Cow manure derived biochar (CMBC) can serve as a promising functional material, and CMBC can be regarded as an ecofriendly approach compared to conventional ones. CM bioadsorbent can be employed for heavy metal immobilization (such as for lead) as well as an amendment to increase soil fertility (e.g., phosphorus). Few studies have examined the surface interactions between pollutants and bioadsorbents when inherent nutrient release is present. In this work, CMBC was prepared and applied for Pb(II) removal, and the vital roles of released phosphorus from CMBC were comprehensively disclosed. Furthermore, CMBC could immobilize part of the Pb(II) in soil and promote plant growth. CM400 was an effective adsorbent whose calculated Q_e reached 691.34 mg·g⁻¹, and it rapidly adsorbed 98.36 mg·g⁻¹ of Pb(II) within 1 min. The adsorption mechanisms of Pb(II) by CMBC include ion exchange, physical adsorption, electrostatic attraction, chemical precipitation, surface complexation, and cation–π bond interaction. Based on the residual phosphorus content and adsorption effect, complexation rather than the chemical precipitation had a greater contribution toward adsorption. Besides, as the concentration of Pb(II) increased, the main adsorption mechanisms likely transformed from chemical precipitation to ion exchange and complexation. CMBC not only had a good effect on Pb(II) removal in the solution, but also immobilized the Pb(II) in soil to restrain plant uptake as well as promote plant growth. The main novelty of this work is providing more insights to the cow manure bio adsorbent on Pb immobilization and phosphorus release. This study is expected to serve as a basis and reference for analyzing the release effects of inherent nutrients and the interfacial behaviors with heavy metals when using CMBC and other nutrient–rich carbon–based fertilizers for pollution control.

In practical wastewater, cationic and anionic dyes usually coexist, while synergistic removal of these pollutants is difficult due to their relatively opposite properties. In this work, copper slag (CS) modified hydrochar (CSHC) was designed as functional material by the one-pot method. Based on characterizations, the Fe species in CS can be converted to zero-valent iron and loaded onto a hydrochar substrate. The CSHC exhibited efficient removal rates for both cationic dyes (methylene blue, MB) and anionic dyes (methyl orange, MO), with a maximum capacity of 278.21 and 357.02 mg·g⁻¹, respectively, which was significantly higher than that of unmodified ones. The surface interactions of MB and MO between CSHC were mimicked by the Langmuir model and the pseudo-second-order model. In addition, the magnetic properties of CSHC were also observed, and the good magnetic properties enabled the adsorbent to be quickly separated from the solution with the help of magnets. The adsorption mechanisms include pore filling, complexation, precipitation, and electrostatic attraction. Moreover, the recycling experiments demonstrated the potential regenerative performance of CSHC. All these results shed light on the co-removal of cationic and anionic contaminates via these industrial by-products derived from environmental remediation materials.

Designing an efficient and recyclable adsorbent for cadmium pollution control is an urgent necessity. In this paper, cow manure, an abundant agricultural/animal husbandry byproduct, was employed as the raw material for the synthesis of magnetic cow manure biochar. The optimal preparation conditions were found using the response surface methodology model: 160 °C for the hydrothermal temperature, 600 °C for the pyrolysis temperature, and Fe-loading with 10 wt%. The optimal reaction conditions were also identified via the response surface methodology model: a dosage of 1 g·L⁻¹, a pH of 7, and an initial concentration of 100 mg·L⁻¹. The pseudo-second-order model and the Langmuir model were used to fit the Cd(II) adsorption, and the adsorption capacity was 612.43 mg·g⁻¹. The adsorption was dominated by chemisorption with the mechanisms of ion-exchange, electrostatic attraction, pore-filling, co-precipitation, and the formation of complexations. Compared to the response surface methodology model, the back-propagation artificial neural network model fit the Cd(II) adsorption better as the error values were less. All these results demonstrate the potential application of CM for Cd(II) removal and its optimization through machine-learning processes.