Air quality in the Canary Islands: A geographical, climatic, and health-based approach

This study analyses air quality in the Canary Islands between 2009 and 2024, being the first to simultaneously examine five pollutants: PM 10 , PM 2.5 , SO 2 , NO 2 , and O 3 . The research focus is justified by the archipelago’s proximity to the Sahara Desert, its high vehicle density, and the recent decommissioning of major industrial sources, including an oil refinery and a thermal power plant in Tenerife. Hourly data from 76 stations of the Canary Islands Air Quality Control and Monitoring Network were combined with daily meteorological observations from 22 stations and ECMWF satellite reanalysis, including temperature, humidity, wind, and the altitude of the characteristic subsidence thermal inversion. Correlations, multivariate models, and an XGBoost machine learning model were applied to evaluate the influence of meteorological conditions on pollutant concentrations. Results show a marked increase in PM 10 and PM 2.5 during Saharan dust intrusions, particularly in winter, when dust is transported at low altitudes, significantly deteriorating air quality. In contrast, SO 2 and NO 2 concentrations have decreased substantially since 2009 (-55% and -27%, respectively), reflecting reduced industrial emissions and moderate improvements in urban mobility, although peak traffic episodes still cause localized pollution in Santa Cruz de Tenerife and Las Palmas de Gran Canaria. O 3 levels remain low and are primarily modulated by meteorological factors, including the altitude of the subsidence inversion and the dispersive effect of trade winds. The subsidence thermal inversion is confirmed as the main predictor of particle and primary pollutant accumulation, while periods of high temperatures and low humidity amplify simultaneous exposure to multiple pollutants, posing elevated public health risks. • First simultaneous analysis of pollution in the Canary Islands (2009–2024). • PM 10 and PM 2.5 rise sharply during Saharan dust intrusions, especially in winter. • SO 2 and NO 2 decreased significantly due to reduced industrial emissions. • Subsidence thermal inversion altitude is the main predictor of particle accumulation. • XGBoost model quantifies meteorology’s influence on pollutant concentrations.