闽江流域水质时间序列变化趋势识别及特征分析

Trend recognition and characteristics analysis of water quality indicator time series in Minjiang River Basin

针对传统方法难以反映水质指标时间序列非线性、局部特征的问题,提出一种结合STL时间序列分解算法和Mann-Kendall趋势检验算法的水质时间序列识别与分析方法。该法首先应用STL时序分解算法对水质指标的时序数据进行回归分解,分离出水质指标趋势项,利用Mann-Kendall检验法,识别并分析出水质指标趋势项的变化趋势及特征。选用2014-2018年闽江流域12个监测站点、8个水质指标时间序列作为分析数据源,结果表明:闽江流域水质状况整体较好且呈现出稳步提高的趋势;闽江上游水质整体上优于闽江下游,但有机物污染较下游更为严重;闽江下游NH3—N、TP浓度下降趋势明显,但DO值较上游偏低并成为影响水质的主导因素。

With conventional methods,the non-linearity and local characteristics of water quality indicator time series are hardly addressed. Aiming at solving this problem,a new water quality time series identification and analysis method combining the merits of STL time series decomposition algorithm and Mann-Kendall trend test algorithm is proposed. In order to isolate the trend terms of the indicators,this method regressively decomposes the time series data of water quality indicators using STL time series decomposition algorithm,then uses Mann-Kendall trend test algorithm to identify and analyze the variation trends and characteristics of the terms. The data source of eight water quality indicator time series from 2014 to 2018 of 12 monitoring stations in Minjiang River Basin was analyzed using this method. The results show that the overall water quality of the Minjiang River Basin is good and is improving steadily. The water quality of the upper reaches of Minjiang River is generally better than that of the lower reaches;however the organic matter pollution is reversed. In the lower reaches of Minjiang River,the concentration of NH3—N and TP has decreased significantly,but the DO value is lower than that in the upper reaches and has become the dominant factor affecting the water quality.