Dataset Information

Urban Stormwater Resilience Assessment Method Based on Cloud Model and TOPSIS.

ABSTRACT: To scientifically and quantitatively evaluate the degree of urban storm resilience and improve the level of urban stormwater resilience, based on the resilience theory, starting from the three attributes of resilience (resistance, recovery and adaptability), this paper establishes the framework of urban resilience evaluation indicator system under the background of stormwater disaster. Firstly, the weight of the indicator system is analyzed by the Delphi method and cloud model, and then the urban stormwater recilience evaluation model is constructed by using the cloud model and approximate ideal solution ranking method. Through the fuzzy description, the corresponding quantitative value is given to the qualitative indicator, so that the stormwater resilience of the city can be measured by accurate values. Finally, the feasibility of the model is verified by case analysis. The results show that the urban stormwater resilience evaluation theory and method based on cloud model and approximate ideal solution ranking method have important guiding significance to improve the level of urban stormwater resilience.

SUBMITTER: Qiao H

PROVIDER: S-EPMC8751000 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Urban Stormwater Resilience Assessment Method Based on Cloud Model and TOPSIS.

Qiao Han H Pei Jingjing J

International journal of environmental research and public health 20211221 1

To scientifically and quantitatively evaluate the degree of urban storm resilience and improve the level of urban stormwater resilience, based on the resilience theory, starting from the three attributes of resilience (resistance, recovery and adaptability), this paper establishes the framework of urban resilience evaluation indicator system under the background of stormwater disaster. Firstly, the weight of the indicator system is analyzed by the Delphi method and cloud model, and then the urba ...[more]

PMID: 35010306

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Project description:BackgroundStormwater is water resulting from precipitation events and snowmelt running off the urban landscape, collecting in storm sewers, and typically being released into receiving water bodies through outfalls with minimal to no treatment. Despite a growing body of evidence observing its deleterious pollution impacts, stormwater management and treatment in cold climates remains limited, partly due to a lack of quality and loading data and modeling parameters. This study examines the quality of stormwater discharging during the summer season in a cold-climate, semi-arid Canadian city (Saskatoon, Saskatchewan).ResultsSeven stormwater outfalls with mixed-land-use urban catchments > 100 km2 were sampled for four summer (June-August 2019) storm events and analyzed for a suite of quality parameters, including total suspended solids (TSS), chemical oxygen demand (COD), dissolved organic carbon (DOC), metals, and targeted polyaromatic hydrocarbons (PAHs). In addition, assessment of stormwater toxicity was done using the two toxicity assays Raphidocelis subcapitata (algae) and Vibrio fischeri (bacteria). Notable single-event, single-outfall contaminant pulses included of arsenic (420 µg/L), cadmium (16.4 µg/L), zinc (924 µg/L), fluorene (4.95 µg/L), benzo[a]pyrene (0.949 µg/L), pyrene (0.934 µg/L), phenanthrene (1.39 µg/L), and anthracene (1.40 µg/L). The IC50 in both R. subcapitata and V. fischeri was observed, if at all, above expected toxicity thresholds for individual contaminant species. Principal component analysis (PCA) showed no clear trends for individual sampling sites or sampling dates. In contrast, parameters were correlated with each other in groups including DOC, COD, TSS, and reduced algal toxicity; and total dissolved solids (TDS), sum of metals, and pH.ConclusionsIn general, stormwater characteristics were similar to those of previous studies, with a bulk of contamination carried by the first volume of runoff, influenced by a combination of rainfall depth, antecedent dry period, land use, and activity within the catchment. Roads, highways, and industrial areas contribute the bulk of estimated contaminant loadings. More intensive sampling strategies are necessary to contextualize stormwater data in the context of contaminant and runoff volume peaks.Supplementary informationThe online version contains supplementary material available at 10.1186/s12302-022-00619-x.

Dataset Information

Urban Stormwater Resilience Assessment Method Based on Cloud Model and TOPSIS.

Publications

Urban Stormwater Resilience Assessment Method Based on Cloud Model and TOPSIS.

Similar Datasets

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