Data

Taking control of microplastics data: A comparison of control/blank data correction methods

Australian Institute of Marine Science
Australian Institute of Marine Science (AIMS)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://apps.aims.gov.au/metadata/view/a7e5a691-5b29-4ba1-b014-b06bbf10250b&rft.title=Taking control of microplastics data: A comparison of control/blank data correction methods&rft.identifier=https://apps.aims.gov.au/metadata/view/a7e5a691-5b29-4ba1-b014-b06bbf10250b&rft.publisher=Australian Institute of Marine Science (AIMS)&rft.description=Establishing a robust strategy to account for unintended processing contamination in microplastics research is of interest to the microplastic community who are currently focussed on developing harmonised methods, and to environmental managers who are calling for accurate risk assessment wrt microplastics.Six commonly used 'core' data correction methods were assessed for their suitablity to microplastics research: a) No correction; b) Subtraction; c) Subtraction of mean; d) Spectral Similarity; e) Limits of detection/ limits of quantification (LOD/LOQ) and f) Statistical analysis. An additional 45 variant methods based on these 6 core methods (n=51 in total) were used to correct a dummy microplastics dataset using control data. The dummy microplastics dataset (n=10 identical samples) was created in the laboratory to mimic the laboratory contamination which may arise throughout sample processing and handling. These were free from ‘sample’ matrix but contained processing solutions and MilliQ water as the surrogate sample matrix. Microplastics processing was conducted following AIMS Microplastics SOPs, and polymer type identified by FTIR and confirmed against the Nicodom Polymer Library.Data was analysed in Excel and R. Bayesian analysis was also assessed for suitablity.This work informs work practices for the IMOS long-term microplastic monitoring project, and for all projects conducted by the AIMS microplastics group. This work will also inform the wider microplastics community by starting the conversation towards harmonisation of microplastics data analysis and reporting.Maintenance and Update Frequency: notPlannedStatement: Sample processing was conducted following the SOPs: SOP SF_T065_3 Filtration System (Schlawinsky et al., 2022. https://doi.org/10.1002/lom3.10504) SOP SF-T075_1 Microplastics IMOS Microscopy SOP BAF-I063_3 FTIR and SOP BAF-I078_5 Microplastics IMOS FTIR Extraneous microplastic contamination was controlled following the SOP SF_T064_2 - Microplastics Avoiding Contamination and other procedures described in the paper such as filtering seawater used in experimental procedures. Extraneous microplastic contamination was identified and characterized following Kroon, F., Motti, C., Talbot, S., Sobral, P., Puotinen, M., 2018. A workflow for improving estimates of microplastic contamination in marine waters: A case study from North-Western Australia. Environ Pollut 238, 26-38. https://doi.org/10.1016/j.envpol.2018.03.010, and SOP BAF-I078_5 Microplastics IMOS FTIR The following versions of R were used: R (4.1.2) and RStudio (2022.02.0).&rft.creator=Australian Institute of Marine Science (AIMS) &rft.date=2025&rft_rights=Creative Commons Attribution 3.0 Australia License http://creativecommons.org/licenses/by/3.0/au/&rft_rights=Use Limitation: All AIMS data, products and services are provided as is and AIMS does not warrant their fitness for a particular purpose or non-infringement. While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.&rft_rights=Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: Australian Institute of Marine Science (AIMS). (2022). Taking control of microplastics data: A comparison of control/blank data correction methods. https://apps.aims.gov.au/metadata/view/a7e5a691-5b29-4ba1-b014-b06bbf10250b, accessed[date-of-access].&rft_subject=oceans&rft.type=dataset&rft.language=English Access the data
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http://creativecommons.org/licenses/by/3.0/au/

Use Limitation: All AIMS data, products and services are provided "as is" and AIMS does not warrant their fitness for a particular purpose or non-infringement. While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.

Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: "Australian Institute of Marine Science (AIMS). (2022). Taking control of microplastics data: A comparison of control/blank data correction methods. https://apps.aims.gov.au/metadata/view/a7e5a691-5b29-4ba1-b014-b06bbf10250b, accessed[date-of-access]".

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Establishing a robust strategy to account for unintended processing contamination in microplastics research is of interest to the microplastic community who are currently focussed on developing harmonised methods, and to environmental managers who are calling for accurate risk assessment wrt microplastics.


Six commonly used 'core' data correction methods were assessed for their suitablity to microplastics research: a) No correction; b) Subtraction; c) Subtraction of mean; d) Spectral Similarity; e) Limits of detection/ limits of quantification (LOD/LOQ) and f) Statistical analysis. An additional 45 variant methods based on these 6 core methods (n=51 in total) were used to correct a dummy microplastics dataset using control data. The dummy microplastics dataset (n=10 identical samples) was created in the laboratory to mimic the laboratory contamination which may arise throughout sample processing and handling. These were free from ‘sample’ matrix but contained processing solutions and MilliQ water as the surrogate sample matrix. Microplastics processing was conducted following AIMS Microplastics SOPs, and polymer type identified by FTIR and confirmed against the Nicodom Polymer Library.


Data was analysed in Excel and R. Bayesian analysis was also assessed for suitablity.


This work informs work practices for the IMOS long-term microplastic monitoring project, and for all projects conducted by the AIMS microplastics group. This work will also inform the wider microplastics community by starting the conversation towards harmonisation of microplastics data analysis and reporting.

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Maintenance and Update Frequency: notPlanned
Statement: Sample processing was conducted following the SOPs: SOP SF_T065_3 Filtration System (Schlawinsky et al., 2022. https://doi.org/10.1002/lom3.10504) SOP SF-T075_1 Microplastics IMOS Microscopy SOP BAF-I063_3 FTIR and SOP BAF-I078_5 Microplastics IMOS FTIR Extraneous microplastic contamination was controlled following the SOP SF_T064_2 - Microplastics Avoiding Contamination and other procedures described in the paper such as filtering seawater used in experimental procedures. Extraneous microplastic contamination was identified and characterized following Kroon, F., Motti, C., Talbot, S., Sobral, P., Puotinen, M., 2018. A workflow for improving estimates of microplastic contamination in marine waters: A case study from North-Western Australia. Environ Pollut 238, 26-38. https://doi.org/10.1016/j.envpol.2018.03.010, and SOP BAF-I078_5 Microplastics IMOS FTIR The following versions of R were used: R (4.1.2) and RStudio (2022.02.0).

Notes

Credit
Motti, C. (AIMS)
Credit
Santana M. (AIMS)
Credit
Nelis, J. Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Credit
Dawson, A. Australian Institute of Marine Science (AIMS)

Modified: 19 09 2025

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Other Information
FTIR spectra files [zip folder size: 2.28 MB]

uri : https://api.aims.gov.au/data-v2.0/a7e5a691-5b29-4ba1-b014-b06bbf10250b/files/FTIR spectra.zip

Dawson et al Raw control data [zip folder containing 1 csv and 1 txt file, size 4 KB]

uri : https://api.aims.gov.au/data-v2.0/a7e5a691-5b29-4ba1-b014-b06bbf10250b/files/Dawson_etal_raw_control_data.zip

Supplementary Information 2: Controls review [csv file 4KB]

uri : https://api.aims.gov.au/data-v2.0/a7e5a691-5b29-4ba1-b014-b06bbf10250b/files/SI 2 Controls review.csv

R scripts: GitHub Repository - Microplastics---control-project

uri : https://github.com/open-AIMS/Microplastics---control-project

Dawson, A. L., Santana, M. F. M., Nelis, J. L. D., & Motti, C. A. (2023). Taking control of microplastics data: A comparison of control and blank data correction methods. Journal of Hazardous Materials, 443, 130218. https://doi.org/10.1016/j.jhazmat.2022.130218

doi : https://doi.org/10.1016/j.jhazmat.2022.130218

Identifiers
  • global : a7e5a691-5b29-4ba1-b014-b06bbf10250b