Chang Ni-Bin, Wimberly Brent, Xuan Zhemin
Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, Florida 32816, USA.
J Environ Monit. 2012 Mar;14(3):992-1005. doi: 10.1039/c2em10574h. Epub 2012 Feb 13.
This study presents an integrated k-means clustering and gravity model (IKCGM) for investigating the spatiotemporal patterns of nutrient and associated dissolved oxygen levels in Tampa Bay, Florida. By using a k-means clustering analysis to first partition the nutrient data into a user-specified number of subsets, it is possible to discover the spatiotemporal patterns of nutrient distribution in the bay and capture the inherent linkages of hydrodynamic and biogeochemical features. Such patterns may then be combined with a gravity model to link the nutrient source contribution from each coastal watershed to the generated clusters in the bay to aid in the source proportion analysis for environmental management. The clustering analysis was carried out based on 1 year (2008) water quality data composed of 55 sample stations throughout Tampa Bay collected by the Environmental Protection Commission of Hillsborough County. In addition, hydrological and river water quality data of the same year were acquired from the United States Geological Survey's National Water Information System to support the gravity modeling analysis. The results show that the k-means model with 8 clusters is the optimal choice, in which cluster 2 at Lower Tampa Bay had the minimum values of total nitrogen (TN) concentrations, chlorophyll a (Chl-a) concentrations, and ocean color values in every season as well as the minimum concentration of total phosphorus (TP) in three consecutive seasons in 2008. The datasets indicate that Lower Tampa Bay is an area with limited nutrient input throughout the year. Cluster 5, located in Middle Tampa Bay, displayed elevated TN concentrations, ocean color values, and Chl-a concentrations, suggesting that high values of colored dissolved organic matter are linked with some nutrient sources. The data presented by the gravity modeling analysis indicate that the Alafia River Basin is the major contributor of nutrients in terms of both TP and TN values in all seasons. With this new integration, improvements for environmental monitoring and assessment were achieved to advance our understanding of sea-land interactions and nutrient cycling in a critical coastal bay, the Gulf of Mexico.
本研究提出了一种综合的k均值聚类和重力模型（IKCGM），用于研究佛罗里达州坦帕湾营养物质及相关溶解氧水平的时空模式。通过使用k均值聚类分析首先将营养数据划分为用户指定数量的子集，有可能发现海湾中营养物质分布的时空模式，并捕捉水动力和生物地球化学特征的内在联系。然后，这些模式可与重力模型相结合，将每个沿海流域的营养源贡献与海湾中生成的聚类联系起来，以协助进行环境管理的源比例分析。聚类分析是基于希尔斯伯勒县环境保护委员会在2008年全年收集的、涵盖坦帕湾55个采样站的水质数据进行的。此外，还从美国地质调查局的国家水信息系统获取了同年的水文和河流水质数据，以支持重力模型分析。结果表明，分为8个聚类的k均值模型是最佳选择，其中2008年坦帕湾下游的聚类2在每个季节的总氮（TN）浓度、叶绿素a（Chl-a）浓度和海洋颜色值均为最小值，并且在连续三个季节中总磷（TP）浓度最低。数据集表明，坦帕湾下游是全年营养物质输入有限的区域。位于坦帕湾中部的聚类5显示出较高的TN浓度、海洋颜色值和Chl-a浓度，这表明有色溶解有机物的高值与一些营养源有关。重力模型分析提供的数据表明，就所有季节的TP和TN值而言，阿拉菲亚河流域是营养物质的主要贡献者。通过这种新的整合，在环境监测和评估方面取得了进展，以增进我们对墨西哥湾这个关键沿海海湾的海陆相互作用和营养物质循环的理解。
