Department of Civil Engineering, Monash University, Clayton, Victoria 3800, Australia.
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park 16802, Pennsylvania United States.
Environ Sci Technol. 2020 Aug 4;54(15):9159-9174. doi: 10.1021/acs.est.9b07511. Epub 2020 Jul 24.
Extensive time and financial resources have been dedicated to address nonpoint sources of nitrogen and phosphorus in watersheds. Despite these efforts, many watersheds have not seen substantial improvement in water quality. The objective of this study is to review the literature and investigate key factors affecting the lack of improvement in nutrient levels in waterways in urban and agricultural regions. From 94 studies identified in the academic literature, we found that, although 60% of studies found improvements in water quality after implementation of Best Management Practices (BMPs) within the watershed, these studies were mostly modeling studies rather than field monitoring studies. For studies that were unable to find improvements in water quality after the implementation of BMPs, the lack of improvement was attributed to lack of knowledge about BMP functioning, lag times, nonoptimal placement and distribution of BMPs in the watershed, postimplementation BMP failure, and socio-political and economic challenges. We refer to these limiting factors as . We also acknowledge the existence of that hinder further improvement in BMP effectiveness and suggest that machine learning, approaches from the field of business and operations management, and long-term convergent studies could be used to resolve these .
人们投入了大量的时间和财力来解决流域的非点源氮磷问题。尽管做出了这些努力，许多流域的水质仍没有显著改善。本研究旨在回顾文献，调查影响城市和农业地区航道中营养物水平改善不足的关键因素。在学术文献中确定的 94 项研究中，我们发现，尽管 60%的研究发现实施流域最佳管理措施（BMP）后水质有所改善，但这些研究大多是建模研究，而不是现场监测研究。对于在实施 BMP 后未能发现水质改善的研究，水质改善不足归因于对 BMP 功能、滞后时间、BMP 在流域中的非最佳位置和分布、实施后 BMP 失效以及社会政治和经济挑战的了解不足。我们将这些限制因素称为 。我们还认识到存在 ，这阻碍了 BMP 有效性的进一步提高，并建议使用机器学习、商业和运营管理领域的方法以及长期的收敛性研究来解决这些 。
