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Microplastics (MPs) have been identified as emerging environmental pollutants classified as primary or secondary based on their source. Composition, shape, size, and colour, among other characteristics, are associated with their capacity to access the food chain and their risks. While the environmental impact of MPs has received much attention, the risks for humans derived from their dietary exposure have not been yet assessed. Several institutions and researchers support that the current knowledge does not supply solid data to complete a solid risk characterization of dietary MPs. The aim of this paper is to review the current knowledge about MPs in foods and to discuss the challenges and gaps for a risk analysis. The presence of MPs in food and beverages has been worldwide observed, but most authors considered the current data to be not only insufficient but of questionable quality mainly because of the outstanding lack of consensus about a standardized quantifying method and a unified nomenclature. Drinking water, crustaceans/molluscs, fish, and salt have been identified as relevant dietary sources of MPs for humans by most published studies. The hazard characterization presents several gaps concerning the knowledge of the toxicokinetic, toxicodynamic, and toxicity of MPs in humans that impede the estimation of food safety standards based on risk. This review provides a tentative exposure assessment based on the levels of MPs published for drinking water, crustaceans and molluscs, fish, and salt and using the mean European dietary consumption estimates. The intake of 2 L/day of water, 70.68 g/day of crustaceans/molluscs, 70.68 g/day of fish, and 9.4 g/day of salt would generate a maximum exposure to 33,626, 212.04, 409.94 and 6.40 particles of MPs/day, respectively. The inexistence of reference values to evaluate the MPs dietary intake prevents the dietary MPs risk characterization and therefore the management of this risk. Scientists and Food Safety Authorities face several challenges but also opportunities associated to the occurrence of MPs in foods. More research on the MPs characterization and exposure is needed bearing in mind that any future risk assessment report should involve a total diet perspective.

Assessing flood severity in urban areas is a pivotal task for urban resilience and climate adaptation. However, the lack of in situ measurements hinders direct spatial estimation of flood return periods, while conventional assumptions about rainstorm‐flood consistency introduce significant uncertainties due to rainstorm spatiotemporal variability (STV). This study proposes a novel framework that utilizes multivariate frequency analysis of flood variables at the street level (50 m) through a stochastic rainstorm‐flood event catalog. The rainstorm events in the catalog are generated by a random field generator and resampled to match the joint distribution of STV variables consistent with radar observations. Urban flood processes are then simulated by a hydrodynamic model for flood hazard assessment (FHA). We applied the framework to a rural‐urban watershed using 3,000 cases randomly resampled from the catalog. Results reveal that inundation characteristics respond more rapidly to increasing rainfall intensities than downstream flood peaks, particularly during the early stages of rainstorms. The complex joint probability structures of rainstorm severity and STV variables obscure the mechanistic control of individual factors on flood response. A significant underestimation of street‐level flood hazards occurs when assuming the same return periods (RPs) as those for watershed‐level hazards. The inconsistency between rainstorm and flood severities results in widespread underestimation of street‐level flood hazards in upstream regions, while traditional storm designs that neglect STV lead to overestimations in mid‐ and downstream areas. This study highlights the complex probabilistic behavior of spatially distributed flood hazards across multiple scales, enhancing the insights and methodologies for street‐level FHA.

Iran is among the driest countries in the world, where many natural hazards, such as floods, frequently occur. This study introduces a straightforward flood hazard assessment approach using remote sensing datasets and Geographic Information Systems (GIS) environment in an area located in the western part of Iran. Multiple GIS and remote sensing datasets, including Digital Elevation Model (DEM), slope, rainfall, distance from the main rivers, Topographic Wetness Index (TWI), Land Use/Land Cover (LULC) maps, soil type map, Normalized Difference Vegetation Index (NDVI), and erosion rate were initially produced. Then, all datasets were converted into fuzzy values using a linear fuzzy membership function. Subsequently, the Analytical Hierarchy Process (AHP) technique was applied to determine the weight of each dataset, and the relevant weight values were then multiplied to fuzzy values. Finally, all the processed parameters were integrated using a fuzzy analysis to produce the flood hazard map with five classes of susceptible zones. The bi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) images, acquired before and on the day of the flood event, were used to evaluate the accuracy of the produced flood hazard map. The results indicated that 95.16% of the actual flooded areas were classified as very high and high flood hazard classes, demonstrating the high potential of this approach for flood hazard mapping.

Floods have destroyed people’s lives as well as social and environmental assets. Flooding is becoming more severe and frequent as a result of climate change and an increase in human-induced land-use changes, which puts pressure on river channels and causes changes in river morphology. The study was aimed to assess flood danger and map inundation areas in Ethiopia’s Teji watershed, which is prone to flooding. The basic flood-producing factors in this study were derived from soil, slope, elevation, drainage-density and land use land cover data. The opinions of public institutions and expert decisions were gathered to determine the weight of the factors in the analytic hierarchy process. The collected data were processed using the ArcGIS environment and the analytic hierarchy method to produce a flood danger map. According to the findings of this study, approximately 43.28 and 13.09% of the area were vulnerable to high and very high flood risk zones, respectively. As a result, flood prediction, early warning and management practices could be implemented on a regular and sustainable basis.

Machine learning, an important branch of artificial intelligence, is increasingly being applied sciences such as forest ecology. Here, we review and discuss three commonly used methods of machine learning including decision tree learning, artificial neural network, and support vector machine, and their applications in five different aspects of forest ecology over the last decade. These applications include: (1) species distribution models (SDMs), (2) carbon cycles, (3) hazard assessment and prediction, and (4) other applications in forest management. While machine learning approaches are useful for classification, modeling, and prediction in forest ecology research, further expansion of machine learning technologies is limited by the lack of suitable data and the relatively “higher threshold” of applications. However, the combined use of multiple algorithms and improved communication and cooperation between ecological researchers and machine learning developers still present major challenges and tasks for the betterment of future ecological research. We suggest that future applications of machine learning in ecology will become an increasingly attractive tool for ecologists in the face of “big data” and that ecologists will gain access to more types of data such as sound and video in the near future possibly opening new avenues of research in forest ecology.

In 2008, the National Institute of Environmental Health Sciences/National Toxicology Program, the U.S. Environmental Protection Agency's National Center for Computational Toxicology, and the National Human Genome Research Institute/National Institutes of Health Chemical Genomics Center entered into an agreement on \"high throughput screening, toxicity pathway profiling, and biological interpretation of findings.\" In 2010, the U.S. Food and Drug Administration (FDA) joined the collaboration, known informally as Tox21. The Tox21 partners agreed to develop a vision and devise an implementation strategy to shift the assessment of chemical hazards away from traditional experimental animal toxicology studies to one based on target-specific, mechanism-based, biological observations largely obtained using in vitro assays. Here we outline the efforts of the Tox21 partners up to the time the FDA joined the collaboration, describe the approaches taken to develop the science and technologies that are currently being used, assess the current status, and identify problems that could impede further progress as well as suggest approaches to address those problems. Tox21 faces some very difficult issues. However, we are making progress in integrating data from diverse technologies and end points into what is effectively a systems-biology approach to toxicology. This can be accomplished only when comprehensive knowledge is obtained with broad coverage of chemical and biological/toxicological space. The efforts thus far reflect the initial stage of an exceedingly complicated program, one that will likely take decades to fully achieve its goals. However, even at this stage, the information obtained has attracted the attention of the international scientific community, and we believe these efforts foretell the future of toxicology.

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India. The flood inventory was constructed through Sentinel-1 SAR analysis and global flood databases. Fifteen flood conditioning factors related to topography, land cover, soil, rainfall, proximity, and demographics were incorporated. Rigorous training and testing of diverse machine learning models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS algorithms, were undertaken for categorical flood hazard mapping. Model optimization was achieved through statistical feature selection techniques. Accuracy metrics and advanced model interpretability methods like SHAP and Boruta were implemented to evaluate predictive performance. According to the area under the receiver operating characteristic curve (AUC), the prediction accuracy of the models performed was around > 80%. RF achieves an AUC of 0.847 at resampling factor 5, indicating strong discriminative performance. AdaBoost also consistently exhibits good discriminative ability, with AUC values of 0.839 at resampling factor 10. Boruta and SHAP analysis indicated precipitation and elevation as factors most significantly contributing to flood hazard assessment in the study area. Most of the machine learning models pointed out southern portions of the study area as highly susceptible areas. On average, from 17.2 to 18.6% of the study area is highly susceptible to flood hazards. In the feature selection analysis, various nature-inspired algorithms identified the selected input parameters for flood hazard assessment, i.e., elevation, precipitation, distance to rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, distance to roads, and gMIS. As per the Boruta and SHAP analyses, it was found that elevation, precipitation, and distance to rivers play the most crucial roles in the decision-making process for flood hazard assessment. The results indicated that the majority of the building footprints (15.27%) are at high and very high risk, followed by those at very low risk (43.80%), low risk (24.30%), and moderate risk (16.63%). Similarly, the cropland area affected by flooding in this region is categorized into five risk classes: very high (16.85%), high (17.28%), moderate (16.07%), low (16.51%), and very low (33.29%). However, this interdisciplinary study contributes significantly towards hydraulic and hydrological modeling for flood hazard management.

The assessment of flooding areas and developing flood hazard maps play a key role in prevention of the many social, economic and environmental damages caused by flood. Most parts of Mazandaran, Iran are at risk of flooding caused by heavy rainfall and rivers. In this paper, the flood hazard map of Mazandaran province is assessed using subjective and subjective–objective weights in an Ordered Weighted Averaging-based GIS analysis. Flood hazard maps are produced based on the two types of weights, along the scale ranging from the pessimistic to optimistic decision strategies. The accuracy of the flood hazard maps was evaluated based on: (1) the percentage of historic flood occurrences that are within the flood hazard maps and (2) the assessment ratio, which is the ratio of known flood areas to whole area in a particular class of flood hazard maps. The results indicate that the percentage of flood areas produced by subjective and subjective–objective weights in “Very high class” are the same in the cases of most pessimistic (14.65%) and optimistic strategy (100%). However, in other strategies (0 < ORness < 1), the subjective–objective weights show better values than subjective weights for percentage of flood hazard areas. Similarly, the assessment ratio results show that flood hazard areas produced by subjective and subjective–objective weights in “Very high class” are the same in the most pessimistic (4.47) and optimistic strategies (1.2).

Rapid flood mapping is crucial in hazard evaluation and forecasting, especially in the early stage of hazards. Synthetic aperture radar (SAR) images are able to penetrate clouds and heavy rainfall, which is of special importance for flood mapping. However, change detection is a key part and the threshold selection is very complex in flood mapping with SAR. In this paper, a novel approach is proposed to rapidly map flood regions and estimate the flood degree, avoiding the critical step of thresholding. It converts the change detection of thresholds to land cover backscatter classifications. Sentinel-1 SAR images are used to get the land cover backscatter classifications with the help of Sentinel-2 optical images using a supervised classifier. A pixel-based change detection is used for change detection. Backscatter characteristics and variation rules of different ground objects are essential prior knowledge for flood analysis. SAR image classifications of pre-flood and flooding periods both take the same input to make sense of the change detection between them. This method avoids the inaccuracy caused by a single threshold. A case study in Shouguang is tested by this new method, which is compared with the flood map extracted by Otsu thresholding and normalized difference water index (NDWI) methods. The results show that our approach can identify the flood beneath vegetation well. Moreover, all required data and data processing are simple, so it can be popularized in rapid flooding mapping in early disaster relief.

The entire world is affected by Coronavirus disease (COVID-19), which is spreading worldwide in a short time. India is one of the countries which is affected most, therefore, the Government of India has implemented several lockdowns in the entire country from April 25, 2020. We studied air pollutants (i.e., PM2.5, Black Carbon (BC), and Polycyclic Aromatic Hydrocarbons (PAHs) level, and observed significantly sudden reduced. In India, most of the anthropogenic activities completely stopped. Therefore, we studied the levels of BC, PAHs and PM2.5 concentrations, their sources apportion, and health risk assessment during normal days, lockdown (from lockdown 1.0 to lockdown 4.0) and unlock down 1.0 situation at Sakchi, Jamshedpur city. It was observed that lockdowns and unlock down situations BC, PAHs and PM2.5 concentrations were significantly lower than regular days. We applied the advanced air mass back trajectory (AMBT) model to locate airborne particulate matter dispersal from different directions to strengthen the new result. The diagnostic ratio analyses of BC shows that wood burning contribution was too high during the lockdown situations. However, during normal days, the PAHs source profile was dedicated toward biomass, coal burning, and vehicle emission as primary sources of PAHs. During the lockdown period, emission from biomass and coal burning was a significant contributor to PAHs. The summaries of health risk assessment of BC quantified an equal number of passively smoked cigarettes (PSC) for an individual situation was studied. This study focuses on the overall climate impact of pandemic situations.Graphic abstract