Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds
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Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA J. E. Smith & M. D. Stocker & J. L. Wolny & R. L. Hill & Y. A. Pachepsky
Received: 1 April 2020 / Accepted: 5 October 2020 # This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020
Abstract 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 Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10661-020-08664-w) contains supplementary material, which is available to authorized users. J. E. Smith : M. D. Stocker : Y. A. Pachepsky Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, ARS-USDA, Beltsville, MD, USA J. E. Smith : M. D. Stocker : R. L. Hill Department of Environmental Science and Technology, University of Maryland, College Park, MD, USA J. E. Smith (*) : M. D. Stocker Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA e-mail: [email protected] J. L. Wolny Resource Assessment Service, Maryland Department of Natural Resources, Annapolis, MD, USA
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 sensorbased 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. Keywords Phycocyanin . Cyanobacteria . Regression trees . Water quality . Irrigation ponds . Monitoring . Ha
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