Center for Environmental Measurement and Modeling, Office of Research and Development, United States Environmental Protection Laboratory, 26 West Martin Luther King Drive, Cincinnati, OH 45268, United States.
Center for Environmental Measurement and Modeling, Office of Research and Development, United States Environmental Protection Laboratory, 26 West Martin Luther King Drive, Cincinnati, OH 45268, United States.
Sci Total Environ. 2024 Nov 15;951:175740. doi: 10.1016/j.scitotenv.2024.175740. Epub 2024 Aug 23.
Stream water quality can be impacted by a myriad of fecal pollution sources and waste management practices. Identifying origins of fecal contamination can be challenging, especially in high order streams where water samples are influenced by pollution from large drainage areas. Strategic monitoring of tributaries can be an effective strategy to identify conditions that influence local water quality. Water quality is assessed using fecal indicator bacteria (FIB); however, FIB cannot differentiate sources of fecal contamination nor indicate the presence of disease-causing viruses. Under different land use scenarios, three small stream catchments were investigated under 'wet' and 'dry' conditions (Scenario 1: heavy residential; Scenario 2: rural residential; and Scenario 3: undeveloped/agricultural). To identify fecal pollution trends, host-associated genetic targets HF183/BacR287 (human), Rum2Bac (ruminant), GFD (avian), and DG3 (canine) were analyzed along with FIB (Escherichia coli and enterococci), viral indicators (somatic and F+ coliphage), six general water quality parameters, and local rainfall. Levels of E. coli exceeded single sample maximum limits (235 CFU/100 mL) in 70.7 % of samples, enterococci (70 CFU/100 mL) in 100 % of samples, and somatic coliphage exceeded advisory thresholds (600 PFU/L) in 34.1 % of samples. The detection frequency for the human-associated genetic marker was highest in Scenario 3 (50 % of samples) followed by Scenario 2 (46 %), while the ruminant-associated marker was most prevalent in Scenario 1 (64 %). Due to the high proportion of qPCR-based measurements below the limit of quantification, a Bayesian data analysis approach was applied to investigate links between host-associated genetic marker occurrence with that of rainfall and fecal indicator levels. Multiple trends associated with small stream monitoring were revealed, emphasizing the role of rainfall, the utility of fecal source information to improve water quality management. And furthermore, water quality monitoring with bacterial or viral methodologies can alter the interpretation of fecal pollution sources in impaired waters.
溪流水质可能受到多种粪便污染源和废物管理措施的影响。确定粪便污染的来源可能具有挑战性，特别是在高等级溪流中，水样受到来自大面积排水区的污染影响。对支流进行战略性监测是确定影响当地水质条件的有效策略。水质使用粪便指示菌 (FIB) 进行评估；然而，FIB 无法区分粪便污染的来源，也无法指示致病病毒的存在。在不同的土地利用情景下，在“湿”和“干”条件下（情景 1：重度住宅；情景 2：农村住宅；情景 3：未开发/农业）调查了三个小溪流域。为了确定粪便污染趋势，分析了与人相关的遗传靶标 HF183/BacR287（人）、Rum2Bac（反刍动物）、GFD（禽类）和 DG3（犬类）以及 FIB（大肠杆菌和肠球菌）、病毒指标（体腔和 F+噬菌体）、六个一般水质参数和当地降雨量。大肠杆菌水平超过单次样本最大限量（235 CFU/100 mL）的样本占 70.7%，肠球菌（70 CFU/100 mL）的样本占 100%，体腔噬菌体超过建议阈值（600 PFU/L）的样本占 34.1%。在情景 3 中，与人相关的遗传标记的检测频率最高（50%的样本），其次是情景 2（46%），而反刍动物相关标记在情景 1 中最为普遍（64%）。由于基于 qPCR 的测量值有很大一部分低于定量下限，因此应用贝叶斯数据分析方法来调查与宿主相关的遗传标记与降雨量和粪便指示物水平之间的联系。揭示了与小溪监测相关的多种趋势，强调了降雨量的作用、粪便源信息在改善水质管理方面的效用。此外，细菌或病毒方法的水质监测可能会改变对受损水域中粪便污染源的解释。
