Source Apportionment of PM2.5 and of its Oxidative Potential in an Industrial Suburban Site in South Italy
Articolo
Data di Pubblicazione:
2019
Abstract:
Some studies suggested a role of the atmospheric particulate matter (PM) and of its oxidative
potential (OP) in determining adverse health effects. Several works have focused on characterisation of
source contributions to PM OP, mainly using three approaches: correlation between OP and chemical
markers of specific sources; use of OP as input variable in source apportionment with receptor models;
and multi-linear regression (MLR) between OP and source contributions to PM obtained from receptor
models. Up to now, comparison of results obtained with different approaches on the same dataset is
scarce. This work aims to perform a OP study of PM2.5 collected in an industrial site, located near
a biogas production and combustion plant (in southern Italy), comparing different approaches to
investigate the contributions of the different sources to OP. The PM2.5 samples were analysed for
determining ions, metals, carbonaceous components, and OP activity with the DTT (dithiotreitol) assay.
Results showed that OP normalised in volume (DTTV) is correlated with carbonaceous components
and some ions (NO3
-, and Ca2+) indicating that PM of combustion, secondary, and crustal origin could
contribute to the OP activity. The source apportionment, done with the Environmental Protection
Agency (EPA)--Positive Matrix Factorization (PMF5.0) model, identified six sources: secondary
sulphate; biomass burning; industrial emissions; crustal; vehicle traffic and secondary nitrate; and sea
spray. A MLR analysis between the source's daily contributions and the daily DTTV values showed a
reasonable agreement of the two approaches (PMF and MLR), identifying the biomass burning and
the vehicle traffic and secondary nitrate as the main sources contributing to DTTV activity
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
biogas plant; PM oxidative potential; source apportionment; positive matrix factorization;; multi-linear regression
Elenco autori:
Gambaro, Andrea; Morabito, Elisa; Merico, Eva; DE NUNTIIS, Paola; Belosi, Franco; Grasso, FABIO MASSIMO; Contini, Daniele; Decesari, Stefano; Rinaldi, Matteo; Cesari, Daniela
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