Project Description

Over the last years, air quality degradation has become an important issue in Portugal. The high concentrations of particulate matter (PM), exceeding the daily limit values, are one of the main concerns for air quality management.

The main objective of this work is to develop a consistent methodology to improve the characterization of aerosol concentrations and to evaluate the contribution of mineral dust for air pollution episodes in Portugal. In order to achieve the objective, firstly the influence of the African dust outbreaks over Portugal were studied. Subsequently, the study and comparison of the air quality modelling results and satellite observation were performed. Finally, we developed and applied a data fusion methodology using modelling and satellite data to improve the quantification of the atmospheric aerosol loading.

The characterization of the spatial distribution of mineral dust over mainland Portugal was performed for the entire year of 2011. The influence of the African dust outbreaks over Portugal that is notorious during the spring period, namely in April and May. For this reason, May 2011 was selected as the study period to perform the comparison and integration of the air quality modelling results and satellite observation. Satellite data obtained by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) instrument with high temporal resolution and air quality modelling results from the Comprehensive Air Quality Model (CAMx) were explored. The data were assessed in terms of aerosol optical depth (AOD) at 550 nm and PM concentrations. The validation of the AOD values obtained from air quality modelling and satellite observations was performed using observations from four Aerosol Robotic Network (AERONET) stations located within the study area. The PM concentrations were validated using measurements from air quality monitoring stations.

The methodology developed in this study revealed that the intercomparison and the integration of satellite observations with air quality modelling contributes to a better understanding of the discrepancies presented in the spatial pattern of AOD and may provide valuable information to in-situ validation with ground-based observations. The data fusion methodology provides an improvement of the modelling results in terms of AOD data and PM concentrations near the surface.

This study constitutes an innovative scientific support for forthcoming air quality management in order to improve the quantification of aerosols, relevant for policy applications in the framework of Air Quality Directives.

Research Team

University of Aveiro

• Ana Patrícia Fernandes
• Carlos Borrego (supervisor)

CITTA

• Oxana Tchepel (supervisor)

University of Bern

• Stefan Wunderle (supervisor)

Financial Support

• FCT

Stage of Progress

• Finished in 2018