Environmental Planning Research Articles

New perspective on the recent challenges of regional environmental management plans under the background of deep-sea mining: from Northwest Pacific to global

New perspective on the recent challenges of regional environmental management plans under the background of deep-sea mining: from Northwest Pacific to global

Research, Analysis, and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments.

Chemical exposure from the Hebei spirit oil spill accident and its long-term effects on mental health

While evidence indicates that exposure to oil spill incidents can affect mental health, it is unclear whether the mental health effects result from the incident itself or from exposure to associated chemicals. Oil contains chemicals that can impact mental health and these chemicals may have long-term effects due to their persistence in the environment. To address the gap in current knowledge, we conducted cross-sectional and prospective analyses of data from adults who participated in the Health Effects of the Hebei Spirit Oil Spill study. To assess chemical exposure from oil spills, we used indirect exposure indicators such as distance from the contaminated oil band to residences and duration of clean-up work, along with direct exposure indicators such as urine metabolite concentrations of volatile organic compounds and polycyclic aromatic hydrocarbons. Mental health assessments covered posttraumatic stress disorder (PTSD), depression, state anxiety, and trait anxiety. In the cross-sectional analyses, all four mental health issues were found to be associated with proximity to the oil band (p-value<0.05) and showed a positive association with clean-up work duration (p-value<0.05). Cox regression analysis revealed that higher urinary t, t-muconic acid levels were associated with an increased risk of depression (Hazard Ratio [HR] = 1.55, 95 % Confidence Interval [CI] = 1.05–2.28), and elevated 1-hydroxypyrene levels increased the risk of PTSD (HR = 1.60, 95 % CI = 1.03–2.48). Additionally, higher urinary 2-naphthol levels were associated with increased state anxiety (HR = 1.39, 95 % CI = 1.00–1.93) and trait anxiety (HR = 1.64, 95 % CI = 1.15–2.32). These associations persisted even after controlling for distance and duration variables related to psychosocial exposure. Our findings suggest that environmental disaster response plans should prioritize minimizing chemical exposure while also considering the duration and nature of the mental health impacts.

Evolutionary computation for unmanned aerial vehicle path planning: a survey

Unmanned aerial vehicle (UAV) path planning aims to find the optimal flight path from the start point to the destination point for each aerial vehicle. With the rapid development of UAV technology, UAVs are required to tackle missions in increasingly complex environments. Consequently, UAV path planning encounters more challenges, causing traditional deterministic algorithms to struggle to find the optimal path within a certain time. Evolutionary computation (EC) is a series of nature-inspired methodologies and algorithms, which have shown effectiveness and efficiency in solving many complex optimization problems in real-world applications. Recently, EC algorithms have been effectively applied in UAV path planning and have shown encouraging performance in obtaining high-quality solutions. Therefore, it is crucial to review the related research experience and literature in the field of using EC for UAV path planning. This paper presents a comprehensive survey to showcase the existing studies on EC in UAV path planning, especially in complex environments. The paper first proposes a novel taxonomy to categorize the relevant studies into three different categories according to the complex environmental properties of the application scenarios. These environmental properties include complex search space, complex time control, and complex optimization objectives. Then, the EC algorithms for UAV path planning in these complex environments are further systematically surveyed as constrained search space and large-scale search space in complex search space, dynamic UAV path planning and multi-UAV concurrent path planning in complex time control, and expensive objective and multiple objectives in complex optimization objectives. Finally, some potential future research directions for applying EC algorithms to UAV path planning are presented and discussed.

UTILIZING GOOGLE EARTH FOR ENVIRONMENTAL PLANNING: EXTRACTION OF POINT SOURCE LOCATIONS AT THE UPSTREAM OF SUNGAI SKUDAI CATCHMENT, JOHOR MALAYSIA

This study explores the application of geospatial technologies, specifically focusing on Google Earth (GE), to extract and mapping point source pollution in the upstream of Sungai Skudai Catchment (SRC). A thorough analysis of the locations of restaurants, launderettes, car washes, and workshops was made practical by the combination of Geographic Information Systems (GIS) tools with satellite images, which offers important data for environmental planning and water quality management. The research area, located in Johor, Malaysia, offers an example of the difficulties driven by rapid urban growth. This study provides comprehensive directions for extracting point sources, with a focus on verifying data via field surveys and Google searches. The upstream region was found to have 373 restaurants, 62 launderettes, 73 car washes, and 80 workshops. The dataset was further improved by the validation process, which determined any inconsistencies and added additional data. This study highlights the importance of field verification for improving point source pollution data accuracy. It also provides a base for comprehensive strategies for pollution management, land-use planning, and preservation efforts in rapidly changing environments, such as the Sungai Skudai Catchment.

An active SLAM with multi-sensor fusion for snake robots based on deep reinforcement learning

Snake-like robots can imitate the movement patterns of animals in nature and enter the space that traditional robots cannot enter, which adapt to environments that humans cannot reach, and expand the field of human exploration. However, it is often challenging to realize autonomous navigation and simultaneously avoid obstacles under an unknown environment, that is, active SLAM (Simultaneous Localization and Mapping). This paper proposes an autonomous obstacle avoidance method combined with SLAM based on deep reinforcement learning for a wheeled snake robot by using a multi-sensor. Firstly, we design a modular wheeled snake robot structure with lightweight materials based on orthogonal joints and build a three-dimensional model of a snake robot in Gazebo. Secondly, the SLAM based on two-dimensional LiDAR and IMU is used to realize autonomous navigation under an unknown environment and detect obstacles. At the same time, a Deep Q-Learning-based path planning method of the snake robot is proposed to realize obstacles avoidance during navigation. Finally, simulation studies and experiments show that the designed snake-like robot can realize effective path planning and environmental mapping in environments with obstacles. The proposed active SLAM algorithm improves the success rate of snake-like robot path planning, has better obstacle avoidance ability for obstacles, and reduces the number of collisions compared with the traditional A* and the sampling-based RRT* algorithms.

Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multi-sensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies.

Refining Environmental Sustainability Governance Reports through Fuzzy Systems Evaluation and Scoring

Environmental, Social, and Governance (ESG) reports have become essential tools for enterprises to showcase their commitment to sustainable development and social responsibility. However, discrepancies persist regarding the criteria, assessments, and ratings disclosed in these reports. Moreover, there is a need for more objective methods to determine the weight distribution of indicator items. This study introduces a novel approach utilizing semantic variables in fuzzy theory and a multiple logic fuzzy inference system to develop an ESG environmental management performance assessment model. Therefore, this paper aims to develop a novel approach utilizing semantic variables and a multiple logic fuzzy inference system to quantitatively evaluate the sustainable performance of an environmental management plan. This research also aims to ensure fair and objective assessment outcomes, providing valuable guidance for enterprises in implementing performance management strategies. Key aspects investigated include the impact of membership functions, the extended utilization of semantic variables and logical rules, a comparative analysis of traditional weight assessments, and the limitations of applying fuzzy theory. Through comprehensive discussions and calculations, it is evident that fuzzy theory offers considerable flexibility in application. By tailoring fuzzy rules and selecting appropriate membership functions, diverse application scenarios can be accommodated. The Fuzzy systems evaluation and scoring EMP model generates EMP evaluation scores ranging from 1.76 to 8.29 for Gaussian membership, 1.80 to 8.19 for Triangular membership-A, 1.92 to 8.00 for Triangular membership-B, and 1.81 to 8.19 for Quadrilateral trapezoidal membership, based on simulated rating scenarios using the semantic variables of completeness and feasibility. This approach successfully incorporates distribution logic from subjective membership degrees to evaluate EMP scores. The findings demonstrate that fuzzy theory enables the consideration of multiple factors and facilitates the provision of objective-level membership, underscoring its potential in addressing complex evaluation challenges. This study illuminates the versatility of the fuzzy system theory, with its applications poised to extend across various domains.

How Can Ecological Product Value Realization Sustainably Enhance the Well-Being of Farmers? A Case Study of Xingyuan Village in China

Although recent years have witnessed a considerable increase in studies on the economic value of ecological products, the extant literature has overlooked the multidimensional enhancement of ecological product value from the perspective of farmers’ well-being. This research aims to examine how the Realization of Ecological Product Value (EPVR) serves as a crucial pathway to promoting the overall well-being of farmers in developing countries. Through a case study of a village in southeastern China, this research reveals that EPVR can enhance farmers’ well-being via various mechanisms as follows: (1) Economic solution to enrich farmers’ livelihood diversity, achieved by leveraging rural resource endowments and comparative advantages; (2) Fair social protection program enabling farmers to enjoy ecological benefits and further achieving urban-rural integration; (3) Environmental protection plan that balances production, living, and ecology; and (4) Grassroots governance tool promoting the governance ability to form collaborative governance model in a community of shared interests. This study offers theoretical support for enhancing human well-being through the realization of ecological product value in rural areas.

Impact of landscape pattern on habitat quality in the Yangtze River Economic Belt from 2000 to 2030

Recently, rapid economic development of the Yangtze River Economic Belt region in China and the growing problem of regional habitat quality have seriously threatened the process of biodiversity conservation in the Yangtze River Basin. Studying the impact of changes in landscape pattern indexes on habitat quality in the Yangtze River Economic Belt can provide policy insights for biodiversity conservation in the Yangtze River Basin. However, existing studies are still lacking in exploring the effects of changing landscape pattern indices on habitat quality under future scenarios. Based on the land use data of the Yangtze River Economic Belt from 2000 to 2020, this study simulates the land use situation in 2030, and analyzes the landscape pattern indexes and habitat quality during the 30 years, to investigate the impact of landscape pattern indexes on habitat quality at global and local scales through various methods. The results show that landscape patterns were significantly affected by human activities and land use types, with the Yangtze River Economic Belt experiencing increased levels of land use and structural stabilization during the three decades (Shannon Diversity Index increased from 0.90 in 2000 to 0.94 in 2030), fragmentation of the landscape (Landscape fragmentation index increase from 0.094 in 2000 to 0.102 in 2030), and weakened connectivity (contagion index decrease from 48.213 in 2000 to 45.437 in 2030). The Habitat Quality has a large variation in spatial distribution at the county scale, with an average change in habitat quality of 1.04 % from 2000 to 2030, and an overall downward trend. There is a strong localized spatial autocorrelation between landscape pattern indices and habitat quality, with significant spatial heterogeneity and some form of variability at time scales. The results of this study help to further understand the eco-environmental problems of the Yangtze River Economic Belt, and provide theoretical references for the formulation of ecological environmental protection policies and ecological functional area planning.

Innovative trend analysis technique with fuzzy logic and K-means clustering approach for identification of homogenous rainfall region: A long-term rainfall data analysis over Bangladesh

Understanding regional climatic trends is crucial for taking appropriate actions to mitigate the impacts of climate change and managing water resources effectively. This study aims to investigate the dissimilarities and similarities among various climate stations in Bangladesh from 1981 to 2021. Fuzzy C-means (FCM) and K-means clustering techniques were employed to identify regions with comparable rainfall patterns. Moreover, the innovative trend analysis (ITA) and the Mann-Kendall (MK) test family were utilized to analyze rainfall trends. The results indicate that both K-means and FCM methods successfully detected two rainfall regions in Bangladesh with distinct patterns. The ITA curve analysis revealed that out of the 29 stations, 13 had a non-monotonic increasing trend having no monotonic increasing trend, 8 had a non-monotonic decreasing trend, and 8 exhibited a monotonic decreasing trend. Additionally, the MK tests employed in the study showed predominantly negative trends across Bangladesh. The majority of stations (65.51%) fell into Cluster 1, while the remaining 34.48% were in Cluster 2. In terms of ITA analysis, 17.24% of stations exhibited a monotonic decrease, while there were no stations with a monotonic increase. However, 37.93% of stations showed a non-monotonic increase, and 44.83% displayed a non-monotonic decrease. These identified regions can provide valuable insights for water resource management, disaster risk reduction, and agricultural planning. Moreover, detailed rainfall analysis can help policymakers and scientists develop sustainable and effective regional-scale policies for managing the country's flood and drought situations, ultimately supporting agricultural development and environmental planning.

Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm

This paper presents an improved multi-objective particle swarm optimization (IMOPSO) algorithm for cooperative path planning of multiple autonomous underwater vehicles (multi-AUVs) in 3D underwater terrain and vortex environment. Traditional PSO algorithms often struggle with local optima in high-dimensional, complex search spaces. To address this, we propose enhancements including Sine double-sequence perturbation interpolation for initial particle population generation, adaptive differential evolution strategies, iterative local search, and traction operations to enhance global search capability and avoid local optima. Additionally, cubic spline interpolation is employed to smooth the planned paths. The objective function is tailored to the ocean currents environment, considering path length, smoothness, deflection angle, and current impact, with a penalty function method to handle multi-AUVs cooperation and environmental constraints optimization for optimal energy consumption. Simulation results demonstrate that the proposed algorithm effectively plans short-range, high-security global paths for multi-AUVs, verifying its effectiveness and practicality in meeting the navigation requirements in ocean currents environments.

Deep learning model for optimizing control and planning in stochastic manufacturing environments

Within the context of Industry 4.0, manufacturing plants implement smart technologies, which adopt machine learning and deep learning, to identify manufacturing problems and provide sensible solutions to the experts. In literature, such applications have demonstrated high-performance in terms of feature extraction and big data analysis compared to typical machine learning methods. However, in the context of control and planning, the research efforts are still focused on integrating ad-hoc solutions, e.g., heuristic algorithms and deep reinforcement learning, thus making it difficult to generate general scheduling rules for manufacturing processes that could be easily applied to a variety of stochastic manufacturing systems. To this end, this paper considers a deep learning application for addressing the scheduling optimization problem in multi-stage stochastic manufacturing environments. The aim is the formulation of a predictive model that could derive suitable authorizations for activities under uncertainty. Findings demonstrate that the proposed predictive model generates policies that improve the performance of the integrated operations within manufacturing systems, constituting to efficient and sustainable manufacturing with effective material management and near-to-zero waste.

Using a comparative of DRASTIC and Bayesian weights of evidence approach to assess transboundary aquifer vulnerability in a data scarcity region: Tuli-Karoo aquifer

Study regionThe Tuli-Karoo Transboundary Aquifer system is shared among Botswana, South Africa and Zimbabwe. It hosts a complex aquifer predominately of sand, heavily tapped in South Africa and sandstone substantially used in Botswana threatening it’s quality. Study focusTwo vulnerability models, the classic DRASTIC (Depth to water table [D], Recharge to the aquifer [R], Aquifer media [A], Soil media [S], Topography [T], Impact of vadose zone [I], Conductivity [C]) and the bayesian based, Weights of Evidence (WofE), were used to delineate zones vulnerable to groundwater contamination under limited nitrate observations. New hydrological insights for the regionThe study revealed that the generic DRASTIC vulnerability results were comparable to the WofE, for the highly vulnerable zones, as both had an area under the curve (AUC) below 0.6. The high vulnerability was attributed to the shallow depth of water level, fractured geological condition (based on transmissivity) and high net recharge rate in and around the Tuli-Karoo Aquifer. However, there are differences in spatial variability in the low/medium vulnerability zones. WofE was superior in capturing spatial variability of groundwater vulnerability. Considerations must therefore be provided, under data scarcity, to incorporate proxies that include landuse-landcover and population density as evidential layers. Overall, WofE performs better due to better prediction power under data-scarcity conditions. This study recommends that under data scarcity, high predictive models like WofE should be used for environmental planning and groundwater protection strategies.

Quantifying marginal utilities of ecosystem services for sustainable management

Understanding the marginal characteristics of ecosystem services (ESs) is crucial for sustainable management. However, there is limited research on quantifying thresholds for ESs, making it challenging for policymakers to plan effectively. This study established a framework to assess the influencing factors and marginal characteristics of cultural and regulatory ecosystem services (RES) in the Taihu Lake Basin of China and its wetland parks. The framework uses a Structural Equation Model to analyze the impact of visitors' attributes on the perception of different types of cultural ecosystem services (CES). It also employed the InVEST model to quantify the spatial patterns of RES in the basin. Multiple regression analysis and marginal utility theory were used to examine the influencing factors and marginal utilities for different types of cultural and regulatory services. The findings reveal that 1) Education level, income, gender, and age significantly influence the perception of five types of CES provision, with different factors affecting each type. 2) Most park characteristics influence the CES provision score, with their maximum and marginal utility peaks. For instance, as patch density (PD) increases, the score for recreational services initially decreases, then increases, and finally decreases again, reaching a peak of 0.50 at a PD of 325.60. The marginal utility of PD follows a similar pattern, initially increasing and then decreasing, with an increase in utility observed in the range of 0 < PD < 187.80. When PD reaches 187.80, it attains its maximum utility, indicating the point of highest efficiency for PD's impact on recreational services. 3) Heterogeneity exists in the spatial distribution of RES within the basin, with different landscape features providing varying marginal benefits for services like water supply, water purification, carbon storage, and soil erosion prevention. Carbon storage demonstrates an 'increase-then-decrease’ pattern in response to rising FP (Percentage of forest area), reaching a maximum value of 13129.3 t/km2 at FP of 66.4. The marginal utility of FP also exhibits a 'rise-then-fall’ trend, increasing up to FP of 30.4, where it peaks at 230.5. This peak represents the point of maximum efficiency in FP's influence on carbon storage services. 4) The effects of park and landscape characteristics on cultural and regulatory services exhibit marginal utilities that can be used to determine the optimal scale and location of these features to maximize ecological benefits. This research aims to expand the scope of ESs assessment and provide insights from wetland parks for broader environmental planning and optimization.

Predicting Particulate Matter (PM 10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method.

Accurate prediction of Particulate Matter (PM 10) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of PM 10 over Morocco for five days. The Analog Ensemble (AnEn) and the Bias Correction (AnEnBc) techniques were employed to post-process PM 10 forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the Root Mean Square Error (RMSE) decreased from 63.83 μg/m 3 in the original forecasts to 44.73 μg/m 3 with AnEn and AnEnBc, while the Mean Absolute Error (MAE) reduced from 36.70 μg/m 3 to 24.30 μg/m 3. Additionally, the coefficient of determination (R 2) increased more than twofold from 29.11% to 65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. This is the first use of this approach for Morocco and the Middle East and North Africa and has the potential for translation into early and more accurate warnings of PM 10 pollution events. The application of such approaches in environmental policies and public health decision making can minimize air pollution health impacts.

TOWARDS HARMONY IN THE CAPITAL OF THE ARCHIPELAGO: VISION OF FUTURE SUSTAINABLE CITY DEVELOPMENT FUTURISTIC-ORIENTED

This research aims to explore and formulate a sustainable and futuristic-oriented development strategy for the capital of the archipelago that can integrate the principles of environmental, economic, and social sustainability in addition to utilizing sustainable advanced technology. This study employs an exploratory qualitative research methodology. and descriptive study design. This approach is carried out by collecting and analyzing data in depth through a document study. This study uses data analysis techniques by making a SWOT analysis to assess the advantages, disadvantages, and opportunities faced during the development of the Indonesian capital. Thus, it can be said that the overall plan for development for the Indonesian capital is sustainable and futuristic using an integrated approach by applying ESG sustainability principles and the use of environmentally friendly technology. Urban development close to the 20th century was accompanied by the growth of urbanization processes without planning so cities experienced gradual inefficiency, which resulted in the erosion of old buildings that were unable to fulfill the requirements of society. Through the above policies, it’s hoped that Indonesian capital will develop into a futuristic-oriented sustainable city by integrating the principles of ESG (environmental, social, and economic) sustainability using advanced, environmentally friendly technology. Recommendation formulation regarding the sustainable and futuristic-oriented development strategy for the Indonesian capital consists of Environmental Policy, Economic Policy, Social Policy, Planning and Supervision Policy, Technology Policy, and Collaboration and Participation for Sustainable City Futuristic.

Assessment of genetically modified maize DP910521 (application GMFF-2021-2473).

Genetically modified (GM) maize DP910521 was developed to confer resistance against certain lepidopteran insect pests as well as tolerance to glufosinate herbicide; these properties were achieved by introducing the mo-pat, pmi and cry1B.34 expression cassettes. The molecular characterisation data and bioinformatic analyses did not identify issues requiring food/feed safety assessment. None of the identified differences in the agronomic/phenotypic and compositional characteristics tested between maize DP910521 and its conventional counterpart needs further assessment except for the levels of iron in grain, which do not raise safety and nutritional concerns. The GMO Panel does not identify safety concerns regarding the toxicity and allergenicity of the Cry1B.34, PAT and PMI proteins as expressed in maize DP910521. The GMO panel finds no evidence that the genetic modification impacts the overall safety of maize DP910521. In the context of this application, the consumption of food and feed from maize DP910521 does not represent a nutritional concern in humans and animals. The GMO Panel concludes that maize DP910521 is as safe as its conventional counterpart and non-GM maize varieties tested, and no post-market monitoring of food/feed is considered necessary. In the case of accidental release of maize DP910521 material into the environment, this would not raise environmental safety concerns. The post-market environmental monitoring plan and reporting intervals are in line with the intended uses of maize DP910521. The GMO Panel concludes that maize DP910521 is as safe as its conventional counterpart and the tested non-GM maize varieties with respect to potential effects on human and animal health and the environment.

Assessment of genetically modified maize MON 95275 (application GMFF-2022-5890).

Genetically modified maize MON 95275 was developed to confer protection to certain coleopteran species. These properties were achieved by introducing the mpp75Aa1.1, vpb4Da2 and DvSnf7 expression cassettes. The molecular characterisation data and bioinformatic analyses reveal similarity to known toxins, which was further assessed. None of the identified differences in the agronomic/phenotypic and compositional characteristics tested between maize MON 95275 and its conventional counterpart needs further assessment. The GMO Panel does not identify safety concerns regarding the toxicity and allergenicity of the Mpp75Aa1.1 and Vpb4Da2 proteins and the DvSnf7 dsRNA and derived siRNAs as expressed in maize MON 95275 and finds no evidence that the genetic modification would change the overall allergenicity of maize MON 95275. In the context of this application, the consumption of food and feed from maize MON 95275 does not represent a nutritional concern in humans and animals. The GMO Panel concludes that maize MON 95275 is as safe as the conventional counterpart and non-GM maize varieties tested, and no post-market monitoring of food/feed is considered necessary. In the case of accidental release of maize MON 95275 material into the environment, this would not raise environmental safety concerns. The post-market environmental monitoring plan and reporting intervals are in line with the intended uses of maize MON 95275. The GMO Panel concludes that maize MON 95275 is as safe as its conventional counterpart and the tested non-GM maize varieties with respect to potential effects on human and animal health and the environment.

Complex network analysis of climate and landscape satellite data to explore spatio-temporal patterns in urban environment: the case of Athens

Motivated by the significance and complexity of exploring spatiotemporal patterns - regions within an urban environment, particularly in the context of extreme heat events- this research analyzes meteorological time series through complex network analysis. The data collected for the examination area is focused on Athens, Greece, and covers sections of the city’s urban landscape. The data was obtained from the Copernicus observation component of the European Union. Initially, the time series are transformed into networks using correlation network methodology, followed by examination of the discriminative capability of the topological measures of networks degree and modularity as community - region detection methods. Of particular interest is that our findings suggest that the proposed complex network analysis can lead to the extraction of spatial urban regions closely linked to land use and building heights in corresponding areas. These results may help investigate the spatial variability of heat in the urban environment and inform urban planning and management strategies in policy decision-making regarding the intensity of urban heat throughout the city and the planning of climate change adaptation strategies.