Search Results (135)

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

Rapid urbanization and industrial development have significantly altered land use and cover across the globe, intensifying urban thermal environments and exacerbating the urban heat island (UHI) effect. Gujranwala, Pakistan, represents an industrial growth that has driven substantial land use/land cover (LULC) changes and temperature increases; however, the directional and distance-based patterns of these changes remain unquantified. Therefore, this study is conducted to examine spatiotemporal changes in LULC and variations in the Urban Thermal Field Variation Index (UTFVI) between 2001 and 2021 and to project future scenarios for 2031 and 2041 using (1) Earth Observation Indices (EOIs) with machine learning (ML) classifiers (Random Forest) for precise LULC mapping through the Google Earth Engine (GEE) platform, (2) Cellular Automata–Artificial Neural Networks (CA-ANNs) for future scenario projection, and (3) Gradient Directional Analysis (GDA) to quantify directional (16-axis) and distance-based (concentric zones) patterns of urban expansion and thermal variation from 2001–2021. The study revealed significant LULC changes, with built-up areas expanding by 7.5% from 2001 to 2021, especially in the east, northeast, and southeast directions within a 20 km radius. Due to urban encroachment, vegetation and cropland decreased by 1.47% and 1.83%, respectively. The urban thermal environment worsened, with the highest land surface temperature (LST) rising from 41 °C in 2001 to 55 °C in 2021. Additionally, the UTFVI showed expanding areas under the ‘strong’ and ‘strongest’ categories, increasing from 30.58% in 2001 to 33.42% in 2041. Directional analysis highlighted severe thermal stress in the southern and southwestern areas linked to industrial activities and urban sprawl. This integrated approach provides a template for analyzing urban thermal environments in developing cities, supporting targeted mitigation strategies through direction- and distance-specific planning interventions to mitigate UHI impacts. Full article

The conservation and monitoring of land cover represent crucial elements for sustainable regional development, especially in fragile high Andean ecosystems. This study evaluates the spatiotemporal changes in land use and land cover (LULC) in the Locumba basin over the period of 1984–2023. A hybrid modeling approach combining artificial neural networks (ANN) and cellular automata (CA) was employed to project future changes for 2033, 2043, and 2053. The results reveal a significant reduction in glaciers and lagoons throughout the Locumba basin, with notable declines from 1984 to 2023, while vegetated areas, particularly grasslands and wetlands, experienced substantial expansion. Specifically, grasslands increased by 273.7% relative to their initial coverage, growing from 57.87 km² in 1984 to over 220.31 km² in 2023, with projections indicating continued growth to over 331.62 km² by 2053. This multitemporal analysis provides crucial information for anticipating future land dynamics and underscores the urgent need for strategic conservation planning to mitigate the continued loss of strategic ecosystems in the high Andean region of Tacna. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

Coenzyme Q₁₀ (CoQ₁₀) has attracted widespread attention in recent years due to its momentous physiological functions. Microbial fermentation is the major method in CoQ₁₀ industrial production, and Rhodobacter sphaeroides is the main strain for the production of CoQ₁₀ by fermentation. Optimization of the culture medium is a popular solution to improve the metabolite production. Culture medium is the material basis for microbial growth and product synthesis, of which inorganic salts are a key ingredient. Uniform design (UD), artificial neural network (ANN), and genetic algorithm (GA) are the main research methods. Through uniform design (UD) and artificial neural network/genetic algorithm (ANN-GA) progressive optimization, an optimal formulation of the inorganic salts in fermentation medium was obtained (g·L⁻¹): MgSO₄ 12, NaCl 2.5, FeSO₄ 1.6, KH₂PO₄ 0.8, MnSO₄ 0.1, CaCl₂ 0.1. Ultimately, the fermentation yield of CoQ₁₀ could reach 255.36 mg·L⁻¹. ANN-GA exhibited a superior prediction capability compared to UD. Compared to UD, the optimization results of ANN-GA had a smaller relative error (ANN-GA 1.23%; UD 3.01%) and a higher increase rate in the fermentation level of CoQ₁₀ (ANN-GA 4.1%; UD 2.04%). R. sphaeroides had a high demand for Mg²⁺. Full article

Annexins (ANNs) are a family of calcium (Ca²⁺)-dependent and phospholipid-binding proteins, which are implicated in the regulation of plant growth and development as well as protection from biotic and abiotic stresses. Corydalis saxicola Bunting, an endangered benzylisoquinoline alkaloid (BIA)-rich herbaceous plant, widely used in traditional Chinese medicine, is endemic to the calciphilic karst region of China. However, whether and how ANNs are involved in the biosynthesis pathway of BIAs and/or help C. saxicola plants cope with abiotic properties, such as calcareous soils, are largely unknown. Here, nine CsANN genes were identified from C. saxicola, and they were divided into three subfamilies, namely subfamilies I, II, and IV, based on the phylogenetic tree. The CsANNs clustered into the same clade, sharing similar gene structures and conserved motifs. The nine CsANN genes were located on five chromosomes, and their expansions were mainly attributed to tandem and whole-genome duplications. The CsANN transcripts displayed organ-specific and Ca²⁺-responsive expression patterns across various tissues. In addition, transient overexpression assays showed that CsANN1 could positively regulate the accumulation of BIA compounds in C. saxicola leaves, probably by directly interacting with key BIA-biosynthetic-pathway enzymes or by interacting with BIA-biosynthetic regulatory factors, such as MYBs. This study sheds light on the profiles and functions of the CsANN gene family and paves the way for unraveling the molecular mechanism of BIA accumulation, which is regulated by Ca²⁺ through CsANNs. Full article

In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature. Full article

Nowadays, reverse osmosis (RO) desalination has become a highly effective and economical solution to address water scarcity worldwide. The membranes used in this type of separation are influenced by both pre-treatment operations and feed water quality, leading to fouling, a complex phenomenon responsible for reducing treatment performance and shortening membrane lifespan. In this study, an autopsy of a RO membrane from the Boujdour plant was performed, and a fouling prediction tool based on transmembrane pressure (TMP) was developed using MATLAB/Simulink (R2015a) with an artificial neural network (ANN) model. The impact of membrane fouling on treatment performance was also examined through one year of monitoring. A detailed analysis of the fouled membrane was conducted using SEM/EDS techniques to characterize the fouling on the membrane’s surface and cross-section. The results revealed significant fractures on the membrane surface, with fouling predominantly consisting of organic deposits (characterized by a high oxygen concentration of 39.69%) and inorganic fouling, including Si (7.99%), Al (2.79%), Mg (1.56%), Fe (1.27%), and smaller quantities of K (0.87%), S (0.36%), and Ca (0.12%). The ANN model for predicting transmembrane pressure was successfully developed, achieving a high R² value of 92.077% and a low mean square error (MSE) of 0.005657. This predictive model demonstrates the ability to forecast future TMP cycles based on historical data. The research provides a detailed understanding of the types of fouling affecting RO membranes and contributes to the development of preventive strategies to mitigate membrane fouling. Full article

The Colombian Coffee Cultural Landscape (CCLC), a UNESCO World Heritage site, faces conservation threats due to changes in land use and land cover (LULC). This study analyzed and predicted the spatiotemporal dynamics of LULC in the CCLC from 2014 to 2034, assessing its effects on the landscape structure. The analyses identified negative impacts and provided insights for developing conservation and land use planning strategies aimed at comprehensive landscape management. A supervised classification methodology using the Random Forest algorithm was implemented by integrating multispectral (Landsat 8) and Synthetic Aperture Radar (SAR) data (Sentinel-1), achieving an overall accuracy of 87.88% and a Kappa coefficient of 84.20%. Future projections were conducted using a hybrid Cellular Automata and Artificial Neural Network model (CA-ANN), reaching an accuracy of 88.12% and a Kappa coefficient of 0.84. The results indicate urban expansion, increasing from 1.46% in 2014 to 15.64% by 2034, accompanied by a forest cover loss of 77.8% and a reduction in coffee-growing areas from 77.91% in 2019 to 68.40% by 2034. Landscape metric analysis revealed increased fragmentation and spatial heterogeneity. The integration of multisensor remote sensing, hybrid predictive models, and landscape metrics within the CCLC provides a quantitative methodological framework to evaluate the transformation of cultural landscapes under anthropogenic pressures. Full article

Rapid urbanization and the consequent alteration in land use and land cover (LULC) significantly change the natural landscape and adversely affect hydrological cycles, biological systems, and various ecosystem services, especially in the developing world. Thus, it is vital to study the environmental conditions of a region to mitigate the negative impacts of urbanization. Out of a wide array of parameters, the Environmental Criticality Index (ECI), a relatively new concept, was used in this study, which was conducted over the Kolkata Metropolitan Area (KMA). It was derived using Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to quantify heat-related impact. An increase in the percentage of land area under high ECI categories, from 23.93% in 2000 to 32.37% in 2020, indicated a progressive increase in criticality. The Spatio-temporal Thermal-based Environmental Criticality Consistency Index (STTECCI) and hotspot analysis identified the urban and industrial areas in KMA as criticality hotspots, consistently recording higher ECI. The correlation analysis between ECI and LULC features revealed that there exists a negative correlation between ECI and natural vegetation and agriculture, while built-up areas and ECI are positively correlated. Bare lands, despite being positively correlated with ECI, have an insignificant relationship with it. Also, the designed built-up index extracted the built-up areas with an accuracy of 89.5% (kappa = 0.78). The future scenario of ECI in KMA was predicted using Modules for Land Use Change Evaluation (MOLUSCE) with an accuracy level above 90%. The percentage of land area under low ECI categories is expected to decline from 50.02% in 2000 to 35.6% in 2040, while the percentage of land area under high ECI categories is expected to increase from 23.93% in 2000 to 36.56% in 2040. This study can contribute towards the development of tailored management strategies that foster sustainable growth, resilience, and alignment with the Sustainable Development Goals, ensuring a balance between economic development and environmental preservation. Full article

To achieve the resource utilization of iron ore tailings (IOTs), two different IOTs were investigated as sustainable adsorbents for ciprofloxacin (CIP) removal from aqueous systems. Through systematic batch experiments, key adsorption parameters including initial pH, adsorbent dosage, contact time, ionic strength, and temperature were comprehensively evaluated. The results showed that CIP adsorption by IOTs remained relatively stable across a broad initial pH range (2–10), with maximum adsorption capacities of 5-IOT and 14-IOT observed at the initial pH values of 10.1 and 9.16, respectively. Competitive ion experiments revealed a gradual decrease in CIP adsorption capacity with increasing ionic strength (Na⁺, Mg²⁺, and Ca²⁺). Thermodynamic analyses indicated an inverse relationship between adsorption capacity and temperature, yielding maximum adsorption capacities (Q_max) of 16.64 mg/g (5-IOT) and 13.68 mg/g (14-IOT) at 288.15 K. Mechanistic investigations combining material characterization and adsorption modeling identified ion exchange as the predominant interaction mechanism. Notably, trace elements (Cd, Co, Cr, Cu, Fe, Ni, Pb, and Zn) were released during leaching tests, with concentrations consistently below environmental safety thresholds. A back-propagation artificial neural network (BP-ANN) with optimized architecture (8-11-1 topology) demonstrated high predictive accuracy (MSE = 0.0031, R² = 0.9907) for adsorption behavior. These findings suggested IOTs as cost-effective, environmentally compatible adsorbents for CIP remediation, offering the dual advantages of pharmaceutical pollutant removal and industrial waste valorization. Full article

Urban expansion reshapes spatial patterns over time, leading to complex challenges such as environmental degradation, resource scarcity, and socio-economic inequality. It is critical to anticipate these transformations in order to devise proactive urban policies and implement sustainable planning practices that minimize negative impacts on ecosystems and human livelihoods. This study investigates LULC changes in the rapidly urbanizing Manisa metropolitan area of Turkey using Sentinel-2 satellite imagery and advanced machine learning algorithms. High-accuracy LULC maps were generated for 2018, 2021, and 2024 using Random Forest, Support Vector Machine, k-Nearest Neighbors, and Classification and Regression Trees algorithms. Among these, the Random Forest algorithm demonstrated superior accuracy and consistency in distinguishing complex land-cover classes. Future LULC scenarios for 2027 and 2030 were simulated using the Cellular Automata–Artificial Neural Network model and the QGIS MOLUSCE plugin. The results indicate significant urban growth, with built-up areas projected to increase by 23.67% between 2024 and 2030, accompanied by declines in natural resources such as bare land and water bodies. This study highlights the implications of urban expansion regarding ecological balance and demonstrates the importance of integrating machine learning and simulation models to forecast land use changes, enabling sustainable urban planning and resource management. Overall, effective policies must be developed to manage the negative environmental impacts of urbanization and conduct land use planning in a balanced manner. Full article

Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts independently, there remains a critical gap in understanding the integrated impacts of land use/land cover (LULC) changes on both ecosystem vulnerability and sustainable development achievements. This study addresses this gap through an innovative integration of multitemporal Landsat imagery (5, 7, and 8), SRTM-DEM, historical land use maps, and population data using the MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling to monitor LULC changes over three decades (1990–2020) and project future changes for 2025, 2030, and 2035, supporting the Sustainable Development Goals (SDGs) in Karachi, southern Pakistan, one of the world’s most populous megacities. The framework integrates LULC analysis with SDG metrics, achieving an overall accuracy greater than 97%, with user and producer accuracies above 77% and a Kappa coefficient approaching 1, demonstrating a high level of agreement. Results revealed significant urban expansion from 13.4% to 23.7% of the total area between 1990 and 2020, with concurrent reductions in vegetation cover, water bodies, and wetlands. Erosion along the riverbank has caused the Malir River’s area to decrease from 17.19 to 5.07 km² by 2020, highlighting a key factor contributing to urban flooding during the monsoon season. Flood risk projections indicate that urbanized areas will be most affected, with 66.65% potentially inundated by 2035. This study’s innovative contribution lies in quantifying SDG achievements, showing varied progress: 26% for SDG 9 (Industry, Innovation, and Infrastructure), 18% for SDG 11 (Sustainable Cities and Communities), 13% for SDG 13 (Climate Action), and 16% for SDG 8 (Decent Work and Economic Growth). However, declining vegetation cover and water bodies pose challenges for SDG 15 (Life on Land) and SDG 6 (Clean Water and Sanitation), with 16% and 11%, respectively. This integrated approach provides valuable insights for urban planners, offering a novel framework for adaptive urban planning strategies and advancing sustainable practices in similar stressed megacity regions. Full article

Amidst the increasingly escalating global concern regarding climate change, adopting a low-carbon approach has become crucial for charting the future developmental trajectory of urban areas. It also offers a novel angle for cities to avoid high-temperature risks. This paper estimates carbon emissions in Wuhan City from both direct and indirect aspects. Then, the ANN (artificial neural network)–CA (Cellular Automata) model is employed to establish three distinct development scenarios (Ecological Priority, Tight Growth, and Natural Growth) to predict future urban expansion. Additionally, the WRF (Weather Research and Forecasting Model)—UCM (Urban Canopy Model) model is used to investigate the thermal environmental impacts of varying urban development scenarios. This study uses a low-carbon perspective to explore how cities can develop scientifically sound urban strategies to meet climate change challenges and achieve sustainable development goals. The conclusions are as follows: (1) The net carbon emission for Wuhan in 2022 is estimated to be approximately 20.8353 million tonnes. Should the city maintain an average annual emission reduction rate of 10%, the carbon sink capacity of Wuhan would need to be enhanced by 382,200 tonnes by 2060. (2) In the absence of anthropogenic influence, there is a propensity for the urban construction zone of Wuhan to expand primarily towards the southeast and western sectors. (3) The Ecological Priority (EP) and Tight Growth (TG) scenarios are effective in alleviating the urban thermal environment, achieving a reduction of 0.88% and 2.48%, respectively, in the urban heat island index during afternoon hours. In contrast, the Natural Growth (NG) scenario results in a degradation of the urban thermal environment, with a significant increase of over 4% in the urban heat island index during the morning and evening periods. (4) An overabundance of urban green spaces and water bodies could exacerbate the urban heat island effect during the early morning and at night. The findings of this study enhance the comprehension of the climatic implications associated with various urban development paradigms and are instrumental in delineating future trajectories for low-carbon sustainable urban development models. Full article

The crystalline state of matter serves as a reference point in the context of studies of properties of a variety of chemical compounds. This is due to the fact that prepared crystalline solids of practically useful materials (inorganic or organic) may be utilized for the thorough characterization of important properties such as (among others) energy bandgap, light absorption, thermal and electric conductivity, and magnetic properties. For that reason it is important to develop mathematical descriptions (models) of properties and structures of crystals. They may be used for the interpretation of experimental data and, as well, for predictions of properties of novel, unknown compounds (i.e., the design of novel compounds for practical applications such as photovoltaics, catalysis, electronic devices, etc.). The aim of this article is to review the most important mathematical models of crystal structures and properties that vary, among others, from quantum models (e.g., density functional theory, DFT), through models of discrete mathematics (e.g., cellular automata, CA), to machine learning (e.g., artificial neural networks, ANNs). Full article