Identification of maladaptive behavioural patterns in response to extreme weather events.

Human behaviour has gained recognition as a critical factor in addressing climate change and its impacts. With extreme weather events posing risks to vulnerable communities, understanding cognitive processes driving behaviours becomes essential for effective risk communication. This study focuses on the 2018 "Vaia" storm, which brought unprecedented precipitation and wind velocity to the mountainous regions of North-eastern Italy. Drawing upon the Protection Motivation Theory (PMT) framework, we employ probabilistic models to identify distinct groups with similar behavioural profiles. By administering a web-based survey to 1500 residents affected by the event, we find that threat appraisal is more influential in shaping protective behaviours than coping appraisal. Our findings indicate that by enhancing coping appraisals and discouraging non-protective measures, we can actively mitigate maladaptive responses and promote the adoption of effective adaptation strategies.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain.

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33 years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

High resolution Tibetan Plateau regional reanalysis 1961-present.

With the rapid global warming in recent decades, the Tibetan Plateau (TP) has suffered severe impacts, such as glacier retreat, glacial lake expansion, and permafrost degradation, which threaten the lives and properties of the local and downstream populations. Regional Reanalysis (RR) is vital for TP due to the limitations of observations. In this work, a 62-year (1961-2022) long atmospheric regional reanalysis with spatial resolution of 9 km (convective gray-zone scale) and temporal resolution of 1 hour over the TP (TPRR) was developed using the Weather Research and Forecasting (WRF) model, combined with re-initialization method, spectral nudging (SN), and several optimizations. TPRR is forced by ERA5 at hourly intervals. TPRR outperforms ERA5, realistically capturing climatological characteristics and seasonal variations of precipitation and T2m (air temperature at 2m above ground level). Moreover, TPRR better reproduces the frequency and intensity of precipitation, as well as the diurnal cycle of precipitation. This study also quantifies the wetting trend of 0.0071 mm/year over the TP amid global warming using TPRR.

Association between exposure to ambient air pollution, meteorological factors and atopic dermatitis consultations in Singapore-a stratified nationwide time-series analysis.

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 20% of children globally. While studies have been conducted elsewhere, air pollution and weather variability is not well studied in the tropics. This time-series study examines the association between air pollution and meteorological factors with the incidence of outpatient visits for AD obtained from the National Skin Centre (NSC) in Singapore. The total number of 1,440,844 consultation visits from the NSC from 2009 to 2019 was analysed. Using the distributed lag non-linear model and assuming a negative binomial distribution, the short-term temporal association between outpatient visits for AD and air quality and meteorological variability on a weekly time-scale were examined, while adjusting for long-term trends, seasonality and autocorrelation. The analysis was also stratified by gender and age to assess potential effect modification. The risk of AD consultation visits was 14% lower (RR10th percentile: 0.86, 95% CI 0.78-0.96) at the 10th percentile (11.9 µg/m3) of PM2.5 and 10% higher (RR90th percentile: 1.10, 95% CI 1.01-1.19) at the 90th percentile (24.4 µg/m3) compared to the median value (16.1 µg/m3). Similar results were observed for PM10 with lower risk at the 10th percentile and higher risk at the 90th percentile (RR10th percentile: 0.86, 95% CI 0.78-0.95, RR90th percentile: 1.10, 95% CI 1.01-1.19). For rainfall for values above the median, the risk of consultation visits was higher up to 7.4 mm in the PM2.5 model (RR74th percentile: 1.07, 95% CI 1.00-1.14) and up to 9 mm in the PM10 model (RR80th percentile: 1.12, 95% CI 1.00-1.25). This study found a close association between outpatient visits for AD with ambient particulate matter concentrations and rainfall. Seasonal variations in particulate matter and rainfall may be used to alert healthcare providers on the anticipated rise in AD cases and to time preventive measures to reduce the associated health burden.

Changes in wet bulb globe temperature and risk to heat-related hazards in Bangladesh.

The rise in temperatures and changes in other meteorological variables have exposed millions of people to health risks in Bangladesh, a densely populated, hot, and humid country. To better assess the threats climate change poses to human health, the wet bulb globe temperature (WBGT) is an important indicator of human heat stress. This study utilized high-resolution reanalysis data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF ERA5) to analyze the spatiotemporal changes in outdoor WBGT across Bangladesh from 1979 to 2021, employing Liljegren's model. The study revealed an increase in the annual average WBGT by 0.08-0.5 °C per decade throughout the country, with a more pronounced rise in the southeast and northeast regions. Additionally, the number of days with WBGT levels associated with high and extreme risks of heat-related illnesses has shown an upward trend. Specifically, during the monsoon period (June to September), there has been an increase of 2-4 days per decade, and during the pre-monsoon period (March to May), an increase of 1-3 days per decade from 1979 to 2021. Furthermore, the results indicated that the escalation in WBGT has led to a five-fold increase in affected areas and a three-fold increase in days of high and extreme heat stress during the monsoon season in recent years compared to the earlier period. Trend and relative importance analyses of various meteorological variables demonstrated that air temperature is the primary driver behind Bangladesh's rising WBGT and related health risks, followed by specific humidity, wind speed, and solar radiation.

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions.

Photonic radar, a cornerstone in the innovative applications of microwave photonics, emerges as a pivotal technology for future Intelligent Transportation Systems (ITS). Offering enhanced accuracy and reliability, it stands at the forefront of target detection and recognition across varying weather conditions. Recent advancements have concentrated on augmenting radar performance through high-speed, wide-band signal processing-a direct benefit of modern photonics' attributes such as EMI immunity, minimal transmission loss, and wide bandwidth. Our work introduces a cutting-edge photonic radar system that employs Frequency Modulated Continuous Wave (FMCW) signals, synergized with Mode Division and Wavelength Division Multiplexing (MDM-WDM). This fusion not only enhances target detection and recognition capabilities across diverse weather scenarios, including various intensities of fog and solar scintillations, but also demonstrates substantial resilience against solar noise. Furthermore, we have integrated machine learning techniques, including Decision Tree, Extremely Randomized Trees (ERT), and Random Forest classifiers, to substantially enhance target recognition accuracy. The results are telling: an accuracy of 91.51%, high sensitivity (91.47%), specificity (97.17%), and an F1 Score of 91.46%. These metrics underscore the efficacy of our approach in refining ITS radar systems, illustrating how advancements in microwave photonics can revolutionize traditional methodologies and systems.

The performance of partially substituted composite ester materials with weathered red-bed soil in ecological restoration.

Ester materials have become a significant topic in ecological restoration because of their degradability and lack of pollution. However, these artificial materials have issues such as high resource consumption and high cost. Therefore, finding a scientific substitute for ester materials is crucial to reduce costs. This study proposes the use of weathered red-bed soil to partially replace ester materials. Orthogonal coupled compounding and ecological effect tests were performed to analyze the soil improvement mechanism based on the mineral composition, soil structure, and electrical conductivity properties of the weathered red-bed soil. The experimental findings indicated that the soil modified using ester materials exhibited improved strength, water retention, and aeration owing to changes in the soil structure. Plant germination and height increased by 55% and 37 mm, respectively, when using a ratio of 15 g/m2 absorbent ester material, 2.5 g/m2 adhesive ester material, and 5% weathered red-bed soil. Through this approach, the amount of ester material to be used could be further reduced by 75%. The weathered red-bed soil offers improved ecological effects by altering the physical, mechanical, and hydraulic properties of the soil structure. This study presents a theoretical foundation for ecological conservation using weathered red-bed soil as a substitute for certain ester materials.

Personalized analysis of pain-weather associations: a pilot study.

Background and purpose:

It is a wellknown belief that weather can influence human health, including pain sensation. However, the current data are controversial, which might be due to the wide range of interindividual differences. The present study aimed to characterize the individual pain–weather associations during chronic pain by utilizing several data analytical methods.

The study included 3-3 patients with (P1, P3, and P4) or without (P2, P5, P6) diabetes mellitus and signs of trigeminal neuralgia or low back pain. Subjective pain scores (0–10) and 12 weather parameters (terrestrial, geomagnetic, and solar) were recorded for one month repeated three times daily. Nonparametric Spearman’s correlation (Sp), multiple regression (Mx), and principal component (PCA) analyses were performed to evaluate associations between pain and meteorological factors obtained at the day of recorded pain value, 2 days before and 2 days after the recorded pain, and the changes in these parameters (5 × 12 parameters). Complex scores were calculated based on the results of these analyses.

While the temperature had the highest effects on the pain levels in most of the participants, huge interindividual dif­ferences in the degree and the direction of the associations between pain and weather parameters could be obtained. The analytic methods also revealed subjectspecific results, and the synthesis of different statistical methods as total scores provided a personalized map for each patient, which showed disparate patterns across the study participants. Thus, Participants 2 and 5 had higher scores for Mx compared to Sp; furthermore, certain factors showed opposite direction in their associations with the pain level depending on the type of analysis (Sp vs Mx). In contrast, P3 had a lower score for Mx compared to Sp, which might suggest a low level of weather sensitivity on the association between the different weather parameters in this subject. Furthermore, participants P4 and P6 had a very high level of weather sensitivity, while P1 had an opposite pattern. Regarding the time point-related effects on the pain level, most patients were sensitive to parameters obtained at the same day or two days before, except the P1 subject, who had the highest sensitivity to weather parameters detected two days after.

The present study highlights the importance of integrating different data analysis approaches to elucidate the individual connections between pain and most of the weather parameters. In conclusion, complex personalized profiling should be considered for the characterization of pain–weather associations by applying different data analytical approaches, which may provide feedback to physicians and patients. 

Environmental plastics in the context of UV radiation, climate change, and the Montreal Protocol.

There are close links between solar UV radiation, climate change, and plastic pollution. UV-driven weathering is a key process leading to the degradation of plastics in the environment but also the formation of potentially harmful plastic fragments such as micro- and nanoplastic particles. Estimates of the environmental persistence of plastic pollution, and the formation of fragments, will need to take in account plastic dispersal around the globe, as well as projected UV radiation levels and climate change factors.

Insights into cork weathering regarding colour, chemical and cellular changes in view of outdoor applications.

A comprehensive analysis of outdoor weathering and soil burial of cork during 1-year experiments was carried out with measurements of CIELAB color parameters, cellular observations by scanning electron microscopy, and surface chemical features analysed by ATR-FTIR and wet chemical analysis. Cork applied in outdoor conditions above and below ground retained its physical structure and integrity without signs of deterioration or fracturing. The cellular structure was maintained with some small changes at the one-cell layer at the surface, featuring cellular expansion and minute cell wall fractures. Surface color and chemistry showed distinct results for outdoor exposure and soil burial. The weathered cork surfaces acquired a lighter color while the soil buried cork surfaces became darker. With outdoor weathering, the cork polar solubles increased (13.0% vs. 7.6% o.d. mass) while a substantial decrease of lignin occurred (about 28% of the original lignin was removed) leading to a suberin-enriched cork surface. The chemical impact on lignin is therefore responsible for the surface change towards lighter colors. Soil-burial induced hydrolysis of ester bonds of suberin and xylan, and the lignin-enriched cork surface displayed a dark brown color. FTIR and wet chemical results were consistent. Overall cork showed a considerable structural and physical stability that allows its application in outdoor conditions, namely for building façades or other surfacing applications. Architects and designers should take into account the color dynamics of the cork surfaces.

High-resolution early warning system for human Puumala hantavirus infection risk in Germany.

The fluctuation of human infections by the Puumala orthohantavirus (PUUV) in Germany has been linked to weather and phenology parameters that drive the population growth of its host species. We quantified the annual PUUV-outbreaks at the district level by binarizing the reported infections in the period 2006-2021. With these labels we trained a model based on a support vector machine classifier for predicting local outbreaks and incidence well in advance. The feature selection for the optimal model was performed by a heuristic method and identified five monthly weather variables from the previous two years plus the beech flowering intensity of the previous year. The predictive power of the optimal model was assessed by a leave-one-out cross-validation in 16 years that led to an 82.8% accuracy for the outbreak and a 0.457 coefficient of determination for the incidence. Prediction risk maps for the entire endemic area in Germany will be annually available on a freely-accessible permanent online platform of the German Environment Agency. The model correctly identified 2022 as a year with low outbreak risk, whereas its prediction for large-scale high outbreak risk in 2023 was not confirmed.

Severe weather events and cryptosporidiosis in Aotearoa New Zealand: A case series of space-time clusters.

Occurrence of cryptosporidiosis has been associated with weather conditions in many settings internationally. We explored statistical clusters of human cryptosporidiosis and their relationship with severe weather events in New Zealand (NZ). Notified cases of cryptosporidiosis from 1997 to 2015 were obtained from the national surveillance system. Retrospective space-time permutation was used to identify statistical clusters. Cluster data were compared to severe weather events in a national database. SaTScan analysis detected 38 statistically significant cryptosporidiosis clusters. Around a third (34.2%, 13/38) of these clusters showed temporal and spatial alignment with severe weather events. Of these, nearly half (46.2%, 6/13) occurred in the spring. Only five (38%, 5/13) of these clusters corresponded to a previously reported cryptosporidiosis outbreak. This study provides additional evidence that severe weather events may contribute to the development of some cryptosporidiosis clusters. Further research on this association is needed as rainfall intensity is projected to rise in NZ due to climate change. The findings also provide further arguments for upgrading the quality of drinking water sources to minimize contamination with pathogens from runoff from livestock agriculture.

Assessing Regional Weather's Impact on Spinal Cord Injury Survivors, Caregivers, and General Public in Miami, Florida.

(1) Background: Climate change is increasing the already frequent diverse extreme weather events (EWE) across geographic locations, directly and indirectly impacting human health. However, current ongoing research fails to address the magnitude of these indirect impacts, including healthcare access. Vulnerable populations such as persons with spinal cord injury (pSCI) face added physiologic burden such as thermoregulation or mobility challenges like closure of public transportation. Our exploratory research assessed commute and transport to healthcare facilities as well as the knowledge, attitudes and behaviors (KAB) of pSCI regarding EWE and climate change when compared to pSCI caregivers (CG) and the general public (GP). (2) Methods: A KAB survey was employed to conduct a cross-sectional assessment of pSCI, CG, and GP in Miami from October through November 2019 using snowball sampling. Descriptive and logistic regression statistical analyses were used. (3) Results: Of 65 eligible survey respondents, 27 (41.5%) were pSCI, 11 (17%) CG, and 27 (41.5%) GP. Overall, pSCI reported EWE, particularly flooding and heavy rain, affecting their daily activities including healthcare appointments, more frequently than CG or GP. The overall models for logistic regression looking at commute to and attendance of healthcare appointments were statistically significant. pSCI self-report being less vulnerable than others, and a large proportion of each group was not fully convinced climate change is happening. (4) Conclusions: This study provided insight to the KAB of 3 population subgroups in Miami, Florida. pSCI are significantly more vulnerable to the effects of regional weather events yet exhibit disproportionate self-perception of their vulnerability. Continued and more comprehensive research is needed to characterize the barriers that vulnerable populations face during weather events.

Association between Weather and Self-Monitored Steps in Individuals with Prediabetes and Type 2 Diabetes in Sweden over Two Years.

BACKGROUND: Many studies have identified key factors affecting the rates of engagement in physical activity in older adults with chronic disease. Environmental conditions, such as weather variations, can present challenges for individuals with chronic diseases, such as type 2 diabetes when engaging in physical activity. However, few studies have investigated the influence of weather on daily steps in people with chronic diseases, especially those with prediabetes and type 2 diabetes. OBJECTIVE: This study investigated the association between weather variations and daily self-monitored step counts over two years among individuals with prediabetes and type 2 diabetes in Sweden. METHODS: The study is a secondary analysis using data from the Sophia Step Study, aimed at promoting physical activity among people with prediabetes and type 2 diabetes, which recruited participants from two urban primary care centers in Stockholm and one rural primary care center in southern Sweden over eight rounds. This study measured physical activity using step counters (Yamax Digiwalker SW200) and collected self-reported daily steps. Environmental factors such as daily average temperature, precipitation, and hours of sunshine were obtained from the Swedish Meteorological and Hydrological Institute. A robust linear mixed-effects model was applied as the analysis method. RESULTS: There was no association found between weather variations and the number of steps taken on a daily basis. The analysis indicated that only 10% of the variation in daily steps could be explained by the average temperature, precipitation, and sunshine hours after controlling for age, gender, and BMI. Conversely, individual factors explained approximately 38% of the variation in the observations. CONCLUSION: This study revealed that there was no association between weather conditions and the number of daily steps reported by individuals with prediabetes and type 2 diabetes taking part in a physical activity intervention over two years. Despite the weather conditions, women and younger people reported more steps than their male and older counterparts.

Positive effects of public breeding on US rice yields under future climate scenarios.

In this study, we model and predict rice yields by integrating molecular marker variation, varietal productivity, and climate, focusing on the Southern U.S. rice-growing region. This region spans the states of Arkansas, Louisiana, Texas, Mississippi, and Missouri and accounts for 85% of total U.S. rice production. By digitizing and combining four decades of county-level variety acreage data (1970 to 2015) with varietal information from genotyping-by-sequencing data, we estimate annual historical county-level allele frequencies. These allele frequencies are used together with county-level weather and yield data to develop ten machine learning models for yield prediction. A two-layer meta-learner ensemble model that combines all ten methods is externally evaluated against observations from historical Uniform Regional Rice Nursery trials (1980 to 2018) conducted in the same states. Finally, the ensemble model is used with forecasted weather from the Coupled Model Intercomparison Project across the 110 rice-growing counties to predict production in the coming decades for Composite Variety Groups assembled based on year of release, breeding program, and several breeding trends. Results indicate positive effects over time of public breeding on rice resilience to future climates, and potential reasons are discussed.

Short-Term Changes in Weather Conditions and the Risk of Acute Coronary Syndrome Hospitalization with and without ST-Segment Elevation: A Focus on Vulnerable Subgroups.

Background and Objectives: Acute coronary syndrome (ACS), a prevalent global cardiovascular disease and leading cause of mortality, is significantly correlated with meteorological factors. This study aims to analyze the impact of short-term changes in meteorological factors on the risk of ACS, both with and without ST-segment elevation, and to identify vulnerable subgroups. Materials and Methods: Daily ACS admissions and meteorological variables were collected from October 2016 to December 2021. A generalized linear model (GLM) with a Poisson distribution was employed to examine how short-term fluctuations in meteorological parameters influence ACS hospitalizations. Subgroup analyses were conducted to identify the populations most vulnerable to climate change. Results: Multiple regression analyses showed that short-term fluctuations in atmospheric pressure (≥10 mbar) and air temperature (≥5 °C) seven days prior increased the number of ACS hospitalizations by 58.7% (RR: 1.587; 95% CI: 1.501-1.679) and 55.2% (RR: 1.552; 95% CI: 1.465-1.644), respectively, notably impacting ST-segment elevation myocardial infarctions (STEMIs). The least pronounced association was observed between the daily count of ACS and the variation in relative air humidity (≥20%), resulting in an 18.4% (RR: 1.184; 95% CI: 1.091-1.286) increase in the risk of hospitalization. Subgroup analysis revealed an increased susceptibility among men and older adults to short-term variations in weather parameters. Conclusions: The findings indicate that short-term changes in weather conditions are associated with an increased risk of ACS hospitalizations, particularly STEMIs. Male and older adult patients exhibit heightened susceptibility to variations in climatic factors. Developing effective preventive strategies is imperative to alleviate the adverse consequences of these environmental risk factors.

'They Talk about the Weather, but No One Does Anything about It': A Mixed-Methods Study of Everyday Climate Change Conversations.

Understanding everyday conversations about climate change may provide insights into framing the issue to promote climate change action. As part of a longitudinal online study in the US launched in June 2021, 805 respondents were asked if they had discussed climate change with a friend or family member in the prior month; if not, why not, and if yes, they were asked to delineate the conversation topic. Concurrent mixed methods were used to analyze the data. The majority (62.6%) of respondents reported not having a conversation about climate change in the prior month. Among those who indicated that they had discussed climate change, five themes were identified from the conversation topics, with many having reported discussing the impact of climate change on weather patterns. Very few discussed actions to address climate change, and most of these discussions focused on individual-level behaviors rather than collective actions. Among participants who had not recently discussed climate change, the most prevalent theme was that it was not a priority or an issue they cared about. Results suggest that conversations may not lead to collective actions and that policymakers and environmental organizations should provide guidance on effectively channeling climate change concerns into action.

Weather or not-Global climate databases: Reliable on tropical mountains?

Global, spatially interpolated climate datasets such as WorldClim and CHELSA, widely used in research, are based on station data, which are rare in tropical mountains. However, such biodiversity hotspots are of high ecological interest and require accurate data. Therefore, the quality of such gridded datasets needs to be assessed. This poses a kind of dilemma, as proving the reliability of these potentially weakly modelled data is usually not possible due to the lack of stations. Using a unique climate dataset with 170 stations, mainly from the montane and alpine zones of sixteen mountains in Tanzania including Kilimanjaro, we show that the accuracy of such datasets is very poor. Not only is the maximum amount of mean annual precipitation drastically underestimated (partly more than 50%), but also the elevation of the precipitation maximum deviates up to 850m. Our results show that, at least in tropical regions, they should be used with greater caution than before.

Wastewater generation model to predict impacts of urine separation on wastewater treatment plants.

Wastewater treatment plants (WWTPs) are under increasing pressure to enhance resource efficiency and reduce emissions into water bodies. The separation of urine within the catchment area may be an alternative to mitigate the need for costly expansions of central WWTPs. While previous investigations assumed a spatially uniform implementation of urine separation across the catchment area, the present study focuses on an adapted stochastic wastewater generation model, which allows the simulation of various wastewater streams (e.g., urine) on a household level. This enables the non-uniform separation of urine across a catchment area. The model is part of a holistic modelling framework to determine the influence of targeted urine separation in catchments on the operation and emissions of central WWTPs, which will be briefly introduced. The wastewater generation model is validated through an extensive sampling and measurement series. Results based on observed and simulated wastewater quantity and quality for a catchment area of 366 residents for two dry weather days indicate the suitability of the model for wastewater generation and transport modelling. Based on this, four scenarios for urine separation were defined. The results indicate a potential influence of spatial distribution on the peaks of total nitrogen and total phosphorus.