Large amounts of fly ash and sewage sludge are produced annually in China. The treatment and disposal of such byproducts have become urgent problems that need to be solved. In order to achieve the possibility of realizing land applications for sewage sludge, fly ash and trimercapto-s-triazine trisodium salt (TMT) were used as immobilizing agents, and their passivation effects on four kinds of heavy metals (Cu, Ni, Pb, and Zn) were evaluated. The results showed that the resulting sewage sludge met Chinese standard GB/T23486-2009. When the addition was 10–20% fly ash or 0.4–0.6% TMT, the optimum immobilization effect was obtained. The synergistic passivation of 20% fly ash +0.5% TMT was superior to that of either fly ash or TMT alone. The addition of sewage sludge during the ryegrass growth process significantly increased the plant height, the number of tillers, the chlorophyll content, and the biomass of the ryegrass over the brown soil. The adverse effect of the heavy metals on the ryegrass growth could be alleviated by the passivation effect of fly ash and TMT. The immobilization performance of the fly ash was mainly due to the formation of precipitation and the ion exchange, while that of TMT was due to chelate precipitation.

In conventional centralized authorization models, the evaluation performance of policy decision point (PDP) decreases obviously with the growing numbers of rules embodied in a policy. Aiming to improve the evaluation performance of PDP, a distributed policy evaluation engine called XDPEE is presented. In this engine, the unicity of PDP in the centralized authorization model is changed by increasing the number of PDPs. A policy should be decomposed into multiple subpolicies each with fewer rules by using a decomposition method, which can have the advantage of balancing the cost of subpolicies deployed to each PDP. Policy decomposition is the key problem of the evaluation performance improvement of PDPs. A greedy algorithm withO(nlgn)time complexity for policy decomposition is constructed. In experiments, the policy of the LMS, VMS, and ASMS in real applications is decomposed separately into multiple subpolicies based on the greedy algorithm. Policy decomposition guarantees that the cost of subpolicies deployed to each PDP is equal or approximately equal. Experimental results show that (1) the method of policy decomposition improves the evaluation performance of PDPs effectively and that (2) the evaluation time of PDPs reduces with the growing numbers of PDPs.