The Mediterranean basin is a region particularly vulnerable to atmospheric pollution. At the tropospheric level, pollutants such as ozone (O3), nitrogen dioxide (NO2), nitrogen monoxide (NO), and particulate matter (PM10 and PM2.5) are particularly harmful. Therefore, it is necessary to have observational air quality databases of sufficient completeness and quality to be able to analyze and extract valuable information regarding the mitigation of air pollution on the population, environment and economy.
In this regard, what the scientific literature provides is that existing air quality databases have notable weaknesses such as lack of inclusion of data from areas close to emission sources, exclusion of cities whose population does not exceed a threshold (Schwela et al., 2020) or the challenge of comparing values due to the presentation of air quality information using city-specific air quality indices (Baldasano et al., 2003).
This study presents a database of tropospheric pollutant concentrations in the Mediterranean basin for the last two decades. The database was constructed from pollution records acquired from thousands of automated air quality stations throughout the European region, through AirBase, provided by the European Environmental Agency (EEA) through the European Air Quality Portal. The data were evaluated using a rigorous quality control process that included detecting manipulation errors, verifying consistency and coherence limits, and assessing spatio-temporal coherence.
The analysis of the database revealed that ozone measurements are the most complete and consistent. Particulate matter stations exhibit more localized behavior, as isolated pollution spikes are more common. With regard to nitrogen oxides, a downward trend in tropospheric pollution has been observed in recent years.
In particular, 3323 measurement stations have been treated for O3, 4727 for PM10, 2317 for PM2.5, 3446 for NO and 4933 for NO2. The quality control employed allows to have available air quality records sufficiently dense and robust for further analysis, achieving a homogenization that allows to reduce the weaknesses presented by other databases. In addition, it is intended in further research to extend these records to a higher spatial resolution by means of interpolation methods.
References
Baldasano J. M., Valera E., Jim ́enez P. (2003). Air quality data from large cities. Science of the Total Environment 307 (1-3), 141–165.
Schwela D. H., Haq G., et al. (2020). Strengths and weaknesses of the who global ambient air quality database. Aerosol and Air Quality
Research 20(5), 1026–1037
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