Centre for Water Technology, Department of Biology, Section for Microbiology, Aarhus University, Ny Munkegade 114, 8000 Aarhus, Denmark.
Grundfos Holding A/S, Poul Due Jensens Vej 7, 8850 Bjerringbro, Denmark.
Sensors (Basel). 2020 Nov 9;20(21):6398. doi: 10.3390/s20216398.
Evaporative cooling towers to dissipate excess process heat are essential installations in a variety of industries. The constantly moist environment enables substantial microbial growth, causing both operative challenges (e.g., biocorrosion) as well as health risks due to the potential aerosolization of pathogens. Currently, bacterial levels are monitored using rather slow and infrequent sampling and cultivation approaches. In this study, we describe the use of metabolic activity, namely oxygen respiration, as an alternative measure of bacterial load within cooling tower waters. This method is based on optical oxygen sensors that enable an accurate measurement of oxygen consumption within a closed volume. We show that oxygen consumption correlates with currently used cultivation-based methods (R = 0.9648). The limit of detection (LOD) for respiration-based bacterial quantification was found to be equal to 1.16 × 10 colony forming units (CFU)/mL. Contrary to the cultivation method, this approach enables faster assessment of the bacterial load with a measurement time of just 30 min compared to 48 h needed for cultivation-based measurements. Furthermore, this approach has the potential to be integrated and automated. Therefore, this method could contribute to more robust and reliable monitoring of bacterial contamination within cooling towers and subsequently increase operational stability and reduce health risks.
蒸发式冷却塔是许多工业中用于散发多余过程热量的必要设施。持续潮湿的环境有利于大量微生物的生长，这不仅会带来操作上的挑战（例如，生物腐蚀），还会因病原体潜在的气溶胶化而带来健康风险。目前，细菌水平的监测主要采用较为缓慢且不频繁的采样和培养方法。在本研究中，我们描述了使用代谢活性（即氧呼吸）作为替代指标来测量冷却塔水中的细菌负荷。该方法基于光学氧传感器，能够在封闭体积内准确测量氧气消耗。我们发现，氧消耗与当前使用的基于培养的方法具有良好的相关性（R = 0.9648）。基于呼吸的细菌定量的检测限（LOD）被发现等于 1.16×10 个菌落形成单位（CFU）/mL。与培养方法相比，这种方法可以更快地评估细菌负荷，测量时间仅为 30 分钟，而基于培养的测量则需要 48 小时。此外，该方法具有集成和自动化的潜力。因此，该方法可以为冷却塔内的细菌污染提供更稳健可靠的监测，从而提高操作稳定性并降低健康风险。
