Modeling microbial water quality in tropical waters using culture-dependent and molecular DNA- based assays for fecal indicator bacteria

Monitoring fecal indicator bacteria (FIB) dynamics in water systems is crucial for assessing water quality, identifying potential health risks, and implementing appropriate management strategies. There is a lack of information regarding the applicability of culture-dependent vs culture-independent microbial methods to develop predictions of FIB levels. The objective of this study is to compare the use of quantitative polymerase chain reaction (qPCR) assays and culture-dependent FIB measurements to assess microbial water quality (MWQ) and further the development of statistical models in tropical watersheds. Water samples were collected from April 2022-December 2023 from rural and urban basins in Puerto Rico and analyzed for E. coli and enterococci levels (culture-based and qPCR assays). Statistical models were developed to predict FIB levels based on environmental variables using Multi Linear Regression (MLR) and Gradient Boosting Machine (XGBoost) learning techniques. Enterococci predictions were highly accurate (0.92) using XGBoost with both methods across watersheds and performed better than the MLR models (R2=0.16 to 0.4). E. coli model accuracy was 0.84-0.96. Temperature, pH, turbidity, dissolve oxygen, and conductivity were all important parameters for FIB prediction, but their influence depended on the type of watershed (urban vs rural).  Results indicate that these rivers are constantly exceeding local water quality criteria, and application of statistical modeling tools may provide a cost-effective methodology to perform MWQ assessments in tropical watersheds.

Presentation Preference: Either

Primary Presenter: Marirosa Molina, US Environmental Protection Agency (molina.marirosa@epa.gov)

Authors:

Marirosa Molina, US Environmental Protection Agency  (molina.marirosa@epa.gov)

Marco Orizondo, ORISE fellow  (orizondolugo.marco@epa.gov)

Antony Tseng, US Environmental Protection Agency  (Tseng.antony@epa.gov)

Mathew Bemis, US Environmental protection Agency  (bemis.mathew@epa.gov)

Jorge Santo Domingo, US Environmental Protection Agency  (santodomingo.jorge@epa.gov)