A two-stage algorithm to estimate ground-level PM2.5 concentration levels in Madrid (Spain) from AOD satellite data and surface proxies

• Published in: Atmospheric pollution research Vol. 16; no. 12; p. 102678
• Main Author: Cordero, J.M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2025
• Subjects: Feature importance (FI) analysis; Machine learning algorithm; MAIAC AOD; PM2.5; Urban air quality; Xgboost; Urban air quality; Xgboost; Feature importance (FI) analysis; MAIAC AOD; Machine learning algorithm; PM2.5

Poor air quality in urban areas is an important health risk; therefore, reducing population exposure to pollutants such as PM2.5 is a major concern. Health assessments regarding this pollutant have typically relied on the measurements from urban networks of Air Quality Monitoring Stations (AQMS) to...