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首页> 外文期刊>Limnology and oceanography, methods >Interpretation of total phytoplankton and cyanobacteria fluorescence from cross-calibrated fluorometers, including sensitivity to turbidity and colored dissolved organic matter
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Interpretation of total phytoplankton and cyanobacteria fluorescence from cross-calibrated fluorometers, including sensitivity to turbidity and colored dissolved organic matter

机译:从交叉梯形荧光仪解释总浮游植物和蓝藻荧光,包括对浊度和有色溶解有机物的敏感性

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In vivo pigment fluorescence methods allow simple real-time detection and quantification of freshwater algae and cyanobacteria. Available models are still limited to high-cost fluorometers, validated for single instruments or individual water bodies, preventing data comparison between multiple instruments, and thus, restricting their use in large-scale monitoring programs. Moreover, few models include corrections for optical interference (water turbidity and colored dissolved organic matter, CDOM). In this study, we developed simple models to predict phytoplankton and cyanobacterial chlorophyll a (Chl a) concentrations based on Chl a and C-phycocyanin in vivo fluorescence, using multiple low-cost handheld fluorometers. We aimed to: (1) fit models to mixed cyanobacterial and microalgal cultures; (2) cross-calibrate nine fluorometers of the same brand and series; (3) correct the CDOM and turbidity effects; and (4) test the algorithms’ performance with natural samples. We achieved comparable results between nine instruments after the cross-calibration, allowing their simultaneous use. We obtained algorithms for total and cyanobacterial Chl a estimation. We developed parametric corrections to remove CDOM and turbidity interferences in the algorithms. Five sampling sites (from a lake, a stream, and an estuary) were used to test the algorithms using eight cross-calibrated fluorometers. The models showed their best performance after CDOM and turbidity corrections (total Chl a: R_2 = 0.99, RMSE = 7.8 μg Chl a L~(?1); cyanobacterial Chl a: R_2 = 0.98, RMSE = 9.8 μg Chl a L~(?1)). In summary, our models can quantify total phytoplankton and cyanobacterial Chl a in real time with multiple low-cost fluorometers, allowing its implementation in large-scale monitoring programs.
机译:体内颜料荧光方法允许简单的实时检测和淡水藻类和蓝藻的定量。可用型号仍然仅限于高成本的荧光仪,验证了单个仪器或单个水体,防止多种仪器之间的数据比较,从而限制了它们在大规模监控程序中的使用。此外,很少有型号包括用于光学干扰的校正(水浊度和有机溶解有机物,CDOM)。在这项研究中,我们使用多个低成本手持式荧光仪,开发了简单的模型来预测基于体内荧光的CHL A和C-浮蛋白的浮游植物和蓝藻叶绿素A(CHL A)浓度。我们的目标是:(1)适合模型以混合蓝藻和微藻培养物; (2)交叉校准相同品牌和系列的九尺寸; (3)纠正CDOM和浊度效应; (4)用天然样本测试算法的性能。我们在交叉校准后达到九个仪器之间的可比结果,可以同时使用。我们获得了总和天生的CHL估计算法。我们开发了参数校正,以删除算法中的CDOM和浊度干扰。使用五个采样网站(来自湖泊,流和河口)使用八个交叉校准的荧光仪来测试算法。该模型在CDOM和浊度校正后显示了它们的最佳性能(总CHL A:R_2 = 0.99,RMSE =7.8μgCHLA L〜(?1);蓝杆菌CHL A:R_2 = 0.98,RMSE =9.8μgCHL A L〜( ?1))。总之,我们的模型可以使用多个低成本荧光仪实时定量浮游植物和蓝藻CHL A,允许其在大规模监控程序中实现。

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