Antibiotics and Antimycotics in Waste Water Treatment Plants: Concentrations, Removal Efficiency, Spatial and Temporal Variations, Prediction, and Ecological Risk Assessment

Abstract

Influent sewage water and treated effluent were collected during three different seasons represented by 19 waste water treatment plants in Tianjin. Analytical protocols were established and the collected samples were then screened for the presence of these selected substances. High performance liquid chromatography tandem mass spectrometry was used to analyze. The detectable rates in influent samples were 22%–100%. The concentration range was nd –547.94 ng/L. For the effluent samples, the detectable rates were 25%–100%. The concentration range was nd–52.97 ng/L. By calculating the removal efficiency, it was found that CIP, OFL and CTR were effectively removed. It concluded that spatial and temporal differences existed with the dry season being significantly higher than the normal and wet seasons, and was characterized by low in the northeast of Tianjin and high in the northwest and southeast. By establishing a data set of Influent and effluent, the priority features were obtained by feature engineering, which were temperature and NH 3 -N. Under the condition of ensuring the best performance of the models, the influent model with 9 features and the effluent model with 4 features were obtained, and the quantitative relationship between the above features and concentration was obtained through partial dependence analysis. The machine learning models were successfully used to predict the concentration to obtain an ecological risk assessment. By calculation, the RQ values for antibiotics and antimycotics were all less than 0.1, except for MOX, NOR and OFL in the influent samples. Among the effluent samples, only NOR had an RQ value greater than 0.1, and OFL, MOX, and PEF had RQ values between 0.01 and 0.1. Comparing the observations and predictions individual RQ values, the predictions were ideal and matched the observations. This work effectively assessed environmental impact and provided a valuable reference for evaluating antibiotic and antimycotics toxicity.

Keywords: Antibiotics, antimycotics, domestic waste water, Machine Learning, risk assessment