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统计模型和 k-C*模型在构建湿地系统中预测处理性能的拟合。

On the fit of statistical and the k-C* models to projecting treatment performance in a constructed wetland system.

机构信息

Centre for Water Resources Research, School of Architecture, Landscape and Civil Engineering, University College Dublin, Belfield, Dublin, Ireland.

出版信息

J Environ Sci Health A Tox Hazard Subst Environ Eng. 2011;46(5):490-9. doi: 10.1080/10934529.2011.551729.

Abstract

The objective of this study was to assess the suitability of statistical and the k-C* models to projecting treatment performance of constructed wetlands by applying the models to predict the final effluent concentrations of a pilot field-scale constructed wetlands system (CWs) treating animal farm wastewater. The CWs achieved removal rates (in g/m(2).d) ranging from 7.1-149.8 for BOD(5), 49.8-253.8 for COD and 7.1-47.0 for NH(4)-N. Generally, it was found that the statistical models developed from multiple regression analyses (MRA) were stronger in predicting final effluent concentrations than the k-C* model. However, both models were inadequate in predicting the final effluent concentrations of NO(3)-N. The first-order area-based removal rate constants (k, m/yr) determined from the experimental data were 200.5 for BOD(5), 80.1 for TP and 173.8 for NH(4)-N and these indicate a high rate of pollutant removal within the CWs.

摘要

本研究的目的是评估统计模型和 k-C模型在预测人工湿地处理性能方面的适用性,将模型应用于预测一个中试规模的人工湿地系统(CWs)处理养殖场废水的最终出水浓度。CWs 对 BOD(5)的去除率(以 g/m(2).d 计)为 7.1-149.8,对 COD 的去除率为 49.8-253.8,对 NH(4)-N 的去除率为 7.1-47.0。通常,发现基于多元回归分析(MRA)建立的统计模型在预测最终出水浓度方面比 k-C模型更强。然而,两种模型在预测 NO(3)-N 的最终出水浓度方面都不足。从实验数据确定的基于面积的一阶去除率常数(k,m/yr)分别为 BOD(5)200.5、TP 80.1 和 NH(4)-N 173.8,这表明 CWs 内的污染物去除率很高。

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