『Abstract
Bioavailable phosphorus (BAP) plays an important role in phosphorus
(P) release from lake and river sediments, as well as serves as
an indicator for the potential P-release risk in sediment. Developing
a feasible model which could predict BAP via other P fractions
is needed for the lakes and reservoirs without regular BAP monitoring.
The algal available P (AAP), NaHCO3 extractable
P (Olsen-P), water soluble P (WAP) and readily desorption P (RDP)
are four fractions of BAP. The vertical and spatial distributions
of BAP fractions of three sediment cores from Jiulongkou Lake
were analyzed. In addition, the P fractions, including total P
(TP), organic P (OP), inorganic P (IP), non-apatite inorganic
P (NAIP), and apatite P (AP) were measured to develop a model
for predicting BAP. The model for each BAP fraction was developed
based on datasets from Jiulongkou Lake and validated by the datasets
collected from Wujin and Wugong Lake. The result showed that all
of the four BAP fractions decreased with depth, along the direction
of contaminant transport. Their rank order was AAP＞Olsen-P＞WSP＞RDP
in all samples. The concentration of BAP was affected by the anthropogenic
input and aquatic macrophyte growth. Each of the four BAP fractions
could be simulated by different P fractions: both AAP and Olsen-P
were expressed by NAIP and OP, WAP had a significant relationship
with OP, and RDP had significant relationship with IP. NAIP and
OP were the major sources of the BAP fraction. The simulated results
in two other lakes further illustrated that this model could be
used to successfully predict the BAP concentration in lakes in
the study area, and holds promise for predicting the BAP levels
in other lakes and reservoirs as well.
Keywords: Bioavailable phosphorus; Phosphorus fractions; Predictive
model; Lake sediments』
Introduction
Materials and methods
Study area
Sampling
Physical parameter determination
Bioavailable P measurement and SMT extraction
Statistical methods
Results
Basic properties of sediments
Environmental evolution of sedimentary BAP
Multiple linear regression model
Discussion
Conclusions
Acknowledgments
References