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人类对易北河携带的营养物质负荷影响的评估。

Estimation of the human impact on nutrient loads carried by the Elbe River.

作者信息

Hussian Mohamed, Grimvall Anders, Petersen Wilhelm

机构信息

Department of Mathematics, Linköping University, Linköping, Sweden.

出版信息

Environ Monit Assess. 2004 Aug-Sep;96(1-3):15-33. doi: 10.1023/b:emas.0000031722.88972.62.

DOI:10.1023/b:emas.0000031722.88972.62
PMID:15327147
Abstract

The reunification of Germany led to dramatically reduced emissions of nitrogen (N) and phosphorus (P) to the environment. The aim of the present study was to examine how these exceptional decreases influenced the amounts of nutrients carried by the Elbe River to the North Sea. In particular, we attempted to extract anthropogenic signals from time series of riverine loads of nitrogen and phosphorus by developing a normalization technique that enabled removal of natural fluctuations caused by several weather-dependent variables. This analysis revealed several notable downward trends. The normalized loads of total-N and NO3-N exhibited an almost linear trend, even though the nitrogen surplus in agriculture dropped dramatically in 1990 and then slowly increased. Furthermore, the decrease in total-P loads was found to be considerably smaller close to the mouth of the river than further upstream. Studying the predictive ability of different normalization models showed the following: (i) nutrient loads were influenced primarily by water discharge; (ii) models taking into account water temperature, load of suspended particulate matter, and salinity were superior for some combinations of sampling sites and nutrient species; semiparametric normalization models were almost invariably better than ordinary regression models.

摘要

德国的统一导致向环境中排放的氮(N)和磷(P)大幅减少。本研究的目的是考察这些异常减少如何影响易北河流入北海的营养物数量。特别是,我们试图通过开发一种归一化技术,从氮和磷的河流负荷时间序列中提取人为信号,该技术能够消除由几个与天气相关的变量引起的自然波动。该分析揭示了几个显著的下降趋势。总氮和硝态氮的归一化负荷呈现出几乎线性的趋势,尽管农业中的氮盈余在1990年急剧下降,然后缓慢增加。此外,发现靠近河口处总磷负荷的减少幅度比上游要小得多。对不同归一化模型预测能力的研究表明:(i)营养物负荷主要受水流量影响;(ii)对于某些采样点和营养物种类的组合,考虑水温、悬浮颗粒物负荷和盐度的模型更优;半参数归一化模型几乎总是比普通回归模型更好。

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本文引用的文献

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Environ Monit Assess. 2006 Nov;122(1-3):335-354. doi: 10.1007/s10661-006-9186-9.
4
Meteorological normalisation and non-parametric smoothing for quality assessment and trend analysis of tropospheric ozone data.用于对流层臭氧数据质量评估和趋势分析的气象归一化及非参数平滑处理
Environ Monit Assess. 2005 Jan;100(1-3):33-52. doi: 10.1007/s10661-005-7059-2.