The development and functional perfection of urban areas have led to increasingly severe fire risks in recent decades. Previous urban fire risk assessment methods relied on subjective judgment, rough data collection, simple linear statistical methods, etc. These drawbacks can lead to low robustness of evaluation and inadequate generalization ability. To resolve these problems, this paper selects the indicator and regression models based on the high-resolution data of the spatial distribution characteristics of Longquanyi distinct in Chengdu, China. and proposes an integrated machine learning algorithm for fire risk assessment. Firstly, the kernel density analysis is used to map the fourteen urban characteristics related to fire risks. The contributions of these indicators (characteristics) to fire risk and its corresponding index are determined by Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). Then, the spatial correlation of fire risks is determined through Moran’s I, and the spatial distribution pattern of indicator weights is clarified through the raster coefficient space analysis. Finally, with these selected indicators, we test the regression performance with a backpropagation neural network (BPNN) algorithm and a geographically weighted regression (GWR) model. The results indicate that numerical variables are more suitable than dummy variables for estimating micro-scale fire risks. The main factors with a high contribution are all numerical variables, including roads, gas pipelines, GDP, hazardous chemical enterprises, petrol and charging stations, cultural heritage protection units, assembly occupancies, and high-rise buildings. The machine learning algorithm integrating RF and BPNN shows the best performance (R² = 0.97), followed by the RF-GWR integrated algorithm (R² = 0.87). Compared with previous methods, this algorithm reduces the subjectivity of the traditional assessment models and shows the ability to automatically obtain the key indicators of urban fire risks. Hence, this new approach provides us with a more robust tool for assessing the future fire safety level in urban areas.

Layered double hydroxide (LDH) is frequently used for phosphate removal in water, while a desirable LDH adsorbent should have sufficiently high adsorption capacity and selectivity. To enhance the removal performance and selectivity of phosphate by LDH, La-doped layered double hydroxide/biochar (La–MgAl–LDH/BC) was synthesized by using a co-precipitation method. Investigation results showed that lanthanum (La) metal loading can significantly enhance the phosphate adsorption performance of La–MgAl–LDH. Then, the introduction of La–MgAl–LDH into BC improved the adsorption capacity of phosphate by the adsorbent, which is 1.8 times that of pure La–MgAl–LDH. The optimum La to Al molar ratio was 0.2 : 0.8 in La–MgAl–LDH materials, and the La–MgAl–LDH to BC mass ratio was 9.3 : 10. Furthermore, both La–MgAl–LDH and La–MgAl–LDH/BC exhibited an enhanced ability to resist interference from co-existing anions (Cl⁻, SO₄ ²⁻, NO₃ ⁻ and CO₃ ²⁻) compared to LDH. For batch experiments, the kinetic results indicated that the adsorption process of phosphate by La–MgAl–LDH/BC follows a pseudo-second-order kinetics model, reaching equilibrium quickly within 1 h. Furthermore, film diffusion and intra-particle diffusion simultaneously took place. The maximum monolayer phosphate adsorption capacity of La–MgAl–LDH/BC could reach 249.3 mg PO₄ ³⁻ g⁻¹ (513.6 mg PO₄ ³⁻ g⁻¹, converting to an equivalent La–MgAl–LDH). After four cycles, La–MgAl–LDH/BC showed good reusability, and over 63.5% of phosphate uptake was retained with minimal metal leaching. Additionally, phosphate could be rapidly removed from actual water and simulated water at low concentrations. The electrostatic attraction, ligand exchange, ion exchange and Lewis acid action jointly facilitate the adsorption of phosphate.

Hydrothermal carbonization is highly applicable to high moisture biomass upgrading due to fact that moisture involved can be directly used as reaction media under subcritical-water region. With this, value-added utilization of hydrochar as solid fuel with high carbon and energy density is one of the important pathways for biomass conversion. In this review, the dewatering properties of hydrochar after the hydrothermal carbonization of biowaste, coalification degree with elemental composition and evolution, pelletization of hydrochar to enhance the mechanical properties and density, coupled with the combustion properties of hydrochar biofuel were discussed with various biomass and carbonization parameters. Potential applications for the co-combustion with coal, cleaner properties and energy balance for biowaste hydrothermal carbonization were presented as well as the challenges.

This study determined the optimal preparation temperature and time of Mg–BC, which has excellent synergistic adsorption capacity for NH ₄ ⁺ and PO ₄ ³⁻ . Furthermore, NP@Mg–BC can serve as a fertilizer to facilitate the resource utilization of N and P.