Mann-Kendall Test Research Articles

Spatiotemporal evolution of PM2.5 and its components and drivers in China, 2000-2023: effects of air pollution prevention and control actions in China.

This study evaluated the Air Pollution Prevention and Control Action Plan (APPCAP) in China using 2000-2023 data. The average annual PM2.5 concentration dropped from 46.11 ± 16.18µg/m3 to 31.75 ± 14.22µg/m3 (P < 0.05) after APPCAP, with components showing a similar decline. Temporal analysis via Mann-Kendall test indicated a decreasing trend (Z < 0, P < 0.05), seasonally peaking in winter and lowest in summer. Spatially, APPCAP reduced concentration distribution, with key regions improving but areas like Shandong and Henan still facing severe pollution. The main PM2.5 driver shifted from human (e.g., population density) to meteorological (e.g., temperature) factors post-APPCAP, and anthropogenic influence varied across regions. In summary, APPCAP has curbed PM2.5 pollution, yet SO42-, NO3-, and NH4+ remain relatively high, and the increasing human impact in central and southeastern China demands attention in future policies.

Historical snow measurements in the central and southern Apennine Mountains: climatology, variability, and trend

Abstract. This work presents an analysis of historical snow precipitation data collected in the period 1951–2001 in central and southern Apennines (Italy), an area scarcely investigated so far. To pursue this aim, we used the monthly observations of the snow cover duration, number of days with snowfall and total height of new snow collected at 129 stations located between 288 and 1750 m above sea level. Such data have been manually digitised from the Hydrological Yearbooks of the Italian National Hydrological and Mareographic Service. The available dataset has been primarily analysed to build a reference climatology (related to the 1971–2000 period) for the considered Apennine region. More specifically, using a methodology based on principal component analysis and k-means clustering, we have identified different modes of spatial variability, mainly depending on the elevation, which reflect different climatic zones. Subsequently, focusing on the number of days with snowfall and snow cover duration on the ground, we have carried out a linear trend analysis, employing the Theil–Sen estimator and the Mann–Kendall test. An overall negative tendency has been found for both variables. For clusters including only stations above 1000 m above the sea level, a significant (at 90 % or 95 % confidence levels) decreasing trend has been found in the winter season (i.e. from December to February), with −3.2 [−6.0 to 0.0] d per 10 years for snow cover duration and −1.6 [−2.5 to −0.6] d per 10 years for number of days with snowfall. Moreover, in all considered seasons, a clear and direct relationship between the trend magnitude and elevation has emerged. In addition, using a cross-wavelet analysis, we found a close in-phase linkage on a decadal timescale between the investigated snow indicators and the Eastern Mediterranean teleconnection Pattern. For both snow cover duration and number of days with snowfall, such connection appears to be more relevant in the full (i.e. from November to April) and in the late (i.e. from February to April) seasons.

Spatiotemporal Dynamics and Driving Mechanism of Aboveground Biomass Across Three Alpine Grasslands in Central Asia over the Past 20 Years Using Three Algorithms

Aboveground biomass (AGB) is a sensitive indicator of grassland resource quality and ecological degradation. However, accurately estimating AGB at large scales to reveal long-term trends remains challenging. Here, single-factor parametric models, multi-factor parametric models, and multi-factor non-parametric models (Random Forest) were developed for three grassland types (alpine meadow, alpine grassland, and swampy meadow) in the Bayanbuluk Grassland using MODIS satellite data and environmental factors, including climate and topography. A 10-fold cross-validation method was employed to assess the accuracy and stability of these models, and an AGB remote sensing inversion model was established to estimate the AGB of the Bayanbuluk Grassland from 2005 to 2024. Moreover, the BEAST mutation test, Theil–Sen median trend analysis, and Mann–Kendall test were used to analyse the temporal trends of AGB, identify the years of mutation points, and explore the changes in AGB across the entire study period (2005–2024) and at 5-year intervals, considering the influence of climatic factors. The results indicated that the machine learning (RF) model outperformed both multi-factor parametric and single-factor parametric models, with specific improvements in R2 and RMSE across all grassland types. For instance, the RF model achieved an R2 of 0.802 in alpine grasslands, outperforming the multi-factor parametric model with an R2 of 0.531. The overall spatial distribution of AGB exhibited heterogeneity, with a gradual increase from northwest to southeast over the study period. Interannual AGB changes fluctuated significantly, with an overall increasing trend. Notably, from 2015 to 2019, 78% of the Bayanbuluk Grassland area showed a nonsignificant increase in AGB. Specifically, 46.7% of the alpine meadow AGB, 23% of the alpine grassland AGB, and 8.3% of the swampy meadow AGB showed non-significant increases. Further, temperature was found to be the dominant driver of AGB, with a stronger effect on alpine meadows and alpine grasslands than on swampy meadows. This is likely due to the relatively constant moisture levels in the swampy meadows, where precipitation plays a more prominent role. This study provides a comprehensive assessment of AGB trends, including both spatial and temporal analyses, which will inform future grassland resource management.

Impact of monthly air pollution and weather conditions on cardiorespiratory mortality in Portuguese Metropolitan Areas

This study analyses cardiorespiratory mortality rates (CARDIO) and their association with air pollutants - particulate matter with aerodynamic diameters lower of equal to 10 or 2.5 (µm) (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3) - and meteorological variables (temperature, humidity, wind speed, direction) in the Lisbon (LMA) and Porto (PMA) metropolitan areas from 2011 to 2020. Monthly analyses reveal regional patterns and seasonal variations. The results show that PMA had a higher average CARDIO rate (202.94 [Deaths per 100 000]) compared to LMA (169.70 [Deaths per 100 000]). Linear and Poisson regression, contingency tables, correspondence analysis and Pearson’s chi-squared tests confirmed significant associations between low temperature and wind speeds, high pollutant concentrations, and increased mortality. Lower temperature (≤ 13 [°C]) and wind speed (≤ 2.5 [m/s]) were consistently associated with increased CARDIO in both regions. High pollutant levels, particularly PM10 (≥ 24 [µg/m³]) and NO2 (≥ 24 [µg/m³]), were also associated with higher CARDIO rates. Additionally, high PM2.5 and CO levels were linked to increased CARDIO in LMA. The seasonal Mann-Kendall test showed no significant trend in CARDIO for LMA, but a statically significant increasing trend of 2.14 [Deaths per 100 000]) per month for PMA. This study shows the importance of mid-term exposure standards and emphasises the need for multifactorial assessments of air quality and meteorological impacts on health, as regional differences in pollutant dynamics and meteorological conditions may significantly impact cardiorespiratory mortality in urban areas.

Assessment of the Rainfed Wheat Productivity in a Changing Climate in Irbid, Jordan Using Statistical Downscaling and Random Forest Regression Prediction Under RCP4.5 & 8.5 Pathways.

Jordan faces risks associated with climate change and endeavors to sustain resources through the SDGs. This study examines the impacts of climate change on rainfed wheat productivity in Irbid Governorate during the historical period (1994-2021) and simulated for the future (2024-2100) using climate models (SDGs 1, 2, 11, 12, 13, & 15). Monthly precipitation, mean temperature, and annual wheat yield data were collected for the period (1994-2021) and analyzed using the Mann-Kendall test with Sen’s slope to investigate the historical trends and elaborate Phi-k correlation coefficient to determine their association. Monthly precipitation and mean temperature projections were collected from CSIRO-MK3.6.0 & GFDL-ESM2M CMIP5 ensemble models under two concentration pathways: RCP4.5 and RCP8.5. The projections were biased and corrected by the dynamic bilinear interpolation method and Delta, EQM, and QM statistical downscaling to enhance the projections' reliability and performance in capturing the region's climate. Taylor diagram was utilized to choose the best model to represent observed data. Cumulative Distribution Function and Probability Density Function Curves were plotted to describe the changes over decades based on climate models. Future wheat yields were predicted using the Random Forest Regression model. The results revealed a non-significant increase in total annual precipitation of 1.8% and 13.8% and a rise in annual mean temperature of 5.4% and 2.7% for Irbid and Baqura stations, respectively during the baseline timeframe on which wheat yield increased by 21.3%. The CSIRO model outperformed the GFDL model with greater fidelity in simulating historical monthly precipitation and mean temperatures. The CSIRO-MK3.6.0 model indicates precipitation and temperature shifts for near and far future periods. Temperature increases are expected to have more severe impacts in the far future (2073-2100) while projected decreases in precipitation of 0.06 mm/day and 0.10 mm/day under RCP4.5 and RCP8.5 affect yield reductions by 11.42 kg/ha and 12.21 kg/ha. Temperature increases of 2.7°C and 4.1°C under RCP 4.5 & 8.5 correspond to yield decreases of 24.825 kg/ha and 33.395 kg/ha, respectively. The study highlights the need for enhanced adaptation strategies; cultivating resilient wheat varieties, promoting crop rotation, improving meteorological monitoring and risk response approaches, and providing timely weather forecasts to mitigate the adverse effects of climate change on wheat productivity and ensure sustainable agriculture in Jordan.

Impacts of 2D/3D building morphology on vegetation greening trends in Hong Kong: An urban-rural contrast perspective

Building morphology profoundly impacts the microclimate, potentially affecting vegetation greening. However, the effects of 2D/3D building morphology on vegetation greening, especially the urban-rural disparities, remains understudied. In this study, we examined the effect of building morphology on vegetation greening in urban and rural areas in Hong Kong by employing a machine learning model. Vegetation greening trends were derived using the Enhanced Vegetation Index (EVI) through the Theil-Sen median method and the Mann-Kendall (MK) test. Results indicated a prevalent greening from 2010 to 2020, with a slope of 0.0024, and more significant in rural. Statistically significant but low correlation existed between building morphology and vegetation greening. Their relationship exhibited notable urban-rural differences and non-monotonic nonlinearity, with 3D indexes showing a stronger impact than 2D indexes. Specifically, sky view factor (SVF) dominated in urban areas, contributing 23.60%, while landscape shape index (LSI) was the key contributor in rural, accounting for 27.30%. SVF, and mean building height (MBH) transitioned from negative to positive effects, whereas landscape patch index (LPI) and edge density (ED) shifted from positive to negative effects, each with distinct "turning points" in urban and rural. LSI’s impact showed a negative-positive-negative shift in urban and a negative-positive shift in rural. Building volume density (BVD) presented a positive to negative shift in urban and negative to positive shift in rural. The identified complicated relationship deepens our understanding of the drivers of vegetation greening in the built environment, informing the optimal building morphology threshold for efficient greening effect toward sustainable development.

Assessing the Snow Water Equivalent Dynamics of the Brahmaputra River Basin

ABSTRACT The Brahmaputra River Basin holds a crucial position in South Asia’s hydrological framework, serving as a vital source of water for a substantial population. Snow Water Equivalent (SWE), a pivotal metric reflecting water availability in regions dominated by snow, assumes a significant role in shaping the hydrological cycle of the basin. Thus, this present work aims to conduct a comprehensive study of the SWE dynamics across the Brahmaputra River Basin from 1980 to 2021. The present study obtains the data of elevation, ERA5-Land and SWE and SWE maximum, snowfall, snow cover area, snow depth, and air temperature, in a Geographic Information System (GIS) environment. Seasonal and annual trends are analyzed using the Theil-Sen slope estimator and the Mann-Kendall significance test. Correlations and linear regressions are conducted to assess the relationships among the variables. The findings unveil intricate SWE distribution patterns across the study area, with high elevations exhibiting prolonged snow cover area and substantial SWE values. Trend analysis shows a consistent decrease in SWE and SWE maximum across all seasons, ranging from -0.006 (post-monsoon) to -0.034 m (pre-monsoon) water equivalent/decade. Similarly, a declining trend (at a rate of -0.014 m water equivalent/decade) is also observed in annual SWE. The declining seasonal and annual SWE is possibly linked to escalating air temperature, diminishing snowfall, snow cover area, and snow depth during the study period. The implications of these findings underscore the significance of incorporating SWE variations into water management and the assessment of ecosystem health in the region.

Innovative trend analysis of precipitation changes over Nigeria: A case study of locations across the Niger and Benue Rivers

Precipitation has become a major topic of discussion in the subject of climate change. Studies have presented correlative relationships between climate change and the existing alterations in the global water cycle, leading to more extreme precipitation events, including both droughts and floods. This has shown severe impacts on agriculture, water resources, ozone levels, and human settlements. Accordingly, this study analyzes the trends in 40 years (1981-2020) of total monthly and annual precipitation data from six selected locations in Nigeria, using both the Mann-Kendall (MK) and new Sen's innovative trend analysis (ITA) method. The stations were chosen because of their close proximity to the Niger or Benue Rivers. The data were visually analyzed using boxplots to identify seasonal patterns and outliers, and the results of the MK test show that four of the six locations detected trends for annual and wet season precipitation variations. However, this was not applicable to dry season variations. The ITA test results show that only Kainji has an increasing trend in total annual and wet season precipitation, especially for high precipitation values, whereas other locations show decreasing trends for maximum precipitation values. A comparison of the two tests suggests that, the ITA test is more sensitive in detecting trends in precipitation data for the study locations. Overall, this study provides useful information for understanding the precipitation patterns in Nigeria, and can assist in planning and implementing appropriate water resource management strategies, with regards to the major rivers in the region.

P-1041. Contemporary Increase in Candidemia Mortality Is Associated with Antifungal Resistance and Patient Demographics

Abstract Background Candida spp. commonly cause bloodstream infections (BSI) in chronically ill patients, and Candida BSI is increasingly due to antifungal-resistant species. This retrospective cohort study examined temporal trends in candidemia epidemiology and associations between epidemiologic changes and mortality.Table 1.Characteristics of patients with different bloodstream infection types. Methods We included patients in a large healthcare system with a first BSI due to Escherichia coli or Klebsiella pneumonia (gram-negative bacteremia [GNB]), Staphylococcus aureus (SAB), or Candida spp. (candidemia) from 6/2016-6/2023. We compared 30-day mortality and demographics between BSI types. We examined changes in candidemia demographics, mortality, and causative species, both by individual year and by period (period 1, 2016-2019 vs. period 2, 2020-2023).Figure 1.Changes in mortality due to bloodstream infection types examined. Results Of 12057 patients, 7801 (64.7%) had GNB, 3602 (29.9%) SAB, and 654 (5.4%) candidemia (baseline characteristics in Table 1). Thirty-day mortality was highest for candidemia (30.9% vs. 13.4% for SAB and 8.0% for GNB, p&amp;lt; .01 by χ2). There was a trend toward increased mortality for patients with candidemia (p=0.06 by Mann-Kendall test, Figure 1). Candidemia mortality was significantly higher in period 2 vs. 1 (36.8% vs. 22.7%, p&amp;lt; .01 by χ2). Candidemia patients in period 2 tended to be older (mean age 66.3±14.3 vs. 63.8±16.2, p=0.04 by t-test); age was associated with mortality among patients with candidemia (OR 1.02, 95% CI 1.01-1.04 per year increase). Of species examined, only the incidence of C. auris increased (p&amp;lt; 0.01 by Mann-Kendall test, Figure 2). C. glabrata was associated with the highest 30-day mortality (36.3%) among common species; C. auris mortality was 17.4%. While the proportion of candidemia cases due to antifungal-resistant species (C. auris, glabrata, krusei, lusitaniae, parapsilosis) did not change between periods (p=0.79), mortality from these species was significantly higher in period 2 vs. 1 (36.1% vs. 21.5%, p&amp;lt; 0.01).Figure 2.Increasing prevalence of candidemia due to C. auris, 2016-2023. Conclusion Candidemia mortality increased significantly starting in 2020, at least in part due to increasing patient age and increased mortality associated with antifungal-resistant candidemia. Urgent efforts are needed to combat candidemia in an ageing population prone to antifungal-resistant Candida infections. Disclosures All Authors: No reported disclosures

P-176. Detecting Evidence for Bystander Selection by Comparing Trends in Antibiotic Resistance among Infection Causing Bacteria in a Large Global AMR Database

Abstract Background Unintentional exposure to antibiotics meant for the treatment of infections caused by other bacteria promote the selection of antibiotic resistant strains in off-target organisms in a phenomenon called bystander selection (BS). Some of these organisms could subsequently cause drug resistant infections or spread to others, promoting antibiotic resistant bacteria in the population. The importance of BS as a driver for antimicrobial resistance (AMR) at the global level is not well understood. We conducted an ecological time trends study to identify and compare trends over time of different clinically relevant bacteria-antibiotic pairs to detect evidence for BS globally and in the United States. Schematic Representation of the Development of Bystander Selection Methods We identified global and US trends in resistance using the Cochran-Armitage and Mann-Kendall tests. We compared trends in resistance using Pearson’s correlation coefficient. Among bacteria pairs with highly correlated trends in resistance, we eliminated pairs that belong to the same genus or commonly colonize the same niche, where horizontal genome transfer may explain the high degree of correlation, to arrive at bacteria pairs that were most likely experiencing BS. Figure showing how we arrived at the 16 and 9 highly correlated antibiotic-bacteria pairs that have the most likely experienced bystander selection globally and in the United States. Results We found 16 and 9 bacteria pairs respectively from our global and United States analysis that most likely experienced BS. Trends in resistance of Acinetobacter baumannii to carbapenems and cefepime were highly correlated with members of the family Enterobacteriace and Psuedomonas. In the United States, rates of levofloxacin resistance among Staphylococcus aureus were significantly correlated with Strepococcus pneumoniae. Figure showing the final 16 bacterial pairs from the global analysis in whom bystander selection was the most likely cause for significantly correlated trends in antimicrobial resistance. Each bubble represents one bacterial species. Intersection between 2 bubbles represents highly correlated trends in resistance between those 2 bacterial species to the antibiotics mentioned in the overlapping area. The size of the bubble represents the total number of correlations for that bacterium. Bubbles of bacterial species with 4 or more correlations are depicted in green. Conclusion To the best of our knowledge this is the first study to find evidence for BS driving the development of AMR in a global AMR surveillance database. Our findings suggest that judicious use of carbapenems for the treatment of Pseudomonas, E. coli, K. pneumoniae and Enterobacter infections could help address carbapenem resistance in A. baumannii. This work highlights the need to study antimicrobial resistance using a systems approach as opposed to studying the development of resistance in single bacteria-antibiotic pairs. Figure showing the final 9 bacterial pairs from the US specific analysis in whom bystander selection was the most likely cause for significantly correlated trends in antimicrobial resistance. Each bubble represents one bacterial species. Intersection between 2 bubbles represents highly correlated trends in resistance between those 2 bacterial species to the antibiotics mentioned in the overlapping area. The size of the bubble represents the total number of correlations for that bacterium. Bubbles of bacterial species with 4 or more correlations are depicted in green. Disclosures All Authors: No reported disclosures

Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China

The dynamics of vegetation changes and phenology serve as key indicators of interannual changes in vegetation productivity. Monitoring the changes in the Nanling grassland ecosystem using the remote sensing vegetation index is crucial for the rational development, utilization, and protection of these grassland resources. Grasslands in the hilly areas of southern China’s middle and low mountains have a high restoration efficiency due to the favorable combination of water and temperature conditions. However, the dynamic adaptation process of grassland restoration under the combined effects of climate change and human activities remains unclear. The aim of this study was to conduct continuous phenological monitoring of the Nanling grassland ecosystem, and evaluate its seasonal characteristics, trends, and the thresholds for grassland changes. The Normalized Difference Phenology Index (NDPI) values of Nanling Mountains’ grasslands from 2000 to 2021 was calculated using MOD09A1 images from the Google Earth Engine (GEE) platform. The Savitzky–Golay filter and Mann–Kendall test were applied for time series smoothing and trend analysis, and growing seasons were extracted annually using Seasonal Trend Decomposition and LOESS. A segmented regression method was then employed to detect the thresholds for grassland ecosystem restoration based on phenology and grassland cover percentage. The results showed that (1) the NDPI values increased significantly (p &lt; 0.01) across all grassland patches, particularly in the southeast, with a notable rise from 2010 to 2014, and following an eastern to western to central trend mutation sequence. (2) the annual lower and upper NDPI thresholds of the grasslands were 0.005~0.167 and 0.572~0.727, which mainly occurred in January–March and June–September, respectively. (3) Most of the time series in the same periods showed increasing trends, with the growing season length varying from 188 to 247 days. (4) The overall potential productivity of the Nanling grassland improved. (5) The restoration of the mountain grasslands was significantly associated with the grassland coverage and mean NDPI values, with a key threshold identified at a mean NDPI value of 0.5 for 2.1% grassland coverage. This study indicates that to ensure the sustainable development and conservation of grassland ecosystems, targeted management strategies should be implemented, particularly in regions where human factors significantly influence grassland productivity fluctuations.

ЖАЙЫҚ ӨЗЕНІНДЕГІ МҰЗ ҚҰБЫЛЫСТАРЫ МЕН МЕТЕОЭЛЕМЕНТТЕРДІҢ КӨПЖЫЛДЫҚ ӨЗГЕРІСІН БАҒАЛАУ

This article examines the changes in ice phenomena and meteorological parameters of the Zhaiyk River during the period from 1936 to 2021. The changes in ice phenomena observed over many years along the Zhaiyk River were analyzed. For this, long-term data on the start and end of ice phenomena at various stations along the river were used. As a result, it was established that the formation of ice on the river has shifted to later dates, the clearing of the river from ice has shifted to earlier dates, and the duration of the ice cover has decreased. The Mann-Kendall test and the Sen’s Slope method were used to assess the changes in long-term average air temperatures in the region. As a result, it was determined that the annual air temperature increase is within the range of 0,032...0,035. Additionally, changes in long-term values of temperature and precipitation, as well as their impact on river discharge, were analyzed. The study results show that global and local climate changes have a significant impact on ice phenomena and the hydrological characteristics of the Zhaiyk River.

Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis.

Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation. This study aimed to evaluate the impact of an AI-assisted diagnostic system on the diagnostic efficiency of radiologists. It specifically examined the report modification rates and missed and misdiagnosed rates of junior radiologists with and without AI assistance. We obtained effective data from 12,889 patients in 2 tertiary hospitals in Beijing before and after the implementation of the AI system, covering the period from April 2018 to March 2022. Diagnostic reports written by both junior and senior radiologists were included in each case. Using reports by senior radiologists as a reference, we compared the modification rates of reports written by junior radiologists with and without AI assistance. We further evaluated alterations in lung nodule detection capability over 3 years after the integration of the AI system. Evaluation metrics of this study include lung nodule detection rate, accuracy, false negative rate, false positive rate, and positive predictive value. The statistical analyses included descriptive statistics and chi-square, Cochran-Armitage, and Mann-Kendall tests. The AI system was implemented in Beijing Anzhen Hospital (Hospital A) in January 2019 and Tsinghua Changgung Hospital (Hospital C) in June 2021. The modification rate of diagnostic reports in the detection of lung nodules increased from 4.73% to 7.23% (χ21=12.15; P<.001) at Hospital A. In terms of lung nodule detection rates postimplementation, Hospital C increased from 46.19% to 53.45% (χ21=25.48; P<.001) and Hospital A increased from 39.29% to 55.22% (χ21=122.55; P<.001). At Hospital A, the false negative rate decreased from 8.4% to 5.16% (χ21=9.85; P=.002), while the false positive rate increased from 2.36% to 9.77% (χ21=53.48; P<.001). The detection accuracy demonstrated a decrease from 93.33% to 92.23% for Hospital A and from 95.27% to 92.77% for Hospital C. Regarding the changes in lung nodule detection capability over a 3-year period following the integration of the AI system, the detection rates for lung nodules exhibited a modest increase from 54.6% to 55.84%, while the overall accuracy demonstrated a slight improvement from 92.79% to 93.92%. The AI system enhanced lung nodule detection, offering the possibility of earlier disease identification and timely intervention. Nevertheless, the initial reduction in accuracy underscores the need for standardized diagnostic criteria and comprehensive training for radiologists to maximize the effectiveness of AI-enabled diagnostic systems.

Extreme Weather Patterns in Ethiopia: Analyzing Extreme Temperature and Precipitation Variability

Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts.

PRESENT TRENDS IN AIR TEMPERATURE IN THE EAST OF KAZAKHSTAN

According to world research, over the past decades there has been a tendency for air temperature to rise and an increase in the frequency of extreme weather events. The study of the climatic predisposition of individual regions to extreme events, in particular fires, is an urgent problem of our time. The main purpose of this study was to define current trends in air temperature in Eastern Kazakhstan on an annual, seasonal and monthly scale. The non-parametric Mann-Kendall test and Sen’s slope estimation were used for the analysis, and the co-kriging method was used to interpolate the data to obtain areal distribution. As a result of the study, it was revealed that in most of Eastern Kazakhstan, there has been an increasing trend in average and maximum temperatures significantly. It was also noted that the main statistically significant changes are observed in the spring and summer seasons. Changes at some stations were reached from 0,2 to 1,0 °C in ten years. The most consistent and significant trends in temperature increase were recorded in March and April.

Are More Adults Bleeding After Tonsillectomy Than in the Past?

Post-tonsillectomy hemorrhage (PTH) rates have increased in children over the last decade. This study aimed to determine whether PTH incidence in adults had also increased over the last 15 years. Retrospective study. TriNetX database. A search was conducted using TriNetX for patients 18 years and older within the US Collaborative Network who had undergone tonsillectomy (CPT codes 42821 or 42826) and yielded 72,232 subjects. PTH diagnoses were identified using ICD-10 codes J95.830 or K91.840, and those who returned to the operating room (RTOR) to control PTH were identified using CPT 42962. PTH events that occurred and analgesics given following 14 days of tonsillectomy were tabulated annually from 2008 to 2022. Mann-Kendall test for monotonic trend examined significant trends in PTH and analgesic use. Mean age at tonsillectomy was 32.8 years (SD = 13.8). 4.1% were diagnosed with PTH in 2008, compared with 6.2% by 2022 (p = 0.0030). In 2008, 0.7% required RTOR to control PTH, compared with 2.4% in 2022 (p < 0.0001). Use of codeine decreased from 6.9% in 2008 to 2.5% in 2022 (p = 0.0022). Use of ibuprofen, acetaminophen, ketorolac, and oxycodone increased from 1.6% to 23.4% (p = 0.0005), 36.1% to 78.6% (p < 0.0001), 1.5% to 11.9% (p < 0.0001), and 20.6% to 63.6% (p < 0.0001), respectively. No change was detected in use of hydrocodone. Post-tonsillectomy analgesia in adults has changed significantly over the last 15 years. Our study highlights a concurrent increase in PTH which bears more investigation. 3 Laryngoscope, 2025.

The Analysis of Hydrometeorological Characteristics in the Yarlung Tsangpo River Basin

Understanding the hydrometeorological processes of the Yarlung Tsangpo River Basin, located on the “Third Pole” Qinghai–Tibet Plateau, is crucial for effective water resource management and climate change adaptation. This study provides a comprehensive analysis of the basin’s hydrometeorological characteristics using long-term observational data from six representative stations across the upper, middle, and lower reaches. We examined trends, periodicity, variability, and correlations of key elements—precipitation, temperature, evaporation, and discharge—employing methods such as linear regression, Mann–Kendall tests, wavelet analysis, and Kendall rank correlation coefficient tests. The results indicated that precipitation and discharge exhibited non-significant upward trends, with fluctuations across decades, while temperature showed a significant increase of 0.39 °C per decade, surpassing the national and global rates. Evaporation generally decreased with increasing precipitation; however, at Lazi Station, evaporation significantly increased due to low precipitation and rising temperatures causing decreased relative humidity. Periodic analysis revealed cycles at multiple temporal scales, particularly at 2–5 years, 10 years, and over 20 years. Correlation analysis demonstrated a strong positive relationship between precipitation and discharge, and a negative correlation between evaporation and discharge. The hydrometeorological characteristics are significantly influenced by climatic factors, especially precipitation and temperature, with the warming trend potentially affecting water’s availability and distribution. These findings offer valuable insights for water resource management and highlight the need for continuous monitoring to understand hydrological responses to climatic and anthropogenic changes in this critical region.

Growth and Survival of a Cohort of Congenital Zika Virus Syndrome Children Born With Microcephaly and Children Who Developed With Microcephaly After Birth.

Little is known about the clinical course of children born with congenital Zika virus syndrome (CZS). This article aimed to analyze the growth and survival of children with CZS born with microcephaly and children who developed with microcephaly after birth in the 36-month period. This is a cohort of children diagnosed with CZS who were monitored in a series of outpatient appointments, with clinical and demographic information and anthropometric measurements collected. The Mann-Kendall test evaluated the trend of the mean Z-score of head circumference (HC) for age and the Kaplan-Meier model described the time to low weight and short length according to the classification of HC at birth. Children born without microcephaly had a reduction in HC growth velocity (P = 0.019) and took longer to reach low weight (P = 0.036) and short length (P = 0.034) when compared with those born with microcephaly. Seven (10%) deaths were reported, and the probability of survival after 36 months of age was 88%. In children with arthrogryposis, the risk of death was 7 times as high as in those without this condition (adjusted hazard ratio: 6.49; 95% confidence interval: 1.31-32.20; P = 0.022). In this cohort of children with CZS, 20% were born without microcephaly and all of them presented a reduction in HC growth velocity, which led to microcephaly. Progression to low weight and short length occurred in the first years of life, and that was faster in those born with microcephaly. Healthcare providers should be aware of these conditions that pose a risk of unfavorable anthropometric measurements and death.

Evaluation and prediction of groundwater quality for irrigation using regression and machine learning models

ABSTRACT This study evaluates and predicts six water quality indices such as sodium adsorption ratio (SAR), Kelly's ratio (KR), percentage sodium (%Na), permeability index (PI), exchangeable sodium percentage (ESP), and irrigation water quality index (IWQI) using multivariate regression models (MLR, PLSR, PCR, and WLSR) and machine learning (ML) algorithms (ANN, SVM, CART, CRRF, and KNN). The study analyzes data from 360 dug wells in Sundargarh district, India, during 2014–2021 with 70% used for training and 30% for testing. Spatial mapping of SAR, KR, ESP, and PI exhibits higher suitability of groundwater. The Mann–Kendall test of trend analysis shows a monotonic increasing and decreasing trend for SAR, KR, %Na, ESP, PI, and IWQI, respectively, at p &amp;gt; 0.05 during 2014–2021. Principal component analysis and discriminant analysis identify Na+, SAR, KR, %Na, and PI as the most influential WQ variables affecting the groundwater quality for this study area. MLR and WLSR models are superior in predicting SAR and ESP, while ANN is the best-suited ML model for SAR, KR, %Na, PI, and ESP. CRRF predicts IWQI with a relatively higher accuracy. These findings demonstrate the effectiveness of ML models in improving irrigation water quality assessment, providing valuable insights for groundwater-based crop management.

The effects of warming on loggerhead turtle nesting counts.

Global trends in marine turtle nesting numbers vary by region, influenced by environmental or anthropogenic factors. Our study investigates the potential role of past temperature fluctuations on these trends, particularly whether warmer beaches are linked to increased nesting due to higher female production (since sea turtles have temperature-dependent sex determination). We selected the loggerhead turtle (Caretta caretta) due to its wide distribution, strong philopatry and vulnerability to environmental changes. We compiled nest counts per year on 35 globally significant rookeries, analysing trends at regional and individual beach levels. We compiled air (CHELSA) and land surface (MODIS) temperature data sets spanning the last four decades (1979-2023) for each location. To analyse temporal trends in nest counts and temperatures, we used generalised additive models and Mann-Kendall trend tests. Additionally, we correlated nest counts with lagged air temperature variables. We found significant warming at 33 nesting locations, 23 of which also showed significant increases in nest counts. Our results suggest that rising temperatures may be boosting nest numbers in regions of the Caribbean, Atlantic Ocean and Mediterranean (sites in Cayman, Mexico, Brazil, Cyprus and Turkey). Furthermore, while some regions temporarily benefit, continued warming could precipitate long-term population declines. This regional variability helps predict species responses to climate change, with the general global increase in nest counts already indicating short-term warming effects. Nesting count trends might reflect a combination of natural ecological phenomena, conservation efforts, and warming effects. Long-term studies are needed to assess global trends in the sex ratio of hatchlings and the extent to which feminisation is driving nest numbers.