Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

Spatiotemporal and weather effects on the reproductive success of piping plovers on Prince Edward Island, Canada.

Piping plovers (Charadrius melodus sp.) rank among North America's most endangered shorebird species, facing compounding environmental challenges that reduce habitat availability and suppress recruitment and survival rates. Despite these challenges, research on the direct effects of climate variability and extremes on their breeding ecology remains limited. Here, we employ a spatiotemporal modelling approach to investigate how location, nest timing and weather conditions influence reproductive success rates in a small breeding population of C. m. melodus in Prince Edward Island (PEI), Canada from 2011 to 2023. Analysis of 40 years of monitoring records from a subset of nesting sites revealed that flooding and predation have been persistent sources of reproductive failures in this population, with unexplained losses increasing in recent years. Contrary to our hypotheses, our modelled results did not support a negative impact of extreme high temperatures and strong precipitation events on reproductive outcomes. Instead, we identified a positive effect of T MAX and no effect of strong precipitation, perhaps due to limited exposure to extreme high temperatures (>32°C) and context-specific risks associated with precipitation-induced flooding. However, trends in regional climate change are likely to increase exposure to-and the influence of-such factors in the near future. Our models also identified spatiotemporal variability in apparent hatch success over the study period, as well as worse hatch outcomes across popular beachgoing regions and for delayed nesting attempts. While our results offer preliminary insights into factors affecting breeding success in this population, further research will be imperative to enhance understanding of constraints on recruitment. To this end, we encourage the collection and analysis of additional time-series data of prey populations, human activities, fine-scale weather data and predator/flood risks associated with each nest on PEI.

Associations between meteorological factors and COVID-19: a global scoping review.

Background: Many respiratory viruses and their associated diseases are sensitive to meteorological factors. For SARS-CoV-2 and COVID-19, evidence on this sensitivity is inconsistent. Understanding the influence of meteorological factors on SARS-CoV-2 transmission and COVID-19 epidemiology can help to improve pandemic preparedness. Objectives: This review aimed to examine the recent evidence about the relation between meteorological factors and SARS-CoV-2/COVID-19. Methods: We conducted a global scoping review of peer-reviewed studies published from January 2020 up to January 2023 about the associations between temperature, solar radiation, precipitation, humidity, wind speed, and atmospheric pressure and SARS-CoV-2/COVID-19. Results: From 9,156 initial records, we included 474 relevant studies. Experimental studies on SARS-CoV-2 provided consistent evidence that higher temperatures and solar radiation negatively affect virus viability. Studies on COVID-19 (epidemiology) were mostly observational and provided less consistent evidence. Several studies considered interactions between meteorological factors or other variables such as demographics or air pollution. None of the publications included all determinants holistically. Discussion: The association between short-term meteorological factors and SARS-CoV-2/COVID-19 dynamics is complex. Interactions between environmental and social components need further consideration. A more integrated research approach can provide valuable insights to predict the dynamics of respiratory viruses with pandemic potential.

Assessment of meteorological parameters in predicting seasonal temperature of Dhaka city using ANN.

In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction of the temperature of Dhaka city. Prior knowledge of temperature is essential, especially in tropical regions like Dhaka, as it aids in forecasting heatwaves and implementing effective preparedness schemes. While various machine learning models have been employed for the prediction of hot weather across the world, research specially focused on Bangladesh is limited. Additionally, the application of machine learning models needs to be curated to suit the particular weather features of any region. Therefore, this study approaches ANN method for prediction of the temperature of Dhaka exploring the underlying role of related weather variables. Using the daily data for the months of February to July collected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (0.25° × 0.25° global grid) for the years 2011-2020, this study focuses on finding the combination of weather variables in predicting temperatures. The densely populated city, Dhaka, has faced severe consequences due to extreme climate conditions in recent years, and this study will pave a new dimension for further research regarding the topic.

The impact of meteorological factors on the incidence of infantile atopic dermatitis.

BACKGROUND: The role of the climate regarding atopic dermatitis (AD) in infants is still unclear. This study aimed to determine the relationship between meteorological conditions and the incidence of early AD. METHODS: The study was conducted using a retrospective design. We analyzed children aged 0-24 months with clinically diagnosed AD (n = 603), including infantile eczema (IE, n = 292), in relation to the mean monthly meteorological data in Minsk. The Mantel-Haenszel method was used to study the association between an AD outcome and meteorological variables, stratifying by potential confounders. Seasons of birth were analyzed in children diagnosed with AD before 6 months of age (n = 567) and at 12 months of age (n = 350) from 2005 to 2019. RESULTS: The incidence rate of IE was negatively associated with air temperature (adjusted incidence rate ratio = 0.75; 95% confidence interval (CI) 0.59-0.94), precipitation (0.74; 95% CI 0.58-0.93), and positively associated with atmospheric pressure (1.31; 95% CI 1.04-1.66). The highest incidence rate of IE was during spring, and the lowest was during summer. Incidences of AD were less frequent among infants born in the spring (18.1% vs. 29.4%, P < 0.001) than among older children. The principal component analysis identified three meteorological combinations where the first one (warm, low humidity) was negatively associated with the incidence rate of AD among children aged 0-24 months (0.77; 95% CI 0.65-0.92), and the third one (rainy, low atmospheric pressure) with IE (0.70; 95% CI 0.54-0.90). CONCLUSION: Continental seasonal cold-humid weather may influence early AD incidence. Moreover, short-term meteorological factors may play an important role in the onset of IE.

A traffic accident dataset for Chattanooga, Tennessee.

This publication presents an annotated accident dataset which fuses traffic data from radar detection sensors, weather condition data, and light condition data with traffic accident data (as illustrated in Fig. 1) in a format that is easy to process using machine learning tools, databases, or data workflows. The purpose of this data is to analyze, predict, and detect traffic patterns when accidents occur. Each file contains a timeseries of traffic speeds, flows, and occupancies at the sensor nearest to the accident, as well as 5 neighboring sensors upstream and downstream. It also contains information about the accident type, date, and time. In addition to the accident data, we provide baseline data for typical traffic patterns during a given time of day. Overall, the dataset contains 6 months of annotated traffic data from November 2020 to April 2021. During this timeframe, and 361 accidents occurred in the monitored area around Chattanooga, Tennessee. This dataset served as the basis for a study on topology-aware automated accident detection for a companion publication [1].

GOES ABI-derived hailstorm polygons and tracking dataset for Brazil.

This paper describes a dataset of convective systems (CSs) associated with hailstorms over Brazil tracked using GOES-16 Advanced Baseline Imager (ABI) measurements and the Tracking and Analysis of Thunderstorms (TATHU) tool. The dataset spans from June 5, 2018, to September 30, 2023, providing five-year period of storm activity. CSs were detected and tracked using the ABI's clean IR window brightness temperature at 10.3 µm, projected on a 2 km x 2 km Lat-Lon WGS84 grid. Systems were identified using a brightness temperature (BT) threshold of 235 K, conducive to detecting convective clusters with larger area and excluding smaller or non-convective cells such as groups of thin Cirrus clouds. Each detected CS was treated as an object, containing geographic boundaries and raster statistics such as BT's mean, minimum, standard deviation, and count of data points within the CS polygon, which serves as proxy for size estimates. The life cycle of each system was tracked based on a 10 % overlap area criterion, ensuring continuity, unless disrupted by dissociative or associative events. Then, the tracked CSs were filtered for intersections in space and time with verified ground reports of hail, from the Prevots group. The matches were then exported to a database with SpatiaLite enabled data format to facilitate spatial data queries and analyses. This database is structured to support advanced research in severe weather events, in particular hailfall. This setting allows for extensive temporal and spatial analyses of convective systems, making it useful for meteorologists, climate scientists, and researchers in related fields . The inclusion of detailed tracking information and raster statistics offers potential for diverse applications, including climate model validation, weather prediction enhancements, and studies on the climatological impact of severe weather phenomena in Brazil.

Extensive pipeline location data resource: Integrating reported incidents, past environmental loadings, and potential geohazards for integrity evaluations in the U.S. Gulf of Mexico.

The U.S. Gulf of Mexico contains a complex network of existing, decommissioned, and abandoned oil and gas pipelines, which are susceptible to a number of stressors in the natural-engineered offshore system including corrosion, environmental hazards, and human error. The age of these structures, coupled with extreme weather events increasing in intensity and occurrence from climate change, have resulted in detrimental environmental and operational impacts such as hydrocarbon release events and pipeline damage. To support the evaluation of pipeline infrastructure integrity for reusability, remediation, and risk prevention, the U.S. Gulf of Mexico Pipeline and Reported Incident Datasets were developed and published. These datasets, in addition to supporting advanced analytics, were constructed to inform regulatory, industry, and research stakeholders. They encompass more than 490 attributes relating to structural information, incident reports, environmental loading statistics, seafloor factors, and potential geohazards, all of which have been spatially, and in some cases temporally matched to more than 89,000 oil and gas pipeline locations. Attributes were acquired or derived from publicly available, credible resources, and were processed using a combination of manual efforts and customized scripts, including big data processing using supercomputing resources. The resulting datasets comprise a spatial geodatabase, tabular files, and metadata. These datasets are publicly available through the Energy Data eXchange®, a curated online data and research library and laboratory developed by the U.S. Department of Energy's National Energy Technology Laboratory. This article describes the contents of the datasets, details the methods involved in processing and curation, and suggests application of the data to inform and mitigate risk associated with offshore pipeline infrastructure in the Gulf of Mexico.

Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations.

Background: The accurate provision of weather information holds immense significance to many disciplines. One example corresponds to the field of air traffic management, in which one basis for weather detection is set upon recordings from sparse weather stations on ground. The scarcity of data and their lack of precision poses significant challenges to achieve a detailed description of the atmosphere state at a certain moment in time. Methods: In this article, we foster the use of physics-informed neural networks (PINNs), a type of machine learning (ML) architecture which embeds mathematically accurate physics models, to generate high-quality weather information subject to the regularization provided by the Navier-Stokes equations. Results: The application of PINNs is oriented to the reconstruction of dense and precise wind and pressure fields in areas where only a few local measurements provided by weather stations are available. Our model does not only disclose and regularize such data, which are potentially corrupted by noise, but is also able to precisely compute wind and pressure in target areas. Conclusions: The effect of time and spatial resolution over the capability of the PINN to accurately reconstruct fluid phenomena is thoroughly discussed through a parametric study, concluding that a proper tuning of the neural network's loss function during training is of utmost importance.

There is currently a great interest in the many uses of artificial intelligence (AI) and how it is affecting our daily lives. From the robotics field to the use of language recognition to interact with different users, we are experiencing how machine intelligence is increasing day by day. In this article, we delve into one of the many applications of artificial intelligence: weather reconstruction. The ability to accurately determine weather conditions is believed to have an impact on various disciplines, e.g. reducing costs at airports due to delays, cancellations and associated compensations. In this particular example, a precise description of the status of the atmosphere is therefore necessary if countermeasures are to be executed. However, conventional weather recording with on-ground stations is often limited to a few sparse locations. Following that line of thought, it is not only necessary to estimate the weather in areas surrounding stations, but also on other target areas which may be subject to lack of weather information. Our strategy is based on the application of neural networks, a type of AI architecture, to infer data based on the underlying physics that drive the measured weather phenomena. For that purpose, we make use of neural networks which are consistent with physics laws, the so-called physics-informed neural networks (PINNs). This article deals with their adoption to weather pattern reconstruction, with the objective of further increasing the precision and availability of information given scarce reference measurements.

Source-sink relationships during grain filling in wheat in response to various temperature, water deficit and nitrogen deficit regimes.

Grain filling is a critical process for improving crop production under adverse conditions caused by climate change. Here, using a quantitative method, we quantified post-anthesis source-sink relationships of a large data set to assess the contribution of remobilized pre-anthesis assimilates to grain growth for both biomass and nitrogen. The data set came from 13 years' semi-controlled field experimentation, in which six bread wheat genotypes were grown at plot scale under contrasting temperature, water, and nitrogen regimes. On average, grain biomass was ~10% higher than post-anthesis aboveground biomass accumulation across regimes and genotypes. Overall, the estimated relative contribution (%) of remobilized assimilates to grain biomass became increasingly significant with increasing stress intensity, ranging from virtually nil to 100%. This percentage was altered more by water and nitrogen regimes than by temperature, indicating the greater impact of water or nitrogen regimes relative to high temperatures under our experimental conditions. Relationships between grain nitrogen demand and post-anthesis nitrogen uptake were generally insensitive to environmental conditions, as there was always significant remobilization of nitrogen from vegetative organs, which helped to stabilize the amount of grain nitrogen. Moreover, variations in the relative contribution of remobilized assimilates with environmental variables were genotype-dependent. Our analysis provides an overall picture of post-anthesis source-sink relationships and pre-anthesis assimilate contributions to grain filling across (non-)environmental factors, and highlights that designing wheat adaption to climate change should account for complex multi-factor interactions.

[Transport Characteristics and Potential Source Areas of PM2.5 in Yangquan City Under Different Pollution Levels].

Based on the PM2.5 monitoring data, NCEP data, and the meteorological data of the weather situation analysis at the corresponding time in Yangquan City from 2020 to 2022, using the HYSPLIT4 backward trajectory model, multi-station potential source contribution factor analysis （MS-PSCF） and trajectory density analysis （TDA） were introduced to study the differentiation and classification of PM2.5 transport channels and potential sources in Yangquan City. The results showed that： ① The PM2.5 pollution in Yangquan was mainly concentrated in Yangquan and Pingding, whereas the pollution in Yuxian was relatively light. The proportion of days with different pollution levels and the average and maximum values of PM2.5 concentration in Yangquan and Pingding were significantly higher than those in Yuxian, and the distribution characteristics of PM2.5 were closely related to the local special terrain. ② The amount of PM2.5 pollution and the concentration of PM2.5 in different pollution levels were the highest in light wind weather. The influence of east-west regional transport on PM2.5 pollution times and PM2.5 concentration of Yangquan and Pingding was obvious, and the contribution of east wind was significant. The influence of local pollution sources was the main factor in the moderate pollution weather in Yuxian County. ③ There were four main ground conditions for the generation and maintenance of moderate or above pollution weather： warm low pressure type （22%）, high pressure front （bottom） type （54%）, high pressure back type （14%）, and pressure equalization field （10%）. High pressure front （bottom） type was the main ground situation causing the increase in PM2.5 concentration. There were two types of upper air conditions, namely, flat westerly flow type （78%） and northwest flow type （22%）. The upper westerly flow type was the main upper air condition that caused the increase in PM2.5 concentration. ④ The results of transport channels and potential source areas of PM2.5 with different pollution levels obtained by MS-PSCF and TDA were consistent. The main transport channels of PM2.5 were the northeast, southeast, and northwest channels, whereas the northeast and southeast channels were short-distance transport routes, which were the main routes causing the increase in PM2.5 concentration. The northwest channel was consistent with the northwest dust transport channel, belonging to long-distance transmission. The main potential source areas of PM2.5 pollution were located in the central and western parts of Hebei and the southeast part of Hebei, the northeast part of Henan and its junction with the southwest part of Shandong, and the southeast part of Shanxi.

Contrasting seasonal responses to wind in migrating songbirds on a globally important flyway.

During spring migration, nocturnal migrants attempt to minimize their travel time to reach their breeding grounds early. However, how they behave and respond to unfavourable conditions during their springtime travels is much less understood. In this study, we reveal the effects of atmospheric factors on nocturnal bird migration under adverse conditions during spring and autumn, based on one of the most detailed bird migration studies globally, using radar data from 13 deployments over a period of seven years (2014-2020) in the Levant region. Using ERA5 reanalysis data, we found that migratory birds maintain similar ground speeds in both autumn and spring migrations, but during spring, when encountering unfavourable winds, they put more effort into maintaining their travel speed by increasing self-powered airspeed by 18%. Moreover, we report for the first time that spring migrants showed less selectivity to wind conditions and migrated even under unfavourable headwind and crosswind conditions. Interestingly, we discovered that temperature was the most important weather parameter, such that warm weather substantially increased migration intensities in both seasons. Our results enhance our understanding of bird migration over the Levant region, one of the world's largest and most important migration flyways, and the factors controlling it. This information is essential for predicting bird migration, which-especially under the ongoing anthropogenic changes-is of high importance.

Incorporating weather in counts and trends of migrating Common Nighthawks.

Effective conservation planning for species of concern requires long-term monitoring data that can accurately estimate population trends. Supplemental or alternative methods for estimating population trends are necessary for species that are poorly sampled by traditional breeding bird survey methods. Counts of migrating birds are commonly used to assess raptor population trends and could be useful for additional taxa that migrate diurnally and are difficult to monitor during the breeding season. In North America, the Common Nighthawk (Chordeiles minor) is challenging to detect during comprehensive dawn surveys like the North American Breeding Bird Survey and is considered a species of conservation concern because of steep population declines across its range. We conducted standardized evening counts of migrating Common Nighthawks at a fixed survey location along western Lake Superior each autumn from 2008 to 2022. To document peak migration activity, counts spanned ~3 hours each evening from mid-August to early September for a mean of 19.4 ± 2.4 days. These count data were then used to assess the effects of weather on daily counts and high-count days and to calculate population trends over this 15-year period. We used generalized linear mixed effects models to determine the relationship between daily counts and high-count days (i.e., ≥1000 migrating nighthawks) and weather variables. Additionally, using our 15-year dataset, we calculated a geometric mean passage rate that accounted for annual differences in weather to estimate count trends. Annual counts averaged ~18,000 (min = 2514, max = 32,837) individuals and high-count days occurred 56 times throughout the course of the study. Model results indicated lighter, westerly winds and warmer temperatures were associated with higher daily counts and greater probability of a large migratory flight. Results from the trend analyses suggest stable or non-significantly increasing trends for Common Nighthawks during this monitoring period; however, the trend models explained a relatively low percentage of the variation in the counts. Results from a power analysis suggest that continued monitoring efforts and adjustments with weather covariates will be necessary to effectively use visible migration count data to estimate Common Nighthawk trends. Establishing annual monitoring programs that use standardized visual counts to document Common Nighthawk migration at key sites across North America may provide supplemental information useful for population trend estimates of this species. Therefore, we advocate for the use of visible migration counts to monitor Common Nighthawks in North America and emphasize the value of long-term monitoring efforts.

Des données de suivi à long terme permettant de calculer avec précision les tendances démographiques sont garantes d'une planification réussie de la conservation d'espèces préoccupantes. Des méthodes complémentaires ou alternatives d'estimation des tendances démographiques sont nécessaires dans le cas d'espèces mal échantillonnées par les méthodes traditionnelles de relevé d'oiseaux nicheurs. Le dénombrement d'oiseaux migrateurs est couramment utilisé pour évaluer la tendance des populations de rapaces et pourrait servir pour d'autres taxons qui migrent de jour et sont difficiles à suivre pendant la saison de nidification. En Amérique du Nord, l'Engoulevent d'Amérique (Chordeiles minor) est difficile à détecter au cours de relevés généraux réalisés à l'aube, tel le Relevé des oiseaux nicheurs d'Amérique du Nord (BBS), et est considéré comme une espèce dont la conservation est préoccupante en raison de la baisse marquée de ses effectifs dans toute son aire de répartition. Nous avons effectué des comptages en soirée standardisés d'engoulevents en migration à un site fixe localisé du côté ouest du lac Supérieur, chaque automne de 2008 à 2022. Afin de caractériser le pic d'activité migratoire, les comptages ont duré ~3 heures chaque soir de la mi-août au début de septembre, durant 19,4 ± 2,4 jours en moyenne. Ces données ont ensuite servi pour évaluer l'effet des conditions météorologiques sur les comptages quotidiens et les jours d'activité migratoire élevée et calculer la tendance démographique au cours de ces 15 ans. Nous avons utilisé des modèles linéaires généralisés à effets mixtes pour déterminer la relation entre les comptages quotidiens et les jours d'activité migratoire élevée (c.-à-d. ≥1000 engoulevents) et les variables météorologiques. En outre, en utilisant notre jeu de données sur 15 ans, nous avons calculé la moyenne géométrique du taux de passage tenant compte des différences météorologiques annuelles afin d'estimer la tendance des comptages. La moyenne des comptages annuels était de ~18 000 (min = 2514, max = 32 837) individus et nous avons observé 56 cas d'activité migratoire élevée au cours de l'étude. Les résultats du modèle ont indiqué que des vents plus légers et de l'ouest, et des températures plus chaudes étaient associés à des comptages quotidiens plus élevés et à une plus grande probabilité d'une activité migratoire importante. Les résultats de l'analyse des tendances indiquent que les engoulevents ont montré une tendance stable ou en augmentation non significative au cours de cette période de suivi; cependant, les modèles de tendances n'ont expliqué qu'un pourcentage relativement faible de la variation des comptages. Les résultats d'une analyse de puissance révèlent que des suivis réguliers et des ajustements avec des covariables météorologiques seront nécessaires pour utiliser efficacement les données de comptages visuels réalisés en migration afin d'estimer la tendance de l'Engoulevent d'Amérique. La mise en place d'un programme de suivis annuels fondés sur des comptages visuels standardisés destiné à caractériser la migration de l'Engoulevent d'Amérique sur des sites clés en Amérique du Nord pourrait fournir des informations supplémentaires utiles pour estimer les tendances démographiques de cette espèce. Par conséquent, nous préconisons l'utilisation de comptages visuels en migration pour suivre l'Engoulevent d'Amérique en Amérique du Nord et soulignons la valeur de suivis à long terme.

Arbovirus Transmission in Australia from 2002 to 2017.

Arboviruses pose a significant global public health threat, with Ross River virus (RRV), Barmah Forest virus (BFV), and dengue virus (DENV) being among the most common and clinically significant in Australia. Some arboviruses, including those prevalent in Australia, have been reported to cause transfusion-transmitted infections. This study examined the spatiotemporal variation of these arboviruses and their potential impact on blood donation numbers across Australia. Using data from the Australian Department of Health on eight arboviruses from 2002 to 2017, we retrospectively assessed the distribution and clustering of incidence rates in space and time using Geographic Information System mapping and space-time scan statistics. Regression models were used to investigate how weather variables, their lag months, space, and time affect case and blood donation counts. The predictors' importance varied with the spatial scale of analysis. Key predictors were average rainfall, minimum temperature, daily temperature variation, and relative humidity. Blood donation number was significantly associated with the incidence rate of all viruses and its interaction with local transmission of DENV, overall. This study, the first to cover eight clinically relevant arboviruses at a fine geographical level in Australia, identifies regions at risk for transmission and provides valuable insights for public health intervention.

Influence of Meteorological Parameters on the Prevalence of TEE Detected Left Atrial Appendage Thrombi.

(1) Background: Meteorological factors seem to exert various effects on human health, influencing the occurrence of diseases such as thromboembolic events and strokes. Low atmospheric pressure in summer may be associated with an increased likelihood of ischemic stroke. The aim of this study was to investigate the potential impact of meteorological conditions on left atrial appendage (LAA) thrombus formation. (2) Methods: A total of 131 patients were included, diagnosed with a first instance of thrombus via 3D transesophageal echocardiography (TEE) between February 2009 and February 2019. Months with frequent thrombus diagnoses of at least 10 thrombi per month were categorized as frequent months (F-months), while months with fewer than 10 thrombus diagnoses per month were labelled as non-frequent months (N-months). The analysis focused on differences in meteorological parameters in two-week and four-week periods before the diagnosis. (3) Results: F-months were predominantly observed in spring and summer (April, May, June, and July), as well as in February and November. During F-months, a higher absolute temperature difference, lower relative humidity, longer daily sunshine duration, and greater wind speed maximum were observed in the two- and four-week periods rather than for N-months. In the two-week period, average temperatures, equivalent temperatures, and temperature maxima were also significantly higher during F-months than N-months. (4) Conclusion: Thrombi in the left atrial appendage are more prevalent during periods characterized by high absolute temperature differences, low relative humidity, and long daily sunshine duration.

Impacts of climate change on water-related mosquito-borne diseases in temperate regions: A systematic review of literature and meta-analysis.

Mosquito-borne diseases are a known tropical phenomenon. This review was conducted to assess the mecha-nisms through which climate change impacts mosquito-borne diseases in temperate regions. Articles were searched from PubMed, Scopus, Web of Science, and Embase databases. Identification criteria were scope (climate change and mosquito-borne diseases), region (temperate), article type (peer-reviewed), publication language (English), and publication years (since 2015). The WWH (who, what, how) framework was applied to develop the research question and thematic analyses identified the mechanisms through which climate change affects mosquito-borne diseases. While temperature ranges for disease transmission vary per mosquito species, all are viable for temperate regions, particularly given projected temperature increases. Zika, chikungunya, and dengue transmission occurs between 18-34 °C (peak at 26-29 °C). West Nile virus establishment occurs at monthly average temperatures between 14-34.3 °C (peak at 23.7-25 °C). Malaria establishment occurs when the consecutive average daily temperatures are above 16 °C until the sum is above 210 °C. The identified mechanisms through which climate change affects the transmission of mosquito-borne diseases in temperate regions include: changes in the development of vectors and pathogens; changes in mosquito habitats; extended transmission seasons; changes in geographic spread; changes in abundance and behaviors of hosts; reduced abundance of mosquito predators; interruptions to control operations; and influence on other non-climate factors. Process and stochastic approaches as well as dynamic and spatial models exist to predict mosquito population dynamics, disease transmission, and climate favorability. Future projections based on the observed relations between climate factors and mosquito-borne diseases suggest that mosquito-borne disease expansion is likely to occur in temperate regions due to climate change. While West Nile virus is already established in some temperate regions, Zika, dengue, chikungunya, and malaria are also likely to become established over time. Moving forward, more research is required to model future risks by incorporating climate, environmental, sociodemographic, and mosquito-related factors under changing climates.

Using Support Vector Machines to Classify Road Surface Conditions to Promote Safe Driving.

Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted. Two learner designs (one-vs-one, one-vs-all) extended their application to multi-class classification. In addition to this non-probabilistic classifier, this study calculated the posterior probability of belonging to each group by applying the sigmoid function to the classification scores obtained by the trained SVM. The results indicate that the classification errors of all the classifiers, excluding the one-vs-all linear learners, were below 3%, thereby accurately classifying road surface conditions, and that the generalization performance of all the one-vs-one learners was within an error rate of 4%. The results also showed that the posterior probabilities can analyze certain atmospheric and road surface conditions that correspond to a high probability of hazardous road surface conditions. Therefore, this study demonstrates the potential of data-driven learning models in classifying road surface conditions accurately.

Enhancing primary healthcare nurses' preparedness for climate-induced extreme weather events.

BACKGROUND: Climate Change is causing frequent and sever extreme weather events globally, impacting human health and well-being. Primary healthcare (PHC) nurses' are at the forefront of addressing these challenges and must be prepared. PURPOSE: This scoping revieww explored literature on the preparedness of the PHC nursing workforce for extreme weather events and identify gaps in knowledge and practice. METHODS: Using Arksey and O'Malley's framework, a comprehensive search was conducted across PubMed, Scopus, CINHAL, Web of Sciences, and ProQuest, on studies from 2014-2024, addressing PHC nurses' preparedness. DISCUSSION: Nine studies were identified and highlighted a need for preparedness training and facility-based preparedness plans. Key themes included prioritizing regional networks, clinical leadership, service delivery, health information, health workforce, medical products and technologies, and financing. CONCLUSION: Strengthening PHC nurses' resilience against extreme weather requires targeted professional development, mental health support, comprehensive planning, and collaborative efforts. Future strategies should enhance PHC nurses' capacity through training, support, and policy development.

Process-Informed Subsampling Improves Subseasonal Rainfall Forecasts in Central America.

Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process-informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly-changing processes. Process-informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases.

Bt corn and cotton planting may benefit peanut growers by reducing aflatoxin risk.

Decades of studies have shown that Bt corn, by reducing insect damage, has lower levels of mycotoxins (fungal toxins), such as aflatoxin and fumonisin, than conventional corn. We used crop insurance data to infer that this benefit from Bt crops extends to reducing aflatoxin risk in peanuts: a non-Bt crop. In consequence, we suggest that any benefit-cost assessment of how transgenic Bt crops affect food safety should not be limited to assessing those crops alone; because the insect pest control offered by Bt crops affects the food safety profile of other crops grown nearby. Specifically, we found that higher Bt corn and Bt cotton planting rates in peanut-growing areas of the United States were associated with lower aflatoxin risk in peanuts as measured by aflatoxin-related insurance claims filed by peanut growers. Drought-related insurance claims were also lower: possibly due to Bt crops' suppression of insects that would otherwise feed on roots, rendering peanut plants more vulnerable to drought. These findings have implications for countries worldwide where policies allow Bt cotton but not Bt food crops to be grown: simply planting a Bt crop may reduce aflatoxin and drought stress in nearby food crops, resulting in a safer food supply through an inter-crop "halo effect."