# Disentangling Multiannual Air Quality Profiles Aided by Self-Organizing Map and Positive Matrix Factorization

## TL;DR

This paper introduces a combined method using Self-Organizing Map and Positive Matrix Factorization to analyze multiyear air quality data and identify pollution sources and patterns.

## Contribution

A novel combined approach for analyzing multiannual, multisite air quality data without prior separation of sites and years.

## Key findings

- The method successfully detected site-specific pollutant sources and yearly variations.
- It identified outliers and provided reliable interpretations of pollution profiles.
- The approach handles noise and sparse missing data effectively.

## Abstract

The evaluation of air pollution is a critical concern due to its potential severe impacts on human health. Currently, vast quantities of data are collected at high frequencies, and researchers must navigate multiannual, multisite datasets trying to identify possible pollutant sources while addressing the presence of noise and sparse missing data. To address this challenge, multivariate data analysis is widely used with an increasing interest in neural networks and deep learning networks along with well-established chemometrics methods and receptor models. Here, we report a combined approach involving the Self-Organizing Map (SOM) algorithm, Hierarchical Clustering Analysis (HCA), and Positive Matrix Factorization (PMF) to disentangle multiannual, multisite data in a single elaboration without previously separating the sites and years. The approach proved to be valid, allowing us to detect the site peculiarities in terms of pollutant sources, the variation in pollutant profiles during years and the outliers, affording a reliable interpretation.