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A mass spectrum is the graphical representation of the relative abundance of the charged fragments in an analyte plotted against their mass-to-charge ratio (m/z). The plot's x-axis represents the ratio of the mass of the charged fragment to the number of charges it carries. The y axis of the plot represents the relative abundance of each charged species. The relative abundance is calculated from the signal intensity of each charged species recorded at the detector. The most intense signal (the...
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Gravimetric analysis is a quantitative method where the analyte is isolated and weighed directly or after conversion into a substance of known composition. Gravimetric analysis can be classified as precipitation, electrogravimetry, volatilization, and particulate gravimetry, based on the method used to isolate the analyte.
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A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization.

Stefano Fornasaro1, Aleksander Astel2, Pierluigi Barbieri1

  • 1Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Giorgieri 1, 34127 Trieste, Italy.

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Summary
This summary is machine-generated.

This study introduces a novel combined approach using Self-Organizing Map (SOM), Hierarchical Clustering Analysis (HCA), and Positive Matrix Factorization (PMF) to analyze complex air pollution data, identifying pollutant sources and variations across sites and years.

Keywords:
COVID-19NOxambient airhierarchical clusteringmultivariate analysisparticulate matterpollutionpositive matrix factorizationself-organizing map

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Area of Science:

  • Environmental Science
  • Data Science
  • Public Health

Background:

  • Air pollution poses significant risks to human health, necessitating robust evaluation methods.
  • Analyzing large, complex air quality datasets with missing data and noise presents a major research challenge.
  • Multivariate data analysis, including neural networks and chemometrics, is increasingly employed for air pollution source identification.

Purpose of the Study:

  • To develop and validate a unified method for analyzing multiannual, multisite air pollution data.
  • To effectively identify pollutant sources, their temporal variations, and site-specific characteristics.
  • To overcome limitations of traditional methods by processing diverse datasets in a single step.

Main Methods:

  • Integration of Self-Organizing Map (SOM) for data clustering.
  • Application of Hierarchical Clustering Analysis (HCA) for grouping similar patterns.
  • Utilization of Positive Matrix Factorization (PMF) for source apportionment.

Main Results:

  • The combined SOM-HCA-PMF approach successfully disentangled complex air pollution data.
  • Site-specific pollutant source profiles and their yearly variations were accurately identified.
  • The method demonstrated robustness in handling noisy and incomplete datasets, revealing outliers.

Conclusions:

  • The integrated approach offers a powerful tool for comprehensive air pollution assessment.
  • It enables reliable interpretation of multiannual, multisite air quality data, improving source identification.
  • This method facilitates a deeper understanding of pollution dynamics and health impacts.