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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]

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]

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]

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]

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.

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]

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]

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]

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]

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]

As the field of cell and gene therapy (CGT) continues to grow, so too must the infrastructure and regulatory guidance supporting the manufacture of these potentially life-saving products—especially early-phase products manufactured at an increasing number of academic or hospital-based facilities providing decentralized (or point of care) manufacturing. An important component of current good manufacturing practices, including those regulating cell and gene therapies, is the establishment of an effective environmental monitoring (EM) program. While several guidelines for establishing an EM program are available, these guidelines do not specifically address the unique aspects of manufacturing CGT products and they do not provide real-world evidence demonstrating the effectiveness of the program. Here, we describe the establishment and evolution of an EM program in a cell therapy manufacturing facility at an academic hospital. With 10 years of EM data, we analyze the effectiveness for identifying trends in environmental conditions and highlight important findings, with the aim of providing practical evidence and guidance for the development of future early-phase EM programs. [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]

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]