Statistical Tools for Air Pollution Assessment: Multivariate and Spatial Analysis Studies in the Madrid Region

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Spatial Analysis

Background:

• Madrid, a major European metropolitan area, has insufficient air quality studies.
• Air pollution poses significant environmental and health risks.

Purpose of the Study:

• To analyze the spatial distribution and temporal trends of key air pollutants in Madrid.
• To identify correlations between different pollutants and their sources.
• To assess compliance with established air quality legislation.

Main Methods:

• Utilized statistical tools for air pollution data interpretation and modeling.
• Employed multivariate analysis, including correlation analysis, principal component analysis (PCA), and cluster analysis (CA).
• Generated contour maps using the geostatistical method of ordinary kriging.

Main Results:

• Established correlations between different pollutants (nitrogen oxides, ozone, particulate matter).
• Classified monitoring stations based on pollutant levels, revealing sources and mechanisms.
• Demonstrated spatial distribution of pollutants, with NO2 exceeding annual limits in central, southern, and eastern Madrid.

Conclusions:

• The study provides a comprehensive overview of Madrid's air quality.
• Identified specific areas with elevated pollutant levels requiring targeted interventions.
• Highlights the effectiveness of integrated statistical and geostatistical methods for air quality assessment.