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Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA

  • Smith, J. E.1, 2, 3
  • Stocker, M. D.1, 2, 3
  • Wolny, J. L.4
  • Hill, R. L.2
  • Pachepsky, Y. A.1
  • 1 Beltsville Agricultural Research Center, ARS-USDA, Beltsville, MD, USA , Beltsville (United States)
  • 2 University of Maryland, College Park, MD, USA , College Park (United States)
  • 3 Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA , Oak Ridge (United States)
  • 4 Resource Assessment Service, Maryland Department of Natural Resources, Annapolis, MD, USA , Annapolis (United States)
Published Article
Environmental Monitoring and Assessment
Publication Date
Oct 16, 2020
DOI: 10.1007/s10661-020-08664-w
Springer Nature


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.

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