Using Artificial Intelligence Tools to Analyze Particulate Matter Data (PM2.5) - Miriam Gómez Marín; Henry O. Sarmiento-Maldonado; Alba Nelly Ardila Arias; William Alonso Giraldo Aristizábal; Rubén Darío Vásquez-Salazar (Atmosphere, 2025)
A multivariable clustering methodology was evaluated using the LAMDA algorithm as an alternative tool for analyzing air quality data. This analysis was based on the assessment of marginal and global adequacy degrees for classification using temporal records of PM2.5 data. This study was conducted before and during the COVID-19 pandemic in the Aburrá Valley, Colombia. A total of 244 samples were collected between 1 December 2018, and 23 November 2020, over 24-h periods at a frequency of three days per week, including weekends. A robust classifier was developed for the PM2.5 dataset, demonstrating that the selected descriptors significantly influenced classification outcomes. The average value for each class fell within the established ranges of the air quality index (AQI). According to AQI scales, the “good” and “acceptable” categories accounted for 95.1% of the monitored days. Class C2 (“acceptable”) was the most prevalent, representing 66% of the records, while the category harmful to sensitive groups (4.5%) was observed in eleven instances. Additionally, only one record (0.4%) fell into the category harmful to health (C4). The proportions of C1 and C2 classifications before and during the pandemic were 93.7% and 97.7%, respectively. The improvement in air quality due to COVID-19 restrictions is evident, as 57% of the observations during the pandemic were classified as “good” (C1), compared to only 13.9% before the pandemic. The visualization of classification results through easily interpretable graphs serves as a valuable decision-making tool, integrating not only real-time PM2.5 measurements but also historical trends of the study area.