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This study optimized a gas chromatography-tandem triple quadrupole mass spectrometry (GC-MS/MS) method for the determination of 21 persistent organic pollutants (POPs) in Irtysh River water, including 14 organochlorines (OCPs) and 7 polychlorinated biphenyls (PCBs). Factors such as column temperature ramping, selection of qualitative and quantitative ion pairs and collision energy were considered to achieve perfect separation and accurate quantification of all 21 target compounds. The limits of detection (LOD) for PCBs and OCPs ranged from 0.21 to 1.18 ng/L. Applying this method to detect POPs in the Irtysh River revealed concentrations of OCPs ranging from ND to 20.2 ng/L and PCBs from ND to 0.411 ng/L. Source analysis indicated that POPs in the Irtysh River mainly originate from historical industrial and agricultural activities, particularly the deliberate use of pesticides. To ensure ecological safety and human health, expanding the range of target analytes and monitoring periods is necessary. This study provides:

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  Qualitative and quantitative analysis methods for 7 PCBs and 14 OCPs.

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  Recoveries achieved ranged between 74.6 to 109 % with RSD less than 15 %.

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  Analysis of sources, transport pathways, accumulation status, and ecological risks of PCBs and OCPs in the Irtysh River.

This study introduces a ground-breaking approach, the Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model, designed for electricity price forecasting. Key features of the proposed WOA-GMHES(1,N) model include leveraging historical data to comprehend the underlying trends in electricity prices and utilizing the WOA algorithm for adaptive optimization of model parameters to capture evolving market dynamics. Evaluating the model on authentic high- and low-voltage electricity price data from Cameroon demonstrates its superiority over competing models. The WOA-GMHES(1,N) model achieves remarkable performance with RMSE and SMAPE scores of 12.63 and 0.01 %, respectively, showcasing its accuracy and reliability. Notably, the model proves to be computationally efficient, generating forecasts in <1.3 s. Three key aspects of customization distinguish this novel approach:

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  The WOA algorithm dynamically adjusts model parameters based on evolving electricity market dynamics.

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  The model employs a sophisticated GMHES approach, considering multiple factors for a comprehensive understanding of price trends.

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  The WOA-GMHES(1,N) model stands out for its computational efficiency, providing rapid and precise forecasts, making it a valuable tool for time-sensitive decision-making in the energy sector.

Urban areas have detrimental impacts on the ecosystems. Nevertheless, they still supply many ecosystem services (ES), such as Pollination, in different urban green spaces (UGS). Lawns are among the most degraded UGS due to very high human impact. Still, flowers such as Dandelions (Taraxacum officinalis) live in these spaces. These flowers are considered a suitable habitat for pollinators. In this work, we develop a methodology to map Pollination ES potential in urban lawns using an Unmanned Aerial Vehicle. A detailed protocol was developed using high-resolution images, consisting of orthomosaic creation, flower vectorisation, field validation, and finally, Pollination ES potential mapping using Kernel and Point Density. This method can be applied to urban lawns and grasslands in Spring and Summer.

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  A novel method was developed to map pollination potential in lawns.

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  Dandelions (Taraxacum officinale) were mapped using UAV high-resolution images.

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  The method is helpful to identify areas with pollination potential in urban lawns.

Thermal sensors mounted on drones (unoccupied aircraft systems) are popular and effective tools for monitoring cryptic animal species, although few studies have quantified sampling error of animal counts from thermal images. Using decoys is one effective strategy to quantify bias and count accuracy; however, plastic decoys do not mimic thermal signatures of representative species. Our objective was to produce heat signatures in animal decoys to realistically match thermal images of live animals obtained from a drone-based sensor. We tested commercially available methods to heat plastic decoys of three different size classes, including chemical foot warmers, manually heated water, electric socks, pad, or blanket, and mini and small electric space heaters. We used criteria in two categories, 1) external temperature differences from ambient temperatures (ambient difference) and 2) color bins from a palette in thermal images obtained from a drone near the ground and in the air, to determine if heated decoys adequately matched respective live animals in four body regions. Three methods achieved similar thermal signatures to live animals for three to four body regions in external temperatures and predominantly matched the corresponding yellow color bins in thermal drone images from the ground and in the air. Pigeon decoys were best and most consistently heated with three-foot warmers. Goose and deer decoys were best heated by mini and small space heaters, respectively, in their body cavities, with a heated sock in the head of the goose decoy. The materials and equipment for our best heating methods were relatively inexpensive, commercially available items that provide sustained heat and could be adapted to various shapes and sizes for a wide range of avian and mammalian species. Our heating methods could be used in future studies to quantify bias and validate methodologies for drone surveys of animals with thermal sensors.

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  We determined optimal heating methods for plastic animal decoys with inexpensive and commercially available equipment to mimic thermal signatures of live animals.

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  Methods could be used to quantify bias and improve thermal surveys of animals with drones in future studies.

The total organic carbon (TOC) concentration of particulate samples is a key parameter to characterize soils and sediments. To demonstrate the applicability and reliability of a modified sample preparation method for the direct measurement of TOC contents in suspended particulate samples, we analyzed five certified reference materials (CRMs) with varying TOC concentrations using a Shimadzu TOC-L CPH analyzer. Measured values were calibrated with a multi-point curve that cover the full range of the expected TOC concentrations and the results were validated using statistical values and measures. The method validation reveals that the measurements are accurate and precise for CRMs from marine and soil contexts, but show a low accuracy for the CRM containing polycyclic aromatic hydrocarbons (PAHs). This demonstrates the applicability and reliability of the modified preparation method for direct TOC determination of suspended particulate samples. Therefore, it is relevant for a broader community, beyond geosciences, and for users employing devices of other manufacturers to analyze TOC in suspended particulate samples.

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  Modified preparation method uses reduced sample weights and yields accurate and precise results.

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  Cost-efficient and environmentally friendly alternative: reduces waste by saving acid and ultrapure water.

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  Avoids incomplete dissolution of dolomite by heating acidified samples.

Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution. Specifically, drone imaging allows for time- and cost-efficient mapping covering larger areas than traditional mapping and monitoring techniques, while also providing more detailed information than those from airplanes and satellites, enabling for example to differentiate various types of coastal vegetation. Here, we present a systematic method for shallow water habitat classification based on drone imagery. The method includes:

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  Collection of drone images and creation of orthomosaics.

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  Gathering ground-truth data in the field to guide the image annotation and to validate the final map product.

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  Annotation of drone images into – potentially hierarchical – habitat classes and training of machine learning algorithms for habitat classification.

Randomized clinical trials are considered the gold standard for studies with dietary interventions, which is mainly due to the fact that they can establish causal relationships between food exposure and body composition measures or biomarkers. The aim of this study was to describe the details of a double-blind, randomized, clinical trial protocol to identify, characterize and evaluate the effects of human dietary exposure to pesticide residues in food. Specific aspects of planning (development of a research question, determination of objectives, selection of participants, randomization and blinding) and performance (recruitment of participants, measures to improve adherence, data collection, follow-up and evaluation of results) are addressed in this study. The study design proved effective in characterizing dietary patterns with foods originating from both conventional and organic agriculture. A total of 148 individuals were recruited for the study. The conventional group was represented by 47 % of the sample and the organic group was represented by 53 %. The practice of evidence-based nutrition has demanded that trials be well designed and systematically performed in the field of clinical nutrition. Therefore, this clinical trial emphasizes the importance of improving studies with toxicological nutrition that assess sources of exposure through food.

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  This double-blind, randomized clinical trial details the protocol for identifying, characterizing, and evaluating the effects of dietary exposure to pesticide residues.

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  The protocol demonstrates that well-designed and systematically conducted trials emphasize the importance of robust methodologies in evidence-based nutrition.

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  In the face of the global climate crisis, this clinical trial underscores the importance of enhancing studies in toxicological nutrition, particularly those evaluating sources of exposure through food, to better understand the dietary impacts on health.

This study introduces an innovative methodology employing Cellular Automata (CA) models to simulate seagrass dynamics in response to global environmental changes. The primary objective is to outline a procedural framework for constructing and deploying CA models applied to seagrass ecosystems, and potentially to other marine or terrestrial environments. The methodology encompasses various components, including conceptualization, workflow delineation, model parameterization, and execution steps. By utilizing Mediterranean and Zanzibari (East Africa) seagrass ecosystems as case studies, we demonstrate the versatility and applicability of the proposed approach across diverse geographical regions, species composition and model components. Through these case studies, we demonstrated how CA models can effectively capture the dynamics of seagrass communities subjected to climate change, invasive species, and nutrient regimes. Despite its strengths, the proposed CA model has limitations, including parameterization complexity and uncertainties related to species-specific environmental thresholds, growth rates and species interactions, alongside the difficulty of validating our models with real-world scenarios. Addressing these limitations in future studies will enhance the model's accuracy and applicability. This study serves as a foundation for future research in other regions and ecosystems, facilitating a better understanding of the complex interactions driving ecosystem dynamics.

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  This study introduces a methodology using Cellular Automata (CA) models to simulate seagrass dynamics detailing conceptualization, workflow, parameterization, and execution.

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  Case studies in Mediterranean and East Africa ecosystems demonstrate the versatility of CA models in capturing the impacts of climate change, invasive species, and nutrient regimes.

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  Despite strengths, the CA model has limitations and uncertainties like parameterization complexity and model validations suggesting future research to enhance accuracy and applicability.

Studies of free-ranging wildlife often involve individual tracking by sequentially recording animals’ positions over a continuous and extended period. Automatic, programmable, operational continuously, and user-friendly thanks to the development of intuitive software, GPS (Global Positioning System) enable the acquisition of large quantities of data, day and night, regardless of field and weather conditions, while allowing for levels of spatial and temporal resolution in the location data never before achieved in wildlife tracking studies. However, GPS collars deployment on wild fauna does not directly translate into scientific outcomes. This article delves into the hidden aspects of telemetry programs, offering a reflective account of our transdisciplinary experience (between researchers and wildlife managers) in GPS tracking of urban wild boar. The described protocol and its discussion aim to outline the necessary conditions to benefit from GPS programs. The program first requires a common construction of the protocol, which meets the objectives of each partner. Second, raw data collection and transformation into information involve four steps. Finally, both technical and human-related dimensions are to be anticipated and considered for further analyses.

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  Transdisciplinary research requires a common construction of the protocol in line with the research question.

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  Technical constraints and negotiations between partners need to be considered.

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  Multiple steps are required to leverage the scientific advantages of the monitoring.

A comprehensive risk assessment method was applied to examine the risks associated with airborne formaldehyde occupational exposure among hospital laboratory staff. The method assessed exposure levels and health impacts by integrating area and personal air sampling, biological monitoring, and self-reported health data. Samples were collected from 74 workplaces across various departments using NIOSH method 3500 and were analyzed via UV-vis spectrophotometry. The data showed significant differences in exposure levels between departments (p≤0.05) and confirmed the efficacy of the method in identifying risk differences. Despite average personal exposure levels being measured lower than occupational limits, individual assessments indicated that some participants surpassed these limits, emphasizing the necessity of personal monitoring for workers with higher risks. The high prevalence of respiratory symptoms, such as cough and wheezing among staff, indicated the need for further investigation and targeted interventions. Although estimated cancer and non-cancer risks were within safe thresholds, the study emphasized the importance of continuous exposure monitoring and the implementation of effective control measures in hospital laboratory departments with formaldehyde emission. This integrated method improved the reliability and generalizability of formaldehyde exposure risk assessments and aided in the development of safe occupational health practices.

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  The method integrated personal and area sampling with advanced calibration for precise occupational exposure evaluation in laboratories.

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  The method used of biomarkers to assess formaldehyde absorption in the body estimating both cancerous and non-cancerous health risks associated with occupational exposure.

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  Addressed traditional method limitations and integrated risk components to improve data reliability for workplace safety and health risk management.