College of Chemistry and Environmental Engineering, Shenzhen University, 518060, Shenzhen, China.
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
Environ Pollut. 2020 Nov;266(Pt 3):115253. doi: 10.1016/j.envpol.2020.115253. Epub 2020 Jul 14.
A range of source tracking approaches have been developed to identify sources in the environmental pollution research field. A comparison of source tracking approaches is essential for a better understanding and practical applications of these approaches. This study compared the commonly used source tracking approaches, namely positive matrix factorization (PMF), Unmix, flag element ratio (FER), and chemical mass-balance based stochastic approach (SCMD). A case study was illustrated for tracing heavy metals (Pb, Zn, Cr, Cu, and Ni) attached to road deposited sediments, which can significantly influence urban road stormwater quality. The results indicated that the accuracy of PMF and Unmix are affected by the number of chemical species used and whether useful markers can be identified for particular sources. However, this does not have an essential influence on FER and SCMD. PMF and Unmix are easier on data preparation and calculation processes but more difficult for source identification process than FER and SCMD. This study also provided recommendations related to the selection of source tracking approach based on different study scenarios and result requirements. These study results are able to provide important guidance for undertaking effective source tracking and devising environmental pollution mitigations.
已开发出一系列源解析方法来识别环境污染研究领域的污染源。比较源解析方法对于更好地理解和实际应用这些方法至关重要。本研究比较了常用的源解析方法,即正矩阵因子分解(PMF)、Unmix、标志元素比(FER)和基于化学质量平衡的随机方法(SCMD)。通过一个案例研究说明了追踪附着在道路沉积物中的重金属(Pb、Zn、Cr、Cu 和 Ni)的方法,这些重金属会显著影响城市道路雨水的水质。结果表明,PMF 和 Unmix 的准确性受所用化学物质数量的影响,以及是否可以为特定源识别有用的标记。然而,这对 FER 和 SCMD 没有本质影响。PMF 和 Unmix 在数据准备和计算过程中较为简单,但在源识别过程中比 FER 和 SCMD 更困难。本研究还根据不同的研究场景和结果要求,就源解析方法的选择提供了相关建议。这些研究结果可为进行有效的源解析和制定环境污染缓解措施提供重要指导。