Dissolved organic matter (DOM) plays a key role in influencing the environmental behavior of heavy metals in lake ecosystems. Through mechanisms such as complexation, ion exchange, and physical adsorption, DOM regulates the speciation, transport, and bioavailability of heavy metals, thereby shaping their ecological risks and fate. Here, we provide a comprehensive review of the sources and molecular composition of lake DOM, with particular attention to humic substances, proteins, and polysaccharides, and highlight the importance of functional groups such as carboxyl group and phenolic hydroxyl group in metal binding. The mechanisms of DOM-heavy metal interactions are discussed in detail. These include σ-ligand bonding, which relies on the donation of lone pair electrons from O/N-containing functional groups to metal orbitals. Also covered are π–d electron interactions, often initiated by photoexcitation of aromatic moieties in DOM to facilitate electron transfer, and multi-site adsorption, a process governed by the combined effects of electrostatic attraction, hydrogen bonding, and the porous structure of DOM. Additionally, the effects of environmental factors (temperature, pH, and light) and biological factors (microbial activity and aquatic plant decomposition) on DOM-heavy metal dynamics are examined. Although substantial progress has been made, key challenges remain in understanding the microscale mechanisms, capturing real-time changes in natural waters, and assessing long-term ecological impacts. Future research should prioritize multi-scale approaches. This entails employing advanced techniques like Fourier-transform ion cyclotron resonance mass spectrometry to elucidate molecular mechanisms, while also advancing in situ monitoring technologies and establishing long-term observation networks to resolve real-time dynamics and assess cumulative ecological impacts. This review provides a theoretical basis for understanding DOM-heavy metal interactions and supports future efforts in ecological risk assessment and the sustainable management of lake environments.
In recent years, with the advancements in machine learning and neural networks, algorithms using physics-informed neural networks (PINNs) to solve PDEs have gained widespread applications. While these algorithms are well-suited for a wide range of equations, they often exhibit a suboptimal performance when applied to equations with large local gradients, resulting in substantially localized errors. To address this issue, this paper proposes an adaptive PINN algorithm designed to improve accuracy in such cases. The core idea of the algorithm is to adaptively adjust the distribution of collocation points based on the recovery-type a-posteriori error of the current numerical solution, enabling a better approximation of the true solution. This approach is inspired by the adaptive finite element method. By combining the recovery-type a-posteriori estimator, a gradient-recovery estimator commonly used in the adaptive finite element method (FEM), with PINNs, we introduce the recovery-type a-posteriori estimator enhanced adaptive PINN (R-PINN) and compare its performance with a typical adaptive sampling PINN, failure-informed PINN (FI-PINN), and a typical adaptive weighting PINN, residual-based attention in PINN (RBA-PINN) as a baseline. Our results demonstrate that R-PINN achieves faster convergence with fewer adaptively distributed points and outperforms the other two PINNs in the cases with regions of large errors.
One-part geopolymer foam concrete (GFC) represents a sustainable alternative to traditional cementitious materials. This study investigates the influence of waste valorization on the performance of GFCs using ground-granulated blast-furnace slag (GBFS), waste concrete sludge (WCS), and waste bamboo powder (WBP), activated with sodium metasilicate. The GFCs were thermally cured and evaluated for workability, mechanical properties, durability, and microstructural changes. Mixes were characterized by compressive strength, flexural strength, density, water absorption, shrinkage, freeze-thaw durability, high-temperature resistance, and thermal conductivity. The results show that mix 0CS-BP5, containing 5 % WBP and no WCS, achieved the highest compressive strength (7 d: 7.6 MPa, 28 d: 11.4 MPa) and flexural strength (28 d: 1.2 MPa) owing to improved pore structure and crack-bridging from WBP. The incorporation of WBP reduced water absorption by 39 % and 91-day drying shrinkage by 52 % compared to the control mix (0CS-BP0). Thermal conductivity ranged from 0.32 to 0.43 W/m & sdot;K across mixes, offering a balance between insulation and mechanical strength. Mix 0CS-BP5 exhibited superior freeze-thaw durability, with only 3.6 % mass loss after 50 cycles. Furthermore, it retained 3.0 MPa compressive strength at 750 degrees C, indicating enhanced high-temperature resistance. However, mixes with higher WCS content (e.g., mix 25CS-BP5) showed a decline in strength and transport properties due to their heterogeneous structure and increased macro-void connectivity. This study demonstrates the potential of utilizing industrial and biowaste to develop high-performance, low-carbon GFCs, advancing the use of sustainable materials for energy-efficient and durable construction applications.
Improving the energy efficiency of building materials is critical for reducing environmental impacts. This study develops and evaluates bio-based polyurethane composites (BPUCs) incorporating lauryl alcohol (LA) as a phase change material (PCM) for lightweight cementitious systems. The composites were synthesized from modified castor oil (MCO), commercial polyether polyol (CPP), and methylene diphenyl diisocyanate (MDI), and systematically characterized to assess their thermal, mechanical, microstructural, and environmental performance. Differential scanning calorimetry, thermogravimetric analysis, hardness, tensile, and thermal conductivity tests were performed, followed by outdoor thermal regulation testing using a full-scale cabin setup. Results show that increasing LA content improves bulk density (38.9-67.6 kg/m3), hardness (7.1-15.2), and thermal conductivity (0.026-0.038 W/m & sdot;K), while moderately reducing tensile strength (243-138 kPa) and strain (89-43 %). The optimized composite, BPUC-LA-6, achieved a latent heat storage of 127.8 J/g and enhanced thermal stability, with activation energy increasing from 108.47 to 164.13 kJ/mol. When incorporated into lightweight cementitious composites (BLWC3), the system reduced peak surface temperatures by up to 6.5 degrees C and maintained nighttime warmth by approximately 2 degrees C, confirming its effective thermal energy storage behavior. Energy simulations across different Turkish climate zones indicated heating energy reductions up to 60 % in severe climates, accompanied by proportional decreases in CO2 emissions. The economic analysis showed annual savings between $0.65 and $4.39 per square meter depending on the heating source, with a payback period of 2-15 years. This work presents a scalable bio-based polyurethane-PCM system that integrates renewable materials with high PCM loading, offering a practical route to energy-efficient and low-carbon building materials.
Urban park visitation in the United States remains markedly unequal, a gap accentuated by post-pandemic mobility shifts. Drawing on anonymized mobile-phone traces in 2022 aggregated to 10,525 census block groups, we mapped weekly trips to parks in New York, Los Angeles, Chicago, Philadelphia, Phoenix, Houston, Dallas, San Diego, and San Jose. One-way ANOVA revealed significant (alpha <= 0.01) associations between visitation and income, race, travel mode, and commute time. An optimized XGBoost model explained 85 % of the variance and identified a log-normalized, per-capita " Dw(weighted distance)" as a strong associative factor, followed by neighborhood car ownership. Partial-dependence plots highlighted non-linear, city-specific responses to mobility intensity. Collectively, higher-income, majority-Asian, and White communities benefited from more frequent park use, whereas predominantly Black and low-income areas were systematically underserved. These findings show that access to urban nature is shaped less by simple proximity than by entrenched socio-economic structures that differ across metropolitan areas. Targeted investments in transit and park infrastructure within disadvantaged neighborhoods are essential to redress these inequities.
