Assessing stormwater control measure inventories from 23 cities in the United States

Assessing stormwater control measure inventories from 23 cities in the United States
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DOI:
10.1088/2634-4505/acc759
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发表时间:
2023-03
期刊:
Environmental Research: Infrastructure and Sustainability
影响因子:
--
通讯作者:
B. Choat;Amber Pulido;A. Bhaskar;R. Hale;Harry X. Zhang;T. Meixner;L. McPhillips;K. Hopkins;J. Cherrier;C. Cheng
B. Choat;Amber Pulido;A. Bhaskar;R. Hale;Harry X. Zhang;T. Meixner;L. McPhillips;K. Hopkins;J. Cherrier;C. Cheng
中科院分区:
其他
文献类型:
--
作者:
B. Choat;Amber Pulido;A. Bhaskar;R. Hale;Harry X. Zhang;T. Meixner;L. McPhillips;K. Hopkins;J. Cherrier;C. Cheng

文献摘要

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自1987年《清洁水法》第319条修正案以来,美国政府已经要求并资助了大约50亿美元的非点源污染项目的发展。尽管有这些支出,来自城市流域的非点源污染仍然是美国水域受损的一个重要原因。近几十年来,城市雨水管理随着地方或城市规模的决策而迅速发展。为了更好地了解不同城市如何实施雨水管理,我们使用了来自美国23个城市的雨水控制措施(SCM)网络数据,并评估了与城市规模的SCM相关的物理、气候、社会经济和/或监管解释变量(如果有的话)。采用Spearman相关检验和Wilcoxon秩和检验研究解释变量与各城市SCM类型和SCM组合的关系。这些分析的结果表明,对于所评估的城市,物理解释变量(如不透水百分比和地下水位深度)解释了SCM组合变化的最大部分。此外,研究还发现,拥有联合下水道的城市更喜欢过滤器、沟槽和条形管道,而不是盆地,而那些得到环境保护局同意的城市倾向于将过滤器更频繁地纳入其SCM清单。未来的工作可以建立在本研究中使用的SCM组合及其解释变量的基础上,以更好地理解城市SCM有效性差异的差异和驱动因素,改进流域建模,并研究SCM组合在城市和流域尺度上的影响。
Since the 1987 Clean Water Act Section 319 amendment, the US Government has required and funded the development of nonpoint source pollution programs with about $5 billion dollars. Despite these expenditures, nonpoint source pollution from urban watersheds is still a significant cause of impaired waters in the United States. Urban stormwater management has rapidly evolved over recent decades with decision-making made at a local or city scale. To address the need for a better understanding of how stormwater management has been implemented in different cities, we used stormwater control measure (SCM) network data from 23 US cities and assessed what physical, climatic, socioeconomic, and/or regulatory explanatory variables, if any, are related to SCM assemblages at the municipal scale. Spearman’s correlation and Wilcoxon rank-sum tests were used to investigate relationships between explanatory variables and SCM types and assemblages of SCMs in each city. The results from these analyses showed that for the cities assessed, physical explanatory variables (e.g. impervious percentage and depth to water table) explained the greatest portion of variability in SCM assemblages. Additionally, it was found that cities with combined sewers favored filters, swales and strips, and infiltrators over basins, and cities that are under consent decrees with the Environmental Protection Agency tended to include filters more frequently in their SCM inventories. Future work can build on the SCM assemblages used in this study and their explanatory variables to better understand the differences and drivers of differences in SCM effectiveness across cities, improve watershed modeling, and investigate city- and watershed-scale impacts of SCM assemblages.