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Biodiversity loss, driven by human activities, significantly affects the environment, human societies, and economies. Using the extended multi‐regional input–output (EEMRIO) and life cycle assessment (LCA) techniques, we offer insights into how these methodologies can be used to inform financial decisions related to biodiversity focusing on two key aspects: biodiversity impacts and ecosystem service dependencies. Our method combines spatially explicit characterization factors from LC‐IMPACT with the Global Resource Input‐Output Assesment (GLORIA) database to estimate biodiversity impacts. As a case study we assess the biodiversity impact of the MSCI All Country World Index (MSCI ACWI) which consist of about 3000 large‐ and mid‐sized companies, from 23 developed and 24 emerging countries. The results demonstrate that most of the biodiversity impact is caused in the Americas, followed by Asia, despite its low representation in the index's country weight (6%). Europe shows the least impact. These results emphasize the need to account for global supply chain linkages as products sold in one country might have significant biodiversity impacts elsewhere due to sourcing of production inputs. Second, our results identify the main determinants of the index's impact: land use, followed by water stress and climate change. Although most of the impact is localized in few sectors, the distinct characteristics of these sectors require industry‐specific mitigation approaches. Finally, double materiality results show both, the influence companies have on biodiversity and the reciprocal effects. Companies neglecting these impacts risk financial setbacks, making it a crucial concern for investors. [ABSTRACT FROM AUTHOR]

The accumulation of sea-surface debris around the coastal waters of Malta poses significant ecological and environmental challenges, negatively affecting marine ecosystems and human activities. This issue is exacerbated due to the lack of an effective system tailored to predict surface-debris movement specifically for the Islands of Malta. To address this gap, a pipeline that combines a machine learning-based prediction system with a physics-based model is proposed. This pipeline uses data on historical sea-surface current velocities to forecast future conditions and visualise debris movement. Central to this system are two machine learning models trained to predict surface velocities for the next 24 h for a specific area. These predictions are then utilised in a Lagrangian model to simulate and visualise the debris movement, providing insights into future dispersion patterns. A comparative evaluation of both models using real-world data is made to determine which one performs best in this application. This method offers a tailored approach to addressing sea-surface debris around Malta by accurately predicting sea-surface current velocities and visualising debris movement, improving cleanup operations and marine conservation strategies. [ABSTRACT FROM AUTHOR]

Countries are looking to reduce their agricultural sector's carbon footprint while encouraging economic and environmental sustainability. One proposed method of making agriculture more sustainable while maintaining farm incomes is through the production of bioenergy feedstocks. We examine the spatial aspect of the production of renewable energy feedstocks in Ireland. Two feedstocks are looked at—grass silage and short‐rotation coppice (SRC) willow. Spatial microsimulation analysis is utilised to assess the spatial suitability for alternative land uses, simulating land‐use change to compare economic returns with the current agricultural use. A farm‐based carbon subsidy is modelled based on avoided agricultural emissions and carbon sequestered. We find that midlands counties, especially those in the north midlands, have the highest proportion of land where feedstocks would be more profitable than the current agricultural use. Counties on the western seaboard have the lowest proportion of land where feedstocks would give greater returns. The amount of land where feedstocks are more profitable increases as greater carbon subsidies are provided. Of the two feedstocks assessed, SRC willow cultivation is more profitable than grass silage production. The overwhelming majority of land where producing feedstocks would be more profitable has cattle farming as its current use. [ABSTRACT FROM AUTHOR]

Peatlands play a key role in the circulation of the main greenhouse gases (GHG) – methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). Therefore, detecting the spatial pattern of GHG sinks and sources in peatlands is pivotal for guiding effective climate change mitigation in the land use sector. While geospatial environmental data, which provide detailed spatial information on ecosystems and land use, offer valuable insights into GHG sinks and sources, the potential of directly using remote sensing data from satellites remains largely unexplored. We predicted the spatial distribution of three major GHGs (CH4, CO2, and N2O) sinks and sources across Finland. Utilizing 143 field measurements, we compared the predictive capacity of three different data sets with MaxEnt machine-learning modeling: (1) geospatial environmental data including climate, topography and habitat variables, (2) remote sensing data (Sentinel-1 and Sentinel-2), and (3) a combination of both. The combined dataset yielded the highest accuracy with an average test area under the receiver operating characteristic curve (AUC) of 0.845 and AUC stability of 0.928. A slightly lower accuracy was achieved using only geospatial environmental data (test AUC 0.810, stability AUC 0.924). In contrast, using only remote sensing data resulted in reduced predictive accuracy (test AUC 0.763, stability AUC 0.927). Our results suggest that (1) reliable estimates of GHG sinks and sources cannot be produced with remote sensing data only and (2) integrating multiple data sources is recommended to achieve accurate and realistic predictions of GHG spatial patterns. Highlights: We compared remote sensing and geospatial data to predict peatland greenhouse gas sinks and sources across Finland. Remote sensing data perform less effectively than habitat and climate-related variables. We recommend integrating various data sources for modeling greenhouse gas sinks and sources. [ABSTRACT FROM AUTHOR]

Degradation and desertification represent serious threats, as they present severe environmental and socio-economic consequences, demanding immediate action. Although a recognized methodology for assessing degradation and desertification is missing, remote sensing has been recognized as a powerful support for its accessibility and efficacy. The aim of this study is to examine the application of remote sensing for assessing land and soil degradation and desertification. A total of 278 research papers retrieved from Scopus/Web of Science database and published over the past decade have been analyzed. From the analysis of scientific publications, a rising interest for these topics and a dominance of research from China has been registered. Established satellite data, Landsat, and MODIS, despite limitations in accuracy and resolution, remain popular due to easy access. This restricts research to broader scales and limits practical applications like land management. The prevalent use of vegetation indexes, while convenient, can be misleading due to their indirect connection to soil health. Consequently, vegetation-based models may not fully capture the complexities involved. To improve understanding, the study suggests a shift towards multi-indexes models and a move away from relying solely on readily available data products. Moreover, the application of data fusion methods could provide a more holistic view. [ABSTRACT FROM AUTHOR]

A four-step dividing algorithm of sampling points for artificial neural networks is presented to select a representative training subset for modelling the spatial distribution. The chromium and manganese contents in the topsoil in Tarko-Sale and Noyabrsk cities (Russian subarctic zone) were used as raw data. The spatial distributions of the content of elements in the topsoil layer were performed using a multilayer perceptron (MLP) with sigmoid and hyperbolic tangential activation functions. The root means squared error (RMSE) was calculated for each element content and area. The MLP with hyperbolic tangential activation function showed better accuracy for both subarctic cities and model areas. For Noyabrsk, the model with a hyperbolic tangent was about 10% better. For Tarko-Sale, the improvement of RMSE was around 200%. We have identified three classes of points: «elite», «middle», and «useless». Given this information when dividing the raw set, the accuracy of the models will increase. [ABSTRACT FROM AUTHOR]

The accumulation of sea-surface debris around the coastal waters of Malta poses significant ecological and environmental challenges, negatively affecting marine ecosystems and human activities. This issue is exacerbated due to the lack of an effective system tailored to predict surface-debris movement specifically for the Islands of Malta. To address this gap, a pipeline that combines a machine learning-based prediction system with a physics-based model is proposed. This pipeline uses data on historical sea-surface current velocities to forecast future conditions and visualise debris movement. Central to this system are two machine learning models trained to predict surface velocities for the next 24 h for a specific area. These predictions are then utilised in a Lagrangian model to simulate and visualise the debris movement, providing insights into future dispersion patterns. A comparative evaluation of both models using real-world data is made to determine which one performs best in this application. This method offers a tailored approach to addressing sea-surface debris around Malta by accurately predicting sea-surface current velocities and visualising debris movement, improving cleanup operations and marine conservation strategies.

The Gore Glen pond is located in the forested floodplain of the Gore River, in the Mid-Lothian region near Edinburgh, Scotland. This work has considered in detail the hydrology, hydrochemistry, and biodiversity of the pond together with all their interactions and in addition interactions with the adjacent Gore River. It is important as it is one of few studies considering all these issues for a pond located in a riverine floodplain. This work shows the pond is connected to the Gore River for discharge events in the river larger than a 1 in 5-year return period. For these events, the pond acts as an online storage feature and will attenuate the flow, thus contributing towards the mitigation of downstream flooding. These large events are also a potential source of the large amount of sediments, as well as adsorbed chemicals, accumulated in the pond. The open water surface of the pond is covered by duckweed, Lemna minor, for most of the year, and that has profound implications for the hydrobiological community and biogeochemical cycling. The system is characterised by nutrient release from sediments due to the extensive hypolimnion anoxia in the summer/autumn period caused by the decomposition of organic matter. Algae are mostly represented by epiphytic diatoms and the cyanobacterium Microcystis, but their development appears to be constrained by light penetration. The eutrophic conditions are also conducive for the population of Difflugia (Protozoa, Rhizopoda). This work improves our understanding of the ecological relations of testate amoebae—an important group which has recently been used as biological indicators for the analysis of paleoecological relationships and paleogeographical reconstructions. [ABSTRACT FROM AUTHOR]

The management of plant-parasitic nematodes (PPNs) still relies on traditional nematicides that threaten the environment and human health. Novel solutions are urgently needed for PPN pest management that are effective while safeguarding non-target organisms. Volatile phytochemicals belong to a structurally diverse group of bioactive metabolites that are believed to hold safer environmental characteristics than synthetic pesticides. Nonetheless, not many studies have analysed the potential environmental benefits of shifting to these novel bionematicides. In the present study, 20 phytochemical volatiles with reported nematicidal activity were compared to traditional pesticides using specific parameters of environmental and human health safety available on applied online databases and predicted in silico through specialised software. Overall, the reviewed nematicidal phytochemicals were reportedly less toxic than synthetic nematicides. They were predicted to disperse to the air and soil environmental compartments and were reported to have a lower toxicity on aquatic organisms. On the contrary, the synthetic nematicides were reportedly toxic to aquatic organisms while showing a predicted high affinity to the water environmental compartment. As alternatives, β-keto or fatty acid derivatives, e.g., aliphatic alcohols or ketones, showed more adequate properties. This study highlights the importance of complementing studies on nematicidal activity with a risk assessment-based analysis to allow for a faster selection of nematicidal phytochemical volatiles and to leverage the development and implementation of bionematicides. [ABSTRACT FROM AUTHOR]

Abstract Countries are looking to reduce their agricultural sector's carbon footprint while encouraging economic and environmental sustainability. One proposed method of making agriculture more sustainable while maintaining farm incomes is through the production of bioenergy feedstocks. We examine the spatial aspect of the production of renewable energy feedstocks in Ireland. Two feedstocks are looked at—grass silage and short‐rotation coppice (SRC) willow. Spatial microsimulation analysis is utilised to assess the spatial suitability for alternative land uses, simulating land‐use change to compare economic returns with the current agricultural use. A farm‐based carbon subsidy is modelled based on avoided agricultural emissions and carbon sequestered. We find that midlands counties, especially those in the north midlands, have the highest proportion of land where feedstocks would be more profitable than the current agricultural use. Counties on the western seaboard have the lowest proportion of land where feedstocks would give greater returns. The amount of land where feedstocks are more profitable increases as greater carbon subsidies are provided. Of the two feedstocks assessed, SRC willow cultivation is more profitable than grass silage production. The overwhelming majority of land where producing feedstocks would be more profitable has cattle farming as its current use.

Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations and when estimating uncertainty in model predictions. However, methods for doing this can be computationally expensive, particularly when the number of unknown model parameters is large. The aim of this study is to develop and test an efficient profile likelihood-based method, which takes advantage of the structure of the mathematical model being used. We do this by identifying specific parameters that affect model output in a known way, such as a linear scaling. We illustrate the method by applying it to three toy models from different areas of the life sciences: (i) a predator–prey model from ecology; (ii) a compartment-based epidemic model from health sciences; and (iii) an advection–diffusion reaction model describing the transport of dissolved solutes from environmental science. We show that the new method produces results of comparable accuracy to existing profile likelihood methods but with substantially fewer evaluations of the forward model. We conclude that our method could provide a much more efficient approach to parameter inference for models where a structured approach is feasible. Computer code to apply the new method to user-supplied models and data is provided via a publicly accessible repository.

Abstract Background Grasslands provide a wide range of ecosystem services (ESs). However, there is currently no method for easily diagnosing the level of ESs produced. Our aim was to develop ES indicators based on botanical surveys, which are readily available data and integrative of grassland spatiotemporal variability. Methods Based on academic knowledge and expertise, we identified several simple vegetation criteria that we aggregated using a multicriteria analysis tool to construct indicators of the level of ESs provided by grasslands. In this study, the indicators were calculated from over 2000 botanical surveys spread over a wide biogeographical gradient. Results Analyses of correlation between the various indicators show that “forage supply” and “diversity conservation” were not correlated. “Forage availability” and “nitrogen availability for the vegetation” were positively linked together and negatively linked to the robustness of the plant community to extreme events. A temporal approach highlights that the “biodiversity conservation” score decreased from 1970 to 2010 and that “nitrogen availability for the vegetation” was lower in 1970 and 1980 than in 2000 and 2010. Conclusions These results show that our aggregation method based on a large data set of botanical surveys could be appropriate for studying temporal dynamics of ESs.

In densely populated cities, residents living near aerodromes may experience heightened exposure to aircraft noise. With hybrid work arrangement, authorities have observed a rise in the number of complaints filed by residents affected by aircraft noise. In view of this problem, urban planners are now placing even more emphasis on exploring solutions that can manage aircraft noise in new and existing residential areas. To achieve this objective, urban planners usually rely on external acoustic consultants to generate noise maps using commercial software. However, urban planners may need to quickly evaluate potential noise issues in the neighbourhood so that noise management strategies can be brainstormed in advance. In this article, we present the development and benchmarking of a package designed to easily generate aircraft noise maps via simplified procedures and a reduced amount of input data, with acceptable accuracy in the results. These benefits distinguish our developed package from commercial software. Our developed package was eventually integrated into an in-house-developed unified urban environmental modelling tool that aims to help urban planners design more liveable and sustainable residential towns in an intuitive and quick manner.

Background: Grasslands provide a wide range of ecosystem services (ESs). However, there is currently no method for easily diagnosing the level of ESs produced. Our aim was to develop ES indicators based on botanical surveys, which are readily available data and integrative of grassland spatiotemporal variability. Methods: Based on academic knowledge and expertise, we identified several simple vegetation criteria that we aggregated using a multicriteria analysis tool to construct indicators of the level of ESs provided by grasslands. In this study, the indicators were calculated from over 2000 botanical surveys spread over a wide biogeographical gradient. Results: Analyses of correlation between the various indicators show that "forage supply" and "diversity conservation" were not correlated. "Forage availability" and "nitrogen availability for the vegetation" were positively linked together and negatively linked to the robustness of the plant community to extreme events. A temporal approach highlights that the "biodiversity conservation" score decreased from 1970 to 2010 and that "nitrogen availability for the vegetation" was lower in 1970 and 1980 than in 2000 and 2010. Conclusions: These results show that our aggregation method based on a large data set of botanical surveys could be appropriate for studying temporal dynamics of ESs. [ABSTRACT FROM AUTHOR]

Context: Models of human agency within research on land systems and landscapes do not fully account for social and cultural factors in decision making. Conversely, within social theory, parallel concepts of agency do not fully take biophysical and spatial factors into account. This calls for a synthesis of conceptual models addressing human decision making in land systems. Objectives: The review identifies parallels between social and ecological perspectives on humans as co-constituent parts of complex land systems. On this basis selected models of agency combining insights from social theory and land systems research are outlined and compared, and improved concepts are outlined. Methods: Elements of agency in modern agricultural land systems are reviewed. A case study illustrating the application of agency concepts in an analysis of decision making among farmers on the Canterbury Plains (New Zealand) is presented. On this basis it is discussed how to improve understandings of human agency in land systems. Results: The review identifies and compares parallel conceptions of agency, practice and holism in landscape ecology and social theory. Taking the agency of farmers in contemporary agricultural landscapes as an example, theories currently used to characterise and interpret the agency of farmers are discussed and improvements considered. Potentials for improvement of current conceptual models are indicated and discussed, and an improved model of agency is suggested. Conclusions: Based on the review, the article presents an improved conceptual model of agency in land systems emphasizing the position of agents in social-ecological contexts of action. [ABSTRACT FROM AUTHOR]

Abstract Intelligent Transport System (ITS) is a typical class of Cyber-Physical Systems (CPS), and due to the special characteristics of such systems, higher requirements are placed on system security. Runtime verification is a lightweight verification technique which is used to improve the security of such systems. However, current runtime verification methods often ignore the effects of the physical environment (e.g., the effects of rain, snow, and other weather changes on road conditions), which results in the inability of the monitor to effectively monitor the system according to the changes in the environment. To address this problem, this paper proposes a method for constructing a runtime monitor with environmental context-awareness capability. First, the physical environment factors affecting the system are formally described and constructed into an environment model, then the system statute is transformed into a Büchi automaton, and then a synthesis algorithm combining the environment model and the Büchi automaton is designed based on the network of automatons, and the corresponding monitor is generated. Finally, the proposed method is applied and verified on simulation and real objects. The experimental results show that the monitors generated based on the method of this paper can effectively monitor unsafe events in different environments, thus improving the safety of intelligent driving systems.

In many regions, a tradeoff exists between draining wetlands to support the expansion of agricultural land, and conserving wetlands to maintain their valuable ecosystem services. Decisions about wetland drainage are often made without identifying the impacts on the services these systems provide. We address this gap through a novel assessment of impacts on ecosystem services via wetland drainage in the Canadian prairie landscape. Draining pothole wetlands has large impacts, but sensitivity varies among the indicators considered. Loss of water storage increased the magnitude of median annual flows, but absolute increases with drainage were higher for larger, less frequent events. Total phosphorus exports increased in concert with streamflow. Our analysis suggested disproportionate riparian habitat losses with the first 30% of wetland area drained. Dabbling ducks and wetland-associated bird abundances respond strongly to the loss of small wetland ponds; abundances were predicted to decrease by half with the loss of only 20%–40% of wetland area. This approach to evaluating changes to key wetland ecosystem services in a large region where wetland drainage is ongoing can be used with an economic valuation of the drainage impacts, which should be weighed against the benefits associated with agricultural expansion.

Cyanobacterial blooms are a persistent concern to water management and treatment, with blooms potentially causing the release of toxins and degrading water quality. However, previous models have not considered the zero inflation of cyanobacteria count data. Typically, a relatively large proportion of measured count data are zeros or non-detects of cyanobacteria, representing either no cyanobacteria was present or the cell number was too low to be detected. Commonly used Poisson and negative binomial models for count data underestimate the probability of zero data, making these models less reliable. This study proposes a Bayesian approach to fit the cyanobacteria abundance data with mixture models that handle zero-inflated data. Predictor variables considered included weather and water quality measures that can easily be obtained day-to-day. The optimal model (zero-inflated negative binomial) was used to predict cyanobacteria alert levels on a separate test set. The ability to predict narrow alert levels was limited, however, 76% accuracy was achieved in predicting cyanobacteria counts above or below 1,000 cells/mL. Parameter estimates were highly variable and demonstrated that complex and uncertain factors influence cyanobacteria count predictions. The modelling approach can be applied to a wide range of environmental problems where zero-inflated data is common. HIGHLIGHTS Bayesian mixture models were used to model zero-inflated cyanobacteria count data.; A Bayesian variable selection method was applied to select important variables.; A zero-inflated model achieved 76% accuracy in predicting binary alert levels.; Bayesian framework produced probabilistic categorization of alert levels.; The model is well suited for management of complex systems with high uncertainty.;

Degradation and desertification represent serious threats, as they present severe environmental and socio-economic consequences, demanding immediate action. Although a recognized methodology for assessing degradation and desertification is missing, remote sensing has been recognized as a powerful support for its accessibility and efficacy. The aim of this study is to examine the application of remote sensing for assessing land and soil degradation and desertification. A total of 278 research papers retrieved from Scopus/Web of Science database and published over the past decade have been analyzed. From the analysis of scientific publications, a rising interest for these topics and a dominance of research from China has been registered. Established satellite data, Landsat, and MODIS, despite limitations in accuracy and resolution, remain popular due to easy access. This restricts research to broader scales and limits practical applications like land management. The prevalent use of vegetation indexes, while convenient, can be misleading due to their indirect connection to soil health. Consequently, vegetation-based models may not fully capture the complexities involved. To improve understanding, the study suggests a shift towards multi-indexes models and a move away from relying solely on readily available data products. Moreover, the application of data fusion methods could provide a more holistic view.

Intelligent Transport System (ITS) is a typical class of Cyber-Physical Systems (CPS), and due to the special characteristics of such systems, higher requirements are placed on system security. Runtime verification is a lightweight verification technique which is used to improve the security of such systems. However, current runtime verification methods often ignore the effects of the physical environment (e.g., the effects of rain, snow, and other weather changes on road conditions), which results in the inability of the monitor to effectively monitor the system according to the changes in the environment. To address this problem, this paper proposes a method for constructing a runtime monitor with environmental context-awareness capability. First, the physical environment factors affecting the system are formally described and constructed into an environment model, then the system statute is transformed into a Büchi automaton, and then a synthesis algorithm combining the environment model and the Büchi automaton is designed based on the network of automatons, and the corresponding monitor is generated. Finally, the proposed method is applied and verified on simulation and real objects. The experimental results show that the monitors generated based on the method of this paper can effectively monitor unsafe events in different environments, thus improving the safety of intelligent driving systems. [ABSTRACT FROM AUTHOR]

A modelling framework suitable for detecting shape shifts in functional profiles combining the notion of the Fréchet mean and the concept of deformation models is developed and proposed. The generalized mean sense offered by the Fréchet mean notion is employed to capture the typical pattern of the profiles under study, while the concept of deformation models, and in particular of the shape-invariant model, allows for interpretable parameterizations of the profile's deviations from the typical shape. The EWMA-type control charts compatible with the functional nature of data and the employed deformation model are built and proposed, exploiting certain shape characteristics of the profiles under study with respect to the generalized mean sense, allowing for the identification of potential shifts concerning the shape and/or the deformation process. Potential shifts in the shape deformation process are further distinguished into significant shifts with respect to amplitude and/or the phase of the profile under study. The proposed modeling and shift detection framework is implemented to a real-world case study, where daily concentration profiles concerning air pollutants from an area in the city of Athens are modeled, while profiles indicating hazardous concentration levels are successfully identified in most cases. [ABSTRACT FROM AUTHOR]

The management of plant-parasitic nematodes (PPNs) still relies on traditional nematicides that threaten the environment and human health. Novel solutions are urgently needed for PPN pest management that are effective while safeguarding non-target organisms. Volatile phytochemicals belong to a structurally diverse group of bioactive metabolites that are believed to hold safer environmental characteristics than synthetic pesticides. Nonetheless, not many studies have analysed the potential environmental benefits of shifting to these novel bionematicides. In the present study, 20 phytochemical volatiles with reported nematicidal activity were compared to traditional pesticides using specific parameters of environmental and human health safety available on applied online databases and predicted in silico through specialised software. Overall, the reviewed nematicidal phytochemicals were reportedly less toxic than synthetic nematicides. They were predicted to disperse to the air and soil environmental compartments and were reported to have a lower toxicity on aquatic organisms. On the contrary, the synthetic nematicides were reportedly toxic to aquatic organisms while showing a predicted high affinity to the water environmental compartment. As alternatives, β-keto or fatty acid derivatives, e.g., aliphatic alcohols or ketones, showed more adequate properties. This study highlights the importance of complementing studies on nematicidal activity with a risk assessment-based analysis to allow for a faster selection of nematicidal phytochemical volatiles and to leverage the development and implementation of bionematicides.

The chamois (Rupicapra rupicapra) is one of the primary mammals on the territory of Romania that still exists nowadays, but one which has restricted its suitable living habitat to alpine and subalpine areas due to global warming and the influence of anthropic factors (poaching, excessive grazing). The main aim of the present study consists of the ecological diagnosis of the fields in the Romanian Carpathians suitable for the chamois by using tools for mapping, as well as the statistical and mathematical analysis of the GIS technology. In order to determine field favorability modelling for the chamois, we used both knowledge expert-type data and official assessments of real actual numbers of specimens, but also, a complete database regarding the main biotic factors (topographic characteristics, hydrographic network, climate characteristics); abiotic factors (the existence of pastures, shrubby and forest vegetation, as well as the presence of feeding areas during winter), and anthropic factors (grazing, mountain tourism). All the hunting grounds at the level of the mountainous area in Romania was analysed in a qualitative and quantitative manner while taking into account the favorability and restriction character of these factors. To validate the modelling results that were obtained, the data from 97 hunting grounds where the chamois is present were used. The main results highlighted the territories situated in the massifs: Rodnei, Retezat, Făgăraş, Pietrosul Maramureşului as being territories that provide the best conditions for Rupicapra rupicapra. With these studies at our disposal, researchers may indirectly evaluate the genetic diversity of the species that are already present as well as the repopulation of the Romanian Carpathians' most advantageous areas. [ABSTRACT FROM AUTHOR]

Submarine outfalls have been employed to convey urban effluents to their fate in the open ocean due to their dilution capacity and organic matter decay. This work analysed Escherichia coli concentrations in the Barra da Tijuca (Rio de Janeiro, Brazil) submarine outfall plume, considering an hourly variable bacterial die-off due to environmental parameters associated with dynamic changes, vertical plume position, and thickness in response to hydrodynamic conditions. The adopted modelling procedure included coupling a near-field mixing zone model, NRFIELD, with the far-field Lagrangian transport and water quality model of the SisBaHiA® (http://www.sisbahia.coppe.ufrj.br). The coupling methodology simulated E. coli concentrations considering simultaneous variations in temperature, salinity, solar radiation, and hydrodynamic conditions. The results showed substantial variability in E. coli concentrations in the marine environment due to variable environmental conditions, regulating solar radiation levels over the submerged plume. [ABSTRACT FROM AUTHOR]

The article presents original, probabilistic models for the quantification of drought risk, based on generalized distribution functions for the distribution of maximum values as well as the standardized precipitation index. Using the models, a probabilistic measurement of drought risk has been made for three regions over four 5-year periods for each region. Three measurement points located in Poland were selected as a case study. The study provides an innovative approach in the field of probabilistic models of quantification; moreover, drought risk levels have been calculated for the selected locations. Furthermore, the method may be used for creating indexes for any climatic region subject to study. [ABSTRACT FROM AUTHOR]

This study explored the first direct and inverse modelling of Carbon monoxide (CO) concentration by applying three different computational approaches namely, Least Square-boost (L-Boost), Hammerstein Weiner (HW), and Multi-variate regression (MVR) models for modelling CO using Sulphur dioxide (SO2), Nitrogen dioxide (NO2) and Ozone (O3). Two filters feature extraction methods were used in the input-combinations selection, which was classified into the direct modelling approach (C1) and inverse modelling approach (C2). Four different statistical metrics, including Nash-Sutcliffe efficiency, Person correlation coefficient, mean absolute error and mean absolute percentage error was used in evaluating the performance of the models. Based on the performance skills of the models, it is indicated that the direct modelling approach showed superior performance to the inverse approach. Furthermore, the non-linear models (HW and L-Boost) depict higher performance skills than the linear MVR model. Overall, the L-Boost-C1 models showed higher performance based on statistical metrics.

Regional assessments of the wind erosion risk are rare and vary due to the methods used and the available data to be included. The adaptation of existing methods has the advantage that the results can be compared directly. We adopted an already successfully applied methodology (ILSWE—applied in East Africa), to investigate the spatiotemporal variability of the wind erosion risk between 2005 and 2019 in Southern Africa. The approach integrates climatic variables, a vegetation index, and soil properties to describe the potential impact of wind erosion at the landscape scale. The annual and seasonal variability is determined by the vegetation cover, whereas droughts and strong El Niño events had only regional effects. We estimated that 8.3% of the study area experiences a moderate to elevated wind erosion risk over the 15‐year period with annual and inter‐annual fluctuations showing a slight upward trend. In general, the desert and drylands in the west have the highest proportion of risk areas, the moist forests in the east are characterized by a very low risk of wind erosion, while the grasslands, shrublands, and croplands in the interior most likely react to changes of climatic conditions. The validation process is based on a comparison with the estimated frequency of dust storms derived from the aerosol optical depth and angstrom exponent and revealed an overall accuracy of 65%. The results of this study identify regions and yearly periods prone to wind erosion to prioritize for further analysis and conservation policies for mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

Atlantic herring (Clupea harengus) are economically and ecologically significant but have been in decline in Atlantic Canada due to an uncertain combination of environmental recruitment controls, predation, and fishing (commercial fishery and poorly documented bait removals). Fisheries and Oceans Canada partially closed Atlantic mackerel (Scomber scombrus) and herring fisheries in March 2022 amid controversy and disagreement. Here, we develop a conceptual model for natural and anthropogenic controls on herring abundance centered on the southern Gulf of St. Lawrence (sGSL) and evaluate the likely importance of each. We provide the first estimates of the magnitude of bait fishery withdrawals in the sGSL. The decline in sGSL herring is likely driven by natural predation exacerbated by fishing. Bait fishery removals were 182 tonnes in 2021, suggesting that previous estimates for removals of spring-spawning herring were ∼30% too low. Fisheries closure is consistent with a precautionary approach given uncertainties and irreversibility of stock collapse. Ecosystemic models are needed but will be difficult to develop given the incomplete understanding of prey substitutability and net effect of interacting environmental processes. [ABSTRACT FROM AUTHOR]

Digital twins are increasingly important in many domains, including for understanding and managing the natural environment. Digital twins of the natural environment are fueled by the unprecedented amounts of environmental data now available from a variety of sources from remote sensing to potentially dense deployment of earth‐based sensors. Because of this, data science techniques inevitably have a crucial role to play in making sense of this complex, highly heterogeneous data. This short article reflects on the role of data science in digital twins of the natural environment, with particular attention on how resultant data models can work alongside the rich legacy of process models that exist in this domain. We seek to unpick the complex two‐way relationship between data and process understanding. By focusing on the interactions, we end up with a template for digital twins that incorporates a rich, highly dynamic learning process with the potential to handle the complexities and emergent behaviors of this important area. [ABSTRACT FROM AUTHOR]

Simple Summary: The aim of the present study was to develop predictive models for the potential generation and emission of ammonia from Brazilian broiler chicken production. For this study, samples of poultry litter (shavings and coffee husks) from thirty commercial poultry houses located in the Zona da Mata Mineira region were analyzed. The poultry litter samples were subjected to different air temperatures in climatic chambers. The models developed and validated showed high accuracy, indicating that they can be used to estimate the potential for generation and emission of ammonia in poultry production, enabling its quantification when its measurement is not possible. Air quality is one of the main factors that must be guaranteed in animal production. However, the measurement of pollutants is still a problem in several countries because the available methods are costly and do not always apply to the reality of the constructive typology adopted, as in countries with a hot climate, which adopt predominantly open facilities. Thus, the objective of the present study was to develop predictive models for the potential generation and emission of ammonia in the production of broiler chickens with different types of litter, different reuse cycles and under different climatic conditions. Samples of poultry litter from thirty commercial aviaries submitted to different air temperatures were analyzed. The experiment was conducted and analyzed in a completely randomized design, following a factorial scheme. Models were developed to predict the potential for generation and emission of ammonia, which can be applied in facilities with ambient conditions of air temperature between 25 and 40 °C and with wood shaving bed with up to four reuse cycles and coffee husks bed with up to six reuse cycles. The developed and validated models showed high accuracy indicating that they can be used to estimate the potential for ammonia generation and emission. [ABSTRACT FROM AUTHOR]

The Mediterranean Basin is one of the most biodiverse areas in the world, harboring 25,000 plant species, of which 60% are endemic. Some of them have narrow distributions, such as Euphorbia orphanidis, which is only known from alpine screes on Mt. Parnassos in Greece. Its exact distribution in this mountain was, however, poorly known, and its phylogenetic origin was also unclear. We performed extensive field work in Mt. Parnassos and could register E. orphanidis only in five patches of limestone screes in the eastern part of this mountain range, emphasizing its very narrow distribution, which is likely limited by topography influencing water availability as indicated by environmental modeling. We also registered 31 accompanying species and thus characterized its habitat. Using nuclear ribosomal internal transcribed spacer and plastid ndhF-trnL and trnT-trnF sequences, we show that it belongs to E. sect. Patellares, despite not having connate raylet leaves typical for this section, and not to E. sect. Pithyusa as previously suggested. The relationships among the species of E. sect. Patellares are poorly resolved, suggesting their simultaneous divergence that dated to the late Pliocene, which coincided with the establishment of the Mediterranean climate. The relative genome size of E. orphanidis is in the range of that for the other members of E. sect. Patellares, suggesting that it is diploid. Finally, we performed multivariate morphological analyses to generate a comprehensive description of E. orphanidis. Based on its narrow distribution and the anticipated negative impact of global warming, we consider this species endangered. Our study demonstrates how microrelief can limit the distribution of plants in topographically heterogeneous mountain environments and likely plays an important, yet neglected, role in shaping the distribution patterns of plants in the Mediterranean Basin. [ABSTRACT FROM AUTHOR]

Route optimization has been used for years to plan the routes for municipal solid waste (MSW) collection trucks to achieve cost reductions. Historically, optimized routes had overlooked a number of aspects and parameters in their design. This study aims to consider MSW odour detection as a performance indicator and a priority constraint in the optimization process by quantifying the impact of objectionable odours from uncollected aged MSW that contains a high percentage of food waste (typically called wet garbage). Odours from 48 aged food waste samples were rated on a scale from 0 to 3 to mark the beginning of the critical time of objectionable odour detection. The critical time was found to take place approximately at the hour 13.6, which was then used, along with the estimated food waste weight in the bin, to define the beginning of a time window that puts the bin on a high priority status for collection over the other, less odoriferous bins. Three optimization scenarios for collection of 100 MSW bins in the city of Madaba, Jordan, were conducted under different constraints: least travelled distance, maximum collected volume and least odour impact. Without the application of the odour consideration, a total travelled distance of 143 km was the shortest travelled distance achieved, with 53 bins emitting odours and leaving 81 m3 of uncollected waste. However, when odour impact was the main routing constraint, a total travelled distance of 161 km was needed and 13 m3 of waste was left uncollected. [ABSTRACT FROM AUTHOR]

Agricultural landscapes are multifunctional and closely connected to the much wider food system. In our review, we explore three specific aspects of modelling approaches for agricultural landscape assessments: (a) how multifunctionality is commonly analysed to support decision-making for sustainable agricultural land management; (b) how the modelling approaches relate to the wider food systems; and (c) how gaps in the existing modelling approaches might be addressed. For this, we identified key elements of agricultural landscape assessments, including ecosystem services (ESS) provided, driving factors, and linkages between crop and livestock production, and to the wider food system. We reviewed 238 publications with respect to these elements. While biodiversity and the ESS ‘water conditions’ and ‘atmospheric composition/conditions’ are widely covered, they are rarely used in combination. Other ESS, such as ‘pest and disease control’, are largely missing. Our results further indicate strong differences between individual approaches regarding model parameterisation and consideration of abiotic, biotic, and management driving factors. Our analysis also shows that the interconnectedness of crop and livestock production is rarely considered and that return flows from the food system are not considered. Furthermore, impacts from the production of external inputs and off-site effects are not considered. Consequently, existing models might overlook trade-offs and synergies between landscape functions. Failure to consider variations in relevant driving factors and food system linkages likely results in studying incorrect levers for change and failing to show decision-makers the full scope of available action. We thus suggest adopting more encompassing modelling approaches to ensure coverage of the full scope of potential actions, whilst hedging against overly costly data requirements by, e.g. employing well-designed sensitivity analyses. In this way, the most relevant components and thus the most important leverage points for interventions can be identified.

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.

The Mediterranean Basin is one of the most biodiverse areas in the world, harboring 25,000 plant species, of which 60% are endemic. Some of them have narrow distributions, such as Euphorbia orphanidis, which is only known from alpine screes on Mt. Parnassos in Greece. Its exact distribution in this mountain was, however, poorly known, and its phylogenetic origin was also unclear. We performed extensive field work in Mt. Parnassos and could register E. orphanidis only in five patches of limestone screes in the eastern part of this mountain range, emphasizing its very narrow distribution, which is likely limited by topography influencing water availability as indicated by environmental modeling. We also registered 31 accompanying species and thus characterized its habitat. Using nuclear ribosomal internal transcribed spacer and plastid ndhF–trnL and trnT–trnF sequences, we show that it belongs to E. sect. Patellares, despite not having connate raylet leaves typical for this section, and not to E. sect. Pithyusa as previously suggested. The relationships among the species of E. sect. Patellares are poorly resolved, suggesting their simultaneous divergence that dated to the late Pliocene, which coincided with the establishment of the Mediterranean climate. The relative genome size of E. orphanidis is in the range of that for the other members of E. sect. Patellares, suggesting that it is diploid. Finally, we performed multivariate morphological analyses to generate a comprehensive description of E. orphanidis. Based on its narrow distribution and the anticipated negative impact of global warming, we consider this species endangered. Our study demonstrates how microrelief can limit the distribution of plants in topographically heterogeneous mountain environments and likely plays an important, yet neglected, role in shaping the distribution patterns of plants in the Mediterranean Basin.

In Singapore, about 900,000 tonnes of plastic waste is being disposed of annually, making it one of the major waste streams. However, at 4% recycling rate (in 2019 and 2020), plastic waste has one of the lowest recycling rate compared to other waste streams such as metal and paper. Most of the plastic waste would be incinerated at waste-to-energy (WTE) plants. In a bid to address the environmental impacts of the various plastic waste end-of-life (EOL) pathways in Singapore, life cycle assessment (LCA) was performed, whereby the treatment of 1 kg of plastic waste was selected as the functional unit. The EOL pathways include domestic mechanical recycling, baling for overseas recycling, and WTE incineration. Three environmental impacts were considered, i.e. global warming potential, cumulative energy demand and water depletion. These indicators were used for hotspot analysis to identify areas within the system boundary that could be prioritised for mitigation actions, as well as sensitivity analysis to ascertain the robustness of the LCA results and their sensitivity to some uncertainty factors in the study. [ABSTRACT FROM AUTHOR]

This study addresses the challenging task of analysing multifunctional landscapes through an innovative integrated modelling approach. Acknowledging the limitations of disciplinary models in assessing diverse landscape functions, we present a conceptual framework for their integration. Demonstrating the feasibility and effectiveness of this approach in a Netherlands case study, we assess alternative land use changes for drought and carbon sequestration. Results underscore the framework's efficacy in elucidating the intricate relationship between carbon and water across multiple model runs and iterations. Notably, the alternative land use scenario reveals an average increase in soil moisture during dry periods and an increase in soil organic carbon content across four model runs. This softly coupled approach offers valuable insights into environmental modelling, facilitating navigation of complex integration challenges for researchers and practitioners. Furthermore, it enhances modelling transparency by elucidating variable representation and processes, providing a foundation for informed decisions in sustainable landscape management. • Increased attention to landscape multi-functionality leads to the need for integrated modelling approaches. • Integrated modelling is perceived as extremely challenging and also time and resource demanding. • A stepwise framework for integrated modelling determines the degree to which disciplinary models need to be coupled. • An illustration with soil carbon and hydrology shows the framework's usefulness and the need to link disciplinary models. [ABSTRACT FROM AUTHOR]

A modelling framework suitable for detecting shape shifts in functional profiles combining the notion of the Fréchet mean and the concept of deformation models is developed and proposed. The generalized mean sense offered by the Fréchet mean notion is employed to capture the typical pattern of the profiles under study, while the concept of deformation models, and in particular of the shape-invariant model, allows for interpretable parameterizations of the profile’s deviations from the typical shape. The EWMA-type control charts compatible with the functional nature of data and the employed deformation model are built and proposed, exploiting certain shape characteristics of the profiles under study with respect to the generalized mean sense, allowing for the identification of potential shifts concerning the shape and/or the deformation process. Potential shifts in the shape deformation process are further distinguished into significant shifts with respect to amplitude and/or the phase of the profile under study. The proposed modeling and shift detection framework is implemented to a real-world case study, where daily concentration profiles concerning air pollutants from an area in the city of Athens are modeled, while profiles indicating hazardous concentration levels are successfully identified in most cases.

Protected forest areas are fraught with severe threats from mining, agriculture and settlement expansion, and unsustainable use of forest resources. Due to funding and technical challenges, the inadequate monitoring and lack of information limits the conservation efforts in Zambia in general and Solwezi district in particular. Field-based methods in monitoring forest quality and suitability are time consuming and inefficient especially in inaccessible areas. However, with the advent of technology, Geographical Information System (GIS) and remote sensing, important data for forest quality can easily be accessed. This study aimed at assessing the state of protected forest areas in Zambia’s Solwezi Copper mining district, prone to forest fragmentation. Furthermore, this study identifies suitable areas for conservation, based on standardized criteria that combine Multi Criteria Decision Analysis (MCDA) and GIS approach. A suitability model was developed for the selection of the suitable areas, and elimination of the unsuitable ones, using GIS and Analytical Hierarchy Process (AHP). Five suitability criteria and two restriction criteria were used in this model. The results show different ranked levels of suitability which include 15.4% restricted, 18.1% lowly suitable, 14.9% moderately suitable, 21.2% highly suitable and 30.4% extremely suitable. Our approach informs conservationists, and other stakeholders about the status of protected forest areas and avails novel opportunities for creating new ones. This study’s modelling approach can be a prerequisite to sustainable forest management by policy makers and practitioners, and an essential input into forest monitoring.

Protected forest areas are fraught with severe threats from mining, agriculture and settlement expansion, and unsustainable use of forest resources. Due to funding and technical challenges, the inadequate monitoring and lack of information limits the conservation efforts in Zambia in general and Solwezi district in particular. Field-based methods in monitoring forest quality and suitability are time consuming and inefficient especially in inaccessible areas. However, with the advent of technology, Geographical Information System (GIS) and remote sensing, important data for forest quality can easily be accessed. This study aimed at assessing the state of protected forest areas in Zambia's Solwezi Copper mining district, prone to forest fragmentation. Furthermore, this study identifies suitable areas for conservation, based on standardized criteria that combine Multi Criteria Decision Analysis (MCDA) and GIS approach. A suitability model was developed for the selection of the suitable areas, and elimination of the unsuitable ones, using GIS and Analytical Hierarchy Process (AHP). Five suitability criteria and two restriction criteria were used in this model. The results show different ranked levels of suitability which include 15.4% restricted, 18.1% lowly suitable, 14.9% moderately suitable, 21.2% highly suitable and 30.4% extremely suitable. Our approach informs conservationists, and other stakeholders about the status of protected forest areas and avails novel opportunities for creating new ones. This study's modelling approach can be a prerequisite to sustainable forest management by policy makers and practitioners, and an essential input into forest monitoring. [ABSTRACT FROM AUTHOR]