WIT Press

A Field Survey In Potenza (southern Italy) For Developing And Testing An Innovative Strategy Of Air Pollution Control On A Local Scale


Free (open access)





Page Range

625 - 634




485 kb

Paper DOI



WIT Press


G. Di Bello, M. Ragosta & O. Salimbene


Climate change and air pollution, both on a global and local scale, are two interrelated environmental policy problems, but usually they are studied separately with very different approaches. In a novel context, in which climate change and air quality become two related aspects of the same problem, we believe that it is necessary to carry out novel local strategies for Air Quality Monitoring Networks’ (AQMNs) innovation, not only applying analytical optimization procedures of the existing networks, but also introducing advanced in situ devices and using data by remote investigations. Advanced instrumentation allows to evaluate the concentration of specific pollutants and to better characterize the local weather conditions. Remote observations (satellite data and vertical profiles of atmospheric variables) may improve the network effectiveness. In this study we present experimental data observed in the urban area of Potenza (Basilicata, southern Italy) during a field survey. In the same sampling period, we compare data atmospheric pollutants concentrations by AQM network of Potenza with data collected in our experimental site and with a satellite map of CO2. Despite the observation scales being very different, the results are encouraging and we believe that other efforts have to be made; the short-term benefits obtaining from air pollution control and innovation may be as effective as the long-term benefits obtainable through strategic climate change measures. So it is important to develop and to support local integrated strategies, for mitigating air pollution and climate change contemporaneously. Keywords: air pollution, AQMN, climate change, in situ data acquisition, remote sensing data.


air pollution, AQMN, climate change, in situ data acquisition, remote sensing data.