A Data-Driven Approach to Stormwater Quality Analysis in Two Urban Catchments
Abstract
:1. Introduction
2. Methods
2.1. Modelling Stormwater Pollution Loads
2.2. Modelling Stormwater Treatment Efficiencies for Stormwater Detention Ponds and Biofilters
3. Case Study Catchments
3.1. Ladbrodammen Catchment
3.2. Sundsvall Biofilter Catchment
4. Results and Discussion
4.1. Characteristics of Stormwater Data Adopted from the StormTac Web Database (SUSQD) for the Test Catchments
- Mean concentrations of the water quality constituents varied among the subcatchments with various land uses by about an order of magnitude, as also reported in [30]. Both Cu and Zn concentrations were the highest in runoff from industrial and trafficked areas. When comparing residential areas with various density of development, multifamily areas displayed higher concentrations of Cu and TSS, possibly because of higher traffic, and in the case of Cu, more frequent use of copper roofs, compared to lower density single-family residential areas. The concentrations of Cu and Zn were the lowest in rural or green lands, including forests, meadows, parks, and agricultural lands. On the other hand, the median concentrations of TP were higher in single-family areas with more greenery and a higher use of fertilizers.
- Among the land uses studied, the RSE band width was the greatest for TP from meadows, terraced housing and parking lots, and smallest for forests and residential land; in the case Cu, the greatest for multifamily housing, smallest in residential areas; for Zn, the greatest in runoff from freeways and agricultural land, smallest in residential areas; and, for TSS, greatest in the parking lots and meadows, the smallest in forests and terraced housing. This was possibly caused by a combination of two factors, low variability of specific constituent concentrations in runoff from those land use areas and a small number of catchments in the database (in some cases, n being as low as 3–4).
4.2. Treatment (Reduction) Efficiencies of Stormwater Control Measures (SCMs) in the StormTac Database (SUSQD)
4.3. Comparison of Calculated and Measured Stormwater Quality Data
4.3.1. Ladbrodammen Stormwater Detention Pond
4.3.2. Sundsvall Biofilter
4.4. Feasibility and Limitations of the Proposed Data-Based Approach to Stormwater Quality Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Subcatchment Land Uses, i | Runoff and Groundwater Contributing Areas A = Ab (ha) | Volumetric Runoff Coefficients | Runoff Generation Potential (Area ∙ Volumetric Runoff Coefficient [ha]) 1 | |
---|---|---|---|---|
1 | Parking lot | 2.0 | 0.80 | 1.6 |
2 | Residential area | 25 | 0.25 | 6.25 |
3 | Terraced house area | 31 | 0.32 | 9.92 |
4 | Multifamily area | 60 | 0.40 | 24.0 |
5 | Downtown area | 10 | 0.60 | 6.0 |
6 | Industrial area | 3.0 | 0.50 | 1.5 |
7 | Park grounds | 10 | 0.10 | 1.0 |
8 | Forest land | 25 | 0.15 | 3.75 |
9 | Agricultural land | 5.0 | 0.26 | 1.3 |
10 | Meadow land | 30 | 0.10 | 3.0 |
Parameter values for the whole catchment | Area 201 (ha) | Area-weighted vol. runoff coeff. 0.29 | Runoff generation potential 58 (ha) |
Land Uses, i | Runoff and Groundwater Contributing Areas A = Ab (ha) | Volumetric runoff Coefficients | Runoff Generation Potential (=Area ∙ Vol. Runoff Coefficient) 2 |
---|---|---|---|
Road 1 (ADT = 13 000 1) | 3.1 | 0.80 | 2.48 |
Road 2 (ADT = 7 000 1) | 1.6 | 0.80 | 1.28 |
Park grounds | 2.5 | 0.10 | 0.25 |
Meadows | 1.0 | 0.10 | 0.10 |
Parameter values for the whole catchment | Area 8.2 (ha) | Area-weighted vol. runoff coeff. 0.50 | Runoff generation potential 4.1 (ha) |
Event | Date | Rain Event Duration [h] | Rainfall Depth [mm] | Antecedent Dry Period [days] | Depth of Rain One Day before the Event [mm] | Overflow Duration [min] | No. of Samples |
---|---|---|---|---|---|---|---|
1 | 15 September 2021 | 8 | 6.4 | 3 | 0 | 0 | 3 |
2 | 26 September 2021 | 11 | 31 | 8 | 0 | 60 | 12 |
3 | 5 October 2021 | 9 | 13 | 7 | 0 | 6 | 10 |
4 | 22 October 2021 | 7 | 3 | 0 | 17 | 0 | 5 |
5 | 1 November 2021 | 22 | 18 | 0 | 0.8 | 30 | 12 |
6 | 3 December 2021 | 34 | 7.6 | 8 | 0 | 0 | 7 |
Stormwater Concentrations Measured in Studies 1 and 2 | Calculated Concentrations | ||||||||
---|---|---|---|---|---|---|---|---|---|
Study 1, 2008–2009 [20] | Study 2, 2009–2010 [19] | StormTac Web v.21.3.3. | |||||||
Constituent | Constituent Concentrations | RE [%] | Constituent Concentrations | RE [%] | Constituent Concentrations | RE [%] | |||
In | Out | In | Out | In | Out | ||||
TP [µg/L] | - | - | - | 210 | 150 | 29 | 180 | 92 | 49 |
Cu [µg/L] | 30 | 14 | 53 | 24 | 11 | 54 | 19 | 9.5 | 50 |
Zn [µg/L] | 175 | 66 | 62 | 90 | 42 | 54 | 74 | 29 | 61 |
TSS [mg/L] | 53 | 15 | 72 | 112 | 19 | 83 | 60 | 19 | 68 |
Constituent | Measured Stormwater Concentrations (2020) | RE (%) | Stormwater Concentrations Calculated with StormTac Web | RE (%) | ||
---|---|---|---|---|---|---|
in | out | in | out | |||
TP [µg/L] | not measured | not measured | not calculated | 140 | 74 | 47 |
Cu [µg/L] | 31 | 6.2 | 80 | 23 | 11 | 52 |
Zn [µg/L] | 300 | 14 | 95 | 180 | 36 | 80 |
TSS [mg/L] | 140 | 6.7 | 95 | 71 | 20 | 71 |
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Larm, T.; Wahlsten, A.; Marsalek, J.; Viklander, M. A Data-Driven Approach to Stormwater Quality Analysis in Two Urban Catchments. Sustainability 2022, 14, 2888. https://doi.org/10.3390/su14052888
Larm T, Wahlsten A, Marsalek J, Viklander M. A Data-Driven Approach to Stormwater Quality Analysis in Two Urban Catchments. Sustainability. 2022; 14(5):2888. https://doi.org/10.3390/su14052888
Chicago/Turabian StyleLarm, Thomas, Anna Wahlsten, Jiri Marsalek, and Maria Viklander. 2022. "A Data-Driven Approach to Stormwater Quality Analysis in Two Urban Catchments" Sustainability 14, no. 5: 2888. https://doi.org/10.3390/su14052888
APA StyleLarm, T., Wahlsten, A., Marsalek, J., & Viklander, M. (2022). A Data-Driven Approach to Stormwater Quality Analysis in Two Urban Catchments. Sustainability, 14(5), 2888. https://doi.org/10.3390/su14052888