditions at day, hour, or minute scales, thus affecting their accuracy in real-time scenarios. These models also generally have difficulties in generalizing predictions due to limited data availability, and they cannot frequently provide multi-step ahead predictions that are crucial for effective disaster preparedness and timely response. We introduced the hierarchical architecture ML model, specifically the hierarchical transformer prediction autoencoder (H-TPA), which is capable of predicting slope movement with high temporal resolution and enhanced generalization capabilities. This study was based on a rich dataset from sixty-four landslide locations over five years. In this work, we utilize 1,066,009 samples for the training set, which were balanced down to 23,328 samples in order to address class imbalance. The validation set contained 100,000 samples, while the test set was made up of 164,082 samples. This work also presents a VSA methodology for determining threshold values of environmental attributes that trigger slope movements. The performance evaluation of the H-TPA model using this dataset demonstrates very good performance with an F1 score of 0.889, 0.760, and 0.746 for the training, validation, and test datasets, respectively, in predicting slope movements 10 min in advance. Moreover, the present study focused on the analyses of weather condition factors and soil moisture affecting the landslide triggers, which indicated the role of temperature, humidity, barometric pressure, rainfall, and sunlight intensity in small or large slope movements according to certain threshold values. This study generally contributes to the present understanding and enhances the knowledge of landslide prediction in the Himalayan region, besides providing recommendations for geo-scientific knowledge improvement and mitigation strategies.

Soil microorganisms play a pivotal role in the biogeochemical cycles of alpine meadow ecosystems, especially in the context of permafrost thaw. However, the mechanisms driving microbial community responses to environmental changes, such as variations in active layer thickness (ALT) of permafrost, remain poorly understood. This study utilized next-generation sequencing to explore the composition and co-occur rence patterns of soil microbial communities, focusing on bacteria and micro-eukaryotes along a permafrost thaw gradient. The results showed a decline in bacterial alpha diversity with increasing permafrost thaw, whereas micro-eukaryotic diversity exhibi ted an opposite trend. Although changes in microbial community composition were observed in permafrost and seasonally frozen soils, these shifts were not statistically significant. Bacterial communities exhibited a greater differentiation between frozen and seasonally frozen soils, a pattern not mirrored in eukaryotic communities. Linear discriminant analysis effect size analysis revealed a higher number of potential biomark ers in bacterial communities compared with micro-eukaryotes. Bacterial co-occurrence networks were more complex, with more nodes, edges, and positive linkages than those of micro-eukaryotes. Key factors such as soil texture, ALT, and bulk density significantly influenced bacterial community structures, particularly affecting the relative abundan ces of the Acidobacteria, Proteobacteria, and Actinobacteria phyla. In contrast, fungal communities (e.g., Nucletmycea, Rhizaria, Chloroplastida, and Discosea groups) were more affected by electrical conductivity, vegetation coverage, and ALT. This study highlights the distinct responses of soil bacteria and micro-eukaryotes to permafrost thaw, offering insights into microbial community stability under global climate change.

The fall armyworm (FAW; Spodoptera frugiperda) has been a persistent threat to global food security due to its strong migratory ability and wide range of host plants. However, most current studies on the suitability distribution of FAW focus on extracting suitable areas in specific regions on an annual basis. Consequently, research on the suitability distribution of FAW at a larger scale and with higher temporal resolution is urgently needed to provide data support for early prevention and control. This study differentiated the historical occurrence records of FAW into annual distribution points and seasonal distribution points. By integrating multi-factor environmental data, including climate, soil, topography, and vegetation, we used MaxEnt to establish annual and monthly models. The annual model extracted the annual suitability distribution of FAW worldwide. Among the nine selected environmental factors, temperature seasonality had the greatest impact on the suitability distribution of FAW, with a single-factor contribution rate of 39.87%. The monthly models analyzed the inter-monthly variations in the global suitability distribution of FAW from January to December. The results indicated that FAW's suitability was highest in July and lowest in March. Under the dominant influence of dynamic environmental factors such as temperature, precipitation, and vegetation index, the expansion and contraction of FAW's suitability distribution corresponded with seasonal changes, exhibiting significant seasonal fluctuations. Our results can provide FAW control personnel with more practical references for formulating preventive strategies in advance, helping to prevent the potentially incalculable damage FAW could cause to crops in invaded areas.

The negative effects of PM2.5 concentration in urban development are becoming more and more prominent. Bernaola-Galvan Segmentation Algorithm (BGSA) and wavelet analysis are powerful tools for processing non-linear and non-stationary signals. First, we use BGSA that reveals there are 41 mutation points in the PM2.5 concentration in Guiyang. Then, we reveal the multi-scale evolution of PM2.5 concentration in Guiyang by wavelet analysis. In the first part, we performed one-dimensional continuous wavelet transform (CWT) on the eight monitoring points in the study area, and the results showed that they have obviously similar multi-scale evolution characteristics, with a high-energy and significant oscillation period of 190-512 days. Next, the wavelet transform coherence (WTC) reveals the mutual relationship between the PM2.5 concentration and the atmospheric pollutants and meteorological factors. PM2.5 concentration variation is closely linked to that of PM10 concentration. But, it is not to be ignored that the increase in the SO2 and NO2 concentrations will cause the PM2.5 concentration to rise on different scales. Lastly, the variation of the PM2.5 concentration can be better explained by the combination of multiple factors (2-4) using the multiple-wavelet coherence (MWC). Under the combination of the two factors, the average temperature (Avgtem) and relative humidity (ReH) have the highest AWC and PASC. In the case of the combination of four factors, CO-Avgtem-Wind-ReH plays the largest role in determining PM2.5 concentration.

Soil freeze-thaw cycles play a critical role in ecosystem, hydrological and biogeochemical processes, and climate. The Tibetan Plateau (TP) has the largest area of frozen soil that undergoes freeze-thaw cycles in the low-mid latitudes. Evidence suggests ongoing changes in seasonal freeze-thaw cycles during the past several decades on the TP. However, the status of diurnal freeze-thaw cycles (DFTC) of shallow soil and their response to climate change largely remain unknown. In this study, using in-situ observations, the latest reanalysis, machine learning, and physics-based modeling, we conducted a comprehensive assessment of the spatiotemporal variations of DFTC and their response to climate change in the upper Brahmaputra (UB) basin. About 24 +/- 8% of the basin is subjected to DFTC with a mean frequency of 87 +/- 55 days during 1980-2018. The area and frequency of DFTC show small long-term changes during 1980-2018. Air temperature impacts on the frequency of DFTC changes center mainly around the freezing point (0 degrees C). The spatial variations in the response of DFTC to air temperature can primarily be explained by three factors: precipitation (30.4%), snow depth (22.6%) and seasonal warming/cooling rates (14.9%). Both rainfall and snow events reduce diurnal fluctuations of soil temperature, subsequently reducing DFTC frequency, primarily by decreasing daytime temperature through evaporation-cooling and albedo-cooling effects, respectively. These results provide an in-depth understanding of diurnal soil freeze-thaw status and its response to climate change. Freeze-thaw transitions of terrestrial landscapes are a common phenomenon in cold regions. The seasonal and diurnal freeze-thaw cycles (DFTC) of shallow soil exhibit substantial differences in response to climate. Understanding of the spatiotemporal patterns of DFTC and their response to climate change remains limited over the Tibetan Plateau (TP), which is characterized by the largest areas of freeze-thaw terrain in the mid- and low-latitudes of the world. We found the frequency and area of DFTC show a slight increase trend in a significantly warming climate in upper Brahmaputra (UB) basin, the largest river basin of the TP. The variation of DFTC depends on climatic conditions, with soils near the freezing point (0 degrees C) being more susceptible to changes in DFTC. Precipitation, snow depth and seasonal warming/cooling rates are the top three factors influencing the response of DFTC to air temperature changes. Snowfall plays a more important role in the temporal variability of DFTC frequency than rainfall. The number of diurnal freeze-thaw cycles (DFTC) in shallow soil increase slightly during the period 1980-2018 in the upper Brahmaputra (UB) basin Air temperature effects on the changes in DFTC frequency center on the freezing point Snowfall plays a more important role in the temporal variability of DFTC than rainfall

Microbially induced carbonate precipitation (MICP) represents a technique for biocementation, altering the hydraulic and mechanical properties of porous materials using bacterial and cementation solutions. The efficacy of MICP depends on various biochemical and environmental elements, requiring careful consideration to achieve optimal designs for specific purposes. This study evaluates the efficiency of different MICP protocols under varying environmental conditions, employing two bacterial strains: S. pasteurii and S. aquimarina, to optimize soil strength enhancement. In addition, microscale properties of carbonate crystals were investigated and their effects on soil strength enhancement were analyzed. Results demonstrate that among the factors investigated, bacterial strain and concentration of cementation solution significantly influence the biochemical aspect, while temperature predominantly affects the environmental aspect. During the MICP treatment process, the efficiency of chemical conversion through S. pasteurii varied between approximately 80% and 40%, while for S. aquimarina, it was only around 20%. Consequently, the CaCO3 content resulting from MICP treatment using S. pasteurii was significantly higher, ranging between 5% and 7%, compared to that achieved with S. aquimarina, which was about 0.5% to 1.5%. The concentration of the cementation solution also plays a pivotal role, with an optimized value of 0.5 M being critical for achieving maximum efficiency and CaCO3 content. The ideal temperature span for MICP operation falls between 20 degrees C and 35 degrees C, with salinity and oxygen levels exerting minor impact. Furthermore, although salinity influences the characteristics of formed carbonate crystals, its effect on unconfined compressive strength (UCS) values of MICP-treated soil remains marginal. Samples subjected to a one-phase treatment, adjusted to pH values between 6.0 and 7.5, exhibit roughly half the UCS strength compared to the two-phase treatment. These findings hold promising potential for MICP applications in both terrestrial and marine environments for strength enhancement.

Cardiovascular disease is a significant cause of morbidity and mortality among non-communicable diseases worldwide. Evidence shows that a healthy dietary pattern positively influences many risk factors of cardiometabolic health, stroke, and heart disease, supported by the effectiveness of healthy diet and lifestyles for the prevention of CVD. High quality and safety of foods are prerequisites to ensuring food security and beneficial effects. Contaminants can be present in foods mainly because of contamination from environmental sources (water, air, or soil pollution), or artificially introduced by the human. Moreover, the cross-contamination or formation during food processing, food packaging, presence or contamination by natural toxins, or use of unapproved food additives and adulterants. Numerous studies reported the association between food contaminants and cardiovascular risk by demonstrating that (1) the cross-contamination or artificial sweeteners, additives, and adulterants in food processing can be the cause of the risk for major adverse cardiovascular events and (2) environmental factors, such as heavy metals and chemical products can be also significant contributors to food contamination with a negative impact on cardiovascular systems. Furthermore, oxidative stress can be a common mechanism that mediates food contamination-associated CVDs as substantiated by studies showing impaired oxidative stress biomarkers after exposure to food contaminants.This narrative review summarizes the data suggesting how food contaminants may elicit artery injury and proposing oxidative stress as a mediator of cardiovascular damage.

With the growing emphasis on environmental protection, there has been extensive research on new green cementitious materials suitable for different conditions. This study focuses on the influence of key environmental factors on proposed biomimetic mineralized composites (BMC) using polyacrylic acid (PAA) as a modifier, based on the biomimetic chemistry induced carbonate precipitation (BCICP) method. The environmental factors examined include the cementing matrices type, the magnesium ions concentration, and the cementing solution pH. The samples treated under various conditions were evaluated for their unconfined compressive strength (UCS) and calcium carbonate content, and their microstructure was characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results demonstrate that the BMC treatment could achieve highstrength cementation for both Toyoura sand and Fujian quartz sand, with a more pronounced enhancement observed for Toyoura sand. Magnesium ions reduce the cementation effect, and they cannot fully replace calcium ions in participating in the cementation process. Moreover, the cementing solution pH significantly influences the cementation effect, with the best acid-base condition for PAA to modify BMC sand samples found at a pH of 11.5. Overall, the BMC exhibits high performance under various environmental conditions. These findings could provide valuable insights into the influence of environmental factors and the potential applications of BMC in diverse environments.

The frequent outbreaks of European spruce bark beetle Ips typographus (L.) have destroyed huge amounts of Norway spruce Picea abies (L.) forests in central and Northern Europe. Identifying the risk factors and estimating the damage level is important for strategic damage control. The risk factors of forest damage by spruce bark beetles have mostly been analyzed on the landscape scale, while the in-stand risk factors have been less investigated. This study aims at exploring the local-scale risk factors in a flat area with spruce-dominated forest in southern Sweden. The investigated factors include four abiotic factors, i.e., soil wetness, solar radiation, slope gradient, and aspect, and three biotic factors, i.e., the number of deciduous trees and trees that died from attacks in previous years that remained (TreesLeft) and removed (TreesRemoved) from the forest stand. We put up 24 pheromone bags in six stands attacked by bark beetle in the previous years, resulting in different numbers of infested trees in each plot. We explored in which microenvironment a pheromone bag resulted in more colonization, the impact radius of each factor, and the necessary factors for a risk model. The environmental factors were obtained from remote sensing-based products and images. A generalized linear model (GLM) was used with the environmental factors as the explanatory variables and the damage levels as the response variables, i.e., the number of attacked trees for the plot scale, and healthy/infested for the single-tree scale. Using 50 m and 15 m radius of the environmental factors resulted in the best fit for the model at plot and individual tree scales, respectively. At those radii, the damage risk increased both at plot and individual tree level when spruce were surrounded by more deciduous trees, surrounded by dead trees that had been removed from the forest, and spruces located at the north and east slopes (315 degrees-135 degrees of aspect, > 2 degrees slope). Soil wetness, solar radiation, and remaining standing dead trees in the surrounding did not significantly impact the damage level in the microenvironment of the study area. The GLM risk model yielded an overall accuracy of 0.69 in predicting individual trees being infested or healthy. Our efforts to investigate the risk factors provide a context for wall-to-wall mapping in-stand infestation risks, using remote sensing-based data.