Intra-seasonal variation of E. coli and environmental covariates in two irrigation ponds in Maryland, USA

Authors: STOCKER, MATTHEW - Orise Fellow; Pachepsky, Yakov; HILL, ROBERT - University Of Maryland; SELLNER, KEVIN - Hood College; MACARISIN, DUMITRU - Food And Drug Administration(FDA); STAVER, KENNETH - University Of Maryland

Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/27/2019
Publication Date: 7/28/2019
Citation: Stocker, M.D., Pachepsky, Y.A., Hill, R.L., Sellner, K.G., Macarisin, D., Staver, K.W. 2019. Intra-seasonal variation of E. coli and environmental covariates in two irrigation ponds in Maryland, USA. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2019.03.121.
DOI: https://doi.org/10.1016/j.scitotenv.2019.03.121

Interpretive Summary: Microbial quality of irrigation waters is an important population health issue. Escherichia coli is used as n indicator of the potential contamination of irrigation and recreation waters with pathogens. Sampling for and measurement of E. coli concentrations is time and labor consuming, and spatial variability of E. coli concentrations is high. The objective of this work was to determine if some other, easier obtainable water quality parameters can indicate locations where E. coli 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 the nearshore concentrations do not provide the information about the E. coli concentration in the bulk of pond water, and that the most influential water quality parameters are dissolved organic matter, chlorophyll and phycocyanin contents, 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 microbial water quality in irrigation water sources using the modern fast measurement technologies for survey and monitoring irrigation water sources.

Technical Abstract: Microbial quality of irrigation water is commonly assessed by measuring concentrations of E. coli. Concentrations of E. coli in irrigation waters are variable in space and time and their determination is resource-demanding in the regular farm environment. E. coli concentrations are known to be affected by several factors affecting bacteria survival. These factors are characterized by water quality parameters that co-vary with E. coli concentrations and can be measured with currently available sensors. The objective of this work was to identify the most influential environmental covariates affecting E. coli concentration during a three month-long monitoring campaigns in two irrigation ponds in Maryland. We performed dense sampling of ponds biweekly during the summer of 2017. Water quality parameters as well as E. coli concentrations were measured. Regression tree algorithms were applied to determine the most influential water quality parameters for prediction of E. coli levels. Correlations between E. coli and water quality covariates were not strong and were inconsistently significant. On average, shoreline samples had higher E. coli concentrations than interior samples and significant differences were observed when comparing these two groups. Regression trees provided fairly accurate predictions of E. coli levels based on water quality parameters with R2 ranging from 0.70 to 0.93. Regression trees varied by sampling date but common leading covariates included cyanobacteria, organic matter, and turbidity. Results of this work indicate the opportunity to use environmental covariates sensed either remotely or in situ to delineate areas with different E. coli survival conditions across the irrigation ponds.