Department of Computer and Information Science, Linköping University, Linköping, Sweden.
Environ Monit Assess. 2010 Jun;165(1-4):217-31. doi: 10.1007/s10661-009-0940-7. Epub 2009 May 12.
Assessing regional trends in groundwater quality can be a difficult task. Data are often scattered in space and time, and the inertia of groundwater systems can create natural, seemingly persistent changes in concentration that are difficult to separate from anthropogenic trends. Here, we show how statistical methods and software for joint analysis of multiple time series can be integrated into a roadmap for trend analysis and critical examination of data quality. Ordinary and partial Mann-Kendall (MK) tests for monotonic trends and semiparametric smoothers for multiple time series constitute the cornerstones of our procedure. The MK tests include a simple and easily implemented method to correct for serial dependence, and the associated software is designed to enable convenient handling of numerous data series and to accommodate covariates and nondetects. The semiparametric smoothers are intended to facilitate detection of synchronous changes in a network of stations. A study of Swedish groundwater quality data revealed true upward trends in acid-neutralizing capacity and downward trends in sulfate but also a misleading shift in alkalinity level that would have been difficult to detect if the time series had been analyzed separately.
评估地下水水质的区域趋势可能是一项艰巨的任务。数据通常在空间和时间上分散，地下水系统的惯性会导致浓度产生自然的、看似持续的变化，难以将其与人为趋势区分开来。在这里，我们展示了如何将用于多时间序列联合分析的统计方法和软件集成到趋势分析和数据质量关键评估的路线图中。普通和部分曼恩-肯德尔 (MK) 单调趋势检验以及用于多时间序列的半参数平滑器是我们方法的基石。MK 检验包括一种简单且易于实现的方法来纠正序列相关性，并且相关软件旨在方便处理大量数据序列，并适应协变量和未检出值。半参数平滑器旨在促进检测站点网络中的同步变化。对瑞典地下水质量数据的研究表明，酸中和能力呈上升趋势，硫酸盐呈下降趋势，但碱度水平也出现了误导性的变化，如果分别分析时间序列，这将很难检测到。
