Search Results (25)

Cloud and quality screening removes approximately 65% of daily TROPOMI tropospheric NO₂ pixels, creating structured data gaps that coincide with meteorological conditions driving pollution extremes. Standard gap-filling methods—kriging, Random Forests and other machine learning methods—act as statistical smoothers that systematically suppress extreme concentrations and ignore the Missing Not At Random (MNAR) character of cloud-induced missingness. Here we present a physically informed framework that treats urban NO₂ as a forced nonlinear dynamical system and reconstructs missing satellite observations through geometric navigation on a shadow manifold rather than statistical interpolation. The framework integrates five components: (i) Multivariate State-Space Reconstruction (MSSR) using multiview embeddings of continuous ground-based NO₂, O₃, and ERA5 meteorology, grounded in Stark’s forced-system embedding theorem; (ii) Short-Time Regime-Conditioned Convergent Cross Mapping (ST-RC-CCM) with a spatial-mismatch negative control for falsifiable causal validation; (iii) Inverse Probability Weighting (IPW) to correct the clear-sky sampling bias; (iv) trajectory-matrix denoising via Singular Spectrum Analysis (SSA) and Robust PCA; (v) topology-inspired fidelity metrics—Manifold Overlap Ratio (MOR) and Dynamic Trend Capture (DTC)—that penalize smoothing artefacts. The physical basis for this coupling is the shared dynamical history of surface and column NO₂: tropospheric NO₂ has a photochemical lifetime of 1–4 h near urban emission sources, comparable to the boundary layer mixing timescale, ensuring that surface and column concentrations are jointly governed by the same emission–photolysis–transport attractor. The planetary boundary layer height (PBLH), solar zenith angle (SZA), and surface O₃—all included as MSSR coordinates—are the dominant physical drivers of the instantaneous surface-to-column scaling, and their joint trajectory in state space constitutes the physically grounded basis for analogue selection. The framework is validated on a synthetic forced Lorenz-96 system, then applied to five European primary cities spanning contrasting regimes (Sofia, Milano, Stuttgart, Kraków, Hamburg) plus five N1 spatial-mismatch control stations (Plovdiv, Genova, Frankfurt, Warszawa, Berlin)—ten urban-background stations across four countries—with structured ablations (A0-A4V-A4K). Across >3600 evaluations, MOR_ext distributions for EDM and non-EDM methods are non-overlapping by a factor exceeding 5× (EDM minimum 0.59 vs. non-EDM maximum 0.10; median non-EDM MOR_ext ≤ 0.05 at every city × mask combination), while EDM achieves MOR_ext up to 0.915 (Milano Po Valley). Under a fair-comparison benchmark that withholds ground-level NO₂ from Random Forest, EDM’s RMSE advantage remains robust at a median of 3.9× (RF_FULL) and increases to 4.2× (RF_METEO), confirming that the performance gap is physical rather than an information artefact. A three-level temporal validation—within-window pseudo-cloud masking, cross-year transfer (full 2022 holdout and DJF 2023/24), and a COVID-19 out-of-distribution test—demonstrates robustness beyond standard train/test splits, with CCM library-length convergence confirmed for 60/60 ablations (p < 0.001) across all ten stations. Spatial-mismatch tests confirm local dynamical specificity at all five primary–control pairs (Δρ = 0.090–0.210), with seasonal modulation driven by orographic and synoptic mechanisms. These results establish manifold-based gap filling as a dynamically informative complement to statistical approaches, particularly in topographically confined, stagnation-prone basins where preserving extreme-event geometry is essential for exposure assessment. Full article

Shallow groundwater systems of rural municipalities are highly vulnerable to long-term contamination from former on-site sanitation systems, while the hydrochemical response of the aquifer after sewerage network development may be delayed by several factors. In the present study, a total of 147 shallow groundwater samples collected during the summer sampling campaigns of 2018, 2019, 2023, and 2024 were analyzed for general water-quality parameters including pH, EC, NH₄⁺, NO₂⁻, NO₃⁻, PO₄⁻, Cl⁻, SO₄²⁻, microelements, and potentially toxic elements, including As, Pb, Cd, Ni, Cu, Zn, Fe, and Mn. The dataset was evaluated using descriptive statistics, Piper, Wilcox, and Gibbs diagrams, hierarchical cluster analysis, principal component analysis, and GIS-based spatial interpolation. The results indicate that, more than ten years after sewerage network development (2014), shallow groundwater in the study area still shows considerable contamination, primarily characterized by elevated mean concentrations of ammonium (0.836 mg/L), nitrate (177.43 mg/L), and chloride (313.26 mg/L), accompanied by high electrical conductivity (3115 µS/cm) and sodium enrichment (378.12 mg/L). Spatial and boxplot analyses of SAR further indicated increasing sodium-related heterogeneity after 2018, with higher local SAR values in 2023–2024. Hydrochemical diagrams revealed a shift towards Ca-Cl type to Na–Cl types, while multivariate analyses confirmed that salinity enrichment, nitrate contamination, water–rock interaction and redox-sensitive trace element mobilization act as overlapping but partly separable controls. The nitrate–chloride source plot indicated mixed contamination origins, dominated by residual sewage influence and manure-related inputs, with diffuse agricultural nitrogen leaching. Arsenic was used as a supporting indicator of mixing with wastewater; however, As was no longer detectable in most of the investigated wells, suggesting a marked reduction in the former wastewater leakage. These results support the slow attenuation of contamination in the shallow groundwater system affected by former wastewater infiltration and highlight the need for continuous monitoring. Full article

Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making. Full article

Multi-sensor systems require precise time synchronization for accurate data fusion. However, currently prevalent software time synchronization methods often rely on clocks provided by the Global Navigation Satellite System (GNSS), which may not offer high accuracy and can be easily affected by issues with GNSS signals. To address this limitation, this study introduces a novel laser pulse-driven time synchronization (LPTS) method in our custom-developed Light Detecting and Ranging (LiDAR) system. The LPTS method uses electrical pulses, synchronized with laser beams as the time synchronization source, driving the Micro-Controller Unit (MCU) timer within the control system to count with a timing accuracy of 0.1 $\mathsf{\mu }$s and to timestamp the data from the Positioning and Orientation System (POS) unit or laser scanner unit. By employing interpolation techniques, the POS and laser scanner data are precisely synchronized with laser pulses, ensuring strict correlation through their timestamps. In this article, the working principles and experimental methods of both traditional time synchronization (TRTS) and LPTS methods are discussed. We have implemented both methods on experimental platforms, and the results demonstrate that the LPTS method circumvents the dependency on external time references for inter-sensor alignment and minimizes the impact of laser jitter stemming from third-party time references, without requiring additional hardware. Moreover, it elevates the internal time synchronization resolution to 0.1 $\mathsf{\mu }$s and significantly improves relative timing precision. Full article

In the present study, the changes in the groundwater quality in a Hungarian settlement, Báránd, were examined, nine years after the construction of a sewerage network. The sewerage network in the study area was completed in 2014, with a household connection rate exceeding 97% in 2023. In the summer of 2023, water samples were taken from 37 dug groundwater wells. Changes in the water quality were assessed using three water quality indicators (the Water Quality Index (WQI), Contamination degree (Cd), and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)) and geographic information (GIS), data visualization systems, and artificial intelligence (AI). During the evaluation of the quality of the groundwater, eight water chemical parameters were used (pH, EC, NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, COD, Na⁺). Based on interpolated maps and water quality indices, it was established that while an increasing portion of the area exhibits adequate or good water quality compared to the pre-sewerage period, a deterioration has occurred relative to recent years. Even nine years after the sewerage network construction, elevated concentrations of inorganic nitrogen forms and organic matter persist, indicating the continued presence of accumulated pollutants, as confirmed by all three water quality indicators to varying degrees and spatial patterns. The interactive data visualization and cloud-based sharing of the data of the water quality geodatabase were made freely available with the help of Tableau Public. A Feed-Forward Neural Network (FFNN) was developed to predict the groundwater quality, estimating the water quality statuses of three water quality indicators based on water chemistry parameters. The results showed that the applied training algorithms and activation functions proved to be the most effective in the case of different network structures. The most accurate prediction of the WQI and CCME WQI indicators was provided by the Bayesian control algorithm (trainbr), which achieved the lowest mean-squared error (RMSE_WQI = 0.1205, RMSE_{CCME WQI} = 0.1305) and the highest determination coefficient (R²_WQI = 0.9916, R²_{CCME WQI} = 0.9838). For the Cd index, the accuracy of the model was lower (RMSE = 0.1621, R² = 0.9714), suggesting that this indicator is more difficult to predict. With regard to our study, it should be emphasized that data visualization is a particularly practical tool for the post-processing of spatial monitoring data, as it is suitable for displaying information in an intuitive, visual form, for discovering spatial patterns and relationships, and for performing real-time analyses. AI is expected to further increase visualization efficiency in the future, enabling the rapid processing of large amounts of data and spatial databases, as well as the identification of complex patterns. Full article

This research describes a multi-objective trajectory planning method for robotic arms based on time, energy, and impact. The quintic Non-Uniform Rational B-Spline (NURBS) curve was employed to interpolate the trajectory in joint space. The quintic NURBS interpolation curve can make the trajectory become constrained within the kinematic limits of velocity, acceleration, and jerk while also satisfying the continuity of jerk. Then, based on the Parrot Optimization (PO) algorithm, through improvements to reduce algorithmic randomness and the introduction of appropriate multi-objective strategies, the algorithm was extended to the Multi-Objective Parrot Optimization (MOPO) algorithm, which better balances global search and local convergence, thereby more effectively solving multi-objective optimization problems and reducing the impact on optimization results. Subsequently, by integrating interpolation curves, the multi-objective optimization of joint trajectories could be performed under robotic kinematic constraints based on time–energy-jerk criteria. The obtained Pareto optimal front can provide decision-makers in industrial robotic arm applications with flexible options among non-dominated solutions. Full article

Accurately valuing large portfolios of Variable Annuities (VAs) poses a significant challenge due to the high computational burden of Monte Carlo simulations and the limitations of spatial interpolation methods that rely on manually defined distance metrics. We introduce a residual portfolio valuation network (ResPoNet), a novel residual neural network architecture enhanced with weighted loss functions, designed to improve valuation accuracy and scalability. ResPoNet systematically accounts for mortality risk and path-dependent liabilities using residual layers, while the custom loss function ensures better convergence and interpretability. Numerical results on synthetic portfolios of 100,000 contracts show that ResPoNet achieves significantly lower valuation errors than baseline neural and spatial methods, with faster convergence and improved generalization. Sensitivity analysis reveals key drivers of performance, including guarantee complexity and contract maturity, demonstrating the robustness and practical applicability of ResPoNet in large-scale VA valuation. Full article

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km²) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO₄ and NH₄. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO₄, Fe, PO₄, and NH₄. In contrast, SK performed worst for the species except for SO₄. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling. Full article

The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement method enhanced by cubic spline interpolation to mitigate stochastic jitter errors. Our method first projects tri-view images onto a mean elevation plane using POS-provided EOPs to generate Level 1 images for dense matching. Matched points are then back-projected to the original Level 0 images for the bundle adjustment based on the Gaussian Markov model. Finally, cubic spline interpolation is employed to obtain EOPs for lines without observations. Experimental comparisons with the piecewise polynomial model (PPM) and Lagrange interpolation model (LIM) demonstrate that our method outperformed these models in terms of geo-referencing accuracy, EOP refinement metric, and visual performance. Specifically, the line fitting accuracies of four linear features on Level 1 images were evaluated to assess EOP refinement performance. The refinement performance of our method showed improvements of 50%, 45.1%, 29.9%, and 44.6% over the LIM, and 12.9%, 69.2%, 69.6%, and 49.3% over the PPM. Additionally, our method exhibited the best visual performance on these linear features. Full article

Transition metals and their oxide compounds exhibit excellent chemical reactivity; however, their easy agglomeration and high cost limit their catalysis applications. In this study, an interpolation structure of a Myriophyllum verticillatum L. biochar-supported Mn/Mg composite (Mn/Mg@MV) was prepared to degrade triphenyl phosphate (TPhP) from wastewater through the activating periodate (PI) process. Interestingly, the Mn/Mg@MV composite showed strong radical self-producing capacities. The Mn/Mg@MV system degraded 93.34% TPhP (pH 5, 10 μM) within 150 min. The experimental results confirmed that the predominant role of IO3· and the auxiliary ·OH jointly contributed to the TPhP degradation. In addition, the TPhP pollutants were degraded to various intermediates and subsequent Mg mineral phase mineralization via mechanisms like interfacial processes and radical oxidation. DFT theoretical calculations further indicated that the synergy between Mn and Mg induced the charge transfer of the carbon-based surface, leading to the formation of an ·OH radical-enriched surface and enhancing the multivariate interface process of ·OH, IO₃, and Mn(VII) to TPhP degradation, resulting in the further formation of Mg PO₄ mineralization. Full article

The mining rope shovel (MRS) is one of the core pieces of equipment for open-pit mining, and is currently moving towards intelligent and unmanned transformation, replacing traditional manual operations with intelligent mining. Aiming at the demand for online planning of an intelligent shovel excavation trajectory, an MRS excavating trajectory planning method based on material surface perception is proposed here. First, point cloud data of the material stacking surface are obtained through laser radar to perceive the excavation environment and these point cloud data are horizontally calibrated and filtered to reconstruct the surface morphology of the material surface to provide a material surface model for calculation of the mining volume in the subsequent trajectory planning. Second, kinematics and dynamics analysis of the MRS excavation device are carried out using the Product of Exponentials (PoE) and Lagrange equation, providing a theoretical basis for calculating the excavation energy consumption in trajectory planning. Then, the trajectory model of the bucket tooth tip is established by the method of sixth-order polynomial interpolation. The unit mass excavation energy consumption and unit mass excavation time are taken as the objective function, and the motor performance and the geometric size of the MRS are taken as constraints. The grey wolf optimizer is used for iterative optimization to realize efficient and energy-saving excavation trajectory planning of the MRS. Finally, trajectory planning is carried out for material surfaces with four different shapes (typical, convex, concave, and convex–concave). The results of experimental validation show that the actual hoist and crowd forces are essentially consistent with the planned hoist and crowd forces in terms of the peak value and trend variations, verifying the accuracy of the calculation model and confirming that the full bucket rate and various parameters meet the constraints. Therefore, the trajectory planning method based on material surface perception are feasible for application to different excavation conditions. Full article

Assessing water quality is necessary to ascertain its viability for domestic, industrial, and agricultural purposes. A total of 48 water samples were, respectively, drawn from the Rivers Karawa and Iyiaji located in Ezeagu and Uzo-uwani which are typical rural areas in Enugu State, Nigeria. These samples were taken in two seasons (early rainy season and late dry season). Physicochemical properties were determined using standard methods. The scaling and corrosivity potentials of the water were evaluated using the Larson-Skold index, aggressive index, Puckorius scaling index, and Ryznar stability index models. Additionally, seven irrigation evaluation criteria, as well as spatial distribution maps, were used to determine the suitability of the river waters for irrigation purposes and to interpolate the spatial distribution of the river water quality parameters. Major ion chemistry was used in the assessments. The physicochemical properties of river waters fell within the recommended standard values. However, NO₃⁻ greatly exceeded the recommended range in both rivers. The cations and anions from River Karawa were Ca²⁺ > Mg²⁺ > K⁺ > Na⁺ and NO₃⁻ > Cl⁻ > SO₄²⁻ > HCO₃⁻ > CO₃⁻ > PO₄⁻, while those from River Iyiaji were Ca²⁺ > Mg²⁺ > Na⁺ > K⁺ and Cl⁻ > SO₄²⁻ >NO₃⁻ > PO₄⁻ > HCO₃⁻ > CO₃⁻. A piper plot showed the predominance of Ca²⁺ and Mg⁺, as well as SO₄²⁻ and Cl⁻, in both rivers. The seven irrigation assessment indices indicated that the water of the Rivers Karawa and Iyiaji is suitable for irrigation purposes. In addition, the scaling and corrosivity models predicted that the river waters have high scaling and corrosivity potentials. Specifically, while the aggressive index suggested that it is severely corrosive, the Langelier saturation index suggested that the water from both rivers is supersaturated and, thus, has potential to scale. Full article

The array position and orientation system (array POS), composed of one main POS and multiple sub-inertial measurement units (sub-IMUs), is key equipment in the aerial remote-sensing system, especially the multi-load system, which can provide motion compensation for the multi-load remote-sensing system to improve imaging quality. Nevertheless, the measurement information of each sub-IMU can only realize the motion information of the corresponding remote-sensing load. Ideally, each remote-sensing load should be equipped with a sub-IMU for motion compensation, which is impossible in actual engineering considering the volume, weight and cost. To solve this problem, a multi-node motion estimation method based on the B-spline of the array POS is proposed to realize the motion compensation of remote-sensing loads without sub-IMUs. Firstly, the transfer alignment method based on fiber-grating multi-dimensional deformation measurement was adopted. Motion parameters of the remote-sensing payload equipped with sub-IMUs at different times can be obtained by observing and correcting the errors between the main POS and sub-IMUs. In this way, the space-time characteristics of each interpolation point are fully utilized. Additionally, the motion information of the main POS and all sub-IMUs is fitted through the estimation method based on the B-spline, during which wing deformation is considered to obtain the motion parameters of the remote-sensing payload equipped without a sub-IMU. In this way, the spatial correlation between the information of each node is fully utilized. Due to the full utilization of the spatiotemporal correlation of the motion information of each sub node, high-precision and highly reliable motion information of the remote-sensing loads not equipped with sub-IMUs is obtained. Furthermore, the proposed method can be modified locally without affecting other nodes, and has the advantages of a simple algorithm and easy engineering implementation. Finally, a semi-physical simulation based on ground-loading test was conducted. The results show that the baseline in the X-axis, Y-axis and Z-axis direction is improved by 0.484 mm, 0.137 mm and 1.225 mm, respectively, and that the measurement accuracy of roll angle is improved by 0.011°. Full article

To investigate the impact of experimental interventions on living biological tissue, ex vivo rodent brain slices are often used as a more controllable alternative to a live animal model. However, for meaningful results, the biological sample must be known to be healthy and viable. One of the gold-standard approaches to identifying tissue viability status is to measure the rate of tissue oxygen consumption under specific controlled conditions. Here, we work with thin (400 $\mathsf{\mu }$m) slices of mouse cortical brain tissue which are sustained by a steady flow of oxygenated artificial cerebralspinal fluid (aCSF) at room temperature. To quantify tissue oxygen consumption (Q), we measure oxygen partial pressure (pO${}_2$) as a function of probe depth. The curvature of the obtained parabolic (or parabola-like) pO${}_2$ profiles can be used to extract Q, providing one knows the Krogh coefficient $K_{\mathrm{t}}$, for the tissue. The oxygen trends are well described by a Fick’s law diffusion–consumption model developed by Ivanova and Simeonov, and expressed in terms of ratio $(Q/K)$, being the rate of oxygen consumption in tissue divided by the Krogh coefficient (oxygen diffusivity × oxygen solubility) for tissue. If the fluid immediately adjacent to the tissue can be assumed to be stationary (i.e., nonflowing), one may invoke conservation of oxygen flux $K·(\partial P/\partial x)$ across the interface to deduce $(K_{\mathrm{t}}/K_{\mathrm{f}})$, the ratio of Krogh coefficients for tissue and fluid. Using published interpolation formulas for the effect of salt content and temperature on oxygen diffusivity and solubility for pure water, we estimate $K_{\mathrm{f}}$, the Krogh coefficient for aCSF, and hence deduce the $K_{\mathrm{t}}$ coefficient for tissue. We distinguish experimental uncertainty from natural biological variability by using pairs of repeated profiles at the same tissue location. We report a dimensionless Krogh ratio $(K_{\mathrm{t}}/K_{\mathrm{f}})=0.562\pm 0.088$ (mean ± SD), corresponding to a Krogh coefficient $K_{\mathrm{t}}=(1.29\pm 0.21)\times {10}^{-14}$ mol/(m·s·Pa) for mouse cortical tissue at room temperature, but acknowledge the experimental limitation of being unable to verify that the fluid boundary layer is truly stationary. We compare our results with those reported in the literature, and comment on the challenges and ambiguities caused by the extensive use of ‘biologically convenient’ non-SI units for tissue Krogh coefficient. Full article

This paper represents the first regional-scale investigation in the Piedmont Po plain about the relationship between groundwater temperature (GWT) and climate variability. The understanding of relationships between air temperature (AT) and GWT is really important, especially in the context of global climate change. The aim of this investigation is to study the relationship between GWT and AT over a 10-year time period (from 2010 to 2019) to analyse how these two parameters interrelate and to evaluate possible trends. To carry out this study, basic statistic interpolations were performed on both parameters to facilitate comparison. Both AT and GWT showed an increase over the observed decade with a more pronounced growth of the AT; this allow to state that GWT is more resilient to climate change than AT. However, some areas in the Piedmont plain showed a behaviour that partially deviated from the standard trend observe for the majority of the region. These areas were influenced by particular anthropic factors (for example the paddy fields in the Novara plain) or natural elements (as the monitoring wells in the “Canavese” area, located downstream of melting glaciers, or the wells located close to the Tanaro River). Moreover, this study wanted to stress the importance of the knowledge of the localization in wells of the instruments for the GWT measurement, to have the most accurate and comparable data. It was proved that as the depth increased, the maximum and minimum peaks of the GWT shifted in time respect to the maximum and minimum peaks of the AT, and, in addition, the GWT fluctuation in the bottom part of the aquifer was milder than the fluctuation observed in the most superficial part. Further investigations will be conducted in future in Piedmont plain areas with different behavior, in order to better understand their dynamics and the factors that may influence GWT and how they are affected by climate change. Full article