3.8 Proceedings Paper

Comparison of the modified Monod and Droop function combined with Logistic function for describing algae growth

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IOP PUBLISHING LTD
DOI: 10.1088/1755-1315/612/1/012057

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资金

  1. National Natural Science Foundation of China [11672139, 51861135314, 5181101344, 41911530081]
  2. Natural Science Foundation of Tianjin [18YFZCSF00510]
  3. Natural Science Foundation of Hebei province [E2020202021]

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Microcystis aeruginosa (M. aeruginosa) are algae found in common freshwater blooms in China, and Dunaliella tertiolecta (D. tertiolecta) are economically important marine algae. Understanding of the microbial growth kinetics plays a significant role in the management of M. aeruginosa's blooms and biodiesel production by D. tertiolecta. This study has shown that the combination of mechanistic models (Logistic and Monod) proved to be efficient in describing relationship between M. aeruginosa growth rates and specific concentrations of total dissolved phosphorus (TDP), orthophosphate (PO43--P), total dissolved nitrogen (TDN) and ammonia (NH4+-N) reasonably with R-2=0.28-0.93. Meanwhile, results also show that both PO43--P and NH4+-N are important forms of TDP and TDN in influencing M. aeruginosa growth. It was also noted that the combination of modified Monod and Logistic functions is suitable for describing specific growth rates of D. tertiolecta versus extracellular nitrate concentrations (R-2=0.24-0.72). In terms of the combination of Droop and Logistic functions, it was analysed to better explain the relationships between M. aeruginosa specific growth rates and cellular P and N concentrations (R-2=0.41-0.86) as compared to the application of Droop function alone. It is also observed that the relationship between D. tertiolecta specific growth rates and intracellular nitrate concentrations also can be well described by the combination of Logistic and Droop functions. In addition, M. aeruginosa growth was affected by less intracellular P concentrations than intracellular N concentrations. In sum, the combination of modified Monod and Logistic functions and the combination of Droop and Logistic functions all can predict algae growth reasonably well, while the combination of Droop and Logistic functions is slightly better. Meanwhile, it is through these two combinations that two sets of better parameters in modified Monod and Droop functions can be respectively obtained to characterize algal population kinetics with changing nutrient concentrations.

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