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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]

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 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]

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]

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.

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]

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]

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., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)

Classification of beaches into morphodynamic states is a common approach in sandy beach studies, due to the influence of natural variables in ecological patterns and processes. The use of remote sensing for identifying beach type and monitoring changes has been commonly applied through multiple methods, which often involve expensive equipment and software processing of images. A previous study on the South African Coast developed a method to classify beaches using conditional tree inferences, based on beach morphological features estimated from public available satellite images, without the need for remote sensing processing, which allowed for a large-scale characterization. However, since the validation of this method has not been tested in other regions, its potential uses as a trans-scalar tool or dependence from local calibrations has not been evaluated. Here, we tested the validity of this method using a 200-km stretch of the Brazilian coast, encompassing a wide gradient of morphodynamic conditions. We also compared this locally derived model with the results that would be generated using the cut-off values established in the previous study. To this end, 87 beach sites were remotely assessed using an accessible software (i.e., Google Earth) and sampled for an in-situ environmental characterization and beach type classification. These sites were used to derive the predictive model of beach morphodynamics from the remotely assessed metrics, using conditional inference trees. An additional 77 beach sites, with a previously known morphodynamic type, were also remotely evaluated to test the model accuracy. Intertidal width and exposure degree were the only variables selected in the model to classify beach type, with an accuracy higher than 90% through different metrics of model validation. The only limitation was the inability in separating beach types in the reflective end of the morphodynamic continuum. Our results corroborated the usefulness of this method, highlighting the importance of a locally developed model, which substantially increased the accuracy. Although the use of more sophisticated remote sensing approaches should be preferred to assess coastal dynamics or detailed morphodynamic features (e.g., nearshore bars), the method used here provides an accessible and accurate approach to classify beach into major states at large spatial scales. As beach type can be used as a surrogate for biodiversity, environmental sensitivity and touristic preferences, the method may aid management in the identification of priority areas for conservation. [ABSTRACT FROM AUTHOR]

Classification of beaches into morphodynamic states is a common approach in sandy beach studies, due to the influence of natural variables in ecological patterns and processes. The use of remote sensing for identifying beach type and monitoring changes has been commonly applied through multiple methods, which often involve expensive equipment and software processing of images. A previous study on the South African Coast developed a method to classify beaches using conditional tree inferences, based on beach morphological features estimated from public available satellite images, without the need for remote sensing processing, which allowed for a large-scale characterization. However, since the validation of this method has not been tested in other regions, its potential uses as a trans-scalar tool or dependence from local calibrations has not been evaluated. Here, we tested the validity of this method using a 200-km stretch of the Brazilian coast, encompassing a wide gradient of morphodynamic conditions. We also compared this locally derived model with the results that would be generated using the cut-off values established in the previous study. To this end, 87 beach sites were remotely assessed using an accessible software (i.e., Google Earth) and sampled for an in-situ environmental characterization and beach type classification. These sites were used to derive the predictive model of beach morphodynamics from the remotely assessed metrics, using conditional inference trees. An additional 77 beach sites, with a previously known morphodynamic type, were also remotely evaluated to test the model accuracy. Intertidal width and exposure degree were the only variables selected in the model to classify beach type, with an accuracy higher than 90% through different metrics of model validation. The only limitation was the inability in separating beach types in the reflective end of the morphodynamic continuum. Our results corroborated the usefulness of this method, highlighting the importance of a locally developed model, which substantially increased the accuracy. Although the use of more sophisticated remote sensing approaches should be preferred to assess coastal dynamics or detailed morphodynamic features (e.g., nearshore bars), the method used here provides an accessible and accurate approach to classify beach into major states at large spatial scales. As beach type can be used as a surrogate for biodiversity, environmental sensitivity and touristic preferences, the method may aid management in the identification of priority areas for conservation.

Nowadays, air pollution has become one of the most serious environmental problems facing humanity and an inescapable obstacle limiting the sustainable development of cities and society. Although air quality sensing and management systems based on artificial intelligence and signal analysis are evolving as essential parts of intelligent cities, the mixture of local emission sources and regional transport of air pollutants still makes PM 2.5 long-term prediction challenging, especially under complex geographical and climatic conditions. In this paper, the complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) is proposed to predict the hourly PM 2.5 concentration, and extensive testing and comparisons are carried out in four typical cities in Shandong, China. Firstly, CEEMD is used for signal pre-processing to construct the intrinsic mode functions (IMFs) based multi-channel representations. Then MsI is specifically designed to learn both temporal and spatial features, and complete the PM 2.5 concentration prediction. To the best of our knowledge, this is the first attempt to predict long-term PM 2.5 concentrations using a deep learning model driven by data collected from monitoring stations spanning long distances and diverse terrains. Finally, test results demonstrate that CEEMD-MsI achieves the best PM 2.5 prediction performance by comparing it with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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.

Global land resources are over-exploited and natural habitats are declining, often driven by expanding livestock production. In Ireland, pastureland for grazing cattle and sheep account for circa 60% of terrestrial land use. The agriculture, forestry and other land use sector (AFOLU) is responsible for 44% of national greenhouse gas (GHG) emissions. A new Grassland Animal response Model (GLAM) was developed to relate livestock-cohort grass and feed requirements to farm-grassland system areas, enhancing environmental assessment of prospective AFOLU configurations. Although land conversion targets are often well-defined, they tend to lack a clear definition of where land sparing can occur. Through analyses of 10 scenarios of milk and beef production and management strategies, we found that displacing beef cows with dairy cows can increase national protein output while sparing up to 0.75 million ha (18%) of grassland (albeit with a minor increase in overseas land requirement for additional concentrate feed). Reducing slaughter age, increasing exports of male dairy calves and increasing grassland use efficiency on beef farms each achieved between 0.19 and 0.32 million ha of land sparing. Sexed semen to achieve more favourable male-female birth ratios had a minor impact. GHG emissions, ammonia emissions and nutrient leaching were only reduced substantially when overall cattle numbers declined, confirming the need for cattle reductions to achieve environmental objectives. Nonetheless, application of GLAM shows potential for improved grass and cattle management to spare good quality land suitable for productive forestry and wetland restoration. This change is urgently needed to generate scalable carbon dioxide removals from the land sector in Ireland, and globally. [Display omitted] • GLAM models land requirements of changing herd dynamics in livestock systems. • Land & environmental impacts of protein output vary in cattle production strategies. • Up to 18% of Irish grassland can be spared without reducing total protein production. • GHG emission reductions across studied scenarios fall far short of climate targets. • Translating bio-physical potential to reality will require careful policy intervention. [ABSTRACT FROM AUTHOR]

Major threats to freshwater ecosystems in the Andean-Amazon region include agriculture, point and nonpoint source pollution, hydroelectric dams, oil extraction, mining and road building, yet little is known about the baseline values and current state of rivers and streams, or how expanding urban, industrial and agricultural activities could affect and change these freshwater ecosystems and their diversity. Therefore, there is a need to understand physicochemical parameters at the basin scale in many large river systems in the Andean-Amazon such as the Napo River, a world biodiversity hotspot. Establishing baselines for these parameters under less developed conditions will be impossible after development in the watersheds increases and hydroelectric projects reach completion. In addition to collecting data to fill in information gaps in geology, vegetation, disturbance, and other parameters that influence water quality, it is important to use existing data in the Napo to predict chemical parameters throughout the watershed to set a baseline from which deviations from development and climate change can be assessed and to guide data collection efforts. In this study, we provide the first basin-wide estimates of physicochemical parameters (pH, conductivity, dissolved oxygen and temperature) for the entire Napo River Basin using a recently developed geostatistical technique called top kriging. Basin-wide predictions aligned well with observed values and a model validation test showed a strong goodness-of-fit for parameter estimates. Furthermore, our predictions aligned well with observed values from independent datasets from the Napo Basin. These predictions could be useful in developing water quality reference baselines for the basin and for monitoring relative changes to parameters, assist in the identification of priority areas within the basin for management and conservation efforts, direct future research, and be applied in other data-scarce basins in remote regions worldwide.

Environmental footprinting, underpinned by systematic life-cycle thinking, is increasingly seen as a critical concept for designing policies to fight climate change. The holistic nature of a life-cycle approach, built using the principles of Life Cycle Assessment (LCA), enables policy makers to understand the potential impacts and benefits of policy options. Although LCA is a widely used and well-established method, methodological aspects such as the quality of background data, model uncertainty, and comparison against existing literature are not usually communicated effectively to wider audiences, in particular policy makers. This paper introduces a novel hybrid data quality assessment method in the context of a case study based on the Scottish Waste Environmental Footprint Tool, a newly developed environmental life-cycle thinking tool. It offers an accessible method to present results of the data quality assessment of environmental models to policy makers and helps identify areas of improvements in future upgrades.

This project lies at the frontier between environmental economics and industrial organization. We use a duopoly setting of a three-stage game; in the first stage, the government chooses an emission tax and aims for maximizing welfare; in the second stage, firms use R&D to reduce their emissions; in the last stage, firms compete a la Cournot with differentiated products. We focus on two policy regimes and three scenarios, namely regimes of competition and merger and scenarios of commitment, non-commitment, and exogenous tax. The study focuses on two major questions: (1) what is the effect of merger on R&D, and the effect of commitment on R&D? (2) what is the effect of merger and commitment on the economy? Results are obtained through numerical simulations of the model. We find that: (i) Merger has a positive effect on R&D under non-commitment and the exogenous tax scenarios. (ii) Under commitment, if goods are imperfect substitutes or homogenous, merger has a negative effect on R&D; if goods are complements or independent, merger has a positive effect on R&D. (iii) For any types of goods under any regime, commitment has a negative effect on R&D.

The purpose of the paper is to propose a model for evaluating land use quality and its variations to be used for planning the use of the space, issuing the permits and producing the environmental impact studies. Elaborated is the case of Quarner bay situated in northern part of the Adriatic Sea. Input parameters considered are variation in size of impervious surfaces, transforming the coastal belt into industrial zone, pressures from maritime transport, road transport, utilities sector, power generation sources and technology and the quality of governance prevailing at national and local level. Fuzzy inference system is used to calculate the output land use quality parameter integrating health, environment, quality of living of the local community and of tourism. Selected input parameters should be further developed and constantly monitored.

In 2020 Ireland missed its EU climate emissions target and without additional measures will not be on the right trajectory towards decarbonisation in the longer 2030 and 2050 challenges. Agriculture remains the single most significant contributor to overall emissions in Ireland. In the absence of effective mitigating strategies, agricultural emissions have continued to rise. The purpose of the review is to explore current research conducted in Ireland regarding environmental modelling within agriculture to identify research gap areas for further research. 10 models were selected and reviewed regarding modelling carbon emissions from agriculture in Ireland, the GAINS (Air pollution Interactions and Synergies) model used for air pollutants, the JRC-EU-TIMES, (Joint Research Council-European Union-The Integrated MARKAL-EFOM System) and the Irish TIMES model used for energy, the integrated modelling project Ireland (GAINS & TIMES), the environmental, economic model ENV-Linkages and ENV-Growth along with the IE3 and AGRI-I models. The review found that data on greenhouse gas emissions for 2019 reveals that emissions can be efficiently lowered if the right initiatives are taken. More precise emission factors and adaptable inventories are urgently needed to improve national CO2 reporting and minimise the agricultural sector’s emissions profile in Ireland. The Climate Action Delivery Act is a centrally driven monitoring and reporting system for climate action delivery that will help in determining optimal decarbonisation from agriculture in Ireland. Multi-modelling approaches will give a better understanding of the technology pathways that will be required to meet decarbonisation ambitions.