Source Apportionment of PM2.5 and of its Oxidative Potential in an Industrial Suburban Site in South Italy

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.

Source apportionment of atmospheric PM<sub>10</sub> Oxidative Potential: synthesis of 15 year-round urban datasets in France

“…We also observed a clear distinction between the intrinsic OP values for the different PM sources, ranging from 0.044 ± 0.064 nmol min −1 µg −1 to 0.223 ± 0.085 nmol min −1 µg −1 for the OP DTT and from −0.018 ± 0.152 nmol min −1 µg −1 to 0.197 ± 0.104 nmol min −1 µg −1 for the OP AA . Such results agree with previous studies reporting different reactivity (or intrinsic OP) for different sources based on receptor-model techniques (Ayres et al, 2008;Bates et al, 2015;Cesari et al, 2019;Costabile et al, 2019;Fang et al, 2016;Paraskevopoulou et al, 2019;Perrone et al, 2019;Verma et al, 2014;Weber et al, 2018;Zhou et al, 2019;Daellenbach et al, 2020).…”

“…The source apportionment of the OP can be performed in two main ways: 1) by including the OP as an input variable for receptor-model (RM) (Verma et al, 2014;Fang et al, 2016;Ma et al, 2018;Cesari et al, 2019) or 2) by conducting source attribution to the PM mass and then, using a multiple linear regression (MLR) model, assigning OP to each of the sources from the source-receptor model (Bates et al, 2015;Verma et al, 2015b;Weber et al, 2018;Cesari et al, 2019;Paraskevopoulou et al, 2019;Zhou et al, 2019;Daellenbach et al, 2020). We decided to use the second approach since adding the OP variable to the PMF may change the source apportionment solution.…”

“…The dithiothreitol (DTT) and ascorbic-acid (AA) assays are widely used in associations with health endpoints (Abrams et al, 2017;Atkinson et al, 2016;Bates et al, 2015;Canova et al, 2014;Fang et al, 2016;Janssen et al, 2015;Strak et al, 2017a;Weichenthal et al, 2016;Yang et al, 2016;Zhang et al, 2016) even if the exact methodologies differ from one study to the other. Results can also differ for the seasonality of OP based on these two assays and some studies report strong seasonality of OP whereas others don't (Bates et al, 2015;Calas et al, 2019;Cesari et al, 2019;Fang et al, 2016;Ma et al, 2018;Paraskevopoulou et al, 2019;Perrone et al, 2016;Pietrogrande et al, 2018;Verma et al, 2014;Fang et al, 2015;Weber et al, 2018;Borlaza et al, 2018;Zhou et al, 2019).…”

“…Finally, several studies have already shown that different sources of PM have different reactivity to OP tests (Verma et al, 2014;Bates et al, 2015;Fang et al, 2016;Weber et al, 2018;Paraskevopoulou et al, 2019;Cesari et al, 2019;Zhou et al, 2019;Daellenbach et al, 2020). In particular, sources with high concentrations of transition metals, such as road traffic, appear to have a higher intrinsic oxidative potential (i.e.…”

Source Identification of Trace Elements in PM2.5 at a Rural Site in the North China Plain

“…The positive matrix factorization (PMF) model developed by the Environmental Protection Agency (EPA) of USA is an advanced factor analysis technique, which employs a flexible modeling method to effectively use information in data and to identify the possible source contributions without the source profiles. This model has been used worldwide for the source apportionment of PM 2.5 [12,13]. The characterization and source identification of trace elements in PM 2.5 play an important role in the prevention and control of air pollution.…”