Future changes (2015–2100) in precipitation and meteorological droughts in Lake Urmia Basin were investigated using an average mean ensemble of eight general circulation models (GCMs) with high-resolution datasets in socioeconomic pathway scenarios (SSPs) from the Coupled Model Intercomparison Project (CMIP6). In order to project the drought, the standardized precipitation index (SPI) was calculated. Overall, the results revealed that precipitation in Lake Urmia Basin will decrease by 3.21% and 7.18% in the SSP1-2.6 and SSP5-8.5 scenarios, respectively. The results based on 6-month-timescale SPI indices projected more “Extremely dry” events in SSP5-8.5 scenarios. The frequency of “Extremely dry” months in SSP5-8.5 compared to SSP1-2.6 is expected to increase by 14, 7, 14, 10, 5, 14, and 7 months for the Mahabad, Maragheh, Saqez, Sarab, Tabriz, Takab, and Urmia stations, respectively. In contrast, the frequency of “Extremely wet” months will decline for all stations in Lake Urmia Basin. The results of this study provide useful insight for considering drought prevention measures to be implemented in advance for Lake Urmia Basin, which is currently experiencing various environmental issues.

https://www.mdpi.com/2073-4441/15/8/1558/pdf?version=1682166594

Projection of Future Meteorological Droughts in Lake Urmia Basin, Iran

Climate change is a threat to mining and other industries, especially those involving water supply and management, by inducing or amplifying some climatic parameters such as changes in precipitation regimes and temperature extremes. Using the latest NASA NEX-GDDP-CMIP6 datasets, this study quantifies the level of climate change that may affect the development of two mine sites (site 1 and site 2) in north–east Kazakhstan. The study analyses the daily precipitation and maximum and minimum temperature a of a number of global circulation models (GCM) over three future time periods, the 2040s, 2060s, and 2080s, under two shared socioeconomic pathway (SSP) scenarios, SSP245 and SSP585, against the baseline period 1981–2014. The analyses reveal that: (1) both maximum and minimum temperature will increase under both SSP in those time periods, with the rate of change for minimum temperature being higher than maximum temperature. Minimum temperature, for example, will increase by 2.2 and 2.7 °C under SSP245 and SSP585, respectively, over the 2040s period at both sites; (2) the mean annual precipitation will increase by an average rate of 7% and 10.5% in the 2040s for SSP245 and 17.5% and 7.5% for SSP585 in the 2080s at site 1 and site 2, respectively. It is also observed that summer months will experience drier condition whilst all other months will increase in precipitation; (3) the values of 24 h precipitation with a 10 year return period will also increase under both SSP scenarios and future time periods for most of the studied GCM and at both mine sites. For instance, over the near future period, a 6.9% and 2.8% increase in 10 year 24 h precipitation is expected to happen over site 1 and site 2, respectively, under SSP245. These predicted changes should be considered as design criteria adjustments for projected water supply and water management structures.

Using CMIP6 Models to Assess Future Climate Change Effects on Mine Sites in Kazakhstan

Climate change has led to the frequent occurrence of high-temperature weather, which has various adverse effects on health, ranging from blood metabolism to systemic organ function. In particular, the sequelae of heat stress injury in most people are related to the nervous system. However, the mechanisms between heat stress and mental health conditions, especially heat stress and anxiety, remain unclear. We attempted to elucidate the effect of heat exposure intervention on anxiety levels in the population and its mechanism. We first carried out randomized controlled trial in 20 college students in Beijing, China, to obsereve the results of the effects of heat exposure intervention on human anxiety. Then, we collected blood samples before and after heat exposure experiment and used metabolomic and transcriptomic approaches to quantify serum metabolites and ELISA measurements to explore the underlying mechanisms. We found that even 1.5-hour heat exposure intervention significantly increased anxiety levels. Heat stress-induced anxiety was mediated by the activation of the HPA axis, inflammation, oxidative stress, and subsequently unbalanced neurotransmitters. Metabolites such as BDNF, GABA, and glucocorticoids released by the adrenal glands are biomarkers of heat stress-induced anxiety. We have demonstrated a causal link between heat stress and anxiety, explored possible biological pathway between heat stress and anxiety. Heat stress can cause the activation of the HPA axis and lead to changes in the body's metabolism, resulting in a series of changes such as inflammation and oxidative stress, leading to anxiety. This study reveals hidden health cost of climate change that has underexplore previously, and also reminds us the importance of immediate climate actions. 

Heat exposure intervention, anxiety level, and multi-omic profiles: a randomized crossover study

In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimization algorithms, such as grey wolf optimizer (GWO) and particle swarm optimization (PSO), to evaluate the potential of a relatively new algorithm and the impact that optimization algorithms can have on the performance of regression models. The Kerala state in India has been chosen as the test site due to the large number of recorded incidents in the recent past. The study started with 18 potential predisposing factors, which were reduced to 14 after a multi-approach feature selection technique. Six susceptibility models were implemented and compared using the machine learning algorithms alone and combining each of them with the two optimization algorithms: SVR, CatBoost, SVR-PSO, CatBoost-PSO, SVR-GWO, and CatBoost-GWO. The resulting maps were validated with an independent dataset. The performance rankings, based on the area under the receiver operating characteristic curve (AUC) metric, are as follows: CatBoost-GWO (AUC = 0.910) had the highest performance, followed by CatBoost-PSO (AUC = 0.909), CatBoost (AUC = 0.899), SVR-GWO (AUC = 0.868), SVR-PSO (AUC = 0.858), and SVR (AUC = 0.840). Other validation statistics corroborated these outcomes, and the Friedman and Wilcoxon-signed rank tests verified the statistical significance of the models. Our case study showed that CatBoost outperformed SVR both in case the models were optimized or not; the introduction of optimization algorithms significantly improves the results of machine learning models, with GWO being slightly more effective than PSO. However, optimization cannot drastically alter the results of the model, highlighting the importance of setting up of a rigorous susceptibility model since the early steps of any research. 

Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility

This systematic literature review examines mercury contamination in the mangrove ecosystems, associating the complex interactions between climate change and mercury contamination, amplifying risks to the environment and human populations. Mercury, a highly toxic non-essential trace element, exist naturally and can be massively produced through anthropogenic activities such as artisanal gold mining, coal combustion, and non-ferrous metals production poses a significant threat to public health, particularly over the ingestion of polluted fish. Atmospheric deposition and conversion of inorganic mercury into the more toxic methylmercury are major pathways for mercury contamination in mangrove ecosystems. Climate change worsens the issue by impacting mercury distribution, bioavailability, and fate in mangrove environments. This has implications for marine and freshwater biodiversity, disrupting ecological interactions and endangering populations. Rising sea levels, altered precipitation patterns, and elevated temperatures associated with climate change influence mercury dynamics and concentrations in aquatic organism such as fish. As mercury in fish, a significant part of diets across the globe, raises concerns for human health, the development of successful measures to reduce mercury pollution and its related dangers relies on a thorough understanding of these relationships. The review will support policymaking aligned with the Minamata Convention and aid in developing effective strategies to mitigate mercury pollution. Protecting human health and the mangrove biodiversity is crucial for the sustainability of fish in the face of climate change. 

Interplay of climate change on mercury in fish within mangrove ecosystems: A brief systematic literature review

Gradually increasing durations of high temperature caused by climate change harm the health of individuals and then lead to death. This study aimed to investigate the relationship between durations of different daily mean air-temperature categories and mortality in China and forecast future mortality changes in China for 2020–2050 under Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios. The daily mean air temperature was divided into 10 categories, and the days under each air-temperature category were counted during the period of 2000–2015. Then, the connection between the days of each of the 10 air-temperature categories and mortality was established using the semi parametric regression model. Results indicate that the days of the >32 °C category have the largest impact on mortality in China, with the death rate increasing by 23‰ for one additional day. Predictions reveal that mortality in China will increase 25.48% and 26.26% under the RCP4.5 and RCP8.5 scenarios, respectively. Moreover, the mortality of 86 regions in western China will increase 30.42%. Therefore, in the future, the increasing duration of days of high temperatures will raise the mortality rate in China and aggravate the mortality gap between developed and underdeveloped regions.

https://www.mdpi.com/2073-4433/13/11/1775/pdf?version=1666941916

Assessing Impact of Temperature Variability of Climate Change on Mortality Based on Multiple GCM Projections in China

The impacts of climate change on regional temperature characteristics and climate zones

Abstract Extreme precipitation-induced landslide events are projected to increase under climate change, which poses a serious threat to human lives and property. In this study, a global-scale landslide risk assessment model was established using global landslide data, by considering landslide hazard, exposure, and vulnerability. The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes. The results show that compared to the 30-year period from 1971 to 2000, the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7% and 10%, respectively, in the future 30-year periods of 2031–2060 and 2066–2095. The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380, respectively (with growth rates of 140% and 160%), during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario. The top 10 countries with the highest casualty risk of landslides are China, Afghanistan, India, the Philippines, Indonesia, Rwanda, Turkey, Nepal, Guatemala, and Brazil, 60% of which are located in Asia. The frequency and intensity of extreme precipitation will increase under climate change, which will lead to an increase in casualties from landslides in mountainous areas globally, and this risk should be taken seriously. The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change, which still has uncertainty in terms of outcomes, and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk. 

Assessing Global Landslide Casualty Risk Under Moderate Climate Change Based on Multiple GCM Projections

Self-management behaviours can be crucial to improving disease symptoms and health outcomes in rheumatoid arthritis (RA) patients. Currently, the tools available for measuring self-management behaviours in RA patients are either generalized for patients with chronic diseases, which lack specificity or have poor reliability in the only specific scale-self-care behaviours scale (SCBS). The aim of this study was to develop a self-management behaviours scale for RA patients and evaluate its psychometric properties.The study included two steps: scale development and the psychometric evaluation. The items were developed from a literature review, in-depth individual interviews, nominal group technique, Delphi expert consultation, and a pilot test. For the psychometric evaluation, a sample of 561 patients with RA was recruited. Item analysis, content validity, exploratory and confirmatory factor analysis, convergent and discriminant validity, and internal consistency reliability were conducted to examine the psychometric properties of the RA-SMBS.The final scale consists of 23 items with 4 dimensions, including medication management, exercise and joint protection, resource utilization and emotional management, and symptom management. The content validity index was 0.78. Exploratory factor analysis explained 61.89% of the total item variance. Confirmatory factor analysis indicated that the RA-SMBS fit well. Good internal consistency reliability was demonstrated (Cronbach's alpha = 0.908), and the test-retest reliability was found to be acceptable (ICC = 0.628, r = 0.780).The scale has good content validity, construct validity, and internal consistency reliability. It can be used to assess the level of self-management behaviours in RA patients. 

/pdf/development-and-psychometric-evaluation-of-a-self-management-2o13kfb7.pdf

Development and psychometric evaluation of a self-management behaviours scale in rheumatoid arthritis patients (RA-SMBS)

The effects of buffer solution and concentration on atomic force microscope (AFM) imaging of DNA molecules were investigated. It was found that the buffer solution type and the DNA solution concentration affected the DNA topological conformations, and had distinct AFM imaging results. Moreover, DNA molecules diluted with the TE buffer under the DC electric field present stretching topological conformations. This study contributes to the understanding of formation factors of various DNA topological conformations and further promotes the application of DNA in nanotechnology and biology.

Effects of Buffer Solution and Concentration on AFM Imaging of DNA Molecules

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