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Updated: Aug 1, 2025

Measuring Phosphorus Release in Laboratory Microcosms for Water Quality Assessment
Published on: July 22, 2019
Dynamic spatiotemporal change of net anthropogenic phosphorus inputs and its response of water quality in the Liao
Tianxiang Wang1, Ya Sun2, Tianzi Wang3
1School of Ocean Science and Technology, Dalian University of Technology, Panjin, 124221, China; Department of Physical & Environmental Sciences, University of Toronto, Toronto, M1C 1A4, Canada; Key Laboratory of Coastal Science and Integrated Management, Ministry of Natural Resources, Qingdao, 266061, China; State Key Laboratory of Lake Science and Environment, Nanjing, 210008, China.
Abstract:
The Liao river is one of the seven major rivers in China, and the process of phosphorus (P) cycling and change of water quality in this basin are influenced to a considerable extent human activities. In this work, the traditional net anthropogenic phosphorus inputs (NAPI) model was improved by considering the dynamic change of wastewater treatment capacity and P deposition (PDEP) and reclassifying the sources of phosphorus into human P consumption (PHUM), agriculture P consumption (PAGR), livestock P consumption (PANIM) and PDEP to analyze its dynamic spatio-temporal change in the Liao river basin. The results showed that the annual mean NAPI was 785.53 kg P km-2 yr-1 (2001-2020), the maximum value was 940.49 kg P km-2 yr-1 in 2009, and the minimum value was 586.04 kg P km-2 yr-1 in 2001. The temporal variation of NAPI presented an increasing-fluctuation-increasing trend and was basically in line with that of the water quality throughout the three stages, and the spatial distribution of NAPI gradually increased from upstream to downstream. During the two decades, PANIM was the predominant component of NAPI with a share of 64.32%. PHUM, PAGR, and PDEP accounted for 15.97%, 11.54%, and 8.17%, respectively, and the point source NAPI (NAPIP) contributed to 4.95% of NAPI. Further, the INAPI (Improved NAPI) -MR (Multiple Regression) -SWAT (Soil and Water Assessment Tool) model was developed to predict the spatial distribution of P flux under two scenarios. The results showed that the Liao river basin experienced a reduction in P flux to different degrees due to the improvement of the wastewater treatment system, which was more significant in its downstream area. Long-term water quality monitoring is encouraged to develop refined water quality models in the future.
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