Occurrence and distribution of selected antibiotics in the surface waters and ecological risk assessment based on the theory of natural disaster.

With increasing population growth and resource depletion, ecological health is a hotspot and urgent topic. Our study investigated the occurrence and distribution of five antibiotics, i.e., metronidazole (MET), sulfamethoxazole (SMZ), ciprofloxacin (CFX), norfloxacin (NFX), and enrofloxacin (EFX), in the surface water from Erlong Lake, China. With the seasonal and spatial variations of antibiotics, this study developed an interdisciplinary approach for the ecological risk of antibiotics considering the natural, human, and socioeconomic elements of watershed based on the risk theory of natural disaster. This approach integrates the geographic information system (GIS) spatial analysis tool, natural disaster theory, "driving force"-"pressure"-"state"-"impact"-"response" (DPSIR) model, and ordered weighted averaging (OWA) operators in terms of various environmental factors, representing a new ecological risk paradigm for environmental managers or decision-makers to identify environmental change. A total of 69 samples were collected in wet, dry, and normal seasons from 2017 to 2018, and laboratory analyses revealed that four antibiotics (MET, SMZ, CFX, and NFX) were widely detected in the lake. The mean concentrations decreased in the order of MET (1041.7 ng L-1) > SMZ (771.4 ng L-1) > CFX (646.4 ng L-1) > NFX (179.0 ng L-1) > EFX (15.3 ng L-1), with their levels in natural surface waters higher than those in other studies. Antibiotic concentrations were higher in dry seasons than in other seasons, and the concentrations were strongly correlated with EC (electrical conductivity), pH, DOC (dissolved organic carbon), and TP (total phosphorus) according to redundancy analysis (RDA). Significant seasonal variations were found in CFX and EFX (ANOVA, p < 0.001). For the whole watershed, the ecological extremely and heavily vulnerable levels were concentrated in the central and northwest regions of the watershed, i.e., Changling county and Lishu county. Hence, the ecological distributions of extreme risk and heavy risk posed by the five selected antibiotics were assessed by using the mixture hazard quotient methods and DPSIR model which were also located in these counties in different seasons. The antibiotic results of ecosystem risk assessment can support decision-makers in identifying and prioritizing the necessary taking of specific measures and different risk attitudes to preserve the quality of ecological health for a city or multiple counties.