Self-organizing maps for indications of airborne polychlorinated biphenyl (PCBs) and organochlorine pesticide (OCPs) dependence on spatial and meteorological parameters

Romanić, Snježana Herceg; Vuković, Gordana; Klinčić, Darija; Antanasijević, Davor

doi:10.1016/j.scitotenv.2018.02.012

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…Zagreb is the one major outlier, with a 1-Jan-2019 ∑ 4 HCH concentration of 818 pg/m 3 (the highest of any POP included in this study), which is nearly 20 times higher than the next highest 1-Jan-2019 ∑ 4 HCH concentration (51.4 pg/m 3 at Zmiinyi Island, UA). Concentrations of ∑ 4 HCH at Zagreb during the initial years of APOPSBAL/MONET sampling (2004–2007) were significantly lower (88–114 pg/m 3 ) and dominated by γ-HCH (55–70%), consistent with the sampling results of Romanić et al during a similar period in the city (2007–2008) . However, since 2011, the ∑ 4 HCH concentrations at the site are highly elevated due to a substantial increase in α-HCH (as high as 3400 pg/m 3 in 2018), which is now the dominant isomer (∼90%) and indicates that the site has been contaminated by local emissions of α-HCH.…”

Section: Resultssupporting

confidence: 85%

“…* Denotes comparisons that are significantly different (p < 0.05). 47 However, since 2011, the ∑ 4 HCH concentrations at the site are highly elevated due to a substantial increase in α-HCH (as high as 3400 pg/m 3 in 2018), which is now the dominant isomer (∼90%) and indicates that the site has been contaminated by local emissions of α-HCH.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Concentrations of ∑ 4 HCH at Zagreb during the initial years of APOPSBAL/MONET sampling (2004–2007) were significantly lower (88–114 pg/m 3 ) and dominated by γ-HCH (55–70%), consistent with the sampling results of Romanić et al during a similar period in the city (2007–2008). 46 However, since 2011, the ∑ 4 HCH concentrations at the site are highly elevated due to a substantial increase in α-HCH (as high as 3400 pg/m 3 in 2018), which is now the dominant isomer (∼90%) and indicates that the site has been contaminated by local emissions of α-HCH.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Spatial and Temporal Trends of Persistent Organic Pollutants across Europe after 15 Years of MONET Passive Air Sampling

White

Kalina

Scheringer

et al. 2023

Environ. Sci. Technol.

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The Global Monitoring Plan of the Stockholm Convention on Persistent Organic Pollutants (POPs) was established to generate long-term data necessary for evaluating the effectiveness of regulatory measures at a global scale. After 15 years of passive air monitoring (2003–2019), MONET is the first network to produce sufficient data for the analysis of continuous long-term temporal trends of POPs in air across the entire European continent. This study reports long-term concentrations of 20 POPs monitored at 32 sites in 27 European countries. As of January 1, 2019, the concentration ranges (pg/m3) were 1.1–52.8 (∑6PCB), 0.3–8.5 (∑12dl-PCB), 0.007–0.175 (∑17PCDD/F), 0.02–2.2 (∑9PBDE), 0.4–24.7 (BDE 209), 0.5–247 (∑6DDT), 1.7–818 (∑4HCH), 15.8–74.7 (HCB), and 5.9–21.5 (PeCB). Temporal trends indicate that concentrations of most POPs have declined significantly over the past 15 years, with median annual decreases ranging from −8.0 to −11.5% (halving times of 6–8 years) for ∑6PCB, ∑17PCDD/F, HCB, PeCB, and ∑9PBDE. Furthermore, no statistically significant differences were observed in either the trends or the concentrations of specific POPs at sites in Western Europe (WEOG) compared to sites in Central and Eastern Europe (CEE), which suggests relatively uniform compound-specific distribution and removal at the continental scale.

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Section: Resultssupporting

confidence: 85%

Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Spatial and Temporal Trends of Persistent Organic Pollutants across Europe after 15 Years of MONET Passive Air Sampling

White

Kalina

Scheringer

et al. 2023

Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…[ 36 ] Variants of the SOM have been developed, such as the counter‐propagation artificial neural network (CPN), supervised Kohonen network (SKN), and the X‐Y‐fused Kohonen network (XYF) that are capable of predicting characteristics of interest. [ 27 ] They have found broad application in diverse fields such as engineering, [ 37 ] environmental science, [ 38 ] hyperspectral imaging, [ 39 ] and finance. [ 40 ] The ability of SOMs to provide a visual interpretation of high dimensional data make them highly valuable in many contexts.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF‐SIMS and Self‐Organizing maps

Wong

Hook

Gardner

et al. 2023

Adv Materials Inter

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Biofilm formation is a major cause of hospital‐acquired infections. Research into biofilm‐resistant materials is therefore critical to reduce the frequency of these events. Polymer microarrays offer a high‐throughput approach to enable the efficient discovery of novel biofilm‐resistant polymers. Herein, bacterial attachment and surface chemistry are studied for a polymer microarray to improve the understanding of Pseudomonas aeruginosa biofilm formation on a diverse set of polymeric surfaces. The relationships between time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data and biofilm formation are analyzed using linear multivariate analysis (partial least squares [PLS] regression) and a nonlinear self‐organizing map (SOM). The SOM models revealed several combinations of fragment ions that are positively or negatively associated with bacterial biofilm formation, which are not identified by PLS. With these insights, a second PLS model is calculated, in which interactions between key fragments (identified by the SOM) are explicitly considered. Inclusion of these terms improved the PLS model performance and shows that, without such terms, certain key fragment ions correlated with bacterial attachment may not be identified. The chemical insights provided by the combination of PLS regression and SOM will be useful for the design of materials that support negligible pathogen attachment.

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“…The Self-Organizing Map is an artificial neural network algorithm for unsupervised pattern recognition (Kohonen, 2001). SOMs have been applied to analyze ambient air data collected with monthly, weekly or irregular frequency (Astel et al, 2013;Zhong et al, 2017;Romanić et al, 2018;de Oliveira et al, 2019), handling few thousands of data points. A much challenging task is represented by data with higher frequency, as it is the case of "quasi-real-time" air monitoring; first case studies have been proposed considering few months for odor nuisance characterization by electronic noses (Licen et al, 2018a, b).…”

Section: Introductionmentioning

confidence: 99%

Assessment and Comparison of Multi-annual Size Profiles of Particulate Matter Monitored at an Urban-industrial Site by an Optical Particle Counter with a Chemometric Approach

Licen

Cozzutto

Barbieri

2020

Aerosol Air Qual. Res.

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The size of airborne particles is a key air quality parameter that is related to their composition, transport properties and effects on human health and the environment. Optical particle counters (OPCs) are increasingly used to dynamically characterize the size of ambient air particles. Monitoring campaigns lasting several months or even years generate millions of individual data values that must be effectively processed to extract information. Data mining algorithms as Self-Organizing Map (SOM) can support exploratory data analysis and pattern recognition in aerosol science. The use of SOMs, which offer powerful visualization features using 2D maps, allows us to interpret a large amount of data while avoiding any loss of information on variability from pre-treatments, such as compacting data recorded every minute to hourly or daily means. In the present study, we processed the data collected with an OPC during a long-term monitoring campaign (almost 3 years) conducted near residential buildings positioned very close to a steel plant and used them to assess and compare particulate matter (PM) profiles. About 12 million individual recorded values in total were handled. The current approach enabled us to identify four main PM profiles, follow their variation over time, and relate the differences to changes in the plant management and processes. Furthermore, it is potentially broadly applicable in high-frequency, long-term air quality monitoring campaigns employing different types of instruments to characterize the particle size and chemical composition of both PM and gases.

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Self-organizing maps for indications of airborne polychlorinated biphenyl (PCBs) and organochlorine pesticide (OCPs) dependence on spatial and meteorological parameters

Cited by 12 publications

References 27 publications

Spatial and Temporal Trends of Persistent Organic Pollutants across Europe after 15 Years of MONET Passive Air Sampling

Spatial and Temporal Trends of Persistent Organic Pollutants across Europe after 15 Years of MONET Passive Air Sampling

Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF‐SIMS and Self‐Organizing maps

Assessment and Comparison of Multi-annual Size Profiles of Particulate Matter Monitored at an Urban-industrial Site by an Optical Particle Counter with a Chemometric Approach

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