Purpose. Attapulgite was modified by sodium dihydrogen phosphate, oxalic acid-activated phosphate rock powder, potassium dihydrogen phosphate, calcium superphosphate, ammonium dihydrogen phosphate, and fused calcium-magnesium phosphate and used in the remediation of Cd, Zn, and Ni. Materials and Methods. Attapulgite was modified by six kinds of phosphate (ratio: 1 : 2), and the improvement effect of passivation material on soil polluted by cadmium, zinc, and nickel was determined. CaCl2-extractable and toxicity characteristic leaching procedure- (TCLP-) extractable Cd, Zn, and Ni were measured in order to estimate the bioavailability and the stabilization efficiency. Pot experiment was conducted to study the enrichment and transport ability of Cd, Zn, and Ni in corn. The ecological risk and ecological toxicity of soil environment were evaluated by calculating SEm, ERIm, CRIm, and BUF. Results and Discussion. Compared with ATP, passivation materials AAPR, AMRP, ASSP, AMAP, and AFMP can improve the stability of CD, Zn, and Ni in soil, and AAPR has the best effect. Compared with CK treatments and ATP treatments, the concentrations of TCLP-extractable Cd decreased by 30.80% and 24.72%, respectively, the concentrations of TCLP-extractable Zn decreased by 15.50% and 11.18%, respectively, and the concentrations of TCLP-extractable Ni decreased by 31.34% and 23.20%, respectively. Compared with ATP treatments, CRI, BUF-Cd, BUF-Ni, and BUF-Zn decreased by 24.67%, 52.88%, 78.73%, and 41.18%, respectively, in the AAPR treatments. Conclusions. Phosphate-modified attapulgite can effectively improve the stability of heavy metals in soil and reduce the migration of heavy metals. In the soil polluted by Cd, Zn, and Ni, the passivation effect of AAPR is the best. Therefore, AAPR can be used as an economical, safe, and effective passivation material to improve Cd-, Zn-, and Ni-contaminated soil, which would have a high utilization value in field applications.

In 2014, the relevant research data from the Ministry of Environmental Protection and the Ministry of Land and Resources showed that the total exceedance rate of soil heavy metal pollution in China had reached 16.1%, and in the construction of ecological civilization in the 13th Five-Year Plan, China has made the prevention and control of soil heavy metal pollution as the focus of prevention and control. Therefore, in this paper, four neural optimization network models, that is, radial basis neural network (RBFNN), generalized regression neural network (GRNN), wavelet neural network (WNN), and fuzzy neural network (FNN), are simulated and created to measure and correlate the soil heavy metal content in a city in northwest China and a city in central China from the actual situation in China. The simulations were conducted. Finally, by analyzing the comparison of predicted and true values of these four models on the test data of two sets of experimental data, the distribution of predicted differences to true values, and the calculation results of three error indicators, we found that WNN has the best prediction performance when using RBFNN, GRNN, WNN, and FNN for soil heavy metal content prediction.