Unraveling antibiotic fate in a highly urbanized estuary: Spatiotemporal patterns, multi-media partitioning, and contaminant prioritization.

Physics-based simulations, while central to transmission tower fragility analysis, are often computationally prohibitive for system-level or regional-scale assessments, where the objective is to evaluate the performance of multiple transmission towers with diverse geometries over large geographic regions. The study addresses these challenges by developing a generalized surrogate wind fragility modeling framework. This framework facilitates rapid and comprehensive vulnerability assessment of transmission towers by integrating structural design, fragility analysis, and deep-learning-based surrogate modeling. This integration allows the framework to explicitly account for site-specific environmental conditions and tower-specific design characteristics across diverse geographic regions in the United States, thereby enhancing its generalizability and applicability to a wide range of tower designs and locations. Moreover, contrary to traditional fragility models, the framework considers a more realistic representation of extreme wind events, where multiple hazard variables (i.e., wind speed, direction, and rainfall intensity) influence tower fragility concurrently. The resulting surrogate models provided high prediction accuracies. For unseen towers, the models achieved a mean square error of 0.0202 and an R 2 of 0.899 and demonstrated consistency with conventional physics-based fragility models. Moreover, the inclusion of location-specific design parameters enabled the models to adapt to different regional performance objectives, while considering tower-specific parameters in the design phase facilitated rapid vulnerability assessments for various tower designs within the transmission network. Overall, the proposed framework can potentially reinforce decision-making for grid resilience planning by offering a practical and computationally efficient solution for system-level vulnerability analysis of transmission tower networks. • The framework combines design and fragility processes for model generalizability. • The interaction of three environmental parameters is considered in tower fragility. • The developed surrogate models provide a high prediction accuracy. • Surrogate models can be generalized for various tower configurations and locations. • The framework provides a computationally-efficient solution for network-level analysis.

Tracking organic mercury bioaccumulation by the brown mussel Perna perna (Linnaeus, 1758) in subtropical bays: Environmental exposure and seasonal effects.

Exploring contributions and responses of microbial quantity and diversity to coastal seasonal hypoxia.

Physiological and immunological resilience of the Pacific oyster Magallana gigas (Thunberg, 1793) to fluctuating salinity.

Nematode metabarcoding as an alternative to conventional benthic macrofauna monitoring of fish farming.

The Adriatic Sea represents one of the most productive areas of the entire Mediterranean basin, with anchovy ( Engraulis encrasicolus ) being among the main targets of fishery. Two decades (2003-2023) of anchovy Catch Per Unit Effort (CPUE) have been made available through continuous data collection derived from the Fishery Observing System (FOS) and the Adriatic Fishery & Oceanography Observing System infrastructure (AdriFOOS), implemented by CNR-IRBIM. These systems leverage commercial fishing vessels as Ships Of OPportunity (SOOPs) by transforming them into autonomous data-collection units capable of recording geo-referenced catch data (supplied by fishermen through a dedicated interface), along with in-situ environmental information (through sensors installed on the fishing gears). By means of Generalized Additive Models (GAMs), the influence of environmental and anthropogenic drivers on anchovy’s CPUE spatio-temporal distribution in the Adriatic Sea was analysed. Temperature and fishing depth were shown to influence anchovy’s CPUEs. In contrast, vessel characteristics (except for vessel power), as well as latitude, exhibited less clear effects on CPUEs. This study characterized the effect of environmental and fleets’ changes, over space and time, on anchovy’s CPUE in the Adriatic Sea. Furthermore, this approach provides a good example of data collection using commercial fishing vessels, demonstrating the potential of SOOPs to advance the investigation on population dynamics. • Fishery observing systems were employed to collect data on European anchovy CPUEs in the Adriatic Sea. • A 20-year time series of georeferenced catches and environmental parameters was used. • CPUEs spatio-temporal distribution was modelled using Generalized Additive Models. • Environmental and anthropogenic drivers significantly influence anchovy’s CPUE. • Commercial fishing vessels resulted effective in characterizing anchovy’s dynamics.

Mechanism study on improving DIC measurement accuracy via air knife parameters in thermal environments

Chitin-rich organic wastes in aquatic environments provide ecological niches for chitinolytic microorganisms capable of converting chitin into chitosan, a high-value biopolymer with applications in agriculture, biotechnology, and environmental management. Microbial chitosan production represents a sustainable alternative to conventional chemical extraction; however, its efficiency is strongly influenced by environmental and physicochemical process parameters. This study investigated the occurrence, chitinolytic potential, and chitosan-producing capacity of indigenous microorganisms isolated from freshwater and sediment samples in Minna, Niger State, Nigeria, and optimised production under controlled fermentation conditions. Physicochemical characteristics of water bodies were determined, followed by the isolation and characterisation of bacterial and fungal chitinolytic strains. Selected isolates were screened for chitinase activity and subjected to fermentation-based chitosan production. Process optimisation was performed using a five-factor central composite design to assess the effects of temperature, pH, glucose level (5-20g L⁻¹), nitrogen source concentration (1-5g L⁻¹), and incubation time, with response surface methodology applied for model development and interaction analysis. Low dissolved oxygen and moderate temperatures favoured chitinolytic genera, including Aspergillus, Bacillus, and Fusarium, with Aspergillus niger exhibiting the highest chitosan yield. Incubation time was the most significant factor (p < 0.001), while temperature, pH, and substrate concentration showed strong interaction effects. Initial batch fermentations yielded up to 0.20g L⁻¹ chitosan from selected isolates, while response surface optimisation with Aspergillus niger increased production to 1.46g L⁻¹ at 32.8°C, pH 5.12, 15.3g L⁻¹ glucose, 2.45g L⁻¹ yeast extract, and 5.2 days incubation. The optimised product exhibited a high degree of deacetylation, indicating suitability for agricultural and environmental applications. These findings highlight inland aquatic ecosystems as valuable reservoirs of chitinolytic microorganisms and provide an optimised, environmentally benign framework for scalable microbial chitosan production.

How the environment shapes the plastisphere of microplastic in a coastal lagoon - a living lab test.

Monitoring whale vocalization is of scientific importance and has practical value for marine ecology, hydroacoustics, and geophysics. Conventional monitoring approaches, including hydrophone arrays, ocean-bottom seismometers, and satellite tagging, are limited by sparse spatial coverage, potential biological disturbance, and high cost. Distributed acoustic sensing (DAS) is an emerging method that uses submarine optical cables as dense acoustic arrays, potentially enabling large-scale, high-resolution monitoring of whale vocalization. We investigated the features of the wavefields of fin whale vocalization by integrating DAS observations with numerical modeling. Three distinct features-insensitive response segments (IRSs), high-frequency component loss, and acoustic notches-were identified in the observed wavefields. DAS response modeling based on ray theory indicates that the length of the IRS is correlated positively with the vertical distance between the source and cable, and the gauge length is responsible for the high-frequency loss in whale calls. Furthermore, wavefield modeling using the spectral-element method demonstrates that the notches represent transitions between transmission zones of waterborne multipath waves entering the seafloor and are sensitive to the seafloor P-wave velocity, water depth, and bathymetry. These findings not only improve our understanding of DAS-observed wavefields but also highlight the potential of DAS for ocean environmental parameter estimation and three-dimensional whale localization.

A macroscopic P-ZIF-8/PDA/MF adsorbent was fabricated by substituting conventional zinc precursors with layered porous zinc oxide (P-ZnO) on polydopamine (PDA)-functionalized melamine foam (MF). The composite exhibited exceptional adsorption capacity for chlortetracycline hydrochloride from wastewater, with a Langmuir-modeled maximum of 1320 mg/g at pH 6. Systematic evaluation of environmental parameters─including pH gradients, ionic strength variations, and competitive ion interference─revealed robust performance under various aqueous conditions. In addition, FT-IR and XPS analyses were performed to characterize P-ZIF-8/PDA/MF both prior to adsorption and following the adsorption process in order to clarify its adsorption behavior. The analysis indicates that the uptake by P-ZIF-8/PDA/MF mainly proceeds through monolayer chemisorption, which results from the combined contributions of hydrogen-bond interactions, π-π stacking, and pH-dependent electrostatic attraction.

Using surfactants to manipulate the interfacial tension (IFT) of oil-water systems represent a critical strategy for enhanced oil recovery (EOR). However, predicting the physical properties of surfactants based on their molecular structure remains a challenging task, as conventional machine learning methods struggle to capture the coupled interactions between molecular structures and environmental parameters, while existing graph neural networks predominantly focus on single-molecule representations and overlook the characteristics of the system environment. Accordingly, a Gated Message-passing Graph Neural Network with an Attention Mechanism (Gated-MPNN-AT) is proposed to integrate molecular graph structures and environmental features, aiming to achieve accurate prediction of interfacial tension in surfactant-oil-water systems. The model dynamically controls the message passing process through a dual gated mechanism, adopts a Cross-Attention mechanism to achieve the in-depth interaction between molecular topological features and environmental parameters, and designs a hybrid robust loss function to handle the IFT data with cross-order-of-magnitude distribution. The research results show that the prediction accuracy of the model is better than that of traditional machine learning methods (such as Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)) and some graph neural network methods (such as Graph Convolutional Network (GCN), and Graph Attention Network (GAT)). Ablation experiments have confirmed that the gated mechanism increases the coefficient of determination (R 2) by 4.8%, and the Cross-Attention fusion strategy reduces the mean absolute error (MAE) by 21.3%. Meanwhile, the model has good generalization ability and strong anti-interference ability against abnormal IFT data.

Species-specific and seasonal variation in trace metal accumulation in intertidal seaweeds from a tropical island's coast.

A mesoscale study on diatoms from the Santos Basin (23°S to 28°S), southern Brazil, was carried out during early spring 2019, based on 60 oceanographic stations stretching from coastal to open-ocean waters. Multivariate analysis based on counting diatom valves on permanent slides was performed in relation to the main environmental parameters (temperature, salinity, nutrients, and depth). Four diatom associations were discriminated, affected by the water masses and environmental drivers operating in the Santos Basin. The inner- to mid-shelf association was influenced by nutrient discharge from rivers and estuaries, as well as the Subtropical Shelf Front; Actinocyclus octonarius, Chaetoceros didymus, Fragilariopsis doliolus, and Pseudo-nitzschia pungens were the highest relative valve contributors. Another association composed of uncommon diatoms occurred as isolated peaks along the continental slope and was affected by meanders and eddies of the Brazil Current, as well as deep waters brought up by local coastal upwelling. Delphineis surirella, Navicula pennata, Pleurosigma diversestriatum, Pseudo-nitzschia cf. delicatissima, Pseudo-nitzschia calliantha, and Skeletonema tropicum were its main components. A typical subtropical/tropical association from oligotrophic warm waters was delimited in outer-shelf and oceanic waters such as Azpeitia spp., Asteromphalus spp., and Roperia tesselata. The fourth association comprised diatoms widely distributed across the Santos Basin, overlapping with the tropical association in certain areas. In this association, Nitzschia bicapitata, Thalassionema nitzschioides, Chaetoceros spp. and Bacteriastrum hyalinum were the most important taxa. Multivariate analysis identified for the first time diatom associations related to water masses and their properties in the Santos Basin on a regional scale. These patterns of mesoscale distribution provide a more detailed understanding of diatom biogeography than previously documented in broader-scale investigations.

This article presents a prototype design and experimental validation of intelligent control of a street light luminaire containing CW (cool white) and WW (warm white) LED based on human or object detection, motion, and weather conditions. The system is mainly controlled by a Raspberry Pi 5(RPi 5) integrated with a wireless CCTV camera for obtaining real-time object presence and speed estimation, along with a DHT11 sensor and LDR for temperature, humidity, and light level detection, and sensing of fog and rain from a weather application programming interface (API). The designed system uses vision-based object detection, determines the type and speed of object movement, and then dynamically adjusts the LED light output, consists of alternate array of CW and WW LED arrays, based on the required illuminance on road surface corresponding to the object's speed. On the other hand, it senses weather behaviour from weather API data to generate control signal and transfer via IoT network to ESP8266 to switch between CW and WW LEDs in a single LED module. In this system, sensor fusion techniques are used to correlate environmental parameters to actuate the situation demanded light level. A hardware prototype is designed and experimentally tested in a controlled environmental scenario to evaluate system response time, light output adjustment variation with speed, and lighting performance under dynamic weather conditions. This control setup helps to develop a smart street lighting solution as well as to provide suitable visual conditions and safety to support the development of a smart city.

Protocol for applying a network-enabled gene discovery pipeline to non-model plant species.

This study aimed to investigate the origin of 1-hydroxyoctan-3-one and octane-1,3-diol, recently identified as chemical markers correlated with fresh mushroom off-flavors in grape must and wine. The objective was to identify their precursors before and during alcoholic fermentation, focusing on linoleic acid and glycosylated precursors. The production of both markers by three grapevine fungi and by an oenological yeast was evaluated in a synthetic must (SM) supplemented with linoleic acid. Glycosylated precursors were detected for the first time in naturally altered must. A full factorial design assessed the impact of environmental parameters on their biosynthesis byBotrytis cinerea andPenicillium crocicola. No production occurred during alcoholic fermentation of SM by yeast, whereas molds biosynthesized both markers in different proportions. Finally, linoleic acid content (P. crocicola) and pH (P. crocicola, B. cinerea) were identified as the main factors impacting free and glycosylated compounds biosynthesis by fungi at the prefermentative stage.

Quantifying the combined effects of substrate microstructure and environmental factors on microalgal biofilm growth: a novel kinetic modeling approach.

Drivers of mantle-governed shell biogenesis gene transcription in Mediterranean mussels from the Northwestern Adriatic Sea: environmental variables, endogenous factors, and the physiological control.