PDF(Pubmed)
Abstract：
The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005-2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey\'s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.
摘要：
浮游植物和附生植物中叶绿素a的浓度代表藻类生物量。我们收集了来自美国（US）水体的18年（2005-2022年）色素数据的记录，以支持开发基于过程的，机器学习,和用于预测有害藻华（HAB）的遥感模型。据我们所知,这个由近84,000个地点和超过1,374,000个色素测量数据组成的数据集是最大的协调离散，实验室提取的美国叶绿素数据。这些数据是根据水质门户网站（WQP）和以前未发布的美国地质调查局国家水质实验室（NWQL）数据编制的。统一了报告单位的数据，颜料类型,重复值，收集深度,站点名称,负值,和一些极端的价值观。全国各地,数据显示采样频率随状态的变化很大，分布，和方法。这些数据的用途包括模型的校准，现场传感器的校准，检查与营养素和其他驱动因素的关系，时间趋势的评估，以及其他解决地方到国家规模问题的应用。
