Search Results (90)

Gram-negative ESKAPE pathogens pose major challenges to global public health due to their multidrug resistance and virulence. The present study aimed to study the prevalence and resistance of Gram-negative ESKAPE pathogens at a tertiary care hospital in Eastern India. A retrospective analysis was conducted on 7343 non-duplicate isolates collected between January 2023 and December 2024. The bacterial isolates and their antibiotic susceptibility testing were identified using Kirby–Bauer disk diffusion techniques and the VITEK 2 Compact system, adhering to CLSI 2025 and EUCAST 2024 guidelines. Our findings indicate that Klebsiella pneumoniae was the most common isolate, followed by Pseudomonas aeruginosa, Acinetobacter baumannii complex, and Enterobacter cloacae complex, predominantly affecting male patients aged 18–64 years. Importantly, most of these isolates exhibit increased multidrug resistance (MDR) to several key antibiotics, including β-lactams and carbapenems, which further complicates the treatment process. The analysis of seasonal dynamics revealed an increased abundance of infections in monsoon and post-monsoon periods. These findings will be useful in understanding AMR in hospital environments and in developing strategies to prevent the occurrence and spread of antimicrobial resistance among pathogens. Full article

Monsoon evergreen broad-leaved forests (MEBFs) represent one of the most species-rich and structurally complex vegetation types, and one of the most widely distributed forests in Yunnan Province, Southwest China. However, they have yet to undergo a comprehensive analysis on their community diversity, spatial differentiation patterns, and underlying drivers across Yunnan. Based on extensive field surveys during 2021–2024 with 548 MEBF plots, this study employed the Unweighted Pair Group Method for forest community classification and Non-metric Multidimensional Scaling for ordination and interpretation of community–environment association. A total of 3517 vascular plant species were recorded in the plots, including 1137 tree species, 1161 shrubs, and 1219 herbs. Numerical classification divided the plots into 3 alliance groups and 24 alliances: (1) Castanopsis–Schima (Lithocarpus) Forest Alliance Group (16 alliances), predominantly distributed west of 102°E in central-south and southwest Yunnan; (2) Castanopsis–Machilus (Beilschmiedia) Forest Alliance Group (6 alliances), concentrated east of 101°E in southeast Yunnan with limited latitudinal range; (3) Castanopsis–Camellia Forest Alliance Group (2 alliances), restricted to higher-elevation mountainous areas within 103–104° E and 22.5–23° N. Climatic variation accounted for 81.1% of the species compositional variation among alliance groups, with contributions of 83.5%, 57.6%, and 62.1% to alliance-level differentiation within alliance groups 1, 2, and 3, respectively. Precipitation days in the driest quarter (PDDQ) and precipitation seasonality (PS) emerged as the strongest predictors of community differentiation at both alliance group and alliance levels. Topography and soil features significantly influenced alliance differentiation in Groups 2 and 3. Collectively, the interaction between the monsoon climate and topography dominate the spatial differentiation of MEBF communities in Yunnan. Full article

Soybean (Glycine max L.) is vulnerable to environmental stresses, such as heavy rainfall and high winds, which promote lodging and reduce plant performance during the monsoon season. To mitigate these issues, we evaluated the effects of plant topping, a practice involving the removal of apical buds, on plant architecture, physiological traits, and grain yield in four soybean cultivars over two growing seasons (2021–2022). Plant topping was performed at the V6-7 stage by cutting 30–35 cm above the ground. Plant topping reduced plant height by up to 23.5% and decreased leaf area index (by 8.0–16.4%), potentially improving light penetration into the lower canopy. Although chlorophyll concentration declined temporarily (297.8 vs. 272.8 mg m⁻² for non-topping vs. topping, respectively), NDVI remained stable, indicating delayed senescence. Chlorophyll fluorescence parameters revealed cultivar-specific stress responses, particularly in Taegwang, which showed elevated ABS/RC, TR₀/RC, and DI₀/CS values under plant topping. Grain yield was generally unaffected, except in Jinpung, which increased by 34% under plant topping in 2021 (2701 kg ha⁻¹ vs. 3621 kg ha⁻¹ for non-topping vs. topping). In conclusion, plant topping may help improve canopy structure and light distribution without compromising yield, potentially reducing lodging risk and offering a cultivar-specific management strategy. Full article

Ammonium nitrate (NH₄NO₃) is a major constituent of fine particulate matter (PM_2.5), playing a critical role in air quality and atmospheric chemistry. However, the dual regulatory role of ammonia (NH₃) in both the formation and volatilization of NH₄NO₃ under ambient atmospheric conditions remains inadequately understood. To address this gap, we conducted high-resolution field measurements at a clean tropical coastal site in China using an integrated system of Aerosol Ion Monitor-Ion Chromatography, a Scanning Mobility Particle Sizer, and online OC/EC analyzers. These observations were complemented by thermodynamic modeling (E-AIM) and source apportionment via a Positive Matrix Factorization (PMF) model. The E-AIM simulations revealed persistent thermodynamic disequilibrium, with particulate NO₃⁻ tending to volatilize even under NH_3gas-rich conditions during the northeast monsoon. This suggests that NH₄NO₃ in PM_2.5 forms rapidly within fresh combustion plumes and/or those modified by non-precipitation clouds and then undergoes substantial evaporation as it disperses through the atmosphere. Under the southeast monsoon conditions, reactions constrained by sea salt aerosols became dominant, promoting the formation of particulate NO₃⁻ while suppressing NH₄NO₃ formation despite ongoing plume influence. In scenarios of regional accumulation, elevated NH₃ concentrations suppressed NH₄NO₃ volatilization, thereby enhancing the stability of particulate NO₃⁻ in PM_2.5. PMF analysis identified five source factors, with NO₃⁻ in PM_2.5 primarily associated with emissions from local power plants and the large-scale regional background, showing marked seasonal variability. These findings highlight the complex and dynamic interplay between the formation and evaporation of NH₄NO₃ in NH_3gas-rich coastal atmospheres. Full article

Precipitation cyclicity plays a crucial role in regional water supply and climate predictions. In this study, we used observational data from 34 representative meteorological stations in the Xinjiang region, a major part of inland arid China, to characterize the interannual cyclicity of regional precipitation from 1951 to 2021 and analyze its contributing factors. The results indicated that the mean annual precipitation in Xinjiang (MAP_XJ) was dominated by a remarkably increasing trend over the past 70 years, which was superimposed by two bands of interannual cycles of approximately 3 years with explanatory variance of 56.57% (Band I) and 6–7 years with explanatory variance of 23.38% (Band II). This is generally consistent with previous studies on the cyclicity of precipitation in Xinjiang for both seasonal and annual precipitation. We analyzed the North Tropical Atlantic sea-surface temperature (NTA_SST), El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Indian Summer Monsoon (ISM) as potential forcing factors that show similar interannual cycles and may contribute to the identified precipitation variability. Two approaches, multivariate linear regression and the Random Forest model, were employed to ascertain the relative significance of each factor influencing Bands I and II, respectively. The multivariate linear regression analysis revealed that the AO index contributed the most to Band I, with a significance score of −0.656, whereas the ENSO index with a one-year lead (ENSO_−1yr) played a dominant role in Band II (significance score = 0.457). The Random Forest model also suggested that the AO index exhibited the highest significance score (0.859) for Band I, whereas the AO index with a one-year lead (AO_−1yr) had the highest significance score (0.876) for Band II. Overall, our findings highlight the necessity of employing different methods that consider both the linear and non-linear response of climate variability to driving factors crucial for future climate prediction. Full article

The medium river basins (MRBs) in Nepal originate from mid-hills. These medium-range rivers are typically non-snow-fed, relying on rain and other water sources. These rivers are typically small, and the sizes of medium river basins vary between 500 and 5000 km². These MRBs are often used for irrigation and other agricultural purposes. In this analysis, we first set up, calibrated, and validated three hydrological models (i.e., HBV, HEC HMS, and SWAT) at the Kankai River Basin (one MRB in eastern Nepal). Then, the best-performing SWAT hydrological model was forced with cutting-edge climate models (CMs) using thirteen CMIP6 models under four shared socioeconomic pathways (SSPs). We employed ten bias correction (BC) methods to capture local spatial variability in precipitation and temperature. Finally, the likely streamflow alteration during two future periods, i.e., the near-term timeframe (NF), spanning from 2031 to 2060, and the long-term timeframe (FF), covering the years 2071 to 2100, were evaluated against the historical period (baseline: 1986–2014), considering the uncertainties associated with the choice of CMs, BC methods, or/and SSPs. The study results confirm that there will not be any noticeable shifts in seasonal variations in the future. However, the magnitude is projected to alter substantially. Overall, the streamflow is estimated to upsurge during upcoming periods. We observed that less deviation is expected in April, i.e., around +5 to +7% more than the baseline period. Notably, a higher percentage increment is projected during the monsoon season (June–August). During the NF (FF) period, the flow alteration will be around +20% (+40%) under lower SSPs, whereas the flow alteration will be around +30% (+60%) under higher SSPs during high flow season. Thus, the likelihoods of flooding, inundation, and higher discharge are projected to be quite high in the coming years. Full article

In March (spring), June (summer), October (autumn), and December (winter) 2022, zooplankton were quantitatively investigated in the lower reaches of the Yellow River in China. A total of 29 sampling points that were separated by about 20 km were set up in the survey area. The purpose of this study is to investigate the seasonal dynamics and spatial distribution characteristics of the zooplankton community in the Yellow River, which has a high sediment content. The main results are as follows: A total of 119 species of zooplankton were found during the survey, including 70 species of rotifers, 29 species of cladocerans, and 20 species of copepods. Because the temperate continental monsoon climate has four distinct seasons, the zooplankton community in the Yellow River showed typical seasonal dynamics. There were significant differences in the richness of zooplankton and dominant species across the four seasons (p < 0.05). There were 15 common species in each of the four seasons. The density and biomass of zooplankton were significantly higher in spring (16.76 ind./L; 0.049 mg/L) and summer (26.17 ind./L; 0.249 mg/L) compared to autumn (5.65 ind./L; 0.042 mg/L) and winter (1.56 ind./L; 0.006 mg/L) (p < 0.05). Additionally, the density and biomass of zooplankton were significantly lower in estuarine areas compared to other areas. The results of multidimensional non-metric ranking (NMDS) based on zooplankton abundance showed four distinct communities: a spring community, a summer community, an autumn community, and a winter community. The spatial heterogeneity of zooplankton communities in spring, summer, and autumn was significantly different (p < 0.05). However, only the estuarine area had a special zooplankton community in winter. Monte Carlo test results showed that pH, water temperature, electrical conductivity, dissolved oxygen, total nitrogen, and total phosphorus were the main environmental factors affecting the community structure of zooplankton (p < 0.05). The areas of the Yellow River affected by human disturbances have lower zooplankton community stability. Overall, the standing stock of zooplankton was very low (less than 15 ind./L), but the species richness was higher (119 species) in the river, which had a high sediment content and a fast flow. Full article

The increasing frequency of extreme weather events such as typhoons and heavy rains, driven by climate change, has intensified debris flow risks during Korea’s monsoon season, causing considerable human and economic losses. In South Korea, where mountainous terrain covers 64% of the country, localized downpours exacerbate the risk of debris flows, endangering communities and critical infrastructure. To enhance resilience and ensure sustainable risk management, the Korea Expressway Corporation developed a quantitative debris flow risk assessment system based on sensitivity and vulnerability indicators. An early warning system utilizing rainfall thresholds was subsequently introduced. However, discrepancies between rainfall data from local AWS stations and actual site conditions compromised its predictive accuracy. This study addresses those limitations by integrating the Parameter-elevation Regressions on Independent Slopes Model (PRISM) into the early warning system to enhance prediction accuracy at debris flow occurrence and non-occurrence points. Comparative analysis revealed that the PRISM-enhanced system significantly improved predictive performance. Furthermore, cumulative rainfall data from five highway sites validated the system’s reliability in short-term prediction while offering a sustainable, data-driven framework for long-term debris flow risk management. This approach strengthens adaptive infrastructure strategies, promoting more resilient transportation networks and improving public safety while minimizing environmental impacts. Full article

Flood disasters are common events in Malaysia, particularly during the monsoon seasons. Hence, disaster management in Malaysia is based on the framework following “Directive 20” by the National Security Council (MKN). This study gathered qualitative information in Shah Alam Municipality through informal interviews with 20 informants following the quadruple-helix multi-stakeholders model in 2023 for flood disaster management (FDM). Thematic analysis of the qualitative information was conducted following the four main priority of action themes of the Sendai Framework for United Nations Disaster Risk Reduction (2015–2030) using the Taguette software. This study found coordination and inter-agency data sharing are two major issues in Shah Alam that require immediate attention for FDM. Thus, this study suggests improving district-level flood management guidelines, especially the involvement of the National Disaster Management Agency (NADMA). The NADMA should have a close look at the flood management plan, which acts as Malaysia’s main disaster management coordinator, as they are usually the first agency on the scene when a disaster occurs. Hence, to prevent and lessen flood disaster impact, disaster risk preparedness and individual management through customized training are crucial in combining non-structural and structural measures for FDM. Full article

One of the manifests of air-sea interactions is the change in sea level due to meteorological forcing through wind stress and atmospheric pressure. When meteorological conditions conducive to water level increase coincide with high tides during spring tides, the sea level may rise higher than expected and pose a flood risk to coastal low-lying areas. In Hong Kong, specifically when the northeast monsoon coincides with the higher spring tides in late autumn and winter, and sometimes even compounded by the storm surge brought by late-season tropical cyclones (TCs), the result may be coastal flooding or sea inundation. Aiming at forecasting such sea level anomalies on the scale of hours and days with local tide gauges using a flexible and computationally efficient method, this study adapts a data-driven method based on empirical orthogonal functions (EOF) regression of non-uniformly lagged regional wind field from ECMWF Reanalysis v5 (ERA5) to capture the effects from synoptic weather evolution patterns, excluding the effect of TCs. Local atmospheric pressure and winds are also included in the predictors of the regression model. Verification results show good performance in general. Hindcast using ECMWF forecasts as input reveals that the reduction of mean absolute error (MAE) by adding the anomaly forecast to the existing predicted astronomical tide was as high as 30% in February on average over the whole range of water levels, as well as that compared against the Delft3D forecast in a strong northeast monsoon case. The EOF method generally outperformed the persistence method in forecasting water level anomaly for a lead time of more than 6 h. The performance was even better particularly for high water levels, making it suitable to serve as a forecast reference tool for providing high water level alerts to relevant emergency response agencies to tackle the risk of coastal inundation in non-TC situations and an estimate of the anomaly contribution from the northeast monsoon under its combined effect with TC. The model is capable of improving water level forecasts up to a week ahead, despite the general decreasing model performance with increasing lead time due to less accurate input from model forecasts at a longer range. Some cases show that the model successfully predicted both positive and negative anomalies with a magnitude similar to observations up to 5 to 7 days in advance. Full article

The upwelling phenomenon plays a vital role within marine ecosystems, transporting essential nutrients from the bottom to the surface and boosting biological productivity. However, the bacterial community structure in upwelling zones along the western coast of India (WCI) is understudied. This research systematically examines bacterial diversity across three seasons—pre-monsoon (PR), monsoon (MN), and post-monsoon (PM)—using next-generation sequencing. Our findings show distinct spatial patterns of bacterial communities in the Arabian Sea and demonstrate that ecological variations influence bacterial distribution in this dynamic environment. During MN, the bacterial community exhibited greater species diversity but lower overall abundance compared to PR and PM. Non-Metric MDS cluster analysis revealed a 78% similarity (at order level) between PR and PM, indicating that MN supports unique bacterial diversity. KEGG analysis showed significant seasonal variations in metabolic functions, with increased functional potential during MN. Additionally, Carbohydrate-Active enZymes (CAZymes) analysis revealed distinct seasonal profiles, among which the GH13 enzymes were the most prevalent glycoside hydrolases during MN, predominantly being sucrose phosphorylase and glucosidase, known for breaking down glucan deposits derived from phytoplankton. The CAZymes profiles supported taxonomic and KEGG pathway findings, reinforcing that microbial communities are seasonally distinct and functionally adapted to changing availability of nutrients. Full article

The Ramganga River basin, comprising three rivers, the Dhela, Dhandi, and Ramganga, plays a vital role in groundwater recharge, sustaining numerous industries, urban areas, and rural communities reliant on these rivers for daily activities. The study’s primary purpose was to analyze the groundwater quality in the context of potability, irrigation, and health risks to the local inhabitants of the Ramganga River basin. In 2021–2022, 52 samples (26 × 2) were collected from 13 locations in two different seasons, i.e., pre-monsoon and post-monsoon, and 20 physico-chemical and heavy metal and metalloids were analyzed using the standard protocols. The result shows that heavy metal and metalloids and metalloid concentrations of Zn (0.309–1.787 and 0.613–1.633); Fe (0.290–0.965 and 0.253–1.720), Cd (0.001–0.002 and 0.001–0.002); As (0.001–0.002 and 0.001–0.002), Cr (0.009–0.027 and 0.011–0.029), and Pb (−0.001–0.010 and 0.00–0.010) values in mg/L are present in both seasons. The groundwater quality index (GWQI), heavy metal pollution Index (HPI), and heavy metal evaluation index (HEI) were used to assess the water quality and metal pollution in the basin area. As per GWQI values, water quality lies from excellent water quality (41.639 and 43.091) to good water quality (56.326 and 53.902); as per HPI values, it shows good (29.51 and 30.03) to poor quality (60.26 and 59.75) and HEI values show the low-level contamination (1.03–2.57 and 1.13–3.37) of heavy metal and metalloids in both seasons. According to the potential health risk assessment, infants show low risk in pre-monsoon and low risk to medium post-monsoon, while children and adults show low risk to high risk in both seasons. From the health risk perspective, it shows that children and adults have more concerns about non-carcinogenic effects, so adequate remedial measures and treatment are required to avoid the groundwater quality of the Ramganga River basin. Full article

The present study, carried out in Gujarat (India) between 2005 and 2021, aims to prepare dew and rain maps of Gujarat over a long period (17 years, from 2005 to 2021) in order to evaluate the evolution of the potential for dew and rain in the state. The ratio of dew to precipitation is also determined, which is an important metric that quantifies the contribution of dew to the overall water resources. Global warming leads, in general, to a reduction in precipitation and non-rainfall water contributions such as dew. The study shows, however, a rare increase in the rainfall and dew condensation, with the latter related to an increase in relative humidity and a decrease in wind amplitudes. Rain primarily occurs during the monsoon months, while dew forms during the dry season. Although dew alone cannot resolve water scarcity, it nonetheless may provide an exigent and unignorable contribution to the water balance in time to come. According to the site, the dew–rain ratios, which are also, in general, well correlated with dew yields, can represent between 4.6% (Ahmedabad) and 37.2% (Jamnagar). The positive trend, observed since 2015–2017, is expected to continue into the future. Full article

Although the effects of invasive alien plants on natural ecosystems are well known, the effects of specific plant species can vary across habitat types and disturbance intensity. This study was carried out to analyze the effects of Lantana camara on associated vegetation and soil physico-chemical properties at invaded and non-invaded sites across three different habitat types (forest edge, fallow land, and roadside) in central Nepal. We sampled 50 pairs of 5 m × 5 m (for shrub species) and 1 m × 1 m (for herbs species) plots at invaded and non-invaded sites in each habitat and recorded community variables for each species within the sampling plots for both wet (monsoon) and dry (pre-monsoon) seasons. Further, we collected soil samples from each quadrat and determined the soil physico-chemical properties. We recorded 137 species of flowering plants (119 from non-invaded and 97 from invaded plots) and classified them in accordance with life form/habit. In invaded sites, we found a significant decline in species diversity as indicated by the Simpson and Shannon diversity indices. Specifically, L. camara reduced the species richness, Simpson index, and Shannon diversity index by 36.84%, 11.84%, and 40.21%, respectively. Soil nutrients such as total nitrogen, soil organic carbon, and available phosphorus were significantly higher in invaded sites than non-invaded ones except for available potassium and soil pH. This study provided evidence that Lantana L. camara has a substantial impact on the understory plant community assemblage and the physico-chemical properties of soil. The results suggest that the protection of native plant community requires management of L. camara by implementing appropriate measures. Full article

We developed a rainfall threshold model with the objective of limiting the effects of uncertainties typically associated with them, such as a lack of robust landslide database, the selection of the contributing rain gauge, seasonal variations in rainfall patterns, and the effect of extreme rainfall conditions. With the aid of gauge-corrected satellite precipitation data and a landslide database compiled from various sources, separate rainfall thresholds were developed for two waves of the monsoon season in the Western Ghats, India. The daily vs. antecedent rainfall distributions for different scenarios of antecedent rainfall were analyzed for landslide occurrence. The different scenarios considered included 1, 2, 3, 5, 10-, 20-, 30- and 40-day antecedent rainfalls along with the monsoon antecedent defined as the cumulative rainfall from the start of the monsoon to the day prior to landslide occurrence, and the event antecedent defined as the cumulative rainfall from the start of a rainfall event to the day prior to landslide occurrence. A statistically defined critical value was used to define the thresholds for extreme rainfall conditions, while ordinary least squares and quantile regression models were compared to identify the best-fit model for the non-extreme rainfall threshold. Receiver Operating Characteristic (ROC) analysis was performed on all these models and the best model was chosen based on the efficiency values. The daily vs. monsoon antecedent threshold was the best model for the first monsoon wave, and the daily vs. event antecedent model was the best model for the second monsoon wave. A separate rainfall threshold was defined for the entire monsoon without subdivision into separate waves, and corresponding ROC statistics were compared with the former approach to analyze the efficacy of intra-seasonal variations in rainfall threshold development. The results suggest that cumulative rainfall makes a significant contribution towards landslide initiation and that intra-seasonal variations should be necessarily considered in rainfall threshold modeling. Full article