Marine and Freshwater Research Marine and Freshwater Research Society
Advances in the aquatic sciences
RESEARCH ARTICLE

Quantitative prediction and typical factor effects of phosphorus adsorption on the surface sediments from the intertidal zones of the Yellow River Delta, China

Baocui Liang A , Xiao Qian A , Xinhui Liu A B , Shengnan Zhao A , Baoshan Cui A and Junhong Bai A
+ Author Affiliations
- Author Affiliations

A State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, P.R. China.

B Corresponding author. Email: xhliu@bnu.edu.cn

Marine and Freshwater Research - https://doi.org/10.1071/MF17104
Submitted: 19 April 2017  Accepted: 1 September 2017   Published online: 8 November 2017

Abstract

Using 13 sediment physicochemical properties and a partial least squares (PLS) regression method, a predictive model was developed for the phosphorus (P)-adsorption capacity of sediments in the intertidal zones of the Yellow River Delta. The cross-validated regression coefficient (Q2cum = 0.823) and correlation coefficient (R2 = 0.854) indicated significantly high robustness of the model. Moreover, P adsorption characteristics of sediments in the intertidal zones were systematically studied. The maximum adsorption rate (274.80 mg kg–1 h–1) was seen for sediment of the site around which there was aquaculture, which could have led to a higher organic matter content in the sediment. The mass fraction of clay and silt (<62.5 µm) in the sediment of this site was 74%. The P-adsorption capacities ranged from 86.63 to 297.49 mg kg–1 for all sites. The quantity of P adsorbed decreased with increasing salinity (2–30), and exhibited an inverted U-trend under the effect of pH (5–11). P adsorption increased with increasing P concentration under oxidation conditions (>400 mV), but decreased under reduction conditions (0 ± 100 mV). These results could contribute to the restoration and management of intertidal zones.

Additional keywords: partial least squares regression.


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