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.

Characteristics and adsorption behavior of typical microplastics in long-term accelerated weathering simulation.

Microplastics can function as carriers in the environment, absorbing various toxins and spreading to diverse ecosystems. Toxins accumulated in microplastics have the potential to be re-released, posing a threat. In this study, two typical plastics, namely polyethylene (PE) and polystyrene (PS), along with the degradable plastic poly(butylene adipate-co-terephthalate) (PBAT), were subjected to a long-term ultraviolet alternating weathering experiment. The study investigated the variations in the weathering process and pollutant adsorption of microplastics of different particle sizes. Furthermore, the adsorption capacity of microplastics for various pollutants was assessed. The findings indicate that particle size significantly influences weathering, leading to variations in adsorption capacity. The weathered PE displays a higher adsorption capacity for azo dyes. Additionally, the adsorption capacity of PBAT for neutral red is double that of antibiotics. Importantly, the maximum adsorption capacity of PBAT for pollutants after aging is approximately 10 times greater than that of PE. Consequently, degradable plastics undergoing weathering in the natural environment may pose a higher ecological risk than traditional plastics.

Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19?

The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus's spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity. HIGHLIGHTS: • The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area. • Weather and climate serve as environmental drivers in propagating COVID-19. • Solar radiation influences the spreading of COVID-19. • The correlation between weather and population as the factor in spreading of COVID-19.

The effect of different extreme weather events on attitudes toward climate change.

Can exposure to extreme weather change political opinion and preferences about climate change? There is a growing literature on both the effects of extreme weather events and the factors explaining attitudes toward global warming, though there remains no clear consensus about whether being exposed to extreme weather influences public opinion about climate change. We contribute to this literature by studying the impact of a variety of extreme weather events associated with climate variability, including severe storms, floods, fires, and hurricanes, on attitudes toward climate change. Specifically, we use a three-wave panel survey and a dynamic difference-in-differences design to analyze public opinion data at the individual level in the US. We find that exposure to only one extreme weather type-fires-has a small but significant effect on acknowledging the existence of climate change and supporting the need for action. However, that impact quickly vanishes, and other types of extreme weather do not appear to have any effect on opinion.

Influence of meteorological variables and air pollutants on measurements from automatic pollen sampling devices.

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency. The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm). Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables. Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

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.

Trends in childhood leukemia incidence in urban countries and their relation to environmental factors, including space weather.

Leukemia is the most common cancer in children. Its incidence has been increasing worldwide since 1910th, suggesting the presence of common sources of the disease, most likely related to people's lifestyle and environment. Understanding the relationship between childhood leukemia and environmental conditions is critical to preventing the disease. This discussion article examines established potentially-carcinogenic environmental factors, such as vehicle emissions and fires, alongside space weather-related parameters like cosmic rays and the geomagnetic field. To discern the primary contributor, we analyze trends and annual variations in leukemia incidence among 0-14-year-olds in the United States, Canada, Australia, and Russia from 1990 to 2018. Comparisons are drawn with the number of vehicles (representing gasoline emissions) and fire-affected land areas (indicative of fire-related pollutants), with novel data for Russia introduced for the first time. While childhood leukemia incidence is rising in all countries under study, the rate of increase in Russia is twice that of other nations, possibly due to a delayed surge in the country's vehicle fleet compared to others. This trend in Russia may offer insights into past leukemia levels in the USA, Canada, and Australia. Our findings highlight vehicular emissions as the most substantial environmental hazard for children among the factors examined. We also advocate for the consideration of potential modulation of carcinogenic effects arising from variations in cosmic ray intensity, as well as the protective role of the geomagnetic field. To support the idea, we provide examples of potential space weather effects at both local and global scales. The additional analysis includes statistical data from 49 countries and underscores the significance of the magnetic field dip in the South Atlantic Anomaly in contributing to a peak in childhood leukemia incidence in Peru, Ecuador and Chile. We emphasize the importance of collectively assessing all potentially carcinogenic factors for the successful future predictions of childhood leukemia risk in each country.

Subaerial weathering drove stabilization of continents.

Earth's silica-rich continental crust is unique among the terrestrial planets and is critical for planetary habitability. Cratons represent the most imperishable continental fragments and form about 50% of the continental crust of the Earth, yet the mechanisms responsible for craton stabilization remain enigmatic1. Large tracts of strongly differentiated crust formed between 3 and 2.5 billion years ago, during the late Mesoarchaean and Neoarchaean time periods2. This crust contains abundant granitoid rocks with elevated concentrations of U, Th and K; the formation of these igneous rocks represents the final stage of stabilization of the continental crust2,3. Here, we show that subaerial weathering, triggered by the emergence of continental landmasses above sea level, facilitated intracrustal melting and the generation of peraluminous granitoid magmas. This resulted in reorganization of the compositional architecture of continental crust in the Neoarchaean period. Subaerial weathering concentrated heat-producing elements into terrigenous sediments that were incorporated into the deep crust, where they drove crustal melting and the chemical stratification required to stabilize the cratonic lithosphere. The chain of causality between subaerial weathering and the final differentiation of Earth's crust implies that craton stabilization was an inevitable consequence of continental emergence. Generation of sedimentary rocks enriched in heat-producing elements, at a time in the history of the Earth when the rate of radiogenic heat production was on average twice the present-day rate, resolves a long-standing question of why many cratons were stabilized in the Neoarchaean period.

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.

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.

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. 

Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.

Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making.

Factors affecting severity of wildfires in Scottish heathlands and blanket bogs.

Temperate heathlands and blanket bogs are globally rare and face growing wildfire threats. Ecosystem impacts differ between low and high severity fires, where severity reflects immediate fuel consumption. This study assessed factors influencing fire severity in Scottish heathlands and blanket bogs, including the efficacy of the Canadian Fire Weather Index System (CFWIS). Using remote sensing, we measured the differenced Normalised Burn Ratio at 92 wildfire sites from 2015 to 2021. We used Generalised Additive Mixed Models to investigate the impact of topography, habitat wetness, CFWIS components and 30-day weather on severity. Dry heath exhibited higher severity than wet heath and blanket bog, and slope, elevation and south facing aspect were positively correlated to severity. Weather effects were less clear due to data scale differences, yet still indicated weather's significant role in severity. Rainfall had an increasingly negative effect from approximately 15 days before the fire, whilst temperature had an increasingly positive effect. Vapour Pressure Deficit (VPD) was the weather variable with highest explanatory value, and predicted severity better than any CFWIS component. The best-explained fire severity model (R2 = 0.25) incorporated topography, habitat wetness wind and VPD on the day of the fire. The Drought Code (DC), predicting organic matter flammability at ≥10 cm soil depth, was the CFWIS component with the highest predictive effect across habitats. Our findings suggest that wildfires in wet heath and blanket bogs are typically characterised by low severity, but that warmer, drier weather may increase the risk of severe, smouldering fires which threaten peatland carbon stores.

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.