Antibiotics in the environment have been a formidable problem and attracted immense research interests on global level. Municipal wastewater treatment plants, as a major source, has been the focus of studies. However, some crucial details were not covered or overlooked in the past, which brought uncertainties to the results and even the knowledge system. In this study, 23 antibiotics were investigated in 7 largest wastewater treatment plants in Beijing. The sampling strategy was specially developed and optimized for 24-h successive composite samples collection to overcome the uncertainties of conventional grab sampling. In the consecutive load fluctuations, clear daytime and nighttime dissimilarity was observed, and the two antibiotics consuming patterns were found conformed to the commuting patterns (see the figure 1). The load fluctuations were decided by both flow quantity and antibiotic concentration, and it was observed that the treatment capacity of WWTPs could be the key to decide which of them were the leading factor. The load also displayed positively linear correlation with both population (R = 0.98) and population density (R = 0.76), which indicated potentials to be used as prediction models. In the study of removal efficiency (RE), advanced clarification and disinfection processes were found more effective. By the comparison of 4 possible RE calculations, HRT-adjusted-cumulative-load measurement was found significantly higher than the grab sampling’s calculation, which was also another possible explanation for the unexpected “increase” of pollutants passing through treatments. Furthermore, canonical correlation analysis was introduced and compared with Pearson’s correlation to explore the relationship between water characteristics and antibiotics. With some differences, both of them revealed that total nitrogen and ammonia nitrogen were more influential water elements for antibiotics.

Intra-day pollution characteristics and load investigation of antibiotics in major wastewater treatment plants of Beijing based on a more reliable sampling approach

255th National Meeting (2018)

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