Datasets of a stormwater treatment train facility consisting of a gross pollutant trap and biofilters/sand filter in Sundsvall, Sweden

SND-ID: 2022-261-1. Version: 1. DOI: https://doi.org/10.5878/nny1-2045

Citation

Creator/Principal investigator(s)

Ali Beryani - Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering orcid

Kelsey Flanagan - Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering orcid

Maria Viklander - Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering orcid

Godecke-Tobias Blecken - Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering orcid

Research principal

Luleå University of Technology - Department of Civil, Environmental and Natural Resources Engineering, Urban Water Engineering rorId

Principal's reference number

1773270

1773260

Description

The data were collected from a stormwater treatment train facility in Sundsvall, Sweden. The facility consists of a gross pollutant trap (GPT) followed by three parallel biofilter cells: a vegetated, chalk-amended biofilter (BFC or F1), a non-vegetated sand filter (SF or F2), and a vegetated biofilter (BF or F3).
One of the objectives of our research project was to assess and monitor stormwater quality received from a major road catchment (incl. E4 highway bridge in Sundsvall) and also evaluate the performance of the various sections of the treatment train in removing organic micropollutants from the stormwater.

The file named "StormwaterRunoffQualityData_SND.csv" contains event mean concentration (EMC) data on stormwater samples collected from 8 rain events (coded by A to H) in one year between September 2020 and September 2021. The samples have been analyzed for organic micropollutants and global water quality parameters (42 parameters in total). EMCs have been mathematically generated by a Monte-Carlo simulation from measured concentrations in sub-samples collected during each event. The

... Show more..
The data were collected from a stormwater treatment train facility in Sundsvall, Sweden. The facility consists of a gross pollutant trap (GPT) followed by three parallel biofilter cells: a vegetated, chalk-amended biofilter (BFC or F1), a non-vegetated sand filter (SF or F2), and a vegetated biofilter (BF or F3).
One of the objectives of our research project was to assess and monitor stormwater quality received from a major road catchment (incl. E4 highway bridge in Sundsvall) and also evaluate the performance of the various sections of the treatment train in removing organic micropollutants from the stormwater.

The file named "StormwaterRunoffQualityData_SND.csv" contains event mean concentration (EMC) data on stormwater samples collected from 8 rain events (coded by A to H) in one year between September 2020 and September 2021. The samples have been analyzed for organic micropollutants and global water quality parameters (42 parameters in total). EMCs have been mathematically generated by a Monte-Carlo simulation from measured concentrations in sub-samples collected during each event. The data elaborate on the generated distribution for each EMC with Q2.5, Q50, and Q97.5 percentiles and standard deviation from the mean. Besides, the number of detected and non-detected (censored) data of sub-samples are mentioned. The list of all pollutants and their abbreviations are included in the documentation file named "StormwaterRunoffQualityData_SND.docx". Stormwater flow data are also presented in the file "VolumeData_Stormwater_SND.csv".

The file named "TreatmentTrainQualityData_SND.csv" presents event mean concentration (EMC) data not only for the stormwater runoff quality but also for the treated stormwater in the GPT-biofilter/sand filter treatment train downstream of the catchment. In addition to the untreated stormwater runoff as the system's inflow (SW), EMCs have been presented for 4 more sampling points: GPT outflow (GPT), vegetated, chalk-amended biofilter outflow (BFC), non-vegetated sand filter (SF), and vegetated biofilter outflow (BF). For this part of the research, a total of 11 rain events (coded by A to K) were covered from Sep. 2020 until Sep. 2021. The samples have been analyzed for organic micropollutants and other conventional water quality parameters (42 parameters in total). EMCs have been mathematically generated by a Monte-Carlo simulation from measured concentrations in sub-samples collected during each event. The data present a distribution for each EMC with Q2.5, Q50, and Q97.5 percentiles and standard deviation from the mean. The number of detected and non-detected (censored) data of sub-samples is also mentioned. The list of all pollutants and their abbreviations are included in the documentation file named "TreatmentTrainQualityData_SND.docx". Flow data are also presented in the file "VolumeData_Treatment train_SND.csv". Show less..

Data contains personal data

No

Language

Method and outcome

Data format / data structure

Data collection
Geographic coverage

Geographic spread

Geographic location: Sundsvall Municipality

Administrative information

Responsible department/unit

Department of Civil, Environmental and Natural Resources Engineering, Urban Water Engineering

Topic and keywords
Publications

Sort by name | Sort by year

Beryani, A., Flanagan, K., Viklander, M., & Blecken, G.-T. (2023). Occurrence and concentrations of organic micropollutants (OMPs) in highway stormwater: a comparative field study in Sweden. In Environmental Science and Pollution Research (Vol. 30, Issue 31, pp. 77299–77317). https://doi.org/10.1007/s11356-023-27623-9
DOI: https://doi.org/10.1007/s11356-023-27623-9
SwePub: oai:DiVA.org:ltu-96463

Beryani, A., Flanagan, K., Viklander, M., & Blecken, G.-T. (2023). Performance of a gross pollutant trap-biofilter and sand filter treatment train for the removal of organic micropollutants from highway stormwater (Field study). In Science of the Total Environment (No. 165734; Vol. 900). https://doi.org/10.1016/j.scitotenv.2023.165734
DOI: https://doi.org/10.1016/j.scitotenv.2023.165734
SwePub: oai:DiVA.org:ltu-96477

If you have published anything based on these data, please notify us with a reference to your publication(s). If you are responsible for the catalogue entry, you can update the metadata/data description in DORIS.

Published: 2023-08-22
Last updated: 2023-08-24