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.

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.

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.

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.

Are we joining the One Health dots? A scoping review of research on the one health effects of extreme weather events in eastern Australia.

Extreme weather events such as floods, bushfires, cyclones, and drought, are projected to increase in eastern Australia. Understanding how these events influence the combined, sustainable well-being of humans, animals, and ecosystems - that is One Health - will enable development of transdisciplinary and ultimately more effective interventions. A scoping review was conducted to explore the research associated with the effects of extreme weather events in eastern Australia using a One Health lens, specifically identifying the type of extreme weather events studied, the research conducted in the context of One Health, and gaps to inform improved One Health implementation. The review followed JBI guidelines (based on PRISMA). Eligible research was peer-reviewed, in English, and published since 2007, in which primary research studies investigated the impact of extreme weather events in eastern Australia on at least two of ecosystems, human health, and animal health. Using structured search terms, six databases were searched. Following removal of duplicates, 870 records were screened by two reviewers. Eleven records were eligible for data extraction and charting. The scope of extreme weather events studied was relatively limited, with studies in flood and bushfire settings predominating, but relatively little research on cyclones. Major health themes included more than the impact of extreme weather events on physical health (zoonotic and vector-borne diseases) through investigation of social well-being and mental health in the context of the human-animal bond in evacuation behaviors and drought. Research gaps include studies across a broader range of extreme weather events and health topics, as well as a more comprehensive approach to including the impacts of extreme weather events on all three domains of One Health. The limited research focus inevitably translates to limited recommendations for policy, planning and response to manage extreme weather event emergencies. Given the expected increase in frequency of these events, there is a critical need for more comprehensive primary research to better identify strategies and facilitate implementation of One Health promotion for improved outcomes in extreme weather event emergencies.

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.

Feeling Younger on Active Summer Days? On the Interplay of Behavioral and Environmental Factors With Day-to-Day Variability in Subjective Age.

Background and Objectives: Subjective age, that is, how old people feel in relation to their chronological age, has mostly been investigated from a macro-longitudinal, lifespan point of view and in relation to major developmental outcomes. Recent evidence also shows considerable intraindividual variations in micro-longitudinal studies as well as relations to everyday psychological correlates such as stress or affect, but findings on the interplay with physical activity or sleep as behavioral factors and environmental factors such as weather conditions are scarce. Research Design and Methods: We examined data from 80 recently retired individuals aged 59-76 years (M = 67.03 years, 59% women) observed across 21 days. Daily diary-based assessments of subjective age, stress, affect, and sleep quality alongside physical activity measurement via Fitbit (steps, moderate-to-vigorous physical activity) and daily hours of sunshine were collected and analyzed using multilevel modeling. Results: Forty-four percent of the overall variance in subjective age was due to intraindividual variation, demonstrating considerable fluctuation. Affect explained the largest share in day-to-day fluctuations of subjective age, followed by stress and steps, whereas sunshine duration explained the largest share of variance in interindividual differences. Discussion and Implications: In our daily diary design, subjective age was most strongly related to self-reported affect as a psychological correlate. We, however, also found clear associations with objective data on daily steps and weather. Hence, our study contributes to contextualizing and understanding variations in subjective age in everyday life.

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].

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."

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.

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.

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.

Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study.

BACKGROUND: Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time. OBJECTIVE: The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures. METHODS: We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes. RESULTS: Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness. CONCLUSIONS: Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study's application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Particle-ozone complex pollution under diverse synoptic weather patterns in the Yangtze River Delta region: Synergistic relationships and the effects of meteorology and chemical compositions.

There is considerable academic interest in the particle-ozone synergistic relationship (PO) between fine particulate matter (PM2.5) and ozone (O3). Using various synoptic weather patterns (SWPs), we quantitatively assessed the variations in the PO, which is relevant to formulating policies aimed at controlling complex pollution in the air. First, based on one-year sampling data from March 2018 to February 2019, the SWPs classification of the Yangtze River Delta (YRD) was conducted using the sum-of-squares technique (SS). Five dominant SWPs can be found in the YRD region, including the Aleutian low under SWP1 (occurring 45 % of the year), a tropical cyclone under SWP2 (21 %), the tropical cyclone and western Pacific Subtropical High (WPSH) under SWP3 (15.4 %), the WPSH under SWP4 (6.9 %), and a continental high pressure under SWP5 (3.1 %). The phenomenon of a "seesaw" between PM2.5 and O3 concentrations exhibited significant spatial heterogeneity, which was influenced by meteorological mechanisms. Second, the multi-linear regression (MLR) model and the partial correlation (PCOR) analysis were employed to quantify the effects of dominant components and meteorological factors on the PO. Meteorological variables could collectively explain only 33.0 % of the PM2.5 variations, but 58.0 % for O3. O3 promoted each other with low concentrations of PM2.5 but was inhibited by high concentrations of PM2.5. High relative humidity (RH) was conducive to the generation of PM2.5 secondary components and enhanced the radiative effects of aerosols and the negative correlation of PO. In addition, attention should be paid to assessing the combined effects of precursor levels, weather, and chemical reactions on the particle-ozone complex pollution. The control of O3 pollutants should be intensified in summer, while the focus should be on reducing PM2.5 pollutants in winter. Prevention and control measures need to reflect the differences in weather conditions and pollution characteristics, with a focus on RH and secondary components of PM2.5.

Weather Radar Calibration Method Based on UAV-Suspended Metal Sphere.

Weather radar is an active remote sensing device used to monitor the full lifecycle changes in severe convective weather with high spatial and temporal resolution. Effective radar calibration is a crucial foundation for ensuring the high-quality application of observational data. This paper utilizes a UAV platform equipped with a high-precision RTK system and standard metal spheres to study the principles and methods of metal sphere calibration, constructing a complete calibration process and calibration accuracy evaluation metrics. Additionally, a collocated radar comparison observation experiment was conducted for cross-validation, and metal sphere calibration tests were performed on problematic radars. The experimental results indicate the following: (1) The combined application of a high-precision RTK system and a laser range camera can provide real-time position information on the metal sphere, improving the efficiency of radar target acquisition. (2) The calibration method based on UAV-suspended metal spheres can periodically conduct the quantitative calibration of Z and ZDR, achieving calibration accuracies within 0.5 dB and 0.2 dB, respectively, and supports the qualitative inspection of key parameters such as beamwidth and pulse width. (3) During field tests, a high success rate "coarse adjustment + fine adjustment + staring" sphere-finding technique was established, based on automatic switching between RHI, PPI, and FIX scanning modes. This method directs the UAV to adjust the metal sphere to the center of the radar distance bin, reducing the impact of uneven beam filling and bin crossing, ensuring the accuracy of scattering characteristic measurements.

Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network.

The presence of fog in the background can prevent small and distant objects from being detected, let alone tracked. Under safety-critical conditions, multi-object tracking models require faster tracking speed while maintaining high object-tracking accuracy. The original DeepSORT algorithm used YOLOv4 for the detection phase and a simple neural network for the deep appearance descriptor. Consequently, the feature map generated loses relevant details about the track being matched with a given detection in fog. Targets with a high degree of appearance similarity on the detection frame are more likely to be mismatched, resulting in identity switches or track failures in heavy fog. We propose an improved multi-object tracking model based on the DeepSORT algorithm to improve tracking accuracy and speed under foggy weather conditions. First, we employed our camera-radar fusion network (CR-YOLOnet) in the detection phase for faster and more accurate object detection. We proposed an appearance feature network to replace the basic convolutional neural network. We incorporated GhostNet to take the place of the traditional convolutional layers to generate more features and reduce computational complexities and costs. We adopted a segmentation module and fed the semantic labels of the corresponding input frame to add rich semantic information to the low-level appearance feature maps. Our proposed method outperformed YOLOv5 + DeepSORT with a 35.15% increase in multi-object tracking accuracy, a 32.65% increase in multi-object tracking precision, a speed increase by 37.56%, and identity switches decreased by 46.81%.

Examining the relationship between wind speed, climatic conditions, and road accidents in Iran.

This study investigates the relationship between wind speed, climatic conditions, and road accidents in Iran, focusing on the type of accidents and collisions. The research aims to identify the causes of accidents and provide insights for prediction and decision-making purposes. The study adopts a developmental research approach, analyzing road accident data and wind speed data. Logistic regression is employed to investigate the correlation between wind speed and the type of accidents and collisions. Data mining techniques, specifically the J48 decision tree algorithm, are used to examine the relationship patterns among wind speed, climatic conditions, collision types, and accident types. Additionally, texts and articles related to atmospheric hazards and road accidents are studied, and interviews are conducted with road accident experts and drivers to extract insights into the causes of road accidents in Iran. The findings indicate that wind speed does not have a direct and significant effect on the type of accidents (fatal or non-fatal), but it does influence the type of collisions in road accidents. The decision tree analysis reveals patterns in the relationships between weather conditions, wind speed, collision types, and accident types, enabling the prediction of collision probabilities in different scenarios. The causes of road accidents in Iran are categorized into human factors, secondary causes, and unique causes. Based on the findings, several recommendations are proposed to enhance road safety and reduce accidents in Iran.