Tuesday, 8 November 2005
14

This presentation is part of: Environmental Aspects of Waste and By-product Materials Applied to Soil

Soil Pollution Assessment on Heavy Metals with Long-Term Application of Organic Wastes Using a Mathematical Mass Balance Model.

SOON IK KWON, Andrew C. Chang, WEIPING CHEN, JUNG DU SHIN, KYU HO SO, and DONG KYU LIM.

A mathematical mass balance model is used to assess heavy metal contaminations of the soils receiving seven consecutive annual applications of organic wastes, demonstrating potentials of recycling wastes for agricultural use in Korea. The organic wastes selected for long-term application experiment were municipal sewage sludge, industrial sewage sludge, leather processing sludge, alcohol fermentation processing sludge, and pig manure compost. The chemical properties and heavy metal contents of these materials were diverse. Heavy metal contents in the soils treated with municipal sewage sludge had a tendency to increase over time with increasing sludge application rates. This tendency was more significant for the industrial sewage sludge treated soil than for the municipal sewage sludge treated soil. The mass balance model assumes that the trace elements present in the root zone are distributed in the soil solution and four solid phases, namely the inorganic mineral, surface adsorbed, organic, and residual phases. Each mass balance term in this equation in turn is defined mathematically by parameters characterizing the biogeochemical processes it represents. A PC-based algorithm is used to solve the equation and the mass balances of Cu and Zn in waste treated soils are simulated for 100 years using this model. Factors that govern the heavy metal element transformations between solid phases in the soils and plant uptake of heavy metals may affect the resulting total heavy metal content of soils. There may be large uncertainties in the model estimations due to uncertainties in defining the values of model parameters. The Monte Carlo simulations were used to estimate the uncertainties of the model.

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