Impacts of climate change on the fate of contaminants through extreme weather events

Elevated mercury wet deposition in the biodiversity hotspot of southwestern China

The West China Rain Zone (WCRZ), one of worldwide biodiversity hotspots, is vulnerable to pollutant exposure. Mercury (Hg), as a global toxic pollutant, could enter into this ecosystem by wet and dry deposition, potentially posing ecological risks. Therefore, it is essential to investigate the patterns, controlling factors, and source contributions of Hg deposition in this region. This study presents a three-year comprehensive analysis of atmospheric Hg wet deposition at Mt. Emei of this biodiversity hotspot region. Results revealed the elevated Hg concentrations with volume-weighted mean concentration (VWMC) of 15.6 ± 9.8 ng L-1 and deposition fluxes of 47.8 ± 4.3 μg m-2 in the WCRZ. The wet deposition flux was twice the magnitude of dry deposition flux in this region. This value exceeded the deposition levels observed in many other regions globally, with WCRZ deposition levels being 1.1-15.6 times that of other biodiversity hotspots worldwide. Source apportionment analysis revealed that Hg in wet deposition predominantly originated from local anthropogenic sources. The transboundary Hg of anthropogenic sources in South Asia contributed to ∼20% of Hg source contribution during winter periods. Finally, we suggest the elevated Hg wet deposition inducing a potentially ecological risk to wildlife in this biodiversity hotspot regions of Southwest China.

Amazon's climate crossroads: analyzing air pollution and health impacts under machine learning-based temperature increase scenarios in Northern Mato Grosso, Brazil

Air pollution has long been a public health concern in South America, now increasingly linked to climate change. In Brazil, this issue is particularly acute in smaller cities with limited monitoring infrastructure. Sinop, located in the Amazon biome of Mato Grosso, exemplifies the intersection of agricultural expansion and environmental vulnerability. This study presents a comprehensive assessment of meteorological conditions, air pollutant levels, and related health impacts in Sinop, focusing on PM2.5, PM10, and ground-level ozone (O3). Using 2022 data, we applied Health Impact Assessment (HIA) via the WHO's AirQ + tool and simulated temperature rise scenarios through a machine learning model. ur findings indicate that O3 levels are highly sensitive to meteorological variation, while PM2.5 and PM10 concentrations are projected to increase by up to 50% and 70%, respectively, under extreme warming scenarios. These changes correspond to an estimated increase in non-external mortality of over 90% for PM2.5 and more than 1000% for PM10. Seasonal simulations revealed that the dry season, marked by intense biomass burning, significantly exacerbates pollutant concentrations. These results underscore the growing burden of air pollution on public health in rapidly urbanizing regions. They also highlight the urgent need for enhanced air quality monitoring and climate-adaptive public health strategies in vulnerable areas such as the southern Amazon.

Nanopesticides ecotoxicity towards traditional ones: A case of study with Daphnia magna and λ-cyhalothrin

Nanotechnology has contributed to agriculture industry with novel products to improve the targeted delivery of active ingredients (a.i.), enable gradual release, avoid premature degradation, and increase efficacy. The properties of nanopesticides make their drift and environmental behavior more unpredictable than traditional formulations. Our aim was to compare the ecotoxicity of two insecticides with λ-cyhalothrin as a.i.: a nano-based one and a traditional emulsified, considering the incidence of temperature according to climate change prospections (20 and 24 °C). We evaluated their effects on Daphnia magna survival, body stores, and butyrylcholinesterase (BChE) activity. Although after 24 h the traditional formulation had greater lethality than the nanopesticide, after 48 h this pattern was reversed. At 24 °C the lethality of both pesticides increased. BChE activity was inhibited at 24 °C by both pesticides. In general, the increase in temperature negatively affected protein and glycogen content. The traditional formulation reduced glycogen content at 24 °C. A decrease in lipid content and the whole energy budget was observed in organisms exposed to the traditional formulation at both temperatures. Although the nano-based pesticide developed a delayed toxicity, it was more lethal than the traditional one in the long term. The temperature rise worsened the pesticides effects in terms of decreased survival, BChE activity inhibition, and energy reserves depletion. The development and regulation of new eco-safer nanopesticides needs to be complemented by their ecotoxicological assessment. It is imperative to analyze their impact in the context of climate change in order to develop mitigation and adaptation strategies.

Mental health measures during wildfires: the case of Ofunato

Climate change is increasing the threat of wildfires, causing severe physical, economic, and psychological impacts. This study analyzed the mental health effects of the February 2025 wildfire in Ofunato City, using government reports, media coverage, and disaster-related mental health studies. Stress, PTSD, social isolation, and economic hard-ship were identified as key issues, particularly among vulnerable groups such as the elderly and those with chronic illnesses. Economic instability from unemployment and delayed recovery may worsen these mental health outcomes. Effective interventions include immediate psychological support, long-term counseling, and rebuilding social connections. Collaboration among local governments, medical institutions, and support groups is essential to provide comprehensive mental health care, enhance community resilience, and support residents affected by wildfires' recovery.

A comprehensive study of recent maximum power point tracking techniques for photovoltaic systems

The percentage of renewable energy in the global mix of energy sources is rising annually, with solar photovoltaics (PVs) accounting for most capacity expansions due to their widespread availability, safety, and cleanliness. Because the amount of energy generated is limited by the poor efficiency of the photovoltaic cells and the characteristics of the connected load and weather fluctuation, maximum power point tracking (MPPT) strategies are crucial for maximizing the power delivered in PV production systems. These MPPT techniques face several issues and limitations, so this paper has focused more on modeling and developing the MPPT techniques in PV systems. The MPPT-based methodologies fall into three categories: artificial intelligence (AI), metaheuristic, and conventional. Five of these techniques have been proposed here to solve the MPPT problem. The perturb & observe (P&O) and incremental conductance (INC) methods have been used as conventional methods. In contrast, particle swarm optimization (PSO) has been used as a metaheuristic method. Finally, the artificial neural network (ANN) and fuzzy logic control (FLC) techniques have been used as AI methods. Each technique is analyzed critically in terms of tracking speed, algorithm complexity, and dynamic tracking in different environmental conditions. Furthermore, this comprehensive study of MPPT methods aims to be a guideline for selecting the best MPPT method for optimal operation under the environmental conditions of PV systems by employing multi-criteria decision-making (MCDM) based on AHP and CRITIC weighting methods, as well as the ranking method (VIKOR), to compare and rank the MPPT methods based on their effectiveness and economic feasibility. The results show AI techniques have a tracking efficiency of almost 99% when compared to other examined approaches, and they give quick and efficient tracking speed.

Identification of key genes and signaling pathways in coconut (Cocos nucifera L.) under drought stress via comparative transcriptome analysis

BACKGROUND

Drought stress has become a pervasive environmental challenge, significantly impacting all stages of plant growth and development under changing climatic conditions worldwide. In coconut, drought stress critically impairs reproductive development, notably reducing the quality of pollen and gametes during fertilization. Therefore, the seedlings of the aromatic coconut variety were subjected to drought stress for varying durations: control (no stress), 7 days, 14 days, and 21 days to find the potential molecular mechanisms and genes related to coconut drought tolerance through transcriptomic analysis. Our study may provide a theoretical basis for investigations into drought stress tolerance that will be useful for further coconut improvement.

RESULTS

We assessed antioxidant enzyme activity and conducted comparative transcriptome analyses of aromatic coconut under different drought conditions (7, 14, and 21 days). Our findings revealed significant rises in superoxide dismutase (SOD), peroxidase (POD) activities and proline (Pro) content across all drought periods compared to control plants, suggesting that these enzymes play a crucial role in the adaptive response of coconuts to drought stress. RNA-seq data identified 280, 729, and 6,698 differentially expressed genes (DEGs) at 7, 14, and 21 days, respectively. Principal Component Analysis (PCA) revealed that coconut samples were scattered and separated across different treatment points, suggesting the presence of differentially expressed genes (DEGs), particularly in the 21 day drought treatment (GH21d). KEGG pathway analysis indicated that DEGs were significantly enriched in pathways related to plant-pathogen interaction, plant hormone signaling, and mitogen-activated protein kinase (MAPK) signaling. Functional annotation of these DEGs revealed key candidate genes involved in several hormone signaling pathways, including abscisic acid (ABA), jasmonates (JA), auxin (AUX), brassinosteroids (BR), ethylene (ET), and gibberellin (GA), along with MAPK pathway which may regulate plant adaptation to drought stress through processes such as plant growth, cell division, stomatal closure, root growth, and stomatal development. This study provides valuable insights into the genetic and molecular basis of drought tolerance in coconuts, paving the way for the improvement of drought-tolerant coconut varieties.

CONCLUSIONS

Under drought stress, the expression of genes related to plant growth, stomatal closure, cell division, stress response, adaptation, and stomatal development appears to play a critical role in drought tolerance in coconut. Our results revealed that multiple genes may contribute to the drought tolerance mechanism in coconut through various hormone signaling pathways, including ABA, JA, auxin, BR, GA, and ethylene. These findings offer new insights into the key molecular mechanisms governing drought tolerance in aromatic coconut. Furthermore, the candidate genes and pathways identified in this study could be valuable for developing strategies to enhance drought tolerance in coconut plants.

CLINICAL TRIAL NUMBER

Not Applicable.

Risk aversion or adaptation? Public choices in sports participation under climate risks

Introduction

The increasing frequency and severity of climate risks have significantly impacted public health behaviors, particularly sports participation. Understanding how individuals respond to these environmental shocks is crucial for designing effective health and climate adaptation policies. This study examines the short-term and long-term effects of climate risks on sports participation among middle-aged and young adults, exploring the underlying mechanisms driving these behavioral changes.

Methods

Using data from the 2014 to 2022 China Family Panel Studies (CFPS), this study employs fixed-effects models, two-stage least squares (2SLS) estimation, and a four-stage mediation model to address potential endogeneity and uncover causal relationships. Climate risks are assessed using multiple proxy variables, and robustness checks ensure the reliability of the findings.

Results

In the short term, climate risks significantly reduce the frequency of sports participation. This effect remains consistent across different model specifications and estimation methods. Mechanism analysis reveals that climate risks lower life satisfaction and increase digital engagement, both of which influence individuals' physical and mental health. While climate risks initially discourage sports participation, long-term adaptation occurs through digital engagement and indoor exercise, leading to improved health outcomes. Heterogeneity analysis indicates that the negative short-term effects are more pronounced in urban and western regions, with rural and western areas experiencing no significant long-term positive effects.

Discussion

This study highlights both the inhibitive short-term effects and adaptive long-term responses to climate risks in sports participation. The findings provide insights into how individuals adjust their health-related behaviors under environmental stress and offer policy recommendations to promote climate adaptation and public health through targeted interventions.

Storm Daniel Extreme Flood Event in Thessaly, Greece: Assessing the Pollution Status of the Impacted Coastal Marine Areas through Extended Screening of Emerging Contaminants Using LC-TIMS-HRMS

In this study, we investigate the pollution status in impacted seawaters and sediments of coastal areas in the region of Thessaly, central Greece, following the Storm Daniel extreme flood event in September 2023, a phenomenon classified as the deadliest Mediterranean tropical-like cyclone in recorded history and one of the costliest cyclones beyond the North Atlantic. For this, an ultra high-performance liquid-chromatography-based wide-scope target screening of more than 2300 LC-amenable emerging contaminants (ECs) was carried out utilizing the technique of TIMS-QTOF-MS. Our results highlight the fact that the extreme floodwater runoff resulted in an extensive transportation of terrestrial derived material from the impacted areas and a major transport of plant protection products, pharmaceuticals, including even illicit drugs, surfactants, industrial chemicals, and per- and polyfluorinated alkyl compounds (PFAS) at sea, as a result of the overflowing of local wastewater treatment plants and the destruction of agricultural, industrial, and port facilities. Overall, the phenomenon resulted in a major alteration of the classification of the determined ECs in seawaters and most importantly in sediments of the study area, with potential implications for their environmental quality status.

Quantifying the influence of climate change on pesticide risks in drinking water

Climate change can influence pesticide contamination and resulting human health risks due by altering weather conditions that drive pesticide fate and transport. However limited research has examined these effects, leaving regulatory frameworks and adaptation strategies unable to address future pesticide risks. This study develops a novel probabilistic model to quantify climate change impacts on pesticide-related human health risks under two different climate scenarios, using study locations in the north-east and south-west of Ireland. Results indicate that pesticide concentrations in drinking water are projected to exceed legal limits more frequently, and by greater amounts, under all climate scenarios, with associated health risks increasing by an average of 18 % under RCP 4.5 (2050) and 38 % under RCP8.5 (2100). The model results also indicate significant regional variation in health risk, with risk 48 % higher in the south-west than the north-east under baseline conditions. Climate change effects intensify these regional variances with risk up to 70 % higher under RCP4.5 (2050), and 85 % higher under RCP8.5 (2100). Despite these increases, overall pesticide human health risks are likely to remain low in Ireland under future climates. This study presents a probabilistic framework that may be applied internationally to quantify the impact of climate change on human health risk at a local-scale and may be adapted for different site conditions and climate projections to suit users' needs. This approach can inform future pesticide management programmes by identifying vulnerable areas and key pesticides under changing climate conditions, emphasizing the importance of incorporating climate change into pesticide risk mitigation and public health strategies.

Hydrological connectivity and dissolved organic matter impacts nitrogen and antibiotics fate in river-lake system before and after extreme wet season

The impact and mechanism of hydrological connectivity and dissolved organic matter on the fate of nitrogen and antibiotics are still lack off in a river-lake connected system under climate extreme events. This study examined the fate of NO3--N, 38 antibiotics, and dissolved organic matter (DOM) in Baiyangdian Basin, through dry and wet seasonal (after extreme rainfall) samplings at 2023. In the system, NO3--N and ∑antibiotics average concentrations were higher in the dry season, while the relative abundance of humic-like components was higher in the wet season. Spatial autocorrelation analysis showed that the high-high clusters of pollutants and DOM components were mainly distributed in rivers, and the temporal difference was significant. MixSIAR and PMF model were respectively applied to nitrogen and antibiotics sources apportionment. The results showed that non-point sources (NPS) of nitrogen and antibiotics exhibited an upward trend, while the point sources decreased from dry to wet seasons. Hydrological connectivity was characterized by using δ18O-H2O, which was higher in the wet season. Partial least squares path model revealed that hydrological connectivity directly impacted humic-like components, which were the direct influencing factor of the concentration and NPS for antibiotics and nitrogen in the connected system. Extreme rainfall weaken the impact of hydrological connectivity on the concentration and NPS of pollutants, while enhanced the impact of humic-like components on pollutants NPS. These findings clarified the impact mechanism of hydrological connectivity and DOM on nitrogen and antibiotics fate in the connected system, which plays an important role in future water quality management under extreme events.

The overlooked interaction of emerging contaminants and microbial communities: a threat to ecosystems and public health

CONTEXT AND AIMS

Emerging contaminants (ECs) and microbial communities should not be viewed in isolation, but through the One Health perspective. Both ECs and microorganisms lie at the core of this interconnected framework, as they directly influence the health of humans, animals, and the environment.The interactions between ECs and microbial communities can have profound implications for public health, affecting all three domains. However, these ECs-microorganism interactions remain underexplored, potentially leaving significant public health and ecological risks unrecognized. Therefore, this article seeks to alert the scientific community to the overlooked interactions between ECs and microbial communities, emphasizing the pivotal role these interactions may play in the management of 'One Health.'

RESULTS

The most extensively studied interaction between ECs and microbial communities is biodegradation. However, other more complex and concerning interactions demand attention, such as the impact of ECs on microbial ecology (disruptions in ecosystem balance affecting nutrient and energy cycles) and the rise and spread of antimicrobial resistance (a growing global health crisis). Although these ECs-microbial interactions had not been extensively studied, there are scientific evidence that ECs impact on microbial communities may be concerning for public health and ecosystem balance.

CONCLUSIONS

So, this perspective summarizes the impact of ECs through a One Health lens and underscores the urgent need to understand their influence on microbial communities, while highlighting the key challenges researchers must overcome. Tackling these challenges is vital to mitigate potential long-term consequences for both ecosystems and public health.

From Cow to Climate-Tracing the Path of Dairy Sustainability: Unveiling the Impact on Sustainable Development Goals Through Bibliometric and Literature Analyses

Archeological evidence shows that dairy farming dates to the early Neolithic era in Europe, the Middle East, Asia, and Africa. Over time, it has evolved from domestication to intensive dairy farms with large, high-tech processing units. Dairy farming has contributed to economic growth, food production, employment, and processing industries. Nonetheless, it has been identified as a major contributor to climate change. This study explores the literature on dairy farming and sustainable development goals (SDGs) to identify current scholarly developments since the formulation and adoption of the SDGs in 2015 and themes for future research. This paper argues that sustainability shortfalls in dairy farming are primarily driven by human processes associated with commercialization and industrialization rather than the animals themselves, although biological emissions remain an inherent factor. Data were analyzed using R package, Excel, NVIVO, and VoS Viewer. A review of the literature showed that dairy farming and its contribution to sustainability has gained more scientific interest since 2015. Moreover, livestock management, feed production and management, stakeholder management, logistics and supply chain management, and waste management are the sources of environmental adversities associated with dairy farming. Notably, these are human processes developed from the commercialization of dairy farming and involve multiple stakeholders across the supply chain. While solutions are embedded within these processes, innovation emerges as a key driver of sustainability and a source of opportunities to strengthen sustainability in the dairy farming sector and achieve SDGs. Sustainability strategies, such as sustainable intensification, multifunctional agriculture, and agro-ecology should be implemented to improve sustainability in the dairy sector.

Novel synthesis and application of biochar for controlling release and exposure of mercury in the farmland: From human health risk perspective

Mercury (Hg)-contaminated farmlands have received wide attention because of the adverse risks posed to food security and human health. In addition, climate change altered the mobility of Hg in the soil, limiting soil productivity and nutrient bioavailability, hence elevating health risks. To adapt to these risks, pot experiments were employed to showcase the impacts of single-pyrolytic synthesized biochar with nitrogen and phosphorus impregnation (NPBC) on the nutrient accessibility, Hg immobilization, and human health risks, compared to pristine and control groups. Results revealed that, with increased surface area and abundant function groups, impregnation amplified bulk nitrogen, phosphorus, and oxygen content from 0.47, 0.25, and 9.47 % to 3.01, 4.50, and 21.4 %, respectively. The pot experiments indicated the effectiveness of NPBC900 in immobilizing soil Hg, hence reducing Brassica rapa chinensis' Hg uptake by 88 %. Notably, NPBC transformed ∼93 % of water soluble and exchangeable Hg species to stable fractions, enhancing the residue concentration three-fold higher than the control. Additionally, NPBC700-900 showcased characteristic phosphorus and nitrogen slow-release (best at NPBC900 and NPBC500, respectively; 5 %) contributing to controlled soil available nutrients. Hg bioaccessible fraction exhibited a notably higher level (1.7 mg kg-1) in the control group measured against BC (0.8 mg kg-1) and NPBC treatments (∼0.1 mg kg-1). Through dietary and soil ingestion pathways, NPBC900 treatment demonstrated the best health risk reduction for farmers and the public by ∼93 and 69 %, respectively. With versatile capabilities, NPBC emerges as a practical, green, and sustainable alternative in Hg remedy technologies, a breakthrough for climate change adaptation.

Drought affects Fe deficiency-induced responses in a purple durum wheat (Triticum turgidum subsp. durum) genotype

Iron (Fe) is essential for plants and humans, with over 2 billion people suffering deficiency disorders because most plant foods, including cereals, are low in Fe. Durum wheat, a staple crop in Mediterranean regions, is facing increased droughts, which reduce plant yield and ability to acquire and use Fe. Therefore, understanding mechanisms underlying Fe acquisition and accumulation in durum wheat under drought is essential for both agronomic and nutritional purposes. Here, a durum wheat (Triticum turgidum subsp. durum) genotype with a purple grain pericarp was grown hydroponically under adequate (80 μM) or limited (10 μM) Fe, with or without water stress (induced with 10% PEG 6000) for 6 days. Fe accumulation decreased under Fe deficiency and drought, with the highest phytosiderophore (PS) release in Fe-deficient plants. Interestingly, despite adequate Fe availability, drought inhibited Fe accumulation in roots. This response was accompanied by increased release of PS from roots, although the increase was less than that observed with single or combined Fe deficiency. Both TdIRT1 and TdYS15 were upregulated by Fe deficiency but downregulated by drought and the combined stress. Drought stress and Fe deficiency led to increased ABA production, being 250-fold higher with respect to controls. TdIRT1 downregulation in plants exposed to the combined stress suggests a trade-off between water and Fe stress responses. Our findings demonstrate that the response to combined stress differs from, and is rarely additive to, the response to a single stressor, reinforcing the complexity of plant adaptation to combined environmental stresses.

Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions

Maintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes in flow rate and increased pollutant loads can affect treatment performance. Traditional physical sensors became both expensive and susceptible to failure under extreme conditions. In this study, we evaluate the performance of soft sensors based on artificial intelligence (AI) to predict the components underlying the calculation of the effluent quality index (EQI). We thus focus our study on three ML models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer. Using the Benchmark Simulation Model no. 2 (BSM2) as the WWTP, we were able to obtain datasets for training the ML models and to evaluate their performance in dry weather scenarios, rainy episodes, and storm events. To improve the classification of networks according to the type of weather, we developed a Random Forest (RF)-based meta-classifier. The results indicate that for dry weather conditions the Transformer network achieved the best performance, while for rain episodes and storm scenarios the GRU was able to capture sudden variations with the highest accuracy. LSTM performed normally in stable conditions but struggled with rapid fluctuations. These results support the decision to integrate AI-based predictive models in WWTPs, highlighting the top performances of both a recurrent network (GRU) and a feed-forward network (Transformer) in obtaining effluent quality predictions under different weather conditions.

Mapping climate change interaction with human health through DPSIR framework: Qatar perspective

This study investigates the interactions between climate change and human health with a particular focus on Qatar, using the DPSIR (Driving Forces, Pressures, States, Impacts, Responses) framework. Key drivers, including economic development and population growth, contribute to increased greenhouse gas (GHG) emissions, exerting pressure on Qatar's climate through rising temperatures and altered precipitation patterns, as modeled by the MIT Regional Climate Model (MRCM). The findings reveal critical gaps in understanding the state of climate-health interactions, including insufficient disease data, incomplete climate-health linkages, and significant research gaps. These limitations hinder targeted responses to climate-sensitive diseases, which have shown an increase over the years. The study identifies the pathways through which climatic shifts contribute to immediate health risks, such as heat-related illnesses and respiratory conditions, as well as long-term impacts, including chronic diseases and mental health challenges. Despite Qatar's efforts through national and international strategies, the DPSIR analysis highlights the urgent need for enhanced research, improved data collection, and tailored actions to address these challenges. Strengthened adaptation, resilience-building, and emission reduction strategies remain essential for safeguarding public health in the face of accelerating climate change.

Dialogue between algorithms and soil: Machine learning unravels the mystery of phthalates pollution in soil

Soil is a major environmental sink for the emerging organic pollutants phthalates (PAEs), and the determination of key factors influencing PAEs accumulation in soil is crucial for agricultural sustainability and food security. Aiming at the time-consuming and inefficient characteristics of traditional batch experiments and statistical prediction models in comprehensively capturing PAEs dynamics in soil, an intelligent analysis framework based on machine learning was proposed and developed. In this study, thirty features were incorporated, including soil PAEs-concentrations, pollutant emissions, agricultural inputs, soil physicochemical properties, and climatic parameters. Six data-driven machine learning models were established: Random Forest Regression (RFR), Gradient Boosting Regression Tree (GBRT), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN). Results showed that the MLP model exhibited optimal performance in predicting soil PAEs concentrations (R²=0.8637), followed by SVR (R²=0.8132) and XGBoost (R²=0.8096). Through feature importance analysis, it was determined that hydrometeorological factors, soil moisture conditions, and nutritional characteristics were the key factors controlling PAEs spatial distribution. Furthermore, non-linear effect analysis elucidated significant synergistic interactions among these environmental covariates. The spatiotemporal prediction model revealed continuous declining trends in PAEs pollution levels in eastern coastal regions over the next 5-10 years, while accumulation tendencies were observed in inland provinces particularly in Guizhou. This study demonstrates the effectiveness and advantages of machine learning in predicting soil PAEs-pollution, providing a new perspective for pollutant risk assessment and management in the era of environmental big data.

Spatiotemporal nonlinear characteristics and threshold effects of China's water resources

China faces shortage of water resources, particularly in the context of rapid population growth and accelerating urbanization, making the changes in its water resources among the most pronounced globally. Additionally, the complex interplay between climate change and human activities leads to nonlinear and non-stationary patterns in China's water resources. This study utilizes high-resolution water storage monitoring data to comprehensively analyze the nonlinear changes in water storage and its relationships with human footprint, precipitation, and temperature, revealing the complex dynamics of water storage changes across China. This study used advanced data analysis techniques to identify the turning points where water storage undergoes nonlinear changes, with categories of nonlinear changes that first decrease then increase and those that first increase then decrease together accounting for 55.62% of the observations. The northeastern and western fringe areas of China are hotspots for these nonlinear changes, and the analysis identifies 2019 as a year with a high frequency of turning points. The piecewise linear regression analysis found that when the human footprint exceeds a specific threshold, its negative impact on water storage significantly intensifies; when precipitation and temperature exceed certain thresholds, their impact on water storage shifts from negative to positive. These findings not only reveal the complex spatiotemporal distribution characteristics of water storage change turning points across China but also emphasize that water resource management strategies in China and globally need to adopt more comprehensive and dynamic approaches in the context of global climate change. To meet future challenges, it is essential to integrate multiple variables and develop flexible, adaptive management strategies to ensure the sustainable use and effective management of water resources.

Polychlorinated biphenyl (PCB) concentrations and breakpoint trends across the waters of the Great Lakes by isotope-dilution high-resolution mass spectroscopy

Water samples were collected during each of the 2012-2019 Cooperative Science and Monitoring Initiative (CSMI) cruises aboard the U.S. EPA R/V Lake Guardian as part of the Great Lakes Fish Monitoring and Surveillance Program (GLFMSP) lower food web contaminant assessment. The CSMI rotates around each of the Great Lakes in a 5-year cycle providing top-to-bottom biological, chemical, and physical environmental assessments, including dissolved-phase surface water studies at two sample locations. Average polychlorinated biphenyl (∑PCB) concentrations across the Great Lakes was 268 pg/L with a station range of 72 pg/L (Keweenaw Point-Lake Superior) to 834 pg/L (Middle Bass Island-Lake Erie). The highest average Great Lakes concentration (pg/L-sample year) were measured in Lake Erie (645 pg/L-2014) and decreased in the order of Lake Huron (378 pg/L-2012) > Lake Erie (364 pg/L-2019) > Lake Ontario (300 pg/L-2013) > Lake Michigan (125 pg/L-2015) > Lake Huron (123 pg/L-2017) > Lake Superior (74 pg/L-2016). Lake Erie registered a 44 % reduction over the 2014-2019 period, attributed to sediment remediation in the late-1990's on the Detroit River, whereas Lake Huron exhibited a 67 % decrease over the 2012-2017 sample period. Our results indicate that dissolved-phase PCB water concentrations in Lake Ontario have significantly increased, rebounding from a low-point in the late-1990's likely due to the bioenergetic diversion of dissolved- and particulate-phase PCBs into the benthic food web by invasive zebra and quagga mussel colonization. Trend analyses uncovered breakpoints in the early 1990's documenting significantly slowing rates of PCB declines for both Lakes Superior and Michigan.

Effect of Flowering Shading on Grain Yield and Quality of Durum Wheat in a Mediterranean Environment

The phenomenon known as "dimming" or shading, caused by the increase in aerosols, air pollutants, and population density, is reducing global radiation, including both direct solar radiation and radiation scattered by the atmosphere. This phenomenon poses a significant challenge for agricultural production in many regions worldwide, with a global radiation decrease estimated between 1.4% and 2.7% per decade in areas between 25° N and 45° N. In particular, in Mediterranean regions, the production of durum wheat (Triticum turgidum L. subsp. Durum) is increasingly constrained by abiotic factors, such as spring/summer heat stress and drought, as well as reductions in solar radiation. Field experiments were conducted in Mosciano Sant'Angelo, Italy, over two cropping seasons (2016-2017 and 2017-2018) to evaluate the effects of photosynthetically active radiation (PAR) availability and nitrogen (N) fertilization on durum wheat. A split-plot design was used with two PAR levels (100% and 20% PAR) and three N rates (0, 100, and 250 kg ha-1). Results highlighted that full sunlight (NoSh) significantly increased grain yield (+25%), thousand kernel weight (+46%), and total gluten fractions (+16%) compared to shaded conditions (Sh). Chlorophyll content and NDVI values were highest under Sh combined with 250 kg N ha-1. Rainfall patterns strongly influenced productivity, with better vegetative growth in 2016-2017 and improved grain filling in 2017-2018. Nitrogen application significantly enhanced grain protein content, particularly under arid conditions. These findings emphasize the interaction between light availability and nitrogen management, suggesting that optimizing these factors can improve yield and quality in durum wheat under Mediterranean conditions.

Investigating the environmental dynamics of emerging pollutants in response to global climate change: Insights from bibliometrics-based visualization analysis

The environmental dynamics of emerging pollutants were profoundly influenced by global climate change, attracting widespread attention to this complex interaction. However, single studies or reviews were insufficient to grasp, clarify, and predict the evolutionary characteristics and coupling patterns of emerging pollutants under global climate change. Here, 2389 research articles collected from the Web of Science Core Collection database for the period 2000-2023 were analyzed using systematic bibliometric visual analysis software. Results suggested a rapid growth trend in this field study, particularly accelerating after 2015. The United States, China, the United Kingdom, and Spain led in the volume of publications, forming a multidisciplinary research network centered on environmental science. Wastewater treatment, personal care products, pharmaceuticals, and heavy metals were identified as current research hotspots, with climate change emerging as the most prominent keyword. Research focus gradually shifted from single pollutants to multi-pollutant composite effects, from local issues to global-scale assessments, and from phenomenon description to mechanism analysis and risk evaluation. It is concluded that climate change is reshaping the environmental behaviors and ecological risks of emerging pollutants, and multidisciplinary, multi-scale research methods are urgent need. Future research is suggested to strengthen interdisciplinary collaboration, integrate climate and pollutant migration models, and investigate impacts of extreme climate events in depth.

Colloid mobilization and transport in response to freeze-thaw cycles: Insights into the heavy metal(loid)s migration at a smelting site

Smelting sites often exhibit significant heavy metal(loid)s (HMs) contamination in the soil and groundwater, which are inevitably subjected to environmental disturbances. However, there is limited information available regarding the migration behaviors of HMs in a disturbed scenario. Thus, this work explored the migration of HMs-bearing colloids in response to freeze-thaw treatments by laboratory simulation and pore-scale study. Ultrafiltration results of soil effluents revealed that 61.5 %, 47.6 %, 68.0 %, and 59.2 % of Zn, Cd, Pb, and As were present in colloidal phase, and co-transported during treatments. Nanoparticle tracking analysis (NTA) further confirmed that freeze-thaw cycles were conducive to the generation of colloidal particles and showed the heteroagglomeration among different particles. Pore-network model (PNM) was used to quantify the soil macropore characteristics (macropore diameter, macropore number, coordination number, and Euler value) after treatments. It is evident that freeze-thaw cycles induced the formation of larger macropores while simultaneously enhancing macropore connectivity, thereby establishing an optimal pathway for colloid migration. These findings underscored the importance of environmental disturbances as a trigger for the release and migration of HMs in the smelting site, offering valuable insights for controlling HMs pollution. ENVIRONMENTAL IMPLICATION: The contaminated site has been subjected to prolonged environmental disturbances, causing the exacerbation of pollutants leaching and frequent occurrences of unstable pollution situations. This work explored the migration of HMs-bearing colloids in response to freeze-thaw treatments by laboratory simulation and pore-scale study. The distinct effects of freeze-thaw treatment on colloidal particle number concentration and macropore characteristics may explain the generation and migration of colloid-associated HMs driven by environmental disturbances. This work revealed the underlying mechanisms driving the redistribution of HMs under freeze-thaw cycles, offering valuable insights for risk assessment of soil and groundwater associated with HMs migration.

Variation pattern, influential factors, and prediction models of PM2.5 concentrations in typical urban functional zones of northeast China

This study investigated the spatial and temporal variations of PM2.5 concentrations in Harbin, China, under the influence of meteorological parameters and gaseous pollutants. The complex relationship between meteorological parameters and pollutants was explored using Pearson correlation analysis and interaction effect analysis. Using the correlation analysis and interaction analysis methods, four mechanical learning models, PCC-Is-CNN, PCC-Is-LSTM, PCC-Is-CNN-LSTM and PCC-Is-BP neural network, were developed for predicting PM2.5 concentration in different time scales by combining the long-term and short-term data with the basic mechanical learning models. The results show that the PCC-Is-CNN-LSTM model has superior prediction performance, especially when integrating short-term and long-term historical data. Meanwhile, applying the model to cities in other climatic zones, the results show that the model performs well in the Dwa climatic zone, while the prediction performance is lower in the CWa climatic zone. This suggests that although the model is well adapted in regions with a similar climate to Harbin, model performance may be limited in areas with complex climatic conditions and diverse pollutant sources. This study emphasizes the importance of considering meteorological and pollutant interactions to improve the accuracy of PM2.5 predictions, providing valuable insights into air quality management in cold regions.

Recent advances in the removal of psychoactive substances from aquatic environments: A review

Psychoactive substances (PS) have become emerging contaminants in aquatic environments, characterized by their wide distribution, high persistence, bioaccumulation and toxicity. They are difficult to be completely removed in sewage treatment plants due to their high stability under different conditions. The incomplete removal of PS poses a threat to the aquatic animals and can also lead to human health problems through accumulation in the food chain. PS has become a huge burden on global health systems. Therefore, finding an effective technology to completely remove PS has become a "hot topic" for researchers. The methods for removal PS include physical techniques, chemical methods and biological approaches. However, there is still a lack of comprehensive and systematic exploration of these methods. This review aims to address this gap by providing a comprehensive overview of traditional strategies, highlighting recent advancements, and emphasizing the potential of natural aquatic plants in removing trace PS from water environments. Additionally, the degradation mechanisms that occur during the treatment process were discussed and an evaluation of the strengths and weaknesses associated with each method was provided. This work would help researchers in gaining a deeper understanding of the methodologies employed and serve as a reference point for future research endeavors and promoting the sustainable and large-scale application of PS elimination.

Rational design and construction of direct Z-scheme ternary heterojunction photocatalyst AgBr/CoWO4/Ag for efficient environmental remediation

The indiscriminate discharge of micropollutants (e.g., dyes, antibiotics, industrial additives, etc.) represents a significant risk to human health, and the removal of these substances from water bodies has become a prominent area of research within the field of environmental remediation. A simple hydrothermal-precipitation-photoreduction method was employed to synthesize novel Z-scheme heterojunction photocatalysts of AgBr/CoWO4/Ag. The catalysts demonstrated remarkable degradation capabilities with regard to a range of micropollutants present in wastewater. Of the catalysts tested, 5AgBr/CoWO4/Ag exhibited the highest degradation rates, reaching 98.58% for Rhodamine B, 86.82% for tetracycline hydrochloride, and 95.60% for 2-mercaptobenzothiazole within 60 min. In particular, the reaction kinetic rate of 5AgBr/CoWO4/Ag towards Rhodamine B degradation (k2 = 0.26278 L mg-1·min-1) is 9 times that of AgBr (k2 = 0.02953 L mg-1·min-1) and 113 times that of CoWO4 (k2 = 0.00233 L mg-1·min-1), which serves to highlight the exceptional photocatalytic activity of the material. The experimental data and subsequent analysis indicated that the enhanced photocatalytic performance can be attributed to two factors: firstly, the electron mediation by Ag nanoparticles leading to improved charge separation efficiency, and secondly, the formation of Z-scheme heterojunctions between AgBr and CoWO4. The cyclic tests provided confirmation of the excellent stability and recyclability of the AgBr/CoWO4/Ag photocatalysts. It is anticipated that this study will facilitate the development of novel methods for the degradation of refractory micropollutants and provide insights into environmental remediation, thereby contributing to the sustainable development of society.

Prediction of urban surface water quality scenarios using hybrid stacking ensembles machine learning model in Howrah Municipal Corporation, West Bengal

In the pursuit of understanding surface water quality for sustainable urban management, we created a machine learning modeling framework that utilized Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splines (MARS), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and their hybrid stacking ensemble RF (SE-RF), as well as stacking Cubist (SE-Cubist), to predict the distribution of water quality in the Howrah Municipal Corporation (HMC) area in West Bengal, India. Additionally, we employed the ReliefF and Shapley Additive exPlanations (SHAP) methods to elucidate the underlying factors driving water quality. We first estimated the water quality index (WQI) to model seven water quality parameters: total hardness (TH), pH, total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD), calcium (Ca), magnesium (Mg). Then six independent factors were utilized (i.e. Precipitation (Pr), Maximum Temperature (Tmax), Minimum Temperature (Tmin), Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and Total Dissolved Solids (TDS)) for predicting the WQI mapping through the different ML models. This study demonstrated that the SE-Cubist model outperforms other ML models. During the testing phase, it achieved the best modeling results with an R2 = 0.975, RMSE = 0.351, and MAE = 0.197. The ReliefF and SHAP analyses identified Pr and Tmax as the most significant factors influencing WQI within the study area.

Climate change influence on the trends of BFRs in the environment and food

Climate change poses new challenges for environmental protection and food safety. With reported consequences including warmer temperatures, melting of Alpine glaciers, higher sea levels, droughts, extreme rainfall events and increased surface UV radiation, concerns about the impact on food contaminants have been raised. While the effects of climate change on POPs were initially expected to have the biggest impact in the arctic region, given the intensity, frequency and spread of extreme weather events, global influence on environmental pollution and food safety is currently anticipated. Warmer temperatures are expected to enhance the volatilization of POPs and influence their partitioning between soil, sediment, water and atmosphere, enhancing their mobility and their potential for long-range atmospheric transport. Floods and strong winds can cause dilution but also spread of pollutants to wider areas. Limited data are available for the impact of climate change on BFRs levels, trends and toxicity. BFRs are widely used to protect people from fire hazards. Numerous BFR containing products are disposed in landfills where climate change could possibly induce increased leaching and resulting impacts on the food chain. Heat and UV exposure can lead to degradation of novel polymeric BFRs with adverse environmental effects. Long-term monitoring data are needed for feed, food and environmental compartments in order to evaluate climate change influence, which will also enable the development of prediction models specific for legacy and novel BFRs, for various climate change scenarios. Furthermore, there is a need to promote further discussion in the scientific community for the design of risk management and remediation activities for contaminated areas, in response to potential future conditions as the climate continues to change.

Multi-scale analysis of nutrient and environmental dynamics in Hongfeng Lake Southwest China

Traditional linear correlation analysis may not fully capture the true relationship between these variables. Therefore, multi-scale running correlation analysis, such as time-dependent intrinsic correlation (TDIC) and continuous wavelet transform based on Hilbert-Huang transform (HHT), provides valuable insights into local correlations and the evolving relationship between nutrients and environmental factors over time. In this study, we investigated seven environmental factors and four water quality nutrient indicators in deep lakes on the Yungui Plateau in southwestern China. The results revealed that there may be strong correlations between environmental factors and nutrient levels during certain periods, while opposite trends may emerge at other times. These variations in correlation could be attributed to uncertain physical processes, spatial heterogeneity, or the impact of different climatic factors on local hydrological processes. Wavelet analysis indicated that changes in environmental factors lag behind those in nutrient levels, particularly on a cycle of about 12 months. This suggests that changes in environmental factors align with natural patterns after the water body has been polluted. These conclusions underscore the complexity and dynamic nature of the relationship between environmental factors and nutrient levels in water bodies, highlighting the importance of employing advanced analysis techniques to capture this complexity.

Use, limitations, and future directions of mixtures approaches to understand the health impacts of weather- and climate change-related exposures, an under-studied aspect of the exposome

The exposome concept aims to account for the comprehensive and cumulative effects of physical, chemical, biological, and psychosocial influences on biological systems. To date, limited exposome research has explicitly included climate change-related exposures. We define these exposures as those that will intensify with climate change, including direct effects like extreme heat, tropical cyclones, wildfires, downstream effects like air pollution, power outages, and limited or contaminated food and water supplies. These climate change-related exposures can occur individually or simultaneously. Here, we discuss the concept of a climate mixture, defined as three or more simultaneous climate change-related exposures, in the context of the exposome. In a motivating climate mixture example, we consider the impact of a co-occurring tropical cyclone, power outage, and flooding on respiratory hospitalizations. We identify current gaps and future directions for assessing the effect of climate mixtures on health. Mixtures methods allow us to incorporate climate mixtures into exposomics.

From port to planet: Assessing NO2 pollution and climate change effects with Sentinel-5p satellite imagery in maritime zones

The growing effects of climate change on Malaysia's coastal ecology heighten worries about air pollution, specifically caused by urbanization and industrial activity in the maritime sector. Trucks and vessels are particularly noteworthy for their substantial contribution to gas emissions, including nitrogen dioxide (NO2), which is the primary gas released in port areas. The application of advanced analysis techniques was spurred by the air pollution resulting from the combustion of fossil fuels such as fuel oil, natural gas and gasoline in vessels. The study utilized satellite photos captured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite to evaluate the levels of NO2 gas pollution in Malaysia's port areas and exclusive economic zone. Before the COVID-19 pandemic, unrestricted gas emissions led to persistently high levels of NO2 in the analyzed areas. The temporary cessation of marine industry operations caused by the pandemic, along with the halting of vessels to prevent the spread of COVID-19, resulted in a noticeable decrease in NO2 gas pollution. In light of these favourable advancements, it is imperative to emphasize the need for continuous investigation and collaborative endeavours to further alleviate air contamination in Malaysian port regions, while simultaneously acknowledging the wider consequences of climate change on the coastal ecology. The study underscores the interdependence of air pollution, maritime activities and climate change. It emphasizes the need for comprehensive strategies that tackle both immediate environmental issues and the long-term sustainability and resilience of coastal ecosystems in the context of global climate challenges.

Polychlorinated biphenyls in the surface and deep waters of the Southern Indian Ocean and Coastal Antarctica

Polychlorinated biphenyl compounds (PCBs) are industrial chemicals whose production was discontinued in the early nineties in most countries. Sill, PCBs are detectable in pristine and remote locations. Occurrence in regions such as Southern Oceans and Antarctica are influenced by the global, and regional, cycling. Here, we studied the surface and deep ocean distribution of indicator- and dioxin-like PCB congeners in the Southern Indian Ocean (SIO), and the coast of Antarctica (COA) during the tenth Indian Southern Ocean Expedition (SOE-10), December 2017-February 2018. ∑21PCBs in SIO surface waters ranged from 3.8 to 167.1 pg L-1 (average ± standard deviation: 35.7 ± 48.4 pg L-1), and in COA from 1.0 to 41.8 pg L-1 (13.8 ± 12.7 pg L-1), respectively. A noticeable gradient was observed, with higher PCBs levels in northern latitudes than southern latitudes in the SIO, and higher levels in the eastern longitudes compared to western longitudes in the COA. Results suggest the influence of secondary sources, or re-emission, of PCBs in the Southern Oceans and Antarctica. Both regions showed notable PCB levels in surface and deep waters (up to 1000 m) due to ongoing surface sources and remineralization processes in deeper waters. Multimedia modeling with the global model (BETR-Global) suggests the SIO act as a net sink for PCBs in the ocean.

Banana Peel Extract-Derived ZnO Nanopowder: Transforming Solar Water Purification for Safer Agri-Food Production

Pure water scarcity is the most significant emerging challenge of the modern society. Various organics such as pesticides (clomazone, quinmerac), pharmaceuticals (ciprofloxacin, 17α-ethynilestradiol), and mycotoxins (deoxynivalenol) can be found in the aquatic environment. The aim of this study was to fabricate ZnO nanomaterial on the basis of banana peel extract (ZnO/BPE) and investigate its efficiency in the photocatalytic degradation of selected organics under various experimental conditions. Newly synthesized ZnO/BPE nanomaterials were fully characterized by the XRD, FTIR, SEM-EPS, XPS, and BET techniques, which confirmed the successful formation of ZnO nanomaterials. The photocatalytic experiments showed that the optimal catalyst loading of ZnO/BPE was 0.5 mg/cm3, while the initial pH did not influence the degradation efficiency. The reusability of the ZnO/BPE nanomaterial was also tested, and minimal activity loss was found after three photocatalytic cycles. The photocatalytic efficiency of pure banana peel extract (BPE) was also studied, and the obtained data showed high removal of ciprofloxacin and 17α-ethynilestradiol. Finally, the influence of water from Danube River was also examined based on the degradation efficiency of selected pollutants. These results showed an enhanced removal of ciprofloxacin in water from the Danube River, while in the case of other pollutants, the treatment was less effective.

Impact of Climate Change on Reproductive Health and Pregnancy Outcomes: A Systematic Review

Climate change has emerged as a significant global health challenge, with growing evidence linking environmental factors to adverse reproductive health outcomes. The primary objective of this review is to assess the effects of climate change-driven environmental factors, such as air pollution and temperature extremes, on reproductive health outcomes, including fertility rates, miscarriage, preterm birth, and congenital anomalies. A comprehensive search of PubMed, Google Scholar, and Web of Science was conducted until July 2024. Studies included in the review were observational, experimental, and randomized controlled trials that reported quantitative data on reproductive outcomes in relation to climate-related environmental exposures. A total of 49 studies were selected for qualitative synthesis. The review found that increased exposure to particulate matter (PM2.5), extreme temperatures, and proximity to traffic were consistently associated with reduced fertility, increased risks of miscarriage, preterm birth, and low birth weight. Adverse effects were particularly pronounced among vulnerable populations, such as pregnant women of lower socioeconomic status and those living in disaster-prone areas. The studies also highlighted potential transgenerational effects, with prenatal exposure to environmental stressors influencing the long-term health of offspring. The findings underscore the urgent need for public health interventions and policies to mitigate environmental exposures that negatively impact reproductive health. Future research should focus on longitudinal and interventional studies to establish causal relationships and inform effective public health strategies.

Spatial-temporal evolution and intrinsic drivers of compound drought and heatwave events in Mainland China

Given the devastating effects and potential rising trends of compound drought and heatwave (CDH) events under the specter of global warming, this study embarks on a comprehensive examination of their spatial and temporal evolution, as well as the intrinsic drivers. This study identified CDH events based on the non-stationary standardized precipitation evapotranspiration index (NSPEI) and the relative threshold method. The study also quantified the spatial and temporal patterns of frequency, intensity, and duration of CDH events across different climatic sub-regions, quantifying the contribution of drought-heatwave interdependence to these events and assessing the impact of single extreme climate events on their proliferation. The study yielded several key findings: 1) The frequency, intensity, and duration of CDH events exhibited high spatial heterogeneity and a significant increasing trend over the study period. 2) A notable positive interdependence was observed between the occurrences of droughts and heatwaves, significantly contributing to the rise in CDH events. 3) Droughts exacerbated the intensity and duration of CDH events compared to heatwaves under non-drought conditions (NDCH). 4) The spatial distribution characteristics and the change indices of heatwaves and CDH events were strikingly similar, indicating a consistent evolution. Notably, the increase in heatwaves had a more pronounced influence on the escalation of CDH events compared to droughts. 5) The West Pacific Subtropical High (WPSH) and the South Asian High (SAH) have had significant impacts on CDH events in mainland China. This research provides vital insights into the dynamics of CDH events, emphasizing their growing frequency and severity in the context of climate change. It offers a crucial perspective for policymakers and disaster management authorities in developing targeted strategies for climate adaptation and mitigation.

Spatio-temporal modeling of lake's ecosystem and dynamism in response to changing environment: a case study of L. Ol Bolossat in Kenya

Lakes' ecosystems are vulnerable to environmental dynamisms prompted by natural processes and anthropogenic activities happening in catchment areas. The present study aimed at modeling the response of Lake Ol Bolossat ecosystem in Kenya to changing environment between 1992 to 2022 and its future scenario in 2030. The study used temperature, stream power index, rainfall, land use land cover, normalized difference vegetation index, slope, and topographic wetness index as datasets. A GIS-ensemble modeling approach coupling the analytical hierarchical process and principal component analysis was used to simulate the lake's extents between 1992 and 2022. Cellular Automata-Markov chain analysis was used to predict the lake extent in 2030. The results revealed that between 1992 and 2002, the lake extent shrunk by about 18%; between 2002 and 2012, the lake extent increased by about 13.58%; and between 2012 and 2022, the lake expanded by about 26%. The spatial-temporal changes exhibited that the lake has been changing haphazardly depending on prevailing climatic conditions and anthropogenic activities. The comparison between the simulated and predicted lake extents in 2022 produced Kno, Klocation, KlocationStrata, K standard, and average index values of 0.80, 0.81, 1.0, 0.74, and 0.84, respectively, which ascertained good performance of generated prediction probability matrices. The predicted results exhibited there would be an increase in lake extent by about 13% by the year 2030. The research findings provide baseline information which would assist in protecting and conserving the Lake Ol Bolossat ecosystem which is very crucial in promoting tourism activities and provision of water for domestic and commercial use in the region.

Has climate change over the last ten years caused a banalisation of diatom communities in Cypriot streams?

To unveil possible changes in diatom communities in Cypriot streams over the last ten years or so, we selected samples from the years 2020, 2021, and 2022 for the "recent" dataset (N = 119) and samples from the years 2010 and 2011 for the "historical" dataset (N = 108). Biotic homogenization has become a truly global phenomenon. Here we show that, over the last ten years, in response to increased water temperature, conductivity, and discharge variability due to climate-change, Cypriot stream diatom communities include a higher number of trivial (= widespread, tolerant, and opportunistic), aerial, and thermophilic species, have reduced β-diversity and increased nestedness. Moreover, IndVal analysis shows that indicator species from the historical dataset were characteristic, often relatively rare species, while the indicators of the recent dataset were a group of typical trivial, eutraphentic, and thermophilic species. As is almost always the case, the diatom communities we studied were subjected to multiple stressors, often affecting them in opposite ways. Besides the increase in trivial species, the reduction in β-diversity, and the rise in nestedness mentioned above, the diatom assemblages we studied also showed an increase in α-diversity that could be due to a moderate reduction in nutrients in several sites. High-ecological-integrity ecosystems, such as springs, waterfalls, and dripping rock-walls, in particular springs that were shown to be excellent hydrologic refugia in climates heavily affected by climate change, and the stream sites close to them should be carefully protected, as they can be refugia for sensitive and characteristic species that can recolonize the adjacent streams after adverse climatic events.

Effects of high temperature and LPS injections on the hemocytes of the crab Neohelice granulata

Temperature fluctuations, particularly elevated temperatures, can significantly affect immune responses. These fluctuations can influence the immune system and alter its response to infection signals, such as lipopolysaccharide (LPS). Therefore, this study was designed to investigate how high temperatures and LPS injections collectively influence the immune system of the crab Neohelice granulata. Two groups were exposed to 20 °C (control) or 33 °C for four days. Subsequently, half were injected with 10 μL of physiological crustacean (PS), while the rest received 10 μL of LPS [0.1 mg.kg-1]. After 30 min, the hemolymph samples were collected. Hemocytes were then isolated and assessed for various parameters using flow cytometry, including cell integrity, DNA fragmentation, total hemocyte count (THC), differential hemocyte count (DHC), reactive oxygen species (ROS) level, lipid peroxidation (LPO), and phagocytosis. Results showed lower cell viability at 20 °C, with more DNA damage in the same LPS-injected animals. There was no significant difference in THC, but DHC indicated a decrease in hyaline cells (HC) at 20 °C following LPS administration. In granular cells (GC), an increase was observed after both PS and LPS were injected at the same temperature. In semi-granular cells (SGC), there was a decrease at 20 °C with the injection of LPS, while at a temperature of 33 °C, the SGC there was a decrease only in SGC injected with LPS. Crabs injected with PS and LPS at 20 °C exhibited higher levels of ROS in GC and SGC, while at 33 °C, the increase was observed only in GC and SGC cells injected with LPS. A significant increase in LPO was observed only in SGC cells injected with PS and LPS at 20 °C and 33 °C. Phagocytosis decreased in animals at 20 °C with both injections and exposed to 33 °C only in those injected with LPS. These results suggest that elevated temperatures induce changes in immune system parameters and attenuate the immune responses triggered by LPS.

Prevalence, serotype, antimicrobial susceptibility, contamination factors, and control methods of Salmonella spp. in retail fresh fruits and vegetables: A systematic review and meta-analysis

This research presents a comprehensive review of Salmonella presence in retail fresh fruits and vegetables from 2010 to 2023, utilizing data from recognized sources such as PubMed, Scopus, and Web of Science. The study incorporates a meta-analysis of prevalence, serovar distribution, antimicrobial susceptibility, and antimicrobial resistance genes (ARGs). Additionally, it scrutinizes the heterogeneous sources across various food categories and geographical regions The findings show a pooled prevalence of 2.90% (95% CI: 0.0180-0.0430), with an increase from 4.63% in 2010 to 5.32% in 2022. Dominant serovars include S. Typhimurium (29.14%, 95% CI: 0.0202-0.6571) and S. Enteritidis (21.06%, 95% CI: 0.0181-0.4872). High resistance rates were noted for antimicrobials like erythromycin (60.70%, 95% CI: 0.0000-1.0000) and amoxicillin (39.92%, 95% CI: 0.0589-0.8020). The most prevalent ARGs were blaTEM (80.23%, 95% CI: 0.5736-0.9692) and parC mutation (66.67%, 95% CI: 0.3213-0.9429). Factors such as pH, water activity, and nutrient content, along with external factors like the quality of irrigation water and prevailing climatic conditions, have significant implications on Salmonella contamination. Nonthermal sterilization technologies, encompassing chlorine dioxide, ozone, and ultraviolet light, are emphasized as efficacious measures to control Salmonella. This review stresses the imperative need to bolster prevention strategies and control measures against Salmonella in retail fresh fruits and vegetables to alleviate related food safety risks.

Maternal exposure to tributyltin alters the breast milk, hormonal profile, and thyroid morphology of dams and induces sex-specific changes in neonate rat offspring

Tributyltin (TBT) is the chemical substance commonly used worldwide to prevent biofouling of vessels. Due to its ability to bioaccumulate and biomagnify, even after being banned, significant concentrations of TBT can be detected in sediment, affecting marine and human life. Although studies have shown that direct exposure to TBT alters physiological parameters in mammals, the relationship between exposure to TBT during pregnancy and lactation, considered critical windows for metabolic programming, has not been fully elucidated. Our hypothesis is that offspring whose mothers were exposed to TBT during critical stages of development may exhibit dysfunctions in endocrine-metabolic parameters. We used pregnant Wistar rats that were divided into groups and received the following treatments from gestational day 7 until the end of lactation by intragastric gavage: vehicle (ethanol 0.01%; Control), low TBT dose (100 ng/kg of body weight (bw)/day; TBT100ng) and high TBT dose (1000 ng/kg bw/day; TBT1000ng). Dams and offspring at birth and weaning (21 days old) were studied. Maternal exposure to TBT promoted dose-dependent changes in dams. The findings for adiposity, milk composition and lipid profile were more pronounced in TBT100 ng dam; however, thyroid morphology was altered in TBT1000 ng dam. Female offspring were differentially affected by the dose of exposure. At birth, females in the TBT100ng group had low body weight, lower naso-anal length (NAL), and higher plasma T4, and at weaning, females in the TBT100ng group had lower insulin and leptin levels. Females in the TBT1000ng group had lower NAL at birth and lower leptinemia and weight of white adipose tissue at weaning. Male offspring from TBT groups showed high T3 at birth, without biometric alterations at birth or weaning. Despite these findings, both sexes exhibited dose-dependent morphological changes in the thyroid gland. Thus, maternal exposure to TBT constitutes an important route of contamination for both dams and offspring.