Zhaohui He

This work mainly studied the Pb(Ⅱ) adsorption properties of cuttlebone-derived materials and the effects of adsorption time, initial solution concentration, and material aging. Cuttlebone is a fishery waste, which is mainly composed of calcium carbonate, and β-chitin. The cuttlebone derivative was prepared by high-temperature carbonization and deacetylation. The results showed that the adsorption of Pb(Ⅱ) by carbonized and deacetylated cuttlebone derivatives was significantly improved from 1644 mg/g to 2216 mg/g and 2118 mg/g, respectively. It resulted that high-temperature carbonization promoted the crystal change of calcium carbonate in cuttlebone from aragonite crystal to calcite crystal, and the latter has a stronger ion exchange capacity for Pb(Ⅱ) than the former. Deacetylation promoted the conversion of chitin in cuttlebone to chitosan, which is beneficial to complexing Pb(Ⅱ) and further improving the adsorption performance. The adsorption kinetics and isotherms of Pb(Ⅱ)on cuttlebone-derived materials conform to the quasi-second-order kinetic model and Langmuir model, respectively. After freeze-thaw aging and hydrogen peroxide aging, the adsorption capacity of Pb(Ⅱ) has been further improved, indicating that the derived materials of cuttlebone are relatively stable. Based on this study, the derived materials of cuttlebone have the potential to treat Pb(Ⅱ) ions in wastewater.

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization—least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.

To assess heavy metal pollution and human health risks, 481 samples from 59 soil sampling points were collected from different soil layers at an underground depth of 10 m in a tin ore area in Yunnan, China. The soil around the mining area was polluted by heavy metals to varying degrees, and high concentrations of Cd, Pb, As, and Cu posed serious ecological risks. Owing to leaching and infiltration of heavy metals, the lower soil samples were also severely polluted as much as the surface soil. The heavy metals mainly accumulated in the soil layers at depths of 0 − 6 m. Meanwhile, the concentration decreased with an increase in soil depth, after which it stabilised. Residents of the area are currently at danger of being diagnosed with non-carcinogenic disorders, while exhibiting a significantly elevated risk of carcinogenicity; children are at a higher risk than adults. In addition, As was the primary threatening factor to the health of residents.