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New devices inspired by flying seeds, or more technically by fruits with dispersal units, could have a significant impact for environmental monitoring and aerial seeding. Among the various types of dispersal units (e.g., winged, gliding), parachuted or plumed units offer the lowest vertical descent speed (i.e., 0.3-0.7 m s-1), making them an appealing solution for wind-driven distribution over large areas. Here, a biodegradable and porous parachute flier based on cellulose acetate, inspired by a Tragopogon pratensis fruit is presented. A micrometric-thick pappus is 3D printed and integrated with a porous colorimetric indicator or a porous beak, with micrometric pores, fabricated through injection molding and leaching techniques. Thanks to the bioinspired design and the lightweight porous structure, the artificial Tragopogon mimics the aerodynamics and descent speed of the natural species. Its feasibility is demonstrated in aerial seeding by integrating the beak with a mustard seed (as a model), and in environmental monitoring by coupling it with colorimetric indicators for rain pH and nitrate levels in soils. The proposed flier represents a significant advancement as it is the first parachute-like biodegradable solution, seamlessly integrated into natural ecosystems, thus contributing to moving a step forward in artificial solutions with zero impact.

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The influence of increasing anthropogenic pressure on ecosystem integrity, such as land use change, is resulting in many ecosystems experiencing a decline in their ability to maintain balanced functions and services. Identifying and quantifying these pressures over different scales is challenging and thus impacting the achievement or maintenance of key environmental outcomes. In this study, a GIS-based and scalable tool was developed, the Relative Environmental Pressure (REP) Tool, to address these challenges. The REP tool combines an ecosystem integrity conceptual framework with a weighted linear combination analysis to quantify and rank relative environmental pressure across the scale of interest. The REP Tool was developed as an automated Python-based model in a PyCharm working environment using ArcGIS Pro Arcpy scripting. The REP Tool was applied to spatially contiguous geospatial data for the Province of Alberta, Canada and dynamically scaled relative to Hydrologic Unit Codes at level 8 (HUC8) along with regional and sub-regional scale sub-watersheds. Both cumulative and individual relative pressure levels were calculated and mapped for specific ecosystem integrity framework-derived Environmental Pressure Groups (EPGs) including Atmospheric Alteration, Sedimentation, Habitat Alteration, Hydrologic Alteration, and Social Pressure. Data driven Jenks natural breaks were then applied to classify the relative environmental pressures into a nine-level ranking system. The resulting visualization and data outputs from the REP Tool clearly show that the highest cumulative relative environmental pressure values align with the distribution of major population centres, zones of intense agriculture and major industrial activity. These regions reflect the physiography of Alberta with the Rocky Mountain and Boreal natural regions dominated by low relative environmental pressure. As scales become smaller and more refined, the location of the higher relative environmental pressure levels typically become more subdivided with greater spatial precision where higher pressured areas are located. These patterns are repeated when looking at individual EPGs but with enhanced differentiation of pressure as scales are refined. The framework and geospatial science driven approach behind the REP Tool can be universally applied to support enhanced understanding of relative environmental pressures in, or between regions, as well as informing adaptive environmental resource management and monitoring activities.

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This study introduces an innovative approach for quantifying isomeric pollutants utilizing an amperometric sensor. The determination of the isomers hydroquinone and catechol is based on the use of a glassy carbon electrode modified with Cu@PtPd/C nanoparticles (Cu@PtPd/C/GCE) in core-shell form, showing significant electrocatalytic activity in the oxidation of the later compounds. The determination was carried out at two different potentials: one at which where only hydroquinone is oxidized, and another in which where both hydroquinone and catechol are oxidized. Using these potentials, two calibration curves were built, one for the quantification of hydroquinone and the other for both isomers. Subsequently, the quantification of catechol was performed using a strategy based on the calculation of a difference using the information collected in the first step. The experiments using hydrogen peroxide as a redox probe demonstrate a clear synergistic effect in the catalytic reduction of hydrogen peroxide at -0.100 V, when Pt, Pd and Cu are incorporated into the core-shell nanostructure. The best performance was achieved with Cu@PtPd/C/GCE 1.00 mg mL-1. For the selected sensor, the analytical parameters are very competitive compared to similar devices reported in recent years for hydroquinone and catechol, with comparable linearity ranges of 0.010-0.200 mmol L-1 (hydroquinone) and 0.005-0.500 mmol L-1 (catechol), low limits of detection (LODs) of 14.0 nmol L-1 (S/N = 3.3) and 1.75 nmol L-1 (S/N = 3.3) for hydroquinone and catechol. The resulting sensor platform has been successfully applied for the quantification of hydroquinone and catechol in river and tap water and could be a promising candidate for environmental monitoring and drinking water safety.

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Laser-induced graphene (LIG) and Laser-scribed graphene (LSG) are both advanced materials with significant potential in various applications, particularly in the field of sustainable sensors. The practical uses of LIG (LSG), which include gas detection, biological process monitoring, strain assessment, and environmental variable tracking, are thoroughly examined in this review paper. Its tunable characteristics distinguish LIG (LSG), which is developed from accurate laser beam modulation on polymeric substrates, and they are essential in advancing sensing technologies in many applications. The recent advances in LIG (LSG) applications include energy storage, biosensing, and electronics by steadily advancing efficiency and versatility. The remarkable flexibility of LIG (LSG) and its transformative potential in regard to sensor manufacturing and utilization are highlighted in this manuscript. Moreover, it thoroughly examines the various fabrication methods used in LIG (LSG) production, highlighting precision and adaptability. This review navigates the difficulties that are encountered in regard to implementing LIG sensors and looks ahead to future developments that will propel the industry forward. This paper provides a comprehensive summary of the latest research in LIG (LSG) and elucidates this innovative material's advanced and sustainable elements.

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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.

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Per- and polyfluoroalkyl substances (PFAS) may cause various deleterious health effects. Epidemiological studies have demonstrated associations between PFAS exposure and adverse neurodevelopmental outcomes. The cytotoxicity, neurotoxicity, and mitochondrial toxicity of up to 12 PFAS including perfluoroalkyl carboxylates, perfluoroalkyl sulfonates, 6:2 fluorotelomer sulfonic acid (6:2 FTSA), and hexafluoropropylene oxide-dimer acid (HPFO-DA) were tested at concentrations typically observed in the environment (e.g., wastewater, biosolids) and in human blood using high-throughput in vitro assays. The cytotoxicity of all individual PFAS was classified as baseline toxicity, for which prediction models based on partition constants of PFAS between biomembrane lipids and water exist. No inhibition of the mitochondrial membrane potential and activation of oxidative stress response were observed below the cytotoxic concentrations of any PFAS tested. All mixture components and the designed mixtures inhibited the neurite outgrowth in differentiated neuronal cells derived from the SH-SY5Y cell line at concentrations around or below cytotoxicity. All designed mixtures acted according to concentration addition at low effect and concentration levels for cytotoxicity and neurotoxicity. The mixture effects were predictable from the experimental single compounds' concentration-response curves. These findings have important implications for the mixture risk assessment of PFAS.

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In response to the pervasive issue of herbicide pollution in environmental water bodies, particularly from herbicides used extensively in agriculture, traditional chemical-based water quality analysis methods have proven costly and time-consuming, often failing to meet regulatory standards. To overcome these limitations, global environmental agencies have turned to rapidly-growing species like duckweed as bioindicators for herbicide and pesticide contamination. However, conventional biological assessment methods, such as the 168-h duckweed growth inhibition test, are slow and lack real-time monitoring capabilities. To address this challenge, we developed an innovative approach by integrating opto-mechanical technology with duckweed to create a cost-effective biosensor for herbicide detection, priced under $10 USD per system. This advancement allows for the rapid detection of herbicide impacts on duckweed growth within just 48 h, significantly improving upon traditional methods. Our biosensor achieves detection limits of 10 ppm (p < 0.05) for glyphosate and 1 ppm (p < 0.05) for glufosinate, both prominent herbicides globally. This mini-biosensing platform offers a practical alternative to the official method, which requires 168 h and higher thresholds (36.4 ppm for glyphosate and 34.0 ppm for glufosinate) for routine environmental analysis. Thus, these duckweed-based optical biosensors represent a promising advancement in environmental monitoring, enhancing accessibility and efficacy for widespread adoption globally.

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Sulfonamide antibiotics were the first synthetic antibiotics on the market and still have a broad field of application. Their extensive usage, wrong disposal, and limited degradation technologies in wastewater treatment plants lead to high concentrations in the environment, resulting in a negative impact on ecosystems and an acceleration of antibiotic resistance. Although lab-based analytical methods allow for sulfonamide detection, comprehensive monitoring is hampered by the nonavailability of on-site, inexpensive sensing technologies. In this work, we exploit functionalized elastic hydrogel microparticles and their ability to easily deform upon specific binding with enzyme-coated surfaces to establish the groundwork of a biosensing assay for the fast and straightforward detection of sulfonamide antibiotics. The detection assay is based on sulfamethoxazole-functionalized hydrogel microparticles as sensor probes and the biomimetic interaction of sulfonamide analytes with their natural target enzyme, dihydropteroate synthase (DHPS). DHPS from S. pneumoniae was recombinantly produced by E. coli and covalently coupled on a glass biochip using a reactive maleic anhydride copolymer coating. Monodisperse poly(ethylene glycol) hydrogel microparticles of 50 µm in diameter were synthesized within a microfluidic setup, followed by the oriented coupling of a sulfamethoxazole derivative to the microparticle surface. In proof-of-concept experiments, sulfamethoxazole, as the most used sulfonamide antibiotic in medical applications, was demonstrated to be specifically detectable above a concentration of 10 µM. With its straightforward detection principle, this assay has the potential to be used for point-of-use monitoring of sulfonamide antibiotic contaminants in the environment.

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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.

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Electrofishing is a common method for sampling fish in rivers. In Sweden, electrofishing has a long history, dating back to the 1950s, but the vast majority of surveys have been conducted by wading in shallow river stretches, leaving a data gap for non-wadable rivers. Boat electrofishing allows for surveys in deeper river sections, but limited numbers of operational electrofishing boats have led to standardisation of methods not being prioritized within Swedish water management. This method protocol describes the first Swedish standardised method for boat electrofishing, based on intermittent shoreline sampling in larger slow-flowing rivers. The paper describes:•General methodology for boat electrofishing operation•Data collection protocols•Discussion of current caveats for the methodIn the future, the methodology will be amended to cover a wider array of river types (e.g. faster flowing river sections). Hence, readers are advised to look for updates to the protocol.

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Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These methods require the availability of large amount of labeled data for training. The labeling work is expensive and laborious, resulting in small open datasets available in the field compared to the comprehensive datasets for computer vision, e.g., ImageNet. Fine-tuning models pre-trained on these larger datasets helps improve litter detection performances and reduces data requirements. Yet, the effectiveness of using features learned from generic datasets is limited in large-scale monitoring, where automated detection must adapt across different locations, environmental conditions, and sensor settings. To address this issue, we propose a two-stage semi-supervised learning method to detect floating litter based on the Swapping Assignments between multiple Views of the same image (SwAV). SwAV is a self-supervised learning approach that learns the underlying feature representation from unlabeled data. In the first stage, we used SwAV to pre-train a ResNet50 backbone architecture on about 100k unlabeled images. In the second stage, we added new layers to the pre-trained ResNet50 to create a Faster R-CNN architecture, and fine-tuned it with a limited number of labeled images (≈1.8k images with 2.6k annotated litter items). We developed and validated our semi-supervised floating litter detection methodology for images collected in canals and waterways of Delft (the Netherlands) and Jakarta (Indonesia). We tested for out-of-domain generalization performances in a zero-shot fashion using additional data from Ho Chi Minh City (Vietnam), Amsterdam and Groningen (the Netherlands). We benchmarked our results against the same Faster R-CNN architecture trained via supervised learning alone by fine-tuning ImageNet pre-trained weights. The findings indicate that the semi-supervised learning method matches or surpasses the supervised learning benchmark when tested on new images from the same training locations. We measured better performances when little data (≈200 images with about 300 annotated litter items) is available for fine-tuning and with respect to reducing false positive predictions. More importantly, the proposed approach demonstrates clear superiority for generalization on the unseen locations, with improvements in average precision of up to 12.7%. We attribute this superior performance to the more effective high-level feature extraction from SwAV pre-training from relevant unlabeled images. Our findings highlight a promising direction to leverage semi-supervised learning for developing foundational models, which have revolutionized artificial intelligence applications in most fields. By scaling our proposed approach with more data and compute, we can make significant strides in monitoring to address the global challenge of litter pollution in water bodies.

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The aim of the present paper was to give a complete picture on the drinking water contamination by pharmaceutical residues all over the world. For this purpose, a systematic review was carried out for identifying all available research reporting original data resulting by sampling campaign and analysis of "real" drinking water samples to detect pharmaceutical residues. The investigated databases were PubMed, Scopus, and Web of Science. A total of 124 studies were included; among these, 33 did not find target analytes (all below the limit of detection), while the remaining 91 studies reported the presence for one or more compounds, in concentrations ranging from a few units to a few tens of nanograms. The majority of the studies were performed in Europe and the most represented categories were nonsteroidal anti-inflammatory drugs and analgesics. The most common analytical approach used is the preparation and analysis of the samples by solid-phase extraction and chromatography coupled to mass spectrometry. The main implications resulting from our review are the need for (a) further studies aimed to allow more accurate environmental, wildlife, and human health risk assessments and (b) developing integrated policies promoting less environmentally persistent drugs, the reduction of pharmaceuticals in livestock breeding, and the update of wastewater and drinking water treatment plants for a better removal of drugs and their metabolites.

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Antimicrobial resistance (AMR) is a major global public health problem. Nevertheless, the knowledge of the factors driving the spread of resistance among environmental microorganisms is limited, and few studies have been performed worldwide. Honey bees (Apis mellifera L.) have long been considered bioindicators of environmental pollution and more recently also of AMR. In this study, 53 bacterial strains isolated from the body surface of honey bees at three ontogenetic stages, collected from ten different geographic locations, were tested for their phenotypic and genotypic resistance to eight classes of the most widely used antimicrobials in human and veterinary medicine. Results showed that 83% of the strains were resistant to at least one antimicrobial and 62% were multidrug-resistant bacteria, with a prevalence of resistance to nalidixic acid, cefotaxime, and aztreonam. A high percentage of isolates harbouring at least one antimicrobial gene was also observed (85%). The gene encoding resistance to colistin mcr-1 was the most abundant, followed by those for tetracycline tetM and tetC. Geographical features influenced the distribution of these traits more than bacterial species or bee stage, supporting the use of honey bee colonies and their associated bacteria as indicators to monitor environmental resistance. This approach can improve the scientific understanding of this global threat by increasing data collection capacity.

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Pesticides in environmental samples pose significant risks to ecosystems and human health since they require precise and efficient detection methods. Imidacloprid (IMI), a widely used neonicotinoid insecticide, exemplifies these hazards due to its potential toxicity. This study addresses the urgent need for improved monitoring of such contaminants by introducing a novel fluorometric method for detecting IMI using nitrogen-doped graphite carbon dots (N-GCDs). The sensor operates by quenching fluorescence through the interaction of Cu2+ ions with N-GCDs. Subsequently, IMI binds to the imidazole group, chelates with Cu2+, and restores the fluorescence of N-GCDs. This alternating fluorescence behavior allows for the accurate identification of both Cu2+ and IMI. The sensor exhibits linear detection ranges of 20-100 nM for Cu2+ and 10-140 µg/L for IMI, with detection limits of 18 nM and 1.2 µg/L, respectively. The high sensitivity of this sensor enables the detection of real-world samples, which underscores its potential for practical use in environmental monitoring and agricultural safety.

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Cell therapy represents a promising treatment modality. A critical component in the production of cell therapy products is maintaining the sterility of cell therapy clean rooms (CTCRs). This study aimed to evaluate the environmental microbial load within CTCRs. We systematically monitored microbial load in CTCRs, following established guidelines. Cultured microbial samples underwent metagenomic sequencing, and alpha and beta diversity analyses, functional annotation, and resistance gene profiling were performed using various bioinformatics tools to assess microbial diversity and function. From November 2023 to January 2024, we collected 42 environmental microbial colony samples from various sources within the CTCR and performed metagenomic sequencing on 39 samples. Alpha diversity analysis revealed no significant differences among surface, settle_plate, and airborne categories, but significant disparities within surface subgroups were revealed. Beta diversity analysis showed notable differences between surface and airborne categories and among surface subgroups. Species distribution analysis identified Bacillus as the predominant genus on surfaces. Functional annotation and resistance gene analysis indicated distinct resistance patterns, with significant variations between subgroups, such as microscopes and transfer windows, and hands and other Grade_B environments. Resistance to hydrogen peroxide was notably higher in the transfer window group. These findings highlight the importance of stringent disinfection protocols and enhanced hand hygiene to maintain sterility in CTCRs. These findings provide valuable insights for implementing effective measures to maintain cleanliness throughout CTCRs. The annotation and study of resistance genes can help rapidly identify methods to control cellular contamination under circumstances of environmental microbial pollution.IMPORTANCEMaintaining the sterility of cell therapy clean rooms (CTCRs) is crucial for the production of safe and effective cell therapy products. Our study systematically evaluated the environmental microbial load within CTCRs, revealing significant microbial diversity and distinct resistance patterns to disinfection methods. These findings underscore the need for stringent disinfection protocols and enhanced hand hygiene practices to ensure CTCR sterility. By identifying key microbial species and their resistance genes, our research provides essential insights into controlling contamination and safeguarding the production environment, ultimately contributing to the reliability and success of cell therapy treatments.

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Coastal heavy-metal contamination poses significant risks to marine ecosystems and human health, necessitating comprehensive research for effective mitigation strategies. This study assessed heavy-metal pollution in sediments, seawater, and organisms in the Pearl River Estuary (PRE), with a focus on Cd, Cu, Pb, Zn, As, Hg, and Cr. A notable reduction in heavy metal concentrations in surface sediments was observed in 2020 compared to 2017 and 2018, likely due to improved pollution management and COVID-19 pandemic restrictions. Spatial analysis revealed a positive correlation between elevated heavy-metal concentrations (Cu, Pb, Zn, Cd, and As) and areas with significant human activity. Source analysis indicated that anthropogenic activities accounted for 63 % of the heavy metals in sediments, originating from industrial effluents, metal processing, vehicular activities, and fossil fuel combustion. Cd presented a high ecological risk due to its significant enrichment in surface sediments. Organisms in the PRE were found to be relatively enriched with Hg and Cu, with average As concentrations slightly exceeding the Chinese food-health criterion. This study identified high-risk ecological zones and highlighted Cd as the primary pollutant in the PRE. The findings demonstrate the effectiveness of recent pollution control measures and emphasize the need for ongoing monitoring and mitigation to safeguard marine ecosystems and human health.

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Metal halide perovskites (MHPs) are emerging gas-sensing materials and have attracted considerable attention in gas sensors due to their unique bandgap structure and tunable optoelectronic properties. The past decade has witnessed significant developments in the gas-sensing field; however, their intrinsic structural instability and ambiguous gas-sensing mechanisms hamper their practical applications. Herein, we summarize the recent advances in MHP-based gas sensors. The physicochemical properties of MHPs are discussed at first. The structure design, including dimension design and engineering design, is overviewed as well as their fabrication methods, and we put forward our insights into the gas-sensing mechanism of MHPs. It is believed that enhanced understanding of gas-sensing mechanisms of MHPs are helpful for their application as gas-sensing materials, and structure design can enhance their stability, sensing sensitivity, and selectivity to target gases as gas sensors. Subsequently, the latest developments in MHP-based gas sensors are summarized according to their different application scenarios. Finally, we conclude with the current status and challenges in this field and propose future perspectives.

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Microfibers are thread-like structures shorter than 5 mm and have natural, semisynthetic, or synthetic origins. These micropollutants are ubiquitous and are emerging in the environment, living organisms, and food sources. Textile laundering is a prominent source of microfibers, but limited research has been conducted on microfiber pollution from domestic washing machines in emerging economies such as India, where consumption and production rates are exorbitantly high. This study aimed to assess the abundance and size distribution of microfibers from the effluent of a semiautomatic domestic washing machine using three categories of "not-new" textiles: cotton, blended, and synthetic under "with" and "without" detergent conditions. Although most Indians still rely on hand washing, this study focused on washing machines due to their increasing use in India driven by improving socioeconomic factors. This study also developed annual emission estimation and forecasting models for India to understand pollution trends. The results revealed that microfibers were highly abundant in washing machine effluent, with a mean abundance of cotton, blended, and synthetic in "with detergent" conditions of 6476.67, 3766.67, and 8645/L, respectively, whereas in "without detergent," it was lower. All identified microfibers were divided into five size classes. The study also found that powdered detergent increased the abundance and emission of tiny fibers. The overall annual emissions estimate was 1.23 × 1011 microfibers, with cotton, synthetic, and blended categories accounting for 2.11 × 1010, 1.40 × 1010, and 6.15 × 109 microfibers, respectively. Time-series-based future estimates (autoregressive integrated moving average [ARIMA] and error-trend-seasonality [ETS]) showed an alarming increase in microfiber emissions, with forecasted annual emission reaching 1.90 × 1011 by 2030. Synthetic and cotton textiles are the most significant contributors to microfiber pollution. This study emphasized the urgent need to address the issue of microfiber pollution caused by washing machine laundering in developing countries, such as India, where sociodemographic factors intensify the problem. Integr Environ Assess Manag 2024;00:1-12. © 2024 SETAC.

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

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Water pollution has become a major concern in recent years, affecting over 2 billion people worldwide, according to UNESCO. This pollution can occur by either naturally, such as algal blooms, or man-made when toxic substances are released into water bodies like lakes, rivers, springs, and oceans. To address this issue and monitor surface-level water pollution in local water bodies, an informative real-time vision-based surveillance system has been developed in conjunction with large language models (LLMs). This system has an integrated camera connected to a Raspberry Pi for processing input frames and is further linked to LLMs for generating contextual information regarding the type, causes, and impact of pollutants on both human health and the environment. This multi-model setup enables local authorities to monitor water pollution and take necessary steps to mitigate it. To train the vision model, seven major types of pollutants found in water bodies like algal bloom, synthetic foams, dead fishes, oil spills, wooden logs, industrial waste run-offs, and trashes were used for achieving accurate detection. ChatGPT API has been integrated with the model to generate contextual information about pollution detected. Thus, the multi-model system can conduct surveillance over water bodies and autonomously alert local authorities to take immediate action, eliminating the need for human intervention. PRACTITIONER POINTS: Combines cameras and LLMs with Raspberry Pi for processing and generating pollutant information. Uses YOLOv5 to detect algal blooms, synthetic foams, dead fish, oil spills, and industrial waste. Supports various modules and environments, including drones and mobile apps for broad monitoring. Educates on environmental healthand alerts authorities about water pollution.

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