Department of Bioresource and Environmental Security, Sandia National Laboratories, 7011 East Ave, Livermore, CA 94550, United States.
Department of Biology, Georgia Southern University, 4324 Old Register Road, Statesboro, GA 30460, United States.
Water Res. 2021 Mar 1;191:116816. doi: 10.1016/j.watres.2021.116816. Epub 2021 Jan 6.
The purpose of this study is to determine the potential for an attached algae flow-way system to efficiently produce algal biomass in estuarine surface waters by utilizing dilute non-point source nutrients from local urban, industrial, and agricultural discharges into the Upper Laguna Madre, Corpus Christi, Texas. The study was conducted over the course of two years to establish seasonal base-line biomass productivity and composition for bioproducts applications, and to identify key environmental factors and flow-way cohorts impacting biomass production. For the entire cultivation period, continuous ash-free biomass production at 4 to 10 g/m/day (corresponding to nutrient recovery at 300 to 500 mg of nitrogen/m/day and 15 to 30 mg of phosphorus/m/day) was successfully achieved without system restart. Upon start-up, a latency period was observed which indicates roles for species succession from relatively low productivity, high ash content pioneer periphytic culture composed primarily of benthic diatoms from the source waters to higher productivity, reduced ash content, and more resilient culture mainly composed of filamentous chlorophyta, Ulva lactuca. Principal Component Analysis (PCA) was used to identify environmental factors driving biomass production, and machine learning (ML) models were constructed to assess the predictive capability of the data set for system performance using the local multi-season environmental variations. Environmental datasets were segregated for ML training, validation, and testing using three methods: regression tree, ensemble regression, and Gaussian process regression (GPR). The predicted ash-free biomass productivity using ML models resulted in root-squared-mean-errors (RSME) from 1.78 to 1.86 g/m/day, and R values from 0.67 to 0.75 using different methods. The greatest contributor to net productivity was total solar irradiation, followed by air temperature, salinity, and pH. The results of the study should be useful as a decision-making tool to application of attached algae flow-ways for biomass production while preventing algal blooms in the environment.
本研究旨在确定附着藻类流道系统在德克萨斯州科珀斯克里斯蒂的上拉古纳马德拉（Upper Laguna Madre）利用当地城市、工业和农业排放的稀释非点源营养物，从河口表层水中高效生产藻类生物质的潜力。该研究进行了两年，以确定生物制品应用的季节性基础生物量生产力和组成，并确定影响生物量生产的关键环境因素和流道群体。在整个培养期间，成功地实现了 4 到 10 g/m/天的连续无灰生物质生产（对应于 300 到 500 mg 的氮/m/天和 15 到 30 mg 的磷/m/天的营养物回收），而无需系统重新启动。启动时，观察到一个潜伏期，表明从相对低生产力、高灰分、主要由源水底层硅藻组成的先驱性周丛培养物到更高生产力、更低灰分和更具弹性的主要由丝状绿藻组成的文化的物种演替作用。主成分分析（PCA）用于确定驱动生物量生产的环境因素，并且使用机器学习（ML）模型构建来评估数据集对系统性能的预测能力，使用当地多季节环境变化。使用三种方法（回归树、集成回归和高斯过程回归（GPR））将环境数据集分离用于 ML 训练、验证和测试。使用 ML 模型预测的无灰生物质生产力导致根均方误差（RSME）为 1.78 到 1.86 g/m/天，并且使用不同方法的 R 值为 0.67 到 0.75。对净生产力贡献最大的是总太阳辐射，其次是空气温度、盐度和 pH 值。研究结果应作为在环境中防止藻类大量繁殖的同时应用附着藻类流道进行生物质生产的决策工具。
