Zeng Xiaoqing, Rasmussen Todd C
Warnell School of Forest Resources, University of Georgia, Athens, 30602-2152, USA.
J Environ Qual. 2005 Oct 12;34(6):1980-91. doi: 10.2134/jeq2004.0337. Print 2005 Nov-Dec.
Watershed monitoring programs depend on water quality characterization data collected for many parameters, at many times and places, and with limited resources. Our objective is to present a strategy that reduces the measured parameters, locations, and frequency without compromising the quality of the monitoring program. One year of twice-monthly (growing season) and monthly (dormant season) water quality data collected from 17 lake and 10 tributary sites are used in conjunction with multivariate statistical techniques to improve the utility of collected data by identifying key parameters and monitoring locations. Factor analysis shows that tributary water quality data consists of three components-stormwater runoff, municipal and industrial discharges, and ground water-which can be distinguished using total suspended solids, total dissolved solids, and alkalinity plus soluble reactive P, respectively. Lake water quality characterization is more ambiguous than tributary water quality characterization, but factor analysis indicates that anoxia associated with lake stratification is the largest source of lake water quality variation, followed by nutrient abundance, and finally by biomass abundance. Cluster analysis suggests that tributary and lake monitoring stations can be consolidated. Reducing the number of parameters and stations frees up resources for increased monitoring elsewhere.
流域监测项目依赖于在许多时间和地点、利用有限资源收集的众多参数的水质特征数据。我们的目标是提出一种策略，在不影响监测项目质量的前提下，减少测量参数、地点和频率。从17个湖泊和10个支流站点收集的一年两次（生长季节）和每月一次（休眠季节）的水质数据，与多元统计技术结合使用，通过识别关键参数和监测地点来提高所收集数据的效用。因子分析表明，支流水质数据由三个成分组成——雨水径流、市政和工业排放以及地下水，分别可以用总悬浮固体、总溶解固体以及碱度加可溶性活性磷来区分。湖泊水质特征比支流水质特征更模糊，但因子分析表明，与湖泊分层相关的缺氧是湖泊水质变化的最大来源，其次是营养物质丰富，最后是生物量丰富。聚类分析表明，可以合并支流和湖泊监测站。减少参数和站点数量可为其他地方增加监测腾出资源。
