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 -