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Exploiting two quasi-natural experiments, we find that firms increase emissions of toxic pollution following decreases in analyst coverage. The effects are stronger for firms with low initial analyst coverage, poor corporate governance, and firms subject to less stringent monitoring by environmental regulators. Decreases in environmental-related questions raised in conference calls, an increased cost of monitoring to institutional shareholders, reductions in pollution abatement investment, and the weakening of internal governance related to environmental performance are channels through which reduced analyst coverage contributes to increases in firm pollution. Our study highlights the monitoring role analysts play in shaping corporate environmental policies. [ABSTRACT FROM AUTHOR]

Purpose: Precooling (PreC) may only benefit performance when thermal strain experienced by an individual is sufficiently high. We explored the effect of mixed-method PreC on 20-km cycling time-trial (CTT) performance under 3 different apparent temperatures (AT). Methods: On separate days, 12 trained or highly trained male cyclists/triathletes completed six 20-km CTTs in 3 different ATs: hot-dry (35 °C AT), moderately hot-humid (40 °C AT), and hot-humid (46 °C AT). All trials were preceded by 30 minutes of mixed-method PreC or no PreC (control [CON]). Results: Faster 2.5-km-split completion times occurred in PreC compared with CON in 46 °C AT (P =.02), but not in 40 °C AT (P =.62) or 35 °C AT (P =.57). PreC did not affect rectal and body temperature during the 20-km CTT. Skin temperature was lower throughout the CTT in PreC compared with CON in 46 °C AT (P =.01), but not in 40 °C AT (P = 1.00) and 35 °C AT (P = 1.00). Heart rate had a greater rate of increase during the CTT for PreC compared with CON in 46 °C AT (P =.01), but not in 40 °C AT (P =.57) and 35 °C AT (P = 1.00). Ratings of perceived exertion (P <.001) and thermal comfort (P =.04) were lower for PreC compared with CON in 46 °C AT only, while thermal sensation was not different between PreC and CON. Conclusion: Mixed-method PreC should be applied prior to 20-km CTTs conducted in hot-humid conditions (≥46 °C AT). Alternatively, mixed-method PreC may be a priority in moderately hot-humid (∼40 °C AT) conditions but should not be in hot-dry (∼35 °C AT) conditions for 20-km CTT. [ABSTRACT FROM AUTHOR]

The accurate detection and quantification of biological species that are rarely present but potentially devastating is of paramount importance for the life sciences, biosecurity, food safety, and environmental monitoring. Consequently, there has been significant interest in the sensitive and accurate detection of nucleic acids, leveraging both chemical and biological methods. Among these, quantitative polymerase chain reaction (qPCR) is regarded as the gold standard due to its sensitivity and precision in identifying specific nucleic acid targets. Despite the widespread adoption of qPCR for nucleic acid detection, the analysis of qPCR data typically depends on the use of calibrated standard curves and a threshold method to interpret signal measurements. In this study, we use a stochastic simulation to show the limitations of the threshold method due to its assumptions on amplification kinetics. We propose a new approach for the absolute quantification of nucleic acids that overcomes these limitations by reconstructing the efficiency profile across amplification cycles and using cumulative amplification folds to build a standard curve, thus avoiding the constant efficiency assumption. Our method, validated through experiments with nucleic acid amplification in the presence of potent inhibitors, demonstrates improved accuracy in quantifying nucleic acids, avoiding the systematic errors of the threshold method. This innovation enhances the reliability of nucleic acid quantification, especially where traditional methods struggle with kinetic variability. [ABSTRACT FROM AUTHOR]

The precise detection of target substances through refractive-index sensing is essential in various fields such as environmental monitoring, food assessment, and optical applications. This study demonstrates a real-time refractive index sensor based on a liquid-infiltrated metal-insulator-metal island (MIMi) structure in a lithography-free manner. In the MIMi structure, the top layer of the metal islands is formed through solid-state dewetting, which serves as an etching mask to form nanopillars in the insulator layer. This allows for the infiltration of external substances, resulting in different structural colors depending on the refractive index. This design results in a redshift of the resonance peak as the infiltrated refractive index increases. Furthermore, by adjusting the area fraction of the metal-island layer and etching depth of the insulator layer, the resonance-peak position can be controlled within the visible range according to the target refractive index. The advantages of our design are the use of cost-effective materials and the ability to implement scalable fabrication. Our proposed method offers a convenient method to sense the target refractive index in real time. [ABSTRACT FROM AUTHOR]

The use of bi-temporal remote sensing images for detecting changes in land cover is an important means of obtaining surface change information, thus contributing to urban governance and ecological environment monitoring. In this article, we propose a deep learning model named Siam-UNet3+ for high-resolution remote sensing image change detection. This model integrates the full-scale skip connections and full-scale deep supervision of the network UNet3+, which can achieve the multi-scale feature fusion of remote sensing images, effectively avoiding the locality disadvantage of convolution operations. Different from UNet3+, Siam-UNet3+ has made major improvements, including the following: (1) incorporating a Siamese network in the encoder, which can process bi-temporal remote sensing images in parallel; (2) leveraging the residual module as the backbone, which can avoid gradient vanishing (or exploding) and model degradation problems; (3) adding a Triplet Attention module to the decoder, which can avoid information redundancy that may occur in full-scale skip connections and increase the ability to focus on changing patterns; and (4) designing a hybrid loss function consisting of focal loss and dice loss, which is more suitable for remote sensing image change detection tasks. In this study, we conducted change detection experiments using the publicly available LEVIR-CD dataset, as well as two local datasets in Beijing. Through comparative experiments with five other models and ablation experiments, the proposed model Siam-UNet3+ in this article demonstrated significant advantages and improvements in four evaluation metrics, namely, precision, recall, F1-score, and overall accuracy (OA), proving to have great potential in the application to highresolution remote sensing image change detection tasks. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Flexible sensors are garnering substantial interests for various promising applications, including medical electronics, environmental monitoring, and wearable devices. Developing a flexible sensor with high compliance, high sensitivity, and high reliability through the construction of a novel composite conductive hydrogel based on the synergistic enhancement effect of conductive polymers and metal ions is a remarkable achievement. Herein, an electronic/ionic‐conductive double network hydrogel (polypyrrole (PPy)/carboxymethyl cellulose (CMC)‐Al3+/polyvinyl alcohol (PVA)) with soft compliance, highly conductivity, and stability is presented. Moreover, owing to the synergistic reinforcement effect of the relatively immobilized "islands" of PPy particles and large amounts of movable "bridges" of aluminum ions (Al3+) within the double network hydrogel, the as‐optimized PPy/CMC‐Al3+/PVA composite gels exhibit excellent conductivity (σ = 3.47 ± 0.25 S m−1) and mechanical properties (E = 18.53 ± 0.67 kPa). Furthermore, it has been developed as strain sensors with relatively high linear sensitivity (gauge factor = 2.58) within a broad linearity range (0–400%). It can also be served as a monitoring devices for subtle physiological signals emanating from various parts of the human body. The robust sensor has great potential to be developed as wearable electronic devices and applied in healthcare monitoring fields. [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]

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]

Natural hazards are considered one of the greatest challenges today. Preventing transformation processes that lead to risk and then, crisis need a structural-strategic approach. An approach that can identify the issues and challenges ahead in a systematic and comprehensive method by formulating an operational plan can provide resilience and reduce vulnerability of human settlements and urban infrastructures. The road networks as one of the most important urban elements having a crucial role in management of crisis during the occurrence of natural crises (such as earthquakes) aid in the transferring the injured and rescue forces. The main purpose of this study was to determine the vulnerability of urban road networks for earthquake risk with neural network and machines learning algorithms with a comparative and systematic approach. In order to identify the most accurate and efficient model, a comparative comparison between neural network model (ANN) and machine learning algorithms including ADTree and KNN was carried out. The results of the present study in evaluating the structural condition of the urban road network with Fractal Dimension on hazardous and vulnerable zones showed that these zones were of low fractal dimension, and the distribution and differentiation of roads were low, reducing the efficiency of the road network at times of crisis. Other results of the present research on the application of machine learning algorithms indicate that the accuracy of the ADTree algorithm was equal to 1. In addition, at the stage of measuring the efficiency of the model with the Classification metrics algorithm, the ADTree algorithm efficiency was equal to 1. However, the accuracy of the KNN algorithm (K-Nearest Neighbors) and the artificial neural network model in predicting the vulnerability of the internal road network was equal to 0.92% and 0.98%, respectively. Therefore, since the degree of accuracy of the ADTree algorithm was higher, it is the most accurate and efficient algorithm to predict the vulnerability of the road network at times of the occurrence of hazardous events, and it can be useful and effective in decision-making of policy makers and planners in pre-crisis management. [ABSTRACT FROM AUTHOR]

Global concern about microplastic (MP) and nanoplastic (NP) particles is continuously rising with their proliferation worldwide. Effective identification methods for MP and NP pollution monitoring are highly needed, but due to different requirements and technical challenges, much of the work is still in progress. Herein, the advanced optical imaging systems that are successfully applied or have the potential for MP identification are focused on. Compared with chemical and thermal analyses, optical methods have the unique advantages of being nondestructive and noncontact and allow fast detection without complex sample preprocessing. Furthermore, they are capable of revealing the morphology, anisotropy, and material characteristics of MP for their quick and robust detection. This review aims to present a comprehensive discussion of the relevant optical imaging systems, emphasizing their operating principles, strengths, and drawbacks. Multiple comparisons and analyses among these technologies are conducted in order to provide practical guidelines for researchers. In addition, the combination of optical and other alternative technologies is described and the representative portable MP detection devices are highlighted. Together, they shed light on the prospects for long‐term MP pollution monitoring and environmental protection. [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]

Understanding societal interest in invasive species is crucial as greater public attention can support the success of conservation efforts. One of the main challenges in monitoring societal interest to support conservation is the absence of near-real-time indicators to track large-scale spatiotemporal dynamics of public attention. However, the digital revolution has opened up new opportunities to develop such indicators in support of invasive species research and management. Here, we aim to evaluate public awareness and interest in the invasive lionfish (Pterois miles) in the Mediterranean Sea and investigate spatiotemporal patterns of public interest in the species along its invasion front by using Google search volumes as a proxy for public attention. We implemented topic searches for the lionfish to download Google search volumes from 2013 to 2022 and used these data to test two hypotheses: (1) countries experiencing lionfish invasion exhibit higher public attention than those without lionfish, and (2) in invaded countries, public attention peaks around the arrival year, followed by a decline to baseline levels. Our study confirmed the first hypothesis, showing higher public attention in invaded countries. However, temporal patterns of attention did not fully align with the year of lionfish arrival, suggesting that the socio-political complexity of the region (e.g., cultural trends or internet accessibility), may be a key aspect to consider in future studies. Overall our study shows that digital data sources provide a unique opportunity to monitor social aspects of biological invasions with low associated costs and quick access to a great amount of data generated by internet users. [ABSTRACT FROM AUTHOR]

Microfibers are thread‐like structures shorter than 5 mm and have natural, semisynthetic, or synthetic origins. These micropollutants are ubiquitous and are emerging in the environment, living organisms, and food sources. Textile laundering is a prominent source of microfibers, but limited research has been conducted on microfiber pollution from domestic washing machines in emerging economies such as India, where consumption and production rates are exorbitantly high. This study aimed to assess the abundance and size distribution of microfibers from the effluent of a semiautomatic domestic washing machine using three categories of "not‐new" textiles: cotton, blended, and synthetic under "with" and "without" detergent conditions. Although most Indians still rely on hand washing, this study focused on washing machines due to their increasing use in India driven by improving socioeconomic factors. This study also developed annual emission estimation and forecasting models for India to understand pollution trends. The results revealed that microfibers were highly abundant in washing machine effluent, with a mean abundance of cotton, blended, and synthetic in "with detergent" conditions of 6476.67, 3766.67, and 8645/L, respectively, whereas in "without detergent," it was lower. All identified microfibers were divided into five size classes. The study also found that powdered detergent increased the abundance and emission of tiny fibers. The overall annual emissions estimate was 1.23 × 1011 microfibers, with cotton, synthetic, and blended categories accounting for 2.11 × 1010, 1.40 × 1010, and 6.15 × 109 microfibers, respectively. Time‐series‐based future estimates (autoregressive integrated moving average [ARIMA] and error‐trend‐seasonality [ETS]) showed an alarming increase in microfiber emissions, with forecasted annual emission reaching 1.90 × 1011 by 2030. Synthetic and cotton textiles are the most significant contributors to microfiber pollution. This study emphasized the urgent need to address the issue of microfiber pollution caused by washing machine laundering in developing countries, such as India, where sociodemographic factors intensify the problem. Integr Environ Assess Manag 2024;20:2116–2127. © 2024 SETAC Key Points: This is the first study in India to estimate textile laundering emissions using time‐series models; the annual emission is 1.23 × 1011 microfibers, with synthetics and cotton contributing the most.The autoregressive integrated moving average (ARIMA) (0,1,0) and error‐trend‐seasonality (ETS) (A,A,N) forecast emissions of 1.90 × 1011 microfibers by 2030.The use of powdered detergent increased the abundance and emission of tiny fibers; all identified microfibers fell into five size classes ranging from 0 to 5 mm.This study highlights the need for novel policy solutions to mitigate microfiber pollution. [ABSTRACT FROM AUTHOR]

Habitat plays a crucial role in shaping the macroinvertebrate community structure in large shallow lakes. In the pursuit of improving the health of freshwater ecosystems, it is imperative to consider their habitat characteristics. To evaluate the impact of habitat variations on lake ecological health, we developed a macroinvertebrate‐based multimetric index (MMI) for both the pelagic and littoral zones of Lake Hongze. Additionally, we employed structural equation models to explore the influence of utilization or phytoplankton biomass on ecological health. Historical data served as reference conditions for the pelagic. Seven key attributes were selected for the pelagic MMI, that is, Biological Monitoring Working Party (BMWP), the percentage of Mollusca taxa, the percentage of filter‐collector taxa, the percentage of predator taxa, the percentage of gather‐collector taxa, and the percentage of sensitive taxa and functional dispersion. The least minimally disturbed conditions and the best attainable conditions were used to develop the littoral. Four key metrics, that is, the percentage of scraper abundance, Mollusca taxa, Biological Pollution Index, and BMWP, were integrated into the littoral MMI. The assessment based on MMI revealed a "poor" health status for the pelagic zone and a "fair" health status for the littoral zone. These findings underscore the high applicability and efficacy of MMIs in assessing and monitoring ecological health in Lake Hongze. Notably, functional feeding groups exhibited heightened sensitivity to disturbance in both zones. Moreover, sediment organic matter strongly influenced the pelagic ecological health, while chlorophyll a and transparency emerged as primary factors influencing the littoral zone, attributable to varying littoral zone utilization. Integr Environ Assess Manag 2024;20:2245–2255. © 2024 SETAC Key Points: Functional feeding groups exhibited heightened sensitivity to disturbance in both zones.Sediment organic matter strongly influenced the pelagic ecological health, and the macroinvertebrate‐based multimetric index in the littoral zone was more affected by water quality. [ABSTRACT FROM AUTHOR]

Military activities in training areas can contaminate soil and groundwater through different persistent pollutants. This article reports the design of the operating procedure to assess and monitor the environmental impact of training activities carried out at the Capo Frasca military firing range (PCF) on the west coast of Sardinia. The procedure includes two steps: (i) a baseline investigation of soil to detect the impact of previous training activities at PCF, and (ii) the development of a post‐training monitoring plan. Results of the baseline investigations revealed that the activities conducted at PCF over many decades have not significantly affected the soil quality. Energetic compounds (ECs) were found below the limit of detection (LOD), and some low exceedances of the screening values (SVs) for Cd, Cu, and Ni were recorded in small areas that coincided with parts of two operational areas. A human health risk assessment (HHRA) identified no relevant issues. Results of baseline investigation allowed the planning of a light, noninvasive post‐training monitoring plan based on two levels, L1 and L2, where L1 involves only manual sampling of topsoil (0–0.2 m). If L1 reveals contamination, a more in‐depth and extensive L2 follow‐up monitoring will be implemented. Results of post‐training monitoring are not yet available. This environmental investigation protocol is intended to be a practical tool for regulatory provisions and is expected to be useful and effective for firing range management. This investigation also emphasized that, compared with the international state‐of‐the‐art, environmental surveys in Italian military ranges require the improvement of ECs set to be analyzed, lowering EC LOD, and establishing ECs SVs. In Sardinia, many military areas, including PCF, are considered areas of significant natural interest. Therefore, it is deemed beneficial to move beyond HHRA and undertake the ecological risk assessment. Integr Environ Assess Manag 2024;20:2060–2075. © 2024 SETAC Key Points: After years of operational activity at the Capo Frasca Range (CFR), baseline environmental investigations have identified a very limited impact on soil quality, attributable to few exceedances of the screening values (SVs) for Cd, Cu, and Ni; in addition, energetic compounds were not detected.Given the exceedance of the SVs, a site‐specific health risk analysis was conducted, revealing that the concentrations of metals (Cd, Cu, and Ni) found in the soils were below the acceptable contamination levels (ACLs).Based on the subsequent results, post‐training monitoring was planned.Compared with the international state‐of‐the‐art, environmental monitoring in Italian military ranges exhibits obvious deficits. [ABSTRACT FROM AUTHOR]

Ensuring hospitalized patients' comfort requires understanding differences in comfort and adaptation to discomfort, as well as the role of the built environment therein. Shortcomings in this understanding result from limitations of existing research methods. Therefore the potential of a mixed methods research design to investigate when and why experiences of and adaption to (dis)comfort differ among hospitalized patients was explored. A convergent mixed methods case study was conducted in two hospital wards of a Belgian hospital. Qualitative data included interviews with 19 patients about their experiences and information about the patients at the two wards during the fieldwork; quantitative data included sensor measurements of indoor environmental quality parameters (i.e. illuminance and indoor temperature), a questionnaire among 238 patients about their assessment of the indoor conditions, and simulations of these conditions. Joint displays allowed comparing situations in which characteristics related to the patients or their setting differed in an integrated way. The displays provide an intermediate step between purely qualitative descriptions of comfort and adaptation and purely quantitative models that predict both. Scenarios that the joint displays allow identifying inform about characteristics that could improve these models, and about roles of the built environment in experiences. [ABSTRACT FROM AUTHOR]

The presence of highly toxic dioxins, specifically polychlorinated dibenzo-p-dioxins (PCDDs), in drinking water is a matter of great concern due to their long-lasting nature and harmful effects. In this study, we detected three out of the five dioxin congeners: 2, 3, 7, 8-tetrachlorodibenzodioxin (TCDD), 1, 2, 3, 7, 8-pentachlorodibenzo-p-dioxin (PeCDD), and octachlorodibenzo-p-dioxin (OCDD). The investigation revealed that three dioxins were present in water samples of winter season, while TCDD and OCDD were found in the summer season. The geometric mean concentrations of PCDDs were 229.9 ng/L (winter) and 108.4 ng/L (summer), exceeded the maximum contaminant level of 30 pg/L set by the USEPA in surface water. The estimated daily intake of PCDDs for residents through drinking water was 273.97 ng-WHO2005-TEQ/kg/days during winter and 78.875 ng-WHO2005-TEQ/kg/days during summer. Our study emphasizes the urgent need for further research on persistent organic pollutants in drinking water to safeguard public health and community well-being. [ABSTRACT FROM AUTHOR]

Satellite-based exposure of fine particulate matters has been seldom used as a predictor of mortality. PM2.5 was predicted using Aerosol Optical Depths (AOD) through a two-stage regression model. The predicted PM2.5 was corrected for the bias using two approaches. We estimated the impact by two different scenarios of PM2.5 in the model. We statistically found different distributions of the predicted PM2.5 over the region. Compared to the reference value (5 µg/m3), 90th and 95th percentiles had significant adverse effect on total mortality (RR 90th percentile:1.45; CI 95%: 1.08–1.95 and RR 95th percentile:1.53; CI 95%: 1.11–2.1). Nearly 1050 deaths were attributed to any range of the air pollution (unhealthy range), of which more than half were attributed to high concentration range. Given the adverse effect of extreme values compared to the both scenarios, more efforts are suggested to define local-specific reference values and preventive strategies. [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]

A crucial tradeoff between material efficacy and environmental impact is often encountered in the development of high‐performance sensors. The use of rare‐earth elements or intricate fabrication techniques is sometimes needed for conventional sensing materials, posing concerns regarding sustainability. Exploring the potential of tomato peel (TP) as a dual‐purpose sensing dielectric layer for pressure and humidity monitoring is a paradigm shift, capitalizing on its porous structure and hygroscopic nature. TP‐based humidity sensor (TP‐HS) exhibits impressive results, with a wide humidity sensing range (5%–95%), fast response/recovery time (6.5/9 s), a high sensitivity (12 500 pF %RH−1), and a high stability (30 days). Additionally, TP‐based pressure sensor (TP‐PS) also shows excellent performance in accurately sensing pressure changes in a wide range (0–196 kPa). TP‐HS can easily distinguish between breathing rates (normal, fast, and slow) and moisture content present in different moisturizers (aloe vera and sanitizer) along with its successful use for proximity sensing. Alternatively, TP‐PS demonstrates weight measurement (490 and 980 N), grip recognition (measuring the pressure exerted by each finger), and gesture detection (by monitoring multiple bending angles 0°, 30°, 50°, and 80°). A wearable, biocompatible dual sensor based on a promising sustainable material for environmental, robotic, and health monitoring applications is successfully demonstrated. [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]

This particular offers a visual logging and automatic modelling apparatus for camp distribution connection employing state-of-the-art deep learning techniques. The gadget that emerged from an interdisciplinary approach inspired by ideas related to data security, quantum computing, and environmental monitoring points to an upward trajectory in increasing the accuracy and efficiency of compassionate coordination in settings for camp distribution. Experiments on the dataset show how successfully connection modelling, anomaly detection, as well as semantic segmentation, are done to generate a more cohesive model. Its reputation is further highlighted by the corresponding study of works from various domains, which finds that it has accuracy, precision, and F1 score measurements above 0.88 per task. As a directed investigation area in non-stipulated regions turns into significant scientific research, it contributes an ever-more significant role in leading authorities through which to make some administrative efforts to optimise such research, as disciplined researchers currently have sufficient knowledge of contemporary trends. Compared to present use, the equipment is more accurate and has superior review values. The research can achieve unprecedented computing efficiency, as seen by its 12-hour setup time and processing velocity measurement of 20 milliseconds per recorded picture. [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]

Metal–organic frameworks (MOFs) as a kind of sensing material plays an important role for environmental monitoring and human health. In this paper, we prepared a luminescent Cd‐MOF: [Cd2L·4H2O]·H2O. (H4L = 2,5‐bis‐(3,4‐dicarboxy‐phenyl)thiophene‐amide) under the solvothermal condition. Single crystal X‐ray diffraction analysis shows that Cd‐MOF has a 2D layer structure, and it exhibits a strong fluorescence emission at 470 nm. The sensing experiment displays Cd‐MOF as multitarget probe can selectively detect Cr (VI) anions and 4‐nitroaniline (4‐NA) with high sensitivity, good anti‐interference ability, and good recyclability. In addition, dual‐emission Eu3+@Cd‐MOF was obtained by encapsulating Eu3+ ions into Cd‐MOF, in which Cd‐MOF sensitizes Eu3+ ion emission. Accordingly, Eu3+@Cd‐MOF also acts as multitarget and self‐calibrated probe to selectively detect Cr (VI) ions and 4‐NA synchronously at the same conditions. However, the changes of fluorescence emissions of Eu3+@Cd‐MOF toward Cr (VI) ions are different from that of the original Cd‐MOF. The possible sensing mechanism be attributed to the competitive energy absorption between Cd‐MOF or Eu3+@Cd‐MOF and the analytes. All the results may provide broad prospects for developing multitarget sensing platform for sensing environment pollutants. [ABSTRACT FROM AUTHOR]

Green burial is an emerging practice in Zimbabwe and is being encouraged as a strategy for enhancing sustainable land use. This study explores stakeholder perceptions towards green burial and its implications on environmental sustainability. Interview data was collected from a purposively selected sample of 26 participants drawn from Harare and Bulawayo, the major metropolitan cities in Zimbabwe. The results of the study showed that the most promising route to promote green burial is through religion by invoking the principles of spirituality and ecological faith. Green burial was also perceived to be more affordable suggesting that cost effectiveness may be used as a positioning strategy. Cultural beliefs and lack of green burial infrastructure were identified as the main inhibiting factors. The findings of this study point to the need for stakeholders to explore strategies to reconcile the conflict between green burial and cultural beliefs. The development of green burial legislation and supporting infrastructure is recommended as a way of promoting the mainstream adoption of the green burial practice. [ABSTRACT FROM AUTHOR]

Baikal amphipods are one of the most unique freshwater species flocks of over 350 morphological species and subspecies playing multiple ecological roles in this ancient lake. However, molecular phylogeny studies accumulate evidence that this number is underestimated. Previously, we showed that an abundant littoral species Eulimnogammarus verrucosus comprised at least three genetic lineages: southern (S), western (W), and eastern (E), inhabiting corresponding parts of Baikal shore. The Angara River is the only outflow of the lake and is inhabited by amphipods originating from Baikal, which were previously analysed only morphologically. The river has parts with strong current or closed bays as opposed to sea‐like conditions of Lake Baikal, which raises the question of which species were able to adapt here. Here we studied the phylogeny of E. verrucosus from the Angara River, which could be reached by two lineages, S and W. We analysed 17 sites from 0.6 to 650 km from the outflow. Cytochrome c oxidase subunit I (COI) and 18S rRNA analysis showed that the sites closest to Baikal were inhabited by the S and W lineages sympatrically. The other sites were inhabited by the S lineage at both banks and also by the novel A (Angara) lineage. The A lineage was the first to split up from the last common ancestor of all E. verrucosus. Our data confirm the importance of considering cryptic diversity in ecological monitoring, and in order to simplify the identification of the genetic lineage, we developed a web‐app speCOIdent. [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]

The use of environmental DNA (eDNA) is becoming prevalent as a novel method of ecological monitoring. Although eDNA can provide critical information on the distribution and biomass of particular taxa, the DNA sequences of an organism remain unaltered throughout its existence, which complicates the accurate identification of crucial events, including spawning. Therefore, we examined DNA methylation as a novel source of information from eDNA, considering that the methylation patterns in eggs and sperm released during spawning differ from those of somatic tissues. Despite its potential applications, little is known about eDNA methylation, including its stability and methods for detection and quantification. Therefore, we conducted tank experiments and performed methylation analysis targeting 18S rDNA through bisulphite amplicon sequencing. In the target region, eDNA methylation was not affected by degradation and was equivalent to the methylation rate of genomic DNA from somatic tissues. Unmethylated DNA, abundant in the ovaries, was detected in the eDNA released during fish spawning. These results indicate that eDNA methylation is a stable signal reflecting targeted gene methylation and further demonstrate that germ cell‐specific methylation patterns can be used as markers for detecting fish spawning. [ABSTRACT FROM AUTHOR]

Necrophagous flies, particularly blowflies, serve as vital indicators in forensic entomology and ecological studies, contributing to minimum postmortem interval estimations and environmental monitoring. The study investigates variations in the predominant cuticular hydrocarbons (CHCs) viz. n-C25, n-C27, n-C28, and n-C29 of empty puparia of Calliphora vicina Robineau-Desvoidy, 1830, (Diptera: Calliphoridae) across diverse environmental conditions, including burial, above-ground and indoor settings, over 90 days. Notable trends include a significant decrease in n-C25 concentrations in buried and above-ground conditions over time, while n-C27 concentrations decline in buried and above-ground conditions but remain stable indoors. Burial conditions show significant declines in n-C27 and n-C29 concentrations over time, indicating environmental influences. Conversely, above-ground conditions exhibit uniform declines in all hydrocarbons. Indoor conditions remain relatively stable, with weak correlations between weathering time and CHC concentrations. Additionally, machine learning techniques, specifically Extreme Gradient Boosting (XGBoost), are employed for age estimation of empty puparia, yielding accurate predictions across different outdoor and indoor conditions. These findings highlight the subtle responses of CHC profiles to environmental stimuli, underscoring the importance of considering environmental factors in forensic entomology and ecological research. The study advances the understanding of insect remnant degradation processes and their forensic implications. Furthermore, integrating machine learning with entomological expertise offers standardized methodologies for age determination, enhancing the reliability of entomological evidence in legal contexts and paving the way for future research and development. [ABSTRACT FROM AUTHOR]

Artisanal gold mining can lead to soil contamination with potentially toxic elements (PTEs), necessitating soil quality monitoring due to environmental and human health risks. However, determining PTE levels through acid digestion is time-consuming, generates chemical waste, and requires significant resources. As an alternative, portable X-ray fluorescence (pXRF) offers a faster, more cost-effective, and sustainable analysis. This study compared total As, Ba, Cr, Cu, Fe, Mn, Ni, Pb, Sr, Ti, V, and Zn obtained from pXRF with their pseudo-total contents obtained through acid digestion (USEPA method 3051A) in areas influenced by artisanal gold mining in the Eastern Amazon, Brazil. pXRF data and machine learning algorithms were used to predict extractable Cu, Fe, Mn, and Zn. Linear regression models were fitted to compare the two methods, and random forest and support vector machine techniques were used to predict extractable contents. The best regression model fits for the pseudo-total PTE contents were those for Cu, Fe, Mn and Pb in agricultural areas (R2 > 0.80); Fe and Mn in gold mining residue (R2 > 0.70); and Ba, Cu and Mn in urban areas (R2 > 0.80). The best models for predicting the extractable PTE contents were those for Cu (R2 = 0.72; RMSE = 2.58 mg dm−3) and Zn (R2 = 0.71; RMSE = 1.44 mg dm−3) in agricultural areas and for Zn (R2 = 0.72; RMSE = 0.43 mg dm−3) in gold mining residue. The results demonstrated that pXRF can characterize and predict PTE contents in mining-impacted areas, offering a sustainable approach to soil quality analysis. [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]

Semantic feature combination/parsing issue is one of the key problems in sound event classification for acoustic scene analysis, environmental sound monitoring, and urban soundscape analysis. The input audio signal in the acoustic scene classification is composed of multiple acoustic events, which usually leads to low recognition rate in complex environments. To address this issue, this paper proposes the Hierarchical-Concatenate Fusion(HCF)-TDNN model by adding HCF Module to ECAPA-TDNN model for sound event classification. In the HCF module, firstly, the audio signal is converted into two-dimensional time-frequency features for segmentation. Then, the segmented features are convolved one by one for improving the small receptive field in perceiving details. Finally, after the convolution is completed, the two adjacent parts are combined before proceeding with the next convolution for enlarging the receptive field in capturing large targets. Therefore, the improved model further enhances the scalability by emphasizing channel attention and efficient propagation and aggregation of feature information. The proposed model is trained and validated on the Urbansound8K dataset. The experimental results show that the proposed model can achieve the best classification accuracy of 95.83%, which is an approximate improvement of 5% (relatively) over the ECAPA-TDNN model. [ABSTRACT FROM AUTHOR]

Nanozymes, synthetic nanomaterials that mimic the catalytic functions of natural enzymes, have emerged as transformative technologies for biosensing, diagnostics, and environmental monitoring. Since their introduction, nanozymes have rapidly evolved with significant advancements in their design and applications, particularly through the integration of machine learning (ML). Machine learning (ML) has optimized nanozyme efficiency by predicting ideal size, shape, and surface chemistry, reducing experimental time and resources. This review explores the rapid advancements in nanozyme technology, highlighting the role of ML in improving performance across various bioapplications, including real-time monitoring and the development of chemiluminescent, electrochemical and colorimetric sensors. We discuss the evolution of different types of nanozymes, their catalytic mechanisms, and the impact of ML on their property optimization. Furthermore, this review addresses challenges related to data quality, scalability, and standardization, while highlighting future directions for ML-driven nanozyme development. By examining recent innovations, this review highlights the potential of combining nanozymes with ML to drive the development of next-generation diagnostic and detection technologies. [ABSTRACT FROM AUTHOR]

Ground weather observations are scarce in many parts of the globe, hampering effective climate monitoring and disaster management. In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fill this gap. Here we present an approach to estimate rainfall based on sound measurements. We identified the best frequency range to estimate rainfall occurrence and intensity, trained classification and regression models with sound and rain gauge data collected in the Central Amazon during 9 months. By training a random forest classifier/regression model based on power spectrum values it was possible to identify and satisfactorily estimate hourly rainfall rates in two vegetation environments distinct from the training site, located 30 km from it. The proposed method is a promising approach for future weather monitoring in remote tropical areas. Plain Language Summary: Understanding and predicting rainfall is a complex task, especially in areas where the availability of data from surface stations is limited, a common feature in many developing regions with insufficient rain gauge coverage. Recently, new opportunistic methods of rainfall measurement have emerged. Among them, is the use of the relationship between rainfall intensity and the sound produced by droplets hitting a surface. Sound recorders offer a low‐cost solution and could provide an interesting means to increase spatial coverage of rainfall measurements, but also to fill information gaps under dense forests where conventional devices do not work. Our study developed a new technique and applied it to the Central Amazon region, by training a supervised machine learning model applied to sound recordings obtained in a tropical rainforest. To our knowledge, for the first time, such techniques are validated in locations far from the calibration site. We showed that reasonable results can be obtained for sites with distinct vegetation types and up to 30 km of distance from where the training data was acquired. Our findings demonstrate a strong capability for estimating hourly rainfall rates. Key Points: Rainfall intensity estimated from sound measurements in the Amazon rainforest, tipping bucket rain gauge, and machine learning modelsThe best model successfully detects rainfall in 88% of the cases, with R2 > 0.87 for hourly rainfall rates on the training siteModel validated over two sites in the Amazon, 97% accuracy identifying rainfall events, R2 of 0.69 and 0.93 for hourly rainfall rate [ABSTRACT FROM AUTHOR]

Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring. [ABSTRACT FROM AUTHOR]