Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization
Abstract
1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Data Analysis Method
3. Results and Discussion
3.1. Data Cleaning
3.2. SOM Analysis
3.3. Hierarchical Clustering
3.4. Positive-Matrix Factorization
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- de Vries, W.; Posch, M.; Simpson, D.; de Leeuw, F.A.A.M.; van Grinsven, H.J.M.; Schulte-Uebbing, L.F.; Sutton, M.A.; Ros, G.H. Trends and Geographic Variation in Adverse Impacts of Nitrogen Use in Europe on Human Health, Climate, and Ecosystems: A Review. Earth Sci. Rev. 2024, 253, 104789. [Google Scholar] [CrossRef]
- Mahakalkar, A.U.; Gianquintieri, L.; Amici, L.; Brovelli, M.A.; Caiani, E.G. Geospatial Analysis of Short-Term Exposure to Air Pollution and Risk of Cardiovascular Diseases and Mortality—A Systematic Review. Chemosphere 2024, 353, 141495. [Google Scholar] [CrossRef] [PubMed]
- Markozannes, G.; Pantavou, K.; Rizos, E.C.; Sindosi, O.; Tagkas, C.; Seyfried, M.; Saldanha, I.J.; Hatzianastassiou, N.; Nikolopoulos, G.K.; Ntzani, E. Outdoor Air Quality and Human Health: An Overview of Reviews of Observational Studies. Environ. Pollut. 2022, 306, 119309. [Google Scholar] [CrossRef]
- Sicard, P.; Agathokleous, E.; Anenberg, S.C.; De Marco, A.; Paoletti, E.; Calatayud, V. Trends in Urban Air Pollution over the Last Two Decades: A Global Perspective. Sci. Total Environ. 2023, 858, 160064. [Google Scholar] [CrossRef]
- Tahir Bahadur, F.; Rasool Shah, S.; Rao Nidamanuri, R. Air Pollution Monitoring, and Modelling: An Overview. Environ. Forensics 2024, 25, 309–336. [Google Scholar] [CrossRef]
- Havemann, S.; Kishcha, P.; Agbehadji, I.E.; Obagbuwa, I.C. Systematic Review of Machine Learning and Deep Learning Techniques for Spatiotemporal Air Quality Prediction. Atmosphere 2024, 15, 1352. [Google Scholar] [CrossRef]
- Alvarez-Guerra, E.; Molina, A.; Viguri, J.R.; Alvarez-Guerra, M. A SOM-Based Methodology for Classifying Air Quality Monitoring Stations. Environ. Prog. Sustain. Energy 2011, 30, 424–438. [Google Scholar] [CrossRef]
- de Oliveira, R.H.; Carneiro, C.C.; de Almeida, F.G.V.; de Oliveira, B.M.; Nunes, E.H.M.; dos Santos, A.S. Multivariate Air Pollution Classification in Urban Areas Using Mobile Sensors and Self-Organizing Maps. Int. J. Environ. Sci. Technol. 2019, 16, 5475–5488. [Google Scholar] [CrossRef]
- Licen, S.; Cozzutto, S.; Barbieri, G.; Crosera, M.; Adami, G.; Barbieri, P. Characterization of Variability of Air Particulate Matter Size Profiles Recorded by Optical Particle Counters near a Complex Emissive Source by Use of Self-Organizing Map Algorithm. Chemom. Intell. Lab. Syst. 2019, 190, 48–54. [Google Scholar] [CrossRef]
- Costa, E.L.R.; Braga, T.; Dias, L.A.; de Albuquerque, É.L.; Fernandes, M.A.C. Self-Organizing Maps Applied to the Analysis and Identification of Characteristics Related to Air Quality Monitoring Stations and Its Pollutants. Neural Comput. Appl. 2024, 36, 11643–11657. [Google Scholar] [CrossRef]
- Song, X.H.; Hopke, P.K. Kohonen Neural Network as a Pattern Recognition Method Based on the Weight Interpretation. Anal. Chim. Acta 1996, 334, 57–66. [Google Scholar] [CrossRef]
- Kohonen, T. Self-Organizing Maps Springer Series in Information Sciences; Springer: Berlin/Heidelberg, Germany, 2001. [Google Scholar]
- Kohonen, T. Essentials of the Self-Organizing Map. Neural Netw. 2013, 37, 52–65. [Google Scholar] [CrossRef] [PubMed]
- Hopke, P.K. Review of Receptor Modeling Methods for Source Apportionment. J. Air Waste Manag. Assoc. 2016, 66, 237–259. [Google Scholar] [CrossRef] [PubMed]
- Zhou, L.; Hopke, P.K.; Paatero, P.; Ondov, J.M.; Pancras, J.P.; Pekney, N.J.; Davidson, C.I. Advanced Factor Analysis for Multiple Time Resolution Aerosol Composition Data. Atmos. Environ. 2004, 38, 4909–4920. [Google Scholar] [CrossRef]
- Paatero, P.; Tapper, U. Positive Matrix Factorization: A Non-Negative Factor Model with Optimal Utilization of Error Estimates of Data Values. Environmetrics 1994, 5, 111–126. [Google Scholar] [CrossRef]
- Fan, W.; Zhou, J.; Zheng, J.; Guo, Y.; Hu, L.; Shan, R. Hydrochemical Characteristics, Control Factors and Health Risk Assessment of Groundwater in Typical Arid Region Hotan Area, Chinese Xinjiang. Environ. Pollut. 2024, 363, 125301. [Google Scholar] [CrossRef]
- Zeng, J.; Liu, K.; Liu, X.; Tang, Z.; Wang, X.; Fu, R.; Lin, X.; Liu, N.; Qiu, J. Driving Factor, Source Identification, and Health Risk of PFAS Contamination in Groundwater Based on the Self-Organizing Map. Water Res. 2024, 267, 122458. [Google Scholar] [CrossRef]
- Trajković, I.; Sentić, M.; Vesković, J.; Lučić, M.; Miletić, A.; Onjia, A. Source-Oriented Health Risks and Distribution of BTEXS in Urban Shallow Lake Sediment: Application of the Positive Matrix Factorization Model. Water 2024, 16, 2302. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhang, Q.; Chen, W.; Shi, W.; Cui, Y.; Chen, L.; Shao, J. Source Apportionment and Migration Characteristics of Heavy Metal(Loid)s in Soil and Groundwater of Contaminated Site. Environ. Pollut. 2023, 338, 122584. [Google Scholar] [CrossRef]
- Hassan, M.S.; Bhuiyan, M.A.H.; Rahman, M.T. Sources, Pattern, and Possible Health Impacts of PM2.5 in the Central Region of Bangladesh Using PMF, SOM, and Machine Learning Techniques. Case Stud. Chem. Environ. Eng. 2023, 8, 100366. [Google Scholar] [CrossRef]
- Liu, H.; Wang, Q.; Liu, S.; Zhou, B.; Qu, Y.; Tian, J.; Zhang, T.; Han, Y.; Cao, J. The Impact of Atmospheric Motions on Source-Specific Black Carbon and the Induced Direct Radiative Effects over a River-Valley Region. Atmos. Chem. Phys. 2022, 22, 11739–11757. [Google Scholar] [CrossRef]
- Kumar, S. Insights on Air Pollution During COVID-19: A Review. Aerosol Sci. Eng. 2023, 7, 192–206. [Google Scholar] [CrossRef]
- Sokhi, R.S.; Singh, V.; Querol, X.; Finardi, S.; Targino, A.C.; Andrade, M.d.F.; Pavlovic, R.; Garland, R.M.; Massagué, J.; Kong, S.; et al. A Global Observational Analysis to Understand Changes in Air Quality during Exceptionally Low Anthropogenic Emission Conditions. Environ. Int. 2021, 157, 106818. [Google Scholar] [CrossRef]
- Bar, S.; Parida, B.R.; Mandal, S.P.; Pandey, A.C.; Kumar, N.; Mishra, B. Impacts of Partial to Complete COVID-19 Lockdown on NO2 and PM2.5 Levels in Major Urban Cities of Europe and USA. Cities 2021, 117, 103308. [Google Scholar] [CrossRef]
- Vesanto, J. SOM-Based Data Visualization Methods. Intell. Data Anal. 1999, 3, 111–126. [Google Scholar] [CrossRef]
- Himberg, J.; Ahola, J.; Alhoniemi, E.; Vesanto, J.; Simula, O. The Self-Organizing Map as a Tool in Knowledge Engineering; World Scientific Publishing: Singapore, 2001; pp. 38–65. [Google Scholar]
- Licen, S.; Astel, A.; Tsakovski, S. Self-Organizing Map Algorithm for Assessing Spatial and Temporal Patterns of Pollutants in Environmental Compartments: A Review. Sci. Total Environ. 2023, 878, 163084. [Google Scholar] [CrossRef]
- Clark, S.; Sisson, S.A.; Sharma, A. Tools for Enhancing the Application of Self-Organizing Maps in Water Resources Research and Engineering. Adv. Water Resour. 2020, 143, 103676. [Google Scholar] [CrossRef]
- Vesanto, J.; Alhoniemi, E. Clustering of the Self-Organizing Map. IEEE Trans. Neural Netw. 2000, 11, 586–600. [Google Scholar] [CrossRef]
- Paatero, P. Least Squares Formulation of Robust Non-Negative Factor Analysis. Chemom. Intell. Lab. Syst. 1997, 37, 23–35. [Google Scholar] [CrossRef]
- Licen, S.; Franzon, M.; Rodani, T.; Barbieri, P. SOMEnv: An R Package for Mining Environmental Monitoring Datasets by Self-Organizing Map and k-Means Algorithms with a Graphical User Interface. Microchem. J. 2021, 165, 106181. [Google Scholar] [CrossRef]
- Melssen, W.; Wehrens, R.; Buydens, L. Supervised Kohonen Networks for Classification Problems. Chemom. Intell. Lab. Syst. 2006, 83, 99–113. [Google Scholar] [CrossRef]
- Wehrens, R.; Kruisselbrink, J. Flexible Self-Organizing Maps in Kohonen 3.0. J. Stat. Softw. 2018, 87, 1–18. [Google Scholar] [CrossRef]
- Carslaw, D.C.; Ropkins, K. Openair—An r Package for Air Quality Data Analysis. Environ. Model. Softw. 2012, 27–28, 52–61. [Google Scholar] [CrossRef]
- Kucheryavskiy, S. Mdatools—R Package for Chemometrics. Chemom. Intell. Lab. Syst. 2020, 198, 103937. [Google Scholar] [CrossRef]
- Kolde, R. Package “Pheatmap”: Pretty Heatmaps. R. package; GitHub, Inc.: San Francisco, CA, USA, 2022; pp. 1–8. [Google Scholar]
- Davies, D.L.; Bouldin, D.W. A Cluster Separation Measure. IEEE Trans. Pattern Anal. Mach. Intell. 1979, PAMI-1, 224–227. [Google Scholar] [CrossRef]
- Todeschini, R.; Ballabio, D.; Termopoli, V.; Consonni, V. Extended Multivariate Comparison of 68 Cluster Validity Indices. A Review. Chemom. Intell. Lab. Syst. 2024, 251, 105117. [Google Scholar] [CrossRef]
- Licen, S.; Tolloi, A.; Briguglio, S.; Piazzalunga, A.; Adami, G.; Barbieri, P. Small Scale Spatial Gradients of Outdoor and Indoor Benzene in Proximity of an Integrated Steel Plant. Sci. Total Environ. 2016, 553, 524–531. [Google Scholar] [CrossRef]
- Astel, A.M.; Giorgini, L.; Mistaro, A.; Pellegrini, I.; Cozzutto, S.; Barbieri, P. Urban BTEX Spatiotemporal Exposure Assessment by Chemometric Expertise. Water Air Soil Pollut. 2013, 224, 1503. [Google Scholar] [CrossRef]
- Kiihamäki, S.P.; Korhonen, M.; Kukkonen, J.; Shiue, I.; Jaakkola, J.J.K. Effects of Ambient Air Pollution from Shipping on Mortality: A Systematic Review. Sci. Total Environ. 2024, 945, 173714. [Google Scholar] [CrossRef]
- Stewart, G.B.; Dajnak, D.; Davison, J.; Carslaw, D.C.; Beddows, A.V.; Phantawesak, N.; Stettler, M.E.J.; Hollaway, M.J.; Beevers, S.D. New NOx and NO2 Vehicle Emission Curves, and Their Implications for Emissions Inventories and Air Pollution Modelling. Urban Clim. 2024, 57, 102103. [Google Scholar] [CrossRef]
- Ghermandi, G.; Fabbi, S.; Veratti, G.; Bigi, A.; Teggi, S. Estimate of Secondary NO2 Levels at Two Urban Traffic Sites Using Observations and Modelling. Sustainability 2020, 12, 7897. [Google Scholar] [CrossRef]
- Muñoz, A.; Muruzábal, J. Self-Organizing Maps for Outlier Detection. Neurocomputing 1998, 18, 33–60. [Google Scholar] [CrossRef]
- Muruzábal, J.; Muñoz, A. On the Visualization of Outliers via Self-Organizing Maps. J. Comput. Graph. Stat. 1997, 6, 355–382. [Google Scholar] [CrossRef]
- Mifka, B.; Telišman Prtenjak, M.; Kavre Piltaver, I.; Mekterović, D.; Kuzmić, J.; Marciuš, M.; Ciglenečki, I. Intense Desert Dust Event in the Northern Adriatic (March 2020); Insights From the Numerical Model Application and Chemical Characterization Results. Earth Space Sci. 2023, 10, e2023EA002879. [Google Scholar] [CrossRef]
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Fornasaro, S.; Astel, A.; Barbieri, P.; Licen, S. Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization. Toxics 2025, 13, 137. https://doi.org/10.3390/toxics13020137
Fornasaro S, Astel A, Barbieri P, Licen S. Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization. Toxics. 2025; 13(2):137. https://doi.org/10.3390/toxics13020137
Chicago/Turabian StyleFornasaro, Stefano, Aleksander Astel, Pierluigi Barbieri, and Sabina Licen. 2025. "Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization" Toxics 13, no. 2: 137. https://doi.org/10.3390/toxics13020137
APA StyleFornasaro, S., Astel, A., Barbieri, P., & Licen, S. (2025). Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization. Toxics, 13(2), 137. https://doi.org/10.3390/toxics13020137