Multivariate assessment of polycyclic aromatic hydrocarbons in surface sediments of the Beijiang, a tributary of the Pearl River in Southern China

To estimate the severity of polycyclic aromatic hydrocarbon (PAH) contamination in the upper sediment of the Beijiang River, 42 sediment samples were analyzed for the presence of 16 key PAHs using gas chromatography–mass spectrometry. The concentrations of PAH in the sediment ranged from 44 to 8,921 ng g^?1 dry weight. The four- to six-ring PAHs, contributing >50 % to PAHs in 34 of the 42 sites, were the dominant species. Based on a principal component analysis, combined with multivariate linear regression, it became clear that the most important contributors of PAH were fossil fuel combustion (48 %), diesel emissions plus oil spillage (33 %), and coke combustion (19 %). The surface sediments of Beijiang River were grossly contaminated by PAHs mainly derived from combustion.  

大数据技术在环境监测中的应用

简述了环境监测信息化建设应用现状及问题;分析了大数据技术的特点及其在环境监测信息化中的优势。提出,基于"大数据"技术,可以建设环境监测大数据中心,突破传统关系型数据库局限性,运用大数据的核心技术,实现环境质量综合评价技术的新突破,提升环境状况综合预警能力;应用大数据可视化技术,提高科学决策水平,提高人体健康风险评价能力;建立环保舆情分析云平台,提升公众服务能力。