A lake-based eutrophication nutrition footprint index evaluation method

Abstract

The invention discloses a lake division eutrophication nutrition footprint index evaluation method which mainly comprises the following steps: a phase space track line establishment step: utilizing lake division data to carry out filtering processing according to a time sequence, and establishing a filtering phase space track line of a consequence index and a reason index; A confinement nutrient identification step of identifying a confinement nutrient using the filtered phase-space trajectory; and a filtering track line segmentation step: carrying out time period division on the filtering phase space track line according to the inflection point of the track line, and establishing a multi-time period curve of an algae density and nutrient concentration response relation and a non-linear multi-time period nutrition index (TFI) relation. The method is mainly applied to lake eutrophication evaluation with algae control requirements and lakes with historical data of more than 10 years.

Description

technical field [0001] The method belongs to the field of environmental protection and management, and relates to a method for evaluating the nutritional footprint index of sub-lake eutrophication. Background technique [0002] Over the past few decades, domestic and foreign scholars have proposed various evaluation methods for eutrophication on the basis of detailed investigations on the eutrophication mechanism, performance characteristics, and main controlling factors of nutritional status of a large number of lakes and reservoirs. Mainly include: characteristic method (Shu Jinhua, 1990), evaluation method of phosphorus concentration in the lake-lake nutrient state response relationship (Vollenwelder model method) (Wu Deling, 1993; Vollenweider R A., 1975), nutrient state index method (Carlson, 1977 ; Aizaki M.et al., 1981; Jin Xiangcan et al., 1990; Li Zuoyong et al., 1993, 2002; Chen Xuhua, 2003), scoring method (Shu Jinhua, 1990; Hoyer, M V, Jones, J R., 1983), princip...

AI Technical Summary

Problems solved by technology

[0073] (2) Defects of TLI and EI Index

[0079] 2) Ignoring the role of sub-indicators: TLIΣ or EI tends to ignore the indicative significance of the differences in individual sub-indices, which in turn affects decision-making analysis

[0080] 3) There is no clear indicator exclusion mechanism: when the sub-item index is too far apart, it has lost its role as a proxy variable; or due to changes in background conditions, the original response relationship is misplaced, and some indicators no longer have an indicative role, so they should be excluded

The current method does not have a clear way to exclude

[0081] 4) The number of indicators has a great influence on the weight and evaluation results

If the SD data are not available and are eliminated, then equal-weight distribution is used, and the weight of the consequence variable drops to 33.3%, which loses the comparability with the evaluation of lakes using 4 indicators

[0082] 5) Misjudgment caused by interference between indicators

[0084] (2) Multi-lake average-translation effect defects

When grading or classification standards are adopted, there is a deviation from the guaranteed rate of nutrient index standards, and it will be unsafe to evaluate based on this

[0088] (3) Average-stationary effect defects regardless of time period

If it is not segmented in time, new and old data are mixed together to establish a response relationship, which will reduce the accuracy and precision of current and future evaluations

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