Ecological security patterns are widely used to support ecological restoration planning in fragile regions, but commonly used resistance-surface approaches often fail to sufficiently account for erosion-related landscape instability in the Loess Plateau. Herein, we developed a planning-oriented ecological security pattern for Linfen, China, by identifying ecological sources using an ecosystem health index and incorporating soil erosion sensitivity as a regional modifier of the ecological resistance surface. Using Linkage Mapper and circuit theory, we extracted modeled ecological corridors, high-current–density pinch points, and candidate barrier areas for restoration priority under an erosion-sensitive resistance scenario, and then compared these outputs with those derived from the baseline resistance surface. The erosion-sensitive scenario yielded 814.22 km of modeled ecological corridors, together with high-current–density pinch points and restoration-priority barrier areas. Incorporating soil erosion sensitivity altered the spatial configuration of corridors and priority areas, indicating that erosion-prone terrain can alter modeled structural connectivity in the Loess Plateau. These outputs should be interpreted as scenario-based structural-connectivity results rather than as independently validated species-movement pathways. Through distinguishing priority areas where ecological connectivity improvement overlaps erosion-sensitive terrain, the results can help align territorial spatial zoning, ecological restoration actions, and soil erosion-control measures in Linfen. This study provides a spatially explicit planning reference for ecological restoration screening and corridor-planning prioritization in the city of Linfen.
Pharmaceutically active compounds (PhACs) are increasingly detected in surface waters and treated effluents. However, their removal during conventional treatment remains inconsistent because of their recalcitrance and variable speciation. Geopolymers offer a sustainable remediation solution, as they are highly tunable and can be synthesised from low-cost aluminosilicate precursors, including industrial byproducts. Nevertheless, research on geopolymer–PhAC is fragmented. Adsorption studies are often conducted in simplified batch systems, and photocatalysis data tend to focus on antibiotics. This review aims to consolidate and critically assess geopolymer-based adsorption and geopolymer-supported photocatalysis for PhAC removal. Emphasis is on the mechanistic connections between PhAC descriptors, geopolymer characteristics, and process variables. Adsorption performance ranges from 19–99
Assessment of soil water infiltration through reliable estimation of the infiltration rate is key to informed decision-making for sustainable ecosystem management. This study was conducted to evaluate the performances of the Kostiakov, Modified Kostiakov, Horton, Swartzendruber, Brutsaert and Philip models in estimating cumulative infiltration depth under loam, sandy loam and loamy sand soil textures. The measured infiltration data from the double ring infiltrometer were fitted to the infiltration models and their goodness-of-fit statistics were verified by the Coefficient of Determination ( R^2 ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The Goodness-of-fit statistics for all Cumulative infiltration estimating models across the three locations within the study area associated with different soil textures show that the Modified Kostiakov model generally outperformed the other models based on the relatively higher mean R^2 and low RMSEs, MAEs and MAPEs. The estimation accuracy was in the order of Modified Kostiakov > Kostiakov > Swartzendruber > Horton > Brutsaert > Philip models. The influence of soil texture on the model accuracy was also in the order of Loam > Sandy loam > Loamy sand soil textures. The results of this study indicate that the use of the Modified Kostiakov model in hydrological evaluations can improve the accuracy of irrigation system design under verified field conditions.
Rapid urbanisation in Ghana has outpaced municipal infrastructure, leaving cities like Accra struggling to manage daily waste. Municipalities and private waste management companies are struggling with significant uncollected municipal solid waste and a high reliance on unsanitary disposal methods. These problems result in serious environmental degradation (increasing greenhouse gas emissions from open dumping and burning) and public health issues. Despite Ghana’s robust legislative framework, a significant functional disconnect exists between policy formulation and local implementation. This study evaluated Ghana’s existing legislative frameworks governing its solid waste management system to identify systemic gaps and explore the potential to integrate informal waste pickers into a formal, inclusive, and sustainable circular economy. Employing a convergent mixed-methods research design, the study utilised a semi-structured questionnaire to collect data from 13 purposively selected expert stakeholders across different governing bodies. This allowed for a quantitative descriptive method to identify systemic trends and a qualitative thematic analysis of open-ended responses. This ensured a complete evaluation of the MSW governance landscape in Ghana. The findings reveal an institutional maze characterised by fragmented law enforcement, financial constraints, and the absence of formal monitoring systems. The findings also show that informal waste pickers lack recognition, social protection, and technical support. The study recommends data-driven modelling approaches that align and integrate institutional frameworks with inclusive social policies such as optimisation, multi-criteria decision-making tools, and life-cycle assessments. This paper provides an evidence-based roadmap for transitioning to a resilient governance framework, offering practical insights for building urban sustainability capable of addressing climate change.
Urban transportation systems face increasing challenges due to traffic congestion, travel delays, and operational inefficiencies, particularly in municipal solid waste collection services. Traditional route planning methods, including shortest-path algorithms such as Dijkstra’s algorithm, primarily optimize a single factor such as travel distance or travel time, which may not adequately represent the complex traffic and infrastructure conditions encountered in urban environments. Despite advances in route optimization, limited research has integrated coverage-based routing with multi-criteria evaluation within a unified framework. To address this gap, this study proposes an integrated route optimization framework that combines the Hamiltonian Circuit concept with the Analytic Hierarchy Process (AHP). The Hamiltonian Circuit ensures complete coverage of waste collection locations without duplication, while AHP evaluates alternative routes using multiple criteria, including travel time, traffic volume, distance, road width, parking effects, encroachment, and intersection density. The framework is validated using a municipal waste collection network in Vellore, Tamil Nadu, India. The results identify Route 3 as the most efficient alternative, providing the best overall balance among operational and transportation-related criteria. Compared with conventional single-factor approaches based solely on distance or travel time, the proposed framework offers a more comprehensive assessment of route performance. The study demonstrates that integrating graph-based routing with multi-criteria decision-making (MCDM) can support more effective municipal service planning and assist urban authorities in improving operational efficiency and sustainable transportation management.
Forest ecosystems are crucial for global carbon sequestration and climate change mitigation. In Northeast India, evaluating forest biomass and carbon are essential because of the region’s high biodiversity and prevailing disturbances such as urbanization and shifting cultivation. This study aimed to estimate biomass, carbon stock, and sequestration potential along an altitudinal gradient in Kolasib district of Mizoram integrating field inventories with Sentinel-2 (S-2) data. A total of 0.6-hectare permanent sample plots were established at low-altitude (LAF) (< 500 m) and high-altitude (HAF) (> 500 m) forests. Field data were collected and species-wise volumetric equations were used to estimate the aboveground biomass, carbon stock, and sequestration for the time period of 2023–2024 and 2024–2025. Corresponding spectral vegetation indices were extracted from S-2 imagery using the Google Earth Engine (GEE). Multiple Linear Regression (MLR) and Random Forest (RF) model was applied to predict spatial biomass and carbon stock distribution. The field-based results showed that the low-altitude forest stores higher mean aboveground biomass (AGB) (456.08 t ha− 1) than the high-altitude forest (438.30 t ha− 1). The findings revealed that biomass was significantly (P < 0.0001) affected by tree diameter. While the MLR and RF models show mean AGB of 436 t ha− 1 and 475 t ha− 1, with corresponding aboveground carbon (AGC) of 113 t ha− 1 and 123 t ha− 1 respectively. Overall, the study area landscape demonstrated a substantial mean carbon stock of 264.83 t ha− 1 and an active carbon sequestration rate of 6.74 t ha− 1 yr− 1. This highlights the vital role of regional carbon sinks in mitigating climate change and supporting REDD+ initiatives.
Understanding the coupling and coordination relationship between water resources resilience and green development is crucial to regional sustainable development. Based on the panel data of 31 provinces in China from 2011 to 2024, the coupling coordination model and regression analysis method are used to evaluate the spatial and temporal evolution and driving mechanism of the two. The study found that the water resources resilience index increased from 0.342 in 2011 to 0.499 in 2024, while the green development index increased from 0.135 at the beginning of the period to 0.541 at the end of the period. The synchronous improvement of the two promotes the coupling coordination degree from the near-disorder stage to the intermediate coordination stage; the gradient improvement pattern of eastern leading, central and western follow-up is formed in space. The low coordination region has contracted significantly, and the high coordination region has continued to expand. Regression analysis shows that the driving mechanism presents heterogeneity in the eastern, central and western regions. The proportion of green credit, the process of urbanization rate and the number of years of education per capita have a positive driving effect, while the low-carbon index of energy consumption structure and the proportion of green patents have an inhibitory effect. The research results can provide a decision-making basis for formulating regional differentiated water resources management and green development strategies and collaboratively promoting human-water harmony and ecological goals from a patio-temporal dynamic perspective.
Winter smog has become critical for the environment and public health in major cities of South Asia, especially Lahore district, Pakistan. The present study is based on an integrated assessment of winter smog using the remote sensing (RS) observations combined with socio-economic survey data, which is used to analyse the spatial distribution of smog and the impacts associated with it. MODIS MAIAC (MCD19A2) and Sentinel-5P TROPOMI (tropospheric nitrogen dioxide - NO₂) Aerosol Optical Depth (AOD) products were used to study the temporal and spatial variation of the atmospheric pollution over winter seasons (2019–2021). At the same time, a structured questionnaire survey was carried out with vulnerable occupational groups such as labourers, street vendors, and traffic police and service workers in order to find out about their perception of health, environment and socio-economic effects. The AOD values exhibited temporal variations during the study period, ranging from 1.062 to 0.809, indicating changes in atmospheric aerosol loading associated with seasonal and anthropogenic emission sources. A temporary reduction in aerosol and NO2 concentrations was observed during 2020, likely associated with COVID-19 mobility restrictions. Risk zone assessment identified Lahore and surrounding districts, including Sheikhupura, Kasur and Gujranwala, as highly vulnerable areas. The survey results indicated that the problem of health effects due to smog is very common, such as discomfort of the respiratory tract, decreased outdoor activity, and economic problems. This study underlines the importance of using remote sensing and socio-economic methods to assess air quality comprehensively. The results serve to enhance knowledge of the dynamics of winter smog and to support policy interventions aimed at reducing air pollution in urban areas based on evidence.
Land surface temperature (LST) indicates that the microclimate of an urban system is significantly warmer than that of its rural counterparts. The effects of building expansion on land use land cover (LULC) are significant because it converts vegetation land to commercial and residential land, along with related infrastructure, thereby hastening LST. This study aimed to examine the effects of LULC variation on LST in Southern Punjab using Google Earth Engine (GEE). The 40-year Landsat images (1984, 1994, 2004, 2014, and 2024) were used to derive vegetation indices and LST in the Multan region. Normalized difference built-up index (NDBI) and normalized difference built-up index (NDBI) were also generated from Landsat images. In 1984, built-up area was noted 1.09
Understanding the spatio-temporal dynamics of precipitation is crucial for monitoring and managing natural hazards such as floods and droughts, which are strongly influenced by variability in precipitation patterns. Traditional precipitation indices often fall short in capturing both the intensity and concentration of precipitation events on multiple time scales. To address this gap, we introduce a new standardized index, the Multiscale Standardized Precipitation Concentration Index (MS–PCI) - designed to assess the spatio-temporal variability of precipitation in a robust and standardized manner. Unlike existing indices, the MS-PCI enables multiscalar characterization of precipitation concentration, allowing for effective comparison across spatial domains and temporal scales. In this study, we used monthly precipitation data collected from 1981 to 2021 in various locations in Pakistan. For the standardization process, we propose the use of K-Component Gaussian Mixture Models (KCGMMs), selected based on their superior performance over traditional univariate probability models as determined by the Bayesian Information Criterion (BIC). To assess spatial interpolation capability, we fit multiple variogram models to both the MS-PCI and the conventional Precipitation Concentration Index (PCI). The results reveal that the MS-PCI offers improved spatial predictive accuracy, outperforming the PCI in capturing the spatial distribution of precipitation at unobserved locations. In summary, MS-PCI provides a comprehensive and scalable tool to characterize spatial and temporal precipitation patterns, while also supporting data-driven decision making in water resource management, disaster preparedness, and climate adaptation planning, particularly in regions vulnerable to hydrometeorological extremes.
Achieving net-zero emissions has become a long-term target to cope with climate change. Local governments are adopting regional collaborations to gather mitigation efforts and address externality issues. From the multilevel governance framework, this study will estimate regional net-zero emissions and social cost of carbon by constructing the Regional Collaborations Dynamic Integrated model of Climate and Economy. Results indicate that: (1) Each regional collaboration would form a localized pathway toward net-zero emissions. The Yangtze River Economic Belt could reach net-zero emissions under almost all scenarios, whereas the Yellow River Basin might pose the main roadblock in China’s climate governance. (2) Social cost of carbon would exhibit regional variation and dynamic change over time. Global temperature targets would increase social cost of carbon in the Pan-Pearl River Delta. The Beijing-Tianjin-Hebei and Northeast China will require a combination of global temperature targets with regional collaborations or national actions to generate larger climate benefits. (3) The uncertainty analysis validates social cost of carbon for regional collaborations by accounting for model uncertainty and structural uncertainty. Based on these findings, this study proposes policy recommendations to strengthen regional collaborations by combining local conditions, following sequencing principle, and consolidating multilevel governance.
Sustainable regional environmental governance has become a core global challenge, while digital governance provides local governments with an innovative approach to resolve complex environmental and governance dilemmas and advance the Sustainable Development Goals. Based on panel data of 31 provincial-level administrative regions in China, this study employs the Technology-Organization-Environment (TOE) framework and integrates Necessary Condition Analysis (NCA) with fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate the complex interactive mechanisms of driving factors for high-level comprehensive performance of digital governance and environmental sustainability (HPG). This research constructs a comprehensive outcome indicator system that couples digital governance performance (DGP) and environmental sustainability performance (ESP) to measure HPG, adopting carbon emission intensity and major pollutant reduction rate as core rigid environmental indicators to guarantee the logical correlation between digital governance practices and regional environmental sustainability. The empirical results reveal that six antecedent factors significantly shape local HPG. This study identifies three valid configurations for high-performance HPG and confirms notable substitution effects across technological, organizational, and environmental conditions. These findings provide practical implications for local governments to optimize digital governance and advance environmental sustainability through rational resource allocation, low-carbon technology adoption and interdepartmental collaboration, while offering empirical references for developing countries to promote environmental governance via digital transformation.
Nanoplastics (NPs), characterized by their diminutive size and extensive specific surface area, readily accumulate in marine environments and propagate through the food web, inducing oxidative stress and immunotoxicity in marine organisms and thereby threatening ecosystem stability. Although the ecotoxicological effects of NPs on marine zooplankton have garnered significant attention, the stage-specific toxicity mechanisms across their developmental lifecycle remain largely elusive. This study used Artemia as a model to investigate toxicity of 100 nm polystyrene nanoplastics (PSNPs; 0, 0.5, 5, 10 µg/mL) across three continuous developmental stages: CN (Cysts to Nauplius), NM (Nauplius to Metanauplius) and MJ (Metanauplius to Juveniles). Physiological responses, oxidative stress, inflammatory gene expression and gut microbiota were analyzed. Results showed that PSNPs induced acute ROS and MDA accumulation in the CN stage, triggering initial oxidative damage. The TLR-NF-κB-AsIL-17-AfRgly1 pathway was activated, the NM stage acted as a sensitive window, with compensatory failure, acute inflammation and sharply reduced feeding and growth. 16 S rRNA sequencing revealed stage-dependent gut microbiota dysbiosis, in which CN showed inhibition then recovery, NM sustained decline, and MJ low-dose adaptation with high-dose damage. This study systematically elucidates the dynamic responses of Artemia to NPs across different developmental stages from individual, cellular, molecular, and microecological dimensions, providing novel insights and theoretical bases for assessing the ecological risks of marine nanoplastic pollution.
While decarbonizing heavy-duty port logistics is essential for regional sustainability, the large-scale transition to green hydrogen in industrial clusters is systematically constrained by a “Low-Load Trap”, where localized resource scarcity and fragmented demand create a self-reinforcing cycle of high costs. Although existing techno-economic frameworks typically treat hydrogen infrastructure as isolated nodes, this study addresses these spatial limitations by modeling the structural transition of such environmental-energy systems through a proposed “Nested RIS-PTE” framework (Regional Innovation System embedded with Policy-Technology-Economy flows). Taking China’s Yangtze River Delta as a case study, we simulate how the spatial-functional coupling of inter-provincial comparative advantages, drawing on regional strengths in R D, manufacturing, and renewable energy, converges in a high-density port application hub to dismantle entrenched cost barriers. Results demonstrate that this regional synergy drives a non-linear Levelized Cost of Hydrogen (LCOH) reduction from 55.5 RMB/kg to approximately 30.6 RMB/kg, close to the diesel parity threshold. Beyond simple cost-cutting, we reveal a “Denominator Effect” triggered by a “Port-Corridor” strategy, which provides a mechanistic pathway for infrastructure to transcend the utilization bottleneck—elevating rates from a stagnant 36
Plant responses to air pollution are complex under dynamic environments, requiring advanced integrated approaches for capturing interactions among pollutant exposure, meteorology, and plant physio-biochemical responses in real-world conditions. This study developed an integrated remote sensing and machine learning framework to predict species-level plant air pollution tolerance from satellite-derived pollutant exposures, reanalysis meteorology, and plant deciduous-state indicator, using laboratory-derived Air Pollution Tolerance Index (APTI) as the response variable. APTI was calculated from pH, relative water content, ascorbic acid concentration, and total chlorophyll content as indicators of plant response to air pollution stress. Sentinel-5P (NO2, SO2, O3), MODIS (PM2.5-derived), and satellite/reanalysis datasets, CHIRPS (rainfall) and ERA5-Land (temperature, humidity) data were processed in Google Earth Engine. Leaf samples of Pterocarpus indicus Willd. were collected across multiple provinces in Thailand under varying air pollutant and meteorological conditions, and laboratory analysis was conducted to calculate APTI. Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), and Multiple Linear Regression (MLR) algorithms were evaluated for APTI prediction. Model performance was evaluated using R2, RMSE, and MAE, and robustness was assessed with cross-validation (CV) and bootstrap resampling. XGB and GBR showed the highest predictive performance, with test R² values of 0.814 and 0.794 and CV R² values of 0.691 and 0.698, respectively. In contrast, SVR and MLR underperformed in capturing the highly complex and nonlinear patterns. The findings demonstrate the potential of multi-source geospatial data fusion for species-level assessment of plant air pollution tolerance under heterogeneous environmental conditions, especially where ground monitoring is limited. The proposed framework provides a useful basis for future plant selection and urban green infrastructure planning, subject to further validation across broader spatiotemporal settings.
The process of industrialization of arid areas can rapidly surpass the pace of environmental monitoring, which leaves a gap in the knowledge of long-term processes of soil degradation. Although conventional monitoring usually involves the use of a static-based evaluation that only records the existing changes in time, this research establishes a dynamic spatio-temporal model to rebuild 30 years of soil dynamics. To support the backcasting of soil conditions in 1986, 1999 and 2010, we combined a Random Forest (RF) model with the Iranian Model of Desertification Potential Assessment (IMDPA). All the data in satellites were strictly radiometrically normalized to provide spectral consistency in Landsat 5, 7 and 8 sensors. The RF model, which has been trained on 201 soil samples in 2016, was able to predict important chemical makers such as Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), soil pH and the Soil Quality Index (SQI). Without the historical ground-truth data, the accuracy of the temporal reconstructions was evaluated using the spatial uncertainty mapping and by cross-checking with the local history. We find that the natural hydrological processes cause groundwater salinity, whereas closeness to mines and cities increases the erosion of soil quality. Spatiotemporal reconstruction showed that the Very High desertification class increased 60-fold from 29 ha in 1986 up to 1766 ha in 2016, mostly concentrated around the Yazd-Ardakan industrial belt. This spatial association between urban growth and land degradation has offered a quantitative diagnostic floor in specific land management and selective reclamation in perilous aridarious settings.
Remote sensing, geographic information science, and machine learning are techniques for modeling the forest ecosystem and analyzing its changes, losses, and risk areas. Forest losses threaten the forest ecosystem. It increases emission of greenhouse gas, soil erosion, local climate, water cycle disruption, and biodiversity loss. In light of this, it has become crucial to manage the forest using continuous cover forestry (CCF) as a nature-based solution (NBS), which is a sustainable forest management strategy, to achieve sustainable development. The study models forest cover losses, assigns weights to thematic variables, and adopts CCF as an NBS to mitigate these losses in Nigeria’s southeastern states during the periods of 2000–2024 and 2030–2050. The study utilized support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning (ML) classifiers, along with Landsat enhanced thematic mapper plus (ETM+) for 2000 and 2012 and operational land imager (OLI) for 2024 as remote sensing data to map and model forest cover changes in Nigeria’s southeastern states from 2000 to 2024 and through 2030 to 2050. AHP was adopted to assign weight to the study’s thematic variables (soil suitability, slope, and distances to rivers, water bodies, settlements, transport, and agricultural land) for normalization. Accordingly, these variables were integrated into the GIS environment for a weighted overlay analysis to create the final forest cover losses suitability map. The Kappa statistics, agreement/disagreement analysis, and the receiver operating characteristics (ROC) were adopted for validation accuracy. The results demonstrate that the forest cover class had declined while the built-up and water body classes had increased progressively throughout the study from 2000 to 2024 for SVM and RF classifiers. Other study classes, including the cultivated and bare land, had also changed for SVM and RF during the study period. The simulation analysis reveals that by 2030–2050, forest cover class will decrease, while other study classes will either increase or decrease and will have a detrimental impact on the forest cover classes. The study’s findings demonstrate that Nigeria’s southeastern states have been experiencing forest cover losses during the past 24 years and are anticipated to decrease further from 2030 to 2050. Since the forest class has been found to support sustainable development, it therefore means that the study’s finding will either limit or hinder it; hence, the study recommends CCF as NBS and as crucial measures to combat forest cover losses in order to achieve sustainability of resources in the study area.
Abstract Egypt’s New Delta Project, one of the country’s largest land reclamation initiatives, aims to boost agricultural production, support population growth, and strengthen food and water security. However, large-scale development in arid regions may alter surface climate, land–atmosphere interactions, and air quality. This study assesses the impact of agricultural expansion on air quality across the New Delta, utilizing multi-source remote sensing data processed through Google Earth Engine (GEE) and Ecosystem Service Value (ESV) analysis. Two periods were compared: pre-development (2019) and post-development (2024). Vegetation increased by 8.35% (from 4,202.96 to 4,553.74 km2), and urban areas expanded by 11.69% (from 1,135.47 to 1,268.22 km2). Correspondingly, all major pollutants except SO₂ showed overall increases, PM2.5 rose from 9.34 to 9.83 µg/m3, and CO from 889.59 to 937.92 µg/m3, indicating a decline in air quality after development. Winter exhibited the highest deterioration, particularly in PM2.5 (348%) and SO2 (101%), linked to heating, dust storms, and stagnant conditions. CO and PM2.5 also increased notably in spring and summer due to agricultural and transport activities, while autumn showed minimal changes. Spatially, NO2 and CO concentrations rose sharply in newly urbanized zones, especially in the southeast. Despite these effects, the total annual ESV increased by approximately USD 309.65 million, primarily due to vegetation and urban expansion. The study demonstrates the effectiveness of remote sensing and cloud-based tools like GEE in monitoring environmental changes and underscores the need for sustainable land management to balance development with air quality and public health.
Abstract Sustainable environmental management requires integrated approaches that combine spatial analysis with field and laboratory assessments. This study integrates field investigations, laboratory analyses, remotely sensed data, and geographic information systems (GIS) to conduct a comprehensive environmental assessment of the western coastal Nile Delta, Egypt. Three multispectral Landsat scenes from 2003, 2013, and 2023 were processed to derive key environmental and climatic indicators, including land use/land cover (LULC), land surface temperature (LST), urban heat islands (UHI), and shoreline dynamics. Water and soil samples were analyzed for physical and chemical properties, with particular focus on heavy metals (Cd, Pb, Fe) and associated pollution indices. Between 2003 and 2023, vegetation expanded by 1,140.19 km2, largely at the expense of barren lands (− 1,212.41 km2). The most pronounced shoreline changes occurred during 2003–2013, with erosion and accretion rates of 1.0209 km2 and 0.8104 km2, respectively. Water quality in the River Nile showed no consistent spatial pattern, though elevated contaminant levels were detected near Kafr El-Zayat, while most canals were classified as good to permissible for irrigation. Soil analyses revealed wide fluctuations in heavy metal concentrations: Fe (3,287.57–42,577.24 ppm), Cd (0.45–4.05 ppm), and Pb (10.17–99.63 ppm). Contamination factor values indicated low Fe contamination but variable Cd levels, ranging from low to very high in the northeastern districts. The geoaccumulation index (Igeo) suggested anthropogenic contributions to Cd pollution, particularly in Abou Homous and Damanhour. Overall, the findings reveal significant land cover transformation, shoreline instability, and spatially clustered pollution linked to intensified anthropogenic activities. This study advances environmental driver analysis by developing an integrated geospatial framework that connects land use dynamics, coastal change, and atmospheric pollution within a sustainability-oriented assessment. The results provide a science-based decision-support tool to guide targeted monitoring and sustainable environmental management in Beheira Governorate and similar regions.
Climate change and global warming are altering weather patterns, threatening crop productivity. The resultant impact can be food insecurity. Japan’s rice farms are in a precarious situation due to a multifaceted crisis driven by climate change and socio-economic factors. Utilizing remote sensing and the MaxEnt predictive modeling approach to identify declining rice fields can significantly enhance agricultural potential and provide a pertinent solution for integrated, place-based sustainable management in the regions. This technique suggests the likely abandonment of rice fields owing to climate change and socio-economic context by identifying the principal factors contributing. The research aims to explore rice cultivation’s scenarios, challenges, and uncertainties to bring policy-relevant, place-based solutions for food security in Himi City, Toyama Prefecture, Japan. The land-use and land-cover analysis shows that rice fields have decreased by 590 ha from 2000 to 2025 in Himi City. The results indicate that summer precipitation and mean temperature, along with the number of agricultural management people, are the primary factors influencing the potential abandonment distribution of rice fields, accounting for a cumulative contribution of > 70
