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Polydiacetylene (PDA) holds promise as a versatile material for biosensing applications due to its unique optical properties and self-assembly capabilities. In this study, we developed a colorimetric detection biosensor system utilizing PDA and aptamer for the detection of microcystin-LR (MC-LR), a potent hepatotoxin found in cyanobacteria-contaminated environments. The biosensor was constructed by immobilizing MC-LR-specific aptamer on magnetic beads, where the aptamer was hybridized with a urease-labelled complementary DNA (cDNA-urease). Upon binding MC-LR, the aptamer undergoes a conformational change to release cDNA-urease. The released cDNA-urease is subsequently captured by PDA bearing a single-stranded DNA (ssDNA). The enzymatic reaction triggers a distinctive color transition of PDA from blue to red. The results demonstrate exceptional sensitivity, with a linear detection range of 5–100 ng/mL and a limit of detection as low as 1 ng/mL. The practicability of the colorimetric method was demonstrated by detecting different levels of MC-LR in spiked water samples. The recoveries ranged from 77.3 to 102% and the color change, visible to the naked eye, underscores the practical utility for on-site applications. Selectivity for MC-LR over other microcystin variants (MC-RR and MC-YR) was confirmed. The colorimetric detection platform capitalizes on the properties of PDA and nucleic acid, offering a robust method for detecting small molecules with potential applications in environmental monitoring and public health. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is crucial for a wide range of applications, including environmental monitoring, precision agriculture, and mining, due to its ability to capture detailed spectral information across numerous wavelengths. However, the high dimensionality and complex spatial-spectral relationships in hyperspectral data pose significant challenges. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown remarkable success in automatically extracting relevant features from high-dimensional data, making them well-suited for handling the intricate spatial-spectral relationships in hyperspectral images.This study presents a hybrid approach for hyperspectral image classification, combining 3D Depthwise Separable Convolution (3D DSC) and Depthwise Squeeze-and-Excitation Network (DSENet). The 3D DSC efficiently captures spatial-spectral features, reducing computational complexity while preserving essential information. The DSENet further refines these features by applying channel-wise attention, enhancing the model's ability to focus on the most informative features. To assess the performance of the proposed hybrid model, extensive experimental studies were carried out on four commonly utilized HSI datasets, namely HyRANK-Loukia and WHU-Hi (including HongHu, HanChuan, and LongKou). As a result of the experimental studies, the HyRANK-Loukia achieved an accuracy of 90.9%, marking an 8.86% increase compared to its previous highest accuracy. Similarly, for the WHU-Hi datasets, HongHu achieved an accuracy of 97.49%, reflecting a 2.11% improvement over its previous highest accuracy; HanChuan achieved an accuracy of 97.49%, showing a 2.4% improvement; and LongKou achieved an accuracy of 99.79%, providing a 0.15% improvement compared to its previous highest accuracy. Comparative analysis highlights the superiority of the proposed model, emphasizing improved classification accuracy with lower computational costs. [ABSTRACT FROM AUTHOR]

Microplastic pollution poses a significant threat to our environment, necessitating effective predictive modelling approaches for better management and mitigation. In this study, we introduce a pioneering methodology that fuses the power of Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) for microplastic predictive modelling. Leveraging a comprehensive dataset, our integrated model exhibits exceptional performance, with an Accuracy of 0.96, Precision of 0.96, Recall of 0.97, and an F1 Score of 0.96. The achieved Accuracy underscores the model's proficiency in distinguishing microplastic and non-microplastic entities, promising robust and reliable predictions. Precision, as a measure of correct positive identifications, demonstrates our model's effectiveness in minimizing false positives, a crucial aspect for environmental monitoring. Moreover, the perfect Recall score signifies the model's ability to detect all relevant microplastic instances, addressing concerns about false negatives. The F1 Score encapsulates this dual proficiency, showcasing a harmonious trade-off between precision and recall. Our research not only advances the field of microplastic prediction but also highlights the potential of synergizing ANN and HMM methodologies for comprehensive environmental assessments. The reported performance metrics underscore the practical applicability of our approach, offering a valuable tool for tackling the pervasive issue of microplastic pollution and fostering proactive environmental stewardship. [ABSTRACT FROM AUTHOR]

Polymers blending has attracted significant interest in recent years owing to the possibility of synergistic interactions between blended materials which can be impressively beneficial over single substrates. Herein, a Tara gum derivative-(PVA-TG) blend was exploited as stabilizing agent for the synthesis of cyto-compatible PAPBA/(PVA-TG)/Ag, colloidal nanocomposite. Based on the in-situ oxidative polymerization strategy, 3-aminobenzene boronic acid (ABBA), was used for the reduction of silver salt, inside highly hydrophilic (PVA-TG) blend. As a result, AgNPs is formed, while ABBA, is oxidized to its conducting polymer conformation (PAPBA), all within the blended polymers solution. PAPBA/(PVA-TG)/Ag, showed dose-dependent cell viability with IC50 of 3.9 µg/mL against human keratinocytes (HaCaT) cells, based on in vitro MTT assay, which attested to its cyto-compatibility. The material was fully characterized using various analytical equipment and was deployed for the detection of metal ion (Au3+ ion) in solution. At the optimal detection conditions, absorbance ratios, (A560/A429) displayed linearity with Au3+ concentrations from 0.10 to 10.0 & 10.0–80.0 µM, with 28.5 nM detection limit (LOD). Further, the mechanistic basis of the detection strategy was proven to be based on galvanic replacement and was applied to Au3+ detection/monitoring in environmental samples with reliable precision and accuracy (99.4–102.3%). In all, we have showcased an innovatively contrived synthesis strategy which can be of huge benefit in toxic metal ions monitoring in water samples. [ABSTRACT FROM AUTHOR]

Irregular waste storage at closed mine sites poses severe environmental problems. This study evaluates the concentrations and effects of trace elements released into the environment by mining activity by analyzing soil, water, and sediment samples taken from a copper mining site. According to the data, acidic mine drainage was the main cause of the high concentrations of trace elements in the soil and sediments, including arsenic (As), cadmium (Cd), lead (Pb), and copper (Cu). The concentrations of As, Cd, Pb, and Cu in soil and sediments were 2734-times, 1189-times, 157-times and 32 times higher, respectively, in comparison to the background values of averaged concentrations of these elements in the upper crust. Geological and statistical studies indicate that the primary constituents of these pollutants are chalcopyrite and pyrite derivatives present in the primary rock structure. Using water analysis data from 2011 to 2020, the release of essential trace element into water was investigated to monitor the environmental effects of acidic mine drainage (AMD) from the closed Kuvarshan copper mine in the Artvin region of Turkey. This study demonstrates that trace elements concentrations may change according to local and seasonal factors and highlights the importance of conducting routine environmental monitoring studies. [ABSTRACT FROM AUTHOR]

An appraisal of the snail (Mollusca: Gastropoda) assemblage of the Sundarbans mangrove region, West Bengal, India, was made through consistent monitoring of three selected sites. In a span of 3 years, 18 species of snails under 11 families, and 9 orders were collected in 304 samples with the Shanon-Weiner diversity index (H') ranging between 0.8445 and 1.6909. In terms of numerical abundance, Stenothyra deltae (34.07 ± 5.71SE) and Cerithidia cingulata (26.01 ± 1.33SE) were dominant, while Nassarius stolatus (0.01 ± 0.01 SE) qualified as a rare species in the three sites. Significant differences (p < 0.05) in abundance in the sites were observed for Cerithidia cingulata, C. alata, Gangetica miliacea, and Stenothyra deltae. A canonical correspondence analysis reflected that the abundance of G. miliacea is shaped by the conductivity, C. cingulata is linked with temperature and the phosphate concentration is a better predictor of the abundance of S. deltae. The pattern of the diversity of the snails may probably reflect the variations in the environmental quality including the food resources. Thus, the snail assemblage pattern may be used as a basis for the biological monitoring of the Sundarbans mangroves and other similar sites. [ABSTRACT FROM AUTHOR]

For a long time, ecological monitoring across Australia has utilised a wide variety of different methodologies resulting in data that is difficult to analyse across place or time. In response to these limitations, a new systematic approach to ecological monitoring has been developed in collaboration between the Terrestrial Ecosystem Research Network and the Australian Department of Climate Change, Energy, the Environment and Water - the Ecological Monitoring System Australia (EMSA). A qualitative approach involving focus groups and semi-structured interviews was undertaken to review perceptions of the introduction of the EMSA protocols amongst Natural Resource Management practitioners and other key stakeholders. We found that environmental management stakeholders recognise there will be many advantages from the standardisation of ecological monitoring. However, key concerns emerged regarding the capacity needed to implement the standard protocols, the utility of the resultant data for regional projects, and the scope for adaptive co-management under the EMSA. Stakeholders emphasised the need for autonomy and flexibility, so their participation in protocol development can facilitate regional adoption of the standards. Respondents' concerns about a perceived lack of genuine consultation and acknowledgement of feedback revealed the importance of clear communication at all stages of an environmental management project aiming to standardise practices. Our findings indicate that reflexivity will be vital to address the complexity involved in standardisation of ecological monitoring. Formal processes of social learning will need to be integrated into environmental management approaches to account for the increasing complexity of socio-ecological systems as they are challenged by global change. [ABSTRACT FROM AUTHOR]

Stratospheric aerosol injection (SAI) and gene drive organisms (GDOs) have been proposed as technological responses to complex entrenched environmental challenges. They also share several characteristics of emerging risks, including extensive uncertainties, systemic interdependencies, and risk profiles intertwined with societal contexts. This Perspective conducts a comparative analysis of the two technologies, and identifies ways in which their research and policy communities may learn from each other to inform future risk governance strategies. We find that SAI and GDOs share common features of aiming to improve or restore a public good, are characterized by numerous potential ecological, societal, and ethical risks associated with deep uncertainty, and are challenged by how best to coordinate behavior of different actors. Meanwhile, SAI and GDOs differ in their temporal and spatial mode of deployment, spread, degree and type of reversibility, and potential for environmental monitoring. Based on this analysis, we find the field of SAI may learn from GDOs by enhancing its international collaborations for governance and oversight, while the field of GDOs may learn from SAI by investing in research focused on economics and decision-modeling. Additionally, given the relatively early development stages of SAI and GDOs, there may be ample opportunities to learn from risk governance efforts of other emerging technologies, including the need for improved monitoring and incorporating aspects of responsible innovation in research and any deployment. [ABSTRACT FROM AUTHOR]

Monitoring the ecological status of rivers is essential for protecting freshwater biodiversity and ecosystem health. The main objective of this work was to predict the ecological quality of Greek rivers using a machine learning approach based on the Extreme Gradient Boosting (XGBoost) classifier. We used a dataset that comprises ecological, physicochemical, geomorphological, and sample-related parameters collected from the national monitoring network of Greek rivers as well as climate parameters from the ERA5-Land dataset. More specifically, we developed multiple models that predicted the ecological quality class derived by four quality elements (QEs) that are benthic macroinvertebrates, benthic diatoms, fish, and physicochemical quality. The Shapley Additive exPlanations (SHAP) approach was implemented for quantifying the contributions of the predictors on the quality class. We finally developed a web interface tool that can simulate what-if scenarios to predict the quality class under altered environmental conditions. Our findings showed that total phosphorus, nitrate, and ammonium were important predictors for benthic macroinvertebrates, benthic diatoms, and physicochemical quality, whereas for fish, predictors related with the geomorphology (e.g., altitude and slope) had a higher influence. The SHAP plots revealed the synergistic effect of predictors on the quality classes, highlighting a negative effect of increased nutrients on achieving the good quality class based on macroinvertebrates and diatoms and a positive relationship between altitude and slope with the good ecological quality class based on fish. Furthermore, the web interface could provide a useful tool for water managers to predict quality classes for water bodies under what-if scenarios. [ABSTRACT FROM AUTHOR]

Hydroquinone is one of the phenolic compounds which is broadly utilized in industries as a raw material for processing. The presence of HQ in the wastewater can cause harmful effects on humans as well as aquatic life. There are enormous analytical methods that have been developed to detect the presence of hydroquinone even at trace levels, but among them, one of the trending methods is electrochemical, which is not only easy to operate but also gives very sensitive and selective results. In the current study, using a microwave-assisted technique, polypyrrole graphene-based nanocomposites were synthesized and then modified on a glassy carbon electrode to serve as a highly efficient replacement for the highly sensitive and selective hydroquinone (HQ) detection in real water samples. Several characterization methods, including Fourier transform infrared spectroscopy, scanning electron microscopy, atomic force microscopy, X-ray diffraction, and Raman spectroscopy, were used to examine the produced nanocomposite. These techniques revealed the presence of nanosheet-like morphologies in sensing nanomaterials. Meanwhile, the cyclic voltammetric method was used to investigate the electrochemical behavior of the surface-modified glassy carbon electrode and set a linear concentration from 0.06 to 1.4 mM for sensing HQ under optimized conditions. The modified method showed a limit of detection and limit of quantification of 0.04 and 0.13 mM, respectively. Moreover, the present research has developed a reliable, sensitive, and fast method for the electrochemical detection of HQ in real water samples. [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]

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]

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]

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

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

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

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]

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]

For decades, the risk of exposure to infectious diseases in recreational beaches has been evaluated through the quantification of fecal indicator bacteria in water samples using culture methods. The analyses of sand samples have recently been developed as a complement to the monitoring of recreational waters in beach quality assessments. The growing use of molecular techniques for environmental monitoring allows for the rapid detection of pathogenic genes, thus providing more accurate information regarding the health risk of exposure to contaminated sand. The aim of this work was to determine the relationship between the fecal indicators abundance in water and sand and the presence of Shiga toxin-producer Escherichia coli (STEC) in sand by analyzing samples from touristic beaches using culture-dependent (fecal coliforms assay) and culture-independent (real-time PCR of stx1, stx2, and eae genes) techniques. We found a high concentration of coliform bacteria in water and sand in several beaches in eastern Uruguay, with different levels of sanitation networks and levels of urbanization. The presence of STEC virulence genes (mainly stx1) was confirmed in 8 out of 20 sand samples. The recreational use of sandy beaches may imply a risk to the health of its users, especially near streams and creek outflows, thus highlighting the need of monitoring sand bacteriological quality and pathogens using molecular tools. [ABSTRACT FROM AUTHOR]

The underground environment and its components are susceptible to various attacks due to their role in monitoring environmental conditions and future predictions. Our review of existing literature reveals that most protocols are vulnerable to impersonation and Man-in-the-Middle attacks, which pose significant risks of data theft from sensors. Additionally, these protocols often impose high computational loads on the sensors. To address these challenges, this article presents an inter-mutual authentication scheme tailored for Wireless Sensor Networks (WSNs). We propose a secure authentication and key agreement protocol using the elliptic curve cryptosystem (ECC), which not only provides session key generation and authentication but also protects against several potential attacks. We conducted comprehensive security analyses, including both formal and informal methods, to validate the robustness of our protocol. The informal analysis, based on a threat model, demonstrates the protocol's effectiveness against various critical attacks. The Random Oracle (RoR) model further establishes that breaking the session key security is challenging. Simulations using Scyther confirm that private parameters remain secure during execution, while AVISPA simulations validate the protocol's resilience against replay attack and Man-in-the-Middle attacks. Furthermore, we assessed the security features, computational and communication costs, and overall performance of the proposed scheme, comparing it with existing studies. Our findings indicate that the protocol offers an average improvement of 36.47% and 48-bits for computation and communication overheads. Additionally, this work also offers enhanced security (secure from all possible attacks), making it an effective solution for securing sensitive data in WSNs. [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]

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]

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]

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]

Biosensors stand at the forefront of innovation in critical fields such as medicine, food safety, and environmental monitoring, offering precise detection of specific biomarkers. Among these, polymer-based biosensors have emerged as powerful tools, evolving from inert materials to intricately tailored structures capable of precise molecular binding. Electrospinning, a prominent fabrication method, offers a cost-effective approach for developing diagnostic kits, pushing the boundaries of biosensors with exceptionally low analyte detection limits. However, the reliance on commercial polymers restricts the versatility of electrospun nanofibers due to the fixed properties and limited functionalization options of off-the-shelf materials. These constraints prevent the tailoring of nanofibers to meet specific or diverse needs, as their molecular structure and surface functionalities are not easily adjustable. A promising solution is integrating Controlled Radical Polymerization (CRP) with electrospinning. CRP provides precise control over polymer properties, enabling the creation of nanofibers with customized functionalities for various applications. This approach combines the benefits of electrospinning, such as high surface area-to-volume ratios, with the advanced capabilities of CRP to enhance the selectivity and sensitivity of biosensors. This review represents recent advancements in biosensors, covering acquisition, characterization, and applications, with a focal point on the role of functional polymers. Exploring biosensor architecture, electrospinning principles, controlled polymer synthesis, CRP techniques, and polymer topologies, the manuscript also discusses challenges and future prospects, including microfluidic integration, multi-detection capabilities, machine learning, and the development of wearable biosensors. [ABSTRACT FROM AUTHOR]

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol's performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network's robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol's exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol's prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications. [ABSTRACT FROM AUTHOR]

The management and design of urban areas in metropolises pose significant challenges. Balancing diverse land uses within a metropolitan structure and addressing spatial and environmental constraints are just some of these challenges. Urban heat islands, which stem from factors such as inappropriate construction materials, inadequate building insulation, improper land use locations, and unsuitable built-up density, reflect environmental imbalances within urban infrastructure. Effectively addressing these temperature discrepancies can lead to energy preservation, reduced environmental hazards, and enhanced comfort for urban residents. This study employed Landsat-8 satellite images to identify and monitor positive and convex temperature disparities across various districts of Tehran over 3 years (2018 to 2020) using three different strategies. These disparities are estimated through the differences in land surface temperature from the thermal trend of each district and their persistence has been assessed in seasonal, semi-annual, and annual strategies. The study found that industrial areas, including warehouses, were the significant contributors to the persistent presence of urban heat islands in summer and winter. Open areas with impervious surfaces or bare soil, particularly those lacking sufficient green cover, also significantly contributed to the heat island effect. Certain large shopping centers, often due to their air conditioning systems, were also consistently identified as persistent heat islands. Evaluations demonstrated that over 78.8% of the identified persistent heat islands were meaningful, with most located in the northern and western parts of Tehran. [ABSTRACT FROM AUTHOR]

This research article proposes a periodically elliptical graphene metamaterial array-based wavelength-tunable absorber. The recent identification of graphene represents a substantial advancement in the field of plasmon-based electronics, which includes design, fabrication, wave distribution, inherent adaptability, and affordable prototyping. The finite difference time domain (FDTD) method is used to study the effects of different parameters. This article shows an apparent absorption peak with maximum absorption of 49.6% occurs at 55.12 µm compared to the existing metamaterial absorber. By extending the ellipse's major axis, increasing the X polarization of the incidence angle within a specific range, and enhancing graphene's chemical potential, the absorption rate of elliptical graphene arrays will be increased. Furthermore, to accurately evaluate the sensing capabilities of the structure, we model the spectra under varying refractive indexes in the environment, resulting in a design with a sensitivity (S) of up to 15,169 nm/RIU and a figure of merit (FOM) value. This research makes it easier to design environmental monitors, biosensors, and optoelectronic devices based on graphene. [ABSTRACT FROM AUTHOR]

Novocherkassk Power Station is one of the energy enterprises of the first hazard class, primarily operating on coal, the emissions of which exert a negative impact on the environment of Rostov oblast. The purpose of this study is to analyze the accumulation of Zn, Pb, Cu, Mn, Ni, Cr, and Cd in soils and various types of medicinal plants in its impact zone. The total content of Cr, Ni, and Cd in soils is to 1.4 times higher than the background and the approximately permissible concentrations. It is revealed that the maximal permissible concentration in soils is exceeded by four times for mobile forms of Cu and to two times for mobile Zn, Pb, Ni, and Cr. Heavy metals are mainly accumulated in the above-ground parts of wormwood, common chicory, and tansy and in roots of yarrow. The maximal permissible concentration of elements is exceeded to 3.5 times for Pb, to five times for Cd, and to three times for Ni in yarrow; to 5.5 times for Pb and to two times for Ni in wormwood; to two and six times for Pb and Cd, respectively, in chicory; and to three times for Pb and Cd in tansy. The content of mobile compounds of heavy metals in soil and their accumulation in plants are the highest within 3 km to the northwest from the power station. The degree of technogenic load on soils and medicinal plants in the impact zone is assessed on the basis of biogeochemical parameters. [ABSTRACT FROM AUTHOR]

Water classification in Synthetic Aperture Radar (SAR) images is an ongoing area of research, which has implications in environmental monitoring and water resource management. Adaptive threshold algorithms provide a fast, reliable and efficient way to perform automated water classification, but users often lack awareness on selecting the best algorithm for their specific application. This paper presents a comprehensive assessment of adaptive threshold algorithms for water delineation applied to L- and C-band SAR backscatter images. We introduce a novel approach for dynamic selection of windows within a SAR image to determine optimum thresholds on sigma naught values. A comparison of five threshold-determination techniques is performed which include Otsu, Kittler and Illingworth (KI), Gaussian Mixture Model (GMM), Quality Index (QI) and Gamma Maximum Likelihood Estimation (GMLE) algorithms. We observed that, for L-band SAR data, convex hull approach produced better kappa coefficient value with GMM, KI and GMLE algorithms. However, for C-band SAR, kappa coefficients were highest for convex hull method with GMM, KI, QI and GMLE approaches and noticeably higher (> 0.89) when compared to split window approach. Our analysis indicates that the proposed convex hull method for window selection performs better in both L- and C-band SAR images. The results of our analysis will help users in identifying the best adaptive algorithm for water delineation in L- and C-band SAR images. [ABSTRACT FROM AUTHOR]

Magnetoelastic biosensors have emerged as a promising technology for the sensitive and label-free detection of a wide range of biological analytes. These biosensors use the magnetoelastic effect, which describes how the mechanical properties of magnetostrictive materials change in response to a magnetic field. This effect is utilized to detect biological analytes by immobilizing specific recognition elements, such as antibodies or nucleic acids, on the magnetoelastic material's surface. The binding of target analytes to the recognition elements induces a mass change, leading to a shift in the resonance frequency of the magnetoelastic material. Magnetoelastic biosensors find applications across various fields, including medical diagnostics, environmental monitoring, and food safety. In medical diagnostics, they offer rapid and sensitive capabilities for detecting pathogens, biomarkers, and toxins. For environmental monitoring, they demonstrate the ability to detect pollutants and heavy metals. Furthermore, in ensuring food safety and quality, magnetoelastic biosensors detect allergens, pathogens, and contaminants effectively. Ongoing research and technological advancements suggest that these biosensors hold great potential for revolutionizing various fields, including healthcare, environmental monitoring, and food safety, contributing to improved disease diagnosis, environmental protection, and public health. This review article provides an overview of the principles, fabrication methods, diagnosis, bacterial density, food, and agricultural applications of magnetoelastic biosensors. [ABSTRACT FROM AUTHOR]

Wireless sensor network is large-scale, self-organizing and reliable. It is widely used in the military, disaster management, environmental monitoring, and other fields. Algorithms for localization can be classified as range-based or range-free based on their ability to achieve effective localization. Range-based algorithms require hardware support, which increases deployment costs and complexity. Instead of measuring distance directly, range-free algorithms estimate the position based on hop counts between nodes. While simpler in terms of hardware requirements, this algorithm suffers from large localization errors. To address this problem, this paper proposes an improved 3D DV-Hop localization algorithm (3D DEHDV-Hop) using a differential evolutionary algorithm. First of all, theoretical analysis shows a correlation between the volume of the intersection area containing the communication range between neighbors and the number of shared single-hop nodes. Then, using the number of shared single-hop nodes between nodes, the number of hops is converted from a discrete value to an exact continuous value. Finally, the localization problem is transformed into a minimum optimization problem by incorporating a differential evolutionary algorithm. As compared to the other four algorithms compared, 3D DEHDV-Hop improves localization accuracy by an average of 10.3% under different anchor node densities, 13.7% under different communication radiuses, and 12.1% under different anchor node numbers. [ABSTRACT FROM AUTHOR]

The remote sensing satellite observation process involves multiple stakeholders and significant costs, so selecting an appropriate observation scheme and reaching an agreement on the chosen scheme among the evaluators/stakeholders is essential. From this perspective, the observation scheme selection problem can be viewed as a large-scale group decision-making (LSGDM) problem, challenging due to its complex group composition and the high consensus level required. Accordingly, this paper investigates an adaptive bi-directional consensus model that incorporates the evolution of social influence to address the LSGDM problem. Firstly, the dual-attribute affinity propagation algorithm is employed to divide the large-group into manageable subgroups. Secondly, the social influence evolution model is established, where evaluators' social influences are determined by considering their opinion similarity and trust level, and subgroups' social influences are updated by measuring their decision risk. Thirdly, the bi-directional feedback mechanism is designed to adaptively generate adjustment strategies corresponding to different scenarios based on the evolution model. Finally, an observation scheme selection case is analyzed using the proposal to demonstrate its practicality. During the process of remote sensing satellite observation, the selection of an appropriate observation scheme can optimize the utilization of existing satellite resources and ensure the quality of satellite observation services, thereby better meeting the demands of diverse application areas such as environmental monitoring, disaster management, and urban planning. [ABSTRACT FROM AUTHOR]

Dujiangyan Irrigation District is the largest irrigation district in China. To understand heavy metal pollution and its potential hazards, the contents and distribution of typical heavy metals in rivers, soils and plant samples from 19 sites were studied and their ecological risks and health risks were evaluated. The results showed that the concentrations of nine heavy metals in the river water did not exceed the safe values (GB 3838-2002). They showed a trend of the highest in the middle reaches followed by the upstream and downstream areas, and the concentration in the dry season (winter) was higher than that in the wet season (summer). The average values of heavy metals in the soil were 0.3–3.08 times the background values in Chengdu. The highest average enrichment factor was Cd (7.52). The Nemerow pollution index indicated that the quality of the river water was at a clean level. However, the heavy metals in the soil at sites B1 and B3 were at a heavy pollution level. The potential ecological hazard index also showed that Cd in the soils of the study area at sites B1 and B3 was very highly hazardous and 26% of the sample area was at a highly hazardous level. The cumulative carcinogenic health risks due to total heavy metals exceeded the safety limits for both children and adults. Furthermore, the risks were higher in children than in adults. This study provides theoretical support for dealing with heavy metal pollution in the irrigation district.Highlights: Heavy metal concentrations were higher during the dry season than during the wet season. Cd posed the most severe ecological risk. Heavy metal contamination in surface water and soils followed a consistent spatial distribution pattern (midstream > upstream > downstream). The health risk associated with carcinogenicity was greater for children compared to adults. [ABSTRACT FROM AUTHOR]

The OECD (Organisation for Economic Co-operation and Development) test guidelines (TG) 229—fish short-term reproduction assay (FSTRA) is one of the gold standard methods used to identify endocrine disrupting chemicals (EDCs). While informative, the FSTRA's 5–6 week duration makes it difficult to use routinely. Prior studies have shown that EDCs' impact on fecundity, vitellogenin (VTG) and steroid levels can be detected after less than 1 week of exposure suggesting the FSTRA could be shortened. This study compares both 7- and 21-day FSTRAs using fathead minnows (Pimephales promelas) for three known EDCs: 17α-ethinylestradiol (EE2; 40 ng/L), 17β-trenbolone (TRB; 50 µg/L), and propiconazole (PRP; 500 µg/L). All three compounds led to arrested fertility after 24 h of exposure, except for the 7-day EE2 treatment which still decreased reproduction. Moreover, independently of time of exposure, EE2 induced VTG production in males, and decreased estrogen levels in females and testosterone levels in males. In contrast, TRB-induced VTG production in males, while the levels were not different from controls in females even though testosterone levels increased, and masculinization was observed. Finally, PRP led to a decrease in VTG levels which was only significant during the 21-day exposure, and surprisingly, no effect on steroid levels were observed despite its known effects on steroidogenesis. For two of the three EDCs tested, both times of exposure led to similar outcomes supporting the shortening of the FSTRA to seven days. This proposed 7-day FSTRA could be used to screen EDCs in routine monitoring of environmental samples. [ABSTRACT FROM AUTHOR]

The response of mesozooplankton is critical in assessing the health of an estuarine ecosystem. Reports on the spatial and temporal dynamics of mesozooplankton community in estuarine and backwaters of the Southern parts of India are scanty. In this scenario, we appraised the community structure of mesozooplankton and their spatio-temporal dynamics based on various multivariate statistical assessments. A total of 31 taxa were identified and the abundance was principally dominated by Copepoda followed by Luciferidae during three sampling seasons. The most abundant species were: Paracalanus parvus parvus, Pseudodiaptomus aurivillii, Temora stylifera, and Pseudodiaptomus serricaudatus. Canonical correspondence analysis and Spearman's correlation coefficients underlined that salinity, turbidity, conductivity, temperature, dissolved oxygen, chlorophyll a (Chl-a), and nutrients were the principal environmental variables strongly linked with mesozooplankton dynamics in Munroe Island. The highest abundance of mesozooplankton was recorded in MoN (monsoon), followed by PrM (pre-monsoon), and PoM (post-monsoon). Hierarchical clustering confirmed that the grouping of sampling stations is based on the estuarine and freshwater influences on mesozooplankton abundance. During the entire investigation, various ecological indices were observed in good condition. Moreover, the optimum environmental conditions during the PoM season are marked with the highest indices values. Overall, multivariate investigations undoubtedly proved the suitability of mesozooplankton communities as potential bioindicators for spatial and seasonal ecological assessments. Our investigation emphasizes the high assemblages of mesozooplankton and their responses to various environmental variables and highlights the significance of long-term ecological monitoring in a threatened ecosystem like Munroe Island. [ABSTRACT FROM AUTHOR]

Fish residing in the aquatic ecosystem are considered the best ecological indicator for monitoring environmental habitat. To evaluate the changes that occurred due to relative restoration in the ecological habitat, a study was conducted in the freshwater zone of the river Ganga between Buxar, Bihar, and Ballia, Uttar Pradesh, between July 2021 and July 2022. In the monitoring, the physico-chemical condition, as well as the food and feeding habits of the fish Gudusia chapra, were monitored with the help of various pollution evaluating indices, namely, the algal pollution index (API) for planktons, the comprehensive pollution index (CPI-WQI), and the National Sanitation Foundation Water Quality Indices (NSF-WQI) for water. The study showed that the relative restoration facilitated the amplification of the fish catch from 5.60 to 98.98% in two consecutive years. The reduction in the API (15 to 4) as well as CPI (0.80 to 0.72) during both years signified the reduction of the pollution status of the river in the region. The NSF-WQI also decreased from 88.27 to 79.27 from 2021 to 2022. The electivity index for the fish showed that fish preferred the groups Cyanobacteria, Rotifera, and Copepoda. The multivariate, as well as univariate analyses, revealed that the fish G. chapra is significantly influenced by multiple abiotic as well as biotic variables, among which the major contributors are riverine velocity, transparency, conductivity, pH, dissolved oxygen, total alkalinity, carbonate, bicarbonate, salinity, total hardness, calcium, silicate, and biochemical oxygen demand. [ABSTRACT FROM AUTHOR]

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter. [ABSTRACT FROM AUTHOR]

A community-driven effort in Cedar Key, Florida, USA, resulted in the construction of three living shoreline retrofits intended to bolster failing coastal infrastructure and restore habitat functions in Daughtry Bayou. A multi-year monitoring program tracked changes in elevation and vegetation communities across the entire shoreline profile from lower-intertidal to upland/transitional zones and measured wave attenuation during typical and extreme (hurricane) conditions. Overall, these living shoreline retrofits served to soften more than 30% of the bayou’s shoreline, dramatically reducing the extent of armored shoreline in direct contact with tidal influence. The extent of vegetated habitat area has increased at all three sites, despite sediment export from higher elevation zones driven largely by repeated impacts from hurricanes and tropical storms. These living shorelines reduced wave energy by 33 to 79% in typical conditions and by up to 28% in hurricane conditions, consistently outperforming armored shorelines, even during an extreme event (Hurricane Idalia). Our monitoring efforts were sufficient to capture project trajectories and assess performance relative to project goals, but our program had limitations that could have been overcome with additional resources and increased focus on capturing spillover effects. The living shoreline retrofit projects assessed here have persisted through and shown signs of recovery after multiple tropical storms and hurricanes, while providing important energy reduction services. Thus, living shoreline retrofits continue to be a cost-effective shoreline management strategy in the short term for this area. However, our analyses suggest that persistence of these shorelines could be threatened by the combination of sea-level rise (by 2040), upland armoring, and an increasing risk of more intense tropical systems. Therefore, future interventions should more carefully consider these threats in conjunction with habitat enhancement goals. [ABSTRACT FROM AUTHOR]

Escalating environmental challenges necessitate paramount decision-making to safeguard ecosystems and resources. However, the burgeoning volume and intricate nature of environmental data often present a formidable challenge in gleaning actionable insights. In this context, integrating data analytics tools within environmental monitoring and management frameworks offers a compelling avenue for progress. These tools facilitate efficient data processing, uncover hidden patterns, and enable predictive modelling, leading to more informed decisions. Despite growing research, a comprehensive understanding of specific data analytics applications, methodologies, and demonstrably effective implementations remains elusive. This systematic review aimed to address this gap. Following PRISMA guidelines, a meticulous search across five databases was conducted using predefined inclusion/exclusion criteria. Rigorous data extraction captured salient study characteristics, methodologies, data analysis techniques, key findings, and acknowledged limitations. The review revealed that data analytics offers a powerful toolkit for environmental management, transforming decision-making across all stages. Big data and advanced techniques enable proactive strategies through earlier issue detection and improved predictive models. However, maximising this potential requires a multifaceted approach, including standardised data collection, data literacy, ethical frameworks, and stakeholder engagement. Highlights: Data analytics as a powerful tool The study underscored that data analytics is a crucial tool for addressing environmental challenges, offering transformative benefits at all stages of environmental decision-making. Enhanced environmental management The study also revealed that advancements in big data and machine learning empower earlier detection of environmental concerns, facilitate accurate prediction, and foster proactive management strategies. Multifaceted approach for success The major implication of the study is that maximising the potential of data analytics requires a multifaceted approach that addresses data quality, human and ethical considerations, interoperability, stakeholder engagement, and cultural factors. [ABSTRACT FROM AUTHOR]

Electrochemiluminescence resonance energy transfer (ECL-RET) is a versatile signal transduction strategy widely used in the fabrication of chem/biosensors. However, this technique has not yet been applied in visualized imaging analysis of intracellular species due to the insulating nature of the cell membrane. Here, we construct a ratiometric ECL-RET analytical method for hypochlorite ions (ClO−) by ECL luminophore, with a luminol derivative (L-012) as the donor and a fluorescence probe (fluorescein hydrazide) as the acceptor. L-012 can emit a strong blue ECL signal and fluorescein hydrazide has negligible absorbance and fluorescence signal in the absence of ClO−. Thus, the ECL-RET process is turned off at this time. In the presence of ClO−, however, the closed-loop hydrazide structure in fluorescein hydrazide is opened via specific recognition with ClO−, accompanied with intensified absorbance and fluorescence signal. Thanks to the spectral overlap between the ECL spectrum of L-012 and the absorption spectrum of fluorescein, the ECL-RET effect is gradually recovered with the addition of ClO−. Furthermore, the ECL-RET system has been successfully applied to image intracellular ClO−. Although the insulating nature of the cell itself can generate a shadow ECL pattern in the cellular region, extracellular ECL emission penetrates the cell membrane and excites intracellular fluorescein generated by the reactions between fluorescein hydrazide and ClO−. The cell imaging strategy via ECL-RET circumvents the blocking of the cell membrane and enables assays of intracellular species. The importance of the ECL-RET platform lies in calibrating the fluctuation from the external environment and improving the selectivity by using fluorescent probes. Therefore, this ratiometric ECL sensor has shown broad application prospects in the identification of targets in clinical diagnosis and environmental monitoring. [ABSTRACT FROM AUTHOR]

Determination of high sulfur content in sulfide minerals is crucial for various applications, including geochemistry, mining, and environmental monitoring. While inductively coupled plasma optical emission spectrometry (ICP-OES) is the preferred method for sulfur analysis, conventional sample preparation techniques often fall short when dealing with high sulfur concentrations. This study investigated optimized digestion conditions for accurate sulfur determination in sulfide minerals using ICP-OES. We employed anti-corrosion acid digestion bombs, an aqua regia–hydrofluoric acid system, and a digestion temperature of 180 °C, achieving exceptional results. Certified reference material recoveries ranged from 99.9 to 100.5%, with relative standard deviations between 0.42 and 0.84%. These results demonstrate the high accuracy and precision of the method. The optimized digestion protocol presented in this study provides a robust and reliable approach for accurate sulfur analysis in sulfide minerals, particularly for high concentrations. [ABSTRACT FROM AUTHOR]

The functional traits of soil protists have been employed in ecological research to enhance comprehension of the underlying mechanisms of ecological processes. Among the numerous soil protists, testate amoebae emerge as a prominent and abundant group, playing a pivotal role in soil micro-food webs. Furthermore, they are regarded as valuable bioindicators for environmental monitoring and palaeoecological studies due to their sensitivity to environmental changes. We screened 372 testate amoebae species widely distributed across Northern Holarctic realm and collected trait data, representing the morphological and feeding characteristics of testate amoebae. The dataset would provide valuable basis for investigation of the functional diversity and ecological roles of testate amoebae, thus facilitating further research on soil protist communities and ecosystem dynamics. [ABSTRACT FROM AUTHOR]

Visceral leishmaniasis (VL) is an urgent public health concern in Brazil. We evaluated the spatiotemporal distribution of VL to better understand the effects of economic activities related to agriculture, livestock, and deforestation on its incidence in the Brazilian Legal Amazon (BLA). The data on newly confirmed cases of VL in Brazilian municipalities from 2007 to 2020 were extracted from the Brazilian Notifiable Diseases Information System (SINAN) and analyzed. The data on agricultural production (planted area in hectares) and livestock (total number of cattle) were obtained from the Brazilian Institute of Geography and Statistics (IBGE), whereas deforestation data (in hectares) were obtained from the Amazon Deforestation Estimation Project (PRODES). SatScan and the local indicators of spatial association (LISA) were used to identify the spatial and temporal patterns of VL and its relationships with economic and environmental variables. The cumulative incidence rate was found to be 4.5 cases per 100,000 inhabitants. Based on the LISA results, areas with a high incidence of VL and deforestation were identified in the states of Roraima, Pará, and Maranhão. Strengthening deforestation monitoring programs and environmental enforcement actions can help implement public policies to control illegal deforestation and mitigate the socio-environmental vulnerability in the BLA. Therefore, areas identified in this study should be prioritized for controlling VL. [ABSTRACT FROM AUTHOR]

Localization is a vital task in underwater wireless sensor networks (UWSNs). Accurate location information is critical for applications such as environmental monitoring and military operations. Received signal strength (RSS)-based localization schemes are commonly employed because of their cost-effectiveness and performance in terrestrial environments. However, underwater acoustic environments are affected by not only path loss, but also absorption bias in which case these schemes perform poorly with large delays. This makes real-time underwater acoustic localization based on RSS challenging and unable to meet the localization requirements of practical applications. Thus, a real-time RSS-based target localization (RRTL) scheme for UWSNs is proposed. Since the derived closed-form expression is complex and nonconvex, an intelligent localization technique based on a backpropagation (BP) neural network is considered which employs improved differential evolution (IDE) to obtain the network weights and biases. First, improved opposition-based learning is used to obtain a robust population. Then an adaptive mutation strategy is utilized to improve global searching and convergence. Finally, optimized network parameters are obtained via IDE to train the BP neural network. Performance results are presented which demonstrate that the proposed method is superior to conventional state-of-the-art methods and is robust to absorption bias. For example, for an error cumulative distribution function (CDF) of 0.8 with N = 6, the proposed method provides an improvement of 69.7% in accuracy and 78.4% in computation time over the well-known WLS technique. [ABSTRACT FROM AUTHOR]

The development of portable and cost-effective sensing system for Hg2+ quantitation is highly demanded for environmental monitoring. Herein, an on-site, rapid and portable smartphone readout device based Hg2+ sensing system integrating nitrogen-doped carbon quantum dots (NCDs) modified paper strip was proposed, and the physicochemical properties of NCDs were characterized by high resolution TEM, FTIR, UV–vis absorption spectrum and fluorescence spectral analysis. The modified paper strip was prepared via "ink-jet" printing technology and exhibits sensitive fluorescence response to Hg2+ with fluorescence color of bright blue (at the excitation/emission wavelength of 365/440 nm). This portable smartphone-based sensing platform is highly selective and sensitive to Hg2+ with the limit of detection (LOD) of 10.6 nM and the concentration range of 0–130 nM. In addition, the recoveries of tap water and local lake water were in the range of 89.4% to 109%. The cost-effective sensing system based on smartphone shows a great potential for trace amounts of Hg2+ monitoring in environmental water samples. [ABSTRACT FROM AUTHOR]

An analysis of the photoluminescence (PL) and thermoluminescence (TL) glow curves from the calcite sample collected from the Meghalaya basin is reported. The as-collected sample is thoroughly ground with an agate pestle-and-mortar set for 4 h and is annealed at 400°C for 2 h. An X-ray diffraction (XRD) analysis of the sample reveals cubic crystal structure. The collected sample morphology shows the presence of agglomeration. The bending and stretching of bonds inside the calcite in the fingerprint region is determined by the Fourier transform infrared (FTIR) spectroscopy. The photoluminescence excitation and emission spectra are monitored; they show well resolved peaks at 252 nm (absorption band), 395 nm and 509 nm (emission bands). An analysis of the thermoluminescence (TL) glow curve of calcite is performed using UV excitation for different UV exposure times (5 and 15 minutes), showing a linear response with the UV dose. The corresponding kinetic parameters, like order of kinetics, activation energy and frequency factor, are calculated using the computerized glow curve deconvolution (CGCD) technique. The collected calcite sample shows a good response with the dose, which may be useful for the environmental UV monitoring systems. [ABSTRACT FROM AUTHOR]

The study utilizes the colorimetric method (involving 1,5-diphenylcarbazide and potassium thiocyanate as complexing agents), computer vision, and machine learning (ML) regression algorithms to determine the content of Cr (VI) and Fe (III) in water samples. To process digital images of water samples, the integration technique utilized a flatbed scanner known as the CanoScan LiDE 100, operating as a digital image capture device, and its performance was compared to that of conventional instruments. The study reveals that PolyReg and SVR-Poly are the most reliable ML regression algorithms for processing color space data (G and B of RGB, c* of CIELch, and b* of CIELab) of digital images of water samples that contain Cr (VI) and Fe (III). The mean absolute percentage error (MAPE) of the ML regression algorithms PolyReg and SVR-Poly for determining the content of Cr (VI) and Fe (III) is < 10% (with 8.48% error for Cr (VI) determination using PolyReg G of RGB and 6.78% error for Fe (III) determination using PolyReg B of RGB) in the estimation algorithm model. The Mean Absolute Percentage Error (MAPE) indicates that the prediction method is highly accurate. The Limit of Detection (LOD) value of the flatbed scanner colorimetric method integrated with PolyReg G of Red–Green–Blue (RGB) for Chromium (VI) and Blue of RGB for Iron (III) is approximately 0.02 mg/L. The Limit of Detection (LOD) for Chromium (VI) and Iron (III) is 0.0209 mg/L and 0.0257 mg/L, respectively. The limit of detection (LOD) values from this technique are superior to those obtained from certain UV–vis spectrometric and colorimetric methods. The low LOD values demonstrate that this technique is suitable for estimating the concentration of Cr (VI) and Fe (III) in water samples for quality assessment purposes, as these values are below the maximum concentration levels established by various regulations, including US-EPA, ASEAN, and EECCA. [ABSTRACT FROM AUTHOR]

A novel photoelectrochemical (PEC) sensor for the detection of microcystic toxins (MC-LR) was developed on the basis of signal-sensitive change strategy. NiO nanoarray as a basic photoactive material was grown directly on the ITO glass electrode via calcination after hydrothermal reaction, while dye N719 was used to sensitize the electrode for enhancing visible light absorption, and the first signal-on stage was obtained. In the meantime, p-type Cu2O was applied as the signal probe attached to probe DNA (DNA2) to improve the sensitivity, and the second "signal-on" stage appeared because of its synergistic effect with NiO nanoarrays. The PEC signal decreases after the target analyte MC-LR is modified on the electrode due to the stronger affinity between MC-LR and its complementary aptamer DNA; part of the Cu2O-DNA2 will dissociate from the electrode. This sensitive signal change strategy allows the detection limit of the MC-LR sensor to be as low as 1.7 pM, which offers an optional method for the sensitive and selective detection of other target molecules, with potential applications in environmental monitoring and toxin determination. [ABSTRACT FROM AUTHOR]