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To enhance the effects of some functional soft drinks, drugs, especially metronidazole (MNZ) and ibuprofen (IBF), are often illegally added. This poses a serious threat to the health of consumers. Therefore, developing simple and rapid detection methods for these additives is crucial. In this study, DNA aptamers of metronidazole and ibuprofen were selected using the library-immobilized method. The best aptamer for metronidazole, named MNZ-1, has a dissociation constant (Kd) value of 4.9 μM and the aptamer for ibuprofen, named IBF-1, shows a Kd of 9.3 μM, as determined by the thioflavin T (ThT) fluorescence assay. The Kd values measured using isothermal titration calorimetry (ITC) were 17.0 μM and 66.7 μM for these two aptamers, respectively. Selectivity experiments indicate that MNZ-1 demonstrates very weak binding to structurally similar drugs, whereas IBF-1 exhibits binding capability to some structurally similar compounds comparable to ibuprofen, enabling the simultaneous detection of these types of drugs. Neither MNZ-1 nor IBF-1 binds to other common drugs. Using ThT, a label-free fluorescent detection method was developed for metronidazole and ibuprofen in soft drinks, showing limits of detection (LODs) of 0.6 μM and 4.7 μM, respectively. Owing to their small size and well-defined secondary structures, these aptamers are expected to be utilized in analytical applications for food and environmental monitoring. [ABSTRACT FROM AUTHOR]

Laccase is an enzyme known for its eco-friendly uses in environmental cleanup and biotechnology. However, it has limitations such as low stability, high cost, and complex recycling. So, there is a need for laccase mimics that can effectively imitate its properties. Herein, we created copper formate–lysine nanoparticles (Cuf-Lys) that mimic laccase's activity. The developed Cuf-Lys demonstrated remarkable polyphenol oxidase-like activity, stability, and recyclability, making them suitable for the fabrication of efficient colorimetric sensors for the detection of epinephrine. These sensors had a specific response and could accurately measure epinephrine concentrations ranging from 2.5 to 50 μM, with a detection limit as low as 1 μM. Furthermore, the biosensor demonstrated high sensitivity and selectivity when applied to the detection of rutin. The limit of detection for rutin was determined to be 0.16 μM while in the linear concentration range of 0.25 to 150.0 μM. We believe that Cuf-Lys provide a new route for the design of laccase mimics, showing potential applications for biomedical diagnosis and environmental monitoring. [ABSTRACT FROM AUTHOR]

Nanozyme technology has gained significant regard and been successfully implemented in various applications including chemical sensing, bio-medicine, and environmental monitoring. Fe-CDs were synthesized and characterized well in this study. As compared to HRP (3.7 mM), the Fe-CDs exhibited a higher affinity towards H2O2 (0.2 mM) using the steady-state kinetic assay and stronger catalytic capability by changing the color of TMB to the blue color of the oxidized state, oxTMB. Additionally, an efficient peroxidase mimic, Fe-CDs/GOx, based on the hybrid cascade system to produce in situ H2O2 for the visual detection of glucose (color change: colorless to blue, and then to green), has been developed in detail, with limits of detection (LODs) for H2O2 and glucose of 0.33 μM and 1.17 μM, respectively. The changes further demonstrate a linear relationship between absorbance and H2O2 concentration, ranging from 10 to 60 μM, and for glucose (1 to 60 μM). To assess the accuracy and detection capability of the Fe-CDs/GOx system, we evaluated a real human serum sample obtained from adult males in a local hospital. In conclusion, Fe-CDs serving as a peroxidase mimic have the potential for various applications in the fields of biomedicine and nanozymes. [ABSTRACT FROM AUTHOR]

Tidal flat ecosystems, are under steady decline due to anthropogenic pressures including sea level rise and climate change. Monitoring and managing these coastal systems requires accurate and up-to-date mapping. Sediment characteristics and macrozoobenthos are major indicators of the environmental status of tidal flats. Field monitoring of these indicators is often restricted by low accessibility and high costs. Despite limitations in spectral contrast, integrating remote sensing with deep learning proved efficient for deriving macrozoobenthos and sediment properties. In this study, we combined deep-learning features derived from Sentinel-2 images and Object-Based Image Analysis (OBIA) to explicitly include spatial aspects in the prediction of tsediment and macrozoobenthos properties of tidal flats , as well as the distribution of four benthic species. The deep-learning features extracted from a convolutional autoencoder model were analysed with OBIA to include spatial, textural, and contextual information. Object sets of varying sizes and shapes based on the spectral bands and/or the deep-learning features, served as the spatial units. These object sets and the field-collected points were used to train the Random Forest prediction model. Predictions were made for the tidal basins Pinkegat and Zoutkamperlaag in the Dutch Wadden Sea for 2018 to 2020. The overall prediction scores of the environmental variables ranged between 0.31 and 0.54. The species-distribution prediction model achieved accuracies ranging from 0.54 to 0.68 for the four benthic species). There was an average improvement of 21% points on predictions using objects with deep learning features compared to the pixel-based predictions with just the spectral bands. The mean spatial unit that captured the patterns best ranged between 0.3 ha and 13 ha for the different variables. Overall, using both OBIA and deep-learning features consistently improved the predictions, making it a valuable combination for monitoring these important environmental variables of coastal regions. [ABSTRACT FROM AUTHOR]

Bioindicators are living organisms that are successfully used for monitoring changes in the environmental health due to natural and/or anthropogenic influences. Dragonflies (Odonata) are considered to be good indicators of water quality; however, research on dragonflies as potential indicators of radioactivity is scarce. The aim of this study was to evaluate dragonflies as potential biological indicators of ionising radiation in nature by measuring 137Cs activity concentrations in the dragonfly and river water samples. Altogether, 11 collective samples of dragonflies were collected from the bank of the Mura–Drava–Danube Biosphere Reserve, in the area of Virovitica–Podravina County. Following the initial sample preparation, the gamma spectrometry technique, employing an ORTEC HPGe detector system, was utilised to determine the activity concentrations of 137Cs. The results show that the activity concentrations of 137Cs in the river water were in the range from 4 to 13 Bq/m3. The results of dragonfly samples showed that 137Cs was in the range from 1.7 to 3 Bq/kg. This was more than a hundred times higher than in the river Drava water. According to the obtained results, we conclude that dragonflies could be used as potential bioindicators of radioactivity. [ABSTRACT FROM AUTHOR]

Background: Invasive management strategies range from preventing new invasive species incursions to eliminating established populations, with all requiring effective monitoring to guide action. The use of DNA sampled from the environment (eDNA) is one such tool that provides the ability to surveille and monitor target invasive species through passive sampling. Technology being developed to eliminate invasive species includes genetic biocontrol in the form of gene drive. This approach would drive a trait through a population and could be used to eliminate or modify a target population. Once a gene drive organism is released into a population then monitoring changes in density of the target species and the spread of the drive in the population would be critical. Results: In this paper, we use invasive Mus musculus as a model for development of an eDNA assay that detects wild-type M. musculus and gene drive M. musculus. We demonstrate successful development of an assay where environmental samples could be used to detect wild-type invasive M. musculus and the relative density of wild-type to gene drive M. musculus. Conclusions: The development of a method that detects both wild-type M. musculus and a gene drive M. musculus (tCRISPR) from environmental samples expands the utility of environmental DNA. This method provides a tool that can immediately be deployed for invasive wild M. musculus management across the world. This is a proof-of-concept that a genetic biocontrol construct could be monitored using environmental samples. [ABSTRACT FROM AUTHOR]

Clouds play a pivotal role in determining Earth's climate, impacting several atmospheric processes influencing the weather. The study explores the temporal variations in cloud base height (CBH) over Pune, a tropical station, from 2017 to 2023 using a Laser-Based Ceilometer with a range of 0–7500 m. Our analysis shows seasonal trends in CBH, with the monsoon season exhibiting unique characteristics showing the lowest average CBH and minimal diurnal changes during the study period. Conversely, the pre-monsoon, post-monsoon, and winter season show a gradual rise of CBH in the morning, maintaining higher levels until late afternoon, followed by a decline in the evening. Analysis of the diurnal frequency of cloud occurrence at 500 m intervals reveals the monsoon season's dominance in the 500 m to 1000 m layer. Throughout the year, low cloud bases (CBH <2000 m) are most frequent, outpacing the frequency of mid and high cloud bases. High cloud base (7500 < CBH > 6000 m) is the least common, showing a semi-diurnal pattern in winter with peaks around noon and midnight. These findings not only enhance our understanding of Pune's atmospheric behaviour but also offer valuable information for weather prediction, climate modelling, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Precise extraction of open-pit mines is crucial for resource management and ecological and environmental dynamic monitoring. Current methods for extracting open-pit mines encounter challenges such as low accuracy and difficulty detecting complex scenes of open-pit mining. To address these issues, this paper proposes an object-oriented intelligent extraction method for complex mining scenes using Gaofen-2(GF-2) high-resolution remote-sensing images, which expresses the pixel-level features at the object level and utilizes the unique feature propagation and aggregation capabilities of the graph structure for the extraction of the mines. First, an object-oriented feature expression strategy is introduced to express multi-level pixel-level features as object-level features by constructing objects with appropriate multi-resolution segmentation parameters. This approach effectively reduces the impact of isolated pixel noise and outliers on classification results. Second, this paper proposes a U-GCN-based open-pit mining extraction method that combines the powerful multi-level feature extraction capabilities of U-GCN to propagate and aggregate information in a graph structure, effectively modelling spatial relationships between different objects. This method achieves high-precision extraction of open-pit mining areas. In experiments conducted on two study areas of varying scales, the F1 scores for open-pit mine extraction reached 93.32% and 83.06%. Comparative experiments demonstrate that the proposed object-oriented U-GCN method performs superiorly in terms of accuracy, stability and robustness across mining scenes with different levels of complexity. The proposed open-pit mine extraction method offers new insights and methodologies for current extraction practices. [ABSTRACT FROM AUTHOR]

Bimetallic Au@Ag nano-sea urchins (Au@Ag NSUs) functionalized with methimazole were synthesized and combined with paper tips as efficient surface-enhanced Raman scattering (SERS) substrates for ultrasensitive Cr(VI) detection. The redox reaction between methimazole and Cr(VI) ensured the reliability of quantitative Cr(VI) analysis. When droplets were continuously dropped on the tip of the paper, based on the gravity effect, the nanoparticles achieved enrichment at the front end of the tip, which further improved the sensitivity of detection. The detection limit is as low as 0.956 ng L−1, demonstrating its excellent performance in ultrarace Cr(VI) detection. Additionally, the SERS sensor was successfully applied to the detection of Cr(VI) in real water samples, such as lake water and tap water, indicating its wide application potential in environmental monitoring. This study not only provides an efficient and reliable new method for detecting Cr(VI) but also shows the great prospect of paper-based SERS sensors in practical applications. [ABSTRACT FROM AUTHOR]

Humidity sensing, as a promising approach for detecting environmental humidity and respiration patterns, is garnering widespread attention. Here, we present a quartz crystal microbalance (QCM) humidity sensor modified with hollow NiCo-LDH polyhedrons for real-time monitoring of both environmental humidity and human respiration. The hollow NiCo-LDH polyhedrons possess a large surface area and loose stacking, which contribute to its excellent humidity sensing performance. In practical applications, our device demonstrates outstanding performance when the relative humidity (RH) ranges from 11% to 97%, exhibiting high sensitivity, rapid response and recovery time (all within 1 s), and high selectivity. Furthermore, the sensor is capable of effectively distinguishing different breathing patterns, such as normal breathing, breath after exercising, and holding the breath, as well as detecting humidity changes before and after drinking water. This demonstrates its potential application prospects in monitoring and analyzing human health conditions. [ABSTRACT FROM AUTHOR]

Artemesia tridentata is a foundational plant taxon in western North America and an important medicinal plant threatened by climate change. Low-cost fabrication of sensors is critical for developing large-area sensor networks for understanding and monitoring a range of environmental conditions. However, the availability of materials and manufacturing processes is still in the early stages, limiting the capacity to develop cost-effective sensors at a large scale. In this study, we demonstrate the fabrication of low-cost flexible sensors using laser-induced graphene (LIG); a graphitic material synthesized using a 450-nm wavelength bench top laser patterned onto polyimide substrates. We demonstrate the effect of the intensity and focus of the incident beam on the morphology and electrical properties of the synthesized material. Raman analyses of the synthesized LIG show a defect-rich graphene with a crystallite size in the tens of nanometers. This shows that the high level of disorder within the LIG structure, along with the porous nature of the material provide a good surface for gas adsorption. The initial characterization of the material has shown an analyte response represented by a change in resistance of up to 5% in the presence of volatile organic compounds (VOCs) that are emitted and detected by Artemisia species. Bend testing up to 100 cycles provides evidence that these sensors will remain resilient when deployed across the landscapes to assess VOC signaling in plant communities. The versatile low-cost laser writing technique highlights the promise of low-cost and scalable fabrication of LIG sensors for gas sensor monitoring. [ABSTRACT FROM AUTHOR]

Accurate extraction of grassland sample coverage is crucial for regional ecological environment monitoring. Due to the strong feature learning capability, high flexibility, and scalability of deep learning methods, they have great potential in grassland sample extraction modelling. However, we still lack a model that can achieve both lightweight structure and effective performance for small object segmentation to considering the small target characteristics of grassland vegetation and the requirements for model deployment in later stages. Here, we combined the UNet model, which performs well in small target segmentation, with the lightweight network Shufflenetv2 model, proposing an improved UNet neural network, Shufflenetv2UNet, for grassland sample coverage extraction. The core of Shufflenetv2UNet is the removal of maximum pooling and double‐layer convolution modules from downsampling in the UNet neural network. In addition, the Inverted Residual Block structure module from Shufflenetv2 was added to achieve a lightweight model and improved extraction accuracy. The Shufflenetv2UNet achieves an accuracy of 98.23%, with a parameter size of 50.74 M, and a model inference speed of 0.004 s. Compared to existing extraction methods, this model has advantages in prediction accuracy, parameter size, and model inference speed. Moreover, Shufflenetv2UNet achieved different types of grassland sample coverage extractions, with good robustness, generalization, and universality, enabling investigators to quickly and accurately obtain grassland sample coverage. This allows more dynamic and accurate ground measurement data for regional grassland environmental monitoring. [ABSTRACT FROM AUTHOR]

For both human health and the environment, air pollution is a serious concern. However, the available air quality monitoring networks have important limitations, such as the high implementation costs, limited portability, and considerable operational complexity. In this context, unmanned aerial systems (UASs) are emerging as a useful technological alternative due to their ability to cover large distances and access areas that are difficult or impossible for humans to reach. This article presents the development of an integrated platform that combines an unmanned aerial system (UAS) with specialized sensors to measure key parameters in relation to air quality, such as carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). In addition, a web application called PTECA is developed to visualize the data gathered by the wireless sensor array in real time. The platform incorporates a system that allows real-time tracking of the UAS route and measurement values during sample collection, employing the LoRa communication protocol. This solution represents a low-cost alternative that mitigates some of the limitations of traditional monitoring networks by offering greater portability and accessibility in terms of data collection. Preliminary tests successfully demonstrate the viability of the proposed system in a controlled airspace using geofencing. [ABSTRACT FROM AUTHOR]

Vegetation segmentation plays a crucial role in accurately monitoring and analyzing vegetation cover, growth patterns, and changes over time, which in turn contributes to environmental studies, land management, and assessing the impact of climate change. This study explores the potential of a multi-scale convolutional neural network (MSCNN) design for object classification, specifically focusing on vegetation detection. The MSCNN is designed to integrate multi-scale feature extraction and attention mechanisms, enabling the model to capture both fine and coarse vegetation patterns effectively. Moreover, the MSCNN architecture integrates multiple convolutional layers with varying kernel sizes (3 × 3, 5 × 5, and 7 × 7), enabling the model to extract features at different scales, which is vital for identifying diverse vegetation patterns across various landscapes. Vegetation detection is demonstrated using three diverse datasets: the CamVid dataset, the FloodNet dataset, and the multispectral RIT-18 dataset. These datasets present a range of challenges, including variations in illumination, the presence of shadows, occlusion, scale differences, and cluttered backgrounds, which are common in real-world scenarios. The MSCNN architecture allows for the integration of information from multiple scales, facilitating the detection of diverse vegetation types under varying conditions. The performance of the proposed MSCNN method is rigorously evaluated and compared against state-of-the-art techniques in the field. Comprehensive experiments showcase the effectiveness of the approach, highlighting its robustness in accurately segmenting and classifying vegetation even in complex environments. The results indicate that the MSCNN design significantly outperforms traditional methods, achieving a remarkable global accuracy and boundary F1 score (BF score) of up to 98%. This superior performance underscores the MSCNN's capability to enhance vegetation detection in imagery, making it a promising tool for applications in environmental monitoring and land use management. [ABSTRACT FROM AUTHOR]

The current study explores a new approach to investigate the CO2 detection capabilities of cobalt-doped zinc oxide (Co-ZnO) combined with molybdenum sulfide (MoS2) hybrid nanomaterials Co-ZnO/MoS2 (CZM). The hydrothermally synthesized CZM composites provide unique structural and compositional properties, with 25 nm as their longest dimension (length), and specific lattice structure. CZM-based electrodes are developed by preparing the nanomaterial-dispersed ink, and potentiometric studies explore the optimal sensing performance. We found significant enhancements in sensitivity, reaction time, and reduction efficiency by systematically changing the electrolyte concentration in the electrode cell. Bode and Nyquist plots explain the influence of electrolyte concentration and the nanomaterial synergy in CO2 sensing and conversion with the 0.1 N electrolyte with the maximum efficiency. By offering important insights into how the electrolyte content affects the performance of Co-ZnO/MoS2 nanocomposite sensors, this study advances the field of CO2 sensing technology. Further, the nanomaterials extend their applicability in environmental monitoring, evaluating indoor air quality, and industrial processes. [ABSTRACT FROM AUTHOR]

Animals have evolved to rapidly detect and recognize brief and intermittent encounters with odor packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results--existing solutions are either slow; or bulky, expensive, and power-intensive--limiting applicability in real-world scenarios for mobile robotics. Here, we introduce a miniaturized high-speed electronic nose, characterized by high-bandwidth sensor readouts, tightly controlled sensing parameters, and powerful algorithms. The system is evaluated on a high-fidelity odor delivery benchmark. We showcase successful classification of tens-of-millisecond odor pulses and demonstrate temporal pattern encoding of stimuli switching with up to 60 hertz. Those timescales are unprecedented in miniaturized low-power settings and demonstrably exceed the performance observed in mice. It is now possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond. [ABSTRACT FROM AUTHOR]

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet. [ABSTRACT FROM AUTHOR]

Detecting ammonia at low concentrations is crucial in various fields, including environmental monitoring, industrial processes, and healthcare. This study explores the development and performance of an ultra-sensitive ammonia sensor using carboxylic group-functionalized multi-walled carbon nanotubes (f-MWCNTs) overlaid on polyvinyl acetate nanofibers coated on a quartz crystal microbalance (QCM). The sensor demonstrates high responsiveness, with a frequency shift response of over 120 Hz when exposed to 1.5 ppm ammonia, a sensitivity of 23.3 Hz ppm−1 over a concentration range of 1.5–7.5 ppm, and a detection limit of 50 ppb. Additionally, the sensor exhibits a rapid response time of only 14 s, excellent selectivity against other gases, such as acetic acid, formaldehyde, methanol, ethanol, propanol, benzene, toluene, and xylene, and good stability in daily use. These characteristics make the sensor a promising tool for real-time ammonia detection in diverse applications. [ABSTRACT FROM AUTHOR]

Nanoclays are layered structures in the nanoscale range with widespread application due to their unique properties such as swelling, cation exchange capacity, and ease of functionalisation using metals, metal oxides, and organic compounds such as carbon paste, polymers, and other biomolecules that form nanoclay composites. Nanoclay functionalisation with silver (Ag), zinc oxide (ZnO), and bimetallic silver–gold (Ag–Au) using hydrophilic and hydrophobic clays is here evaluated and discussed. The composites' synthesis and morphological, crystallinity, and electroactive properties in comparison with pure nanoclay are also assessed. The layered structure and crystallinity of all these nanoclay composites were slightly changed. The clumped layered structures on the surface of the nanocomposites had dispersed white spots that indicated possible surface modification. The nano-films of the composites' electroactivity were comparatively high, as seen from the increase in current in the cyclic voltammetry characterisation voltammograms and the differential pulse voltammograms of the pharmaceutical detection. Efavirenz, nevirapine, and zidovudine detection was improved by modification of the nanocomposite with human serum albumin (HSA), as shown by the higher current, thus indicating improved conductivity of the composites compared to the pure nanoclays. Applying HAS-modified nanocomposites in the analysis of efavirenz, nevirapine, and zidovudine on a glassy carbon electrode (GCE) showed good linearity and acceptable detection limits comparable to those of previous studies. Therefore, it has potential for application in pharmaceutical quality control and environmental monitoring. [ABSTRACT FROM AUTHOR]

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23–30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources. [ABSTRACT FROM AUTHOR]

We present a terahertz (THz) sensing chip capable of detecting trace biomolecules. Its operating bandwidth stretches from 0.2 to 1.35 THz, capable of signal strengths exceeding 38 dB above noise levels, achieved by the mitigation of mode competition issues. The high sensitivity of the sensing chip is used to observe the absorption fingerprints of α-lactose monohydrate, L-tyrosine, and L-histidine powders at their characteristic frequencies for sample masses as low as 0.3 mg. Using a solution titration technique, we demonstrate detection of the absorption fingerprint of α-lactose monohydrate for concentrations of 72 μg/μl, adding just 57.6 μg of the powder onto the chip after evaporation of the ethanol solvent, representing a twofold advancement over prior work by providing a significantly broader operating spectrum and higher sensitivity. The relatively easy manufacturability, low cost, and scalability of the sensing chip paves the way for studies of enhanced light–matter interaction between THz waves and trace analytes, while also unlocking innovative potential applications in biomolecular sensing, medical diagnostics, and environmental monitoring. [ABSTRACT FROM AUTHOR]

This article presents a 45-year data series (from 1978 to 2023) acquired under the IGA ("Impact des Grands Aménagements" in French; Impacts of Major Developments) program, conducted by Ifremer for EDF (Électricité de France, the French multinational electricity utility company). The IGA program was established to monitor the ecological and environmental quality of the coastal area surrounding the Gravelines Nuclear Power Plant (NPP), located in the southern bight of the North Sea. The main objective of this program is to assess medium- and long-term environmental evolution by providing means to identify possible changes in the local marine habitats. Since 1978, the IGA program has measured key parameters, including temperature, salinity, nutrient concentrations, oxygen levels, chlorophyll- a concentrations, as well as the abundance of phytoplankton and zooplankton species. These measurements have been taken at different sampling stations around the NPP, including the "Canal d'amenée" sampling station, for which hydrological and biological characteristics are considered as representative of broader coastal area of the southern bight of the North Sea. This data paper provides an overview of the main statistical characteristics of the time series, including long-term trends and shifts analysis. Despite the importance and length of this dataset, one of the longest available for this region, its application in advancing knowledge of hydrological and biological processes has been surprisingly limited. The aim of this paper is to make this valuable dataset available to the scientific community, stakeholders, and society to help decipher the local and global influence of anthropogenic activities in a world increasingly affected by climate change. Since all the main statistics and patterns are still available thanks to our analysis, the user should be able to use this data and combine it with other sources (in situ , satellite, modelling), in order to dive into deeper analyses, and to investigate new key scientific challenges as well as more specific ones. [ABSTRACT FROM AUTHOR]

The interaction of substrate surfaces with the environment (moisture, heat, pressure, chemicals) has cascade effects on their efficiency, performance, durability due to their susceptibility towards microbial adhesion, spontaneous leaching, impairment, corrosion etc. Many scientific, technological innovations such as smart coating have been discovered for the protection of these surfaces. It is a high‐tech, high value coating capable of responding to environmental impulses. Incorporation of nanofillers, nanomatrices in smart coating improve surface performance by extending service life, provide high durability, high physical coverage, adhesive strength etc. Real‐time monitoring of environmental conditions, structural health could be made possible by sensing coatings. This review predominantly highlights the essential features, design, advancements, applications of smart nanocoatings over elementary nanocoatings. To cover the whole study, drawbacks of traditional coating methods are also discussed. Finally, the research gaps, future perspectives of smart nanocoatings will be overlooked. [ABSTRACT FROM AUTHOR]

Introduction: The Common Agricultural Policy (CAP) is a vital policy framework implemented by the European Union to regulate and support agricultural production within member states. The Land Parcel Identification System (LPIS) is a key component that provides reliable land identification for administrative control procedures. On-the-spot checks (OTSC) are carried out to verify compliance with CAP requirements, typically relying on visual interpretation or field visits. However, the CAP is embracing advanced technologies to enhance its efficiency. Methods: This study focuses on using Sentinel-2 time series data and a two-level approach involving recurrent neural networks (RNN) and convolutional neural networks (CNN) to accurately identify permanent pastures. Results: In the first step, using RNN, the model achieved an accuracy of 68%, a precision of 36%, a recall of 97% and a F1-score of 52%, which indicates the model's ability to identify all the true positive parcels (correctly identified permanent pasture parcels) and minimize the false negative parcels (non-identified permanent pasture parcels). This occurs due to the difficulty in distinguishing between permanent pastures and other similar land covers (such as temporary pastures and shrublands). In the second step, it was possible to distinguish the permanent pasture parcels from the others. The obtained results improved significantly from the first to the second step. Using CNN, an accuracy of 93%, a precision of 89%, and a recall of 98% were achieved for the "Permanent pasture" class. The F1-score was 94%, indicating a balanced measure of the model's performance. Discussion: The integration of advanced technologies in the CAP's control mechanisms, as demonstrated, has the potential to automate the verification of farmers' declarations and subsequent subsidy payments. [ABSTRACT FROM AUTHOR]

Forest fires pose a significant threat to ecosystems, property, and human life, making their early and accurate detection crucial for effective intervention. This study presents a novel, lightweight approach to real-time forest fire detection that is optimized for resource-constrained devices like drones. The method integrates multi-task knowledge distillation, transferring knowledge from a high-performance DenseNet201 teacher model that was trained on a hierarchically structured wildfire dataset. The dataset comprised primary classes (fire vs. non-fire) and detailed subclasses that account for confounding elements such as smoke, fog, and reflections. The novelty of this approach lies in leveraging knowledge distillation to transfer the deeper insights learned by the DenseNet201 teacher model—specifically, the auxiliary task of recognizing the confounding elements responsible for false positives—into a lightweight student model, enabling it to achieve a similar robustness without the need for complex architectures. Using this distilled knowledge, we trained a MobileNetV3-based student model, which was designed to operate efficiently in real-time while maintaining a low computational overhead. To address the challenge of false positives caused by visually similar non-fire elements, we introduced the Confounding Element Specificity (CES) metric. This novel metric, made possible by the hierarchical structure of the wildfire dataset, is unique in its focus on evaluating how well the model distinguishes actual fires from the confounding elements that typically result in false positives within the negative class. The proposed approach outperformed the baseline methods—including single-task learning and direct multi-task learning—achieving a primary accuracy of 93.36%, an F1-score of 91.57%, and a higher MES score, demonstrating its enhanced robustness and reliability in diverse environmental conditions. This work bridges the gap between advanced deep learning techniques and practical, scalable solutions for environmental monitoring. Future research will focus on integrating multi-modal data and developing adaptive distillation techniques to further enhance the model's performance in real-time applications. [ABSTRACT FROM AUTHOR]

Fire detection is a critical task in environmental monitoring and disaster prevention, with traditional methods often limited in their ability to detect fire and smoke in real time over large areas. The rapid identification of fire and smoke in both indoor and outdoor environments is essential for minimizing damage and ensuring timely intervention. In this paper, we propose a novel approach to fire and smoke detection by integrating a vision transformer (ViT) with the YOLOv5s object detection model. Our modified model leverages the attention-based feature extraction capabilities of ViTs to improve detection accuracy, particularly in complex environments where fires may be occluded or distributed across large regions. By replacing the CSPDarknet53 backbone of YOLOv5s with ViT, the model is able to capture both local and global dependencies in images, resulting in more accurate detection of fire and smoke under challenging conditions. We evaluate the performance of the proposed model using a comprehensive Fire and Smoke Detection Dataset, which includes diverse real-world scenarios. The results demonstrate that our model outperforms baseline YOLOv5 variants in terms of precision, recall, and mean average precision (mAP), achieving a mAP@0.5 of 0.664 and a recall of 0.657. The modified YOLOv5s with ViT shows significant improvements in detecting fire and smoke, particularly in scenes with complex backgrounds and varying object scales. Our findings suggest that the integration of ViT as the backbone of YOLOv5s offers a promising approach for real-time fire detection in both urban and natural environments. [ABSTRACT FROM AUTHOR]

Background/Aims: This study aimed to assess the impact of INTERCEPT Foam Spray (IFS) application on delayed endoscope reprocessing through microbiological surveillance culture (MSC). Methods: A quasi-experimental, matched-comparison pilot study was conducted using gastrointestinal endoscopy. IFS was applied to the endoscopes after precleaning and before reprocessing the next day. An equal number of endoscopes, matched by endoscope type, were subjected to routine reprocessing. The MSC were subjected to high-level disinfection to detect any contamination. Data were analyzed using the chi-square test or Fisher exact test (categorical data) and Student t-test (continuous data). Results: In total, 150 MSCs were collected from 42 endoscopes. Positive MSCs were observed in 4.0% (4/75) of the sprayed group and 1.3% (1/75) of the control group (95% confidence interval, 30.34–0.31; p>0.05), all of which were contributed by colonoscopes. Colonoscope were more prone to positive MSC (mean difference in percentage, p<0.05). Mean spraying hours were not associated with detected growth (11.7% vs. 13.6%; 95% confidence interval, 1.43 to –5.27; p>0.05), with environmental and skin flora being the primary contaminants. Conclusions: IFS may be applied when delayed endoscope processing is necessary, but with caution when applied to colonoscopes. However, further research is warranted to verify the result. [ABSTRACT FROM AUTHOR]

The article explores the complexities of regulating emerging technologies in environmental, energy, and climate law. Topics include the role of AI and data-driven technologies in improving efficiency, resource allocation, and environmental monitoring; the adequacy of existing local, national, EU, and international legal frameworks to manage the uncertainties and risks posed by these innovations; and the importance of integrating climate, environmental, and energy law.

A dual-responsive ratio electrochemical-colorimetric method for nitrite (NO2−) is established based on the combination of nanoenzyme (Mn3O4) catalysis with diazotization reactions. The Mn3O4 can oxidize colorless 3,3′,5,5′-tetramethylbenzidine (TMB) into blue TMBox. The NO2− induces the diazotization reaction of TMBox, leading to a decrease of the signal at 652 nm and the generation of a new signal from diazotized TMBox at 445 nm. Furthermore, the presence of NO2− reduces the electrochemical oxidation signal of TMB and simultaneously provides its electrochemical signal. Compared with traditional single-mode detection, dual-mode detection offers higher sensitivity, lower detection limits, and better interference resistance. The inherent advantages of this method make it feasible to detect NO2− in real samples, offering broad prospects for applications in food safety and environmental monitoring. [ABSTRACT FROM AUTHOR]

A highly sensitive, portable, rapid, and accurate colorimetric sensing method is presented. It is based upon exfoliated g-C3N4 nanosheets (E–g-C3N4 NSs), having peroxidase nanozyme-like properties. The as-prepared catalyst (E–g-C3N4 NSs) tends to oxidize the colorless tetramethyl-benzidine (TMB) into oxidized-TMB in the presence of hydrogen peroxide (H2O2) generating a dark blue color and corresponding ultraviolet visible-spectral changes following a Michaelis–Menten kinetic. The prepared colorimetric sensor exhibited response within the range 0.001–0.450 μM having R2 value of 0.999 and a detective limit (LOD) of 0.15 ± 0.04 nM. Furthermore, the sensor also displayed outstanding selectivity, ample stability (10 weeks), and excellent practicability in real sample applications. All these outstanding properties were highly attributed to the large surface area with exposed actives sites, high surface energy, and large conductive structure of E–g-C3N4 NSs. For comparison of the catalytic study, we have also explored the sensing mechanism of B-g-C3N4, using the same optimized experimental conditions. Resultantly, we concluded that the proposed sensor (E–g-C3N4 NSs) will gain considerable attention for on-site environmental and health monitoring in future endeavor. [ABSTRACT FROM AUTHOR]

Solvothermal synthesis of 1D n-In2O3@n-WO3 heterojunction nanowires (HNWs) and their NO2 gas sensing characteristics are reported. The n-In2O3@n-WO3 HNWs have been well-characterised using XRD, Raman spectroscopy, XPS, SEM and HRTEM analyses. The NO2 sensing performance of n-In2O3@n-WO3 HNWs showed superior performance compared with pristine WO3 NWs. Due to the distinctive configuration of WO3-In2O3 heterojunctions, the n-In2O3@n-WO3 HNWs demonstrated remarkable sensitivity reaching 182% in response towards 500 ppb of NO2 gas at operating temperature of 200°C which is nearly 3.5 times greater than the response observed with pristine WO3 (50%). Moreover, the n-In2O3@n-WO3 HNWs also exhibited fast response (8–13 s)/recovery (54–62 s) time characteristics. A plausible sensing mechanism has been discussed. The enhancement in sensor characteristics shows that n-In2O3@n-WO3 HNWs could serve as a promising material for high-performance NO2 gas sensors for real-time environmental monitoring applications. This work could provide new understandings of the sensing mechanism of n-In2O3@n-WO3–based heterojunction nanowires, which can be applied to the design of novel n–n type MOS heterojunction materials for the application of low-temperature real-time high-performance NO2 sensors. [ABSTRACT FROM AUTHOR]

Secondhand smoking may contribute to cognitive decline, but evidence on this subject is limited in the working economically active older adults. This study aimed to investigate the association between cognitive decline and exposure to secondhand smoke at work in economically active adults aged 65 years or above. Cross-sectional study using nationwide data. Data were from the 2022 Korea Community Health Survey. Chi-squared test was used to perform descriptive statistics. The association between cognitive decline and exposure to secondhand smoke at work was analysed using multivariable logistic regression analysis. Subgroup analysis was performed based on to exposure status to secondhand smoke at home and diabetes mellitus. Of a total of 28,197 adults, 8767 (28.5%) individuals reported cognitive decline. Compared to individuals without exposure to passive smoking at work, those with such exposure were more likely to report cognitive decline (odds ratio: 1.30, 95% confidence interval: 1.14–1.47). This association was particularly strong in individuals with exposure to passive smoking at both work and home and in those diagnosed with diabetes mellitus. Exposure to secondhand smoking at work is associated with a higher likelihood of cognitive decline in older-aged economically active individuals. The findings infer the importance of monitoring passive smoking and implementing public health measures to reduce workplace secondhand smoke exposure. [ABSTRACT FROM AUTHOR]

The production of home and personal care products demands stringent contamination control to ensure safety and quality. Microbial contamination poses significant risks to both product integrity and consumer safety, leading to potential recalls and brand damage. This article explores the critical role of environmental monitoring (EM) programs in identifying, tracking, and trending sources of microbial contamination. A robust contamination control strategy includes species-level microbial identifications, early warning systems, and adherence to regulatory standards such as the Modernization of Cosmetics Regulation Act (MoCRA) and USP chapters <60>, <61>, and <62>. These regulations emphasize the importance of tracking and trending microorganisms to increase visibility and improve the state of control of your processes and facility. Drawing on the analogy of a railway system, we illustrate how undetected microbial contamination can spread across production lines. By implementing proactive contamination controls, utilizing advanced identification techniques like MALDI-TOF and DNA sequencing, and managing microbial risks through accurate data analysis, manufacturers can stay ahead of potential threats. The article underscores the value of harmonized EM programs, which shift manufacturers from reactive to proactive responses, minimizing contamination risks and ensuring product safety. Investing in such programs ultimately protects consumers, ensures regulatory compliance, and preserves the brand's reputation. [ABSTRACT FROM AUTHOR]

2,2-bis(4-hydroxyphenyl)butane (BPB), one of the 2,2-bis(4-hydroxyphenyl)propane (BPA) alternatives, has not received sufficient attention so far due to the limited environmental concentration and toxicological data available. In this study, the acute toxicity of BPB was investigated using three aquatic organisms, i.e., Tetradesmus obliquus, Daphnia magna and Danio rerio. It was showed that Daphnia magna was the most sensitive organism with a half effective concentration of 3.93 mg/L. Subsequently, Daphnia magna was exposed to BPB solutions for 21 days to explore its chronic toxicity. Results indicated that BPB restricted the body length of parent Daphnia magna and reduced the total number of broods and neonates. The no-observed effect concentration of BPB to Daphnia magna was as low as 0.01 mg/L, which was two orders of magnitude lower than that of BPA (0.86–5.00 mg/L). Furthermore, the ecological risk of BPB was quantitatively assessed using a risk quotient (RQ) method. The RQ values of BPB ranged from 0.003 to 0.914, with the mean of 0.197. BPB posed a moderate risk at most sampling sites, while BPA posed a low ecological risk with RQ < 0.1 at nearly all the sampling sites. Hence, environmental monitoring and management should also be focused on BPB contamination. [ABSTRACT FROM AUTHOR]

The community stability of coral reefs and fish is the focus of ecological monitoring of coral reefs. Among them, the realization of effective metrics of variations in reef fish communities (i.e., the combined communities of coral reefs and fish) is important for analyzing the stability of communities as well as maintaining the ecological balance of coral reefs. Based on coral reef and fish data collected at St. John's Island from 2004 to 2010, this study proposes a symbiotic graph modeling method to express the biological relationships of reef fish communities, and a Pyramid Match graph kernel method for fusing Attributes (PMA) to quantify community fluctuations to measure interannual variability of communities. The results showed that the community similarity was low in 2006, 2007, and 2008. The total coral cover rate in the study area decreased by 32.04% from 2006 to 2007 and increased by 24% in 2008. The total number of fish fell from 3780 in 2006 to 2596 in 2007 and rose to 6249 in 2008. Among them, the proportion of herbivorous fish decreased to 30.84% in 2007. Furthermore, we have combined the Louvain algorithm with the proposed PMA method to effectively identify the regions that should be prioritized for protection. Experiments were conducted on real datasets with good results, demonstrating the potential of the proposed method to assist in the analysis of community stability and identification of priority conservation areas. [ABSTRACT FROM AUTHOR]

This work extends recent developments in diacetylene-based, sprayable sensors by identification and assessment of formulations which facilitate their use for wearable sensing. Diacetylene-based spray-on sensors have the potential to be a widely deployed sensing technology, as they require no power and can be applied as thin coatings onto surfaces to provide a colorimetric response to target exposure. In responding to radiation, liquid-phase targets, or gas-phase targets specifically determined by the formulation of the sprayable sensor used, this technology is amenable to wearable sensors for measuring exposure to different environmental risks. Here, we provide the means to improve wear resistance, reduce false-positive signals due to wetting, and enhance color fastness for coatings of sprayable, diacetylene-based sensor formulations on cotton fabric. These sensor formulations possess polymethyl methacrylate (PMMA), which enhances the coating stability to only 8% color loss due to wear compared to 18–25% without PMMA, while maintaining the inherent ability of diacetylene-component formulations to detect radiation as well as gas or liquid phase analytes. This represents a significant step toward the use of diacetylene-based sensing formulations for wearable sensing. In the future, the form of spray-on sensor materials demonstrated here may find use in wearable sensing applications for detection of cumulative exposure to UV radiation, hydrogen peroxide vapors, or solvent exposure. We expect trends toward applications toward other wearable sensors for environmental monitoring given the well-known customizability in target response of diacetylene-containing monomers by modifying their headgroup chemistry. [ABSTRACT FROM AUTHOR]

The present study characterizes gamma dose rate (GDR) concentrations in Bilbao, located in the northern Iberian Peninsula, utilizing a comprehensive 10-min interval database spanning from 2009 to 2018. This station belongs to the radiological environmental monitoring of the Basque Country network. The daily average GDR was found to be 0.07624 ± 0.00004 µSv/h, with the daily 95th percentile averaging 0.08026 ± 0.00007 µSv/h throughout the entire period. Our analysis specifically addresses the impact of precipitation on GDR, revealing a higher correlation coefficient for daily 95th percentile values compared to daily averages. Additionally, the influence of the Galerna (GL) event, a natural meteorological phenomenon in this region, on GDR was investigated, noting that it can develop both with and without precipitation. Understanding the interaction between GDR and this meteorological scenario is vital for the development of more reliable radiological monitoring strategies and for safeguarding public health. For this purpose, 40 GL events were analyzed. The present results indicate that GDR values frequently exceed alarm levels when a GL is formed. These GDR peaks should be considered natural radiological events, necessitating the classification of such GDR peaks as false alarms within the radiological monitoring network. To explain them in detail, 10-min time series of precipitation and radon outdoor concentrations were analyzed. The results demonstrate that the GL event with precipitation is a meteorological scenario that can be associated with false alarms. The present analysis provides a distinct contrast in radon behavior under the same meteorological event in case of being developed with precipitation or without precipitation. The findings from this analysis are crucial for avoiding and understanding false radiological alarms triggered in the monitoring network, thereby enhancing the accuracy of radiological data interpretation and improving public safety measures. [ABSTRACT FROM AUTHOR]

The use of surface-enhanced Raman spectroscopy (SERS) in liquid solutions has always been challenging due to signal fluctuations, inconsistent data, and difficulties in obtaining reliable results, especially at very low analyte concentrations. In our study, we introduce a new method using a three-dimensional (3D) SERS substrate made of silica microparticles (SMPs) with attached plasmonic nanoparticles (NPs). These SMPs were placed in low-concentration analyte solutions for SERS analysis. In the first approach to perform SERS in a 3D environment, glycerin was used to immobilize the particles, which enabled high-resolution SERS imaging. Additionally, we conducted time-dependent SERS measurements in an aqueous solution, where freely suspended SMPs passed through the laser focus. In both scenarios, EFs larger than 200 were achieved, which enabled the detection of low-abundance analytes. Our study demonstrates a reliable and reproducible method for performing SERS in liquid environments, offering significant advantages for the real-time analysis of dynamic processes, sensitive detection of low-concentration molecules, and potential applications in biomolecular interaction studies, environmental monitoring, and biomedical diagnostics. [ABSTRACT FROM AUTHOR]

The ecosystem of the Qinghai–Tibet Plateau is highly fragile due to its unique geographical conditions, with vegetation playing a crucial role in maintaining ecological balance. Thus, accurately monitoring the distribution of vegetation in the plateau region is of paramount importance. This study employs UAV multispectral imagery in combination with four machine-learning models—Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Random Forest (RF)—to investigate the impact of different features and their combinations on the fine classification of shrubs and grasses on the Qinghai–Tibet Plateau, including Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea. The results indicate that near-infrared spectral information can improve classification accuracy, with improvements of 5.21%, 1.65%, 6.64%, and 5.03% for Salix psammophila, Populus simonii Carrière, Kobresia tibetica, and Kobresia pygmaea, respectively. Feature selection effectively reduces redundant information and enhances model classification accuracy, with all four machine-learning models achieving the best performance on the optimized feature set. Furthermore, the RF model performs best on the optimized feature set, achieving an overall accuracy (OA) of 95.32% and a kappa coefficient of 0.94. This study provides important scientific support for the fine classification and ecological monitoring of plateau vegetation. [ABSTRACT FROM AUTHOR]

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been observed in the past, but never with this intensity or duration, which typically occurs during periods of drought. In this study, carotenoid concentrations were analyzed in relation to precipitation and temperature using field and remote sensing data from February 2016 to December 2023. In November 2023, samples showed unusually high concentrations of carotenoids. The study confirmed the effectiveness of a new algorithm for estimating carotenoids using Sentinel-2 imagery to complement chlorophyll-a data. Results showed that temperature and precipitation significantly influenced carotenoid/chlorophyll-a ratio, highlighting a climatic control of phytoplankton community structure. These results highlight the importance of long-term monitoring and conservation efforts to address climate change and human impacts. This research is a first step in using optical properties of lakes as an indicator of phytoplankton dynamics under environmental stress and warns of the potential for increased occurrence or persistence of such phenomena with future climate trends. [ABSTRACT FROM AUTHOR]

Understanding the flow processes and pattern optimization of ecosystem services (ESs) supply and demand is crucial for integrated regional ecological management. However, the understanding of the flow process of ESs at the 1 km grid scale is still limited, especially in areas dominated by mineral resource development. The landscape in these areas has undergone significant changes due to mining activities. It is urgent to construct a regional management model that integrates the flow of ecosystem services and mine restoration. This study developed a framework that links ecosystem service flows (ESFs) and ecological security patterns (ESP) based on multi-source ecological monitoring data, constructed an ES supply-demand flow network through the flow properties, and determined the sequence and optimization strategies for mine rehabilitation to achieve integrated regional management. The results show that, except for food production (FP), other services were in surplus overall, mostly in synergistic relationships, but the spatial distribution of their supply and demand was not coordinated. Surplus areas were located mainly in the eastern woodlands, and deficit areas were located in the northwestern production agglomeration centers, suggesting that areas of supply-demand imbalance can be mitigated through ecological integration. Among these, water yield (WY) had a small number of sources and sinks and is limited in area range. Habitat quality (HQ) sources and sinks had the largest area coverage and the highest number. The distribution of ESF corridors, influenced by factors such as the number of sources and sinks, flow characteristics, and spatial resistance, varied significantly. HQ exhibited a more uniform distribution range, while WY had a longer average length of flow path. Overlaying ecological and mining factors, we identified ecological strategic spots, important supply areas, beneficiary areas, and mine priority restoration areas to further optimize the overall layout and rationally allocate the intrinsic structure of the patches based on ES supply and demand. [ABSTRACT FROM AUTHOR]

Semantic change detection (SCD) is a newly important topic in the field of remote sensing (RS) image interpretation since it provides semantic comprehension for bi-temporal RS images via predicting change regions and change types and has great significance for urban planning and ecological monitoring. With the availability of large scale bi-temporal RS datasets, various models based on deep learning (DL) have been widely applied in SCD. Since convolution operators in DL extracts two-dimensional feature matrices in the spatial dimension of images and stack feature matrices in the dimension termed the channel, feature maps of images are tri-dimensional. However, recent SCD models usually overlook the stereoscopic property of feature maps. Firstly, recent SCD models are usually limited in capturing spatial global features in the process of bi-temporal global feature extraction and overlook the global channel features. Meanwhile, recent SCD models only focus on spatial cross-temporal interaction in the process of change feature perception and ignore the channel interaction. Thus, to address above two challenges, a novel fine-grained feature perception network (FFPNet) is proposed in this paper, which employs the Omni Transformer (OiT) module to capture bi-temporal channel–spatial global features before utilizing the Omni Cross-Perception (OCP) module to achieve channel–spatial interaction between cross-temporal features. According to the experiments on the SECOND dataset and the LandsatSCD dataset, our FFPNet reaches competitive performance on both countryside and urban scenes compared with recent typical SCD models. [ABSTRACT FROM AUTHOR]

Farmstead dairy processing facilities may be particularly susceptible to Listeria spp. contamination due to the close physical proximity of their processing environments (PE) to associated dairy farm environments (FE). In this case study, we supported the implementation of interventions focused on improving (1) cleaning and sanitation efficacy, (2) hygienic zoning, and (3) sanitary equipment and facility design and maintenance in a farmstead dairy processing facility, and evaluated their effect on Listeria spp. detection in the farmstead's PE over 1 yr. Detection of Listeria spp. in the farmstead's PE was numerically reduced from 50% to 7.5% after 1 yr of intervention implementation, suggesting that these interventions were effective at improving Listeria spp. control. In addition, environmental samples were also collected from the farmstead's FE to evaluate the risk of the FE as a potential source of Listeria spp. in the PE. Overall, detection of Listeria spp. was higher in samples collected from the FE (75%, 27/36) compared with samples collected from the PE (24%, 29/120). Whole-genome sequencing performed on select isolates collected from the PE and FE supported the identification of 6 clusters (range of 3 to 15 isolates per cluster) that showed ≤ 50 high-quality single nucleotide polymorphism differences. Of these 6 clusters, 3 (i.e., clusters 2, 4, and 5) contained isolates that were collected from both the PE and FE, suggesting that transmission between these 2 environments was likely. Moreover, all cluster 2 isolates represented a clonal complex of L. monocytogenes commonly associated with dairy farm environmental reservoirs (i.e., CC666), which may support that the farmstead's FE represented an upstream source of the cluster 2 isolates that were found in the PE. Overall, our data underscore that although the FE can represent a potential upstream source of Listeria spp. contamination in a farmstead dairy processing facility, implementation of targeted interventions can help effectively minimize Listeria spp. contamination in the PE. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [ABSTRACT FROM AUTHOR]

Recent years have witnessed an exponential increase in the research on gold nanoparticles (AuNPs)-based colorimetric sensors to revolutionize point-of-use sensing devices. Hence, this review is compiled focused on current progress in the design and performance parameters of AuNPs-based sensors. The review begins with the characteristics of AuNPs, followed by a brief explanation of synthesis and functionalization methods. Then, the mechanisms of AuNPs-based sensors are comprehensively explained in two broad categories based on the surface plasmon resonance (SPR) characteristics of AuNPs and their peroxidase-like catalytic properties (nanozyme). SPR-based colorimetric sensors further categorize into aggregation, anti-aggregation, etching, growth-mediated, and accumulation-based methods depending on their sensing mechanisms. On the other hand, peroxidase activity-based colorimetric sensors are divided into two methods based on the expression or inhibition of peroxidase-like activity. Next, the analytes in environmental and food samples are classified as inorganic, organic, and biological pollutants, and recent progress in detection of these analytes are reviewed in detail. Finally, conclusions are provided, and future directions are highlighted. Improving the sensitivity, reproducibility, multiplexing capabilities, and cost-effectiveness for colorimetric detection of various analytes in environment and food matrices will have significant impact on fast testing of hazardous substances, hence reducing the pollution load in environment as well as rendering food contamination to ensure food safety. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have advanced technologically, allowing for a wide range of Internet of Things (IoT) applications. The Internet of Things (IoT) provides a platform for connecting items to achieve a specific goal with minimal human intervention. Various practical sectors, such as environmental monitoring and defense, demand IoT support with maximum object connectivity, and low human participation, yet face security concerns from strong unauthorized entities. As location privacy is one of these major vulnerabilities, continual monitoring of contextual information may lead the attacker to the asset's position. The location privacy of the source node that detects and reports the presence of an event is critical. In this study, we provide a source location privacy (SLP) preservation technique based on confounding domain angular routing (SLP-CDAR) that attempts to improve security while increasing network lifespan. To avoid traffic analysis attacks, packets first undergo a random walk, followed by a perplexing time domain routing that creates additional diversionary pathways. The packets are then routed via angle routing, followed by a random path, and finally delivered to the base station using the forward random walk technique. The experimental results prove that the proposed scheme enhances the location privacy strength while providing an improved network lifetime, and a swift increase in the entropy. [ABSTRACT FROM AUTHOR]

Applied nucleation and other spatially patterned restoration methods are promising approaches for scaling up projects to meet ambitious international restoration commitments in an ecologically and economically sound manner. Much of the corresponding literature to date, however, has centered around theoretical discussions and small‐scale studies that are largely divorced from constraints faced by restoration practitioners. We briefly review recent academic literature about applied nucleation and other spatially patterned restoration methods and discuss practical challenges to their implementation. We offer several recommendations to move spatially patterned restoration from an academic conversation to scalable application, including: (1) comparing different planting designs and natural regeneration within the same system at an appropriate scale; (2) monitoring ecological outcomes throughout the restored area over sufficient time to evaluate recovery; (3) quantifying costs and documenting other logistical constraints to implementation; and (4) exploring methods for using unplanted areas to provide benefits to landholders until planted vegetation establishes. [ABSTRACT FROM AUTHOR]

Deep, site-specific research is needed to understand ecosystem complexity. Improving conservation outcomes should be a driving force for the development of a holistic understanding of ecosystems. Holistic ecosystem models and digital twin ecosystems could guide conservation interventions in real-time. New digital and sensor technology provides a huge opportunity to revolutionise conservation, but we lack a plan for deploying the technologies effectively. I argue that environmental research should be concentrated at a small number of 'super-sites' and that the concentrated knowledge from super-sites should be used to develop holistic ecosystem models. These, in turn, should be morphed into digital twin ecosystems by live connecting them with automated environmental monitoring programmes. Data-driven simulations can then help select pathways to achieve locally determined conservation goals, and digital twins could revise and adapt those decisions in real-time. This technology-heavy vision for 'smart conservation' provides a map toward a future defined by more flexible, more responsive, and more efficient management of natural environments. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance is an ever-increasing threat. The widespread usage of ciprofloxacin has led to the manifestation of resistance due to chromosomal mutations or the acquisition of plasmid-mediated quinolone resistance (PMQR) traits. Some particular PMQR traits, qnr genes, have been identified globally in clinical and environmental isolates. This study aimed to determine the prevalence of ciprofloxacin-resistant bacteria in aquatic environments in southern Ontario and investigate the extent of dissemination of ciprofloxacin resistance traits among the bacterial communities. We surveyed the prevalence of plasmid encoding qnr genes using a multiplex PCR assay of associated PMQR genes, qnrA, qnrB, and qnrS, on 202 isolates. Despite the absence of significant impacts on minimum inhibitory concentration levels, the presence of qnr genes correlates with heightened resistance to quinolones and nalidixic acid in some isolates. Taxonomic analysis highlights distinct differences in the composition and diversity of ciprofloxacin-sensitive (CipS) and ciprofloxacin-resistant (CipR) populations, with Proteobacteria dominating both groups. Importantly, CipR populations exhibit lower genetic diversity but higher prevalence of multiple antibiotic resistances, suggesting co-selection mechanisms. Co-occurrence analysis highlights significant associations between ciprofloxacin resistance and other antibiotic resistances, implicating complex genetic linkages. The results of our study signified the critical role of environmental monitoring in public health. [ABSTRACT FROM AUTHOR]

The Anil River estuary (ARE), which is crucial for São Luís Island's socioeconomic well-being, faces substantial human pressure due to its urban location, hastening the eutrophication process. This study, which was conducted from 2022 to 2023, aimed to assess trophic levels across the ARE. Using a multiparametric probe, we analysed the pH, temperature, dissolved oxygen, oxygen saturation, salinity, and total dissolved solids quarterly during the rainy and dry seasons at six sampling points. The nutrient analysis included nitrite, nitrate, ammonium ions, phosphate, total nitrogen (TN), and total phosphorus (TP). Trophic index (TRIX) values categorised the ARE as eutrophic (sector 1) or hypereutrophic (sector 2). The dry season exhibited relatively high trophic levels, indicating that the ARE was hypereutrophic. Sector 2, influenced by concentrated nutrients and chlorophyll-a, exhibited increased trophic status from domestic and industrial effluent discharge. Rainy season data at the downstream from the point 3 recorded the maximum DIN:DIP ratio, indicating phosphorus limitation. Monitoring estuarine trophic states is vital for kerbing eutrophication and preserving local biodiversity in the anil river estuary. [ABSTRACT FROM AUTHOR]

The application of innovative systems using low-cost microcontrollers in human biometeorology studies is a promising alternative to conventional monitoring devices, which are usually cost-intensive and provide measurements at specific points, as in stationary meteorological stations. A Portable Low-cost Environmental Monitoring System (PLEMS) aimed at the pedestrian scale is introduced. The backpack-type equipment consists of a microcontroller with attached sensors that assess environmental conditions in a broad sense, integrating measurements of air quality, lighting and noise levels alongside variables typically measured at meteorological stations. The application of the system took place in altogether 12 environmental walks carried out with questionnaire-surveys with concurrent environmental monitoring with the PLEMS in Curitiba, Brazil, a subtropical location characterized by a Cfb climate type. Results allowed us to test the equipment and method of data gathering within a limited period (approximately 50 min) and for a short walking circuit of 800 m. The equipment was successfully able to capture even slightest differences in environmental conditions among points of interest, whereas subjective responses (n= 3843 responses to a total of 11 questions) showed consistency with measured data. From a multi-domain perspective, relevant insights could be obtained for the measured variables. [ABSTRACT FROM AUTHOR]