Non-target Analysis and Chemometric Evaluation of a Passive Sampler Monitoring of Small Streams
Research output: Journal contributions › Journal articles › Research › peer-review
Standard
In: Environmental Science and Technology, Vol. 56, No. 9, 03.05.2022, p. 5466-5477.
Research output: Journal contributions › Journal articles › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Non-target Analysis and Chemometric Evaluation of a Passive Sampler Monitoring of Small Streams
AU - Hohrenk-Danzouma, Lotta L.
AU - Vosough, Maryam
AU - Merkus, Valentina I.
AU - Drees, Felix
AU - Schmidt, Torsten C.
N1 - Publisher Copyright: © 2022 American Chemical Society. All rights reserved.
PY - 2022/5/3
Y1 - 2022/5/3
N2 - Complex multivariate datasets are generated in environmental non-target screening (NTS) studies covering different sampling locations and times. This study presents a comprehensive chemometrics-based data processing workflow to reveal hidden data patterns and to find a subset of discriminating features between samples. We used ANOVA-simultaneous component analysis (ASCA) to disentangle the influence of spatial and seasonal effects as well as their interaction on a multiclass dataset. The dataset was obtained by a Chemcatcher passive sampler (PS) monitoring campaign of three small streams and one major river over four sampling periods from spring to summer. Monitoring of small streams is important as they are impacted by non-point source introduction of organic micropollutants (OMPs). The use of a PS provides a higher representativeness of sampling, and NTS broadens the range of detectable OMPs. A comparison of ASCA results of target analysis and NTS showed for both datasets a dominant influence of different sampling locations and individual temporal pollution patterns for each river. With the limited set of target analytes, general seasonal pollution patterns were apparent, but NTS data provide a more holistic view on site-specific pollutant loads. The similarity of temporal pollution patterns of two geographically close small streams was revealed, which was not observed in undecomposed data analysis like principal component analysis (PCA). With a complementary partial least squares-discriminant analysis (PLS-DA) and Volcano-based prioritization strategy, 223 site- and 45 season-specific features were selected and tentatively identified.
AB - Complex multivariate datasets are generated in environmental non-target screening (NTS) studies covering different sampling locations and times. This study presents a comprehensive chemometrics-based data processing workflow to reveal hidden data patterns and to find a subset of discriminating features between samples. We used ANOVA-simultaneous component analysis (ASCA) to disentangle the influence of spatial and seasonal effects as well as their interaction on a multiclass dataset. The dataset was obtained by a Chemcatcher passive sampler (PS) monitoring campaign of three small streams and one major river over four sampling periods from spring to summer. Monitoring of small streams is important as they are impacted by non-point source introduction of organic micropollutants (OMPs). The use of a PS provides a higher representativeness of sampling, and NTS broadens the range of detectable OMPs. A comparison of ASCA results of target analysis and NTS showed for both datasets a dominant influence of different sampling locations and individual temporal pollution patterns for each river. With the limited set of target analytes, general seasonal pollution patterns were apparent, but NTS data provide a more holistic view on site-specific pollutant loads. The similarity of temporal pollution patterns of two geographically close small streams was revealed, which was not observed in undecomposed data analysis like principal component analysis (PCA). With a complementary partial least squares-discriminant analysis (PLS-DA) and Volcano-based prioritization strategy, 223 site- and 45 season-specific features were selected and tentatively identified.
KW - ASCA
KW - chemometric feature prioritization
KW - LC-HRMS screening
KW - spatiotemporal trend analysis
KW - Chemistry
UR - http://www.scopus.com/inward/record.url?scp=85129295415&partnerID=8YFLogxK
U2 - 10.1021/acs.est.1c08014
DO - 10.1021/acs.est.1c08014
M3 - Journal articles
C2 - 35443133
AN - SCOPUS:85129295415
VL - 56
SP - 5466
EP - 5477
JO - Environmental Science and Technology
JF - Environmental Science and Technology
SN - 0013-936X
IS - 9
ER -