Laureano-Rosario Abdiel E, Duncan Andrew P, Symonds Erin M, Savic Dragan A, Muller-Karger Frank E
College of Marine Science, University of South Florida, 140 7th Avenue South, Saint Petersburg, FL 33701, USA E-mail:
Centre for Water Systems, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UK.
J Water Health. 2019 Feb;17(1):137-148. doi: 10.2166/wh.2018.128.
Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.
预测休闲用水水质是保护公众健康免受与废水相关病原体暴露影响的关键。对所有病原体监测休闲用水是不可行的；因此，监测项目使用粪便指示菌（FIB），如肠球菌，来识别废水污染。人工神经网络（ANN）被用于预测在波多黎各圣胡安埃斯卡姆布隆海滩可培养肠球菌浓度何时超过美国环境保护局（EPA）的休闲用水水质标准（RWQC）。对十年的可培养肠球菌数据与卫星衍生的海表面温度（SST）、直接法向辐照度（DNI）、浊度和露点，以及当地的降水量和平均海平面（MSL）观测数据进行了分析。基于美国EPA RWQC确定的与肠球菌超标预测最相关的因素是DNI、浊度、48小时累计降水量、MSL和SST；根据接收操作特征曲线和F-Measure指标，它们预测可培养肠球菌超标情况的准确率为75%，功效大于60%。结果表明卫星衍生数据和人工神经网络在预测埃斯卡姆布隆海滩休闲用水水质方面的适用性。未来的工作应纳入当地卫生调查数据，以预测危险的休闲用水状况并保护人类健康。
