Search Results (157)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

Subtropical plateau lakes, which are distinguished by their elevated altitudes and subtropical climates, display distinct ecological dynamics. Nevertheless, the spatial and seasonal variations in the plankton community structure, as well as their interactions with environmental factors, remain inadequately understood. This study investigated the alterations in the phytoplankton and zooplankton community structure across different geographical regions (southern, central, and northern) and seasonal periods (rainy and dry) in Erhai lake, located in a subtropical plateau in China. The results indicated that the average values of total nitrogen (TN), total phosphorus (TP), chlorophyll-a (Chla), pH, and conductivity are significantly higher during the rainy season in comparison to the dry season. Furthermore, during the rainy season, there were significant differences in the concentrations of TN, TP, and Chla among the three designated water areas. Notable differences were also observed in the distribution of Microcystis, the density of Cladocera and copepods, and the biomass of copepods across the three regions during this season. Conversely, in the dry season, only the biomass of Cladocera exhibited significant variation among the three water areas. The redundancy analysis (RDA) and variance partitioning analysis demonstrated that the distribution of plankton groups (Cyanophyta, Cryptophyta, and Cladocera) is significantly associated with TN, Secchi depth (SD), and Chla during the rainy season, whereas it is significantly correlated with TP and SD during the dry season. These findings underscore the critical influence of environmental factors, shaped by rainfall patterns, in driving these ecological changes. In the context of the early stages of eutrophication in Lake Erhai, it is essential to ascertain the spatial distribution of water quality parameters, as well as phytoplankton and zooplankton density and biomass, during both the rainy and dry seasons. Full article

The Amazon faces significant challenges related to mercury contamination, including naturally elevated concentrations and gold mining activities. Due to mercury’s toxicity and the importance of fish as a protein source for local populations, assessing mercury levels in regional fish is crucial. However, there are gaps in knowledge regarding mercury concentrations in many areas of the Amazon basin. This study aims to synthesize the existing literature on mercury concentrations in fish and the exposure of urban and traditional social groups through fish consumption. A systematic review (1990–2022) was conducted for six fish genera (Cichla spp., Hoplias spp. and Plagioscion spp., Leporinus spp., Semaprochilodus spp., and Schizodon spp.) in the Web of Science (Clarivate Analytics) and Scopus (Elsevier) databases. The database consisted of a total of 46 studies and 455 reports. The distribution of studies in the region was not homogeneous. The most studied regions were the Madeira River sub-basin, while the Paru–Jari basin had no studies. Risk deterministic and probabilistic assessments based on Joint FAO/WHO Expert Committee on Food Additives (JECFA, 2007) guidelines showed high risk exposure, especially for traditional communities. Carnivorous fish from lakes and hydroelectric reservoirs, as well as fish from black-water ecosystems, exhibited higher mercury concentrations. In the Amazon region, even if mercury levels in fish muscle do not exceed regulatory limits, the high fish consumption can still elevate health risks for local populations. Monitoring mercury levels across a broader range of fish species, including both carnivorous and non-carnivorous species, especially in communities heavily reliant on fish for their diet, will enable a more accurate risk assessment and provide an opportunity to recommend fish species with lower mercury exposure risk for human consumption. The present study emphasizes the need to protect regions that already exhibit higher levels of mercury—such as lakes, hydroelectric reservoirs, and black-water ecosystems—to ensure food safety and safeguard public health. Full article

The present study investigates the extent and spatial distribution of metal concentration in stream sediments that flow into Keban Dam Lake, Turkey. Sediment samples were analysed for trace and potentially toxic elements (PTEs), including V, Cr, Co, Ni, Cu, Zn, Pb, Tl, Th, and U. Enrichment Factor (EF), Contamination Factor (CF), Geo-accumulation Index (Igeo), and Pollution Load Index (PLI) were employed to assess contamination levels. Results reveal that Cr exhibited very high enrichment (EF = 15.95) in downstream urban samples, while Cu and Zn showed high enrichment in samples collected from the middle to lower reaches of the stream, probably indicating anthropogenic contributions. Most other elements, such as Pb, Tl, Th, and U, were within natural background levels. Sediment Quality Guidelines (SQGs) indicate that Cr, Ni, and Cu may pose potential ecological risks, especially in samples from urban-influenced and downstream areas where concentrations exceed the Probable Effect Levels (PEL; Cr: 160 mg/kg, Ni: 42.8 mg/kg, Cu: 108 mg/kg). Multivariate statistical analyses, including Pearson correlation and hierarchical clustering, reveal three distinct geochemical groupings. Among these, the most contaminated cluster—corresponding to midstream and downstream regions—is characterized by elevated Cu and Zn concentrations. Strong correlations among Cu–Zn, Ni–Cu, and Th–U suggest there is a combination of anthropogenic and lithogenic sources for most metals. While most sites showed low to moderate pollution, urban downstream locations exhibited significant metal accumulation, necessitating the region’s continued environmental monitoring and management strategies. Full article

A multimethodic approach based on infrared, Raman, electron spin resonance and photoluminescence spectroscopy, absorption spectroscopy in near infrared, visible and ultraviolet regions, single-crystal X-ray diffraction as well as electron microprobe analyses was applied to the characterization of a new commensurately modulated cubic haüyne analogue with the modulation parameter of 0.2 and unit-cell parameter of 45.3629(3) Å (designated as haüyne-45Å) from the Malobystrinskoe lazurite deposit, in the Baikal Lake area, Siberia, Russia, as well as associated SO₃²⁻-bearing afghanite. Haüyne-45Å is the second member, after vladimirivanovite, of the sodalite group with a commensurately modulated structure. The average structure is based on the tetrahedral aluminosilicate sodalite-type framework with sodalite cages of different sizes. The simplified formula of haüyne-45Å is Na₆Ca_2−x(Si₆Al₆O₂₄)(SO₄²⁻,HS⁻,S₂^●−,S₄,S₃^●−,S₅²⁻)_2−y. The structural modulations of the haüyne-45Å framework are presumably related to the regular alternation of SO₄²⁻ anions with polysulfide S₂^●−, S₃^●−, S₄, and S₅²⁻ groups detected by the spectroscopic methods. Mechanisms of thermal conversions of S-bearing groups in haüyne-45Å under oxidizing and reducing conditions at temperatures up to 800 °C are studied, and their geochemical importance is discussed. Full article

In this study, a large portion of data on the chemical composition of precipitation falling in the South Baikal region shows the main factors determining their formation in 2017–2024. Taking into account the high variability of meteorological conditions in the region, both in time and in space, a method of observing the chemical composition of atmospheric precipitation has been developed, which makes it possible to determine its composition depending on the conditions of air mass formation. Using statistical analysis, marker substances characterizing the main groups of sources influencing the composition of atmospheric precipitation were identified. Joint analysis of air mass trajectories and data on chemical composition of precipitation allowed for establishing the areas of location of potential sources of precipitation pollution. All precipitation events were categorized based on the similarity of air mass formation conditions and chemical composition. Precipitation composition data collected on the shores of Lake Baikal reflect the influence of different types of pollutants such as industrial emissions, motor vehicles, dust storms, and forest fires. The results of the study are relevant for air quality assessment in the region and demonstrate the potential of using precipitation chemistry data to understand the long-range transport of pollutants, which contributes to sustainable development by increasing the availability of air quality data in ecologically significant regions such as Lake Baikal. Full article

The Huoqiu Group is located in the southern margin of the North China Craton and is considered an Archean geologic body. Its supracrustal rocks are divided into the Huayuan, Wuji, and Zhouji formations in ascending order. The Wuji and Zhouji formations contain large BIF-type iron deposits. The BIFs show geological and geochemical features of Paleoproterozoic Lake Superior-type rather than Archean Algoma-type. The study of the formation ages and evolutionary history of the Huoqiu Terrane will provide significant guidance for the mineralization and exploration of the Huoqiu iron deposits. In this paper, we collected all available isotopic ages and Hf isotopic compositions obtained from the Huoqiu Terrane and reassessed their accuracy and geological meanings. We conclude that the Wuji and Zhouji formations were not older than 2343 Ma. Therefore, the BIFs hosted in the Wuji and Zhouji formations must be of Paleoproterozoic age. The magmatic zircons from the TTG gneisses and granite yield U-Pb ages of Neoarchean Era, indicating that the Wuji and Zhouji formations of the Huoqiu Group were deposited on an Archean granitic basement that mainly comprises the trondhjemite-tonalite-granodiorite (TTG) gneisses and granites of the “Huayuan Formation”. The Early Precambrian crystalline basement in the Huoqiu area can be divided into the Huayuan Gneiss Complex and the Huoqiu Group, comprising the Wuji and Zhouji formations. The tectonic scenario of granitic complexes overlain by supracrustal rocks in the Huoqiu Terrane has been recognized in the Songshan, Zhongtiao, Xiaoshan, and Lushan Early Precambrian terranes in the southern margin of the North China Craton. As indicated by the zircon U-Pb ages and εHf(t) data, the crustal growth of the Huoqiu Terrane occurred mainly at ~2.9 Ga and ~2.7 Ga. Based on the sedimentary age, environment, and rhythm, the BIFs in the Huoqiu region are considered to be of Lake Superior type and of great potential for Fe ore exploration. Full article

The western region of Lake Erie has been experiencing severe water-quality issues, mainly through the infestation of algal blooms, highlighting the urgent need for action. Understanding the drivers and the intricacies associated with algal bloom phenomena is important to develop effective water-quality remediation strategies. In this study, the influences of multiple bloom drivers were explored, together with Harmonized Landsat Sentinel-2 (HLS) images, using the datasets collected in Western Lake Erie from 2013 to 2022. Bloom drivers included a group of physicochemical and meteorological variables, and Chlorophyll-a (Chl-a) served as a proxy for algal blooms. Various combinations of these datasets were used as predictor variables for three machine learning models, including Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Random Forest (RF). Each model is complemented with the SHapley Additive exPlanations (SHAP) model to understand the role of predictor variables in Chl-a estimation. A combination of physicochemical variables and optical spectral bands yielded the highest model performance (R² up to 0.76, RMSE as low as 8.04 µg/L). The models using only meteorological data and spectral bands performed poorly (R² < 0.40), indicating the limited standalone predictive power of meteorological variables. While satellite-only models achieved moderate performance (R² up to 0.48), they could still be useful for preliminary monitoring where field data are unavailable. Furthermore, all 20 variables did not substantially improve model performance over models with only spectral and physicochemical inputs. While SVR achieved the highest R² in individual runs, XGB provided the most stable and consistently strong performance across input configurations, which could be an important consideration for operational use. These findings are highly relevant for harmful algal bloom (HAB) monitoring, where Chl-a serves as a critical proxy. By clarifying the contribution of diverse variables to Chl-a prediction and identifying robust modeling approaches, this study provides actionable insights to support data-driven management decisions aimed at mitigating HAB impacts in freshwater systems. Full article

Background/Objectives: Sepsis is a costly and life-threatening condition caused by a dysregulated host response to infection. Lack of a reliable, timely diagnostic for sepsis leads to under- and overdiagnosis, suboptimal outcomes, and strained hospital resources. Our Lady of the Lake Regional Medical Center (OLOLRMC) implemented a sepsis learning health program to evaluate and improve outcomes through standardized ED workflows and the incorporation of a novel sepsis diagnostic test. Methods: We report the results of the first year of experience following the implementation of the learning health initiative and sepsis testing. Data from the Epic EHR were analyzed across two groups: pre-implementation (April 2023–July 2023) vs. post-implementation (August 2023–July 2024), and temporally matched cohorts (April–July 2023 vs. April–July 2024). We assessed clinical outcomes (sepsis-associated mortality, hospital length of stay, or HLOS), and resource utilization (antibiotic use, blood cultures). Results: Post-implementation, sepsis-associated mortality dropped from 10.9% to 6.6% in the temporally matched group (p < 0.001). There was also a 0.76-day reduction in mean HLOS among sepsis DRG patients (p < 0.05). Blood culture utilization fell from 50.8% to 45.7%, driven by reductions in blood culture utilization among patients receiving a Band 1 IntelliSep score. Conclusions: The FMOLHS experience demonstrated significant benefits to patient outcomes and resource utilization after implementing a sepsis QI initiative including protocolized and standardized ED workflows via a nurse-driven triage system with sepsis testing for the early risk stratification of patients who present to the ED with signs and symptoms of infection. Full article

The scientific determination of reference conditions for lake nutrients is fundamental for establishing ecologically sustainable nutrient criteria. This study developed a novel method for determining lake reference nutrient conditions based on generalized extreme value distribution theory. The method establishes a unified framework by integrating Weibull, Gumbel, and Fréchet distributions. It was applied to estimate the reference nutrient conditions in Hongze Lake, the fourth-largest freshwater lake in China. The results indicated that the extreme value sequences of total nitrogen (TN) and total phosphorus (TP) followed the generalized extreme value distribution, thereby confirming the method’s feasibility. The recommended reference conditions for TN and TP in Hongze Lake were 0.65 mg/L and 0.031 mg/L, respectively, with 95% confidence intervals of 0.56–0.74 mg/L for TN and 0.026–0.035 mg/L for TP. The proposed method could avoid the artificial errors associated with data grouping in descriptive statistical methods and assist in formulating adaptive water management strategies by providing confidence intervals for reference nutrient concentrations. The spatial distribution of the reference TN and TP concentrations across various watersheds in China revealed that the eastern watersheds exhibited higher reference concentrations of TN and TP compared to the western watersheds. This study provides valuable insights for developing nutrient criteria for lakes, contributing to the sustainable management of water quality in regional lake ecosystems. Full article

High-alkalinity water bodies can disrupt normal ammonia metabolism in fish, leading to ammonia poisoning. In China, there exists a highly tolerant group of Amur ide (Leuciscus waleckii) that can survive in extreme alkaline lakes with alkalinity up to 53.57 mM (pH 9.6), making it an excellent model for elucidating the high-alkalinity tolerance mechanism in fish. We have discovered that this species has evolved a special ammonia excretion mechanism to maintain ammonia efflux in high-alkalinity environments. Compared to the freshwater forms of Amur ide, the ammonia excretion protein RHBG plays a prominent role in the ammonia excretion process of the alkali forms of Amur ide; however, the regulatory mechanism of RHBG expression in fish remains unclear. Through DNA pull-down, RNA-Seq, qPCR, Western blotting, immunofluorescence, and dual-luciferase reporter assays, this study demonstrates that the transcription factor HIF1A can inversely regulate the expression of Rhbg by binding to its promoter region, thereby participating in the high-alkalinity adaptation process of fish. The findings of this study provide a theoretical basis for elucidating the ammonia excretion mechanism and revealing the alkalinity tolerance mechanism in fish. Full article

The structure and density of plankton communities greatly influence carbon and nutrient cycling as well as the environmental status of lake ecosystems. This community can respond to a range of environmental drivers, including those influenced by human perturbations on local and regional scales, causing abrupt changes and imbalances. While the implications of climate and land-use changes are evident for a range of tropical lake conditions, their impacts on planktonic population dynamics are less understood. In this study, we aimed to investigate how distinctive levels of nutrients, allochthonous organic matter (OM), and sunlight availability change phytoplankton and zooplankton density and structure in a natural tropical lake. Using an in situ mesocosm facility, we manipulated the addition of nutrients and OM, in addition to sunlight availability and a combination of these treatments. We monitored limnological parameters, plankton count, and identification for 12 days. The mesocosms included eight different combinations in a 2 × 2 × 2 factorial design, each with two replicates. Inorganic nutrient addition reduced phytoplankton species richness, favoring the dominance of opportunistic species such as Chlorella sp. at much higher densities. Organic matter also increased light attenuation and caused the substitution of species and changes in dominance from Pseudanabaena catenata to Aphanocapsa elachista. On the other hand, physical shading had less influence on these communities, presenting densities similar to those found in the control mesocosms. Zooplankton presented a group dominance substitution in all mesocosms from copepod to rotifer species, and copepod growth seemed to be negatively affected by Chlorella sp. density increase. Furthermore, this community was associated with the light attenuation indices and bacterioplankton. These results indicate that tropical planktonic responses to environmental changes can effectively occur in just a few days, and the responses can be quite different depending on the nutritional source added. The punctual nutrient addition was sufficient to provide changes in this community, evidencing the strength of anthropic events associated with strong nutrient input. Understanding tropical plankton dynamics in response to environmental changes, such as those simulated in this work, is important for understanding the effects of climate and anthropogenic changes on tropical lake functioning. This knowledge can strengthen measures for the conservation of freshwater systems by allowing predictions of plankton community changes and the possible consequences for the aquatic food chain and water quality. Full article

Groundwater is one of the most critical drinking water resources on Earth, and its safety significantly impacts the ecological environment and human health. This study focuses on the pollution characteristics, sources, and health risks of potentially toxic elements (PTEs) in the groundwater of the Dongting Lake basin in China. It highlights the concentration distribution and pollution causes of common toxic elements such as manganese (Mn), copper (Cu), zinc (Zn), arsenic (As), mercury (Hg), iron (Fe), chloride ions (Cl⁻), and fluoride ions (F⁻). The results indicate that the Mn concentration reached 28.6 times the background value, followed by Cu at 16.7 times. The groundwater pollution level in the study area reached a severe contamination level, with Zn classified as severely polluted and Mn categorized as moderately to heavily polluted. Approximately 47.05% of the study area was severely contaminated by PTEs. The study further reveals that the primary sources of pollution are anthropogenic activities, including agricultural fertilization, industrial discharges, and urbanization processes, which have significantly elevated PTE concentrations in groundwater. Additionally, natural geochemical processes contribute substantially to the high concentrations of specific elements in certain areas. Health risk assessments indicate that long-term exposure to PTEs in groundwater may pose various potential threats to human health, particularly in terms of carcinogenic and non-carcinogenic risks. Children are identified as the most vulnerable group. This research provides a systematic scientific basis for the management of groundwater pollution in the Dongting Lake basin, emphasizing that the current pollution levels pose serious threats to regional ecosystems and public health. The findings not only offer guidance for groundwater management in the Dongting Lake basin but also serve as a valuable reference for groundwater pollution management in similar regions. Full article

The phytoplankton communities in lakes change seasonally within competitive areas, referred to as seasonal succession, which results in high compositional diversity if conditions remain stable. However, glacial lakes are generally far from human and terrestrial influences due to their location so very few species can be identified and large changes in phytoplankton composition cannot be anticipated. Nonetheless, molecular techniques, as well as classical methods, help us to determine the existence of different species. Additionally, these techniques allow us to evaluate the ecology of glacial lakes from different perspectives with developing technology. Horseshoe Island is located in the area known as Marguerite Bay on the Peninsula region in western Antarctica. This study was carried out to determine phytoplankton genome biodiversity by using the metagenomic analysis method used in 18S rRNA, 16S rRNA, and 23S rRNA gene analyses. 16S rRNA and 23S rRNA gene analyses revealed that bacteria belong to broadly distributed Cyanobacteria taxa, whereas 18S rRNA gene analysis revealed other eukaryotic phytoplankton groups. This method was used for the first time for Horseshoe Island lakes (Col 1, Col 2, Skua, and Zano), and species belonging to Cyanobacteria, Chlorophyta, Ochrophyta, and Bacillariophyta were identified. As a result, the phytoplankton genomic diversity of shallow and oligotrophic glacial lakes was determined, and benthic algal species were also identified in the water samples. These results indicate that benthic algae associated with the sediment can also contribute to aquatic phytoplankton communities in addition to oligotrophic lake phytoplankton biodiversity. Cyanobacterial biodiversity can also be recognized as a sentinel by which to monitor adaptation responses to climate change in this rapidly warming region. Full article