Hagedorn C, Robinson S L, Filtz J R, Grubbs S M, Angier T A, Reneau R B
Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA.
Appl Environ Microbiol. 1999 Dec;65(12):5522-31. doi: 10.1128/AEM.65.12.5522-5531.1999.
Nonpoint sources of pollution that contribute fecal bacteria to surface waters have proven difficult to identify. Knowledge of pollution sources could aid in restoration of the water quality, reduce the amounts of nutrients leaving watersheds, and reduce the danger of infectious disease resulting from exposure to contaminated waters. Patterns of antibiotic resistance in fecal streptococci were analyzed by discriminant and cluster analysis and used to identify sources of fecal pollution in a rural Virginia watershed. A database consisting of patterns from 7,058 fecal streptococcus isolates was first established from known human, livestock, and wildlife sources in Montgomery County, Va. Correct fecal streptococcus source identification averaged 87% for the entire database and ranged from 84% for deer isolates to 93% for human isolates. To field test the method and the database, a watershed improvement project (Page Brook) in Clarke County, Va., was initiated in 1996. Comparison of 892 known-source isolates from that watershed against the database resulted in an average correct classification rate of 88%. Combining all animal isolates increased correct classification rates to > or = 95% for separations between animal and human sources. Stream samples from three collection sites were highly contaminated, and fecal streptococci from these sites were classified as being predominantly from cattle (>78% of isolates), with small proportions from waterfowl, deer, and unidentified sources ( approximately 7% each). Based on these results, cattle access to the stream was restricted by installation of fencing and in-pasture watering stations. Fecal coliforms were reduced at the three sites by an average of 94%, from prefencing average populations of 15,900 per 100 ml to postfencing average populations of 960 per 100 ml. After fencing, <45% of fecal streptococcus isolates were classified as being from cattle. These results demonstrate that antibiotic resistance profiles in fecal streptococci can be used to reliably determine sources of fecal pollution, and water quality improvements can occur when efforts to address the identified sources are made.
已证实,向地表水排放粪便细菌的非点源污染难以识别。了解污染源有助于恢复水质、减少流出流域的营养物质数量,并降低因接触受污染水体而导致传染病的风险。通过判别分析和聚类分析对粪链球菌中的抗生素耐药模式进行了分析,并用于识别弗吉尼亚州一个乡村流域的粪便污染源。首先从弗吉尼亚州蒙哥马利县已知的人类、牲畜和野生动物源建立了一个由7058株粪链球菌分离株模式组成的数据库。整个数据库中粪链球菌来源的正确识别平均为87%,范围从鹿分离株的84%到人类分离株的93%。为了对该方法和数据库进行实地测试,1996年在弗吉尼亚州克拉克县启动了一个流域改善项目(佩奇溪)。将该流域892株已知来源的分离株与数据库进行比较,得出的平均正确分类率为88%。将所有动物分离株合并后,动物源与人类源之间的分离正确分类率提高到≥95%。来自三个采集点的溪流样本受到高度污染,这些地点的粪链球菌主要被分类为来自牛(>78%的分离株),小部分来自水禽、鹿和不明来源(各约7%)。基于这些结果,通过设置围栏和牧场内饮水站来限制牛进入溪流。三个地点的粪大肠菌群平均减少了94%,从围栏前每100毫升平均15900个的数量降至围栏后每100毫升平均960个的数量。围栏后,<45%的粪链球菌分离株被分类为来自牛。这些结果表明,粪链球菌中的抗生素耐药谱可用于可靠地确定粪便污染源,并且当针对已识别的污染源采取措施时,水质可以得到改善。