Heal the Bay, 1444 9th Street, Santa Monica, CA 90401, USA.
UCLA, 2248 Murphy Hall, 410 Charles E. Young Drive East, Los Angeles, CA 90095, USA.
J Environ Manage. 2018 Oct 1;223:633-643. doi: 10.1016/j.jenvman.2018.06.058. Epub 2018 Jun 30.
Fecal indicator bacteria like Escherichia coli and entercococci are monitored at beaches around the world to reduce incidence of recreational waterborne illness. Measurements are usually made weekly, but FIB concentrations can exhibit extreme variability, fluctuating at shorter periods. The result is that water quality has likely changed by the time data are provided to beachgoers. Here, we present an automated water quality prediction system (called the nowcast system) that is capable of providing daily predictions of water quality for numerous beaches. We created nowcast models for 10 California beaches using weather, oceanographic, and other environmental variables as input to tuned regression models to predict if FIB concentrations were above single sample water quality standards. Rainfall was used as a variable in nearly every model. The models were calibrated and validated using historical data. Subsequently, models were implemented during the 2017 swim season in collaboration with local beach managers. During the 2017 swim season, the median sensitivity of the nowcast models was 0.5 compared to 0 for the current method of using day-to-week old measurements to make beach posting decisions. Model specificity was also high (median of 0.87). During the implementation phase, nowcast models provided an average of 140 additional days per beach of updated water quality information to managers when water quality measurements were not made. The work presented herein emphasizes that a one-size-fits all approach to nowcast modeling, even when beaches are in close proximity, is infeasible. Flexibility in modeling approaches and adaptive responses to modeling and data challenges are required when implementing nowcast models for beach management.
粪便指示细菌，如大肠杆菌和肠球菌，在世界各地的海滩进行监测，以降低因娱乐性用水而导致的疾病发病率。这些测量通常每周进行一次，但 FIB 浓度可能会发生极端变化，在较短的时间内波动。结果是，当数据提供给海滩游客时，水质可能已经发生了变化。在这里，我们提出了一个自动化的水质预测系统（称为实时预测系统），该系统能够为许多海滩提供每日水质预测。我们使用天气、海洋学和其他环境变量作为输入，为 10 个加利福尼亚海滩创建了实时预测模型，以预测 FIB 浓度是否超过单一样本水质标准。几乎每个模型都使用了降雨量作为变量。这些模型使用历史数据进行了校准和验证。随后，在与当地海滩管理人员合作的情况下，在 2017 年游泳季节实施了这些模型。在 2017 年游泳季节，实时预测模型的中位灵敏度为 0.5，而当前使用每天到每周的旧测量值来做出海滩发布决策的方法为 0。模型特异性也很高（中位数为 0.87）。在实施阶段，当没有进行水质测量时，实时预测模型平均为每个海滩提供了 140 多天的更新水质信息，为管理人员提供了便利。本文强调，即使海滩彼此接近，使用一刀切的实时预测建模方法也是不可行的。在实施海滩管理的实时预测模型时，需要灵活的建模方法和对建模和数据挑战的适应性反应。
