State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Anwai Dayangfang 8, Beijing, 100012, People's Republic of China.
Environ Monit Assess. 2013 Nov;185(11):9549-58. doi: 10.1007/s10661-013-3272-6. Epub 2013 Jun 9.
The identification of contamination "hotspots" are an important indicator of the degree of contamination in localized areas, which can contribute towards the re-sampling and remedial strategies used in the seriously contaminated areas. Accordingly, 114 surface samples, collected from an industrially contaminated site in northern China, were assessed for 16 polycyclic aromatic hydrocarbons (PAHs) and were analyzed using multivariate statistical and spatial autocorrelation techniques. The results showed that the PCA leads to a reduction in the initial dimension of the dataset to two components, dominated by Chr, Bbf&Bkf, Inp, Daa, Bgp, and Nap were good representations of the 16 original PAHs; Global Moran's I statistics indicated that the significant autocorrelations were detected and the autocorrelation distances of six indicator PAHs were 750, 850, 1,200, 850, 750, and 1,200 m, respectively; there were visible high-high values (hotspots) clustered in the mid-bottom part of the site through the Local Moran's I index analysis. Hotspot identification and spatial distribution results can play a key role in contaminated site investigation and management.
污染“热点”的识别是局部污染程度的重要指标，可以为严重污染区域的重新采样和补救策略提供依据。因此，对中国北方一个工业污染场地采集的 114 个表层土壤样品进行了 16 种多环芳烃（PAHs）的评估，并采用多元统计和空间自相关技术进行了分析。结果表明，主成分分析（PCA）将初始数据集的维度降低到两个分量，以 Chr、Bbf&Bkf、Inp、Daa、Bgp 和 Nap 为主，很好地代表了 16 种原始 PAHs；全局 Moran's I 统计量表明，存在显著的自相关，六个指示性 PAHs 的自相关距离分别为 750、850、1200、850、750 和 1200 m；通过局部 Moran's I 指数分析，发现该场地中下部存在明显的高-高值（热点）聚集。热点识别和空间分布结果可以在污染场地调查和管理中发挥关键作用。
