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    The objective of this study is to explore the optimal pre-treatment procedures and statistics methods for live/dead bacterial staining using nitrite oxidizing organism (NOO) as the research aim. This staining method was developed and widely utilized to evaluate activated bacterial survival situation, because it is direct and convenience to count live and dead bacteria amount by colour distinguishes (green/red) from pictures taken by microscope. The living cell (green colour) percentage and initial bacterial chemical oxygen demand (COD) could be used for accurate reaction rate calculation at the beginning of tests. While according to the physiological principles, the detection target was limited as the organism has a complete cell shape, that was applicable for the initial phase for decay stage (live cell → particulate dead cell), but it is impossible to evaluate the decayed soluble COD from particulate dead cell during whole reaction. To model the decay stage scientifically, a two-step decay model was developed to cater to the live/dead bacterial staining analysis of biological nitrite oxidizer under inhibition condition of high nitrite concentrations at 35 °C. As results of optimal pre-treatment, a three level ultrasonic wave with 45 seconds was explored, as a reasonable observed picture number, 30 sets with 95% confident interval for datasets statistics was summarized. A set of nitrite oxidizer inhibition test (total COD and oxygen uptake rates) under high nitrite concentrations was simulated using the above model and obtained experimental schemes. Additionally, the disintegration enhancement from particulate dead cell to soluble COD by nitrite was inspected and modelled on the basis of experimental datasets.

    Citation

    Bing Liu, Yifan Li, Jinzhu Wu, Yuanyuan Shao, Feiyong Chen, Jer-Horng Wu, Rajeev Goel, Mitsuharu Terashima, Hidenari Yasui. Evaluating nitrite oxidizing organism survival under different nitrite concentrations. Water science and technology : a journal of the International Association on Water Pollution Research. 2020 Jul;82(2):273-280

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    PMID: 32941169

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