Phosphorus immobilization/release behavior of lanthanum-modified bentonite amended sediment under the dual effects of pH and dissolved organic carbon.

Chemosphere(2024)

引用 0|浏览0
暂无评分
摘要
Lanthanum modified bentonite (LMB) is typical P-inactivating agent that has been applied in over 200 lakes. Dissolved organic carbon (DOC) and high pH restrict the phosphorus (P) immobilization performance of LMB. However, the P immobilization/release behaviors of LMB-amended sediment when suspended to overlying water with high pH and DOC have not yet been studied. In the present work, batch adsorption and long-term incubation experiments were performed to study the combined effects of pH and DOC on the P control by LMB. The results showed that the coexistence of low concentration of DOC or preloading with some DOC had a negligible effect on P binding by LMB. In the presence of DOC, the P adsorption was more pronounced at pH 7.5 and was measurably less at pH 9.5. Additionally, the pH value was the key factor that decided the P removal at low DOC concentration. The increase in pH and DOC could significantly promote the release of sediment P with a higher EPC0. Under such condition, a higher LMB dosage was needed to effectively control the P releasing from sediment. In sediment/water system with intermittent resuspension, the alkaline conditions greatly facilitated the release of sediment P and DOC, which increased from 0.087 to 0.581 mg/L, and from 11.05 to 26.56 mg/L, respectively. Under the dual effect of pH and DOC, the P-immobilization performance of LMB was weakened, and a tailor-made scheme became essential for determining the optimum dosage. The desorption experiments verified that the previously loaded phosphorus on LMB was hard to be released even under high pH and DOC conditions, with an accumulative desorption rate of less than 2%. Accordingly, to achieve the best P controlling efficiency, the application strategies depending on LMB should avoid the high DOC loading period such as the rainy season and algal blooms.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要