Long-Term Dynamics and Seasonality of Bacteriological Water Quality Parameters of the Lower Don: Trends and Forecasts

Introduction: When conducting environmental public health surveillance, the key areas for preventing the spread of waterborne diseases include microbiological testing aimed to assess and predict the quality of surface sources for drinking water and of recreational water bodies and watercourses.

Objectives: To analyze the results of bacteriological testing of the Lower Don water samples, to establish long-term pollution trends and seasonality, and to compare the accuracy of medium-term forecasting based on regression modeling and artificial neural networks.

 Materials and methods: We used the results of bacteriological testing of 540 river water samples taken in the water area of the city of Azov, Rostov Region, in 2005–2020 for pathogenic Enterobacteriaceae (Salmonella spp.), opportunistic pathogens (Klebsiella spp. and Pseudomonas aeruginosa), and general, thermotolerant and glucose-positive coliforms. Specially created software, as well as IBM SPSS Statistics version 20.0 and Matlab R2021a with the Neural Network Toolbox module were used for statistical data analysis.

Results: We established high levels of bacterial water pollution in the lower reaches of the Don River with opportunistic and pathogenic microorganisms posing a high epidemic risk of the occurrence and spread of intestinal infections. We also observed rising trends in the density of thermotolerant coliforms, including Klebsiella spp. and Pseudomonas aeruginosa, in river water and their seasonal fluctuations.

 Conclusions: Compared to extrapolative forecasting based on regression analysis, the use of neural network models in prospective analysis enables more accurate medium-term forecasts of bacteriological quality parameters of river water. Identification of their seasonal fluctuations facilitates determination of time spans with a high probability of the spread of waterborne infectious diseases.

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