The effect of fish feed quality has gained increasing attention to alleviate the harmful environmental impacts induced by intensive aquaculture. in current research, we have conducted an incubator experiment to highlight the effect of fish feed quality on aquaculture water environment. Fish feed from three manufactures with two different dosages (0.1000 g, 0.2000 g) was added to the culture medium with and without Microcystis aeruginosa. treatments with Microcystis aeruginosa were named as MHt, MHp and MZt; while the treatments without Microcystis aeruginosa named as Ht, Hp and Zt. Microcystis aeruginosa densities and nutrients concentrations were measured in the study. Results have shown that fish feed quality (manufactures) has a great effect on nutrients concentrations in the absence of Microcystis aeruginosa (P < 0.05). Meanwhile, fish feed can stimulate Microcystis aeruginosa growth that is also influenced by fish feed quality excluding lag phase (0~12 day) significantly in general (P < 0.05). The maximum Microcystis aeruginosa density (N max) is 1221.5, 984.5, 581.0, 2265.9, 2056.8 and 1766.6 1 × 10 4 cells mL −1 for MHT 0.1 g, MHP 0.1 g, MZT 0.1 g, MHT 0.2 g, MHP 0.2 g and MZT 0.2 g, respectively. In treatments with algae, fish feed quality affect total phosphorus (TP) concentrations (except the difference between MHT and MHP) and total nitrogen (TN) concentrations significantly (P < 0.05). For most of consumed nutrients, the obvious differences among all treatments were observed excluding lag phase in general (P < 0.05), which suggest that the nutrient utilization is also dependent on fish feed quality. Keeping in mind the above facts it is concluded that fish feed quality is a key factor in impacting aquaculture water environment. Aquaculture is one of the fastest growing food producing sectors around the world. Global production of aquaculture increased from 4.17 × 10 7 tonnes in 2000 to 8.0 × 10 7 tonnes in 2016, and the annual growing rate reached 5.2% during this period 1. Freshwater aquaculture is probably the most important form of aquaculture for the time being, and fish is by far the dominating product in freshwater aquaculture 2,3. In fact, aquaculture production heavily depends on the external aquafeeds or nutrients supply to the aquaculture system 4. Aquafeeds production has been widely recognized as one of the fastest expanding agricultural industries in the world 5 , and the annual growth rate of aquafeeds production reached 17% in China 6. In 2018, total output of global aquafeeds was 40.1 million tonnes, of which Asia-Pacific's aquafeeds production reached 28.5 million tonnes 7. In practice, fish feed is the most important kind of aquafeeds with China being the top 1 in the world production of the fish feed 8. Currently, the rapid development and low entry barriers for China's feed industry have led to the emergence of aquafeeds enterprises with insufficient conditions 9. Meanwhile, production of carp and other omnivorous species is intensifying in China, and commercial aquafeeds enterpris...

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Fish feed and faeces are known to be the most intensive wastes of aquaculture systems resulting in enriching the nutrient pool of re-

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 (PO4 3--P), total dissolved nitrogen (TDN) and ammonia (NH4 +-N) reasonably with R2=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 (R2=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 (R2=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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