Predictive models of phosphorus concentration and load in stormwater runoff from small urban residential watersheds in fall season
Urban street trees are a key part of public green infrastructure in many cities, however, leaf litter on streets is a critical biogenic source of phosphorus (P) in urban stormwater runoff during Fall. This study identified mass of street leaf litter (Mleaf) and antecedent dry days (ADD) as the top t...
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Veröffentlicht in: | Journal of environmental management 2022-08, Vol.315, p.115171-115171, Article 115171 |
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Sprache: | eng |
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Zusammenfassung: | Urban street trees are a key part of public green infrastructure in many cities, however, leaf litter on streets is a critical biogenic source of phosphorus (P) in urban stormwater runoff during Fall. This study identified mass of street leaf litter (Mleaf) and antecedent dry days (ADD) as the top two explanatory parameters that have significant predictive power of event end-of-pipe P concentrations through multiple linear regression (MLR) analysis. Mleaf and volume of runoff (Vol) were the top two key explanatory parameters of event end-of-pipe P loads. Two-predictor MLR models were developed with these explanatory parameters using a 40-storm dataset derived from six small urban residential watersheds in Wisconsin, USA, and evaluated using storms specific to each study basin. The MLR model validation results indicated sensitivity to storm composition in the datasets. Our analysis shows selected parameters can be used by environmental managers to facilitate end-of-pipe P prediction in urban areas. This information can be used to reduce the amount of P in stormwater runoff by adjusting the timing and frequency of municipal leaf collection and street cleaning programs in urban areas.
•Fall contributions of phosphorus in urban runoff are largely attributed to trees.•Mass of leaf litter and number of dry days can predict phosphorus concentrations.•Mass of leaf litter and runoff volume can predict phosphorus loads.•Predicting phosphorus load can guide municipal leaf management programs. |
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ISSN: | 0301-4797 1095-8630 |
DOI: | 10.1016/j.jenvman.2022.115171 |