Environmental Health Program, Western Carolina University, 3971 Little Savannah Road, 1 University Drive, Cullowhee, NC, 28723, USA.
Environmental Health Program, Western Carolina University, 3971 Little Savannah Road, 1 University Drive, Cullowhee, NC, 28723, USA.
J Environ Manage. 2021 Jan 1;277:111413. doi: 10.1016/j.jenvman.2020.111413. Epub 2020 Oct 6.
Surface waters listed on impaired waters (303 d) lists due to pathogen contamination pose a significant environmental and public health burden. The need to address impairments through the Total Maximum Daily Load (TMDL) process has resulted in method developments that successfully identify nonpoint fecal pollution sources by maximizing available resources to improve water quality. However, the ability of those methods to effectively and universally identify sources of fecal pollution requires further evaluation. The objective of this research was to assess the usefulness of a previously described multivariate statistical approach to identify common patterns influencing fate and transport of fecal pollutants from sources to receiving streams using the Tuckasegee River watershed in Western North Carolina as a test watershed. Two streams were routinely monitored using a targeted sampling approach to assess fecal pollution extent and identify nonpoint sources using canonical correlation and canonical discriminant analyses. Fecal pollution in the watershed varied spatially and temporally with significantly higher fecal coliform concentrations observed in Scott Creek (f = 9.49, p = 0.002) and during the summer months (f = 14.8, p < 0.0001). Canonical correlations described 62-67% of water quality variability and indicate that fecal pollution in portions of the watershed are influenced by stormwater runoff and fecal indicator bacteria resuspension from sediment, while fecal pollution in other portions are influenced by soil erosion and surface runoff. Canonical discriminant analyses indicate that LULC significantly influences the nature and extent of fecal pollution. These results demonstrate that chemical parameters are useful predictors of fecal pollution and can help identify nonpoint fecal pollution sources in relation to land use patterns and land management practices. This approach to water quality monitoring program design and data analysis may effectively and efficiently identify parameters that best predict fecal pollution to aid in development and implementation of effective TMDLs to remediate impaired waters.
受病原体污染而被列入受损水体（303d）清单的地表水体给环境和公共健康带来了重大负担。通过全面最大日负荷（TMDL）处理来解决这些受损水体的问题，已经促使人们开发出一些方法，通过最大限度地利用现有资源来改善水质，从而成功地确定非点源粪便污染源。然而，这些方法有效地普遍识别粪便污染源的能力需要进一步评估。本研究的目的是评估先前描述的多元统计方法在识别影响粪便污染物从源头到接收溪流的归宿和传输的共同模式的有用性，以西卡罗莱纳州的塔卡塞格河流域作为测试流域。使用有针对性的采样方法定期监测两条溪流，以评估粪便污染的程度，并使用典范相关和典范判别分析来识别非点源。流域内的粪便污染具有空间和时间变化，斯科特溪（f=9.49，p=0.002）和夏季的粪便大肠菌群浓度明显更高，f=14.8，p<0.0001）。典范相关描述了 62-67%的水质变化，表明流域部分地区的粪便污染受雨水径流和沉积物中粪便指示菌再悬浮的影响，而其他部分地区的粪便污染受土壤侵蚀和地表径流的影响。典范判别分析表明，土地利用/土地覆被（LULC）显著影响粪便污染的性质和程度。这些结果表明，化学参数是粪便污染的有用预测因子，并有助于根据土地利用模式和土地管理实践来识别非点源粪便污染源。这种水质监测方案设计和数据分析方法可以有效地识别出最佳预测粪便污染的参数，有助于制定和实施有效的 TMDL 来修复受损水体。
