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

Authors: SMITH, JACLY - University Of Maryland; STOCKER, MATTHEW - Orise Fellow; WOLNY, JENNIFER - Maryland Department Of Natural Resources; HILL, ROBERT - University Of Maryland; Pachepsky, Yakov

Submitted to: Environmental Monitoring and Assessment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/5/2020
Publication Date: 10/16/2020
Citation: Smith, J.L., Stocker, M., Wolny, J.L., Hill, R.L., Pachepsky, Y.A. 2020. Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-020-08664-w.
DOI: https://doi.org/10.1007/s10661-020-08664-w

Interpretive Summary: Cyanobacteria blooms are negatively affecting water quality of irrigation ponds used in agriculture. Toxins from these harmful algal blooms can be carried from irrigation waters to nearby crop fields, and ultimately fresh produce. The pigment phycocyanin, which is found only in cyanobacteria, is often used to indicate cyanobacteria presence in waters. The objective of this work was to determine if some other, easier obtainable water quality parameters can indicate locations where phycocyanin concentrations are expected to be relatively high and where they are expected to be low in irrigation ponds. The intensive monitoring of two irrigation ponds in Maryland combined with artificial intelligence-based -data processing showed that near-shore waters typically contained higher phycocyanin concentrations than interior waters, and the most influential water quality parameters were chlorophyll, colored dissolved organic matter and turbidity; these parameters can be measured with existing sensors in situ and estimated with remote sensing methods. Results of this work can be used by irrigation water managers and consultant in that they indicate the opportunity for evaluating potentially toxic harmful cyanobacteria in irrigation water sources using the modern fast measurement technologies for survey and monitoring irrigation water sources.

Technical Abstract: Cyanobacteria have recently become a concern regarding 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 two 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 during which time 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. Near-shore 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.