ABSTRACT
Quaternary ammonium compounds (QACs) are one type of widely used cationic biocide, and their usage amount is growing rapidly due to the flu and COVID-19 pandemic. Many QACs were released into the environment in or after the course of their use, and thus they were widely detected in water, sediment, soil, and other environmental media. QACs have stronger surface activity and non-specific biotoxicity, which poses a potential threat to the ecosystem. In this study, the environmental fate and potential toxicity of QACs were documented in terms of their migration and transformation process, biological toxicity effects, and the main mechanisms of bacterial resistance to QACs. Aerobic biodegradation was the main natural way of eliminating QACs in the environment, and the reaction was mainly initiated by the hydroxylation of C atoms at different positions of QACs and finally mineralized to CO2and H2O through decarboxylation, demethylation, and ß-oxidation reaction. Toxicological studies showed that QACs at environmental concentrations could not pose acute toxicity to the selected biotas but threatened the growth and reproduction of aquatic organisms like Daphnia magna. Their toxicity effects depended on their molecular structure, the tested species, and the exposed durations. Additionally, our team first investigated the toxicity effects and mechanisms of QACs toward Microcystis aeruginosa, which showed that QACs depressed the algae growth through the denaturation of photosynthetic organelles, suppression of electron transport, and then induction of cell membrane damage. In the environment, the concentrations of QACs were always lower than their bactericidal concentrations, and their degradation could induce the formation of a concentration gradient, which facilitated microbes resistant to QACs. The known resistance mechanisms of bacteria to QACs mainly included the change in cell membrane structure and composition, formation of biofilm, overexpression of the efflux pump gene, and acquisition of resistance genes. Due to the similar targets and mechanisms, QACs could also induce the occurrence of antibiotic resistance, mainly through co-resistance and cross-resistance. Based on the existing data, future research should emphasize the toxicity effect and the potential QACs resistance mechanism of microorganisms in real environmental conditions.
Subject(s)
Ammonium Compounds , COVID-19 , Humans , Ecosystem , Pandemics , Quaternary Ammonium Compounds/toxicity , Quaternary Ammonium Compounds/chemistry , Anti-Bacterial Agents/pharmacologyABSTRACT
Objective • To explore the spatial distribution and spatial-temporal clustering of coronavirus disease 2019(COVID-19) in Jingzhou City. Methods • Data of COVID-19 cases in Jingzhou City from January 1 to March 12, 2020 were collected. Trend surface analysis, spatial autocorrelation and spatial-temporal scanning analysis were conducted to understand the spatial-temporal distribution of COVID-19 at town (street) level in Jingzhou City, and the spatial-temporal clustering characteristics of local cases and imported cases were compared. Results • Trend surface analysis showed that the incidence rate of COVID-19 in Jingzhou City was slightly "U" from west to east, slightly higher in the east, and inverted "U" from south to north, slightly higher in the south. Global autocorrelation showed that the incidence rate of COVID-19 in Jingzhou City was positively correlated (Moran's I=0.410, P=0.000). Local spatial autocorrelation analysis showed that the highly clustered areas and hot spot areas were mainly in Shashi District, Jingzhou District and the main urban area of Honghu City (Xindi Street) (P<0.05). Five clusters were found by spatial-temporal scanning of imported cases. The cluster time of the main cluster was from January 18 to February 3, 2020, and it was centered on Lianhe Street, covering 15 towns (streets) in Shashi District and Jingzhou District (LLR=174.944, RR=7.395, P=0.000). Five clusters were found by spatial-temporal scanning of local cases. The cluster time of the main cluster was from January 20 to February 24, 2020, which was located in Xindi Street, Honghu City (LLR=224.434, RR=16.133, P=0.000). Conclusion • Obvious spatialtemporal clustering of COVID-19 was found in Jingzhou City, and Shashi District, Jingzhou District and Honghu City were the most prevalent areas.