基于随机森林评价的兰州市主城区校园地表灰尘重金属污染

将2018年1~12月兰州市主城区校园地表灰尘重金属元素含量计算得到的综合污染指数（P_N）和潜在生态风险指数（RI）作为训练集，使用11个影响地表灰尘重金属污染和积累的特征参数，利用随机森林算法对信息采样点的P_N、RI进行估算，分析了地表灰尘重金属污染的时空变化特征，并对传统算法插值结果和随机森林插值结果进行了比较.结果表明，研究区地表灰尘重金属各元素浓度均高于本地背景值；研究区P_N排序为城关区 > 西固区 > 安宁区 > 七里河区，RI排序为城关区 > 西固区 > 七里河区 > 安宁区，P_N和RI在空间分布特征上很相似，都位于交通枢纽或市中心；P_N在冬季和夏季出现高值，RI高值则出现在冬季，冬季高值主要源于采暖燃煤源的增加；空间插值结果对比表明随机森林插值结果优于传统算法插值结果.

Assessment of Heavy Metal Pollution in Surface Dust of Lanzhou Schools Based on Random Forests

In this study, seven types of heavy metal elements and 11 types of characteristic parameters affecting heavy metal pollution and accumulation in surface dust were selected. Based on the comprehensive pollution index (P_N) and potential ecological risk index (RI) calculated from the heavy metal element content of the school dust in the main urban area of Lanzhou City in 2018 as the training set, the P_N and RI of the information sampling points were estimated using random forests. Then, the temporal and spatial characteristics of heavy metals in school dust in the main urban area of Lanzhou were analyzed. Finally, the correlation coefficient was used to evaluate the advantages and disadvantages of the traditional interpolation results and the random forest interpolation results. The results showed that the concentrations of heavy metals in the dust were higher than the local background values. The over standard rate of a single sample is 100%, Zn is 5 times higher than the background value, and Pb is 4 times higher than background value. P_N in the study area was in the order Chengguan > Xigu > Anning > Qilihe, and RI was in the order Chengguan > Xigu > Qilihe > Anning. P_N and RI exhibited very similar spatial distribution characteristics, both located in transportation hubs or downtown. In winter and summer, P_N exhibited a high value, whereas RI had a high value. The reason for the high value of P_N and RI in winter was the increase of coal sources in winter. The comparison of spatial interpolation results shows that the correlation coefficient between the results of random forest interpolation and traffic flow and normalized building index is greater than that of the traditional algorithm.