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As marine ecosystems and habitats continue to degrade, scientists urgently need a simple and effective method to monitor and assess the ecological environment. Due to the sensitivity of indicator species to environmental changes and pollution, indicator species are widely used to monitor environmental changes and assess management practices. In recent years, indicator species have received significant attention from ecologists. Therefore, systematic introduction of indicator species holds considerable practical and theoretical significance. Firstly, the definition and development history of indicator species are introduced, including six macrobenthic taxa identified as indicator species, such as annelids, echinoderms, cnidarians, mollusks, crustaceans, and demersal fishes. Additionally, ten related terms are explained, and two main problems are identified: unclear meanings and repeated definitions. Next, the advantages of indicator species and their limitations in practical applications are discussed, considering human, species-specific, and environmental factors. Finally, specific examples of the application of indicator species are presented in ecological monitoring, habitat selection, and ecological early warning. Through this multi-dimensional introduction of indicator species, further discussions are expected to be stimulated, and their application and development in marine ecology promoted, thereby enhancing their role in habitat and ecological environment selection and assessment in the future.

Soil heavy metal contamination has emerged as a global environmental concern, posing significant risks to human health and ecosystem integrity. Hyperspectral technology, with its non-invasive, non-destructive, large-scale, and high spectral resolution capabilities, shows promising applications in monitoring soil heavy metal pollution. Traditional monitoring methods are often time-consuming, labor-intensive, and expensive, limiting their effectiveness for rapid, large-scale assessments. This study introduces a novel deep learning method, SpecMet, for estimating heavy metal concentrations in naturally contaminated agricultural soils using hyperspectral data. The SpecMet model extracts features from hyperspectral data using convolutional neural networks (CNNs) and achieves end-to-end prediction of soil heavy metal concentrations by integrating attention mechanisms and graph neural networks. Results demonstrate that the OR-SpecMet model, which utilizes raw spectral data, achieves optimal prediction performance, significantly surpassing traditional machine learning methods such as multiple linear regression, partial least squares regression, and support vector machine regression in estimating concentrations of lead (Pb), copper (Cu), cadmium (Cd), and mercury (Hg). Moreover, training specialized OR-SpecMet models for individual heavy metals better accommodates their unique spectral-concentration relationships, enhancing overall estimation accuracy while achieving a 20.3 % improvement in predicting low-concentration mercury. The OR-SpecMet method showcases the superior performance and extensive application potential of deep learning techniques in precise soil heavy metal pollution monitoring, offering new insights and reliable technical support for soil pollution prevention and agricultural ecosystem protection. The code and datasets used in this study are publicly available at: https://github.com/zhang2lei/metal.git.

An innovative and exceptionally responsive optical sensing device engineered to selectively identify Bi(III) ions in water-based solutions. The sensing component, 5-(2′,4′-dimethylphenylazo)-6-hydroxypyrimidine-2,4-dione (DMPAHPD), is incorporated into a plasticized polyvinyl chloride (PVC) membrane. The sensor demonstrates an exceptional selectivity for Bi(III) within a broad dynamic range spanning from 7.5 × 10−10 to 4.2 × 10−5 M at pH 2.25. Notably, it achieves lower quantification and detection limits of 7.25 × 10−10 and 2.15 × 10−10 M, respectively. The optode membrane’s response to Bi(III) proves to be entirely reversible, demonstrating remarkable selectivity for Bi(III) ions over a diverse range of other cations and anions. The sensor exhibits favorable performance characteristics, including good reversibility, a wide dynamic range, a prolonged lifespan, sustained response stability over the long term, and high reproducibility. This visual chemical sensor exhibits potential for real-world usage, offering consistent outcomes when assessing Bi(III) levels in matrices such as water, soil, plants, biological and synthetic mixtures. Importantly, the sensor’s performance is comparable to corresponding data achieved from inductively coupled plasma atomic emission spectroscopy (ICP-AES).

Assessment of cytogenetic homeostasis of indicator animals is of great importance in ecological monitoring. The simplest method of its study is micronucleus analysis. Animals with nuclear erythrocytes are often used as indicator animals. In addition to the micronuclei usually recorded, a wide range of cytological nuclear and cellular abnormalities (cytomic analysis) is encountered when assessing the spontaneous level and under the influence of anthropogenic factors. Spontaneous frequency of cytogenetic disorders in 36 species of fish, amphibians, reptiles and birds was studied. Ecological monitoring of territories of Kazakhstan with different types of pollution (radiation, petrochemical, pesticide, heavy metals, due to rocket and space activities) was carried out with the help of separate species of animals. The results of the study include comparative descriptions, schematics and microphotographs clearly demonstrating a wide range of cytological anomalies of nuclear erythrocytes of animals of different classes. The greatest spectrum of nuclear anomalies in the studied animals was registered at petrochemical and pesticide contamination of territories. Depending on the tasks and climatic-geographical conditions, all investigated species can be used as bioindicators. Testudo horsfieldii is an exception for desert regions due to high spontaneous micronuclei level in this species. A review of the names of the main nuclear anomalies is carried out and variants of its ordering are proposed.

Sensing trivalent chromium ion (Cr(III)) is widely applied in different areas, such as clinical analysis, marine, environmental monitoring, or even chemical industry applications. Cr(III) has a significant role in the physiological process of human life. It is classified as an essential micronutrient for living organisms. Herein, we developed and designed a novel optical Cr(III) ions sensor film. The investigated sensor has a relatively small dynamic range of 1.24 × 10−3 to 0.5 μM. We report a highly sensitive optical sensor film for Cr(III) ions based on diethyl 3,4-diaminothieno[2,3-b]thiophene-2,5-dicarboxylate (3D) probe. The optical characteristics of the chemical probe exhibit substantial emission at 460 nm under 354 nm excitation. Besides, the interaction of the Cr(III) ions with 3D involves a complex formation with a 2:1 (metal: ligand) ratio, which is convoyed by the main peak enhancement that centered at 460 nm of 3D, and the main peak is red-shifted to 480 nm. The easily discernible fluorescence enhancement effect is a defining characteristic of the complexation reaction between the 3D probe and Cr(III). On the basis of the substantial fluorescence mechanism caused by the formation of a (Cr(III)-3D complex, which inhibits the photo-induced electron transfer (PET) process, the devised optical sensor was proposed. This film exhibits exceptional sensitivity and selectivity due to its notable fluorescence properties, stock shift of less than 106 nm, and detection capabilities at a significantly low detection limit of 0.37 × 10−3 μM. The detection procedure is executed by utilizing a physiological pH medium (pH = 7.4) with a relative standard deviation RSDr (1 %, n = 3). In addition, the 3D sensor demonstrates a high degree of affinity for Cr(III), as determined by the calculation of its binding constant to be 1.40 × 106. We present an impressive optical sensor that is constructed upon a three-dimensional molecule.

Herein, we reported the dual functions of molybdenum disulfide/sulfur-doped graphitic carbon nitride (MoS2/SGCN) composite as a sensing material for electrochemical detection of 4-NP and a catalyst for 4-NP degradation. The MoS2 nanosheet, sulfur-doped graphitic carbon nitride (SGCN) and MoS2/SGCN were characterized using field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) spectroscopy and X-ray photoelectron spectroscopy (XPS). Electrochemical characterization of these materials with electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) in 1 mM K4[Fe(CN)6]3-/4- show that the composite has the lowest charge transfer resistance and the best electrocatalytic activity. The limit of detection (LOD) and the linear range of 4-nitrophenol at MoS2/SGCN modified glassy carbon electrode (MoS2/SGCN/GCE) were computed as 12.8 nM and 0.1 – 2.6 μM, respectively. Also, the percentage recoveries of 4-NP in spiked tap water samples ranged from 97.8 – 99.1 %. The electroanalysis of 4-NP in the presence of notable interferons shows that the proposed electrochemical sensor features outstanding selectivity toward 4-NP. Additionally, the results of the catalytic degradation of 4-NP at MoS2/SGCN show that the nanocatalyst catalyzed the transformation of 4-NP to 4-aminophenol (4-AP) with a first-order rate constant (k) estimated to be 4.2 ×10−2 s−1. The results of this study confirm that the MoS2/SGCN nanocatalyst is a useful implement for electroanalytical monitoring and catalytic degradation of the hazardous 4-NP in water samples.

The application of Artificial Intelligence (AI) in environmental monitoring offers accurate disaster forecasts, pollution source detection, and comprehensive air and water quality monitoring. This article provides an overview of the value of environmental monitoring, the challenges of conventional methods, and potential AI-based solutions. Several significant AI applications in environmental monitoring are highlighted, showcasing their contributions to effective environmental management. AI technologies enhance environmental monitoring by enabling better understanding, prediction, and mitigation of environmental risks. However, realizing the full potential of AI faces hurdles such as a shortage of specialized AI experts in the environmental sector and challenges related to data access, control, and privacy. These issues are more pronounced in regions with developing technological infrastructure. The paper advocates for proactive data governance measures by governments to protect sensitive information. Despite these challenges, the future of AI in environmental monitoring remains promising, with advancements in AI algorithms, data collection techniques, and computing power expected to further improve accuracy and efficiency in pollution monitoring and management.

In situ measurements and laboratory analysis considering i) marine water quality measurements, ii) Physical, chemical, and heavy metals characteristics of desalination brine, and iii) Conductivity- Temperature-Depth (CTD) profiles, were conducted. For marine water quality, the data collection aimed to evaluate the distribution of salinity, temperature, and heavy metals both at the surface and close to the seabed and other water quality parameters at specific locations. For salinity and temperature, a salinity tester model HQ40d was used. Moreover, marine water and brine samples were collected and analyzed in a laboratory for multiple parameters, including heavy metals such as Cr and Cu, and the alkaline metal ion Mg. The data indicated that greater salinity levels are at the bottom and closer to the brine discharge point. As for temperature, lower values are prevalent near the bottom, while higher values are present at the water surface. Last, Conductivity–Temperature–Depth (CTD) profiles showed variation along the water column in salinity and temperature, especially close to the brine discharge. This dataset can potentially be useful for researchers, policymakers, and locals to raise awareness through data, modeling, and solutions for the water quality conditions under a brine and wastewater discharge on an island.

In this study, Sediment Microbial Fuel Cells (SMFCs) prototypes have been developed to operate under open-air conditions and power sensors for environmental monitoring. Two SMFCs with a volume of 50 l each, consisting of two types of anodic materials – graphite and coke, were operated on-field for over a year. The electrical outputs have been recorded and compared with the measured environmental parameters such as temperature, light illumination, atmospheric pressure, humidity, etc. The statistical analysis of the obtained data shows that temperature changes between 0 and 14 °C do not affect the power achieved. On the contrary, the sunlight irradiation showed a second-order polynomial correlation with the current generated by the SMFCs, increasing the latter during the days. The cathode reactions significantly impacted the power density achieved by both explored SMFCs and the system's sustainability. The metallurgical coke is suggested to be used as an inexpensive and convenient anode material for SMFCs giving compatible results to the widely used graphite.

The convergence of Artificial Intelligence (AI) and nanotechnology is a transformative frontier, holding vast potential for scientific and technological advancements. This review explores the integration of AI and nanotechnology, aiming to uncover current trends, challenges, and future directions across various domains. A scientometric approach was employed, gathering and analyzing existing literature and research in the fields of AI and nanotechnology. Through a systematic examination of recent studies and developments, we identified key trends, challenges, and potential pathways for future research and applications. Findings from this review highlight the significant impact of AI-driven nanotechnology in materials discovery and design, healthcare, environmental monitoring, energy conversion, and quantum computing. These areas have witnessed remarkable advancements, such as the development of novel nanomaterials, precise drug delivery systems, advanced nanosensors, and efficient energy storage solutions. However, challenges persist, including insufficient interdisciplinary collaboration, data quality, ethical concerns, and regulatory frameworks. While challenges require careful consideration and mitigation, the potential for innovation and progress in diverse domains is undeniable. This review underscores the importance of responsible AI use and ongoing research to unlock the full potential of this convergence, ultimately shaping the future of science and technology.

Bees play a crucial role in terrestrial ecosystems. However, beekeepers are unable to monitor the state of beehives (bees and environment) all the time, which often results in bees escaping or even dying. Currently, some researchers provided the scheme of intelligent beehive monitoring system equipped with the Internet of Things (IoT), There remain two challenges: accurately monitor the environmental status around the hive and accurately track and monitor bees in real time. With the development of the IoT and computer vision algorithms, we hope to provide an automated and efficient system to meet the above challenges. In this paper, we proposed a hive monitoring system, and build a visualization module in the cloud to monitor the activity of bee colonies and the environmental dynamic changes. (1) We proposed a multi-bee tracking algorithm to solve the problem of monitoring bees at the door of the hive; (2) we constructed a dataset containing various complex scenes, named BEE22, for training and testing the performance of our algorithm; (3) we designed a bee counting rule, based on results of multi-bee tracking algorithm, to reasonably count the bees entering or leaving the beehive; (4) we have deployed multiple sensors around(center, margin, door, and environment) the hive to accurately reflect the changes in the environment around the hive. Experimental results demonstrate the effectiveness and excellence of our system. In particular, the tracking performance of the multi-bee tracking algorithm reaches 83.5 % ± 0.7 % Multiple Object Tracking Accuracy (MOTA) and 77.3 %±0.2 % Multiple Object Tracking Precision (MOTP), speeds up to 16 frames per second, compared with other algorithms, MOTA and Identity F1 Score (IDF1) are improved by 5.4 % and 8.2 % respectively. Moreover, our counting algorithm also achieved excellent results, with root mean square error (RMSE) of 1.3 ± 0.1, 0.2 ± 0.0, and 1.6 ± 0.1 in counting the number of bees current, entry, and out scene in an episode, respectively. After that, the system will be deployed and monitored for a long time in the actual scenario, it was found that the activity of bees decreased significantly under heavy rainfall conditions. Additionally, the activity of the bee colony will also increase accordingly, when the amplitude is 500 dB to 2000 dB, the temperature of the center of the beehive is 25 °C to 37 °C, and the humidity is 48 % to 67 %. In summary, our system can provide valuable information for bee farmers to make control decisions on hives.

The objective of this study was to evaluate the levels of desethylatrazine (DEA), a hydrophilic metabolite of atrazine, and its impact on plant health. This was achieved by utilizing multispectral imagery captured by Unmanned Aerial Vehicles (UAVs) in combination with ground-measured data to assess photosynthetic pigment levels in Green Cos lettuce following atrazine application in agricultural soil. Strong correlations were found between DEA levels and chlorophyll a, chlorophyll b, and anthocyanin levels in lettuce (R² > 0.70), while the correlation with carotenoid levels was weaker (R² = 0.55). This disruption to the pigments could interfere with photosynthesis, potentially hindering the plant's growth and development, and ultimately leading to a reduction in yield. The Anthocyanin Reflectance Index (ARI) demonstrated a robust positive correlation with DEA, whereas the Normalized Difference Red Edge (NDRE), Leaf Chlorophyll Index (LCI), and Normalized Difference Vegetation Index (NDVI) displayed pronounced negative correlations. Incorporating ARI, LCI, and NDRE, with or without NDVI, provided the most accurate prediction of DEA levels, with an R² exceeding 0.96. NDRE emerged as the most efficient index for forecasting chlorophyll a and chlorophyll b levels. Modified Chlorophyll Absorption in Reflectance Index (MCARI) demonstrated the best fit for carotenoids, while ARI performed exceptionally well in describing actual measurements of anthocyanins (R² = 0.90). The best-performing VI models, developed from the selection of effective single variables, exhibited the best fit to actual pigment measurements (R² > 0.83). These findings underscore the role of UAV-derived multispectral imagery in assessing DEA levels and improving environmental monitoring, aiding in better planning for agriculture and environmental remediation to enhance ecosystem health and resilience.

We searched for an extraction method that would allow a precise quantification of metal(loid)s in milligram-size samples using high-resolution graphite furnace atomic absorption spectrometry (HR-GFAAS). We digested biological (DORM-4, DOLT-5 and TORT-3) and sediment (MESS-4) certified reference materials (CRMs) using nitric acid in a drying oven, aqua regia in a drying oven, or nitric acid in a microwave. In addition, we digested MESS-4 using a mixture of nitric and hydrofluoric acids in a drying oven. We also evaluated the effect of sample size (100 and 200 mg) on the extraction efficiency. Nitric acid extraction in a drying oven yielded the greatest recovery rates for all metal(loid)s in all tested CRMs (80.0 %–100.0 %) compared with the other extraction methods tested (67.3 %–99.2 %). In most cases, the sample size did not have a significant effect on the extraction efficiency. Therefore, we conclude that nitric acid digestion in a drying oven is a reliable extraction method for milligram-size samples to quantify metal(loid)s with HR-GFAAS. This validated method could provide substantial benefits to environmental quality monitoring programs by significantly reducing the time and costs required for sample collection, storage, transport and preparation, as well as the amount of hazardous chemicals used during sample extraction and analysis. • Sample digestion with nitric acid in a drying oven yielded the greatest recovery rates of metal(loid)s from biological and sediment certified reference materials. • The recovery rates of metal(loid)s from biological and sediment certified reference materials using nitric acid digestion in a drying oven ranged from 73 % to 100 %. • Digestion with nitric acid in a drying oven is a simple and reliable method to extract small size environmental samples for metal(loid)s quantification by high-resolution graphite furnace atomic absorption spectrometry.

This study aims to construct a comprehensive evaluation model for efficiently assessing appropriate technologies within green buildings. Initially, an Internet of Things (IoT)-based environmental monitoring system is devised and implemented to collect real-time environmental parameters both inside and outside the building. To evaluate the technical suitability of green buildings, this study employs a multifaceted approach encompassing various criteria, including energy efficiency, environmental impact, economic benefits, user comfort, and sustainability. Specifically, it involves real-time monitoring of environmental parameters, analysis of energy consumption data, and indoor environmental quality indicators derived from user satisfaction surveys. Subsequently, a Multi-Layer Perceptron (MLP) is selected as a conventional artificial neural network (ANN) model, while a Long Short-Term Memory (LSTM) model is chosen as an advanced recurrent neural network model in the realm of deep learning. These models are utilized to process and explore the collected data and assess the technical suitability of green buildings. The dataset comprises physical quantities such as temperature, humidity, and light intensity, as well as economic indicators including energy efficiency and building operating costs. Furthermore, the assessment process considers the building's life cycle assessment and indoor environmental quality factors such as health, comfort, and safety. By incorporating these comprehensive criteria, a holistic evaluation of green building technologies is achieved, ensuring the selected technologies' suitability and effectiveness. The model prediction results demonstrate that the proposed hybrid evaluation model exhibits high accuracy and robust stability in predicting building environmental parameters. For instance, the Root Mean Square Error (RMSE) for temperature prediction is 1.2 °C, the Mean Absolute Error (MAE) is 0.9 °C, and the determination coefficient (R2) reaches 0.95. Similarly, for humidity prediction, the RMSE, MAE, and R2 are 3.5 %, 2.8 %, and 0.88. Compared to the traditional MLP and LSTM models alone, the proposed hybrid model shows significant improvements in predicting building energy consumption, with approximately 15 % and 12 % reductions in RMSE and MAE, respectively, and an increase in R2 values of approximately 7 percentage points. These findings indicate that by amalgamation of the IoT and ANNs, this study successfully establishes a comprehensive model for accurately assessing technologies suitable for green buildings. This approach offers a novel perspective and methodology for the design and evaluation of green buildings.

Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method.

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy.An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations.Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

Summary: Microorganisms are critical to the stability of aquatic environments, and understanding the ecological mechanisms of microbial community is essential. However, the distinctions and linkages across biogeographic patterns, ecological processes, and formation mechanisms of microbes in rivers and lakes remain unknown. Accordingly, microbiome-centric analysis was conducted in rivers and lakes in the Yangtze River watershed. Results revealed significant differences in the structure and diversity of microbial communities between rivers and lakes, with rivers showing higher diversity. Lakes exhibited lower community stability, despite higher species interactions. Although deterministic processes dominated microbial community assembly both in rivers and lakes, higher stochastic processes of rare and abundant taxa exhibited in rivers. Spatial factors influenced river microbial community, while environmental factors drove differences in the lake bacterial community. This study deepened the understanding of microbial biogeography and formation mechanisms in large watershed rivers and lakes, highlighting distinct community aggregation patterns between river and lake microorganisms.

This study conducted in the Kyungpook National University Eco-friendly Agriculture Research Centre between 2022 and 2023 investigates the environmental implications of fence-type solar photovoltaic (PV) systems in diverse agricultural settings. Despite the increasing adoption of solar energy for climate change mitigation, there is a noticeable gap in research regarding the potential environmental impact of these specific PV systems. Focusing on heavy metal concentrations, including Cadmium (Cd), Copper (Cu), Arsenic (As), Mercury (Hg), Lead (Pb), Hexavalent Chromium (Cr+6), Zinc (Zn), and Nickel (Ni), across distinct fields, the study reveals significant fluctuations. Notably, the Rice Field experienced a substantial increase in Cd levels from 0.47 mg/kg in 2022 to 1.55 mg/kg in 2023, while Cu and Pb concentrations decreased to acceptable levels in 2023. The findings underscore the dynamic nature of heavy metal concentrations, emphasizing the importance of continuous soil quality monitoring to prevent contamination. This research provides valuable insights into the impact of fence-type solar PV system installations on agricultural soil quality, emphasizing the urgent need to secure these ecosystems through vigilant monitoring and environmental management practices.

In recent years, electronic noses, or more generally Instrumental Odor Monitoring Systems (IOMS), have aroused increasing interest in the field of environmental monitoring.One of the most interesting applications of these instruments is the real-time estimation of the odor concentration at plant fencelines to continuously monitor odor emissions and identify anomalous conditions. In this type of application, it is possible to setting a “warning” threshold, enabling the continuous check of proper functioning of the plant and sudden intervention in case of malfunctions, preventing, at the same time, the risk of odor events at the receptors. For this purpose, it is necessary to provide a continuous, fast and reliable measurement of the odor concentration, which is nowadays one of the main challenges of this technology.In this context, this work proposes the development of a quantification model for quantifying odors detected at the fenceline of a landfill characterized by very different odor fingerprints. A double-step quantification model, firstly identifying the different odor classes to which the ambient air monitored at the fenceline by the IOMS belong to, and then developing different specific PLS regression models for each of the odor classes identified, was developed. The results of the proposed quantification model were compared to the ones obtained developing a “global” quantification model, which implements the regression on the globality of the training set, without differentiating between the odor classes. Then, they were further evaluated by comparison with the odor events detected at the sensitive receptor by another electronic nose. Moreover, the combined evaluation of the odor events at the plant fenceline and the receptor, respectively, together with the meteorological data highlighted the need of identifying variable warning thresholds for the odor concentrations at the fenceline according to effectively account for meteorological conditions and produce an output that is more correlated with the probability that an odor is perceived outside of the plant.

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).

Antineoplastic drugs (ADs) are primarily used to manage cancer and are becoming more prevalent as cancer cases increase globally. A primary route of exposure for workers administering these hazardous drugs is via dermal absorption from contact with contaminated surfaces. To assess the risk in a workplace, wipe sampling is used to inspect and assess suspect workplace surfaces (i.e., as a form of worker exposure and environmental monitoring). Despite widespread use of ADs, there are no standards or proficiency testing programs (at this time) for surface wipe sampling protocols specifically for residues that contain ADs. Current literature provides many different analytical methods developed by research groups for detecting one or more ADs in residue found in the workplace. These studies contain significant variability in the techniques and materials used and, therefore, also vary in their outcomes. This review highlights the variability observed in the results obtained from current methods and points to opportunities that might assist in addressing these inconsistencies towards preparing standard methodologies for wipe sampling of ADs. This review also discusses critical factors to consider when optimizing the steps performed for surface wipe sampling. The inter-dependent steps discussed in this review for surface wipe sampling are: (i) adsorption of analyte onto a wipe; (ii) desorption of the analyte from the wipe; and (iii) detection of the sampled analyte. The first two steps require optimization of both chemical and physical factors to create a successful sampling method. The detection step has largely been optimized due in part to the sensitivity of analytical instrumentation, but there remain opportunities to develop more effective methodologies for timely feedback and an increased sensitivity to platinum-based ADs. This review also provides additional recommendations to improve reporting of results from the wipe sampling of ADs and highlights the need for additional research on the occupational and surface exposure limits for ADs.

One of the most fascinating and important research areas in recent years is supply chain risk management (SCRM), which helps to boost customer support, high-profit revenues, and organizational growth. The main goal of this effort is to carry out a thorough case study on the top 5 businesses that use the SCRM model globally. The following reasons are why organizations use the SCRM model: risk identification and assessment, risk analysis, environmental monitoring, and risk management. The organization's SCRM is more complicated and constantly changing, and numerous teams, including those for quality, resilience, security (both cyber and physical), compliance, and sustainability, support its implementation. Additionally, a system based on Artificial Intelligence (AI) and Adaptive Logistic Regression Classifier (ALRC) is employed to determine the right choices for risk identification. They calculated the supply base throughout the risk assessment process using the intrinsic risk factors of the production, social, and environmental processes. Here, the effectiveness of organizations is evaluated in light of their success rate, resilience, and value to their customers.

The paper focuses on environmental monitoring in European regions using the Sustainable Development Goals (SDG) framework. Adopted in 2015, the United Nations SDG are a set of global goals developed to guide countries towards sustainable development. Although designed for the national level, it is growingly argued that achieving the SDG requires action at the sub-national, local level. With this in mind, in this paper, we use indicators co-designed by researchers and SDG practitioners from European regional administrations to monitor the environmental status of selected European regions over a period of time. We consider environmental monitoring a domain that encompasses activities beyond the monitoring and reducing CO2 emissions alone and widely regards biodiversity and conservation, ecosystems, climate change, environmental pollution and human ecology. By default, this touches upon different sectors of society (from energy to mobility, and production to consumption), and the latter is also reflected in the SDG and their targets. To this end, we isolate indicators that directly and indirectly address the environment as a domain to highlight areas where there is a need for improvement. Finally, the paper provides insights into how the SDG framework can be used to monitor environmental progress at the regional level in Europe and how the current policy framework might also contribute to this progress.

Validated alternative test methodologies may be used in place of culture-based methods recommended for environmental monitoring programs (EMPs) for Listeria in food production facilities. In order to help guide decisions on which testing method to use to simplify Listeria EMP implementation in food production facilities, alternative methods were compared to the culture-based method in actual EMPs for Listeria. Seventy-two samples collected from two facilities of souzai production businesses that use meat and meat products as ingredients, one facility of processed meat product production business, and one facility of processed meat product and souzai production business were applied to EMPs for Listeria using the culture-based method, 3MTM Molecular Detection System (MDS), and InSite L. mono Glo (InSite). The kappa coefficient in MDS was 0.65 for Listeria monocytogenes and 0.74 for Listeria spp., both of which were deemed substantial compared with the culture-based method. The kappa coefficient in InSite was −0.01 for L. monocytogenes and 0.50 for Listeria spp., which indicated poor and moderate reproducibility, respectively. When the medium of InSite was smeared on agar medium, 7 of the 19 samples tested positive only for Listeria spp. (negative for L. monocytogenes) but L. monocytogenes was cultured, indicating that the sensitivity of detecting L. monocytogenes via fluorescence may be low. MDS was considered a useful alternative for both L. monocytogenes and Listeria spp. as targets, and InSite was not possible as a substitute for detecting L. monocytogenes; however, it is considered a helpful alternative method for detecting Listeria spp. EMPs for Listeria often target Listeria spp. as an indicator of L. monocytogenes. The alternative methods studied in this study are rapid, simple, and useful in EMPs for Listeria. However, the data in this study were a comparatively small sample set and impacted by variability, so more robust comparisons are desirable in the future.

It is well known that rodenticides are widely used, and there are multiple routes by which they can reach non-target wildlife species. Specifically, in the Canary Islands, a high and concerning incidence of these compounds has been reported. However, in this scenario, reptiles remain one of the least studied taxa, despite their potential suitability as indicators of the food chain and environmental pollution has been noted on several occasions. In this context, the California Kingsnake (Lampropeltis Californiae), widely distributed on the island of Gran Canaria, occupies a medium trophic level and exhibits feeding habits that expose it to these pollutants, could be studied as a potential sentinel of exposure to these compounds. For this reason, 360 snake livers were analyzed by LC-MS/MS. Similarly, 110 livers of birds of prey were sampled. Thus, we present the analysis of 10 anticoagulant rodenticides (warfarin, diphacinone, chlorophacinone, coumachlor, coumatetralyl, brodifacoum, bromadiolone, difethialone, difenacoum and flocoumafen) in both data series; snakes, and raptors. Furthermore, this dataset includes biological data (weight, length, sex, colour, and design pattern), geographic data (distribution area and municipalities) and necropsy findings that could be of interest for a better understanding of this snake species and for future studies.

Whole genome sequencing (WGS) is a powerful tool that may be used to assist in identifying Listeria contamination sources and movement within environments, and to assess persistence. This study investigated sites in a produce packinghouse where Listeria had been historically isolated; and aimed to characterize dispersal patterns and identify cases of transient and resident Listeria. Environmental swab samples (n = 402) were collected from 67 sites at two time-points on three separate visits. Each sample was tested for Listeria, and Listeria isolates were characterized by partial sigB sequencing to determine species and allelic type (AT). Representative isolates from the three most common L. monocytogenes ATs (n = 79) were further characterized by WGS. Of the 144 Listeria species positive samples (35.8%), L. monocytogenes was the most prevalent species. L. monocytogenes was often coisolated with another species of Listeria. WGS identified cases of sporadic and continued reintroduction of L. monocytogenes from the cold storages into the packinghouse and demonstrated cases of L. monocytogenes persistence over 2 years in cold storages, drains, and on a forklift. Nine distinct clusters were found in this study. Two clusters showed evidence of persistence. Isolates in these two clusters (N = 11, with one historical isolate) were obtained predominantly and over multiple samplings from cold storages, with sporadic movement to sites in the packing area, suggesting residence in cold storages with opportunistic dispersal within the packinghouse. The other seven clusters demonstrated evidence of transient Listeria, as isolation was sporadic over time and space during the packing season. Our data provide important insights into likely L. monocytogenes harborage points and transfer in a packinghouse, which is key to root cause analysis. While results support Listeria spp. as a suitable indicator organism for environmental monitoring surveys, findings were unable to establish a specific species as an index organism for L. monocytogenes. Findings also suggest long-term persistence with substantial SNP diversification, which may assist in identifying potential contamination sources and implementing control measures.

The steady conditions of temperature, humidity and air flux within beehives make them a valuable location for conducting environmental monitoring of pollutants such as PAHs. In this context, the selection of an appropriate apicultural matrix plays a key role in these monitoring studies, as it maximizes the information that will be obtained in the analyses while minimizing the inaccurate results. In the present study, three apicultural matrices (honey bees, pollen and propolis) and two passive samplers (APIStrips and silicone wristbands) are compared in terms of the number and total load of PAHs detected in them. Samplings took place in a total of 11 apiaries scattered in Austria, Denmark, and Greece, with analyses performed by GC-MS/MS. Up to 14 different PAHs were identified in silicone wristbands and pollen, whereas the remaining matrices contained a maximum of five contaminants. Naphthalene, 1-methylnaphthalene, 2-methylnaphthalene, and pyrene were found to be the most prevalent substances in the environment. Recovery studies were also performed; these suggested that the chemical structure of APIStrips is likely to produce very strong interactions with PAHs, thus hindering the adequate desorption of these substances from their surface. Overall, silicone wristbands placed inside the beehives proved the most suitable matrix for PAH monitoring through honey bee colonies.

Portable biosensors are emerged as powerful diagnostic tools for analyzing intricately complex biological samples. These biosensors offer sensitive detection capabilities by utilizing biomolecules such as proteins, nucleic acids, microbes or microbial products, antibodies, and enzymes. Their speed, accuracy, stability, specificity, and low cost make them indispensable in forensic investigations and criminal cases. Notably, portable biosensors have been developed to rapidly detect toxins, poisons, body fluids, and explosives; they have proven invaluable in forensic examinations of suspected samples, generating efficient results that enable effective and fair trials. One of the key advantages of portable biosensors is their ability to provide sensitive and non-destructive detection of forensic samples without requiring extensive sample preparation, thereby reducing the possibility of false results. This comprehensive review provides an overview of the current advancements in portable biosensors for the detection of sensitive materials, highlighting their significance in advancing investigations and enhancing sensitive sample detection capabilities.

In the context of sustainable applications, this research investigates using artificial intelligence (AI) in interactive visualization for hyperspectral pictures. Detailed spectrum data about the Earth's surface is provided through hyperspectral imaging, allowing for the monitoring and analyzing several phenomena about agriculture, land use, and environmental sustainability. However, processing, analyzing, and interpreting the massive data produced by hyperspectral sensors is challenging. AI methods and interactive visualization provide practical tools for deriving useful information from hyperspectral data and assisting in decision-making for environmentally friendly applications. This study examines the key elements of an interactive visualization framework powered by AI and emphasizes the advantages and implications for sustainable agricultural operations. This sector's difficulties and potential possibilities are also discussed, focusing on the need for data processing optimization, technology integration, user-friendly interfaces, and ethical issues. In general, interactive hyperspectral image visualization powered by AI shows potential for improving sustainability in agriculture and other related fields.

The lack of adequate and good water quality is one of the serious issues that most developing countries are facing, yet environmental monitoring programs are not efficiently protecting ecosystem health. Here, we aimed to assess the suitability of different water quality assessment frameworks in order to define the most suitable method, especially for data-scarce environments, to quantify river ecosystem health. The study was conducted in the largest north-western river of Cambodia, the Sangker River in the Tonle Sap Lake (TSL) basin (UNESCO Biosphere Reserve). Five water quality assessment methods were selected for the study: Basic & Single Prati indices, Mekong River Commission water quality index (MRC-WQI), French water quality assessment system (SEQ-EAU) and US Environmental Protection Agency (US-EPA) framework. Overall, a physico-chemical gradient of water quality was observed along the river course. The river head was defined by high levels of dissolved oxygen (DO), low nutrients and chloride, while the middle course was marked by a gradual decrease of DO and increased concentration of nutrients up to the TSL floodplains. This degradation was explained by multiple impacts from human activities, e.g. domestic wastes, livestock, crop farming, causing simultaneous eutrophication and oxygen depletion along the river course. Despite those multiple pressures, the Sangker river health status was scored as fairly good reflecting the current situation of the river. So far, we found a convergence of the five water quality assessment methods; however, SEQ-EAU and Basic Prati seemed to be less suitable for tropical river health assessment. The study defined the US-EPA assessment method as the most suitable technique to quantify the Sangker river health. This method could potentially be used to monitor river ecosystem health in data-scarce environments. This would help overcome environmental monitoring challenges in developing countries faced by rapid global environmental changes.

Open biomass burning (OBB) in agriculture presents a significant and well-documented challenge, posing severe consequences for both environmental and human health. OBB releases air pollutants that degrade air quality and contribute to climate change, leading to premature deaths in regions with high concentrations of open crop straw burning (OCSB) emissions. Although policies aimed at prohibiting OBB are in place, the efficacy of these regulations in mitigating OCSB emissions remains ambiguous. Consequently, early prevention and monitoring of open biomass combustion are imperative for environmental preservation. Traditional monitoring techniques, reliant on fixed-position cameras, are constrained by their location and monitoring intensity, making concealed fire recognition a complex problem. To address this limitation and monitor the human living environment more flexibly and accurately, we propose a new method to identify straw fires in UAV Aerial Image Using CNN Branch Reinforce Transformer which named BranTNet, enabling early detection and rapid response to crop straw fires. By integrating computer vision technology and deep learning algorithms, straw fires in UAV-acquired aerial survey images can be detected and categorized. In the realm of artificial intelligence algorithms, we skillfully merge convolution and attention mechanisms, harnessing the full potential of both methodologies. Moreover, we seamlessly incorporate transfer learning, skillfully unifying self-training convolution modules with pre-trained transformer modules. This strategic amalgamation not only minimizes time costs but also ensures optimal experimental outcomes. Regarding data, we meticulously collected a substantial number of authentic samples, ensuring the sufficiency of our experimental dataset. The experimental results demonstrate that our proposed method exhibits exceptional accuracy and robustness in detecting and identifying straw fires in UAV aerial survey images. Our approach outperforms the use of convolution or attention mechanisms alone. By integrating this approach with drone technology, we unlock the potential for developing more versatile and precise monitoring solutions, expanding the application of drones to diverse domains. This progress contributes significantly to the early detection and prevention of crop straw fires, fundamentally reducing environmental pollution, curbing carbon emissions, and advancing the cause of carbon neutrality. This innovative technique for monitoring and preventing OBB holds substantial promise in mitigating the adverse effects of OBB on the environment and human health.

Spatial interpolation has been applied for mapping various variables in a wide range of environmental disciplines. This study aims to develop novel tools for examining the relative performance of different interpolation methods. We shall quantify and compare the quality of interpolation models by applying, among others, some inequality indices of error distributions. Such indices can generally be classified as non-dimensional and global. The performance measures explored here provide a valuable supplement to the conventional accuracy assessment, and have so far received only scant attention in the relevant literature. Given a wide range of potential applications for the methods discussed here, the main focus of the paper will be on empirical research concerning variability of soil properties. The eight interpolation methods, i.e., Inverse Distance Weighting (IDW), Modified Shepard’s Method (MS), Radial Basis Function (RBF), Natural Neighbour (NaN), Nearest Neighbour (NeN), Triangulation with Linear Interpolation (TIN), Local Polynomial (LP) and Ordinary Kriging (OK) were applied to estimate spatial distribution of soil pH, nitrogen, potassium and phosphorus content. Biplot methods were applied to visually examine the numerical results on the assessment of prediction quality. The ordinary kriging showed superior performance compared to the competing methods in majority of the cases. Significantly, predictions by kriging approaches revealed substantial improvement by considering data transformations. As concerns the other tested methods, the IDW and the LP algorithms tend to share similar characteristics. In turn, the NeN, RBF and MS algorithms scored relatively small inequality indices, when compared to the other methods. The use of new proposed measures will enable practitioners to gain more insightful and comprehensive evaluations of spatial interpolation techniques.

Summary: Plastic pollution and climate change are two major environmental focuses. Having the forming potential due to ambient plastic pollution, the environmental fate of microplastics shall be inevitably impacted by global warming. This manuscript discusses the destiny of environmental microplastics and characterizes their fate considering the framework of the planetary boundary. The major routes for microplastic discharge include the release of microplastic stored in the ice into the sea when the ice melts as a result of global temperature increase, flushing of the plastic/microplastic debris from the shorelines into the adjacent water bodies as a result of increased rainfall, redistribution of the microplastics away from the source of plastic debris as a result of increased wind, and accumulation of microplastics in the soil as a result of drought. A perspective on the impact of climate change and microplastic pollution on aquatic and soil organisms was discussed as well.

This study investigated the concentrations of 16 polycyclic aromatic hydrocarbons (PAHs) in sediments from the shoreline of the Red Sea. 16 PAH concentrations ranged from 1.43 to 4990.69 µg/g dry weight to 1.64 to 6397.69 µg/g dry weight during spring and autumn, respectively. Most sites with the highest PAH concentrations were (4–6 rings) compounds. The sources of PAHs were analyzed using principal component analysis (PCA) and diagnostic ratios between combinations of PAHs. The results indicated that the pyrogenic source was the primary source of PAHs. The results showed that surface sediment from all sample sites presents low to sporadic toxicity concerns to marine organisms. Total carcinogenicity and mutagenicity of eight priority PAHs ranged from 0.03 to 863.96 and 0.09 to 1197.85 µg/g dry weight, respectively (TEQBaP and MEQBaP). BaP accounted for most of the influence among the eight carcinogenic PAHs and could be employed as a particular PAH toxicity indicator. Using the toxic equivalent quotient (TEQcarc) and incremental lifetime cancer risk (ILCR) to estimate human health risk, it has been determined that sediment contaminated with PAHs in some study area stations may possess the capacity to cause cancer, particularly in children. The ecological risk posed by PAHs was evaluated using a risk quotient (RQ). The RQPAHs estimated for the samples revealed that the study region was at a high-risk level. Anthropogenic hydrocarbon inputs that were more pronounced in locations connected to maritime activity are described in this study. Further monitoring studies encompassing different environmental matrices about PAHs pollution and their toxicological relevance should be conducted.

Water is the source of life and a very important part of life. However, the current water resources security is facing global challenges. Affected by climate change and human factors, how to quickly realize the continuous monitoring of water resources changes is crucial to the sustainable management of water resources. In recent years, the amount of multi-temporal remote sensing data has grown rapidly, which has caused problems such as weak single-computer processing for change detection and difficulty in mass image storage management. Traditional single-computer processing methods can no longer meet the needs of remote sensing big data change detection. It provides cutting-edge methods and means for monitoring large-scale changes in water resources. Therefore, a distributed parallel method for remote sensing image change detection based on cloud computing is proposed. Through this method, the change detection efficiency of massive remote sensing images is improved, and the high-resolution remote sensing image change detection based on fully connected conditional random fields is deeply analyzed. By summarizing the experimental data of this method and comparing with other data, it can be seen that when the parallelism is higher, the advantage of running time is more obvious. The performance improvement is also as high as 89.49%. Therefore, distributed parallel processing through cloud computing can complete the efficient change detection of massive remote sensing images without affecting the accuracy. This helps us to better monitor and monitor water resources at multiple scales.

Summary: Equitable SARS-CoV-2 surveillance in low-resource communities lacking centralized sewers is critical as wastewater-based epidemiology (WBE) progresses. However, large-scale studies on SARS-CoV-2 detection in wastewater from low-and middle-income countries is limited because of economic and technical reasons. In this study, wastewater samples were collected twice a month from 186 urban and rural subdistricts in nine provinces of Thailand mostly having decentralized and non-sewered sanitation infrastructure and analyzed for SARS-CoV-2 RNA variants using allele-specific RT-qPCR. Wastewater SARS-CoV-2 RNA concentration was used to estimate the real-time incidence and time-varying effective reproduction number (Re). Results showed an increase in SARS-CoV-2 RNA concentrations in wastewater from urban and rural areas 14–20 days earlier than infected individuals were officially reported. It also showed that community/food markets were “hot spots” for infected people. This approach offers an opportunity for early detection of transmission surges, allowing preparedness and potentially mitigating significant outbreaks at both spatial and temporal scales.

Continuous systematic monitoring is necessary to measure the success or failure of mineland rehabilitation. Here, we aimed to outline how measures of phylogenetic diversity and phylogenetic community structure complement existing monitoring frameworks along a waste pile rehabilitation chronosequence in Carajás National Forest, Brazil, including primary rainforest as the desired outcome. We pruned the megatree R20160415.new to tree species found in this study and computed the phylogenetic diversity (PD), mean pairwise distance (MPD) among taxa and individuals and their standard effect sizes (ses.PD and ses.MPD) via the 'phylogeny pool' null model. PD and MPD increased with rehabilitation progress. Dominance of Fabales species caused phylogenetic clustering during early rehabilitation stages. This clustering disappeared in older stands and switched to phylogenetic overdispersion at reference sites, indicating that mechanisms impeding the recruitment of species from certain phylogenetic lineages during initial stages, such as environmental filtering, tend to decrease with rehabilitation advancement. Furthermore, this pattern indicates the recovery of coexistence-stabilizing interactions with rehabilitation time, even if a constrained number of species was used to launch rehabilitation. Positive associations between all phylogenetic variables and previously determined measures of environmental quality indicate the importance of community assembly processes and niche diversification for ecosystem performance in rehabilitating minelands.

We determined the levels of 52 organic pesticides (OPs), including 17 organochlorine pesticides (OCPs), 14 organophosphate pesticides (OPPs), 8 organic nitrogen pesticides (ONPs), and 13 pyrethroids (PYRs) for four types of contour feathers (breast, tail, back, and wings) from three species of migratory birds overwintering in the Tibetan Plateau. The studied species included the Pacific golden plover (Pluvialis fulva), a long-distance migratory omnivore; common snipe (Gallinago gallinago), a mid-distance migratory omnivore; and bar-headed goose (Anser indicus), a short-distance migratory herbivore. The primary and emerging pollutants included malaoxon and paraoxon from the OPP group, and alachlor and metalaxyl from the ONP group, respectively. No significant differences were observed between the levels of OP from different types of feathers, suggesting the suitability of contour feathers for biomonitoring. Significant species-level differences were observed between the levels of OP, with common snipe > Pacific golden plover > bar-headed goose. Pesticide levels were generally higher for feathers from omnivorous species. Additionally, species with longer migration distances tended to have higher pesticide levels for feathers, indicating migratory birds could influence the global transport of pollutants.

The Colour Pattern Regression (CPR) algorithm complement for QGIS is presented for determining and quantifying the relationship between aerial images and raster maps. Aerial images can be readily decomposed into their standard RGB spaces that assign numerical values to their colours. According to them, a linear regression can be established to correlate raster values with the colour patterns, and if the model performance is considered satisfactory, the result will provide a raster interpolation with finest resolution according to colour nuances within the aerial image. The CPR Algorithm allow both, the calculation of the regression coefficient and the evaluation of the goodness of fit of the model. The use of the present QGIS complement could enable the study of the relationships of aerial images and earth surface products – e.g. soil moisture content, landcover, vegetation and forests, soils, urban heat islands – or marine products – e.g. chlorophyll, total suspended solids. The tool is open source and will be readily adapted with additional features and improved general performance ratings thresholds for the physical problems to be solved.

Living benthic foraminifera were investigated in three tourist marinas of the Central Adriatic in order to test the suitability of these organisms for a quick and cost-effective assessment of the ecological quality status (EcoQS). Local high concentrations of biocidal metals (Cu, Zn, Cr, Sn, Ni and Pb) were measured in the sediments nearby the boathouse areas, i.e. where the careening activity takes place, suggesting that the accumulation of current and past antifouling residues is the main source of pollution in these particular maritime spaces. Factors such as the capacity of the marina, its shape as well as the location of the boathouse area concur to the degree of antifouling residues accumulation. Foraminifera responded to the different environmental conditions and to metal contamination in terms of abundance and species composition. At the boathouse stations these organisms were scarcely numerous and no living specimen was observed in the most polluted sediments. Three categories of indices were tested based on: (1) the percentages of abnormal tests, (2) biodiversity and (3) the sensitive-tolerant species occurrence (i.e. Foram-AMBI). The most diverse assemblage was observed at intermediate metal levels and seemed to be influenced by other environmental factors such as the presence of submerged vegetation that likely provides additional resources and increased heterogeneity. Unexpectedly, the station with the lowest metal levels was characterized by the least biodiverse assemblage. Notwithstanding, among the few species observed, at this site the dominant one was the sole species encountered in the present study that is recognized as sensitive, i.e. Ammonia parkinsoniana. Consequently, while the diversity indices provided very low values, Foram-AMBI outputs indicated the presence of an assemblage not exclusively dominated by opportunistic taxa. This result suggests a complementarity of these two categories of indices that should be taken into account for an accurate EcoQS assessment of tourist marinas.

A gas sensor based on light-induced thermo-elastic spectroscopy (LITES) capable to detect methane (C1) and ethane (C2) in a wide concentration range, from percent down to part-per-billion (ppb), is here reported. A novel approach has been implemented, exploiting a compact sensor design that accommodates both a custom 9.8 kHz quartz tuning fork (QTF) used as photodetector and the gas sample in the same housing. The resulting optical pathlength was only 2.5 cm. An interband cascade laser (ICL) with emission wavelength of 3.345 µm was used to target absorption features of C1 and C2. The effects of high concentration analytes on sensor response were firstly investigated. C1 concentration varied from 1% to 10%, while C2 concentration varied from 0.1% to 1%. These ranges were selected to retrace the typical natural gas composition in a 1:10 nitrogen dilution. The LITES sensor was calibrated for both the gas species independently and returned nonlinear but monotonic responses for the two analytes. These univariate calibrations were used to retrieve the composition of C1-C2 binary mixtures with accuracy higher than 98%, without the need for further data analysis. Minimum detection limits of ∼650 ppb and ∼90 ppb were achieved at 10 s of integration time for C1 and C2, respectively, demonstrating the capability of the developed LITES sensor to operate with concentration ranges spanning over 6 orders of magnitude.

Multi-species indicators (MSIs) have been useful tools for reflecting the state of taxa and ecosystems at global, regional, and national levels. However, most indicators are from Europe and North America, and there are few from the world’s major tropical and subtropical biodiversity hotspots, often in large part because of insufficient data availability. We modelled the population trajectories of 107 regularly-occurring breeding bird species in Taiwan (100 native and seven introduced species) and developed MSIs for (i) forest, (ii) farmland, and (iii) introduced bird species based on the Taiwan Breeding Bird Survey dataset between 2011 and 2019. Individual population trajectories for 87 species did not show a significant change, those for 11 species grew significantly, and those for two species declined significantly. All MSIs show significant growth. Based on the phylogenetic generalised least squares (PGLS) results, the forest bird indicator increased somewhat more rapidly than the farmland bird indicator, perhaps reflecting very low rates of deforestation contrasting with more rapid land use change on farmlands in Taiwan. Some of the forest and farmland species, however, showed rapid declines, and most of these atypical decliners were common species or carnivores. Further, the PLGS results show that the introduced species indicator had more rapid growth than native species indicators, posing a potential risk for the integrity of native bird communities in the near future as well as compromising broader ecosystem intactness. Our study provides important information on bird population changes in subtropical Asia. The MSIs will be updated regularly and will be used to provide information to support conservation policies in Taiwan.

The increase in fatal accidents and chronic illnesses caused by hydrogen sulfide (H2S) exposure occurring in various workplaces is pushing the development of sensing systems for continuous and in-field monitoring of this hazardous gas. We report here on the design and realization of a Near-IR quartz-enhanced photoacoustic sensor (QEPAS) for H2S leaks detection. H2S QEPAS signal was measured in matrixes containing up to 1 % of methane (CH4) and nitrogen (N2) which were chosen as the laboratory model environment for leakages from oil and gas wells or various industrial processes where H2S and CH4 can leak simultaneously. An investigation of the influence of CH4 on H2S relaxation and photoacoustic generation was proposed in this work and the sensor performances were carefully assessed with respect to CH4 content in the mixture. We demonstrated the high selectivity, with no cross talk between H2S, H2O and CH4 absorption lines, high sensitivity, and fast response time of the developed sensor, achieving a minimum detection limit (MDL) of 2.5 ppm for H2S with 2 s lock-in integration time. The employed 2.6 µm laser allowed us to employ the sensor also for CH4 detection, achieving an MDL of 85 ppm. The realized QEPAS sensor lends itself to the development of a portable and compact device for industrial monitoring.

Summary: Owing to the impact of the western development of China, there have been signs of air pollution over the Qinghai-Tibet Plateau in recent years. However, monitoring data on atmospheric volatile organic compounds (VOCs) are lacking in plateau areas. Here, VOCs concentrations in urban and background areas in North China and the Qinghai-Tibet Plateau were observed from 2012 to 2014 and 2020 to 2022, respectively. Compared to 2012–2014, the concentration of VOCs increased to 2.5 times in urban areas on the Qinghai-Tibet Plateau, which was equivalent to that in North China. Oil, gas, and solvent evaporation caused by a low atmospheric pressure is the primary factor for the increase in VOCs in plateau areas, and weak VOCs degradation is the secondary factor. Hence, we put forward the VOCs control strategies in plateau areas and point out the defects in the current research.

The Water Framework Directive (WFD) aims to attain ‘good quality’ status for all European water bodies through the achievement of good ecological status. To this purpose, the WFD advocates the creation of cost-effective monitoring tools to deliver appropriate data that help to create links between chemical and ecological indicators, as those from ecotoxicological research. Here, it was evaluated whether the integration of ecotoxicological tools, as bioassays and biomarkers, did (or did not) strengthen the robustness of the assessment of the ecological status obtained through well stablished biotic indices in two Atlantic estuaries. For that, a battery of in-situ bioassays, including five macroinvertebrate species (the crab Carcinus maenas, the amphipod Echinogammarus marinus, the isopod Cyathura carinata, the snail Peringia ulvae and the polychaete Hediste diversicolor, each one providing complementary information regarding key ecological functions) and a set of biomarkers, was used. In addition, the concentrations of heavy metals in sediments, and in macroalgae (Fucus), were determined, along with sediment granulometric, water physicochemical and nutrients characterization. We show that by interpreting the values of all indicators together, along with environmental components, it is possible to perform a more holistic description of the quality status of a waterbody – and, to begin to allude to factors limiting that quality. Ecotoxicological tools (in situ bioassays and biomarkers) appear to provide an added value of useful information for monitoring programmes regarding the true state - within which, both known and unknown contaminants are potentially present at concentrations sufficient to cause biological effects. Our results support the use of in situ bioassays and biomarkers within protocols aimed at fulfilling the goals of the WFD. In doing so, more complete and informative assessments of the ecological quality status of water bodies can take place.

Managers and decision makers need to know if variables measured by environmental monitoring programs are increasing or decreasing, both at individual sites and at larger spatial scales, and the degree of statistical support for these assessments. Traditionally, null hypothesis significance testing (NHST) has been used to evaluate whether an assessed trend is a reliable estimate of the true (i.e., population) trend but has two shortcomings. First, failure to achieve “statistical significance” is often falsely interpreted as evidence that there was no trend. Second, the acceptable Type 1 error risk tends to be chosen arbitrarily and without consideration of the risk of failing to identify important trends. As an alternative to NHST, we propose a continuous measure of confidence in the direction of an individual site trend based on the posterior probability distribution. Confidence that the trend direction is correctly inferred (i.e., that the assessed trend direction has the same sign as the population value) is expressed as a probability. The approach is extended to assessing confidence in the direction of aggregate trends (i.e., trends observed over multiple sites representing a spatial domain such as a geographic region). The aggregate trend assessment accounts for the confidence in the individual site trends and spatial correlation in the observations, which reduces the effective size of the dataset. The approach is demonstrated for site and aggregate river water quality trends for 352 sites in New Zealand. Compact graphical reporting of the results indicated appreciable variation in trend direction between sites for all variables, as well as patterns in trend direction at larger spatial scales. The new method provides decision makers with a more complete description of the statistical support for the assessment of trend direction than an arbitrary “significant/not significant” designation associated with NHST.

We report on a gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) able to detect multiple gas species for environmental monitoring applications, by exploiting a Vernier effect-based quantum cascade laser as the excitation source. The device emission spectrum consists of ten separated emission clusters covering the range from 2100 up to 2250 cm−1. Four clusters were selected to detect the absorption features of carbon monoxide (CO), nitrous oxide (N2O), carbon dioxide (CO2), and water vapor (H2O), respectively. The sensor was calibrated with certified concentrations of CO, N2O and CO2 in a wet nitrogen matrix. The H2O absorption feature was used to monitor the water vapor within the gas line during the calibration. Minimum detection limits of 6 ppb, 7 ppb, and 70 ppm were achieved for CO, N2O and CO2, respectively, at 100 ms of integration time. As proof of concept, the QEPAS sensor was tested by continuously sampling indoor laboratory air and monitoring the analytes concentrations.

Industrialization and the extensive use of chemicals have raised significant concerns about their environmental impacts, particularly on aquatic ecosystems. This study evaluated the sub-lethal effects of Celcron (Cec), an organophosphate insecticide, on the Java barb (Barbonymus gonionotus) through erythrocyte morphology and acetylcholinesterase (AChE) activity, aiming to refine biomarkers for environmental health assessments. We hypothesized that sub-lethal Cec exposure would induce significant erythrocyte abnormalities and decrease AChE activity in Java barb, with variable recovery rates between gill and kidney tissues. To test this, we exposed the juvenile Java barbs to two sub-lethal Cec concentrations - 0.01 ppm (10 % of the LC 50 ) and 0.05 ppm (50 % of the LC 50 ) -for 60 days. After the exposure period, the fish were placed in pesticide-free water to allow for recovery. Results indicated a significant decline in AChE activity in both liver and kidney tissues, with activity levels showing gradual recovery over time. Erythrocyte abnormalities, including nuclear and cellular changes, were significantly elevated in response to Cec exposure. The frequency of nuclear abnormalities such as micronuclei and binucleation increased in a concentration- and duration-dependent manner, with the gill blood exhibiting higher sensitivity and slower recovery compared to kidney blood. Cellular abnormalities such as twin, teardrop and spindle-shaped cells were also more prevalent in Cec-treated fish. Recovery from these abnormalities was observed but varied between gill and kidney blood, with gill blood showing higher sensitivity and slower recovery compared to kidney blood. This study underscores the utility of AChE activity and erythrocyte abnormalities as biomarkers for assessing pesticide impacts on aquatic organisms. The findings highlight the sensitivity of fish erythrocytes to environmental contaminants and emphasize the need for continued research to better understand the long-term effects of pesticide exposure on aquatic health and ecosystem stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The frequency and magnitude of extreme events, such as heat waves, are predicted to increase with climate change. However, assessments of the response of biological communities to heat waves are often inconclusive. We aimed to assess the responses in abundance, taxonomic and functional diversity indices of stream invertebrate communities to heat waves using long-term monitoring data collected across Europe. We quantified the heat waves' magnitude, analyzed the spatial (i.e., long-term mean) and temporal (anomaly around the long-term mean) components of variation in the magnitude of heat waves, and their interaction with anthropogenic stressors (ecological quality and land cover). For the spatial component of variation, we found a negative association of the community indices to the increasing magnitude of heat waves. Sites undergoing heat waves of higher magnitude showed fewer species and lower trait diversity compared with sites experiencing lower magnitude heat waves. However, we could not detect an immediate temporal response of the communities to heat waves (i.e., the temporal component). Furthermore, we found that the effects of heat waves interacted with the ecological quality of the streams and their surrounding land cover. Diversity declined with increasing heat waves' magnitude in streams with higher ecological quality or surrounded by forest, which may be due to a higher proportion of sensitive species in the community. Heat waves' impacts on diversity were also exacerbated by increasing urban cover. The interaction between heat waves' magnitude and anthropogenic stressors suggests that the effects of extreme events can compromise the recovery of communities. Further, the predicted increase in heat waves will likely have long-term effects on stream invertebrate communities that are currently undetected., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

African ecosystems are expected to be significantly affected by climate change, making it crucial to understand these changes for effective management. We provide a framework and application to assess ecoregional vulnerability to climate change, considering environmental exposure, sensitivity, and adaptive capacity. We assessed environmental exposure using projections for consecutive dry days, precipitation, and temperature changes. Sensitivity was determined based on forest fragmentation and grassland degradation. Adaptive capacity was represented by protected areas network and biodiversity intactness. These factors were combined to create overall vulnerability index and specific categories to guide management decisions. Under the SSP5 8.5 scenario, 16 % of ecoregions will be highly impacted by 2050, with vulnerable areas emerging in Montane forest-grassland and flooded savanna. Impacted ecoregions are disproportionately biodiverse. By intersecting the likely climate impacts and adaptive capacity, we highlight where conservation actions are needed to enhance the resilience of ecoregions to climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)