Sorry, you need to enable JavaScript to visit this website.

Spatial-temporal assessment of air quality in Rome (Italy) based on anemological clustering

TitoloSpatial-temporal assessment of air quality in Rome (Italy) based on anemological clustering
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2023
AutoriDi Bernardino, A., Iannarelli A.M., Casadio S., Pisacane Giovanna, and Siani A.M.
RivistaAtmospheric Pollution Research
Volume14
ISSN13091042
Abstract

The relationship between atmospheric circulation and air pollution is investigated by analysing in-situ measurements collected at four monitoring stations located in the coastal area of central Italy over the period 2014–2020. The study is based on the prior identification of three typical circulation patterns, obtained via the k-mean clustering of surface anemological data. The present analysis explores the relation between atmospheric dynamics and concentrations of nitrogen oxides (NO and NO2), NO2/NOx (NOx = NO + NO2), ozone (O3), and particulate matters (PM2.5 and PM10). When local circulation systems prevail, the best air quality conditions are observed, as the onset of the sea breeze permits clean, marine air masses to be advected to the urban area of Rome. On the other hand, when synoptic winds persistently blow from the northeast, the highest concentrations of atmospheric pollutants are recorded. Finally, when both synoptic and local winds blow from the southeast, the complex anemological regime results in low ventilation and quite poor air quality conditions. The largest differences among clusters are observed during winter, when the north-easterly winds can persist for more than ten consecutive days, with the enhanced atmospheric stability limiting the development of the mixed layer, causing the increase of ground-level pollutants concentration. © 2023 Turkish National Committee for Air Pollution Research and Control

Note

cited By 0

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85147814286&doi=10.1016%2fj.apr.2023.101670&partnerID=40&md5=f0cdaf593953f0eddccbd940b9f3a502
DOI10.1016/j.apr.2023.101670
Citation KeyDiBernardino2023