Sustainable triethylenetetramine modified sulfonated graphene oxide/chitosan composite for enhanced adsorption of Pb(II), Cd(II), and Ni(II) ions.

A novel sulfonated group and triethylenetetramine modified GO/chitosan (GO-CS) adsorbent (T-SGO-CS) was successfully prepared and utilized for the adsorption of heavy metal ions from single-metal, binary-metal, and ternary-metal solutions. In a single system, the adsorption capacity was 312.28 mg/g for Pb2+, 260.52 mg/g for Cd2+, and 84.61 mg/g for Ni2+, whereas, Adsorption of Pb(II), Cd(II), and Ni(II) in binary and ternary systems was systematically studied. In tertiary systems, the effect of competitive adsorption was more pronounced. In addition, T-SGO-CS exhibited a high adsorption capacity and was recyclable for Pb2+, Cd2+, and Ni2+. T-SGO-CS is a novel and highly efficient adsorbent for omnidirectionally enhancing the adsorption of Pb2+, Cd2+, and Ni2+, as demonstrated by these results. Therefore, T-SGO-CS could be investigated as a potential new material for future applications in heavy metal removal.

A novel approach to forecast dust concentration in open pit mines by integrating meteorological parameters and production intensity.

Mine dust pollution poses a hindrance to achieving green and climate-smart mining. This paper uses weather forecast data and mine production intensity data as model inputs to develop a novel model for forecasting daily dust concentration values in open pit mines by employing and integrating multiple machine learning techniques. The results show that the forecast model exhibits high accuracy, with a Pearson correlation coefficient exceeding 0.87. The PM2.5 forecast model performs best, followed by the total suspended particle and PM10 models. The inclusion of production intensity significantly enhances model performance. Total column water vapor exerts the most significant impact on the model's predictive performance, while the impacts of rock production and coal production are also notable. The proposed daily forecast model leverages production intensity data to predict future dust concentrations accurately. This tool offers valuable insights for optimizing mine design parameters, enabling informed decisions based on real-time forecasts. It effectively prevents severe pollution in the mining area while maximizing the use of natural meteorological conditions for effective dust removal and diffusion.