The widespread use of rare earth elements (REEs) has resulted in problems for soil and human health. Phytolacca americana L. is a herbaceous plant widely distributed in Dingnan county of Jiangxi province, China, which is a REE mining region (ion absorption rare earth mine) and the soil has high levels of REEs. An investigation of REE content of P. americana growing naturally in Dingnan county was conducted. REE concentrations in the roots, stems, and leaves of P. americana and in their rhizospheric soils were determined. Results showed that plant REEs concentrations varied among the sampling sites and can reach 1040 mg/kg in the leaves. Plant REEs concentrations decreased in the order of leaf > root > stem and all tissues were characterized by a light REE enrichment and a heavy REE depletion. However, P. americana exhibited preferential accumulation of light REEs during the absorption process (from soil to root) and preferential accumulation of heavy REEs during the translocation process (from stem to leaf). The ability of P. americana to accumulate high REEs in the shoot makes it a potential candidate for understanding the absorption mechanisms of REEs and for the phytoremediation of REEs contaminated soil.
Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods.
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