Green Hyatt, Wilder Maxwell, Wiedmann Martin, Weller Daniel
Department of Environmental Biology, College of Environmental Science and Forestry, State University of New York, Syracuse, NY, United States.
Department of Food Science, Cornell University, Ithaca, NY, United States.
Front Microbiol. 2021 Aug 12;12:684533. doi: 10.3389/fmicb.2021.684533. eCollection 2021.
Aquatic fecal contamination poses human health risks by introducing pathogens in water that may be used for recreation, consumption, or agriculture. Identifying fecal contaminant sources, as well as the factors that affect their transport, storage, and decay, is essential for protecting human health. However, identifying these factors is often difficult when using fecal indicator bacteria (FIB) because FIB levels in surface water are often the product of multiple contaminant sources. In contrast, microbial source-tracking (MST) techniques allow not only the identification of predominant contaminant sources but also the quantification of factors affecting the transport, storage, and decay of fecal contaminants from specific hosts. We visited 68 streams in the Finger Lakes region of Upstate New York, United States, between April and October 2018 and collected water quality data (i.e., , MST markers, and physical-chemical parameters) and weather and land-use data, as well as data on other stream features (e.g., stream bed composition), to identify factors that were associated with fecal contamination at a regional scale. We then applied both generalized linear mixed models and conditional inference trees to identify factors and combinations of factors that were significantly associated with human and ruminant fecal contamination. We found that human contaminants were more likely to be identified when the developed area within the 60 m stream buffer exceeded 3.4%, the total developed area in the watershed exceeded 41%, or if stormwater outfalls were present immediately upstream of the sampling site. When these features were not present, human MST markers were more likely to be found when rainfall during the preceding day exceeded 1.5 cm. The presence of upstream campgrounds was also significantly associated with human MST marker detection. In addition to rainfall and water quality parameters associated with rainfall (e.g., turbidity), the minimum distance to upstream cattle operations, the proportion of the 60 m buffer used for cropland, and the presence of submerged aquatic vegetation at the sampling site were all associated based on univariable regression with elevated levels of ruminant markers. The identification of specific features associated with host-specific fecal contaminants may support the development of broader recommendations or policies aimed at reducing levels of aquatic fecal contamination.
水生粪便污染通过将病原体引入可能用于娱乐、消费或农业的水中，对人类健康构成风险。识别粪便污染物来源以及影响其运输、储存和衰减的因素，对于保护人类健康至关重要。然而，使用粪便指示菌（FIB）时，识别这些因素通常很困难，因为地表水中的FIB水平往往是多种污染物来源的产物。相比之下，微生物源追踪（MST）技术不仅可以识别主要污染物来源，还可以量化影响特定宿主粪便污染物运输、储存和衰减的因素。2018年4月至10月期间，我们走访了美国纽约州北部手指湖地区的68条溪流，收集了水质数据（即MST标记物和物理化学参数）、天气和土地利用数据，以及其他溪流特征数据（如河床组成），以识别区域尺度上与粪便污染相关的因素。然后，我们应用广义线性混合模型和条件推断树来识别与人类和反刍动物粪便污染显著相关的因素和因素组合。我们发现，当溪流60米缓冲区内的开发区面积超过3.4%、流域内的总开发区面积超过41%，或者采样点上游紧邻雨水排放口时，更有可能识别出人类污染物。当不存在这些特征时，若前一天的降雨量超过1.5厘米，则更有可能检测到人类MST标记物。上游露营地的存在也与人类MST标记物的检测显著相关。除了降雨和与降雨相关的水质参数（如浊度）外，与上游养牛场的最小距离、60米缓冲区内用于农田的比例，以及采样点处水生沉水植物的存在，根据单变量回归分析，均与反刍动物标记物水平升高相关。识别与宿主特异性粪便污染物相关的特定特征，可能有助于制定更广泛的建议或政策，以降低水生粪便污染水平。
