Li Min, Tang Jian-Feng, Chen Li-Ding, Zhao Fang-Kai, Feng Qing-Yu, Yang Lei
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environment Sciences, Chinese Academy of Sciences, Beijing 100085, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Huan Jing Ke Xue. 2020 May 8;41(5):2264-2271. doi: 10.13227/j.hjkx.201911114.
Quantitively identifying the effect of land use patterns on antibiotics in surface water has significance in maintaining water quality and protecting residents' health in urban and rural regions. In this study, a typical peri-urban watershed, located in the Yangtze River Delta, was selected as the study area. Based on surface water sampling, laboratory analysis, and source-sink landscape model (SSLM) analysis, the component and distribution characteristics of antibiotics in surface water in different sub-watersheds were analyzed. The effects of source and sink landscape patterns on antibiotic concentrations in surface water were identified. The results of this study showed substantial differences in types and concentrations of antibiotics in surface water in different sub-watersheds. The total concentrations of antibiotics in surface water ranged from 1.12 ng·L to 53.74 ng·L. From upstream to downstream, the area of "source" landscape increased, and the area of "sink" landscape decreased based on landscape pattern analysis. The results of non-metric multidimensional scaling (NMDS) showed that sub-watersheds with similar "source-sink" landscape patterns were detected as having similar antibiotics types and concentrations in surface water. Land use composition, distance, elevation, and slope degree had substantial impacts on antibiotic concentrations in surface water. The results of this study also found that location-weighted landscape index (LWLI) was positive correlated with antibiotics concentrations in surface water based on correlation analysis and redundancy analysis. The sub-watersheds with high LWLI values usually had relatively higher antibiotic concentrations in surface water. This study indicated that optimization of "source" and "sink" landscapes at the watershed scale can decrease antibiotic contamination in surface water. Furthermore, SSLM is an effective tool in landscape optimization at the watershed scale.
定量识别土地利用模式对地表水抗生素的影响，对于维护城乡地区的水质和保护居民健康具有重要意义。本研究选取位于长江三角洲的一个典型城郊流域作为研究区域。基于地表水采样、实验室分析和源汇景观模型（SSLM）分析，分析了不同子流域地表水抗生素的组成和分布特征。识别了源汇景观模式对地表水抗生素浓度的影响。本研究结果表明，不同子流域地表水抗生素的类型和浓度存在显著差异。地表水抗生素总浓度范围为1.12 ng·L至53.74 ng·L。根据景观格局分析，从上游到下游，“源”景观面积增加，“汇”景观面积减少。非度量多维尺度分析（NMDS）结果表明，具有相似“源 - 汇”景观模式的子流域，其地表水抗生素类型和浓度相似。土地利用组成、距离、海拔和坡度对地表水抗生素浓度有显著影响。本研究结果还通过相关性分析和冗余分析发现，位置加权景观指数（LWLI）与地表水抗生素浓度呈正相关。LWLI值高的子流域，其地表水抗生素浓度通常相对较高。本研究表明，在流域尺度上优化“源”和“汇”景观可以减少地表水抗生素污染。此外，源汇景观模型是流域尺度景观优化的有效工具。
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