Understanding temporal and spatial changes of O₃ or NO₂ concentrations combining multivariate data analysis methods...

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Data Analysis

Background:

• Air quality monitoring is crucial for understanding pollution dynamics.
• Ozone (O3) and Nitrogen Dioxide (NO2) are key air pollutants with complex spatial and temporal variations.
• Air quality modeling systems like CALIOPE provide valuable data for analysis.

Purpose of the Study:

• To apply multivariate curve resolution (MCR) to analyze hourly O3 and NO2 concentrations in Catalonia.
• To investigate the temporal and spatial variability of O3 and NO2 using ground measurements and CALIOPE model data.
• To understand the contributions of different factors to air pollutant concentrations.

Main Methods:

• Utilized multivariate curve resolution (MCR) factor analysis.
• Analyzed hourly O3 and NO2 data from 19 monitoring stations in Catalonia (2015).
• Incorporated ground-based measurements and CALIOPE model predictions at multiple spatial resolutions (12x12 km, 4x4 km, 1x1 km).

Main Results:

• MCR successfully identified contributions from sunlight, seasonal patterns, traffic emissions, and local environments (urban, suburban, rural).
• CALIOPE model predictions showed variations across different spatial resolutions.
• NO2 predictions generally outperformed O3 predictions, particularly in urban areas.

Conclusions:

• The trilinearity constraint in MCR is effective for decomposing air quality data and understanding variability sources.
• MCR provides a robust method for analyzing complex air pollution datasets.
• The study enhances understanding of O3 and NO2 dynamics in Catalonia.