Smith J E, Stocker M D, Wolny J L, Hill R L, Pachepsky Y A
Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, ARS-USDA, Beltsville, MD, USA.
Department of Environmental Science and Technology, University of Maryland, College Park, MD, USA.
Environ Monit Assess. 2020 Oct 16;192(11):706. doi: 10.1007/s10661-020-08664-w.
Recently, cyanobacteria blooms have become a concern for agricultural irrigation water quality. Numerous studies have shown that cyanotoxins from these harmful algal blooms (HABs) can be transported to and assimilated into crops when present in irrigation waters. Phycocyanin is a pigment known only to occur in cyanobacteria and is often used to indicate cyanobacteria presence in waters. The objective of this work was to identify the most influential environmental covariates affecting the phycocyanin concentrations in agricultural irrigation ponds that experience cyanobacteria blooms of the potentially toxigenic species Microcystis and Aphanizomenon using machine learning methodology. The study was performed at two agricultural irrigation ponds over a 5-month period in the summer of 2018. Phycocyanin concentrations, along with sensor-based and fluorometer-based water quality parameters including turbidity (NTU), pH, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), conductivity, chlorophyll, color dissolved organic matter (CDOM), and extracted chlorophyll were measured. Regression tree analyses were used to determine the most influential water quality parameters on phycocyanin concentrations. Nearshore sampling locations had higher phycocyanin concentrations than interior sampling locations and "zones" of consistently higher concentrations of phycocyanin were found in both ponds. The regression tree analyses indicated extracted chlorophyll, CDOM, and NTU were the three most influential parameters on phycocyanin concentrations. This study indicates that sensor-based and fluorometer-based water quality parameters could be useful to identify spatial patterns of phycocyanin concentrations and therefore, cyanobacteria blooms, in agricultural irrigation ponds and potentially other water bodies.
最近，蓝藻水华已成为农业灌溉水质的一个问题。大量研究表明，这些有害藻华（HABs）产生的蓝藻毒素在灌溉水中存在时，可传输至作物并被作物吸收。藻蓝蛋白是一种仅在蓝藻中出现的色素，常用于指示水体中蓝藻的存在。这项工作的目的是使用机器学习方法，确定影响经历产毒微囊藻和束丝藻蓝藻水华的农业灌溉池塘中藻蓝蛋白浓度的最具影响力的环境协变量。该研究于2018年夏季在两个农业灌溉池塘进行，为期5个月。测量了藻蓝蛋白浓度，以及基于传感器和荧光计的水质参数，包括浊度（NTU）、pH值、溶解氧（DO）、荧光溶解有机物（fDOM）、电导率、叶绿素、有色溶解有机物（CDOM）和提取的叶绿素。回归树分析用于确定对藻蓝蛋白浓度影响最大的水质参数。近岸采样点的藻蓝蛋白浓度高于内部采样点，并且在两个池塘中都发现了藻蓝蛋白浓度持续较高的“区域”。回归树分析表明，提取的叶绿素、CDOM和NTU是对藻蓝蛋白浓度影响最大的三个参数。这项研究表明，基于传感器和荧光计的水质参数可用于识别农业灌溉池塘以及潜在的其他水体中藻蓝蛋白浓度的空间模式，从而识别蓝藻水华。
