基于APCS-MLR模型和地统计学相结合的矿区农田土壤砷源解析

为了研究矿区周边农田土壤As等重金属的污染特征和来源情况，在云南会泽铅锌矿区采集了42个农田土壤样品，测定了9种元素含量，利用绝对主成分得分–多元线性回归(APCS-MLR)受体模型定量解析了土壤中As的来源和贡献率，并结合地统计学方法揭示了As的不同来源及其贡献的空间分布状况。结果表明：(1)研究区土壤As的平均含量为73.3mg/kg，超过农用地土壤污染风险筛选值的1.8倍；(2)根据APCS-MLR模型计算得到的As污染源的贡献率分别为源1(58.6%)，源2(14.7%)和其他源(26.7%)；(3)结合PCA和源贡献率的空间分布状况，推断源1可能为矿山开采所造成的人为源，源2可能是燃煤及化肥施用所造成的人为源，其他源可能为地质背景等自然源。可见，基于APCS-MLR受体模型和地统计学方法相结合，可以有效解析矿区农田土壤As等重金属的来源及贡献。

Source Apportionment of Arsenic in Agricultural Soils from a Typical Mining Area Based on APCS-MLR Model and Geostatistics

In order to evaluate the pollution characteristics and sources of As and other heavy metals in farmland around Huize lead-zinc mining area in Yunnan Province, 42 soil samples were collected and nine elements in the soils were determined. The absolute principal component scores/multiple linear regression (APCS-MLR) receptor model was used to quantitatively analyze the contribution rate of soil As, and the geostatistical method was used to reveal the different sources of As and its spatial distribution. The results showed that:1) the average content of As in the study area was 73.3 mg/kg, which was 1.8 times higher than the risk screening values for soil contamination of agricultural land, 2) according to APCS-MLR receptor model, the contribution rates of As pollution sources were calculated as source I (58.6%), source II (14.7%) and other sources (26.7%), 3) based on PCA and the spatial distribution patterns of the source contributions, source I may originate from the mining related activities, source II may come from coal combustion and fertilization, other sources may be natural sources such as geological background. This study indicated that combining the APCS-MLR receptor model and geostatistics was an effective method for apportioning soil heavy metal pollution sources and contribition.