Key Laboratory of Smart City and Environmental Modeling of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi, 830046, People's Republic of China.
Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, 830046, Xinjiang, People's Republic of China.
Sci Rep. 2018 Aug 30;8(1):13099. doi: 10.1038/s41598-018-31265-0.
The key problem in the reasonable management of water is identifying the effective radius of surface water pollution. Remote sensing and three-dimensional fluorescence technologies were used to evaluate the effects of land use/cover on surface water pollution. The PARAFAC model and self-organizing map (SOM) neural network model were selected for this study. The results showed that four fluorescence components, microbial humic-like (C1), terrestrial humic-like organic (C2, C4), and protein-like organic (C3) substances, were successfully extracted by the PARAFAC factor analysis. Thirty water sampling points were selected to build 5 buffer zones. We found that the most significant relationships between land use and fluorescence components were within a 200 m buffer, and the maximum contributions to pollution were mainly from urban and salinized land sources. The clustering of land-use types and three-dimensional fluorescence peaks by the SOM neural network method demonstrated that the three-dimensional fluorescence peaks and land-use types could be grouped into 4 clusters. Principal factor analysis was selected to extract the two main fluorescence peaks from the four clustered fluorescence peaks; this study found that the relationships between salinized land, cropland and the fluorescence peaks of C1, W2, and W7 were significant by the stepwise multiple regression method.
水资源合理管理的关键问题在于确定地表水的有效污染半径。本研究采用遥感和三维荧光技术来评估土地利用/覆被对地表水污染的影响，选用平行因子分析（PARAFAC）模型和自组织映射（SOM）神经网络模型进行分析。结果表明，通过 PARAFAC 因子分析成功提取了四种荧光成分：微生物腐殖质类（C1）、陆源腐殖质类有机物质（C2、C4）和蛋白类有机物质（C3）。本研究共选取了 30 个水质采样点，建立了 5 个缓冲区。结果发现，土地利用与荧光成分之间的最显著关系存在于 200 m 缓冲区范围内，对污染的最大贡献主要来自城市和盐渍化土地源。通过 SOM 神经网络方法对土地利用类型和三维荧光峰进行聚类，结果表明，三维荧光峰和土地利用类型可以分为 4 类。选择主成分分析从聚类的四个荧光峰中提取两个主要荧光峰，采用逐步多元回归方法发现，盐渍化土地、耕地与 C1、W2 和 W7 荧光峰之间的关系显著。
