U.S. Geological Survey, Ohio Water Science Center, 6480 Doubletree Avenue, Columbus, OH 43229, USA.
U.S. Geological Survey, Ohio Water Science Center, 6480 Doubletree Avenue, Columbus, OH 43229, USA.
Harmful Algae. 2016 Sep;58:23-34. doi: 10.1016/j.hal.2016.07.003. Epub 2016 Aug 6.
Cyanobacterial harmful algal blooms (cyanoHABs) and associated toxins, such as microcystin, are a major global water-quality issue. Water-resource managers need tools to quickly predict when and where toxin-producing cyanoHABs will occur. This could be done by using site-specific models that estimate the potential for elevated toxin concentrations that cause public health concerns. With this study, samples were collected at three Ohio lakes to identify environmental and water-quality factors to develop linear-regression models to estimate microcystin levels. Measures of the algal community (phycocyanin, cyanobacterial biovolume, and cyanobacterial gene concentrations) and pH were most strongly correlated with microcystin concentrations. Cyanobacterial genes were quantified for general cyanobacteria, general Microcystis and Dolichospermum, and for microcystin synthetase (mcyE) for Microcystis, Dolichospermum, and Planktothrix. For phycocyanin, the relations were different between sites and were different between hand-held measurements on-site and nearby continuous monitor measurements for the same site. Continuous measurements of parameters such as phycocyanin, pH, and temperature over multiple days showed the highest correlations to microcystin concentrations. The development of models with high R values (0.81-0.90), sensitivities (92%), and specificities (100%) for estimating microcystin concentrations above or below the Ohio Recreational Public Health Advisory level of 6μgL was demonstrated for one site; these statistics may change as more data are collected in subsequent years. This study showed that models could be developed for estimates of exceeding a microcystin threshold concentration at a recreational freshwater lake site, with potential to expand their use to provide relevant public health information to water resource managers and the public for both recreational and drinking waters.
蓝藻有害藻华(cyanoHABs)和相关毒素,如微囊藻毒素,是一个主要的全球水质问题。水资源管理者需要工具来快速预测何时何地会发生产毒蓝藻水华。这可以通过使用特定地点的模型来实现,这些模型估计引起公共健康关注的毒素浓度升高的可能性。在这项研究中,从俄亥俄州的三个湖泊采集样本,以确定环境和水质因素,开发线性回归模型来估计微囊藻毒素水平。藻类群落(藻蓝蛋白、蓝藻生物量和蓝藻基因浓度)和 pH 的测量值与微囊藻毒素浓度的相关性最强。对一般蓝藻、普通微囊藻和多列螺旋藻的蓝藻基因以及微囊藻毒素合成酶(mcyE)进行了定量分析,包括微囊藻、多列螺旋藻和束丝藻。对于藻蓝蛋白,其关系在不同地点之间以及在现场手持式测量值和同一地点附近连续监测测量值之间是不同的。连续多天对参数(如藻蓝蛋白、pH 值和温度)的测量与微囊藻毒素浓度的相关性最高。在一个地点,开发了模型,其 R 值(0.81-0.90)、灵敏度(92%)和特异性(100%)较高,用于估计超过俄亥俄州休闲公共卫生咨询水平(6μgL)的微囊藻毒素浓度;随着未来几年收集更多的数据,这些统计数据可能会发生变化。这项研究表明,可以为休闲淡水湖泊地点的微囊藻毒素阈值浓度超过估计值开发模型,有可能将其扩展到为水资源管理者和公众提供有关休闲和饮用水的相关公共卫生信息。