Kengo Nakamura

Characteristics of element distributions in an MSW ash melting treatment system

Thermal treatment of municipal solid waste (MSW) has become a common practice in waste volume reduction and resource recovery. For the utilization of molten slag for construction materials and metal recovery, it is important to understand the behavior of heavy metals in the melting process. In this study, the correlation between the contents of elements in feed materials and MSW molten slag and their distributions in the ash melting process, including metal residues, are investigated. The hazardous metal contents in the molten slag were significantly related to the contents of metals in the feed materials. Therefore, the separation of products containing these metals in waste materials could be an effective means of producing environmentally safe molten slag with a low hazardous metals content. The distribution ratios of elements in the ash melting process were also determined. The elements Zn and Pb were found to have a distribution ratio of over 60% in fly ash from the melting furnace and the contents of these metals were also high; therefore, Zn and Pb could be potential target metals for recycling from fly ash from the melting furnace. Meanwhile, Cu, Ni, Mo, Sn, and Sb were found to have distribution ratios of over 60% in the metal residue. Therefore, metal residue could be a good resource for these metals, as the contents of Cu, Ni, Mo, Sn, and Sb in metal residue are higher than those in other output materials.

Extraction of heavy metals characteristics of the 2011 Tohoku tsunami deposits using multiple classification analysis.

Tsunami deposits accumulated on the Tohoku coastal area in Japan due to the impact of the Tohoku-oki earthquake. In the study reported in this paper, we applied principal component analysis (PCA) and cluster analysis (CA) to determine the concentrations of heavy metals in tsunami deposits that had been diluted with water or digested using 1 M HCl. The results suggest that the environmental risk is relatively low, evidenced by the following geometric mean concentrations: Pb, 16 mg kg(-1) and 0.003 ml L(-1); As, 1.8 mg kg(-1) and 0.004 ml L(-1); and Cd, 0.17 mg kg(-1) and 0.0001 ml L(-1). CA was performed after outliers were excluded using PCA. The analysis grouped the concentrations of heavy metals for leaching in water and acid. For the acid case, the first cluster contained Ni, Fe, Cd, Cu, Al, Cr, Zn, and Mn; while the second contained Pb, Sb, As, and Mo. For water, the first cluster contained Ni, Fe, Al, and Cr; and the second cluster contained Mo, Sb, As, Cu, Zn, Pb, and Mn. Statistical analysis revealed that the typical toxic elements, As, Pb, and Cd have steady correlations for acid leaching but are relatively sparse for water leaching. Pb and As from the tsunami deposits seemed to reveal a kind of redox elution mechanism using 1 M HCl.

Development of a predictive model for lead, cadmium and fluorine soil–water partition coefficients using sparse multiple linear regression analysis

In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters. The SMLR analysis indicated that fluorine adsorption to soil may be associated with organic matter, whereas cadmium or lead adsorption to soil is more likely to be influenced by soil pH, IL. We found that an accurate K value can be predicted from more than three soil parameters for most soils. Approximately 65% of the predicted values were between 33 and 300% of their measured values for the K value; 76% of the predicted values were within ±30% of their measured values for the n value. Our findings suggest that adsorption properties of lead, cadmium and fluorine to soil can be predicted from the soil physical and chemical properties using the presented models.

Application of multivariate analysis to investigate the trace element contamination in top soil of coal mining district in Jorong, South Kalimantan, Indonesia

Multivariate analysis is applied to investigate geochemistry of several trace elements in top soils and their relation with the contamination source as the influence of coal mines in Jorong, South Kalimantan. Total concentration of Cd, V, Co, Ni, Cr, Zn, As, Pb, Sb, Cu and Ba was determined in 20 soil samples by the bulk analysis. Pearson correlation is applied to specify the linear correlation among the elements. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to observe the classification of trace elements and contamination sources. The results suggest that contamination loading is contributed by Cr, Cu, Ni, Zn, As, and Pb. The elemental loading mostly affects the non-coal mining area, for instances the area near settlement and agricultural land use. Moreover, the contamination source is classified into the areas that are influenced by the coal mining activity, the agricultural types, and the river mixing zone. Multivariate analysis could elucidate the elemental loading and the contamination sources of trace elements in the vicinity of coal mine area.